thesis multidimensional poverty

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• Published: 17 May 2016

Multidimensional poverty measure and analysis: a case study from Hechi City, China

• Yanhui Wang 1 , 2 &
• Baixue Wang 1  

SpringerPlus volume  5 , Article number:  642 ( 2016 ) Cite this article

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Aiming at the anti-poverty outline of China and the human–environment sustainable development, we propose a multidimensional poverty measure and analysis methodology for measuring the poverty-stricken counties and their contributing factors. We build a set of multidimensional poverty indicators with Chinese characteristics, integrating A–F double cutoffs, dimensional aggregation and decomposition approach, and GIS spatial analysis to evaluate the poor’s multidimensional poverty characteristics under different geographic and socioeconomic conditions. The case study from 11 counties of Hechi City shows that, firstly, each county existed at least four respects of poverty, and overall the poverty level showed the spatial pattern of surrounding higher versus middle lower. Secondly, three main poverty contributing factors were unsafe housing, family health and adults’ illiteracy, while the secondary factors include fuel type and children enrollment rate, etc., generally demonstrating strong autocorrelation; in terms of poverty degree, the western of the research area shows a significant aggregation effect, whereas the central and the eastern represent significant spatial heterogeneous distribution. Thirdly, under three kinds of socioeconomic classifications, the intra-classification diversities of H , A , and MPI are greater than their inter-classification ones, while each of the three indexes has a positive correlation with both the rocky desertification degree and topographic fragmentation degree, respectively. This study could help policymakers better understand the local poverty by identifying the poor, locating them and describing their characteristics, so as to take differentiated poverty alleviation measures according to specific conditions of each county.

As the world’s most populous developing country, China had experienced various poverty alleviation and development programs in rural areas, resulting in that the absolute poverty population had dropped from 94.22 million in 2000 to 26.88 million in 2010, and the poverty rate had fell from 10.2 to 2.8 %, according to National Statistics Bureau of China ( 2012 ). However, due to the complex geographic and socioeconomic conditions of rural China, the development gaps among different regions have been increasing, and the region-related characteristic have also been obvious (Wang and Qian 2015 ), indicating that China’s previous income-based poverty identification policy had to overcome great difficulties to precisely target those poverty-stricken households (Lu 2012 ). Therefore, the policymakers need specific information and tools to systematically identify those areas where development lags and where the poor live (Henninger and Snel 2002 ; Okwi et al. 2007 ), and further specifically target them to solve the poverty one by one with special policies.

In this context, China have been adjusting its poverty elimination policy from a purely monetary perspective to a more multidimensional view on poverty (Wang and Alkire 2009 ). It announced the ‘China’s rural poverty alleviation and development Outline (2011–2020)’ (hereinafter referred to as the ‘New Outline’) in 2011, assigning 14 contiguous destitute areas to act as the main battlefields to basically eliminate absolute poverty in 2020. In this policy document, Chinese government proposed the specified anti-poverty goal of the poor households that the poor themselves need not have to worry about their eating and wearing in the future; in addition, their obligatory education, basic medical treatment, and basic housing should be guaranteed by the government. In 2014, when the precisely targeting and identifying of the poor in rural areas is still a primary problem to be solved in the new anti-poverty stage, China further released a new policy of ‘Precise Poverty Reduction’ aiming at the implement of ‘New Outline’, and emphasized the precondition of accurately targeting the poor and their poverty factors to achieve the objective of building a full well-off society. However, driven by Chinese historical routine of national economic-core poverty identification, the previous practice on identifying the poor had still been done based on a single standard of economic income, which obviously ignored the basic rights of the poor in terms of housing, health, education, etc. In addition, the authenticity and reliability of income data is limited, resulting in that the traditional income-based method has been unable to meet China’s strategic needs of currently precise poverty alleviation. Therefore, new methods are needed to be introduced to identify the complex and multidimensional poverty in rural China, while multidimensional measures provide such an alternative lens through which poverty may be viewed and understood, as it is quite a departure for traditional unidimensional and multidimensional poverty measurement-particularly with respect to identification step-further elaboration may be warranted (Alkire and Foster 2011 ).

In this context, from the views of multidimensional poverty, bringing housing, health, education and other indicators into the evaluation system to comprehensively measure and analyze the rural poverty has increasingly been becoming a hot topic of domestic and foreign research (Wang and Alkire 2009 ; Alkire and Foster 2011 ; Guedes et al. 2012 ; Wang et al. 2013 ). Accordingly, in accordance with China’s current precise anti-poverty policy, the objective of this study is to develop a multidimensional poverty measure and analysis methodology, taking typical area of rural China as one case to accurately measure multidimensional poverty and its contributing factors under given socioeconomic and geographical conditions.

Related work

Internationally, the previous poverty identification was often designed from the unidimensional view, e.g., according to the most popular poverty standard line of $ 1.25 per person per day developed by World Bank, or the poverty line of 2300 Chinese RMB yuan per person per year in China in 2011; however, this method had showed its distinct limitation due to its immoderate simplicity (Wang and Qian 2015 ).

With the increasing understanding that poverty is of multidimensional and dynamic natures, many studies had responded with new measures and tools that comprehensively measure poverty to the strong demands of governments and international communities (Anand and Sen 1997 ; Bourguignon and Chakravarty 2003 ; Maasoumi and Lugo 2008 ; Alkire and Foster 2011 ; Guedes et al. 2012 ). For examples, poverty was measured by use of Human Development Index (UNDP 2000), and also by Multidimensional Poverty Index ( MPI ) proposed by Alkire and Foster ( 2010 ), both of them stressing that human poverty was caused by inequalities of their achieved rights and abilities, such as education, health, job, policy and so on. This view has increasingly been becoming a hot topic of domestic and foreign research, and special practice had also been done aiming at different study areas in different countries and regions (Anand and Sen 1997 ; Bourguignon and Chakravarty 2003 ; Thomas et al. 2009 ; Alkire and Foster 2011 ; Gilvan et al. 2012).

On the other hand, since adoption of a multidimensional approach to deprivation poses the challenge of understanding the interaction between different dimensions (Atkinson 2003 ), the need for such a multidimensional approach to the robustness measurement of multidimensional inequality had also been emphasized in other literatures (Atkinson and Bourguignon 1982 ; Tsui 1985 ; Maasoumi 1986 ; Sen 1999 ; Bourguignon and Chakravarty 2003 ). Aiming at the ‘right’ poverty-line that should be concerned with the union of all those deprived on at least one dimension or with the intersection of those deprived on all dimensions, diverse approaches to the study of poverty appear to fall into two categories: non-axiomatic approach in which different indicators are combined in order to obtain a multidimensional index (Betti et al. 2015 ), and axiomatic approaches that had been developed by Chakravarty et al. ( 1998 ), Bourguignon and Chakravarty ( 2003 ). Atkinson ( 2003 ) brought out key features of different approaches and sets them in a common framework. Duclos et al. ( 2001 ) compared union and intersection method by which to decide who is poor in multiple dimensions, demonstrating how to check whether the comparisons are robust to aggregation procedures and to the choice of multidimensional poverty lines. Additionally, the ‘counting’ approach, widely used in applied studies (UNDP 1997 ; Wang et al. 2013 ), is devoted to counting the number of dimensions in which people suffer deprivation, in which both union and intersection conditions may be necessary. Considering poverty as a matter of degree rather than an attribute that is simply present or absent for individuals in the population, Cheli and Lemmi ( 1995 ), and Betti et al. ( 2015 ) adopted a fuzzy set approach to draw a distinction between those who adopt a union approach and those who use an intersection measure.

On the other hand, according to some authors (UNDP 1997 ; Wang et al. 2013 ; Wang et al. 2015 ), these weights of the indicators should be equal while the composite welfare index is an average of the responses to the different variables. On the other hand, some others suggested that the weights allocated to the indicators must vary as a function of their contribution to welfare, and they developed such methodologies to response to this view, e.g., entropy approach (Maasoumi 1999 ), multiple correspondence analysis (Ningaye and Njong 2015 ), fuzzy set approach (Cheli and Lemmi 1995 ; Njong and Baye 2010 ; Betti et al. 2015 ), and so on.

