Poverty Alleviation and Assessment: Utilizing Data Science Methods for Improved Estimation and Efficient Response of Rural Poverty in China

Word Count: 3,018

Introduction

Among all the 17 sustainable development goals of the United Nations until 2030, “End poverty in all its forms everywhere” is the first on the list (Tollefson, 2015). Poverty here entails more than its literal meaning of lack of income or resources for basic livelihood. It is important and necessary to realize that it considers malnutrition, limitation access to education, basic service and infrastructures, even social recognition and discrimination as well. By 2018, almost 8 per cent of the world’s workers and their families lived on less than US$1.90 per person – the internationally agreed poverty line and millions of people lived just beyond this line. The United Nations has clarified what poverty alleviation and ending poverty means to us: “Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programs and policies to end poverty in all its dimensions.” (Goal 1: End poverty in all its forms everywhere, 2020) In other words, poverty alleviation can develop the basement of the social development, freedom. Just as Amartya Sen stated in the book Development as Freedom, economic unfreedom can breed social unfreedom, just as social unfreedom can also foster economic unfreedom. Poverty alleviation process is the key to break this vicious circle. China becomes under the spotlight when President Xi Jinping has vowed to fulfill the Communists’ original intent, staking his legacy on an ambitious plan to complete the eradication of rural poverty by 2020, ahead of the world target by 10 years. (Xi Jinping Vows No Poverty in China by 2020. That Could Be Hard., 2017) During the last four decades, China has been praised worldwide for its economic growth represented by a series of impressive numbers. Among this data, China is frequently shown as the leading Asian country in terms of the number of billionaires created each year as a consequence of capital shifting from West to East. Cities such as Beijing, Shanghai, Hong Kong and Shenzhen have constantly climbed up the rankings of cities where the highest number of millionaires and billionaires live and work. This glittering data cannot hide the fact that China is still a developing country where poverty remains present, especially in rural areas and the western part of the country. The data released on Feb 15 of this year by the Chinese National Bureau of Statistics show that at the end of 2018, there were over 16 million people living below the official poverty line. Most of the poor in China live in the countryside, where farming is generally the only form of employment. One option could be migration to big cities, which have the best job opportunities, but the household registration system in China, which is called “Hukou”, creates a lot of limitations, such as living conditions in the outskirts of the cities and restricted access to schools for children. According to the goals of China’s government, poverty reduction will have been accomplished when standards such as access to food, clothing, compulsory education for children, basic medical treatment and good living conditions are all met. In order to deliver these goals by 2020, China should lift out of poverty an average of 8 million rural residents each year from now to 2020. The country has seen a steady decline in the number of impoverished rural residents from nearly 100 million in late 2012 to 16 million by the end of 2018, as shown in data from China’s National Bureau of Statistics. It should reach zero by the end of 2020. (China on its way to end poverty by 2020, 2020)

But many people who had barely escaped extreme poverty could be forced back into it by the convergence of COVID-19, conflict, and climate change. All these factors have trapped China and the whole world from economic and social development. The poverty alleviation system in China is rigorous and precise. As the final year of poverty alleviation in 2020, the work plan has been well arranged earlier. But a plan made in advance will always be challenged by unexpected accidents. Due to the outbreak of the Covid-19 epidemic, the process of poverty alleviation passed from the central government to the local government has been disturbed to varying degrees. On one hand, a considerable part of the work had to be postponed again and again, and the pace of poverty alleviation was slowed down; on the other hand, after the outbreak and when the pandemic has been well restrained and controlled, in order to speed up the progress of work in rural areas, the risk that the quality of poverty alleviation might decline. In addition, the people who have been lifted out of poverty will also be impacted due to their vulnerability. The epidemic has brought the risk of returning to poverty and we need to be highly vigilant against this problem. Due to the dual influence of instability and accidental factors from poverty alleviation process, the poverty-stricken population has not yet completely got rid of the problem of vulnerability and their abilities to resist risk and accident are weak. There is a possibility of returning to poverty even after poverty alleviation. As an accidental factor, the epidemic situation will not only impact the vulnerability of the people who have been lifted out of poverty, but also may induce new poverty causing factors such as epidemic, which will lead the people who have been lifted above the poverty line to return to the level below the poverty line. Moreover, as the comparative cost rises, efficiency declines and uncertainty increases, the mechanism of cooperating and interactive supporting becomes difficult to well generate. As the range of this pandemic covers almost everywhere in China and it starts in the critical period of production in spring, it has a wide and serious impact on poverty alleviation and key assistance mechanisms. Key assistance measures such as industrial supporting, education, medical treatment and relocation are almost affected by altering degrees. The factory production is suspended. The start-ups are delayed. Migrant workers are delayed working and the procedures for migration are increased. They not only increase the delivery cost of various assistance measures and lead to low efficiency of assistance; and in addition, the changes of situation of macroeconomics at home and abroad caused by the epidemic, such as the pressure of maintaining economic growth and the increase of international environmental adverse factors, will also bring more uncertainty to the implementation of various assistance measures, especially some of the hidden problems which are difficult to predict at present may not be revealed until the end of the year, and the overall difficulty of completing the task of poverty alleviation on time will be increased.

