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Causes and measures of poverty, inequality, and social exclusion: a review.

Graphical Abstract

1. Introduction

2. literature review: poverty, inequality, and social exclusion.

‘It is the poor person, the “ aporos ”, who is an irritation, even to his own family. The poor relative is considered a source of shame it is best not to bring to light, while it is a pleasure to boast of a triumphant relation well situated in the academy, politics, art, or business. It is a phobia toward the poor that leads us to reject individuals, races, and ethnic groups that in general lack resources and that therefore cannot—or appear unable to—offer anything’. ( Cortina 2022 )

2.1. Poverty

2.1.1. monetary poverty, 2.1.2. multidimensional poverty, 2.2. inequality, 2.2.1. types of inequality.

‘The nation-state is still the right level at which to modernize any number of social and fiscal policies and to develop new forms of governance and shared ownership intermediate between public and private ownership, which is one of the major challenges for the century ahead. But only regional political integration can lead to effective regulation of the globalized patrimonial capitalism of the twenty-first century’. ( Piketty 2014 )

2.2.2. Measuring Inequality

2.3. is the middle class disappearing, 2.4. social exclusion: what is going on with aporophobia, 2.5. the sdgs overview, 3. discussion and future directions, 4. policy implications and conclusions.

• In order to diminish social exclusion and aporophobia, further utilization of poverty and inequality indices for the most needed target groups is necessary.
• Discrepancies between indicators provided by institutions (i.e., the World Bank and the UN) ought to be adjusted in order to have a unique poverty indicator.
• More focus on how to cover Maslow’s hierarchy of needs should be given from governments and international institutions, as it provides a framework for basic needs necessary for a decent life and is in tandem with the proposed indices of World Bank and the UN.
• The diversification of the SDGs not only in their targets, but also in their sub-targets, ought to be conducted.
• There is not a common rule on the acceptance and implementation of a specific poverty or inequality index: the application of two or more indices and their comparison might lead to better interpretation of the extent and depth of poverty or inequalities.
• It is also suggested that inequality measures should be further compared with polarization, as the former measures focus on the tails of a population distribution and the latter polarization index delves into the disappearance of the middle class.

Author Contributions

Informed consent statement, data availability statement, conflicts of interest.

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Halkos, G.E.; Aslanidis, P.-S.C. Causes and Measures of Poverty, Inequality, and Social Exclusion: A Review. Economies 2023 , 11 , 110. https://doi.org/10.3390/economies11040110

Halkos GE, Aslanidis P-SC. Causes and Measures of Poverty, Inequality, and Social Exclusion: A Review. Economies . 2023; 11(4):110. https://doi.org/10.3390/economies11040110

Halkos, George E., and Panagiotis-Stavros C. Aslanidis. 2023. "Causes and Measures of Poverty, Inequality, and Social Exclusion: A Review" Economies 11, no. 4: 110. https://doi.org/10.3390/economies11040110

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• DOI: 10.6007/ijarbss/v11-i15/10637
• Corpus ID: 241764759

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The paper provides an overview of definitions, measurements and applications of the concept of multidimensional poverty through a systematic review. The literature is classified according to three research questions: (1) what are the main definitions of multidimensional poverty?; (2) what methods are used to measure multidimensional poverty?; (3) what are the dimensions empirically measured?. Findings indicate that (1) the research on multidimensional poverty has grown in recent years; (2) multidimensional definitions do not necessarily imply to leave behind the dominance of the economic sphere; (3) the most popular methods proposed in the literature deal with the Alkire–Foster methodology, followed by latent variable models. Recommendations for future research emerge: new methodologies or the improvement of current ones are rather relevant; intangible aspects of poverty start to deserve attention calling for new definitions; there is evidence of under researched geographical areas, thereby calling for new empirical works that expand the geographical scope.

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1 Introduction

The human capital is an essential resource for the growth of a country. Individuals or groups who are in poverty have to be helped to improve their conditions in order to experience a dignified life. With this in mind, poverty, its understanding, measuring, and reduction are at the center of socio-economic and political programs of governments in developing and non-developing countries. In particular, the way how it is measured determines the directions of governments’ lines of interventions. The other side of poverty is wealth. As reported in Peichl and Pestel ( 2013 , p. 4551) “the rich are an important source of both economic growth and inequality and have considerable economic and political power.” Therefore, in terms of design of public policies it becomes important not only who the poor are but also who the rich are.

We started our review from the belief, not new in the literature (see for example Petrillo 2018 ), that people well-being is far from being a unidimensional concept based only on the monetary aspects (i.e. income). Instead, other aspects of human life have to be included in order to enrich the idea of well-being.

As a matter of fact, the conceptualization of poverty ranges from income and/or consumption-based definitions to others that consider its multidimensional nature and its many manifestations: lack of productive resources to sustain livelihoods, limited or no access to basic services such as water, health and education, malnutrition, increased morbidity and mortality, living in an unsafe or insecure environment, poor or no housing, lack of participation in social, cultural and political life, social exclusion (Botchway 2013 ).

Originally, the literature on poverty has dwelt a great deal on the economic dimension as poverty manifestations and measurement were based on the GDP (at a national level) and on the poverty line.

Only recently, poverty has been increasingly conceptualized and measured from a multidimensional perspective in order to provide policy makers and the general public with the necessary tools for effectively monitoring social changes (Iglesias et al. 2017 ). For instance, policy makers who have often underestimated the need to define poverty multidimensionally (Kana Zeumo et al. 2011 ), started to consider it as a multidimensional concept. A number of factors made the multidimensional poverty concept appealing to them: (1) different measurements based on single indicators may produce different results (Lister 2004 ; Barnes et al. 2002 ) and the consideration of multidimensionality may prevent such a risk when policy makers evaluate policy impacts and targets to reduce poverty, (2) as income-based poverty and multidimensional poverty do not overlap, policies need to be addressed to different aspects of citizens’ lives, other than economic wellness.

However, such a relatively new conceptualization is still far from consolidation (Aaberge and Brandolini 2014 ). Furthermore, how many aspects of multidimensionality are jointly measured remains still an open debate.

Yet this growing literature is highly fragmented and to the authors’ knowledge no systematic review has been recently carried out on the concept of multidimensional poverty. It is acknowledged that a systematic literature review is considered the gold standard for evidence assessment and it is “the most efficient and high-quality method for identifying and evaluating extensive literature” (Mulrow 1994 ). It makes explicit the values and assumptions underpinning a review and enhances the legitimacy and authority of the resulting evidence (Tranfield et al. 2003 ). Systematic reviews use a rigorous method of study selection and data extraction and typically involve a detailed and comprehensive plan and search strategy derived a priori that reduce selection bias, which is very common in narrative reviews.

Using the systematic literature review (SLR) methodology, the aim of this paper is to identify the main definitions of poverty, to review how the concepts of “multidimensional poverty” and “multidimensional poverty measurement” have been developed, and which are the dimensions considered in empirical analysis, ultimately.

This specific objective leads to the achievement of a more general goal, which is to serve as a bibliometric reference for researchers who will need to deal with the topic of multidimensional poverty in the three areas investigated: definitions, methods, and empirical analysis.

Specifically, the method followed is the SLR procedure as transferred from medicine to business and economics research by Tranfield et al. ( 2003 ), employing specific criteria for inclusion and exclusion of articles in and from the review.

Through the SLR we aim at identifying the main definitions of poverty with special emphasis on different aspects encompassed in the definition, the methods proposed in the literature to study the multidimensional concept of poverty, and the dimensions included in the empirical applications.

