Thursday, 23 May 2013

‘Middle Cities’ – Are we forgetting to nurture the engines of our future growth?

By Jaideep Gupte
With the world now mostly urban, nearly 60% of our global GDP is generated in only 600 urban centres. Moreover, large urban centres are quite simply the places where growth has been occurring – this is a function of concentrated economic activity. But this story is really about what is yet to come. For the first time, a country like India, with only a third of its population currently urbanised, which is far less than Brazil (86%) or China (47%), is reporting higher population growth in urbanised areas than across its vast rural landscape. In sub-Saharan Africa, the urban population is projected to double by 2030. This growth can be categorised into two significant trends: just under 30% is projected to occur due to what is classically understood as rural-urban migration. Significantly, the rest will occur due to natural increases in urban population, that is, cities and towns generating their own population growth. National planning bodies also have a say in this when they classify peri-urban or peripheral areas as under municipal administration.

This ‘urban-shift’ is going to require resources at a monumental scale – China for example, predicts it will need $8.1 trillion in new investment by 2020 to accommodate its new urban dwellers. Current rates of investments into infrastructure are falling far behind these levels. And not just in terms of scale, but importantly, also in terms of location: focussing on new or projected population growth, the mega-cities of the developing world are quickly being overtaken by a vast number of small and medium sized urban areas, each numbering approximately 100,000 in population.

These ‘middle’ cities and towns across sub-Saharan Africa and Asia are going to be the main hosts of urban growth. Understandably, these town and cities are also the weakest in terms of human capacity, infrastructure or service provision, and have a very thin local tax-base to use for future investment. Local revenue of most of these municipalities is often less than 1% of their country’s GDP. This has created a critical mismatch across a range of sectors, from basic service provision, law and order, to disaster preparedness, which directly impacts our progress on poverty eradication.

This is the theme of this year’s Global Monitoring Report - Rural Urban Dynamics and the MDGs, which provides an in-depth analysis on urbanisation as a force for poverty reduction and progress towards the Millennium Development Goals. I speak with Jos Verbeek, Lead Economist at the World Bank and Manager of the Global Monitoring Report, on what impact urban development has on rural poverty, what roles and responsibilities the private sector has in fostering equitable urban growth, and how ‘middle’ cities can be supported in becoming engines of our future growth.

Thursday, 16 May 2013

When an economist goes to the field – and yes it does happen!

By Alia Aghajanian
This post is mainly in response to the reactions of surprise and suspicion when talking to colleagues about my fieldwork. Yes, I am an economist, and yes, I have gone to the field!

Most PhD students at IDS spend the better half of the second year of their PhD programme conducting primary research with real research participants. This research can take the form of life history interviews, short focus group discussions, participant observation, ethnographic research and so on. So how does a research project that relies on numbers, rather than people, involve fieldwork[1]? Hopefully, this post will shed some light on what happens when an economist goes to the field, by describing the field data collection conducted for my PhD thesis.

Two months ago I conducted a household survey amongst residents of a Palestinian refugee camp in Lebanon (Nahr el Bared) who had been displaced to various locations throughout the North of Lebanon and beyond, due to a conflict that destroyed the camp in 2007. The idea behind the research is to evaluate the consequences of returning home after conflict-induced displacement – in terms of social and economic reintegration. While some qualitative research has shown that formerly displaced persons often face difficulties upon returning home[2], the quantitative literature is yet to address this question in a rigorous manner.

Once I had finessed my research questions, I set to work designing my household survey. Palestinian refugees in Lebanon are registered with the United Nations Relief and Works Agency (UNRWA), and after the conflict and the initiation of the reconstruction of the camp, UNRWA created a database of displaced refugees (including their addresses) which is updated on a quarterly basis. Using this database I was able to create my sample, and I aimed for 600 households – around 10% of the population. In order to guarantee that the sample collected was representative of the geographical distribution of the population under study, I set the sample size in each geographical area equal to 10% of its actual population size.

