Have Water Conservancy Project Resettlers in Contemporary China Really Been Lifted Out of Poverty? Re-Measurement Based on Relative Poverty and Consumption Poverty

Abstract

Those who have been forced to resettle by water conservancy projects (WCP) have always been a group that is characterised by high poverty and livelihood vulnerability, mainly due to insufficient compensation and the fragmentation of their social networks. In 2020, the Chinese government announced that China had achieved comprehensive poverty alleviation, implying that all WCP-induced resettlers, have been lifted out of poverty. However, China’s current poverty line is based on the minimum subsistence standard, namely the absolute poverty line, which fails to objectively reflect China’s uneven development and individuals’ actual consumption needs. Therefore, in order to comprehensively analyse the poverty status of WCP-induced resettlers in contemporary China, this paper reassessed the poverty status of contemporary WCP-induced resettlers from the perspective of development-based poverty and consumption-based poverty. Based on survey data from over 1000 households who were forced to resettle due to China’s ‘Yangtze River to Huai River Inter-basin Water Diversion’ project, this paper concludes that: (1) China’s current absolute poverty line is outdated for contemporary WCP-induced resettlers, due to the fact they had basically been lifted out of absolute poverty by 2018, and those who remain poor need to be addressed through the bottom line guarantee of local governments; (2) the role of land as a form of basic insurance can alleviate income inequality and mitigate the risk of force majeure. Therefore, those resettled from rural areas have stronger income stability and greater resilience to risks; (3) the poverty status of contemporary WCP-induced resettlers is mainly consumption-based, and it is worse for resettlers from urban areas. Based on these conclusions, we suggest that the government should try to avoid large-scale relocation of WCP-induced resettlers to urban areas, and try to provide more insurances to them, such as providing arable land and sharing the benefits of water conservancy projects with the resettlers.

1. Introduction

Large-scale water conservancy projects (WCP), such as dam construction and inter-basin water transfers, result in a large number of people having to resettle [1]. According to statistics, in the last century, approximately 80 million across the world individuals have had to resettle due to WCP [2]. Displacement and relocation have often had a very significant impact on the livelihoods of such individuals, henceforth referred to as ‘resettlers’ [3]. There are several reasons for this. First, the loss or severe reduction of farmland frequently forces resettlers to transform their mode of production [4]. Second, compensation is often low, resulting in insufficient funds for resettlers to build new family houses and restore their production capacity and living standards to their previous levels [5]. Third, the resettlers’ social networks are commonly destroyed during the process of relocation, which affects their ability to adapt to their new environment and overcome cultural barriers [6]. Taken together the combined effect of these shocks can make it easy for resettlers to fall into long-term poverty [7]. Furthermore, the effects are not limited to just the resettlers themselves. Instead, chronic poverty can also lead to social instability [8] and environmental degradation [9]. Therefore, the poverty alleviation of WCP-induced resettlers is a complex and multifaceted social problem [10], which cannot be ignored.

Since WCPs are initiated and managed by governments, and the resettlement they induce occurs involuntarily [11], the subsequent poverty alleviation that they necessitate should also be led by governments. However, in some developing countries, due to the lack of systematic livelihood development planning and capital investment by the government, resettler poverty has become a long-term problem, which restricts the economic and social development of resettlement areas [12]. In order to help governments improve livelihoods and eliminate poverty, the World Bank has provided extensive technical and financial assistance, such as tailored development plans and special funds, for WCP-induced resettlers [13]. With the help of the World Bank, governments of many developing countries have gradually formed localised resettlement standards and compensation levels based on their specific laws and cultural traditions [14,15,16]. These efforts have effectively helped many resettlers to overcome poverty by restoring their livelihoods [17,18].

