No.79-When Poverty Reduction Meets Democracy: The Effectiveness of Dibao Using Different Evaluation Methods

Zhan, Peng; Li, Shi; Shen, Yangyang; Wang, Xiaobing
Published: 2019/8/25 12:17:50    Updated time: 2019/8/25 12:19:19

Abstract: Effective targeting is crucial for the success of any social programme. Different evaluation methods may yield different results for any given social programme. This paper evaluates the minimum living standard guarantee programme (Dibao) in rural China using several methods, such as the income approach, the multi-dimensional poverty approach, and a proxy means test approach, and finds the targeting accuracy increases the more comprehensive the evaluation method. As the Dibao fund allocation is largely decided in the villagers’ meeting democratically with a holistic view, it may appear to suffer from a low level of targeting accuracy when simply using an income approach, but may in fact more accurate in alleviating real poverty. This paper argues that a democratically allocate social assistance fund may be a better way in combating real poverty in many developing countries, as it requires less administrative capacity and overcomes the difficulties of identifying the poor.
Keywords: Policy Effectiveness; Poverty Reduction; multi-dimensional poverty; proxy means test; Social Rate of Return; China

Authors:

Peng Zhan (School of Economics, Nanjing University of Finance & Economics)

Shi Li (China Institute for Income Distribution, Beijing Normal University)

Yangyang Shen (School of Economics and Resurce Management, Beijing Normal University)

Xiaobing Wang (Department of Economics, Manchester University, United Kingdom)

 

Introduction:

Accurate targeting is key to the success of a social program in combating poverty and inequality (Ravallion 2009, Kakwani and Son 2016). Targeting involves administrative costs in identifying the households that qualify for the social program, and in distributing transfer payments. Social programmes are of more use in poor countries, but those countries usually suffer from the lack of state capacity, constraints in bureaucratic capacity, and many governance issues like high levels of corruption, which makes targeting even more important. In this paper we discuss a form of practice that has been used to identify the recipients of social transfers. We use one of the biggest social transfer programs in China as an example to test and explain it.

Poor countries face a lot of problems in combating poverty and inequality, and fairness and effectiveness are big concerns for those running social programs. However, the administrative ability and efficiency of the government cannot be improved over night. Many social programs often suffer inclusion errors where some households that should not be included are included in programmes, and exclusion errors where households that should be included are being excluded.

We argue that poverty is multi-dimensional and measuring poverty using only the income or consumption approach may not accurately reflect the real situation of the households and might exclude many of the households in real difficulties. With the constraint of administrative capacity and costs that identifying the poor involves, the best practice may be leave some of the identification to villagers themselves, i.e. use democracy as a supplement for the lack of administrative capacity from the government.

According to a World Bank report, The State of Social Safety Nets 2015, as many as 1.9 billion people are beneficiaries of safety net programs, and these programs have become an essential pillar of economic development policies (Kakwani and Son 2016). China’s minimum living standard guarantee programme (Dibao) is the largest social safety-net programme in the world. The programme was introduced in urban areas in the 1990s and was considered to be very useful in reducing urban poverty. The Chinese government extended the Dibao programme to rural areas in the early 2000s. In 2013, the rural Dibao provided cash benefits to 29.3 million households covering 53.9 million individual beneficiaries (Ministry of Civil Affairs, 2014).

Rural and urban areas have separate Dibao programmes run by their respective local authorities; different regions have a distinct Dibao line, qualifying criteria and ways of distribution. Given the scale and the popularity of the rural Dibao, rigorous evaluation can demonstrate the extent to which the programme meets its intended objective of reducing poverty, thus being able to reduce wasteful spending, increase target accuracy and improve policy effectiveness. Kakwani et al (2018) examine the effectiveness of the rural Dibao programme in China, using the income approach with data from the Chinese Household Income Project (CHIP2013). It finds low targeting accuracy, and large inclusion and exclusion errors, along with a negative social rate of return.

In this paper we use the rural Dibao program as an example to demonstrate our argument. As we will show, using the income and consumption approach, that the Dibao suffers very severe problems including high inclusion and exclusion errors. However, when multi-dimensional measures, and a proxy means test are used, its targeting efficiency increased significantly. In the case studies conducted, we found a much higher targeting efficiency that is not captured by either the income approach or the multi-dimensional measures. Thus we believe that the democratic approach of identifying households is more accurately targeted and of lower cost.

This paper is part of a bigger study that the authors have undertaken to enhance the understanding of poverty and inequality reduction in theory and improve targeting efficiency of social programmes in practice. Although this paper is China-focused, the findings should be of interest to development economists working in the area of social protection in other developing countries.

Targeting is a means to improve programme efficiency so that programme objectives of maximizing poverty reduction can be achieved with minimum cost. Kakwani and Son (2016) argue that there are two distinct issues in designing targeted programmes: the first is identifying the deserving beneficiaries who are the neediest, and the second is deciding the amount of transfers they receive so that their minimum basic needs are met.

The primary objective of social assistance programmes is to reduce poverty. It is therefore essential to be able to identify the genuine poor who need help from the government. To identify the poor objectively, we need to know a metric of household welfare, which accurately informs the economic situation of households of different sizes.

This paper uses several approaches to identify poor households, and evaluate the target accuracy of Dibao with these three approaches. Firstly, it follows Kakwani et al (2018), which evaluated the rural Dibao using both beneficiary and benefit incidence analyses. Secondly it applies an augmented multi-dimensional method based on the principles outlined in Alkire and Foster (2011). Thirdly, it follows the poor means test method developed by Brown, Ravallion, and Van de Walle (2018). We argue that a social program that is evaluated as being ineffective using income approach may in fact very accurate in targeting and very effective in achieving its goals. We provide some evidence from our field studies to support the usefulness of this new approach.

The data used is the fifth round of the Chinese Household Income Project (CHIPs) covering rural households in the year 2013 (CHIP 2013). These surveys are large in size and are representative of China as a whole (Gustafsson, Li, and Sicular, 2008). They are the best publicly available data source on Chinese household income and expenditures (Riskin, Zhao, and Li, 2001).

This paper is organized as follows: Section 2 provides a discussion of China’s Dibao programme, the poverty line used, and the Dibao targeting in practice. Sections 3 to 6 provide our evaluation results using different approaches and discuss the applications of it. Section 7 concludes and discusses policy implications and recommendations.