A total of 229 articles were finally included. The key information related to these articles was stored in a data repository Footnote 1 specifically designed for recording their characteristics. After that, the main information has been summarized and discussed. The most relevant findings of the SLR can be outlined as follows. First, the analysis of the definitions of multidimensional poverty showed that only few studies proposed a new definition (10 studies out of 229). Also, most definitions included the income-based poverty as focus. Second, among the new methodological proposals, the relative majority of studies proposed modifications of the Alkire–Foster method (Alkire and Foster 2007 , 2009 , 2011a ) in terms of weighting schemes or methods of identification and aggregation of the dimensions. Only few studies (about 10%) proposed a comparison among different methodological approaches. Last, with reference to empirical applications, it emerged that not all the hypothesized dimensions are jointly considered and there was not observed a uniform geographical coverage of the continents. In this respect, a lack of studies related to USA emerged. Moreover, a certain preference for secondary data was observed with a predominant use of surveys that clearly show the extant need of producing internationally comparable “poverty” data with harmonized questionnaires by country and by year.

The main contribution of the paper is to bring together in one single research the most relevant studies about multidimensional poverty in order to find possible avenues for future research.

The rest of the paper is organized as follows. Section  2 describes the methodology employed to conduct the systematic review; Sect.  3 describes the main characteristics of the studies included in the final data repository and provides the results from an in-depth review of the studies; Sect. 4 summarizes the main findings, discusses and concludes.

2 Methodology of the literature review

According to the Cochrane Handbook (Higgins and Green 2011 ; Higgins et al. 2020 ) “A systematic review attempts to collate all empirical evidence that fits pre‐specified eligibility criteria in order to answer a specific research question. It uses explicit, systematic methods that are selected with a view to minimizing bias, thus providing more reliable findings from which conclusions can be drawn and decisions made”. The SLR here conducted follows three main stages—planning, executing, and reporting, as described in Tranfield et al. ( 2003 ). At the same time, we rely on the methodological guide summarized by Mohamed Shaffril et al. ( 2021 ), who have provided an all-encompassing and up-to-date guide to conducting systematic review for non-health researchers.

2.1 Planning

2.1.1 conceptual development and research questions.

Scholars and practitioners agree that one indicator alone cannot capture the multiple aspects of the poverty that is undisputedly considered a multidimensional concept (see Kana Zeumo et al. 2011 for a review).

According to the World Bank’s ( 2001 ) report, poverty is a state of deprivation which encompasses not only material but also non-material aspects. Furthermore, the concept of poverty is evolutive (Kana Zeumo et al. 2011 ) and its manifestations are related to the structures of the society and to the period in which poverty is discussed. Therefore, defining poverty is not a simple task as various studies do not agree on a common and conclusive definition.

With this in mind, we posit the following research question:

What are the main definitions of poverty and related concepts proposed in a multidimensional setting?

Poverty measurement is a crucial task. Indeed, only through its measurement authorities and policy makers are able to quantify its extent, intensity, and potential effect so as to gauge subsequent actions. We start from considering that the operationalization of a multidimensional poverty concept has to deal with different theoretical and methodological choices (see Dewilde 2004 ). Therefore, technically speaking, the problem becomes how to construct a multidimensional index. With this in mind, we posit the following research question:

What are the methods proposed to measure the multidimensional poverty concept?

Poverty can be declined with respect to several dimensions: income, human rights, food, education, health to cite the most common. However, in empirical contexts it may be difficult to effectively measure all the dimensions as assumed in conceptual frameworks. We expect that the literature review will reflect the fact that the notion of poverty has gradually been enlarged from an income-based to a multidimensional concept, and in the same fashion of Dewilde ( 2004 ), that the operationalization of the concept has not followed the same development. To put it differently, we might expect a mismatch between the dimensions conceptually developed and the number of dimensions empirically measured. In light of this view, we posit the following research question:

What are the dimensions measured in empirical works?

2.2 Executing

2.2.1 identification of studies and data collection, 2.2.1.1 selection of keywords.

We selected keywords that in our conceptual view were relevant for finding articles addressing the afore mentioned research questions and that were specific enough to avoid the inclusion of non-relevant publications and formulated in order to avoid the exclusion of potentially relevant and insightful works.

The chosen keywords, namely, ‘multidimensional inequality’, ‘multidimensional poverty’, ‘multidimensional well-being’, and ‘multidimensional wellbeing’, all refer to the broad concept of poverty. The concept of poverty from a stand-alone viewpoint (e.g., income-only poverty) was not considered. It is worth to note that in the selection of keywords, we did not differentiate among terms that describe methodology (e.g., ‘measures’, ‘indicators’) or terms addressing the type of investigation (e.g., ‘case study’, ‘empirical’, ‘theoretical’, ‘analysis’).

2.2.1.2 Selection of databases

Like in other studies (e.g., Dangelico and Vocalelli 2017 ; Vivas and Barge-Gil 2015 ) we chose the following databases for this research: (a) Elsevier Scopus and (b) Clarivate Analytics Web of Science (WoS). Descriptions of the search options are provided in Table 1 .

All databases were searched using the four abovementioned keywords. Table 2 reports the number of results obtained for each keyword within each database. Specifically, in the last two rows, the total numbers of retrieved studies within each database and across keywords (total, net of duplicates) are reported.

2.2.2 Selection of studies

Once the results of the searches reported in Table 2 were collected and the duplicated studies, within and across databases, were discharged, we obtained a list of 669 results that were archived in a Microsoft Excel file. In a SLR it is critical to operationally define which types of studies to include and exclude (Uman 2011 ). To this end, we decided to include studies that were clearly able to satisfy at least one of the three research questions (RQ1–RQ2–RQ3) reported in Sect.  2.1 . In particular, we included studies which provide a new definition of poverty in a multidimensional setting, propose a new method to study multidimensional poverty, or deal with a real-data application to support evidence on this topic. The exclusion criteria were defined as follows:

studies that did not strictly focus on the concept of multidimensional poverty as they did not propose a definition, a new method, or an application to real data on this topic;

studies that dealt with “multidimensional inequality” only from a mathematical point of view;

studies that dealt with economic or income aspects of poverty only, and therefore were not strictly considering a multidimensional concept;

studies that focused on well-being from a medical point of view only;

studies that did not focus on individuals or households, but, for example, on firms;

studies that dealt with specific categories of subjects only (e.g., patients, children, females, people with disabilities, aging population, workers, …) as the main focus of the systematic review is on households or individuals in general and not on specific categories of the population;

studies where multidimensional inequality was studied in relation to other aspects (e.g., mental health, gender) or as their determinant;

theoretical studies investigating the statistical and mathematical properties of inequality measures already proposed in the literature, as their focus was not on proposing a new definition, method or an application to real data;

studies that dealt with applications in a very limited geographical area (e.g. small rural areas of a specific region of a country, very small sample size);

studies whose abstract did not clear up the focus of the study;

studies where the dimensions considered were not clearly defined;

In this phase, it was important to balance sensitivity (retrieving a high proportion of relevant studies) with specificity (retrieving a low proportion of irrelevant studies). A total of 314 potentially relevant articles has been retrieved once the title and abstract were reviewed according to the above-mentioned inclusion and exclusion criteria.

2.2.2.1 Study quality assessment

Once a comprehensive list of abstracts has been retrieved and reviewed, the 314 articles were fully analyzed and their quality was assessed. More precisely, as 14 full-text files were not available and 3 studies were written in Spanish or German, Footnote 2 a number of 297 studies were fully read. The final sample was reduced to a total of 229 articles (see a list of the studies in “ Appendix ”). The steps of the study selection process are reported in Fig.  1 .

Steps of the study selection process

As shown in Fig.  1 , we identified 608 records from Scopus and 531 from WoS, net of duplicates within each database. After removing duplicates, 669 abstracts were screened, which resulted in removing 355 records with not relevant abstract, leaving to 314 potentially relevant records to be screened by the lead authors. Of these 314 records, 68 not relevant, 14 with a not available full-text, and 3 not written in English articles were excluded, thus leaving a final sample of 229 studies to be included in the systematic review for data extraction.

