Development Co-operation Report 2017 Data for Development © OECD 2017 PART I Chapter 2 The value of data for development by William Hynes, New Approaches to Economic Challenges Unit, OECD This chapter discusses how thinking on development and development co-operation have been informed by the availability and use of data, and what now needs to change to efficiently exploit traditional data sources and take advantage of new ones. It argues that the data revolution is contributing to three shifts in focus: from gross domestic product to multi-dimensional well-being; from aggregate to micro data; and from administrative data to smart data. 45 I.2. THE VALUE OF DATA FOR DEVELOPMENT Life can only be understood backwards; but it must be lived forwards. (Søren Kierkegaard) How do you know if an anti-poverty strategy is working if you don’t know how many poor people there are? How do you know if a school or a clinic is the better investment if you have no information base with which to estimate or track their impact? Data and statistics provide the essential basis for understanding the practicalities of the development process, the interactions and feedbacks between different systems, and the factors that should shape decisions. Data are also vital for answering larger questions about the development process. Identifying the factors behind differential rates of growth, development and well-being have been central questions of development economics. Proposed explanations include factors ranging from geography, history, institutions and culture to politics and governance. Another central debate has been over the role and importance of development co-operation in promoting economic growth. While some progress has been made on these deeper questions, neither debate has reached anything like closure, and many of the obstacles to resolving them stem from data limitations. New and better data sources offer the opportunity to let questions determine the data to be obtained, instead of the data determining the questions that can be asked. But what data? The quality, availability, timeliness and use of basic economic and demographic data remain deficient in many parts of the developing world. While progress is being made, much more work is needed to improve census and other population data which form the traditional basis for policy making. At the same time, completely new sources of data are emerging through telecommunications, social media and e-commerce. New and better data sources offer the opportunity to let questions determine the data to be obtained, instead of the data determining the questions that can be asked (Duflo, 2006: 2), and new sources are already leading to the emergence of new policy-oriented analytics (Dum and Johnson, 2016: 278). From relying on gross domestic product to looking at multi-dimensional well-being Modernisation theory, e.g. Rostow’s theory of the five stages of economic growth, suggested that development progress occurred in a linear fashion. Structural transformation would see an evolution from an agricultural economy to a modern industrialised one. Data on returns to capital and structural transformation were key to tracking and guiding this progress. The model led to an almost exclusive focus in aid programmes on financing capital goods and infrastructural investment, which were considered essential in driving developing countries’ rise through the stages of development. Unfortunately, early aid-financed capital projects were sometimes premature in scale or technology and lacked provision for management and maintenance. Though projects gradually became more cost-effective and their successors were built with greater attention to long-term feasibility, the perception grew that infrastructure spending had been relatively ineffective, especially in poor, narrowly based and vulnerable economies that had limited margin for error (OECD, 1985: 16). 46 DEVELOPMENT CO-OPERATION REPORT 2017 © OECD 2017 I.2. THE VALUE OF DATA FOR DEVELOPMENT Moreover, even by the end of the 1960s, it was realised that high rates of gross domestic product (GDP) growth had not made a real dent in the prevailing social conditions (Emmerij, 2002). The potential for a disconnect between GDP and welfare has long been recognised. Robert Kennedy observed almost 50 years ago that GDP “measures everything, except that which makes life worthwhile”. At almost the same time, Gunnar Myrdal’s vision that development was “the movement of the whole social system upwards” led to the prioritisation of basic human needs in the 1970s and the improvement of social data on health, education and poverty. Measurement difficulties in developing countries exacerbate the problems of relying just on GDP to measure and understand progress. Morten Jerven (2013) highlighted the difficulties arising from a pervasive shadow economy, differing standards, errors and guestimates. He also highlighted the impact of rebasing – revising the methods and base data used to calculate GDP. For instance, Ghana rebased in 2010, and the GDP estimate rose by 62%. Nigeria rebased in 2014, the GDP figure rose by 89%, and Kenya saw a 25% rise after rebasing in 2014. The revisions took into account formerly omitted economic activities performed by informal businesses, as well as recent booms in several sectors, such as information and communications technologies, telecommunications, banking, and real estate. This provided a much more precise assessment of the economies’ current sizes and of the contributions of different sectors to GDP, but rendered historical data practically unusable (Sy, 2015). Any measure is, by definition, a quantity that is at best only roughly correlated with quality of life as it is actually experienced by individuals. By many measures, especially those having to do with material sufficiency, the average person’s quality of life has clearly improved over the past 100 years. By others, especially those having to do with the environment, social harmony and individual fulfilment, the quality of life may well have declined. But any measure is, by definition, a quantity that is at best only roughly correlated with quality of life as it is actually experienced by individuals. Similarly, the Pearson Report (1969) argued that economic statistics alone could not give a true comparison between the living standards and satisfaction of a tenant in a high-rise housing development in a packed and polluted megalopolis and those of a village in sunny Ceylon. Sen (1989) has long criticised the danger of using one number to try to capture the breadth of the development experience. He conceptualised development as having the capabilities to live the kind of life one values, for example in terms of political freedom, economic facilities, social opportunities, transparency guarantees and protective security. In his way of thinking, development is not about what you have, but about what you can do. The UN Human Development Index (HDI), first published in 1990, took this approach forward with its three components of life expectancy, literacy/schooling and GDP per capita. Subsequent UNDP reports tweaked the measure and added further indices such as Inequality adjusted HDI, the Gender Inequality Index and Multidimensional Poverty Index, but the very proliferation of these indexes testifies to the difficulty of capturing overall well-being with any single metric. More sophisticated measures of welfare, such as the OECD’s Better Life Index, now allow users to establish their own priorities among dimensions of well-being, and construct international comparisons accordingly. But data limitations severely constrain such approaches in all but the most advanced developing countries. Even tracking such basic well-being objectives as those contained in the Millennium Development Goals proved a major challenge. According to a report by an independent UN advisory group, the availability of annual data on 55 core indicators for 157 countries never exceeded 70%. The Sustainable Development Goals (SDGs) now pose a much larger data DEVELOPMENT CO-OPERATION REPORT 2017 © OECD 2017 47 I.2. THE VALUE OF DATA FOR DEVELOPMENT In my view: We need to rebalance the political economy of statistics Morten Jerven, Professor, Norwegian University of Life Sciences The Sustainable Development Goals (SDGs) have launched us into a new era of global development measurement and monitoring in a world increasingly focused on gauging progress against quantifiable targets. Yet data remain unavailable or scarce for many SDG indicators. The global demand for data often overshoots the capacity of national governments to supply statistics, putting pressure on their national statistical offices. The resulting stress lessens their survey capacity even further, hampering their efforts to collect, report and disseminate data. The resulting distortion of our knowledge base on data for development is double-edged: we know less than we need to about poor countries; but our knowledge is even slimmer when it comes to the poor people who live in these countries. These knowledge gaps result from problems at various levels. At the design level, there is a lack of compatibility between statistical categories that were conceived for industrialised societies and the developing contexts they are applied to. At the implementation level, lack of capacity and poor record keeping in national statistical offices, compounded by other challenges, make the transaction costs of recording certain activities much higher than the value of the activities