Demographic Data in Small Island Developing States: - PDF document

Demographic Data in Small Island Developing States: State-of-the-art, Challenges and Opportunities Alessio Cangiano & Andreea Torre The University of the South Pacific, Fiji Islands Paper for the IUSSP XXVIII International Population Conference, Session 72: Data quality and time trends Cape Town, South Africa, 29 October - 4 November 2017 Draft (please do not quote) Introduction Out of 232 countries listed by the United Nations, 54 are Small Island Developing States (SIDS). Shared features of small insular economies and environments – such as their narrow resource base; reliance on a limited number of industries; costly per capita production, service provision and infrastructure; remoteness from markets; and vulnerability to economic shocks, political upheavals and environmental hazards (House 2013) – have catalyzed a growing interest of the international community in the unique development challenges of island states. The critical development challenges facing SIDS are compounded with recurring demographic characteristics and dynamics shared by insular populations: high population growth and density associated with rapid and concentrated urbanization; numerous examples of delayed demographic transitions caused by stagnating life expectancies and/or stalling fertility; and some of the highest emigration rates worldwide, especially highly skilled. Yet, analysis of the development implications of demographic trends has been undermined by SIDS’ limited statistical capacity and specific challenges of demographic data collection – for example, some of the highest per capita cost of census data gathering worldwide and the reliance of statistical systems on foreign donors’ fu nding and technical assistance. As a result of the lack of an appropriate evidence base, policy formulation, implementation and evaluation, including the monitoring of the Millennium Development Goals (MDGs) and of ICPD Program-of-Action, has suffered. This poses critical challenges for the transition from the MDG framework to the post-2015 Sustainable Development Goals (SDGs) which will require significant expansion in the availability and use of demographic statistics in SIDS. Demographic research focusing on SIDS has equally been lagging behind, and has somehow lost momentum. Over the recent decades, demographers have devoted limited attention to the study of small and isolated populations – especially in comparison with other academic disciplines such as economics, geography and environmental sciences. Demographic research on SIDS has been carried out in relative isolation, with little or no systematic exercise to learn from comparative analyses of island states located in different world regions. 1

This paper reviews the state-of-the-art of population and development data in SIDS, along with the situational challenges experienced by SIDS in the production and dissemination of demographic statistics. In doing so, we attempt to identify good practices and priority actions that could be pursued to enhance the development of SIDS statistical systems. This paper relies on a combination of data and information sources. A systematic mapping of statistical capacity in SIDS is attempted based on existing quantitative measures, namely the World Bank Statistical Capacity Indicators and available data on the completeness of vital event registrations. OECD data (PRESS database) on international aid allocated to the development of demographic and social statistics are also used to investigate reliance of SIDS statistical systems on donor assistance. Only independent countries are considered in the analysis. For comparative purposes SIDS across regions are referred to according to geographical groupings (Pacific, Caribbean and AIMS 1 ) and classified according to level of economic development 2 . Further insights on specific data sources and issues were obtained through a review of technical documents and research reports on the production and use of demographic data in SIDS. Finally, reflections provided in this paper are based on the author’s exposure to Pacific Island NSOs daily work practices over five years in his role as coordinator of the Population and Demography and Official Statistics programs at the University of the South Pacific; as member of the Pacific Statistics Steering Committee; and as researcher visiting National Statistical Offices (NSOs) around the region to access unit-level Census and immigration data. Mapping of statistical capacity The World Bank’s Statistical Capacity Indicator (SCI) provides a quantitative assessment of the capacity of a country’s statistical system. It is a composite score summarizing statistical systems’ performance in three areas: methodology; data sources; and periodicity and timeliness 3 . According to this measure, SIDS as a group have an average statistical capacity lower than all developing countries combined (figure 1). There is however great variation across SIDS. Some of the largest countries such as Mauritius, Dominican Republic and Jamaica have SCI scores that are higher than the developing country average. Trends of statistical capacity over the last ten years are equally diversified. While few SIDS with very low SCI scores in the mid-2000s have since experienced remarkable increases in statistical capacity (e.g. Timor-Leste and Solomon 1 Atlantic, Indian Ocean, Mediterranean and South China Sea. 2 Based on the World Bank’s classification by income groups (low, lower -middle, upper-middle and high income). 3 The first dimension, statistical methodology, measures a country’s ability to adhere to internationally recommended standards and methods. The second dimension, source data, reflects whether a country carries out regular data collection activities, and whether key administrative data are used for statistical estimation purposes. The third dimension, periodicity and timeliness, captures the accessibility and periodicity of key socioeconomic indicators, including child and maternal mortality. This dimension aims to report the extent to which timely statistical outputs are timely disseminated for the benefits of the users. Countries are scored against 25 criteria in these three areas, using publicly available information and/or country input. The overall Statistical Capacity score is then calculated as simple average of all three area scores on a scale of 0-100. More details can be found at http://datatopics.worldbank.org/statisticalcapacity/files/Note.pdf 2

Islands), this is not a consistent outcome – with countries such as Comoros and Maldives experiencing significant capacity loss. Interestingly, the performance of SIDS in terms of statistical capacity seems to be only partly related to their level of economic development. Amongst high income SIDS, only Seychelle has a SCI score in line with the developing countries’ average. On the other hand, there are exceptions at both ends of the spectrum, i.e. lower- middle income countries such as Cabo Verde, Timor-Leste and Sao Tome and Principe – all former Portuguese colonies – that perform better than the SIDS average and an upper-middle income country (the Marshall Islands) that has the lowest statistical capacity of all SIDS. Figure 1 – Overall Statistical Capacity Score in selected SIDS, 2006 & 2016. Source: World Bank Statistical Capacity Indicators Disaggregated analysis for the three dimensions of the World Bank’s SCI reveals that the area where SIDS’s statistical capacity is lowest compared to the developing country average is the periodicity and timeliness of data dissemination (figure 2). SIDS performance in this dimension has even worsened between 2006 and 2016. On the other hand, significant improvements have been recorded for SIDS average methodology score (up from 41 to 48 in 2016), resulting in a reduced gap with other developing countries. This suggests that some progress has been made in implementing international standards in the data production cycle. 3