After 1949, China rapidly developed a large number of WCP and by 1989, more than 80,000 reservoirs had been constructed across the country. These reservoirs have resulted in 10 million resettlers, more than 60 percent of whom were in absolute poverty after resettlement [19]. Therefore, WCP-induced resettlers in China are characterised by high poverty and vulnerability, and consequently are a group that the Chinese government has paid close attention to. After several revisions, the State Council of China and the Ministry of Water Resources issued two important decrees in 2006: (1) The State Council Decree No. 471 (2006) on large and medium-scale hydraulic and hydropower projects’ land acquisition and resettlement compensation rules, and (2) Suggestions of the State Council No. 17 (2006) on the improvement of follow-up support for people affected by large and medium-scale reservoirs. These decrees have been effective in alleviating poverty among the resettled via pre-resettlement compensation, resettlement subsidies and follow-up support [20]. In addition, with the effective implementation of the Chinese government’s targeted poverty alleviation policy in 2013, poverty among the resettled has been greatly reduced [21].

Currently, China uses the absolute poverty line to measure poverty. Specifically, the standard for absolute poverty adopts the per capita annual disposable income of 2300 RMB from 2011, adjusted annually according to the consumer price index (CPI) of each province [22]. This absolute poverty line, equivalent to $1 per person per day, is the minimum level for basic subsistence first proposed by the World Bank. Based on this standard, in 2020, the Chinese government announced that China had achieved comprehensive poverty alleviation [23], implying that all of the country’s WCP-induced resettlers have been lifted out of poverty. However, some Chinese scholars have argued that this ‘subsistence-based’ poverty standard fails to accurately reflect the poverty status of the resettled, because their poverty characteristics have become ‘development-based’, that is, based on income inequality caused by differences in resource endowments [24]. Some qualitative studies have also concluded that increases in living costs and changes in consumption behaviour mean that resettlers’ income is often insufficient to support their consumption [25,26,27]. This situation is termed ‘consumption-based’ poverty. However, despite the rationale of these arguments, there is currently no quantitative standard for development-based and consumption-based poverty. In fact, based on the experience of some developed countries, the poverty measurement criteria have experienced the transition from absolute poverty to development poverty and consumption poverty [25]. Since the resettlers are considered by the Chinese government to have got rid of absolute poverty, it is necessary to adopt a new method to measure poverty. In order to more fully understand the poverty status of WCP-induced resettlers in contemporary China, this paper has dynamically reinterpreted their situation by analysing their development-based and consumption-based poverty using the relative and consumption poverty lines, as well as the absolute poverty line.

This study takes the WCP resettlement programme relating to the ‘Yangtze River to Huai River Inter-basin Water Diversion’ project (YtoH Diversion) as a case study. Specifically, the study makes three contributions to the existing literature on the poverty study of WCP-induced resettlers. First, it reassessed the poverty status of contemporary WCP-induced resettlers from the perspective of development-based poverty and consumption-based poverty. Second, it dynamically reinterprets the poverty status of the resettlers arising from the YtoH Diversion project. Third, based on our assessment of multiple poverty measurement models, suggestions are put forward to improve the livelihood level and sustainable development of the WCP-induced resettlers in the future.

The remainder of the article is structured as follows. Section 2 provides an overview of the differing poverty measurement methods and outlines the methods used in this study. Section 3 describes the characteristics of the case study region, and the research and data collection methods. Section 4 presents the results of the various poverty measures used to assess the resettlers’ poverty status. Section 5 dynamically reinterprets their poverty status. Section 6 presents the study’s main conclusions.

2. Literature Review

When poverty research was in its infancy, scholars usually determined the poverty line, also known as the absolute poverty line, by the minimum resources necessary for long-term health [28]. The most commonly used measurements include: the Martin method [29], the market-basket method [30], and the calorie expenditure method [31]. The current universal absolute poverty line is the International Poverty Line (IPL), which was formulated by the World Bank. This provides the benchmark which the Chinese government has used to formulate the poverty line in China. By comparing the absolute poverty line with the income of WCP-induced resettlers, impoverished households can be easily identified. The advantages of the absolute poverty line relate to the convenience of data collection it enables and the consistency of evaluation criteria. However, with improvements to resettlement compensation and follow-up support, alongside the Chinese government’s sustained implementation of its targeted poverty alleviation programme, food and clothing shortages of the resettled have been mitigated to a large degree. Consequently, the issue of poverty alleviation has shifted from subsistence to sustainable development, including issues such as excessive income inequality and social exclusion [32]. Therefore, some Chinese scholars who focus on resettlement have proposed using relative poverty to evaluate the poverty caused by the unbalanced development of the resettled [33]. Commonly used relative poverty measures include the income proportion method [34], and the lifestyle method [35]. The relative poverty line’s advantages are that it can effectively measure the overall income inequality of the resettled, as well as providing convenience in terms of data collection.