Alkire and Foster ( 2011 ) attempted to offer a practical A–F approach as the methodology of measurement and analysis multidimensional poverty, identifying people as poor depending upon achievements of household members. A–F measure with desirable axiomatic properties could reflect the breadth, intensity and components of deprivations and improve the counting-based headcount measures of multidimensional poverty, presenting indicators of multiple dimensions with a single summary index that can be broken down among the dimensions and different groups (Alkire and Roche 2011 ; Wang et al. 2015 ). It has also been widely applied to the investigations of the multidimensional poverty status in India, China and Latin American countries (Alkire and Seth 2013 ; Yu 2013 ; Battiston et al. 2013 ). In regards to the weight of these dimensions, most of the indexes apply equal weights implicitly or explicitly (Alkire and Roche 2011 ; Wang et al. 2013 ; Qi and Wu 2015 ; Wang et al. 2015 ). To simplify interpretation, Alkire and Roche ( 2011 ) argued that equal weight was plausible and commonly adopted.

Meanwhile, following the international multidimensional poverty view, some studies had also been done to aim at China’s multidimensional poverty identification by using A–F method, i.e., Wang and Alkire ( 2009 ) collected sampling data from China Health and Nutrition Survey of 2006, using the eight indicators of health, education, housing, and living standard to carry out the study area’s multidimensional poverty estimations. Li ( 2009 ) implemented the four dimensions of education, health, environment and consumption to perform a poverty measurement for 30 poor counties. Wang et al. ( 2013 ) conducted the village-level case study of multidimensional poverty. Multidimensional child poverty index and its dynamic changes in China were also studied by Qi and Wu ( 2015 ), and Wang et al. ( 2015 ). Maasoumi and Xu ( 2015 ) combined multidimensional welfare analysis and entropy metrics to derive not only the best relative weights but also substitution degree among different attributes to construct multidimensional indices of well-being with CHIPS 2002 data. Recently, Yang and Mukhopadhaya ( 2016 ) measured multidimensional poverty in China at the national, rural–urban, regional and provincial levels using the China Family Panel Studies data from 2010, and observed that when they adopted four kinds of different methods to measure multidimensional poverty, the variation of weights did not change the results much.

Although the above multidimensional poverty research had made great strides, China’s poverty evaluation indicator system has not completed yet due to the absence of an objective standard. Since scholars often pursued the quantity and all-sided indexes during measuring poverty, however, most of them have not delve into the detailed poverty types and poverty contributing factors, resulting in inconsistence with the goal of China’ existing precisely targeting the poor. What’ more, most of the cases’ data source had their natural limitations, the reason is that the sampling principle commonly used for poor households’ information collection was 120 households per county, however, this would overlook the differences among various poor households, insufficiently depicting various characteristics of China’s poverty, due to the higher population density, huger gaps among rural areas, complex contributing poverty factors, and special anti-poverty policy. As a result of the less sample data, it often caused deviations between the measurement results and the actual poverty (Li et al. 2005 ; Hayati et al. 2006 ; Islam and Maitra 2012 ; Thongdara et al. 2012 ).

On the other hand, Poverty is also geographical related, geographical environment has a significant influence on the state of poverty particularly in mountainous regions (Madulu 2005 ; Vista and Murayama 2011 ). So considering the impact of the natural environment on poverty, the geographical distribution of the poor has also become another current hot topic. For examples, some studies explored the case area’s poverty determinants in terms of poverty fragility (Christiaensen and Subbarao 2005 ; Islam and Maitra 2012 ). Some attempted to use GIS to evaluate poverty (Hentschel et al. 2000 ; Akinyemi 2008 ; Wang et al. 2013 ). However, most research of the above focused on the perspective of sociology and economics, seldom quantitatively considering the influence of geographical environment on poverty under different geographic constraints.

Therefore, to address the above problems to fulfill China’s precise poverty reduction strategy, taking the whole poor households’ census data from Hechi City, China, as our test case, we combines quantitative GIS analysis with RS digital image processing technology, attempting to develop a multidimensional poverty measurement and analysis methodology to assess each county’s poverty level and the poverty contributing factors. According to study area’s geographic and socioeconomic characteristics, the poverty analysis could help policymakers better understand the local poverty by identifying the poor, locating them and describing their characteristics, so as to take differentiated poverty alleviation measures according to specific conditions of each county.

Research area and data sources

Research area.

Hechi City belongs to Guangxi Zhuang Autonomous Region, China, and is a part of Yunnan and Guangxi and Guizhou rocky desertification areas amongst the 14 contiguous destitute areas. As a historic revolutionary base, gathered minority, border and mountainous region, Hechi is underdeveloped with the typical Chinese poverty-stricken characteristics. In other words, its conflict among population, resources and environment is very prominent, e.g., it has a total population of 4,500,000, mainly made up of Zhuang, and the minority nationalities account for 83.67 % of the total population. It is mountainous, mainly distributed at the border, with the diverse terrain that is high in the northeast and low in the southeast. On the other hand, with its widespread karst geomorphology, it is considered as the worst rocky desertification and fragmentation area in China, the area of rocky desertification being 722,600 hectares.

Further specifically, 11 counties are under the jurisdiction of Hechi City, e.g., Jincheng, Yizhou, Luocheng, and Bama, etc. Among which, there are 7 ones belonging to the national key poverty-stricken counties, and the other 4 counties are classified as province-level ones. On the other hand, there are 5 minority autonomous counties and 4 historic revolutionary base ones. As shown in Fig.  1 , ‘national-level’ refers to that the county belongs to national-level poverty-stricken counties; ‘revolutionary base’ refers to that the county belongs to historic revolutionary bases; ‘autonomous county’ refers to minority autonomous county; ‘other’ refers to counties that do not belong to any kind of above.

Illustration of the study area

Data sources and preprocessing

The data collected for this study includes socioeconomic dataset and basic geographic information dataset. The former stems from 2013 census archiving data of rural poor households, i.e., household sheets, provided by official poverty alleviation department of Hechi City, covering a wide range of headcount information about each households, e.g., housing conditions, production conditions and living conditions, and all adding up to 1578 villages and approximately 1,100,000 individuals. The latter geographic dataset includes mainly the 1:250,000 geographic data, national 90 m DEM data, and landsat8 raster image of the same period in the study area.

These data are pre-processed before putting into use, e.g., by eliminating unreasonable socioeconomic data, spatial data’s georeferencing, clipping, as well as joining spatial and corresponding socioeconomic data. In addition, as a result of interactions between vulnerable ecological environment and unreasonable human activities, the rocky desertification degrees of the study area are calculated from landsat8 image by using supervised classification and utilizing ENVI software to combine 4, 3, 2 wave bands for pseudocolor synthesis.

Research methods

In light of the complex poverty characteristics of the study area, this article constructs a set of multidimensional poverty measurement indicators and evaluation methodology with Chinese characteristics, taking the rural poverty-stricken households as the evaluation unit, and county as the output unit, designing multidimensional measure indicators, adopting A–F identification and spatial geographic statistics to compare and analyze the diversities of poverty characteristics, as well as their spatial distribution under different natural and socioeconomic situations.

• Multidimensional poverty measurement

According to Alkire and Foster ( 2011 ), A–F ‘dual cutoff’ identification approach can be seen as a general framework for measuring multidimensional poverty since many key decisions are left to us. These includes the selection of dimensions and indicators, derivation cutoffs (to determine when a person is deprived in a dimension or indicator), weights (to indicate the relative importance of the different deprivations), and a poverty cutoff (to determine when a person has enough deprivations to be considered to be poor). So, our work in this section is to give the response to the above to fit the purpose of the precise poverty measure and intervenes, and as well as to embody normative judgements regarding what it means to be poor in the study area.

Multidimensional poverty measure indicator system

The principles by which we build multidimensional poverty measure indicator system are as follows, (1) responding to the implement demand of Chinese precise poverty alleviation of the ‘New Outline’; (2) In accordance with the indeed requirements of the scientific nature, typicality, data availability, policy relevance and practicality for building common indicators; (3) Referring to the poverty-related identification methodology put forward by a wide range of research scholars (Li 2009 ; Ravallion 2011 ; Lu 2012 ; Hu and Ou 2013 ).

To be specified, after the collection of the indicator candidates from the census archiving data of households, we integrates the above principles with Chinese special poverty reduction objective of ensuring that the poor farmers do not have to worry about food and clothing and they have to be guaranteed for compulsory education, basic medical treatment and housing, and filter out four dimensions, namely, housing, health, education, living conditions. Then, taken income as the independent variable, logistic regression is used here to check the validity of each deprivation dimension by investigating whether or not dimensional deprivation are significantly associated with income that are known to be correlated with poverty. Results showing all the coefficients are statistically significant which guarantee the validity of all the four dimensions. Besides, to objectively eliminate redundant and non-critical ones, each candidate in the above four dimensions goes through the threshold sensitivity examination of the correlation coefficient by use of the exploratory factor analysis method in SPSS software environment. At last, a set of multidimensional poverty measure indicators for Hechi City is constructed, as shown in Table  1 , there are altogether 4 dimensions and 10 basic indicators in the system. Where, as the basis for determining who is deprived and in which indicator, the derivation cutoff depends on anti-poverty development goal under Chinese current special conditions, and its special definition is given in the Table to satisfy dimensional monotonicity and key properties for policy and analysis, e.g., one farmer is poor if his health is weakly below the cutoff.

