TECHNOLOGY AND HUMAN RIGHTS

Poor Enough for the Algorithm? Exploring Jordan’s Poverty Targeting System

The Jordanian government is using an algorithm to rank social protection applicants from least poor to poorest, as part of a poverty alleviation program. While helpful to those individuals who receive aid, the system is excluding beneficiaries in need, as it is failing to accurately reflect the complex realities of poverty. It uses an outdated poverty measure, weights imperfect indicators—such as utility consumption—and relies on a static view of socioeconomic status.

On November 28, 2023, the Digital Welfare State and Human Rights project hosted the sixteenth episode in the Transformer States conversation series on Digital Government and Human Rights. Victoria Adelmant and Katelyn Cioffi interviewed Hiba Zayadin, a senior researcher in the Middle East and North Africa division at Human Rights Watch (HRW), about a report published by HRW on the Jordanian government’s use of an algorithmic system to rank applicants for a welfare program based on their poverty level, using data like electricity usage and car ownership. This blog highlights key issues related to the system’s inability to reflect the complexities of poverty and its algorithmic exclusion of individuals in need.

The context behind Jordan’s poverty targeting program 

‘Poverty targeting’ is generally understood to mean directing social program benefits towards those most in need, with the aim of efficiently using limited government resources and improving living conditions for the poorest individuals. This approach entails the collection of wide-ranging information about socioeconomic circumstances, often through in-depth surveys and interviews, to enable means testing or proxy means testing. Some governments have adopted an approach in which beneficiaries are ‘ranked’ from richest to poorest, and target aid only to those falling below a certain threshold. The World Bank has long advocated for poverty targeting in social assistance. For example, since 2003, the World Bank has supported Brazil’s Bolsa Família program, which is a program targeted at the poorest 40% of the population. 

Increasingly, the World Bank has turned to new technologies to seek to improve the accuracy of poverty targeting programs. It has provided funding to many countries for data-driven, algorithm-enabled solutions to enhance targeting. Similar programs have been implemented in countries including Jordan, Mauritania, Palestine, Morocco, Iraq, Tunis, Jordan, Egypt, and Lebanon.

Launched in 2019 with World Bank support, Jordan’s Takaful program, an automated cash transfer program, provides monthly support to families (roughly US $56 to $192) to mitigate poverty. Managed by the National Aid Fund, the program targets the more than 24% of Jordan’s population that falls under the poverty line. The Takaful program has been especially welcome in Jordan, in light of rising living costs. However, policy choices underpinning this program have excluded many individuals who are in need: eligibility restrictions limit access solely to Jordanian nationals, such that the program does not cover registered Syrian refugees, Palestinians without Jordanian passports, migrant workers, and the non-Jordanian families of Jordanian women—since Jordanian women cannot pass on citizenship to their children. Initial phases of the program entailed broader eligibility, but criteria were tightened in subsequent iterations.

Mismatch between the Takaful program’s indicators and the reality of people’s lives

In addition, further exclusions have arisen because of the operation of the algorithmic system used in the program. When a person applies to Takaful, the system first determines eligibility by checking whether an applicant is a citizen and whether they are under the poverty line. It subsequently employs an algorithm, relying on 57 socioeconomic indicators, to rank people from least poor to poorest. The National Aid Fund uses existing databases as well as applicants’ answers to a questionnaire – that they must fill out online. Indicators include household size, geographic location, utilities consumption, ownership of businesses, and car ownership. It is unclear how these indicators are weighted, but the National Aid Fund has admitted that some indicators will lead to the automatic exclusion of applicants from the Takaful program. Applicants who own a car that is less than five years old or a business valued at over 3000 Jordanian Dinars, for instance, are automatically excluded. 

In its recent report, HRW highlights a number of shortcomings of the algorithmic system deployed in the Takaful program, critiquing its inability to reflect the complex and dynamic nature of poverty. The system, HRW argues, uses an outdated poverty measure, and embeds many problematic assumptions. For example, the algorithm gives some weight to whether an applicant owns a car. However, there are cars in people’s names that they do not actually own; some people own cars that broke down long ago, but they cannot afford to repair them. Additionally, the algorithm assumes that higher electricity and water consumption indicates that a family is less vulnerable. However, poorer households in Jordan in many cases actually have higher consumption—a 2020 survey showed that almost 75% of low- to middle-income households lived in apartments with poor thermal insulation.

Furthermore, this algorithmic system is designed on the basis of a single assessment of socioeconomic circumstances at a fixed point in time. But poverty is not static; people’s lives change and their level of need fluctuates. Another challenge is the unpredictability of aid: in this conversation with CHRGJ’s Digital Welfare State and Human Rights team, Hiba shared the story of a new mother who had been suddenly and unexpectedly cut off from the Takaful program, precisely when she was most in need.

At a broader level, introducing an algorithmic system such as this can also exacerbate information asymmetries. HRW’s report highlights issues concerning opacity in algorithmic decision-making—both for government officials themselves and those subject to the algorithm’s decisions—such that it is more difficult to understand how decisions are being made within this system.

Recommendations to improve the Takaful program

Given these wide-ranging implications, HRW’s primary recommendation is to move away from poverty targeting algorithms and toward universal social protection, which could cost under 1% of the country’s GDP. This could be funded through existing resources, tackling tax avoidance, implementing progressive taxes, and leveraging the influence of the World Bank to guide governments towards sustainable solutions. 

When asked during this conversation whether the algorithm used in the Takaful program could be improved, Hiba noted that a technically perfect algorithm executing a flawed policy will still lead to negative outcomes. She argued that it is the policy itself – the attempt to rank people from least poor to poorest – that is prone to exclusion errors, and warns that technology may be shiny, promising to make targeting accurate, effective, and efficient, but that it can also be a distraction from the policy issues at hand.

Thus, instead of flattening economic realities and leading to the exclusion of people who are, in reality, in immense need, Hiba recommended that support be provided inclusively and universally—to everyone during vulnerable stages of life, regardless of their income and their wealth. Therefore, rather than focusing on using technology that will enable ever-more precise targeting, Jordan should focus on embracing solutions that allow for more universal social protection.

Rebecca Kahn, JD program, NYU School of Law;  and  Human Rights Scholar at the Digital Welfare State & Human Rights project. Her research interests relate to responsible AI governance, digital rights, and consumer protection. She previously worked in the U.S. House and Senate as a legislative staffer.