Both the absolute poverty line and the relative poverty line attempt to measure poverty in relation to income. However, after the Chinese government announced in 2020 that all Chinese citizens had been successfully lifted out of absolute poverty, Han et al. questioned this assessment due to their study’s finding that domestic migrants’ poverty characteristics are mainly consumption-based [36]. Similarly, other qualitative studies have showed that WCP-induced resettlers also frequently experience consumption-based poverty [27]. However, our understanding of this issue is currently hampered by a lack of quantitative evidence. Wang et al. and Xu et al. have pointed out that the absolute and relative poverty lines, namely the measures which adopt an income-based approach, fail to effectively reflect the potential benefits that resettlers accrue from the government’s social development programmes and poverty alleviation policies. Therefore, they analyszed the poverty status of WCP-induced resettlers from multiple dimensions based on the global multidimensional poverty index (MPI) published by the United Nations Development Program (UNDP) and Oxford University [32,37]. However, while the MPI is effective in comprehensively revealing individuals’ poverty status and the outcomes of policies designed to address poverty, it has the following deficiencies: (1) there are differences in the selection of its indicators; (2) the weight of each indicator is not uniform; (3) the measurement standard of each indicator is different in different countries. Therefore, to sum up, the income-based approach is still the most common model used to analyse the poverty of WCP-induced resettlers, due to the convenience of data collection and the consistency of evaluation criteria. However, there is currently a lack of relevant research measuring poverty in relation to consumption. Therefore, in addition to the commonly used absolute and relative poverty lines, this study has constructed a consumption poverty line by extending the linear expenditure system (ELES) method to comprehensively analyse the poverty status of WCP-induced resettlers.

3. Materials and Methods

3.1. Research Region and Sample

Our research site was the Anhui province section of the ‘Yangtze River to Huai River Inter-basin Water Diversion’ project. The original residents of the area were all relocated to resettlement sites by 2016, as seen in Figure 1. Our research group conducted fieldwork for five consecutive years (2017 to 2021). In January 2017, we firstly conducted a survey to establish the resettlement baseline-resettlers’ production and life data up to 2016. We repeated our surveys with the same households every December from 2017 to 2021. The total number of relocated households was 2745, and our sample was 1098, a sampling rate of 40%. A stratified sampling method was used to select our participants. Households with low income, relatively low income, middle income, relatively high income and high income accounted for 15%, 20%, 30%, 20% and 15% of the sample, respectively. Before the resettlement, all those who were subsequently resettled had rural hukou (household registration) status, and they were mainly engaged in agricultural production. The local government adopted a mixed resettlement model: 79.6% of them were resettled in urban resettlement sites and 20.4% in rural resettlement sites. Those resettled in rural areas mainly continued to engage in agricultural production, while those resettled in urban areas had to find non-farming occupations, such as manufacturing and services.

3.2. Poverty Measurement Method

This study first calculated the Lorenz curve of the resettled induced by the YtoH Diversion project [6]. Then, the formula of the Foster–Greer–Thorbecke indices (FGT indices) was calculated based on the curve. FGT indices is commonly used to analyse the poverty status from three dimensions, namely, poverty headcount ratio, poverty gap and poverty severity. By adding different poverty lines (absolute, relative and consumption) into the formula, the corresponding poverty status was obtained.