In regards to the weight of these dimensions in A–F method, considering that the policy-related criterion that each aspect of households’ living conditions should be synchronously improved to keep the same pace with building national moderately prosperous society of China, as well as that the equal weighting method is also a commonly adopted scheme in international MPI (Wang et al. 2015 ), this paper assigned equal weight to all four dimensions as well as the indicators within each dimension. To be specified, each dimension has the same weight value of 1/4, and each indicator within each dimension shares the same weights of the dimension.

Multidimensional poverty measure methods

A–F method describes how poor people are identified by using ‘dual cutoffs’ (Alkire and Foster 2011 ), so this article introduces the A–F dual cutoffs poverty line to identify that a person is poor if multiply deprived enough. Here, dual cutoffs means deprivation cutoff and poverty cutoff between the target group and the remaining population, respectively indicating who is deprived and in which indicator, and whether a person is deprived enough to be called poor. To be specified, this article conducts multidimensional poverty identification for the targeting of ‘who is poor?’, addressing whether each indicator of one household has been deprived by employing the deprivation cutoff, as well as whether the household is overall multidimensional poverty-stricken with the help of poverty cutoff.

Further, during the dimensional poverty measurement, A–F dimensional aggregation and decomposition methodology is also adopted to respectively reflect the overall level of poverty in a given poverty line, i.e., addressing ‘how much poverty there is overall?’ for evaluation and monitoring, the joint distribution of deprivations, as well as the breakdown by dimension after identification. As a result, households’ deprived capabilities are identified, in which the multidimensional poverty incidence ( H ), average deprivation quota ( A ), multidimensional poverty index ( MPI ), and the contribution degree of each indicator ( C ) are measured.

So this article takes county as the output unit to conduct poverty analysis, synthesizing the multidimensional measure results of households in each county. The whole flows are shown in Fig.  2 , as well as the specific explanation in Table  2 .

Poverty measurement flow

Diversity analysis on multidimensional poverty’s characteristics

To some extent, China government attaches great importance to the balanced development of different rural regions, especially for those different types of historical poverty-stricken counties that other departments have compulsory duties to specially provide tilted support on assistance policies and resource.

Under the current conditions of Hechi City, each county is classified into different types according to its historic poverty characteristic, and Theil - T coefficient is introduced here to conduct inter-class and intra-class difference analysis, so as to measure the effectiveness and efficacy of different third-party departments on the anti-poverty development, also contributes them to taking specific measures to facilitate the next step. Compared with other difference analysis methods such as Gini coefficient and variable coefficient, Theil - T coefficient model can break down the overall differences of the research area into inter-regional differences and intra-regional differences (Hu et al. 2009 ), so that the gap or inequality between different types of counties can be better revealed. Employing the decomposability of Theil - T coefficient, this article measures the overall differences ( T t ), inter-regional differences ( T r ) and intra-regional ( T a ) differences under different poverty indicators respectively. As shown in formula ( 1 )–( 3 ).

where, n refers to the number of the classes after each county is been classified; Y i represents the portion of the counties in Class i in the given indicator; P i represents the multidimensional poverty headcount ratio of the given class i . Y ij and P ij represent the given indicator’s poverty contribution portion of county j in the class i of counties, and multidimensional poverty headcount ratio of the county j within class i , respectively. The larger the Theil - T Index , the bigger the differences of poverty characteristics, and vice versa.

Results analysis on multidimensional poverty measurement of the research area

According to the above methodology of multidimensional poverty measurement and analysis, Hechi City’s poverty indexes, i.e., H, A, MPI , are achieved. And then, multidimensional poverty characteristic and their structural differences are revealed by use of spatial statistical analysis.

The overall characteristic of multidimensional poverty of the research area

As stated in “ Research area ” section, K represents the number of multidimensional poverty cutoffs, which can be used to determine whether the household is under multidimensional poverty or not. In terms of different K values, the households’ poverty differences may be very big, as well as that of each indicator’s deprivation ratio. In this study, there are ten basic indicators, therefore, setting K  = 1, 2,…, 10, we calculate the changes of H , MPI and A under given different K values of the research area, analyzing their changes, and then selecting a reasonable value of K to explore the poverty contributing factors according to the contribution of each indicator.

The characteristic of the study area’s multidimensional poverty indexes

As shown in Table  3 , with the increase of K, H and MPI show a decreasing trend. When K  =  8 , H and MPI being 0, indicating that there are no more than 8 basic indicators in the research area that are deprived extremely as poor counties. Under different K values, each identification indicator’s contribution degree changes little, showing that these indicators’ deprivation degrees are stable for the households. When K ∈ [1, 6], the contribution of ‘adults’ illiteracy’ is small; While K  > 6, it increases, indicating that those households affected by this indicator are also affected by at least other five indicators at the same time. Since the ratio of the households under illiteracy poverty is small when K  > 6, while the number is large when K ∈ [1, 6], so the contribution of the indicator is lower when assigning K a small value. Overall, the contribution of ‘fuel type’ is opposite to that of ‘adults’ illiteracy’, indicating that the number of households affected by the ‘fuel type’ is large.

Two basic indicators of ‘house safety’ and ‘members’ health’ are of great contribution degrees that has been maintained at around 30 % under different K values, showing when K is smaller, the proportion of households with the deprivation of housing and health indicators is larger; and vice versa, further indicating that households in high-dimensional poverty are of great deprivation in terms of these two indicators. In other words, when K is smaller, the covered poverty indicators are not so comprehensive; however, when K value is larger, the number of those households in higher dimensional poverty is too smaller to reflect the common poverty situation in the research area. Therefore, this article follows the UNDP standard, defining those households under about 30 % of deprived indicators as poverty-stricken ones (Lu 2012 ; Hu and Ou 2013 ), i.e., K = 10/3 ≈ 4. On the other hand, to scientifically select a cutoff, the ANOVA and logistic regression model, introduced by Gordon et al. ( 2000 ), Qi and Wu ( 2015 ), are also applied to find out which poverty cutoff could best distinguish the poor and non-poor. As a result of using F value and Chi 2 value respectively, it can be seen that both the F value and Chi 2  value are the biggest when K is defined as 4 for counting the valid deprivation indicators. Therefore, the following analysis are done when given K  = 4.

Multidimensional poverty degree of the research area

According to the ‘Dimensional Aggregation’ algorithm in “ Data sources and preprocessing ” section, we obtain MPI , A and H of each county, respectively denoting the multidimensional poverty degree, poverty intensity and poverty occurrence. As shown in Fig.  3 , the MPI of each county ranks from high to low: Fengshan > Donglan > Huanjiang > Luocheng > Bama > Du’an > Dahua > Nandan > Tian’e > Yizhou > Jincheng. As far as H is concerned, the three values, the average, the minimum and the maximum, are obviously different, showing that the proportion of multidimensional poverty varies greatly. A value of every county is about 0.40, showing that the difference among each county’s deprivation is smaller. In addition, it can be seen that high H is often accompanied by high MPI , indicating that proportion of the poor population with greater poverty is also higher.

H , A , MPI of each county in the study area

According to each county’s MPI value, we use equal interval classification in ArcGIS (Atreshiwal 2012 ; Thongdara et al. 2012 ), dividing the 11 counties of the research area into three categories, as shown in Fig.  4 . From an overall perspective, MPI shows a tendency of ‘higher in the rim and lower in the middle’. Regarding Jincheng County as the center, the surrounding counties are much poorer; instead of the central areas’ less poverty. Fengshan, Donglan and Huanjiang County belong to highly impoverished county, and Jincheng and Yizhou belong to mildly poor ones. MPI is increasing from south to north, also from west to east. In addition, those minority autonomous counties, i.e., Huanjiang, Du’an, Luocheng, Dahua and Bama, all belong to middle or high poverty.

MPI spatial distribution for each counties when K  = 4

Multidimensional poverty contributing factors

Each county’s poverty factors may be different due to the MPI differences. The contribution degree, i.e., C , indicating each factor’s contributing to the county’s comprehensive poverty, is figured out by using ‘dimensional decomposing’ algorithm in “ Data sources and preprocessing ” section. Then, according to the average C value of each indicator, these indicators are divided into three classes in a descending order, namely, main poverty factors, general ones and minor ones, seeing Table  4 , and Fig.  5 is the distribution of each indicator’s contribution degree.