The reason to exclude some articles was that they did not match any of the specified inclusion criteria, but matched at least one of the exclusion criteria, although this was not clear from the abstracts. Among the exclusion criteria previously defined, the top motives for the exclusions were:

the study was theoretical only (21 records; 25%);

the study did not answer to any of the three research questions and therefore does not strictly focus on multidimensional poverty (15 records; 18%);

the full-text was not available (14 records; 16%).

the study investigated multidimensional inequality in relation to other aspects (e.g., mental health, gender) or as their determinant (11 records; 13%);

the geographical area or the sample size were very limited (11 records; 13%);

the study focused on females or children only (4 records; 5%);

the dimensions of poverty considered in the study were not made explicit (3 records; 4%);

the language was not English (3 records; 4%);

the only dimension considered was income (2 records; 2%);

the study was a review (1 record; 1%).

The Pareto chart (Fig.  2 ) reports the top motives of exclusions along with their cumulative frequencies.

Pareto Chart representing the main exclusion criteria

Apart from the fact that the study ‘only theoretical’ was the main exclusion criterion, the Pareto chart makes clear that ‘theoretical only’, ‘no match to any RQ’, ‘unavailable full-text’, ‘limited geographical area and/or sample size’ and ‘multidimensional inequality not the main focus’ together represent the 80% of the exclusion criteria.

2.2.2.2 Data extraction and data repository

Three types of information from each article were retrieved and stored in the data repository: (1) general information from the articles (authors, year, journal name, title, bibliographic database, keyword matching), (2) information about the matching with the three research questions (definition, method, application), and (3) information about the application, if any. For empirical applications, we reported the following details: methods of analysis, sample description (size, statistical units), geographical area (country or other), years covered, data collection type (cross sectional or longitudinal), data source (primary or secondary, source name), data representativeness (national, country comparisons), dimensions considered (economic/income, education, health, living standards, others), number and name of dimensions, number of indicators, main findings.

3 Reporting

3.1 characteristics of studies included in the review.

Figure  3 shows the distribution of studies over time and lead us to conclude that research associated with multidimensional poverty has grown in recent years. The year distribution of the sample is from 1999 to 2019. Over 60% of the sample is from studies published between 2015 and 2019.

Publications per year

The first study included in the review dates back to 1999. Until 2006 there has been a quite constant and limited number of studies, while after 2006 there has been an increase in the number of studies with a picking up speed starting from 2013 and a peak in 2019. Hence, most of the articles are recent, thus evidencing an increasing interest for poverty as a multi-dimensional concept in the literature.

Table 3 reports the name of the main journals where the reviewed studies were published. The journal that published most of the studies included in the review is “Social Indicator Research” (23% of studies), followed by “World Development” (4.8%) and by “The Journal of Economic Inequality” (4.4%). Interestingly, about 36% of the studies (82 out of 229) have been published in journals that host only one paper of this review. The journals publish work related to the economic, statistical, and social fields, mainly.

3.2 What is known about the multi-dimensional concept of poverty

Articles included in the review were classified into three different clusters (C1, C2, C3) according to the research questions they have addressed: (1) RQ1: what are the main definitions of poverty and related concepts in a multidimensional setting? (C1, 10 articles); (2) RQ2: what are the methods to measure the multidimensional poverty concept? (C2, 116 articles); and (3) RQ3: what are the relevant dimensions measured in empirical works? (C3, 214 articles). Table 4 reports the classification of the studies according to the three research questions. As can be noticed, the three clusters of studies examined were not mutually exclusive, as some of them (around 45%) addressed more than one research question. More in detail, as reported in Table 4 , 111 studies answer to RQ3 only, 94 studies involve both methods and applications to real data (i.e., they satisfy both RQ2 and RQ3), 14 studies satisfy RQ2 only, 7 studies satisfy all the RQs, 2 studies provide both a definition of multidimensional poverty and an empirical application (RQ1 and RQ3), and only 1 paper has been classified as proposing both a definition and a method (RQ1 and RQ2).

In “ Appendix ”, the full list of articles included in the review is reported.

In the following sections, the evidence coming from the studies belonging to the three clusters are described (Table 5 ).

3.2.1 What are the main definitions of poverty and related concepts in a multidimensional setting?

About 4% of the articles from the review were classified into C1 (10 articles). Out of the ten articles from C1, three defined the poverty as a multidimensional concept with a clear mention of the dimensions to be considered in addition to economic and monetary dimensions. Two articles considered more than one dimension, but still limited the definition to the economic and material spheres only (e.g., Annoni et al. 2015 ). The remaining definitions went beyond the material and economic spheres and included intangible or fuzzy dimensions like, for instance, ‘achievements’, ‘quality of life’, ‘living right’.

What emerged from the above definitions was that the consideration of more than one dimension did not necessarily imply to overcome the dominance of economic and material aspects in the conceptualization of poverty. Nevertheless, new dimensions belonging to the non-material sphere complemented the material ones.

3.2.2 What are the methods to measure the multidimensional poverty concept?

Among the selected studies, 116 have been classified in cluster C2 as they answer the RQ2 research question by discussing methods to measure the multidimensional poverty concept. These studies proposed a new method or an alternative version (or improvement) of an existing method to investigate the multidimensionality structure of poverty, inequality, or well-being from an original point of view.

Examining the methods, 105 articles have been classified as reporting a single method, 10 articles using two methods, and 1 article as reporting a comparison of three methods. Table 6 shows the list of studies and their classification, where the studies reporting more than one method are identified by a star (see the note to Table 6 ). The second column of Table 6 shows the 11 studies that explicitly mention the Sen’s capability approach (Sen 1985 ) as theoretical framework of reference in their research. According to this approach, multidimensional well-being should be understood in terms of peoples’ capabilities to achieve valuable functionings (beings and doings), emphasizing their freedom to choose and achieve well-being. As a matter of fact, this approach has been developed as an alternative approach to the traditional “welfarist” approach focusing on the utility only. The capability approach represents a general principle and therefore needs to be operationalized through a specific method.

As can be seen in Table 6 , the most frequently used methodology in the literature is the Alkire–Foster (AF) method (Alkire and Foster 2007 , 2009 , 2011a ) and its extensions, which have been employed in 34 studies overall (about 29% of all the studies). Among these, 30 studies use the AF method only while 4 studies report the use of different methods, besides the AF one. The AF method builds on the Foster-Greer-Thorbecke (FGT) poverty measures (Foster et al. 1984 ) with the aim of constructing a multidimensional index of poverty (MPI). By adopting a flexible approach, different dimensions of poverty are identified as different types of deprivations. The method is based on the counting approach as it counts the weighted number of dimensions in which people suffer deprivation by defining proper cut-offs.

The main innovations introduced by the studies using the AF method deal with the following issues: weighting schemes, methods of identification and aggregation of the dimensions. In fact, as reported in Mitra et al. ( 2013 ), “the Alkire Foster method is sensitive to the selection of dimensions and the methods used to derive rankings and weights”. In Datt ( 2019 ), alternative weighting schemes, methods of identification and aggregation are proposed, finding evidence that the contribution of different dimensions involved in the estimation of multidimensional poverty may vary depending not only on the weighting schemes, but also on the interaction between them and the choices made in terms of identification and aggregation of dimensions. A new strategy for deriving weighting schemes comes from Cavapozzi et al. ( 2015 ), who proposed a hybrid approach based on the hedonic regression, where value judgements about the dimensions are combined to statistical evidence. With respect to the identification of the dimensions, a modification of the MPI was proposed by Nowak and Scheicher ( 2017 ) to include individuals who are extremely poor in only few dimensions and the differences with the respect to the original formulation have been showed in an empirical setting. Alkire et al. ( 2017 ), followed by Nicholas et al. ( 2019 ), combined the classical counting approach of the AF method (Alkire and Foster 2011a ) for the analysis of multidimensional poverty at single time points, and the duration approach of Foster ( 2009 ) for over time analysis. In particular, Nicholas et al. ( 2019 ) showed that a large proportion of poverty may be attributed to over time deprivations. Finally, most studies propose modifications to the original formulation of the MPI based on the AF method by testing them in empirical settings (see, e.g., García-Pérez et al. 2017 ; Goli et al. 2019 ).

