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Posts Tagged ‘transfer’

The November 14, 2012, Technology Salon NYC (TSNYC) focused on ways that ICTs can support work with children who migrate. An earlier post covers the discussion around Population Council’s upcoming ‘Adolescent Girls on the Move’ report. The current post focuses on the strategic use of data visualization for immigration advocacy, based on opening points from Brian Root and Enrique Piracés of Human Rights Watch (HRW).

Visualizing the US Detention Network and the transfers between detention centers.

The project

The HRW initiative used data to track and visualize the movement of people through the US immigration detention system after noticing that U.S. Immigration and Customs Enforcement (ICE) was moving people very freely without notifying their families or attorneys. HRW was aware of the problem but not its pervasiveness. The team obtained some large data sets from the US government via Freedom of Information Act (FOIA) requests. They used the data to track individuals’ routes through the immigration detention system, eventually mapping the whole system out at both aggregate levels and the level of individual. The patterns in the data informed HRW’s advocacy at the state and federal levels. In the process, HRW was able to learn some key lessons on advocacy and the importance of targeting data visualizations to specific advocacy purposes.

Data advocacy and storytelling

The data set HRW obtained included over 5.4 million records of 2.3 million people, with 10-12 variables. The team was able to connect these records to individuals, which helped tell a meaningful story to a broad audience. By mapping out all the US facilities involved and using geo-location to measure the distance that any individual had been transferred, the number of times an individual from Country X in Age Range X was transferred from one facility to another was visible, and patterns could be found. For example, often people on the East Coast were transferred to Texas, where there is a low ratio of immigration lawyers per detainee.

Even though the team had data and good stories to tell with the data, the two were not enough to create change. Human rights are often not high priority for decision makers, but budgeting is; so the team attached a cost to each vector that would allow HRW to tell decision makers how much was being spent for each of these unnecessary transfers.

They were also able to produce aggregated data at the local level. They created a state dashboard so that people could understand the data at the state level, since the detention facilities are state-run. The data highlighted local-level inefficiencies. The local press was then able to tell locally relevant stories, thus generating public opinion around the issue. This is a good example of the importance of moving from data to story telling in order to strengthen advocacy work.

HRW conveyed information and advocated both privately and publicly for change in the system. Their work resulted in the issuing of a new directive in January 2012.

FOIA and the data set

Obtaining data via FOIA acts can be quite difficult if an organization is a known human rights advocate. For others it can be much easier. It is a process of much letter sending and sometimes legal support.

Because FOIA data comes from the source, validation is not a major issue. Publishing methodologies openly helps with validation because others can observe how data are being used. In the case of HRW, data interpretations were shared with the US Government for discussion and refutation. The organization’s strength is in its credibility, thus HRW makes every effort to be conservative with data interpretation before publishing or making any type of statement.

One important issue is knowing what data to ask for and what is possible or available. Phrasing the FOI request to obtain the right data can be a challenge. In addition, sometimes agencies do not know how to generate the requested information from their data systems. Google searches for additional data sets that others have obtained can help. Sites such as CREW (Citizens for Responsibility and Ethics in Washington), which has 20,000 documents open on Scribd, and the Government Attic project, which collects and lists FOI requests, are attempting to consolidate existing FOI information.

The type of information available in the US could help identify which immigration facilities are dealing with the under-18 population and help speculate on the flow of child migrants. Gender and nationality variables could also tell stories about migration in the US. In addition, the data can be used to understand probability: If you are a Mexican male in San Jose, California, what is the likelihood of being detained? Of being deported?

The US Government collects and shares this type of data, however many other countries do not. Currently only 80 countries have FOI laws. Obtaining these large data sets is both a question of whether government ministries are collecting statistics and whether there are legal mechanisms to obtain data and information.

Data parsing

Several steps and tools helped HRW with data parsing. To determine whether data were stable, data were divided by column and reviewed, using a SHELL. Then the data were moved to a database (MySQL), however other programs may be a better choice. A set of programs and scripts was built to analyze the data, and detention facilities were geo-located using GeoNames. The highest quality result was used to move geo-location down to the block level and map all the facilities. Then TileMill and Quantum GIS (QGIS) were used to make maps and ProtoViz (now D3) was used to create data visualizations.