Contribution degree ( C ) of each indicator

As shown in Table  4 and Fig.  5 , the three indicators, including ‘dangerous housing’ with contribution degree C ∈ [0.3, 0.4], ‘poor health’ with C ∈ [0.2, 0.3], and ‘adults’ illiteracy’ with C ∈ [0.05, 0.15], are classified as the main poverty factors due to their absolutely high contribution level. Similarly, ‘broadcasting access’ and ‘electricity access’ can be classified as minor poverty factors due to their C values between 0 and 0.05. The rest five indicators are classified as general poverty-contribution factors with their C values of 0.05–0.1.

From the perspective of spatial clustering, we also found that, H and MPI show a significantly high value aggregation in Fengshan, Donglan and Bama of the west, while there exists a significantly low value aggregation of A and MPI in the middle and southeast sections; what’s more, A shows a high spatial aggregation in Nandan, Luocheng and Dahua. All these mean that the middle and southeast of Hechi city are in medium poverty and the poverty differences among counties are small; on the contrary, the west and the north, Fengshan and Nandan, are in relatively deep poverty, as well as the middle and the southeast are of less poverty-stricken. The indicator of ‘children enrollment rate’ shows a very high value aggregation in Jincheng and Donglan of the middle, as well as a low value aggregation in the marginal area; ‘adults’ illiteracy’ shows a high value aggregation in Dahua of the south, and a low value aggregation in other counties. One of main poverty factors, ‘housing’, shows a high value aggregation in Yizhou of the east and Dahua of the south, rather than a significantly low value aggregation in Jincheng and Donglan of the middle. On the other hand, the ‘health’ indicator shows an insignificantly high value aggregation in Tian’e and Nandan of the northwest, while in other areas, it shows an insignificantly low value aggregation. In a word, in terms of poverty degree, the west of the study area shows a significantly aggregation, while the middle-east indicates a significant spatial difference; in terms of the poverty factors, Dahua and Bama of the south, and Nandan of the north showing significantly high value aggregation.

Multidimensional poverty characteristics under different classes of social economic conditions

At present, Chinese government is conducting classified poverty-alleviation policies based on the poverty characteristics of each county and its locational conditions, and the trinity of poverty reduction policy that aids the poor in social development, industrial development, special aid-the-poor projects, respectively, has become an important measure to stimulate the development-oriented poverty reduction program for rural China. How to improve the local government behaviors to enhance the efficiency of poverty alleviation is one of the core problems for the poverty alleviation and development from now on. Therefore, when it comes to applying the Theil index’s great subgrouping decomposition strength to monitor the execution efficacies of different government departments’ efforts in aiding work, it will be very easy to find their vulnerability on aiding system, further to help take targeted measures in poverty alleviation.

In this context, we divide these counties of the study area into three classifications, according to each county’s locational conditions mentioned in “ Related work ” section, namely, national-level poverty-stricken county, minority autonomous county, historic revolutionary base county, adopting T t , T r and T a Theil indexes in formula ( 1 )–( 3 ) to reflect the poverty diversity among intra- and inter-classifications, respectively. As shown in Table  5 and Fig.  6 that sheds a light on visually understanding the development differences, T rp and T ap represents T r and T a in percent terms, respectively.

Intra-classification and inter-classification differences

In terms of the classification of national level poverty-stricken counties, the intra-classification differences of different indicators’ contributions are larger than those of other classes, except for the housing indicator; the inter-classification differences of different indicators are similar to those of other classes, except for the two indicators of drinking water’s safety and sanitary facilities. This indicates that the multidimensional poverty difference is quite larger among different poverty level of counties, while the internal difference among those counties at the same poverty level is comparatively smaller. In terms of the classification of minority autonomous counties, the intra-classification differences of indicators’ contributions are larger than those of historic revolutionary base classes, except for the indicators of adult’s illiteracy, drinking water’s safety and sanitary facilities. The inter-classification differences of different indicators are similar to those of other classes, except for drinking water’s safety and sanitary facilities. This indicates that the multidimensional poverty difference is greater between minority autonomous counties and non-minority autonomous counties, while the internal difference of the same classes is comparatively smaller. Overall, the poverty contributions of the two indicators, i.e., housing and adults’ illiteracy, show bigger intra-classification differences, while those of the other two indicators, drinking water’s availability and sanitary facilities, show bigger difference not only in intra-classification, but also in inter-classification. Which indicates there exist significant difference among those counties from three different classifications, as well as from the same classification, when regarding to the poverty degree of two indicators of drinking water’s availability and sanitary facilities.

Compared Table  3 with Table  5 , it could be seen that, the contributing influence of different poverty identification indicators on the poverty degree is different from that on poverty diversity, also different under different classifications. As a whole, it seems that greater poverty contributing degree does not necessarily play a more important role in poverty diversity, due to the uneven development among the same classification of counties, as well as among different classification of ones. As a matter of fact, Table  3 comes from A–F measurement and component decomposition, resulting in three multidimensional poverty indexes and each indicator’s contribution degree to the three indexes, respectively; while Table  5 is from Theil index that reveals the diversities among different poverty indexes, as well as each indicator’s contribution degree to these diversities respectively, under different subjective poverty-stricken classifications. On the other hand, when given K  = 4, the indicator ‘house safety’ becomes a greatest contributing factor to MPI. Correspondingly, it also shows a greatest T t and a greatest T r compared to other indicators under different classifications; However, T a is an exception. Similarly, the second greatest contributing indicator, ‘member’s health’, and the least two greatest ones, ‘broadcasting access rate’ and ‘power access rate’, also show their corresponding ranks on T t and T r , partly indicating that the poverty contributing factor has a positive correlation with T t , as well as with T r , the indicator w ith a larger poverty contributing degree having a larger T t and T r .

Multidimensional poverty characteristics under different topographic and geomorphic conditions

As a part of typical rocky desertification areas in Yunnan and Guangxi and Guizhou provinces of China, Hechi City’s natural environment constrains, e.g., topographic and geomorphic features, rocky desertification degree, etc., play an obvious role on the local social and economic development. In this context, the analysis on multidimensional poverty characteristics under different natural environments helps guide policies for effective poverty interventions, adapting to local conditions.

Multidimensional poverty characteristics under different topographic conditions

Figure  7 shows the overlay distribution of each counties, between MPI, H, A , respectively, and the local elevation. From Fig.  7 a, it can be seen that, mild poor counties, Jincheng and Yizhou, are of lower elevation and flatter terrain, compared with moderate poor counties, Nandan and Tian’e with a higher elevation; whereas, Fengshan and Donglan with relatively flat terrain are highly poor counties. So it could be partly said that comprehensive poverty degree has little relationship with elevation. From Fig.  7 b, it can be seen that, Jincheng and Yizhou, with lower elevation and flatter terrain, have lower H values; whereas, Fengshan, with higher elevation and mountains terrain, has a larger H value. This indicating that, most of the households that are deprived of fewer poverty indicators, are mainly aggregated in relatively flat areas. Figure  7 c shows that, all A values of Donglan, Huanjiang and Yizhou are the greatest, while these counties’ elevations are relatively lower; Jincheng, Du’an and Luocheng, all with middle A , are of lower elevation and flatter terrain; Nandan and Tian’e are located in the highest elevation and the most complex terrain, while they have the lowest A . All these show that A has a negative correlation with elevation, the higher the elevation, the lower the A .

Overlay between DEM and MPI , H , A , respectively

On the other hand, topographic fragmentation degree of Hechi City is represented by the slope difference between the slope value of 90 m DEM and that of 1000 m DEM, and is classified by natural interval classification. By synchronously taking county as output unit to redraw it in ArcGIS and then spatially overlaying the fragmentation with MPI, A and H , respectively, it can be seen that, overall, the fragmentation degree has a positive correction with elevation, the more serious fragmentation comes with the higher elevation, and vice versa. As far as the relation of fragmentation degree with multidimensional poverty indexes is concerned, it can be similarly found that, MPI , A and H of each county, all are increasing with the increasing of fragmentation degree. The influence of fragmentation degree on these three indexes is larger in northwestern counties than that in central ones. All these are consistent with the spatial aggregation features of multidimensional poverty discussed in the above “ The overall characteristic of multidimensional poverty of the research area ” section.

Multidimensional poverty characteristics under different karst rocky desertification geomorphic conditions

There exist serious contradictions among population, resources and environment in the Karst study area. We explore the relation between rocky desertification degree and multidimensional poverty of the study area. Similar to the above fragmentation degree’s representation, the rocky desertification degrees are also classified into three levels, i.e. mild, moderate and severe level, showed by using gray image and ArcGIS color rendering, respectively, as shown in Fig.  8 .