After designing the sample I started work on the questionnaire. Many months were spent developing this, as I drew from various different standardised questionnaires and then adapted the questions to suit my context. Particularly, I was interested in measuring social capital, and after an extensive literature review I developed a relatively large range of questions that aimed to capture different elements of social capital, from trust to social interactions. Unfortunately, a household survey does not allow the luxury of changing the questionnaire once data collection starts, so many run-throughs with friends, supervisors, and finally members of the data collection team ensured that it was as perfect as possible (inevitably, in hindsight there are many questions I wish I had also included!).

Planning the logistics of the actual data collection came next. As much as I would have liked to interview 600 households myself, time would not allow it[3]. So I needed to hire a team that I could trust, and more importantly that the research participants could trust. In an ideal world, data collectors would be complete strangers to the respondents, to ensure confidentiality and unbiased responses. On the other hand, the refugee camps that we were conducting research in were not safe environments, and can often turn hostile. I did not think it a good idea to bring in a team of strangers to the field sites and draw unnecessary attention to ourselves. For this reason, data collectors were local residents of the camps in the sample, but were assigned to sectors in the camp as far as possible from the sector that they lived in.

Prior to fieldwork starting we set up meetings with relevant persons in the field site communities (this included the camp services officers, leaders of political parties, and members of the camps’ popular committees) to obtain the necessary permissions and explain the aim of the household survey. I was questioned on the importance of my research, what new information my research could provide, and what policy implications my research could have, as there has already been quite a lot of research conducted on the residents of Nahr el-Bared[4]. A few days prior to fieldwork, these meetings were a reminder of the commitment I was making to the research participants. While no direct policy changes would necessarily result from my research (a point made clear to consenting participants), I made a commitment to understand how these households had been affected by the conflict, subsequent displacement, and return. As well as to do my best to communicate these findings not only to the academic world, I will try to disseminate my findings to policy makers who are concerned (or should be concerned) with the reintegration process of refugees and displaced persons around the world.

With the necessary permissions granted, we arrived to the field with a specific target of households to be interviewed for each field site. But we still needed to ensure that households were selected randomly within these areas. In areas where displaced households were clustered together, data collectors used what is known as the “right-hand-rule”. This means that after the first interview was completed, the data collector crossed eight[5] consecutive households on his/her right-hand path and interviewed the eighth household. Whenever data collectors reached an intersection, they took the lane on their right hand side and completed the route by turning to their right until they reached the main route again. In this way all areas of the camp were covered, and we ensured that all households had an equal probability of being selected. However, in areas where households were spread apart, households were tracked using addresses, phone numbers, and the key-contacts of the field supervisors[6].

As it was important that these protocols were followed correctly and consistently, and that data collectors were not interviewing the first household they could find (or worse, sitting under a tree and completing the required number of questionnaires), the field supervisors, coordinators and I conducted random checks on the data collectors and asked them to retrace their path with us.

Data collectors interviewed households using tablet PCs programmed with a structured questionnaire (which in turn required many weeks of testing!). This meant that at the end of each day I was able to extract all the data collected and convert it to an excel file. To the annoyance of the data collectors, I was then able to check for any inconsistencies and clear this up with them the next morning. Some examples of these inconsistencies are the sex of an individual not reflecting his/her name, or a 50 year old woman being the mother to a 45 year old man. If I was not able to clear these issues up with the data collectors, they went back to the household for further clarification.

In addition to making data validation easier, there was an all round consensus among the team that the tablet PCs were a great bonus to the data collection. Many questions in the questionnaire were related to the responses of previous questions, but luckily there was no need for data collectors to navigate through these difficult and complex skip codes, as the programme[7] used automatically calculated these. Inevitable errors that occur during data entry were minimised, as responses were only entered once. In addition, I was able to observe the data in real time, which was quite satisfactory after a long hard day of data collection. Also, rather than finding the tablet PCs intimidating, respondents were initially only interested in the tablets, and at this point data collectors found it much easier introducing themselves and the research project once their attention had been caught. Even during training, data collectors were eager to learn about this new and exciting technology, practicing the questionnaire amongst themselves and then later at home with their families.