The second most commonly employed method is based on latent variables. Indeed, 15 articles (about 13%) have proposed latent variable models, such as factor analysis, or methods based on the concept of latent variables, such as principal component analysis or multiple correspondence analysis. In particular, one third of these studies use factor analysis, either in its confirmatory or exploratory version (see, e.g., Betti et al. 2015 ; Iglesias et al. 2017 ), one third use multiple correspondence analysis (see, e.g., Berenger et al. 2013 ), 4 of them use principal component analysis (see, e.g., Li et al. 2019 ) while, in the remaining study, latent class analysis based on discrete latent variables is proposed (Moonansingh et al. 2019 ). The aim of these studies has been to build a synthetic multidimensional measure of poverty or well-being, where each dimension is conceived as a latent, non-observable, construct. Particularly worthy of note is the fact that about half of the studies using latent variable models or methods are published in the journal “Social Indicators Research” (7 articles out of 15).

The third most frequently proposed method to study multidimensional poverty is the fuzzy theory (12 articles, 10%). Specifically, the fuzzy set theory is used to propose new weighting schemes for the poverty dimensions (see, e.g., Belhadj 2012 , 2013 ) and to overcome the classical notion of binary poverty (poor or not poor) by using fuzzy measures (Behlhadj and Limam 2012 ; Betti et al. 2015 ).

A total of 8 articles (6.9%) use the Gini index and its generalization to the multidimensional case for studying multidimensional poverty (see, e.g. Banerjee 2010 ). In this field of literature, the Gini coefficient is used to measure the extent of inequality.

Moreover, the stochastic dominance and partial order theory have been proposed in 6 studies (about 5%) for synthetizing multidimensional data as opposed to the classical approaches based on composite indicators, such as the AF method or factor analysis. In particular, the posetic (partially ordered set) approach has been proposed in this field to deal with the issues of weighting and aggregating for ordinal data (Iglesias et al. 2017 ) by following the proposal of Fattore ( 2016 ) and Fattore et al. ( 2012 ). Interestingly, the studies by Fattore ( 2016 ) and Fattore et al. ( 2012 ) have not been included in the list of studies of our systematic review, despite they developed the initial proposal based on the posetic approach. Specifically, Fattore ( 2016 ) has not been included due to the choice of the research keys, which omitted the word “deprivation” while Fattore et al. ( 2012 ) is not a journal article.

In addition, a number of 4 studies (3.4%) use generalized mean aggregation as method for building multidimensional measures of well-being or poverty (see, e.g., Pinar 2019 ).

The 49 remaining studies (about 42%) use different methods from the ones reviewed above (“other”). The “other” category contains less common methods that are used in less than 4 studies. Among these methods, we find clustering (Kana Zeumo et al. 2014 ), structural equations and causal theory (Rodero-Cosano et al. 2014 ), spatial Bayesian models (Greco et al. 2019 ), and axiomatic approaches (Decanq et al. 2009 ; Croci Angelini and Michelangeli 2012 ).

To sum up, about half of the studies included in this SLR (116 out of 229) have been classified as proposing a method to synthetize multidimensional poverty or well-being. This means that introducing a new methodological approach or improving current approaches used in the literature is rather relevant in this research field. Despite the AF method is predominant, a number of different and minor approaches have been proposed which borrow from different research fields. Another interesting aspect that clearly emerges from the findings of this review is that most studies (about 90%) include an empirical application to show the effectiveness of the proposed method in practice. The presence of empirical results enriches the study of multidimensional poverty and well-being with data-based socio-economic interpretations. Finally, another finding is that most studies use a single methodological approach to analyze poverty data. Comparisons among different methods are rather uncommon and involve about 10% of the studies only.

3.2.3 What are the dimensions measured in empirical works?

A total of 214 articles (93.45% of the sample) were classified as C3. Footnote 3 The dimensions of poverty considered in the studies reviewed are reported in Fig.  4 . The first most frequently dimension considered was ‘education’, followed by the second most frequently dimensions ‘health’ and ‘income’.

Poverty dimensions considered in the studies reviewed

The dimensions reported in Fig.  4 are not mutually exclusive. This becomes clearer from Fig.  5 that reports the number of dimensions together considered in the studies reviewed. Only 6 studies out of 214 focused on a single dimension of poverty. The fact that they were included as considering poverty multidimensionally depends on the number of sub-items considered to measure the single dimension or in the case of the dimension ‘other’. In fact, under that category there may fall more than one dimension, such as ‘life satisfaction’, ‘civic engagement’, ‘women empowerment’, to cite a few. Moreover, 83 articles (around 39%) considered three dimensions and only around 9% considered all dimensions. Focusing on articles that considered three dimensions, we observed that the most frequent combination of poverty dimensions was “Education, Health, Living Standards” (37 studies out of 83, about 44.5%) followed by the combination “Income, Education, Health” (15 studies out of 83, about 18%), while the less frequent combination was “Education, Health, Other” (2 studies out of 83, about 2.4%).

Number of dimensions together considered in the articles reviewed

The next descriptive analysis concerns the place where empirical studies refer to. The countries with the greatest number of studies were China (13), Pakistan (12) and India (11). Distributed by continent (Fig.  6 ), the studies were mainly made in Asia (30.84%, 66), in Europe (21.96%, 47) and in Africa (15.89%, 34). For some studies (9.35%, 20), the continent could not be clearly identified since the ‘many countries analysed’ may belong to different continents. Only six studies (2.8%) were found to refer to North America, in particular to the United States of America (USA). As the most powerful economy in the world, with one of the highest rates of poverty in the developed world, and an extreme extent of income and wealth inequality when compared to other industrialized countries, we could have expected the country to be one of the main fields of research. However, the USA does not predominate in empirical studies of multidimensional poverty. In this respect, the information presented in Fig.  6 gives researchers an important opportunity for empirical investigation on multidimensional poverty in the USA, as few relevant studies were identified in recent years in this review. On the other hand, Asian and European researchers in poverty wishing to study empirically the multidimensional poverty shall benefit of the various studies published on Asian or European countries for comparative purposes in order to provide more robust conclusions.

Geographic location of studies distributed by continent

As a matter of fact, as shown in Fig.  7 , apart from the categories ‘many OECD countries’, ‘Mediterranean countries’, ‘many countries’, within the same continent only few studies (e.g., related to Europe and South America) involve more than one country. This evidence calls for comparative studies among countries on multidimensional poverty.

Country comparison by continent

Aiming to help future research on this subjects, the main data sources used by authors were also checked. A marked preference for secondary data (202 out of 214) was observed (Fig.  8 ).

Secondary data type

Among secondary data type, it is evidenced that the use of surveys is still predominant. Notwithstanding the era of big data, survey research is still needed. Future works might explore the way how the two data sources may be used together in order to provide richer dataset and enhance poverty measurement.

Figure  9 focuses on the most researched continents, namely Asia and Europe, and for each one considers the main survey source. It clearly emerges that in Asia there is a fragmented use of surveys, while in Europe the use of EU-SILC data is predominant as it is a cross-sectional, longitudinal, harmonized survey with a full coverage of all European Union member states.