Karst rocky desertification Classification and its correlations with three poverty indexes, respectively

Figure  8 reflects that rocky desertification is widely distributed in Hechi City and possesses typical regional differentiation characteristics. Six out of seven national-level poverty-stricken counties are located in moderate and severe level rocky desertification area. In term of H , those counties with higher rocky desertification, e.g., Bama, Dahua, Huanjiang, Yizhou etc., have higher H values, whereas counties with lower rocky desertification, like Tian’e, Fengshan, Jincheng etc., have lower H values. This partly indicates the area with high rocky desertification has high multidimensional poverty incidence, vice versa. Rocky desertification degree and multidimensional poverty incidence have positive correlation with each other.

In terms of MPI index, rocky desertification has the least influence on Jincheng, then Tian’e and Fengshan; however, its influence on MPI of the other counties is moderate level or above. This shows that most of the poverty-stricken households in the study area are subjected to rocky desertification. For index A , Huanjiang, Bama and Dahua have the largest A and the most severe rocky desertification, indicating that the households in those counties are deeply subjected to rocky desertification, and are deprived of more indexes than those in Tian’e, Fengshan and Jincheng, where there exist mild levels of rocky desertification.

Generally, H , A and MPI increase with the increasing of rocky desertification degree. This shows that the geographical distribution of poverty-stricken counties has an obviously positive correlation with rocky desertification. The reason is that there exists internal interaction effect between rocky desertification and poverty. The vulnerable ecological environment of the research area and human’s irrational activities cause current situation of karst rocky desertification, while human’s irrational activities are caused by poverty, vice versa. As a result, available resources become less and less, which further increases poverty. Therefore, poverty alleviation measures should be targeted, differentiated and precise, according to local natural environment and socioeconomic development conditions.

Conclusion and discussion

Aiming at national ‘Precise Poverty Alleviation’ strategy of China, this article proposed a multidimensional poverty measure and analysis methodology, using GIS to measure poor households and their contributing factors, and taking 11 counties of Hechi City as study area, census archiving data of households as data source, respectively explored the poor’s multidimensional poverty characteristics under different geographic and socioeconomic conditions, as well as their spatial distribution diversities.

The main methodology includes: A set of multidimensional poverty measure indicators with Chinese Characteristics was proposed, consisting of 4 dimensions and 10 basic indicators; A multidimensional poverty measure model based on A–F double cutoffs was developed to evaluate the poor’s multidimensional poverty characteristics; A GIS spatial analysis method was introduced to descript the spatial diversity under different geographic and socioeconomic conditions.

The case test of 11 counties in Hechi City showed that, firstly, each county existed at least four respects of poverty, the whole poverty level showed the spatial trend of surrounding higher versus middle lower, as well as that of northern higher versus southern lower; Secondly, the main poverty contributing factors of the research area were followed in descending order: unsafe housing, family health and adults’ illiteracy. Thirdly, under three kinds of socioeconomic classification systems, the intra-classification differences of H, A and MPI are greater than their inter-classification differences. Fourthly, these three multidimensional poverty indexes increased with the increasing of rocky desertification degree and rocky desertification of the study area, A having an obvious negative relation with the study area’s elevation.

In closing, this article tried to analyze multidimensional poverty from the perspective of combining socioeconomics and human geography, developing multidimensional poverty measurement and analyzing methodology with Chinese Characteristics, realizing GIS application in multidimensional poverty identification and measurement. Such efforts not only provide scientific basis for precisely targeting poor people and aiding decision-making for further special poverty alleviation of China, but also offer references for both domestic and foreign related research.

It is noteworthy that, due to data limit, this article could not be able to monitor the multidimensional poverty change at a series of spatial and temporal scales, and conducting spatial analysis at county level is not so sufficient to support for the village- level ‘entire-village advancement’ poverty reduction work of China, all these are also our research directions to the next work.

Further, there still exists some issues on how to decide a ‘right’ deprivation cutoff, poverty line and weights, although there are many studies in line with {0,1} dichotomy of the derivation cutoff and equal weights in A–F applied studies, considering poverty also as a matter of degree rather than an attribute that is simply present or absent for individuals in the population, we had also tried to adopt the fuzzy measure to address the poverty-line robust, and the result is not so variable due to the relative little difference when compared to a simple {0,1} dichotomy. To be specified, three poverty indexes, A , H , MPI , have not obvious changes until K  = 6, when it may not make much sense for the practical work due to the improper K value that is the ideal value of 4. So only for the purpose of simplification, this paper adopted {0, 1} dichotomy estimates as the derivation cutoff. We are conscious of the limitations that this test is only the preliminary work, and obviously, exploring cutoff robustness of the study area needs a further improvement in the future work.

On the other hand, to examine the possible result diversities that result from various weights, this article also adopted two different kinds of weighting methods, equal weights and combined weights that integrate subjective equal weight and objective entropy one based on game theory, to make a comparative analysis. Essentially, the combined weight model based on the game theory is devoted to finding a consensus or compromise among different weights, and the most reliable weights can be represented in a form of optimized weight set by minimizing the respective deviation between the possible actual weight and various basic weights. i.e., in this paper, based on game theory, the combined weight can be defined as an optimal weight between equal weight method and entropy value method. The specific calculation can refer to such literatures as Wang and Qian ( 2015 ). From the two different weight, we found that the results are various. For example, randomly taken out one case study, the two results are shown in Fig.  9 . When given K  = 1–3 and 6–9, the three indexes, M, H, MPI, and their differences calculated by different weights have no significant changes, respectively. When given K  = 3–6, H and MPI have been greatly changed with different K ; correspondingly, their differences under different weighting methods have also changed greatly. The most obvious is in the case of K  = 4–5, when the differences of all the three indexes under different weighting methods began to mutate, especially MPI changing its value from 0.227 under the equal weight method to 0.438 under combined weight method, indicating that almost all the households in this case have been deprived from at least three indicators. From the beginning of K  = 4, the number of the households under the multidimensional poverty becomes less. Similarly, when given K  = 4, we also found the contribution degrees of different indicators had also changed under these two different weighting methods. To be specified, under the equal weights, the top three contributing factors include house safety, health, and fuel type; however, taken the combined weights into consideration, the ranking is changed into the order of house safety, health, and sanitary facilities. From the above, it can be known the results may be different due to different weighting methods and different poverty cutoffs.

The correlations between K and different poverty indexes with two different weighting methods

In terms of the identification accuracy, we also adopted ‘overlap ratio’ of the poverty-stricken villages between multidimensional profile and monetary profile as the test indicator aiming to the difference result only from different weighting methods. Here, we used equal interval classification in ArcGIS, dividing all the villages of the research area into three categories, similarly as shown in “ Multidimensional poverty degree of the research area ” section. We examined how the highly and moderately poverty-stricken villages match with the national assigned key villages list. As shown in Fig.  10 , compared to the list of national designated key poverty-stricken villages that are recognized according to the 2300 Chinese RMB Yuan of income poverty line, there are 66.3 % overlap ratio, while this number changed to 64.6 % in the condition of combined weighting method. In this case, it seems that the two approaches produced similar results, and the difference is not significant with regard to multi-poverty.

Overlap between identification results and national designated ones under equal weighting method

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Authors’ contributions

YW carried out the design of the study studies and drafted the manuscript. BW participated in the case test of the study and performed the statistical analysis. Both authors read and approved the final manuscript.

Acknowledgements

This materials is based upon work supported by Natural Science Foundation of China (No. 41371375), as well as by Twelve-Five science and technology support program of China (No. 2012BAH33B03). We also thank the anonymous referees for their helpful suggestions.