3.2.1. Lorentz Curve Equation

The Lorenz curve is a convenient graphical method used to summarise information on the distribution of income and wealth [38]. Its formula is shown in as:

$$L=L\left(P;\pi \right)$$

(1)

where, L is the cumulative share of income earned and P is the cumulative share of people or households from lowest to highest incomes. $\pi$ is the parameter vector to be estimated. The two commonly used Lorenz curve equations are General Quadratic (GQ) Lorenz curve [39] and Beta (β) Lorenz curve [40], which are based on models proposed by Villasenor and Kakwani [41,42]. This paper used the GQ model, and its formula is:

$$L\left(1-L\right)=a\left(P^2-L\right)+bL\left(P-1\right)+c\left(P-L\right)$$

(2)

After the corresponding values of P and L are counted, the ordinary least squares (OLS) method can be used to estimate the values of a, b, and c in the formula.

3.2.2. FGT Indices

FGT indices are often used when analyzing income-based poverty [43]. The indices can measure the poverty headcount ratio, poverty gap and income inequality. Each index is derived by substituting different values of the parameter α into the following equation:

$$FG{T}_{\alpha }=\frac{1}{N}{\displaystyle \sum }_{i=1}^H{\left(\frac{z-y_i}{z}\right)}^{\alpha }$$

(3)

where, $z$ is the poverty line, $N$ is the number of people in the economy, H is the number of poor (those with income at or below z), and $y_i$ is the income of each individual $i$. With $\alpha =0$, the formula reduces to the poverty headcount ratio (H). With $\alpha =1$, the formula reduces to the poverty gap index (PG). With $\alpha =2$, the formula reduces to the poverty severity index (PS). After a, b, and c of the GQ model are estimated, the Lorentz curve equation and the corresponding FGT indices can be obtained, as shown in

Table 1. FGT indices derived from the parameterized GQ Lorenz curve.

GQ Lorenz curve	$L\left(P\right)=\frac{-1}{2}\left(bP+e+\sqrt{m{P}^2+nP+e^2}\right)$
$FG{T}_1=H$	$\frac{-1}{2m}\left(n+r\frac{\left(b+2z/\mu \right)}{\sqrt{{\left(b+2z/\mu \right)}^2-m}}\right)$
$FG{T}_2=PG$	$H-\left(\frac{\mu }{z}\right)L\left(H\right)$
$FG{T}_3=PS$	$2PG-{\left(\frac{\mu }{z}\right)}^2\left[aH+bL\left(H\right)-\frac{r}{16}ln\left(\frac{1-H/s_1}{1-H/s_2}\right)\right]$
Note:	$e=-\left(a+b+c+1\right)$
	$m=b^2-4a$
	$n=2be-4c$
	$r=\sqrt{n^2-4m{e}^2}$
	$s_1=\left(r-n\right)/2m$
	$s_2=-\left(r+n\right)/2m$

. In the equation, $\mu$ is per capita net income and $z$ is the poverty line.

3.2.3. Calculation of the Poverty Lines

(1) Absolute poverty line

The absolute poverty line is determined based on a person’s basic subsistence needs, including food, safe drinking water, sanitation, healthcare, shelter, education and information [44]. The World Bank set the absolute poverty line at $1 per person per day in 1996, and adjusted it to $1.25 and $1.9 in 2005 and 2015, respectively [45,46]. The poverty standard in China is the per capita annual disposable income of 2300 RMB from 2011, adjusted annually according to the CPI of each province [22]. Therefore, the formula of the absolute poverty line $z_1$ can be expressed as:

$$z_1=2300\times CP{I}_{2011}$$

(4)

where, $CP{I}_{2011}$ represents the CPI value with 2011 as the base period.

(2) Relative poverty line

Relative poverty means lower income compared to others in a country: for example, relative poverty may be defined as having income below 60% of a country’s median income [47]. Unlike the definition of absolute poverty, relative poverty takes into account people’s observed socioeconomic circumstances. It is based on the assumption that whether a person is poor or not depends on their income share relative to other people living in the same society [47]. The relative poverty line can be set differentially, e.g., 50%, 40% or 30% of the median income of a certain group or country [48]. Therefore, the formula of the relative poverty line $z_2$ can be expressed as:

$$z_2=X\times MI\left(X=30\%\vee 40\%\vee 50\%\vee 60\%\right)$$

(5)

where, MI represents the median income in a certain group or country. In this study X = 60%.