Survey source in the two most researched continents

Moreover, from Fig.  10 another issue emerges: the inadequate timeliness, namely the period between the year when the study has been published and the reference period of the survey wave. However, it is acknowledged that such a weakness is common to all empirical studies that make use of secondary data produced by Bureaus of Statistics. This finding emerged from the SLR paves the way for future research that might experiment the combined use of traditional data sources (e.g., surveys, census and administrative data) and modern big data. Both data sources can significantly reduce the cost of reporting and improve the timeliness, as the data collection is less time and resource intensive than for conventional data.

Timeliness of reviewed surveys

4 Discussion and conclusions

The purpose of this paper was to provide a systematic framing of the literature on multi-dimensional poverty and related concepts until 2019. In particular, the review was conducted by querying the Scopus and the Web of Science databases, according to the keywords ‘multidimensional poverty’, ‘multidimensional inequality’, ‘multidimensional well-being’, and ‘multidimensional wellbeing’. A number of 669 studies was found, which was reduced to 314 after the abstract review. Next, the analysis of the full-text studies brought to the final number of 229 articles included in the review. Three main research questions were formulated to select and to analyze the studies, related to the definition of multidimensional poverty, the introduction of methods to synthetize and measure the multidimensional poverty, and the use of different dimensions in empirical applications.

The current work found that the amount of scientific literature devoted to enlarge the study of poverty or well-being from an income-based only perspective to a multidimensional one, has increased in the last few years, and especially from 2017 to 2019. In particular, besides the economic dimension, other three important poverty-related dimensions clearly emerged from the review: education, health, and living standards. However, one interesting finding is that the definitions of multidimensional poverty proposed in the literature often move around the income/consumption dimension, which has been considered as the main, most important conceptualization of poverty.

Another important issue which emerged from this study is that several different methods have been employed in the reviewed studies such as the fuzzy theory, the Gini index, and models and methods based on latent variables, but the most frequently used approach relies on the well-established Alkire–Foster method. In fact, the framework developed under the AF method for the measurement of multidimensional deprivations has turned to be very flexible so that it is currently used for large scale studies such as the computation of the multidimensional poverty index (MPI) by the United Nations, based on the three dimensions of health, education, and standard of living. Despite the primacy of the AF method, some limitations have been raised in the literature. Likely the main practical limitation is that the method requires that the data are available from the same survey and linked at the individual or household level (Alkire and Foster 2011b ). Consequently, different data sources cannot be used, thus limiting the applicability of the method and, for example, the number of countries that could be compared within this framework. The investigation of multidimensional poverty measures based on the AF method requires efforts in collecting data uniformly and systematically. From the methodological point of view, as discussed in Sect.  3.2.2 , the limitations identified in the literature are concerned with the sensitivity of the AF method to the methods of identification and aggregation of the dimensions and the weighting schemes. The authors of the AF method themselves identified some common misunderstandings of their approach in Alkire and Foster ( 2011b ). In particular, they clarify that the method is sensitive to the joint distribution of deprivations, unlike other unidimensional or marginal methods, and that this is a distinctive feature of their proposal. The method represents a general framework for poverty measurement in a multidimensional perspective and should be operationalized by making proper choices which depend on the objectives of the single empirical studies.

The SLR here conducted allows us to conclude that the multidimensionality is not an unambiguous concept. Various dimensions may contribute to its definition and, notwithstanding we can observe frequently common dimensions (e.g., economic, health, education, living standards), their combined use is not obvious nor the items used to measure each specific dimension. On the empirical side, we found that some countries are under researched (e.g., USA). On the other hand, some geographical area, namely Asia or Europe, shall benefit of a vast empirical literature. Notwithstanding the high number of studies in these areas, a lack of comparative studies clearly emerged and paves the wave for future research. Moreover, a predominant use of surveys for data collection was observed that take along with it the often-inadequate timeliness issue. Future works might experiment the combined use of traditional surveys and new data sources based, for example, on big data.

It should be noted that the current literature review has some limitations. The most important one lies in the choices made during the systematic review design. Firstly, the review was solely restricted to the two “Scopus” and “Web of Science” databases since they represent the two biggest bibliographic databases covering literature from almost any discipline. Then, the research was limited to journal articles only. As a consequence, working papers, conference proceedings, books or book chapters, even if consistent with the research keys, were omitted from the results (for instance, the following references Alkire and Foster 2007 , rev 2008 , 2009 ; Fattore et al. 2012 ; Foster 2009 ; Sen 1985 , were not caught by the queries). Thirdly, efforts were focused on articles published in English while articles published in other languages (despite the abstract in English) were excluded, as their inclusion may have increased challenges with respect to time and expertise in non-English languages, thus conducting to a knowledge loss. However, we are aware of the fact that limiting the SLR to English-only studies may increase the risk of bias. Future works may consider the inclusion of non-English studies in order to prevent such a risk.

An additional limitation is the time range of the systematic review, as the last publication year recorded is 2019. This choice is motivated by the fact that the following year 2020 has been characterized by the spread of the COVID-19 pandemic. We believe that the difficult epidemiological situation, still affecting people’s life, may have deeply changed the impact of the different dimensions of poverty and gave much importance to dimensions such as psychological well-being, social exclusion, and technological and digital gaps. Future works might explore emerging issues related to poverty.

A natural progression of this work is to conduct a post-COVID systematic review of the literature including studies from 2020 onwards, considering a time horizon after the initial year of the COVID-19 pandemic of at least five years, and to compare the findings with the current ones.

Moreover, a limitation concerns the search method, which was through “keywords” (see Sect.  2.2.1 ). It is likely that some relevant articles that used different words in the title, abstract, keywords or topic were omitted from the systematic review. An example is the paper by Fattore ( 2016 ), cited in Sect.  3.2.2 , which title contains the word “deprivation” instead of “poverty”, “well-being”, or “inequality” and was therefore excluded from the results. Future research might consider additional keywords such as: “social exclusion”, “deprivation”, “vulnerability”, “inequality of opportunity”, or “quality of life”. Moreover, future works might make use of text mining techniques to analyse in deep the occurrence of words in the definition and conceptualization of poverty in order to extrapolate the main dimensions considered behind the well-known group of four: health, economics, education, and living standards.

No less important, a limitation might lie in the fact that we do not suggest what is the best way to treat poverty from a multidimensional perspective, but simply analyse the trend of consideration of multidimensionality in scientific publications. After all, a systematic review has precisely the goal of bringing order in scientific publications by soughing and describing, in this particular case, the characteristics of the multidimensional poverty related papers in the considered period. In this respect, one might object that the adoption of a systematic review does not allow us to critically interrogate the extant literature, but solely to summarize/systematize extant knowledge. Consequently, future research avenue might opt for embracing a methodological approach that better suits a critical evaluation, like for instance the problematizing review (Alvesson and Sandberg 2020 ).

The data repository is available upon request.

Although these articles have an abstract written in English, the main text was not written in English.

The table with the summary of C3 articles is available upon request.

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1.1 List of articles from the literature review

Abraham, R.A. and Kumar, K.S.K. (2008) Multidimensional poverty and vulnerability. Economic and Political Weekly 43(20):77, 79-87.

Adepoju, A.O. and Akinluyi, O.I. (2017) Multidimensional poverty status of rural households in Nigeria: Does family planning have any effect?.  International Journal of Social Economics 44(8): 1046-1061.

Alkire, S., Apablaza, M., Chakravarty, S. and Yalonetzky, G. (2017) Measuring chronic multidimensional poverty. Journal of Policy Modeling 39(6): 983-1006.

Alkire, S. and Fang, Y. (2019) Dynamics of multidimensional poverty and uni-dimensional income poverty: An evidence of stability analysis from China.  Social Indicators Research  142: 25-64.