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Wang, Y., Wang, B. Multidimensional poverty measure and analysis: a case study from Hechi City, China. SpringerPlus 5 , 642 (2016). https://doi.org/10.1186/s40064-016-2192-7

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• Poverty identification indicators
• Spatial pattern
• Diversity analysis
• Geographical constraint

Multidimensional poverty in India: a study on regional disparities

• Published: 04 August 2021
• Volume 87 , pages 3987–4006, ( 2022 )

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• Pinaki Das 1 ,
• Sudeshna Ghosh   ORCID: orcid.org/0000-0002-2026-1676 2 &
• Bibek Paria 1  

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The primary objective of this study is to investigate the regional disparities of multidimensional poverty (MPI) in the context of India. This study to our knowledge is the first of its kind which examined MPI disparity at a regional level. The study has classified the geographic area of India into six regions: Northern, Eastern, North Eastern, Central, Western and Southern region. Further, we explore MPI across population sub-groups within a region. Using the latest available household data from the National Family and Health Survey over 2005–2006 and 2015–2016 we explored how at the regional level multidimensional poverty changed within a decade. The paper estimates MPI in India at a regional level following the methodology of Alkire and Foster (2011). The Eastern rural region has the highest MPI 0.43 (2005–2006) and 0.21 (2015–2016). The lowest MPI is in the Northern region 0.14 and 0.03 respectively. The Northern region further has lowest MPI across all social sub-groups. The results also demonstrate regional concentration of MPI particularly in the Central and Eastern regions. A major disquieting feature is that the regional variation in MPI across the Eastern and the Northern region increased by four times in 2015–2016 compared to the earlier period. The study further obtains that though multidimensional poverty has reduced significantly over the decade the decline is regressive. It can be traced to the nature of regressivity in the decline in the different deprivation indicators. The present study suggests that India must endeavour the process of balanced regional development.

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Dynamics of multidimensional poverty and its determinants among the middle-aged and older adults in China

• Qun Wang 1   na1 ,
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• Xiaojun Lu   ORCID: orcid.org/0000-0003-3480-9089 1  

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The aim of this study was to understand the dynamics of multidimensional poverty and its determinants among mid-aged and older adults. We used 2011, 2013, 2015, and 2018 data from the China Health and Retirement Longitudinal Study. We utilised basic descriptive statistics, the poverty transition matrix, Kaplan–Meier estimates, and the discrete-time proportional hazards model for data analysis. From 2011 to 2018, the incidence of multidimensional poverty among mid-aged and older people basically decreased, but the average poverty intensity remained stable. Most mid-aged and older adults had transient multidimensional poverty. The longer an individual remained multidimensionally poor, the smaller the probability of exit from poverty; The longer an individual remained nonpoor after escaping from poverty, the smaller the probability of returning to poverty. As to other factors, individual characteristics, family structure, living arrangements, social capital, and living areas significantly affected the risks of multidimensional poverty exit and reentry. Based on these results, the government should implement targeted interventions for frail older adults with the identified characteristics to prevent them from persistent multidimensional poverty or return to poverty.

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Introduction.

Poverty is one of the main problems plaguing most countries worldwide (OECD, 2021 ). By 2020, China had eliminated absolute poverty. However, similar to other low- and middle-income countries (LMICs) that made significant achievements in combating absolute poverty, China still faces great challenges of relative poverty reduction (Wan et al., 2021 ). The relatively poor people often face problems in employment, education, medical care, pension, etc., due to resource shortages, poor environment, and lack of individual ability. Essentially, their poverty is multidimensional (Guo et al., 2022 ). In addition, in LMICs, some deep poverty-stricken or marginal poverty-stricken households have a high risk of returning to poverty due to poor foundations (Échevin, 2013 ; Wan et al., 2021 ). Therefore, it is urgently needed to understand the dynamics of multidimensional poverty and its determinants to reduce relative poverty.

Academically, poverty has long been a heated research topic. Thanks to Sen’s innovative contribution to the capacity approach, many poverty researchers have gradually shifted from a single monetary perspective to a multidimensional approach (Alkire and Santos, 2014 ). A series of approaches were developed to measure multidimensional poverty. Among them, the most widely used measurement follows the Alkire-Foster (A-F) method. Many researchers used cross-sectional data and applied this method to analyse the current situation and influencing factors of multidimensional poverty (United Nations Economic Commission for Europe, 2017 ).

Another shift in poverty studies is that more and more researchers have moved from static poverty analysis to dynamic poverty analysis (Jenkins, 2011 ). For those in poverty, it is essential to know whether they are in poverty for most of their lives or only for a brief period. Therefore, from a policy perspective, it is crucial to understand the process of falling into poverty or escaping from poverty, which gives rise to the problem of poverty dynamics. The studies on poverty dynamics mainly focus on two aspects: poverty durations and poverty transitions, including exiting from poverty, falling into poverty, persistent poverty, reentering into poverty, etc. (Addison et al., 2009 ).

Among the existing research on poverty dynamics, the majority were on income poverty dynamics, and only a very small part were on multidimensional poverty dynamics. Some studies on the latter topic analysed multidimensional poverty dynamics in adjacent years by constructing a Multidimensional Poverty Index (MPI) and poverty transition matrix (Madden, 2022 ; Guo et al., 2022 ). Other related studies estimated the persistence of multidimensional poverty by constructing chronic MPI (Apablaza and Yalonetzky, 2016 ; Alkire et al., 2017 ). However, the above studies have separated the analysis of durations of multidimensional poverty and its state transition, thus ignoring the cumulative effect that durations may have on poverty dynamics. Thus, there is a lack of studies systematically exploring multidimensional poverty dynamics over the whole period. Additionally, among the existing studies on multidimensional poverty, only a few centred on older adults. And the few such studies were all based on the static analysis of multidimensional poverty (Amarante and Colacce, 2022 ; Kamal et al., 2022 ). To our knowledge, no study has been conducted so far on multidimensional poverty dynamics among older adults. According to the global experience of poverty alleviation, this population is more vulnerable to poverty than the general population (OECD, 2021 ). This study is designed to fill the above knowledge gap. The aim of this study is to understand the dynamics of multidimensional poverty and its determinants among mid-aged and older adults using the China Health and Retirement Longitudinal Study (CHARLS) survey from 2011 to 2018 in a country in the stage of rapid population ageing.

The paper is organised into five sections. Section ‘Methodology’ introduces the methodology, including conceptual framework and hypothesis, data, measurement and variables, and statistical methods. Section ‘Results’ depicts the results. Section ‘Discussions’ discusses the main findings. Section ‘Conclusions’ concludes.

Methodology

Conceptual framework and hypothesis.

We formed a conceptual framework to systematically analyse the determinants of multidimensional poverty dynamics (Fig. 1 ). Based on Schultz’s ( 1961 ) human capital theory, individual characteristics such as gender, age, and marital status can affect the accumulation process of human capital, which in turn can affect multidimensional poverty dynamics. According to Rowntree and Bradshaw’s ( 2000 ) life cycle of poverty theory, poverty is closely related to significant life events in the family. Therefore, household size, labour status, and living arrangements can affect multidimensional poverty dynamics. Based on Bourdieu’s ( 1986 ) work on social capital, the lack of social capital means the lack of the possibility of obtaining development opportunities and social resources, which will lead to poverty. From the perspective of spatial poverty traps (Bird, 2019 ), different regions have differences in geographic capital in terms of economic development, culture, etc., affecting residents’ poverty status. Based on the literature on poverty dynamics (Devicienti and Gualtieri, 2010 ; You, 2011 ), the state of poverty usually changes over time, and poverty durations usually affect the change of its state. According to the above theories and our understanding of China, we postulated the following hypotheses:

Conceptual framework.

H1: In terms of individual characteristics, women, those with higher age, and those not married are more difficult to exit from multidimensional poverty and easier to return to multidimensional poverty among mid-aged and older people.

H2: In terms of family structure and living arrangement, the household size, the number of working population within a household, and living with children have a positive effect on the probability of exiting from multidimensional poverty, but have a negative effect on the probability of returning to multidimensional poverty.

H3: In terms of social capital, economic transfer and social activities have a positive effect on the probability of escaping from multidimensional poverty, but have a negative effect on the probability of returning to multidimensional poverty.

H4: In terms of living areas, the mid-aged and older people in rural or western regions have more difficulties escaping from multidimensional poverty and are more likely to return to multidimensional poverty.

H5: With durations increase, the probability of multidimensional poverty transitions became smaller.

We used data from CHARLS, which representatively collects social, demographic, economic, health, retirement, etc. data among a population aged ≥45 years in China. In 2018, CHARLS collected data on a sample size of 190,000 respondents in 124,000 households. In this study, we used all four waves of data, i.e., 2011, 2013, 2015, and 2018 rounds. We kept those who participated in all four rounds and deleted those below 45 years in 2011. In the end, we included 11,566 respondents in the analysis.

Measurement

Construction of multidimensional poverty index.

We utilised the Alkire-Foster (A-F) method to construct the MPI, which sets a double cutoff in poverty identification. First, the deprivation cutoffs were utilised to determine whether the respondent was deprived in each dimension. Second, the deprivation scores of all dimensions were weighted and summed. Third, the poverty cutoffs were applied to determine whether the respondent was multidimensionally poor. Forth, the MPI (or M 0 ) was calculated as the product of headcount ratio H and poverty intensity A (Alkire and Seth, 2015 ). H , A , M 0 are expressed as follows:

where n is the sample size, q is the number of those who were multidimensionally poor, c i is the deprivation scores of individual i in all dimensions, and k is a poverty cutoff that identifies who is poor. When c i  ≥  k , individual i is defined as multidimensionally poor and c i ( k ) =  c i ; otherwise c i ( k ) = 0. M 0 has good decomposability, which can be decomposable by subpopulations or indicators (Alkire and Santos, 2014 ; Kamal et al., 2022 ).