(3) Consumption poverty line

The consumption poverty line is determined based on the actual consumption needs of a certain group. It varies between different groups from different countries or regions [36]. A suitable measure is the extended linear expenditure system (ELES) which was proposed by Stone [49]. ELES assumes that people’s daily consumption structure reflects the basic needs and non-basic needs of life. Basic needs will not be affected by differences in income, and only when basic needs are met will different preferences for non-basic needs arise [50]. The formula can be expressed as:

$$p_i{q}_i=p_i{r}_i+{\beta }_i\left(I-{\displaystyle \sum }_{i=1}^n{p}_i{r}_i\right)=p_i{r}_i-{\beta }_i{\displaystyle \sum }_{i=1}^n{p}_i{r}_i+{\beta }_{i}I$$

(6)

where, $p_i$ is the price of commodity $i$; $q_i$ is the actual consumption for commodity $i$ in daily consumption; $r_i$ is the basic demand for commodity $i$ derived from the daily consumption structure; $I$ is the income; ${\beta }_i$ is the non-basic demand coefficient which indicates the willingness to consume commodity $i$ after its basic need has been met. Therefore, the consumption poverty line $z_3$ can be expressed as:

$$z_3={\displaystyle \sum }_{i=1}^n{p}_i{r}_i$$

(7)

Let $V_i=p_i{q}_i$, $p_i{r}_i-{\beta }_i{{\displaystyle \sum }}_{i=1}^n{p}_i{r}_i={\alpha }_i$, then Equation (6) can be simplified as $V_i={\alpha }_i+{\beta }_{i}I$. Since $V_i$ and $I$ are survey data. Therefore, the parameters ${\alpha }_i$ and ${\beta }_i$ can be estimated by the OLS method. Summing over ${\alpha }_i$ gives:

$${\displaystyle \sum }_{i=1}^n{\alpha }_i={\displaystyle \sum }_{i=1}^n{p}_i{r}_i-{\displaystyle \sum }_{i=1}^n{\beta }_i{\displaystyle \sum }_{i=1}^n{p}_i{r}_i={\displaystyle \sum }_{i=1}^n{p}_i{r}_i\left(1-{\displaystyle \sum }_{i=1}^n{\beta }_i\right)$$

(8)

Therefore, $z_3$ can be calculated as:

$$z_3=\frac{{{\displaystyle \sum }}_{i=1}^n{\alpha }_i}{\left(1-{{\displaystyle \sum }}_{i=1}^n{\beta }_i\right)}$$

(9)

3.3. Data Collection and Processing

3.3.1. Data for Parameter Estimation of Lorenz Curve

This study used k-means cluster analysis to divide the per capita net income of the sample households into 10 levels from low to high. Then, the cumulative share of people from all the levels was calculated as an independent variable. At the same time, the counterpart cumulative share of net income earned was obtained as a dependent variable. Based on the above process, 10 sample points (P_i, L_i) (i = 1, 2, …, 10) were available. Using the Equation (2) for parameter estimation, the Lorenz curve was obtained. This study used SPSS 19 for its cluster analysis and nonlinear regression.

3.3.2. Poverty Line and Per Capita Net Income

When using FGT indices to measure absolute poverty, relative poverty and consumption poverty, the poverty line $z$ and per capita net income $\mu$ need to be obtained. The absolute income poverty line $z_1$ was determined based on the annual per capita disposable income of 2300 RMB set by China in 2011, adjusted according to the CPI of Anhui Province; the relative income poverty line $z_2$ was set at 60% of the median per capita household income of resettlers; and the consumption poverty line $z_3$ was calculated using the ELES model. In this model, we use the various types of residents’ consumption obtained from the survey for regression. The consumption data were collected according to China’s national standard (Regulations for Supervision and Evaluation of Resettlement of Water Conservancy and Hydropower Projects, SL716-2015). They include housing expenditure, food expenditure, communication expenditure, medical expenditure, education & entertainment expenditure and daily necessities expenditure. To help readers understand the calculation process of the consumption poverty line, a calculation example of the consumption poverty line for urban and rural resettlers in 2016 is provided in Appendix B. μ was obtained from the statistics of the sampled data.