Alkire, S. and Foster, J. (2011) Counting and multidimensional poverty measurement. Journal of Public Economics 95: 476-487.

Alkire, S., Jindra, C., Aguilar, G.R. and Vaz, A. (2017) Multidimensional poverty reduction among countries in sub-Saharan Africa. Forum for Social Economics 46(2): 178-191.

Alkire, S., Roche, J.M., Seth, S. and Sumner, A. (2015) Identifying the poorest people and groups: Strategies using the global multidimensional poverty index.  Journal of International Development  27: 362-387.

Alkire, S., Roche, J.M. and Vaz, A. (2017) Changes over time in multidimensional poverty: Methodology and results for 34 countries. World Development 94: 232-249.

Alkire, S. and Santos, M.E. (2014) Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Development 59: 251-274.

Alkire, S. and Seth, S. (2013) Selecting a targeting method to identify BPL households in India.  Social Indicators Research  112: 417-446.

Alkire, S. and Seth, S. (2015) Multidimensional poverty reduction in India between 1999 and 2006: Where and how?. World Development 72: 93-108.

Altamirano Montoya, Á.J. and Teixeira, K.M.D. (2017). Multidimensional poverty in Nicaragua: Are female-headed households better off?.  Social Indicators Research  132: 1037-1063.

Angulo, R., Díaz, Y. and Pardo, R. (2016) The Colombian multidimensional poverty index: Measuring poverty in a public policy context. S ocial Indicators Research  127: 1-38.

Annoni, P. and Weziak-Bialowolska, D. (2016) A Measure to target antipoverty policies in the European Union regions.  Applied Research in Quality of Life  11: 181-207.

Annoni, P., Bruggemann, R. and Carlsen, L. (2015) A multidimensional view on poverty in the European Union by partial order theory.  Journal of Applied Statistics  42(3): 535-554.

Antoniades, A., Widiarto, I. and Antonarakis, A.S. (2019) Financial crises and the attainment of the SDGs: An adjusted multidimensional poverty approach.  Sustainability Science (published online: 28 December 2019).

Aristei, D. and Perugini, C. (2010) Preferences for redistribution and inequality in well-being across Europe. Journal of Policy Modeling 32(2): 176-195.

Arndt, C., Hussain, A. M., Salvucci, V., Tarp, F. and Østerdal, L. P. (2016) Poverty mapping based on first-order dominance with an example from Mozambique.  Journal of International Development  28: 3-21.

Arndt, C., Mahrt, K., Hussain, M.A. and Tarp, F. (2018) A human rights-consistent approach to multidimensional welfare measurement applied to sub-Saharan Africa. World Development 108: 181-196.

Artha, D.R., and Dartanto, T. (2018) The multidimensional approach to poverty measurement in Indonesia: Measurements, determinants and its policy implications.  Journal of Economic Cooperation and Development 39: 1-38.

Ashaal, A. and Bakri, A. (2019) A multidimensional poverty analysis: Evidence from Lebanese data. International Journal of Recent Technology and Engineering  7(6S5): 2277-3878.

Awan, M.S., Waqas, M. and Aslam, M.A. (2015) Multidimensional measurement of poverty in Pakistan: Provincial analysis.  Nóesis. Revista de Ciencias Sociales y Humanidades 24: 54-71.

Ayala, L., Jurado, A. and Pérez-Mayo, J. (2011) Income poverty and multidimensional deprivation: Lessons from cross-regional analysis. Review of Income and Wealth 57: 40-60.

Ayoola Oni, O. and Adenike Adepoju, T. (2014) Analysis of rural households’ wellbeing in Nigeria: A capability approach.  International Journal of Social Economics 41(9): 760-779.

Bader, C., Bieri, S., Wiesmann, U. and Heinimann, A. (2016a) Differences between monetary and multidimensional poverty in the Lao PDR: Implications for targeting of poverty reduction policies and interventions. Poverty & Public Policy 8: 171-197.

Bader, C., Bieri, S., Wiesmann, U. and Heinimann, A. (2016b) Different perspective on poverty in Lao PDR: Multidimensional poverty in Lao PDR for the years 2002/2003 and 2007/2008.  Social Indicators Research  126: 483-502.

Bader, C., Bieri, S., Wiesmann, U. and Heinimann, A. (2017) Is economic growth increasing disparities? A multidimensional analysis of poverty in the Lao PDR between 2003 and 2013. The Journal of Development Studies , 53(12): 2067-2085.

Ballon, P. and Duclos, J.-Y. (2016) A Comparative analysis of multidimensional poverty in Sudan and South Sudan. African Development Review 28: 132-161.

Banerjee, A.K. (2010) A multidimensional Gini index. Mathematical Social Sciences 60(2): 87-93.

Banerjee, A.K. (2018) Normative properties of multidimensional inequality indices with data-driven dimensional weights: The case of a Gini index. International Journal of Economic Theory 14: 279-288.

Bangun, W. (2019) Multidimensional poverty index (MPI): A study in Indonesian on ASEAN. International Journal of Recent Technology and Engineering 8(1S): 207-212.

Basarir, H. (2011) Poor, multidimensionally speaking: Evidence from South Africa. Journal of African Economies 20(3): 463-504.

Batana, Y.M. (2010) Aid and poverty in Africa: Do well-being measures understate the progress?. African Development Review 22: 452-469.

Battiston, D., Cruces, G., Lopez-Calva, L.F., Lugo, M.A. and Santos, M. E. (2013) Income and beyond: Multidimensional poverty in six Latin American countries.  Social Indicators Research  112: 291-314.

Bautista, C.C. (2018) Explaining multidimensional poverty: A household-level analysis.  Asian Economic Papers 17(3): 183-210.

Belhadj, B. (2011a) A new fuzzy unidimensional poverty index from an information theory perspective.  Empirical Economics  40: 687-704.

Belhadj, B. (2011b) New fuzzy indices of poverty by distinguishing three levels of poverty. Research in Economics 65(3): 221-231.

Belhadj, B. (2012) New weighting scheme for the dimensions in multidimensional poverty indices. Economics Letters 116(3): 304-307.

Belhadj, B. (2013) New fuzzy indices for multidimensional poverty. Journal of Intelligent & Fuzzy Systems 24(3): 587-591.

Belhadj, B. and Limam, M. (2012) Unidimensional and multidimensional fuzzy poverty measures: New approach. Economic Modelling 29(4): 995-1002.

Bellani, L. (2013) Multidimensional indices of deprivation: The introduction of reference groups weights.  The Journal of Economic Inequality 11: 495-515.

Bennett, C.J. and Mitra, S. (2013) Multidimensional poverty: Measurement, estimation, and inference. Econometric Reviews 32(1): 57-83.

Bérenger, V. (2017) Using ordinal variables to measure multidimensional poverty in Egypt and Jordan.  The Journal of Economic Inequality 15: 143-173.

Bérenger, V. (2019) The counting approach to multidimensional poverty: The case of four African countries. South African Journal of Economics 87(2): 200-227.

Bérenger, V. and Celestini, F. (2006) Is there a clearly identifiable distribution function of individual poverty scores?.  Journal of Income Distribution  15(3-4): 55-77.

Bérenger, V., Deutsch, J. and Silber, J. (2013) Durable goods, access to services and the derivation of an asset index: Comparing two methodologies and three countries. Economic Modelling 35: 881-891.

Betti, G., D’Agostino, A. and Neri, L. (2002) Panel regression models for measuring multidimensional poverty dynamics.  Statistical Methods & Applications  11: 359–369.

Betti, G., Gagliardi, F., Lemmi, A. and Verma, V. (2015) Comparative measures of multidimensional deprivation in the European Union.  Empirical Economics  49: 1071–1100.

Betti, G., Gagliardi, F. and Verma, V. (2018) Simplified jackknife variance estimates for fuzzy measures of multidimensional poverty.  International Statistical Review  86: 68-86.