Once the data were there, common variables were noted throughout the different fields and used to group and link information and records to individuals. Many individuals had been in the system multiple times. The team then looked at different ways that the information could be linked. They were able to measure time, distance and the “bounce factor”, eg.., how many times an individual was transferred from one place to the other.

Highlighting problematic cases: One man’s history of transfers.

Key learning:

Remember the goal. Visualization tools are very exciting, and it is easy to be seduced by cool visualizations. It is critical to keep in mind the goal of the project. In the HRW case the goal was to change policy, so the team needed to create visualizations that would specifically lead to policy change. In discussions with the advocacy team, they defined that the visualizations needed to 1) demonstrate the complexity 2) allow people to understand the distance 3) show the vast numbers of people being moved.

Privacy. It is possible to link together individual records and other information to tell a broader story, but one needs to be very careful about this type of information identifying individuals and putting them at risk. For this reason not all information needs to be shared publicly for advocacy purposes. It can be visualized in private conversations with decision makers.

Data and the future

Open data, open source, data visualization, and big data are shaping the world we are embedded in. More and more information is being released, whether through open data, FOIA or information leaks like Wikileaks. Organizations need to begin learning how to use this information in more and better ways.

Many thanks to the Women’s Refugee Commission and the International Rescue Committee for hosting the Salon.

The next Technology Salon NYC will be coming up soon. Stay tuned for more information, and if you’d like to receive notifications about future salons, sign up for the mailing list!

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A recipient signs for a cash transfer (Photo: http://www.plan-international.org)

The popularity of cash transfer programs in the academic and aid blogosphere over the past few years, got me wondering what the difference is between the kind of cash hand-out programs that sponsorship organizations were doing in the early days and today’s cash transfer and conditional cash transfer programs.

What prompted the shift in thinking from ‘line up and get your cash’, to ‘cash handouts are paternalistic, ineffective, unsustainable and create dependency,’ to ‘cash transfers are innovative ways of achieving development gains’ and/or ‘cash transfers empower local people to purchase what they really need?’ How are cash transfers different today from 40+ years ago?

I happen to work for an organization that raises a good percentage of its funding through child sponsorship. From what I’ve heard, for the first few decades of our existence, cash handouts were simply how the organization worked. Along with most other development agencies, we moved away from direct handouts in the 80s. Like some other organizations, by the end of the 1990s we had adopted a rights-based approach. We are also now doing cash grants again in some cases such as this program in Vietnam. I’ve asked around a bit internally and haven’t found anyone able to point me to documentation on what in particular prompted the move from cash handouts to community-based development in the 80s. Obviously it was a change happening most everywhere, not just in the organization where I work. I assume there was a process and a lot of discussion around this like there is with any change in approach, but it’s most likely on paper and not on-line. I do wonder what has been or could be learned about cash transfers from that process of discussion and change in methods.

There is certainly a lot of debate today about cash transfers. When I’ve asked people outside my organization what the difference is between today’s cash transfers and those of 40 years ago, most pro-cash transfer folks say that today’s approach to cash transfers is different or that cash transfers are included as part of broader programs, or that cash transfer programs that succeed are done by governments and not INGOs.

The anti-cash transfer folks tend to feel that cash transfers are not sustainable development, encourage dependency, and cause community conflict, and that they do nothing to improve systems or infrastructure in the long run; eg., what good is having cash if there is no health system? no food to purchase?  no school to attend? Or they consider cash transfers to be individualistic rather than a way to support an entire community or district’s development or worry that conditioning cash transfers can cause unintended consequences. (Here’s a fun piece that talks about what the cash transfer debate says about the international humanitarian community.)

There are tons of studies (mostly by economists it seems) showing that cash transfer and conditional cash transfer programs have improved health, nutrition and education enrollment. Some caution that cash transfer programs such as Brazil’s Bolsa Familia are not a panacea and need to be complemented with other types of programs.