4. Results

4.1. Inter-Temporal Calculation Results of Poverty Line and Per Capita Net Income

According to the formula outlined in Section 3.2, the absolute poverty line, relative poverty line, consumption poverty line and per capita net income of the urban and rural resettlers were calculated. The results are shown in

Table 2. Poverty line and per capita net income from 2016 to 2021 (unit: RMB).

Year	${\mathit{z}}_1$		${\mathit{z}}_2$		${\mathit{z}}_3$		$\mathit{\mu }$
	Urban	Rural	Urban	Rural	Urban	Rural	Urban	Rural
2016	3004	2961	3987	3713	8498	7245	8643	7930
2017	3043	2996	4322	4017	8503	7540	8509	8157
2018	3104	3056	4242	4097	8412	7738	8386	8471
2019	3188	3138	4191	4079	8446	7756	8411	8342
2020	3267	3224	4137	4059	8479	7771	8437	8202
2021	3330	3265	4077	4038	8505	7742	8466	8050

Note: $z_1$, $z_2$ and $z_3$ are absolute poverty line, relative poverty line and consumption poverty line respectively, while $\mu$ is per capita net income.

.

As can be seen from Table 2, compared with the relative poverty line and the consumption poverty line, China’s current absolute poverty line is relatively low. From the perspective of relative poverty, we can see that the relative poverty line of both the urban and rural resettlers’ increased sharply in 2017. This is because resettlement compensation was issued in that year. After 2017, the relative poverty line of the urban resettlers began to decline, while that of the rural resettlers fluctuated around 4000 RMB. This indicates that the overall income of the urban resettlers declined, while that of the rural resettlers was relatively stable. From the perspective of consumption poverty, we can see that the consumption poverty line of the rural resettlers was lower than their per capita net income from 2016 to 2021, while that of the urban resettlers exceeded their per capita disposable income after 2018. This reveals the phenomenon of overdraft consumption among the urban resettlers, which is not conducive to their sustainable development.

4.2. Poverty Measurement Results

The calculation results of Lorentz curve are shown in Appendix A. The FGT indices of absolute poverty, relative poverty and consumption poverty can be obtained by using the formula in Table 2.

4.2.1. Absolute Poverty

When $z_1$ is used as the poverty line, the absolute poverty incidence, absolute poverty gap and absolute poverty severity can be obtained. These are represented by $H_1$, $P{G}_1$ and $P{S}_1$, respectively. The inter-temporal calculation results are shown in Figure 2. As can be seen from Figure 2, the $H_1$ of both the urban and rural resettlers declined before 2018, indicating that absolute poverty was alleviated during this period. Specifically, the poverty alleviation rate of the rural resettlers was 0.29%, and that of the urban resettlers was 0.13%. However, the $H_1$ of both the urban and rural resettlers increased rapidly after 2018, indicating that many households returned to poverty during this period. It is worth noting that the re-impoverishment rate of the urban resettlers (5.91%) was much higher than that of the rural resettlers (2.96%). $P{G}_1$ and $P{S}_1$ showed the same trend as $H_1$ for both the urban and rural resettlers.

4.2.2. Relative Poverty

When $z_2$ is used as the poverty line, the relative poverty incidence, relative poverty gap and relative poverty severity can be obtained. These are represented by $H_2$, $P{G}_2$ and $P{S}_2$, respectively. Their inter-temporal calculation results are shown in Figure 3. As can be seen from Figure 3b, the $H_2$ of the urban resettlers increased between 2016 and 2021, indicating that their overall income gap widened during this period. At the same time, the urban resettlers’ $P{G}_2$ and $P{S}_2$ exhibited the same trend as their $H_2$. However, in contrast to the urban resettlers, the $H_2$ of the rural resettlers declined after 2018, and their $P{G}_2$ and $P{S}_2$ showed the opposite trend to their $H_2$, indicating that although the overall income gap of the rural resettlers narrowed between 2018 and 2021, the poverty gap and poverty severity of the relatively poor rural resettlers has further increased.