Dimensions, indicators, and weights of MPI

There is no unified standard for selecting dimensions and indicators of MPI, but most studies chose the dimensions of education, health, and living standards (Alkire and Santos, 2014 ; Alkire and Seth, 2015 ). Some related studies using cross-sectional data pointed out that the ageing of physical function and the weakening of labour ability are the reasons that make older adults more vulnerable to poverty. Thus, social security, work, and income are essential for older people to cope with poverty risks and maintain welfare (Zhang and Yang, 2020 ; Amarante and Colacce, 2022 ; Kamal et al., 2022 ). Therefore, in this study, we included education, health, living standards, social security, work, and income as the five dimensions of multidimensional poverty on 14 indicators (Table 1 ). Notably, in the education dimension, considering that the mid-aged and older people generally had a low level of education in the CHARLS sample (Wang and Tian, 2018 ), the deprivation cutoff was defined as poverty if they had not completed primary school. In the health dimension, we included both physical and mental health indicators. As one indicator of physical health, chronic diseases included 14 diseases, such as hypertension, diabetes, asthma, stroke, etc. We covered social and commercial pension/health insurance in the social security dimension. In the work and income dimension, we used the annual per capita net income within a household as the indicator of income. As to weights of MPI, we chose a normative weighting structure like most prior studies did (Alkire and Santos, 2014 ; Alkire and Seth, 2015 ) and selected k = 2 as the poverty cutoff, poverty in at least two dimensions was considered to be with multidimensional poverty.

Measurement of the selected determinants

As described in conceptual framework, we selected five groups of determinants: poverty or nonpoverty durations, personal characteristics, family structure and living arrangement, social capital, and living areas (Table 2 ). Only two groups of determinants needed explanations. Social capital was defined as a collection of actual or potential resources, mainly embodied in some kind of social relationship network familiar or recognised by the actors with specific institutionalised but informal characteristics (Lin, 1999 ). Similar to previous studies (Sun et al., 2016 ; Cao et al., 2022 ), we used economic transfer with relatives and social activities to measure social capital. As to durations, poverty durations referred to the time from being identified as multidimensional poor to escaping from it. At the same time, nonpoverty durations referred to the time from exiting poverty to returning to poverty.

Analytical methods

First, we used basic descriptive statistics to outline the trend and decomposition of multidimensional poverty.

Second, we applied the poverty transition matrix to analyse the multidimensional poverty dynamics in adjacent survey years (Apablaza and Yalonetzky, 2016 ; Madden, 2022 ). The sample size in this part was the whole sample included in this study, i.e., 11,566 respondents. In adjacent survey years, the transition had four types: from poverty to poverty (persistent multidimensional poverty), from poverty to nonpoverty (exit from multidimensional poverty), from nonpoverty to poverty (fall into multidimensional poverty), from nonpoverty to nonpoverty (persistent nonpoverty). Let 1 denote poverty, 0 denote nonpoverty, and p denote the probabilities of the four poverty transition situations. Furthermore, the multidimensional poverty transition matrix from time t to time t  + 1 is:

Third, considering that poverty transitions between adjacent survey years cannot explore the overall dynamics of poverty through all survey years, we applied the Kaplan–Meier method to estimate poverty’s survival and hazard rates at each time point (Devicienti and Gualtieri, 2010 ; Glauben et al., 2011 ). The Kaplan–Meier method is a common nonparametric survival analysis. Survival analysis is becoming a popular method in social sciences since it can model the occurrence of a social event (such as marriage, poverty, and migration) over time. The Kaplan–Meier method is based on all non-left-censored spells (Kleinbaum and Klein, 2012 ). We included 3,155 respondents starting a poverty spell in 2011 to analyse exit rates. Moreover, we included 1,616 respondents who escaped from poverty in 2013 to analyse reentry rates. The survival rate (hazard rate) is calculated from the survival function (hazard function). Taking exit from poverty as an example, the survival function is expressed as the probability that an individual remains poor after time t:

where T i is the duration of individual i being identified as multidimensionally poor. T a is anytime not exceeding time t . n a is the number of individuals being identified as multidimensional poor at t a . d a is the number of individuals escaping from multidimensional poverty at t a . The hazard function is expressed as the probability that individual i with T i  ≥  t will escape from multidimensional poverty at time t .

Forth, in line with methods used in studies on income dynamics (Stevens, 1994 ; Devicienti and Gualtieri, 2010 ; Glauben, et al., 2011 ), we selected the discrete-time proportional hazards model, a type of semi-parametric survival analysis, to clarify the determinants of multidimensional poverty dynamics. After deleting the observations with massive missing values in the selected determinants associated with multidimensional poverty dynamics, the final sample size in the discrete-time proportional hazards model was 11,315. We set a complementary log-log hazard rate. Moreover, the hazard function can be expressed as follows:

where h 0 ( t ) is the baseline hazard. X n is the matrix of the determinants influencing multidimensional poverty dynamics. β is the parameter to be estimated.

The trend, decomposition, and dynamics of multidimensional poverty

From 2011 to 2018, the MPI (M 0 ) showed a downward trend on the whole. This trend mainly depended on reducing the headcount ratio (H). At the same time, the poverty intensity (A) remained at about 0.57 without significant reduction, indicating that the poor population’s welfare status had not significantly improved in these years. Specifically, the sharp drop in the headcount ratio (H) mainly occurred in 2011–2013 and 2015–2018, while H slightly increased during 2013–2015, indicating that poverty entry, poverty exit, and poverty return might coexist during this time (Table 3 ).

To further explore which dimensions or indicators were the leading causes of poverty, we decomposed the poverty index over the years. From the perspective of dimensions, education had always been the dimension with the highest contribution, reflecting that the mid-aged and older adults were generally poorly educated in the CHARLS sample. The contribution of the health dimension increased year by year, indicating that with age growth, the importance of physical function and mental health to the welfare of mid-aged and older adults gradually increased. As to the dimensions of a standard of living and social security, their contributions showed a downward trend as a whole. The contribution of work and income dimension first increased and then decreased during this period. In terms of indicators, in 2018, the top contributors to overall multidimensional poverty were schooling, employment, activities of daily living, chronic diseases, and depression. From 2011 to 2018, the indicators of running water, housing structure, medical insurance, durable goods, etc. were always kept at a low contribution, reflecting that China’s “two assurances and three guarantees” policies (i.e., free from worries about food and clothing and access to compulsory education, basic medical services, and safe housing) were basically effective. Moreover, the contribution of income and pension insurance dropped sharply from 2015 to 2018, showing that China had greatly achieved eliminating absolute poverty (Table 4 ).

Based on the analysis from transition matrices, the rate of persistent multidimensional poverty gradually decreased between adjacent survey years (from 13.31% (2011–2013), to 11.42% (2013–2015), to 8.01% (2015–2018)), while the proportion of persistent nonpoverty gradually increased (from 67.72% (2011–2013), to 72.16% (2013–2015), to 73.84% (2015–2018)).

In addition, when comparing the rates of exiting from and falling into poverty, although the number of people who were identified as multidimensional poor gradually decreased as a whole, there were always new people aged ≥45 years who fell into multidimensional poverty (Table 5 ).

Further explorations of multidimensional poverty dynamics and its determinants

Results from kaplan–meier estimates.

Among those who fell into multidimensional poverty in 2011, the survival rate of maintaining multidimensional poverty gradually declined from 2011 to 2018. By 2013, only 48.78% of the 3155 individuals in poverty in 2011 remained poor, indicating that more than half of the individuals were transient multidimensionally poor. By 2018, only 17% of the 3155 individuals were still poor, and such a group had spent seven years in multidimensional poverty. The hazard rate of exit from poverty showed a downward trend, on the whole, indicating that the longer the time spent in poverty, the smaller the probability of escaping from poverty. Notably, the hazard rate of exit from poverty rebounded from 2015 to 2018. This may be because China entered a critical period of poverty alleviation after 2015, and more interventions related to poverty alleviation had been carried out. Concerning reentry into poverty, the survival rate of keeping nonpoor after exiting from poverty also gradually decreased from 2013 to 2018. Specifically, 64% and 55% of the 1616 individuals who escaped from poverty in 2013 remained nonpoor in 2015 and 2018, respectively, indicating that 36% and 9% returned to poverty in 2015 and 2018, respectively. The hazard rate of returning to poverty decreased from 2015 to 2018, indicating that as nonpoverty durations increased after exiting from poverty, the probability of returning to poverty decreased (Table 6 ).