To further investigate the results of poverty alleviation process and estimate whether China can achieve Xi’s great announcement by 2020, the new geospatial data methods and techniques based on traditional data researching methods including mathematical models and advanced technologies are established mainly focusing on poverty assessment, especially in rural areas of China where all kinds of obstructions occur. Satellite data and other remote sensing techniques become commonly introduced to detect the changing phases of the poverty level during the procedure of the poverty alleviation. They have been proved useful and accurate by cross-matching the data indexed into evaluating indicators from these remote sensors and the recorded data about poverty level by National Bureau of Statistics of China. Essentially, these remote sensors provide dynamic information about poverty indicators that realize a more immediate and better response to the changing situation under pandemic from local governments in these rural areas where data are challenging to be collected accurately and time-savingly by traditional data collecting methods.

Spiritual and cultural poverty

Spiritual poverty is a general metaphor for the phenomenon that people’s spiritual world is empty, ideals and beliefs are weakened. There is no higher pursuit of work and life, and no motivation to improve their own status quo. Oscar Lewis thinks that the living environment of the poor has certain uniqueness and the corresponding lifestyle. This kind of environment and lifestyle promote the internal communication and interaction within the poor environment, which forms a kind of poverty subculture that isolates from the mainstream society and solidifies over time. Their next generation grows up under the continuous influence of this poverty culture, which makes it difficult for them to make the right choice to get rid of poverty and this invisible atmosphere. (Lewis, 2000) Moreover, because of this culture, in the implementation of targeted poverty alleviation policy in China, with the expansion of social insurance function, some farmers appear to be “acceptable even proud to be poor households”. This results in the loss of internal motivation for the poor to change their lives.

Lack of human capital

The most difficult part in poverty alleviation is that the structure of the poverty-stricken population is mainly composed of groups lacking capacity or ability to work. According to the local data, children under the age of 16, the disabled, the mentally ill and the elderly account for a large proportion of the population who have still not been lifted out of poverty yet. Even though the current health poverty alleviation policy has greatly reduced the medical expenditure of the poor population, for the poverty-stricken households who lost their ability to work due to the limitation of medical and health conditions or suffering from incurable diseases, they will usually lose hope for the future and thus lose the faith and motivation to get rid of poverty as well. The labor force of poverty-stricken people is seriously insufficient, that is to say, the limitation to feasible capacity is very great, which makes the endogenous motivation of poverty alleviation insufficient, and the poverty alleviation measures will be difficult to achieve the expecting results.

Insufficient vitality of industrial poverty alleviation

The development of agriculture is the foundation to ensure the poverty-stricken population to shake off poverty steadily. As the backbone of stable power to alleviate poverty for a long time, capital investment and good industrial projects can lead a large number of poor people, even the whole village and township to escape from poverty, which is an important measure to solve the problem of middle and long-term labor income of the poor population. However, the effect of industrial poverty alleviation is not ideal in the deep-set areas where poverty alleviation is hard to overcome. Some industrial poverty alleviation projects are implemented in a hurry, and the problems of homogenization and simplification are prominent. When the supply of most agricultural products in the neighboring areas exceeds their demand, these homogeneous projects have the risk of collective collapse, which turns these households fragile and repeatedly living back to poverty level. The single industrial form, lack of foresight and sustainability of industrial projects, will become common problems for extremely poor households to improve their income levels and living standards.

Migration and shift to urban area

Migration and relocation is a key part of the implementation of targeted poverty alleviation policy to help the poor get rid of poverty. Most poverty-stricken areas and villages are located remotely with poor living environment such as like lack of infrastructure such as transportation, education and medical treatment; while wasteland and ecological protection land covers much areas, resulting in the shortage of resources and means of production. All these lead to poverty taking root and therefore, migration and relocation to urban areas seems to become the first choice to solve the serious poverty caused by living environment. However, during the implementation of the policy, the poor households are often lack of motivation or unwilling to move into the new relocation house, who often choose to give the house to their children to live in. Even after the relocation, other policies may not match with the situation after the relocation, for example high living price, which makes the effect of poverty alleviation by migration not really ideal.