4.2.3. Consumption Poverty

When $z_3$ is used as the poverty line, the consumption poverty incidence, consumption poverty gap and consumption poverty severity can be obtained. These are represented by $H_3$, $P{G}_3$ and $P{S}_3$, respectively. Their inter-temporal calculation results are shown in Figure 4. It can be seen from Figure 4 that the $H_3$ of both the urban and rural resettlers exceeded 50%, which is far greater than their $H_1$ and $H_2$, indicating that all the resettlers generally relied on overdrafts. The $H_3$ of both the urban and rural resettlers increased between 2016 and 2018, indicating that their overall consumption demand relative to their income increased during this period. However, between 2018 and 2021, the $H_3$ of the rural resettlers declined, while that of the urban resettlers remained unchanged, indicating that rural resettlers’ overall consumption demand relative to their income declined, while urban resettlers’ overall consumption demand relative to their income remained unchanged. $P{G}_3$ and $P{S}_3$ basically showed the same trend as $H_3$ for both the urban and rural resettlers.

5. Discussion

5.1. Absolute Poverty Analysis

The $H_1$ of both the urban and rural resettlers declined between 2016 and 2018. Specifically, that of the urban resettlers dropped to 1.63%, while that of the rural resettlers dropped to 1.97%. Compared with the $H_1$ of the whole of Anhui Province in 2018, which was 2.2% [51], the $H_1$ of the resettled due to the YtoH Diversion project was relatively low. According to our survey of the poor households, we found that the causes of their poverty were mainly severe disease, disability and incapacity to work [52]. That is to say, the remaining poor households can only rely on the government’s bottom line which is called ‘dibao’ minimum income guarantee, and the resettlers who are able to work generally have been lifted out of poverty.

The $H_1$ of both the urban and rural resettlers increased rapidly after 2018, and the re-impoverishment rate of urban resettlers’ (5.91%) was much higher than that of rural resettlers (2.96%), indicating that the WCP-induced resettlers were vulnerable to returning to poverty, with the urban resettlers more prone to this outcome. One of the main explanation for this phenomenon concern the impact of the China-United States trade war, which had a significant impact on employment in China’s manufacturing sector and service sector in 2019. After that, COVID-19 pandemic made the employment of manufacturing sector and service sector even more worse in 2020 [53,54]. In the meantime, despite the decline in the agricultural sales of the rural resettlers, the pressure of unemployment and lower income was partially mitigated due to having land as a basic guarantee. However, the urban resettlers suffered more in terms of unemployment, resulting in their higher rate of re-impoverishment compared to the rural resettlers over the same timeframe.

5.2. Relative Poverty Analysis

The income gap among the urban resettlers widened from 2016 to 2021, which was consistent with the trend in China’s overall Gini coefficient [54]. Before 2018, the development of internet platforms such as Meituan, Alipay, JD.com, and Didi Taxi provided a large number of jobs in the service sector [55], which greatly increased the income of the urban resettlers who engaged in services. However, in 2019, the China-United States trade war caused the employment rate and income of China’s manufacturing sector and service sector to decline to varying degrees, which led to a slowdown in the growth of the income gap [56]. Moreover, the COVID-19 pandemic in 2020 had an even more severe impact on businesses in manufacturing sector and service sector. Unemployment and income reduction have further slowed the expansion of the income gap [57].

The income gap among the rural resettlers expanded from 2016 to 2018. Like the urban resettlers, the main reason for this was that the rural resettlers who engaged in service sector also benefitted from the growth of China’s platform economy. However, due to the effect of the China-United States trade war and COVID-19 pandemic, more than half of the rural resettlers were forced to change their mode of production from manufacturing and service to agriculture [52]. Since the income gap within the agricultural sector is relatively small and the income of jobs in the manufacturing sector and services sector had declined, the income gap among the rural resettlers narrowed between 2018 and 2021.