After two weeks of intense data collection, we were done! I now have a dataset of 590[8] households ready to analyse. This dataset will allow me to describe certain demographic characteristics of the sample, being representative of the larger population. For example, I will be able to estimate the employment rates and education levels of the population under study, as well as observe community levels of social capital, cohesion, and integration. Collecting a representative and unbiased sample allows me to make scientific conclusions, such as whether the average literacy rate is statistically different for females and males. More importantly, I hope to estimate causal relationships and their statistical significance: For example, what is the effect of returning compared to prolonged displacement on levels of trust in neighbours, and is this effect significant?

While my sample is relatively large, it can be argued that I will not have as much depth to my research that qualitative research allows. Although a properly collected large dataset increases the representativeness and the unbiased position of the participants in the research, more focused research can provide insight and information which is not necessarily limited to a structured questionnaire. While I acknowledge this trade-off between depth and breadth, I still believe there is room for these two methods to complement each other, rather than to conveniently ignore and debase the other. In fact, much of the qualitative research on return migration and return after displacement has guided the hypotheses that I will empirically test with the data collected.

Unfortunately not many economists get to collect their own primary data. Organizing a household survey can be expensive and time-consuming. Researchers need to think twice before conducting a large survey. Is it worth the required resources? Is it worth the time of the respondents? And can another existing dataset answer the research questions just as effectively? Chris Blattman writes an interesting blog on the questions researchers (and I think this is especially relevant for quantitative researchers) should ask themselves before going to the field. On the other hand, collecting your own data can be a very rewarding experience, attaching human faces to what can otherwise be just numbers.

[1] In fact, most applied analysis conducted by micro-economists involves secondary datasets that were initially collected from the field. This blog gives an example of the collection of these kinds of datasets.
[2] Often refugees and internally displaced persons are away for so long that “home” has become a foreign environment. See Black, Richard and Saskia Gent. 2006. "Sustainable Return in Post-Conflict Contexts." International Migration, 44(3), 15-38.
[3] The role that I played during the fieldwork was that of a field supervisor, assigning tasks for each member of the team at the beginning of the day and checking up on the data collection process and resulting data throughout the day - and unfortunately long into the night.
[4] Having said that, during one meeting the relevant person seemed to be more interested in an upcoming Premier League football match than the relevance of my household survey.
[5] This number was calculated by dividing the number of households in the sample by the total population, and allowing a margin for refusals and unavailable households. Data collectors asked neighbours or directly knocked on the doors of dwellings to enquire as to whether households were originally from Nahr el Bared camp. This was because Nahr el Bared families are living alongside other Palestinian families and more recently alongside Syrian refugees, and we did not want the number of non-Nahr el Bared households to inflate the count number. If households were not from Nahr el-Bared then the household was not included in the count.
[6] Before heading to the field the supervisors met with various key informants, such as the Palestinian popular committee for relevant camps, the relevant camp services officer, local NGOs who had provided aid to displaced persons, the national Palestinian scouts group, notable media persons, and notables of Palestinian political parties.
[7] I used ODK, an open source programme developed by researchers at the University of Washington.
[8] This is slightly lower than the target as there was a difficulty in tracing certain households. While this is a very small “attrition rate”, I will still try to include robustness checks later on in my analysis to correct for this.