Geospatial data techniques and data sets

Traditionally, regional poverty assessment, basically socio-economic development assessment, is based on statistics collected by local governments. GDP is the most popular indicator of economic performance and has been used in a wide range of socio-economic development studies in China. However, there are limits to this type of data, as economic census is usually collected once every five years in China and it takes substantial manpower and generates huge amount of economic costs. It also needs a long period to update existing data and sometimes may become impossible because of various reasons, for example change of local administrative units. It cannot meet special demands also due to the lack of spatial information. And even every different approach used to calculate indicators of living standards for a population has its advantages and disadvantages, and each indicator discerns different characteristics of the population. Consumption data can be highly noisy due to recall error or because expenditures occurred outside the period captured in surveys, but provide a better shorter term concept of poverty Asset-based measures have been regarded as a better proxy for the long-term status of households as they are thought to be more representative of permanent income or long-term control of resources. (Steele, 2017)

Satellite remote sensing has an advantage to provide efficient and accuracy spatial data for various physical and social science research purposes due to its high temporal resolution and extensive spatial coverage. The night-time radiance data has been proven to be capable of providing strong estimation of population, GDP and electricity consumption based on the strong correlation between lights and human activities. (Elvidge et al., 1997)

Elvidge et al. (2009) have already produced a global poverty map using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP/OLS night-time lights). DMSP/OLS night-time data are annual night-time cloud-free image composites of lights of the globe collected by the DMSP/OLS sensors on a low-earth orbiting satellite (at 833 km altitude above earth). DMSP operates satellites in sun-synchronous orbits with night-time overpasses at 8– 10 pm local time. With a swath width of 3000 km and 14 orbits per day, each OLS instrument is capable of generating a complete coverage of night-time data in a 24-hour period. (Wang,2012) The data collected will be organized and calculated to give out the average light index (ALI), which can be conducted from total regional luminance of night-time light (Brightness, B) and the number of total pixels in satellite imagery (N). Generally, ALI=B/N. (Wang,2012) By modelling the average light index with the socio-economic statistical data for the selected 31 provinces and municipalities from the National Bureau of Statistics of China, a close and positive correlation has been found with a coefficient of determination (R^2) of 0.854., which firmly indicates the effectiveness by using satellite techniques and intensity of lights to show economic and poverty level. (Wang,2012) Since this data will have much higher accuracy and better reliability in comparison with old data collected by traditional methods as GIS technique has been introduced into census, bringing the native population grid data into the future studies on Chinese poverty issues is reasonable and expecting to enjoy the benefit from the new geospatial technique.

Conclusion and Reflection

It is generally accepted that villages are still the “short planks” in China’s ambition of building a moderately prosperous society until 2020. The average living and income standard in the rural area in the provinces Yunnan, Gansu, Guizhou and Xizang are way behind other regions in China, proved by both official data and the night-time brightness index. Nonetheless, the impoverished villages are considered as the “short planks” of a prosperous countryside in China. To a large extent, prosperous society can hardly be achieved if there are quite many less developed and backward villages. Thus, it is vital important to lift those households out of poverty by stimulating rural development and providing specific assistance.

It is an important goal for governments and local policy makers to eradicate poverty in China and other countries. In order to tackle the excessively wide gap of socio-economic development levels in different regions, the measurement of the overall poverty situation at a regional scale is the primary task. It is fortunate that by the support of advanced technology, accurate and detailed data can be tracked and accessible in a short period of time, which is especially needed for Chinese poverty eradication process at the very last stage.

Moreover, the correlations between socio-economic measures and night-time light intensity, access to roads and cities, the entropy of contacts and mobility features help to build up a poverty assessment system which involves various methods instead of a single technique to improve its certainty and accuracy. For instance, remote sensing and geographic information system data (RS data) obtain information like distance to roads and cities, which can reflect access to market and information to a certain degree; and similarly, and mobile operator call detail records (CDR data) like monthly credit consumption on mobile phones and the proportion of people in an area using mobile phones indicate household access to financial resources. Therefore, methods that exploit information from, and correlations between, many different data sources will provide the greatest benefit in understanding the distribution of not only poverty level, but also human living conditions.

This research conducted intend to figure out how to improve data collection by introducing advanced geospatial technologies in the field of poverty alleviation. Assessing poverty level is always a challenging task, especially in the rural area of China where only scraps of information have been recorded and the dynamic identification of impoverished people is trapped by underdeveloped environment. But a multi-dimensional poverty assessment, combining various traditional social indicators from GDP per capita to illiteracy rate and sex ratio and indirect data based on advanced technology such as average light index and call detail records, may develop a dynamic data set with geospatial data which can accurately detect the changing phases of the poverty alleviation process and map the poverty level.

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