Development Statistics: Understanding Development better through Data

Globally, we have come a long way since the 1950’s, when global life expectancy was only 48 years, 36 percent of the population was literate and 54.8 percent lived in extreme poverty (Our World in Data). Global life expectancy now stands at just over 71 years (WHO 2016), global adult literacy is estimated at 86% (UNESCO 2015) and 10.6 percent of the global population is poor i.e. 767 million people living on less than 1 US$ 1.90 per day (World Bank 2013). Data has played a key role in being able to track progress towards national and global development outcomes such as the Millennium Development Goals (MDGs) and the adoption of the Cape Town Global Action Plan for Sustainable Development Data during the UN World Data Forum in January of this year is an explicit recognition of the importance of data to achieve the Sustainable Development Goals (SDGs).

So what does development really mean?

Traditional welfare economics approaches merely defined development as economic growth measured in terms of Gross National Product (GNP) or Gross Domestic Product (GDP) as a proxy for development. In the 1970s and 1980s, alternative definitions sought to go beyond GNP and GDP, by putting greater emphasis on employment, followed by redistribution with growth, and then whether people had their basic needs met. In the 1980’s and 1990’s under the influence of scholars like Amartya Sen, development became more broadly defined as a process of expanding the real freedoms that people enjoy in terms of political freedoms, economic facilities, social opportunities, transparency guarantees and protective security. With the SDGs, the emphasis is now on “sustainable development” i.e. development that meets the needs of the present without compromising the ability of future generations to meet their own needs, connecting the three core elements economic growth, social inclusion and environmental protection.

While there has been some common understanding on what development means, there surprisingly has never been an agreed upon definition of what constitutes a "developed" and "developing" country despite the wide use of this distinction within the development sector. The categorization seems to imply that any given country is developing towards something, and that there is only one way to get there. Yet, many of the so-called “developing” countries have made significant progress and in many instances differences between countries have dramatically reduced. During a series of Ted Talks, Hans Rosling, world renowned statistician and founder of Gapminder, who sadly passed away recently, dispelled the myth of the dichotomy between "developed" and "developing" countries by using data and statistics to clearly show that the term "developing" country has lost its relevance if it had it in the first place. Giving the example of child mortality, he showed that child mortality rates in poorer countries have been steadily decreasing over the past 50 years and how the numbers of the so-called “developing” countries are almost equal to the numbers of the wealthier nations. According to his predication, by 2030, there will be no difference between the two sets of countries.

While this realization has been slow to sink in within the development sector, there has been some recognition that the term "developing" country is antiquated. Thus, in 2016, the World Bank, in an unprecedented move, decided to no longer distinguish between “developed” countries and “developing” ones in the presentation of its data. However, the distinction remains widely used and in many people’s perception, the myth of the "developed" and "developing" world still persist. As Rosling put it, “It is only by measuring, that we can cross the river of myths.”

The next question is then how we measure development?

There have been many debates, different schools of thought and practices of defining and measuring development with a shift away from merely using economic terms based on average levels of income to a human development centered approach to measurement based on indicators such as life expectancy, means years of schooling etc. The SDGs provide the most recent global framework of measurement with 169 targets and a proposed 230 individual indicators. Still one of the main challenges remains how to measure development accurately without falling into the trap of one size fits all. Development by its very nature seems to be highly subjective, contextual and difficult to capture. There is no blue print for how to develop as the experience of different countries shows that there are different ways to achieve development.

The SDGs acknowledge the need for country differentiation in target setting by defining targets as aspirational and global, with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances. However, there is an inherent tension in between harmonization – staying close to the global targets themselves, which will make comparisons and global progress monitoring easier – and internalization – reinterpreting the targets to make them fit better the national legal framework, social fabric, and political discourse. Until now, a lot of emphasis has been placed on harmonization, but it is important to recognize that prior to the adoption of the SDGs, national governments already had existing policy objectives and commitments that are were articulated in national and sectoral strategies and plans, as well as in commitments to regional and international agreements. Therefore, rather than introducing a whole new set of parallel processes, it is far better to integrate the SDG agenda into existing agendas. Given the complexity of understanding and measuring development, this is not something that can be left to development agencies with their own methodologies, but rather warrants its own discipline -“development statistics”- to better understand the political, economic, social, cultural and other dimensions of a country’s development, their interrelatedness and interdependency with external factors.

An attempt to understand and measure a country’s development is not possible without data. One the main challenges remains the lack of reliable and accurate data at country level, where most of the data originates from and is produced. In Nepal, there is a lot of data that is being generated, but it is scattered with many of the datasets not easily accessible. Available data is spread over various places, mostly in not in open format and the process to obtain data is time consuming. There is no central place where data on different sectors can be easily accessed, compared and analyzed to connect the dots of development and can be used for evidence based policy and decision-making, development programming, research, reporting and monitoring and evaluation. To address this issue, Bikas Udhyami, developed “Nepal in Data” (www.nepalindata.com), an open data and statistics portal to make data in relation to Nepal’s development more available and accessible for all. The portal by now contains more than 1000 datasets spread over 12 sections covering various sectors including agriculture and land, social and human development, state and politics; infrastructure communication and technology etc. Users can select and compare different indicators with each other and the portal allows them to visualize their data selection in tabular, chart and map format in order to make data more easy to understand and download and print their preferred selection.

However, opening data in itself is not enough in order to have impact on development. In order for data and statistics to become truly accessible to everyone, it is crucial to increase data literacy- the ability to read, build knowledge from data and statistics, and to communicate that meaning to others. This is why Bikas Udhyami will build capacity to use and analyze data and statistics, develop tools such as infographics to help people to understand data and statistics and work with stakeholders to address specific data and statistical needs to solve to specific problems so that they can use data and statistics in meaningful ways for whatever purpose be it policy making, advocacy, business development or research.

Only if people are able to understand and use data, we can talk about data as having an impact on people’s lives and thus contribute to development.

Visit Nepal in Data: www.nepalindata.com