5.3. Consumption Poverty Analysis

The $H_3$ of both the urban and rural resettlers was far greater than their $H_1$ and $H_2$. This indicates that consumption-based poverty was the main poverty characteristic of the YtoH-induced resettlers. In the first two years after the resettlement was completed, the overall consumption of both the urban and rural resettlers increased faster than their income, and the gap between consumption and income was larger for the urban resettlers. This is a common phenomenon, because after any WCP-induced relocation is completed, the market participation, consumption structure and consumption behavior of those who are resettled generally change significantly, especially for those who are resettled from urban areas [36]. Our survey data further confirms this phenomenon. Moreover, our data showed that cooking fuel and asset ownership changed significantly after resettlement. In terms of cooking fuel, most households switched from wood or charcoal to electricity, coal or natural gas. In terms of assets, the ownership of household electrical devices, such as refrigerators, washing machines, air conditioners, electric fans, water heaters, rice cookers, induction cookers, and microwave ovens, also increased significantly. Those changes greatly increased the resettlers’ consumption costs.

After 2018, the $H_3$ of the rural resettlers declined, while that of the urban resettlers remained unchanged. This indicated that the rural resettlers managed to gradually balance their consumption and income by adjusting their non-rigid consumption, while the urban resettlers were not able to do so. Through sample interviews, we found that the urban resettlers’ housing loans and food consumption constituted their main consumption costs, both of which are forms of rigid consumption. As for the price of housing, for the urban resettlers, this was higher than the compensation they received, and every month the urban resettlers needed to make mortgage repayments; at the same time, the change of their mode of production led to them losing their self-sufficiency in terms of food. Hence the $H_3$ of the urban resettlers remained stable.

5.4. Deficiencies and Prospects

This paper only measures the poverty status of YtoH-induced resettlers in contemporary China from poverty headcount, poverty gap and poverty severity. The method fails to intuitively reflect the changes of living standard among the poor and non-poor families over time. In order to make up for the deficiency, the following study can adopt A-F model to discuss the changes from a microscopic perspective. A-F method is adopted for the assessment of multidimensional poverty, which has the advantages of being highly intuitive and suitable for policy analysis [58]. Coupled with the use of Global Multidimensional Poverty Index (GMPI) published by the United Nations Development Program (UNDP) and Oxford University, the A-F model can be used to analyze the changes of GMPI and the main factors that cause poverty [59].

6. Conclusions

In this paper, the absolute poverty line, relative poverty line and consumption poverty line have been used to comprehensively measure the poverty status of those resettled as a result of China’s ‘Yangtze River to Huai River Inter-basin Water Diversion’ project. By analysing absolute poverty, we found that China’s current absolute poverty line is outdated for contemporary WCP-induced resettlers, because they had basically been lifted out of absolute poverty by 2018, and the remaining poor need to be addressed via the bottom line guarantee of local governments. Therefore, the poor measured by China’s current absolute poverty line are not representative. Given this, the Chinese government needs to consider adopting other methods with higher poverty discrimination to remeasure the poverty status of WCP-induced resettlers. From the results we have generated, it is clear that relative poverty line and consumption poverty line are better choices.

Our analysis of relative poverty suggests that rural resettlers are more resilient to force majeure, such as the COVID-19 pandemic and the China-United States trade war. This is because they have land as a guarantee of employment and food supply, which means that they can avoid the secondary destruction of their livelihoods. This result suggests that the Chinese government should recognise that forcibly relocating rural residents to urban areas is not necessarily a good option and may even threaten the resilience of their livelihoods. Once people are relocated to urban areas and lose their land, they will immediately lack food and income if they lose their new city-based jobs. In fact, apart from YtoH -induced resettlers, other Chinese city dwellers are also affected by force majeure, such as China-United States trade war and COVID-19 pandemic. But because they generally have certain savings and enjoy the long-term urban social security system, they have a higher ability to resist risks.

Through the analysis of consumption poverty, we found that the poverty of contemporary WCP-induced resettlers is mainly consumption-based. This fact once again proves that China’s absolute poverty measurement standard is no longer applicable. The consumption poverty line may be a more effective measurement model after the Chinese government announces comprehensive poverty alleviation in 2020. The measurement result of consumption poverty reflects the real problem of making ends meet due to changes in consumer demand after resettlement. Therefore, the Chinese government needs to give greater consideration to how to improve their basic security. One feasible option is to share the benefits of water conservancy projects with them. This approach has been found to be effective in practice in certain other countries [58].