Thursday, 2 May 2013

Social transfers: all good for children?

by Keetie Roelen
The positive impact of social protection programmes is now widely acknowledged. They help to increase household income, smooth consumption and protect against shocks. With respect to children, social protection has been found to increase school enrolment and attendance rates, improve nutritional outcomes and reduce child labour. Given these positive impacts, interest in the role of social protection is now also widening to other aspects of children’s lives. Momentum is growing around questions of the potential impact of social protection programmes on child protection outcomes. Last week, UNICEF IRC Office of Research published a paper on the link between social transfers and child protection, a draft version of which was presented and discussed during an expert meeting in Florence last month.

A notable observation from the paper and discussions at the expert meeting refers to the lack of knowledge about the linkages between social transfers and child protection outcomes. Whilst we know quite a lot about the effect of transfers on children’s education and health, few evaluations have considered the effect of programmes on issues of child protection, which would include early marriage, family separation, violence, abuse and neglect. This thin evidence base is not limited to social transfers only; in general we know little about the linkages between social protection and child protection.

The lack of knowledge can be attributed to a number of factors. Firstly, issues of child protection are largely outside of the ‘theory of change’ of social protection programmes. Whilst many programmes include the increase in school enrolment rates and immunization coverage in their objectives, few spell out the reduction of early marriage and prevention of loss of parental care, for example, as explicit goals. Secondly, the effect of social protection on child protection is much harder to observe and study. Child protection violations are much less visible to outsiders, easier to hide and sensitive to investigate. Finally, the linkages between social protection and child protection outcomes are everything but straightforward. Whilst improved nutritional outcomes or school attendance will be highly correlated with an increase in household income through social transfers, the link between household wealth and child protection is much more tenuous.

As such, evidence on the link between social protection and child protection remains thin on the ground. However, the modest information that is available suggests that social protection programmes can far-reaching impacts in terms of child protection – both positive and negative, and that there is a dire need for more investigation in this area.

On the positive side, social protection has the potential reduce child protection violations, largely through its reduction of poverty. Improved household income can prevent the loss of parental care and family separation by avoiding parents having to migrate or leaving their children in the care of others. Poverty reduction is also likely to reduce stress levels in the household, thereby improving the quality of care for children. The provision of transfers can also work as a positive incentive for foster or kinship carers, particularly in areas with many orphans.

That said, the provision of social protection can also have negative impacts in terms of child protection, mostly through perverse incentives or due to unintended side effects of programme implementation. For example The incentivisation of foster care through social transfers can result in low-quality care and ‘commodification’ of children when unaccompanied by careful screening and training of carers. Unintended side effects can result from trying to comply with conditions in CCTs (such as overfeeding or keeping a child underweight when having to meet certain weight requirements) or the requirement to work in public works programmes (including the lack of child care or children having to substitute for domestic or farm work).

There is no doubt that social protection holds great potential for improving children’s lives. However, more information is needed about programmes’ impact on outcomes outside of the more easily observable ones, such as education, health and nutrition. It has been argued before that child-sensitive social protection needs a nuanced perspective, rather than be based on assumptions about we think works and does not work for children (Roelen and Sabates-Wheeler 2012), and this also holds with respect to the link between social transfers and child protection outcomes. An important step in gaining better insights into this link is not merely to do more research, but to do research that goes beyond the parameters as set by social transfer programmes’ ‘theories of change’. This requires an acknowledgement that child protection outcomes are not only shaped by the interventions directly (i.e. the provision of cash or the condition of school attendance or regular health check-ups) but also, or maybe primarily, by the design and implementation features of those interventions. As long as the potential impact, foreseen and unforeseen, of these features is not adequately considered, the picture about the link between social transfer programmes and child protection will remain incomplete and partial, and programmes may cause as much harm to children as they do good.

Barrientos, A., Byrne, J., Villa, J.M. and Pena, P. (2013) Social Transfers and Child Protection. Office of Research  Working Paper WP-2013-05. Florence: UNICEF IRC Office of Research.

Roelen, K. and Sabates-Wheeler, R. (2012) A child sensitive approach to social protection: serving practical and strategic needs. Journal of Poverty and Social Justice, 20:3, pp. 309-324.