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It is worth quoting him: By necessaries I understand, not only the commodities which are indispensably necessary for the support of life, but whatever the custom of the country renders it indecent for creditable people, even of the lowest order, to be without … Under necessaries therefore, I comprehend, not only those things which nature, but those things, which the established rules of decency have rendered necessary to the lowest rank of people. Credit, Insurance, and Financial Markets In addition to being dependent on agriculture, the rural poor have to cope with uncertainties, some of which relate to the environment for production and consumption such as weather and disease and others of which are idiosyncratic health and mortality rates among humans and livestock.
Product Markets The efficient functioning of product markets at home and abroad is vital for poor producers and consumers. Labor Markets Most often the only asset that the poor have is their own labor. Opportunities for and Returns from Accumulation I have already referred to returns from investment if any by the poor in financial instruments. Institutions I have discussed above the effects of the absence or poor functioning of one of the most important institutions, namely, the market.
Poverty Eradication Mechanisms The mechanisms for alleviating, if not eradicating, poverty can be divided into two broad categories.
Tenancy Reform It is unlikely that radical land reforms would come about in poor agrarian societies through nonrevolutionary domestic political processes. Raising the Productivity of the Poor Redistributive land and tenancy reforms are only two reforms among many that could augment the resources commanded by the poor or raise the returns on the assets they own.
Raising the Productivity of the Poor Raising the productivity or returns to the assets of the poor would alleviate their poverty. Human Capital Accumulation Public policy interventions could influence directly the investment in human capital by the poor by subsidizing the out-of-pocket costs to the poor of using publicly provided educational and health-care services.
Openness In the standard Heckscher-Ohlin-Samuelson model of international trade, opening an economy to trade or reducing trade barriers raises the return to its most abundant factor and reduces that of its least abundant factor. Domestic Market Integration In some developing countries, particularly large ones such as India, there are restrictions, including taxes, on internal trade.
Infrastructure Spending I have already discussed the possible pro-poor impact of public spending on social sectors such as education and health. Macroeconomic Stability An unstable macroeconomy, especially an inflationary environment, is particularly damaging to the poor. Growth The instrumental roles of rapid aggregate growth and, secondarily, of a more equitable distribution of the fruits of growth in eradicating poverty were recognized long ago by policymakers in developing countries.
The overarching objective of the strategy … was to insure an adequate standard of living for the masses; in other words, to get rid of the appalling poverty of the people … the irreducible, in terms of money, had been estimated by economists at figures varying from Rs 15 to Rs 25 per capita, per month at prewar prices … [To] insure an irreducible minimum standard for everybody, the national income had to be greatly increased, and in addition to this increased production there had to be a more equitable distribution of wealth.
In his response, Nehru said: Again it is said that the national incomes over the First and Second Plans have gone up by 42 percent and income per capita by 20 percent. Growth, Openness, and Poverty East Asia Since the mids—when they adopted an outward-oriented development at a time almost every other developing country was inward-oriented—the East Asian economies Hong Kong SAR, Korea, Singapore, and Taiwan Province of China achieved spectacularly high rates of growth and succeeded in achieving an economic and social transformation that had taken the industrialized economies of Europe centuries to accomplish.
Impact of Growth, Globalization, and Inequality One of the claims of those opposed to globalization is that it widens inequalities in income and wealth both within countries and between countries.
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 and [ 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 ]. 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 ]. 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 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 s, 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 ].
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 ].
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.
We conducted the study with the aid of the FCM-based approach introduced by Kosko in [ 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 ].
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 ].
The FCM methodology does have its share of weaknesses. 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 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. 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 this approach, the researcher is able to exercise a higher degree of control over how the system is defined. 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.
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. 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 ].
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. We used this instrument during the second stage to obtain FCMs. The remaining FCMs were obtained from 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 experts and project implementers.
Most participants produced FCMs individually and some in pairs. The participants produced FCMs. The participants were given the instrument and were instructed to assign weights to each concept, wherever applicable, and leave other cells blank. Later, the participants were asked to assign weights to all pre-established links between the 22 concept categories.
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 concepts S3 Fig. 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 values were then coded into a square adjacency matrix whenever a connection existed between any two concepts [ 60 , 62 — 64 , 66 ]. 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: 1. 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, ; 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 ]. Aggregation of all the individual cognitive maps produced a social cognitive map S1 Table.
This shows the cumulative strength 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.
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. 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 ]: 4. 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 ]: 5.
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. 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: 6.
The simulation outcomes also depend on the type of transformation function used. When the values of concepts can only be positive, i. Following is the mathematical equation of the sigmoid transformation function: 7. Hence, the derivative becomes higher when increasing the activation value [ 95 ].
Identifying pivotal concepts is a traditional approach in scenario planning that helps linking storylines to the quantitative model [ 96 ]. 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.
This scenario tries to evaluate how good governance is crucial for poverty alleviation. This scenario tries to assess how the most critical concepts, identified by the participants, are crucial for poverty alleviation. 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.
Each concept in the system has an initial state vector A 0 that varies from 0 to 1. A new state of the concepts can be calculated by multiplying the adjacency matrix with the state vector [ 69 ]. Self-loops and feedback cause a repeated activation of concepts, introducing non-linearity to the model [ 61 , 70 , 88 ]. 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. 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. The FCM system has a complexity index of 0.
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, C multi-sectoral collective enterprise development, C livelihood diversification and C entrepreneurship have higher degree centrality.
These concepts should be interpreted as the greatest strength of poverty alleviation strategies. The most influential concepts i. 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, C affordable and approachable education and healthcare, C6: good governance systems and processes, C2: strong institutions of the poor, C 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. 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.
Social Protection. Social and Solidarity Economy. Welcome to the United Nations. Toggle navigation. Home Issues Poverty Eradication. Poverty Eradication Poverty entails more than the lack of income and productive resources to ensure sustainable livelihoods.
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