Results from the discrete-time proportional hazards model

Based on the discrete-time proportional hazards model, we found that the likelihood of multidimensional poverty transitions became smaller with the rise of durations of poverty/nonpoverty. Specifically, the longer an individual remained multidimensionally poor, the smaller the probability of exit from poverty; The longer an individual remained nonpoor after escaping from poverty, the smaller the probability of returning to poverty (Table 7 ). These results verified the above results based on Kaplan–Meier estimates.

As to other factors, we found that men, being married, larger household size, economic exchanges with close relatives or distant relatives, participation in social activities, and living in urban areas significantly increased the likelihood of escaping from multidimensional poverty. Compared with those aged between 45 to 55 years, those aged ≥65 years had a lower risk of escaping from multidimensional poverty. Compared with those living in the eastern region, those in the middle region were less likely to exit from multidimensional poverty. Additionally, men, being married, having larger household sizes, living with children, having economic exchanges with distant relatives, and participating in social activities significantly decreased the possibility of returning to multidimensional poverty (Table 7 ). In sum, based on the discrete-time proportional hazards model results, H5 was supported, while H1, H2, H3, and H4 were partially supported.

Discussions

This study makes great contributions to the field of multidimensional poverty dynamics as it is the first attempt to explore multidimensional poverty dynamics among older adults globally. The following aspects need further discussion. First, we found that the incidence of multidimensional poverty among mid-aged and older adults basically decreased from 2011 to 2018, but the average poverty intensity seemed not to drop. We also showed that the contribution of health indicators to the overall poverty of mid-aged and older adults increased every year. In 2018, besides schooling and employment, which were hard to improve for mid-aged and older adults, the three health indicators were the top contributors to multidimensional poverty. These results suggest that health has become the most deciding contributor to the overall welfare of mid-aged and older adults in China. That means, to further reduce the multidimensional poverty of such a population, the government should strengthen the health monitoring system for them, provide routine health examinations for them, and pay special attention to their needs related to chronic diseases, activities of daily living, and mental health.

Second, we showed that the probability of multidimensional poverty transitions was negatively associated with poverty or nonpoverty durations. These results are consistent with many existing types of research on income poverty dynamics (Devicienti and Gualtieri, 2010 ; You, 2011 ). The government should apply these findings in the dynamic monitoring of poverty. To those who fall into multidimensional poverty for a long time, the government should give more targeted interventions and help since they are the most challenging group to leave the poverty state. For those who fall into multidimensional poverty, the government should provide interventions early when they are most likely to escape from it. For those who are just out of multidimensional poverty, the government should continuously monitor them for some time to prevent them from returning to poverty.

Third, as to personal characteristics, we found that men were more likely to escape from multidimensional poverty and were less likely to return to poverty than women. Women are disadvantaged in acquiring social resources, so they are more vulnerable than men (Admasu et al., 2022 ; Klasen and Lahoti, 2021 ; OECD, 2021 ). Older people (aged ≥65 years) had a significantly lower probability of escaping from multidimensional poverty than the mid-aged (aged between 45 and 55 years). This is probably because older people cannot resist risks caused by physical decline and loss of labour ability (Alkire and Yingfeng Fang, 2019 ; OECD, 2021 ; Olarinde et al., 2020 ). The married were found to be more likely to escape from poverty and to be less likely to return to poverty than those not married. Those not married tend to bear more financial pressure and spiritual loneliness, so they have higher risks of suffering from multidimensional poverty (Olarinde et al., 2020 ).

Fourth, we identified that household size was positively associated with the probability of exiting from poverty but negatively associated with the likelihood of returning to poverty. Moreover, living with children was negatively associated with the possibility of returning to poverty. These results to some extent, contradict other studies in LMICs, showing that large household sizes led to a higher risk of falling into multidimensional poverty (Olarinde et al., 2020 ; Bautista, 2018 ). Another study in LIMCs showed that family size had a U-shaped correlation with the MPI (Lekobane, 2022 ). These discrepancies could be attributed to the corresponding regions’ economic development or cultural background. In China, for mid-aged and older adults, especially older adults, the larger family size means that their care, economic, and spiritual needs can be better shared, which suggests that the overall welfare of mid-aged and older adults in China is largely dependent on family support. However, with the increasing miniaturisation of family size in China, older adults’ traditional family support model is challenging to sustain. Therefore, the government should develop diversified care models for older people, thus comprehensively enhancing their ability to resist multidimensional poverty risks.

Fifth, we uncovered that all proxies of social capital significantly increased the probability of escaping from poverty and all proxies except for economic transfer with close relatives decreased the likelihood of returning to poverty. These results verify the role of social capital in alleviating poverty (Abdul Hakim et al., 2010 ; Devicienti and Gualtieri, 2010 ; Glauben et al., 2011 ). The government should promote communities and social organisations to organise more social activities for older adults and encourage more older people to participate in these activities, thus enhancing their social capital.

Sixth, in terms of living area characteristics, mid-aged and older adults in cities were more accessible to escape from poverty than those in rural areas. Our results are similar to another study based in China (Alkire and Fang, 2019 ), but are different from one study based in Tunisia using the fuzzy nets approach, showing that urban areas were marked with significantly higher rates of extreme vulnerability to poverty than rural areas (Nasri and Belhadj, 2022 ). Based on our analysis of the decomposition of MPI, the welfare of mid-aged and older adults mainly depended on physical and mental health. People in cities have better access to high-quality medical services and facilities, which is more conducive to alleviating multidimensional poverty. Additionally, the probability of escaping from poverty in the central region was found to be significantly lower than that in the eastern region. In China, the eastern region has the higher level of economic development than the central region. Our findings verify the positive effect of the macroeconomic level on poverty alleviation (Wang et al., 2020 ). At the same time, we found that the probability of escaping from poverty in the western region was similar to that in the eastern region. This may be because in the regions with the lowest level of economic development (i.e., the western region), government interventions related to poverty alleviation are intense, which increases the probability of escaping from poverty.

Conclusions

This study mainly found: (1) from 2011 to 2018, the headcount ratio of multidimensional poverty among mid-aged and older adults showed a downward trend, but the average poverty intensity remained basically stable. (2) Health has become the most deciding contributor to multidimensional poverty of mid-aged and older adults. (3) In the whole period, most mid-aged and older adults identified as multidimensionally poor had transient poverty. And in adjacent survey years, the phenomenon of poverty exit and poverty reentry coexisted. (4) Durations of poverty or nonpoverty, individual characteristics, family structure, living arrangements, social capital, and living areas significantly affected the risks of multidimensional poverty exit and reentry. And most of these factors had opposite influences on the risks of multidimensional poverty exit and reentry. These results have far-reaching implications for the government to carry out prevention interventions and also largely enrich the studies of multidimensional poverty dynamics. Considering that we had limited rounds of data in the analysis of reentry to poverty, future studies involving more rounds of data are needed in this regard. Future studies are also needed to explore the influence of COVID on multidimensional poverty.

Data availability

The data used in this study can be found here: https://charls.charlsdata.com/pages/data/111/zh-cn.html .

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Acknowledgements

The study was supported by the National Natural Science Foundation of China (72274027), the Key Project of Liaoning Social Science (L22AGL007), the Major Program of Philosophy and Social Science of the Chinese Ministry of Education (21JZD034), and the Basic Scientific Research of Central Universities (DUT22LAB123).

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These authors contributed equally: Qun Wang, Lu Shu.

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Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, China

Qun Wang, Lu Shu & Xiaojun Lu

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Conceptualization: QW and XL. Methodology: LS, QW, and XL. Formal analysis: LS; Writing—original draft preparation: QW and LS; Writing—review and editing: QW, LS, and XL; Funding acquisition: QW.

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Correspondence to Xiaojun Lu .

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This study used open data. Ethical approval for this type of study is not required by our institute. The CHARLS survey was approved by the Ethical Review Committee of Peking University.

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Wang, Q., Shu, L. & Lu, X. Dynamics of multidimensional poverty and its determinants among the middle-aged and older adults in China. Humanit Soc Sci Commun 10 , 116 (2023). https://doi.org/10.1057/s41599-023-01601-5

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Received : 01 December 2022

Accepted : 27 February 2023

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DOI : https://doi.org/10.1057/s41599-023-01601-5

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