Bibi, S. and El Lahga, A.R. (2008) Robust ordinal comparisons of multidimensional poverty between South Africa and Egypt. Revue d'Économie du Développement 16(5): 37-65.

Bosmans, K., Decancq, K. and Ooghe, E. (2015) What do normative indices of multidimensional inequality really measure?. Journal of Public Economics 30: 94-104.

Bosmans, K., Lauwers, L. and Ooghe, E. (2018) Prioritarian poverty comparisons with cardinal and ordinal attributes. Scandinavian Journal of Economics 120: 925-942.

Bossert, W., Chakravarty, S.R. and D'Ambrosio, C. (2013) Multidimensional poverty and material deprivation with discrete data. Review of Income and Wealth 59: 29-43.

Bucheli, J.R., Bohara, A.K. and Villa, K. (2018) Paths to development? Rural roads and multidimensional poverty in the hills and plains of Nepal.  Journal of International Development  30: 430-456.

Callander, E.J., Schofield, D.J. and Shrestha, R.N. (2012) Capacity for freedom – using a new poverty measure to look at regional differences in living standards within Australia. Geographical Research 50: 411-420.

Calvo, C. (2008) Vulnerability to multidimensional poverty: Peru, 1998–2002. World Development 36(6): 1011-1020.

Castro, J.F, Baca, J. and Ocampo, J.P. (2012) (Re)counting the poor in Peru: A multidimensional approach.  Latin American Journal of Economics 49(1): 37-65.

Cavapozzi, D., Han, W. and Miniaci, R. (2015) Alternative weighting structures for multidimensional poverty assessment.  The Journal of Economic Inequality 13: 425-447.

Chakravarty, S.R., Deutsch, J. and Silber, J. (2008) On the Watts multidimensional poverty index and its decomposition. World Development 36(6): 1067-1077.

Chowdhury, T.A. and Mukhopadhaya, P. (2012) Assessment of multidimensional poverty and effectiveness of microfinance-driven government and NGO projects in the rural Bangladesh. The Journal of Socio-Economics 41(5): 500-512.

Chowdhury, T.A. and Mukhopadhaya, P. (2014) Multidimensional poverty approach and development of poverty indicators: The case of Bangladesh. Contemporary South Asia 22(3): 268-289.

Ciani M., Gagliardi F., Riccarelli S. and Betti G. (2019) Fuzzy measures of multidimensional poverty in the Mediterranean area: A focus on financial dimension.  Sustainability 11(1):143.

Ciommi, M., Gentili, A., Ermini, B., Gigliarano, C., Chelli, F.M., and Gallegati, M. (2017) Have your cake and eat it too: The well-being of the Italians (1861–2011).  Social Indicators Research  134: 473-509

Ciommi, M., Gigliarano, C., Emili, A., Taralli, S. and Chelli, F.M. (2017) A new class of composite indicators for measuring well-being at the local level: An application to the equitable and sustainable well-being (BES) of the Italian provinces. Ecological Indicators 76: 281-296.

Coromaldi, M. and Zoli, M. (2012) Deriving multidimensional poverty indicators: Methodological issues and an empirical analysis for Italy.  Social Indicators Research  107(37): 37-54.

Croci Angelini, E. and Michelangeli, A. (2012) Axiomatic measurement of multidimensional well-being inequality: Some distributional questions. The Journal of Socio-Economics 41(5): 548-557.

Cuenca García, E., Navarro Pabsdorf, M. and Moran Alvarez J.C. (2019) Factors determining differences in the poverty degree among countries.  Resources 8(3): 122.

de Silva, A.F., de Sousa, J.S. and Araujo, J.A. (2017) Evidences on multidimensional poverty in the Northern Region of Brazil. Revista de Administração Pública 51(2): 219-239.

Datt, G. (2019a) Distribution-sensitive multidimensional poverty measure. World Bank Economic Review 33(3): 551-572.

Datt, G. (2019b) Multidimensional poverty in the Philippines, 2004–2013: How much do choices for weighting, identification and aggregation matter?.  Empirical Economics  57: 1103–1128.

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D’Attoma, I., Matteucci, M. Multidimensional poverty: an analysis of definitions, measurement tools, applications and their evolution over time through a systematic review of the literature up to 2019. Qual Quant 58 , 3171–3213 (2024). https://doi.org/10.1007/s11135-023-01792-8

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Accepted : 28 October 2023

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DOI : https://doi.org/10.1007/s11135-023-01792-8

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International Journal of Academic Research in Business and Social Sciences

Open access journal.

ISSN: 2222-6990

Poverty: A Literature Review of the Concept, Measurements, Causes and the Way Forward

Rusitha wijekoon, mohamad fazli sabri, laily paim.

• Pages 93-111
• Received: 09 May, 2021
• Revised: 30 May, 2021
• Published Online: 22 Jul, 2021

http://dx.doi.org/10.6007/IJARBSS/v11-i15/10637

Open access

In spite of the fact that there is some lucidity within the field of poverty with respect to the concept, measurements, causes, and the way forward, those exterior to the field are confronted with an apparently complex poverty literature, overlapping terminology, and several published measures. Therefore, the objective of this review was to give an overview of concepts, measurements, causes and the way forward on poverty. A systematic literature review was performed by searching websites, and electronic databases from January 2000 to September 2020, and the selected articles were then analyzed thematically. Twenty research articles, and 23 website articles were incorporated in the analysis. In the current paper authors try to develop a guidance to the academicians and policy makers who are looking to use poverty in their work. Further, it gives an outline of the various conceptualizations of poverty, and afterward give a set of recommendations for researchers, and practitioners with respect to the most appropriate measures of poverty for a scope of various purposes, and policy implications for the way forward the poverty.

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CBN Economic and Financial Review, 39(4), 1-17. Olowa, O. W. (2012). Concept, measurement and causes of poverty: Nigeria in perspective. American Journal of Economics, 2(1), 25-36. Panday, P. K. (2008). The extent of adequacies of poverty alleviation strategies: Hong Kong and China Perspectives. Journal of Comparative Social Welfare, 24(2), 179-189. Ravallion, M. (2012). Why don’t we see poverty convergence? American Economic Review, 102(1), 504-523. Sen, A. K. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44, 219-231. Sen, A. K. (1983). Poor, relatively speaking. Oxford economic papers, 35(2), 153-169. Sen, A. K. (1994). Poor, relatively speaking, in resources, values and development, Oxford, Basil Blackwell. SILC (Survey on Income and Living Conditions): The preliminary report. (2010). Accessed on 14th September 2020 from http://www.cso.ie/en/media/ csoie/releasespublications/ documents/ silc/ 2010/. Streeten, P., & Burki, S. J. (1978). Basic needs: Some issues. World Development, 6(3), 411-421. Streeten, P. (1994). Poverty concepts and measurement, in poverty monitoring: An international concern, UNICEF. The U. S. Census Bureau. (2019). U. S. Census 2020. Accessed on 14th September 2020 from https://www. https://2020census.gov/. Tendulkar, S. D., & Jain, L. R. (1995). Economic reforms and poverty. Economic and Political Weekly, 30(23), 1373-1377. UNDP. (1997). Human Development Report, Oxford University Press, New York. UNDP. (2017). Asia-Pacific Sustainable Development Goals Outlook. https://www.adb.org/sites/default/files/publication/232871/asia-pacific-sdgoutlook 2017. pdf. (Accessed 6 September 2020). Wikipedia. (2019a). Measuring poverty. Accessed on 14th September 2020 from https://en.wikipedia.org/ wiki/Measuring_poverty. Wikipedia. (2019b). Causes of poverty. Accessed on 14th September 2020 from https://en.wikipedia.org/ wiki/Causes_of_poverty. World Bank. (2016). Measuring and analyzing poverty. Accessed on 14th September 2020 from http://web. worldbank. org/WBSITE/ EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/ 0, contentMDK :22405907~menuPK:6626650~pagePK:148956~piPK:216618~theSitePK:430367, 00.html. World Bank. (2018). Decline of global extreme poverty continues but has slowed. Accessed on 4th September 2020 from https://www.worldbank.org/en/news/press-release/2018/09/19/ decline-of-global-extreme-poverty-continues-but-has-slowed-world-bank. World Vision. (2018a). Global poverty: Facts, FAQs, and how to help. Accessed on 10th September 2020 from https://www.worldvision.org/sponsorship-news-stories/global-poverty-facts. World Vision. (2018b). 2018 Annual Review. Accessed on 4th September 2020 from https://www.worldvision.org/wp-content/ uploads/ 2019/01/annual-report.pdf.