I liked this recent paper ‘Richer but resented: What do cash transfers do to social relations and does it matter‘ by MacAusland and Riemenschneider (HT @rovingbandit). It questions the impact of cash transfers on less visible, more contextualized local and national relationships and power dynamics and suggests a need to go beyond material analysis during design, implementation and impact evaluations of cash transfer programs.

Especially helpful for someone like me who is trying to better understand the discussion around cash transfer programs is the paper’s reference to Copestake’s (2006) aspects of well-being (material, relational and symbolic) and three views on social protection as applied to cash transfers.

I’m pasting in the paragraphs I found especially useful to tempt you into reading the whole paper. I liked the excerpt below because it provides good insight into how different development theories color the objectives set in cash transfer programs and the way that success and impact are measured.

‘…An „income-first view of social protection focuses on the consequences of cash transfers for recipients’ incomes and on their costs, including fiscal costs and perverse incentives to stop working or to seek rents. Second, a needs-first view starts from a more multidimensional view of poverty and focuses on the states role in guaranteeing access to basic needs, including livelihoods, assets, and public action. This would criticise the income-first view for being too narrow. Third, a rights-first view identifies injustice as a key cause of poverty, and criticises the „needs-first approach for being paternalistic.

Very broadly, these views can be identified with philosophical approaches to development. The income-first view is most closely identified with a  modernisation theory and Washington Consensus approach, which is rationalist, individualist and utilitarian in nature, measuring utility primarily in terms of income. The appeal of this view in part lies in the measurability and equivalence of outcomes and costs – so that outcomes measured in dollars can be compared to costs measured in dollars. This possibility is very attractive for planners, since it enables an unambiguous (on this single metric) judgement of whether an intervention should proceed. In terms of approaches to social protection, the income-based view is reflected most clearly in the safety nets approaches of the early 1990s (World Bank 1990).

The needs-first view starts from a similarly utilitarian and individualist standpoint but broadens this by introducing other dimensions of well-being, largely adding material dimensions (such as education, health, and livelihoods) but in some cases relational aspects (such as a capacity for social action). This draws in part from Sens capability perspective (Sen 1985) and is currently being operationalised through the Millennium Development Goals and now multidimensional poverty indices (see e.g. Alkire and Foster 2009). In the social protection literature, this view is closest to the transformative social protection approach (Devereux and Sabates-Wheeler 2004) that emphasises the role of social protection in overcoming not only material shortcomings but in enhancing self-esteem and social status.

The rights-first view has developed rather differently, in part from Latin American traditions of dependency theory and structuralism, which place more emphasis on relational and symbolic aspects of well-being. One application of this tradition can be found in Figueroa (2001) who argues that persistent inequality in Latin America can be explained by processes of social exclusion (based on cultural difference) leading to political exclusion from social protection programmes and education, and resulting economic exclusion. As Copestake (2006b: 4) summarises, this interpretation highlights: “the extent to which economic growth and inequality reduction are dependent upon cultural and political mobilisation, not least through advocacy of human rights. This is in stark opposition to the more common assumption of economists that improved human rights are more likely to follow economic development than to be a precondition for it.”

The consequences of these different views for assessments and planning of cash transfers are quite profound. For instance, the different views will put quite different weights on the negative consequences of excluding members of the community from controlling payments or targeting as opposed to the problems associated with additional costs of targeting. The decision whether to pay for additional community participation will look very different depending on which view is held. Similarly, the different views will imply quite different judgements on whether cash transfer programmes should be replicated, given different material, relational and symbolic outcomes.’

I still don’t really know what I think about cash transfers, (I suppose “it depends’ is always a good answer) but at least I have a bit of a better framework for thinking about them and analyzing what I read about them. Copestake’s three areas (material, relational and symbolic) also give a good framework for analyzing other types of aid and development programs, beyond cash transfers (such as Gift In Kind, as @cynan_sez points out).

I also still haven’t figured out how the old style sponsorship cash handouts were different from today’s ‘innovative’ models. Any old timers out there with insight to share on that?

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