In-Text Citation: (Wijekoon et al., 2021) To Cite this Article: Wijekoon, R., Sabri, M. F., & Paim, L. (2021). Poverty: A Literature Review of the Concept, Measurements, Causes and the Way Forward. International Journal of Academic Research in Business and Social Sciences, 11(15), 93–111.

Copyright: © 2021 The Author(s) Published by HRMARS (www.hrmars.com) This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode

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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.

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PONE-D-19-26538

Evaluating Poverty Alleviation Strategies in a Developing Country

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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.

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Ethiopia Population 2024 (Live)

Ethiopia ’s current population is about 115 million and is expected to surpass 200 million by the end of 2049. Ethiopia’s population is growing about 2.7% annually with no projected peak year or period of decline.

The birth rate in Ethiopia is 36 births per 1,000 people. The fertility rate is 4.1 births per woman. Religion plays a major role in Ethiopia’s high birth rate, as well as the lack of contraceptives.

The disproportionate population increase has hindered the economy’s ability to grow and develop at a more rapid pace due to the increased need for more resources. Ethiopia remains one of the poorest countries in the world due to its rapid population upsurge.

Ethiopia Population Growth

Ethiopia is a nation that has been beset by hunger and poverty for most of its long history. A land where child starvation and subsequent death have been prevalent for such a long time requires assistance from the more privileged and prosperous nations of the world. It is the responsibility of all members of the peaceful international community to step in with more rigor and determination to empower the Ethiopians. This population has proven to be one of the strongest on the face of the earth, having endured massive hardships. If it is given a little assistance, Ethiopia will be able to build on the strength of its inhabitants in order to increase the strength of the nation itself.

Ethiopia Population Projections

Ethiopia is currently one of the fastest growing countries in the world, with a growth rate of 3.02% per year. If Ethiopia follows its current rate of growth, its population will double in the next 30 years, hitting 210 million by 2060. Most of the world's population growth in the next 40-50 years is expected to come from Africa , and Ethiopia will be a large part of the growth.

Ethiopia Growth Rate

Ethiopia population clock.

Net increase of 1 person every 9 seconds

Population estimates based on interpolation of data from World Population Prospects

Components of Population Change

Ethiopia Population Density Map

Ethiopia Area and Population Density

The surface area in Ethiopia is currently at 1,104,300 km² (or 426,372.6137 miles square). Ethiopia has a population density of 83 people per square mile (214/square mile), which ranks 123rd in the world.

Largest Cities in Ethiopia

The largest city and capital of Ethiopia is Addis Ababa , or Addis Abeba, which has an estimated population of 3.6 million in the city proper and a metro population of more than 4.6 million. Being as old as two millenniums, its cultures and traditions hold family as a significant part of Ethiopian life, sometimes even surpassing the significance their careers or businesses might have.

Other major cities include Adama (324,000), Gondar (324,000), Mek'ele (324,000), and Hawassa (302,000).

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Ethiopia Population by Year (Historical)

Ethiopia Population by Year (Projections)

Ethiopia population pyramid 2024, ethiopia median age, ethiopia population by age.

There are people over age 18 in Ethiopia .

Census Years

Ethiopia Population Pyramid

With one of the highest poverty levels in the world, Ethiopia is considered by many to be one of the most under-developed nations in the world. But within its African boundaries lies a nation filled with a rich culture and heritage. Bordered by Kenya , South Sudan , Sudan , Djibouti , Eritrea , and Somalia .

Ethiopia is the most populous landlocked country in the continent of Africa and the second-most populous country of Africa after Nigeria . This estimate of how many people live in Ethiopia is based on the most recent United Nations projections, and makes Ethiopia the 14th most populous country in the world. The most recent census in 2007 found an official population of 73.7 million.

Ethiopia Demographics

Ethiopia is home to various ethnicities, predominantly the Oromo at 34.4% of the country's population and the Amhara, who account for 27% of the population. Other major ethnic groups include the Somali (6.2%), Tigray (6.1%), Sidama (4%), Gurage (2.5%), Welayta (2.3%), Afar (1.7%), Hadiya (1.7%), and Gamo (1.5%).

In 2009, Ethiopia had an estimated 135,000 asylum seekers and refugees, mostly from Somalia (64,000), Eritrea (42,000) and Sudan (23,000). The government requires refugees to live in designated refugee camps. According to a 2013 report, the number of refugees hosted by Ethiopia has grown to 680,000.

Ethiopia Religion, Economy and Politics

Ethiopia has close ties with all three major Abrahamic religions, and it was the first in the region to officially adopt Christianity in the 4th century. Christians account for 63% of the country's population, with 44% belonging to the Ethiopian Orthodox Church. Ethiopia has the first Hijra in Islamic history and the oldest Muslim settlement on the continent. Muslims account for 34% of the population.

Despite its wealth in culture, Ethiopia, unfortunately, does not suffer the same fate economically. With a significantly agriculture-based economy, it is not surprising that in today's technologically thriving world, Ethiopia has one of the lowest incomes per capita. Its reliance on domestic investment restricts foreign investment, which could otherwise account for a comparatively successful economy. However, improvement in agricultural practices has shown a decrease in the level of starvation that the country had been previously accustomed to. The GDP is also increasing, showing a 7% increase in 2014. The composition of the labor force is almost 40%, accounting for another step toward progress. However, only if the conditions of the average Ethiopian get better will the country be able to witness a better tomorrow.

The median age in Ethiopia is approximately 17.9 years of age. 60% of the population in Ethiopia is under the age of 25.

In terms of access to clean drinking water and sanitation, the numbers are still quite grim in this country. According to the World Factbook, only 57% of the country has improved access to clean drinking water, while 42% still struggle to find clean water. Only 28% of the population has access to improved sanitation services, while 72% struggle to maintain sanitation. This likely contributes greatly to the very high degree of risk with transmittable diseases and illnesses in the area.

Only 49% of the population over 15 years of age is literate and many children only attend school for 8 or 9 years.

Ethiopia Population History

The conditions of poverty entail deterioration in health for many of Ethiopia's inhabitants. The most common diseases that cause mortality among many Ethiopians are AIDS, tuberculosis, malaria, and various communicable diseases that occur due to improper sanitation and malnutrition. Most women give birth to children outside of the vicinity of hospitals. Often the mothers are only attended to by an elderly midwife. The mortality rate of mothers while giving birth is high. Various organizations, governmental and non-governmental, seek to improve the deplorable health conditions in Ethiopia. The World Health Organization is working to initiate a healthy Ethiopia. Low literacy levels also support the inferior health conditions. Therefore, it is important to provide the Ethiopians with adequate knowledge regarding common diseases and their appropriate medication and cure. The empowerment of women could also help achieve improvements in the circumstances pertaining to the well-being of Ethiopians.

• National Bank of Ethiopia
• World Population Prospects (2024 Revision) - United Nations population estimates and projections.