Sustainability of OSS is an important, but often overlooked issue. The private sector is struggling to find the right model to maintain and sustain OSS. The International Development Agencies need viable long-term strategies to sustain the OSS projects they are developing, funding, or using.
Two young colleagues invited me for coffee to discuss their proposal to develop an open source software (OSS) system for administering government programs in developing countries. The idea of replacing costly, custom-built proprietary systems with open-source solutions tailored for specific country requirements was very appealing.
“Why pay millions of dollars for a proprietary solution when an open source system will be free?” exclaimed one of the colleagues.
I inquired cautiously, “Have you considered how to maintain these systems once they are deployed? Who will pay for customization and on-going support to the country clients? How do you consistently ensure the quality of the code?”
“The international OSS community will volunteer their time to maintain and improve these systems.” was the reply.
Our Gender Data Portal is the World Bank Group’s comprehensive source for historical and current data disaggregated by sex. This data site brings together high-quality, curated data on women and men (and girls and boys) in an easy-to-use platform that covers a wide range of topics such as demography, education, health, economic opportunities, public life and decision-making, and agency. The Gender Data Portal is the go-to place for reliable data disaggregated by sex for countries and regions around the world.
Here are 5 things you can do in our Gender Data Portal:
The launch of the Human Capital Project has galvanized global action to close human capital gaps, and has highlighted the importance of investments in the knowledge, skills, and health that people accumulate throughout their lives, to realize their potential as productive members of society.
Improving both the quantity andquality of education is pivotal to empowering young people to fulfill their potential. Science, Technology, Engineering, and Mathematics (STEM) education is critical not only for fulfilling the needs of the future workforce, but also for producing researchers and innovators who can help to solve intractable challenges.
The underrepresentation of women and girls in STEM gets a lot of attention, but the data on access to, and quality of, education shows that the story is more nuanced.
At primary school level globally, there is gender parity in both enrollment and completion–a remarkable achievement of recent times. Gender gaps emerge in a number of low-income countries, mostly in Sub-Saharan Africa, and in some Latin American countries there are ‘reverse’ gender gaps (with boys less likely to attend or complete primary school). Overall, gender gaps (where they exist) are modest in comparison to the gaps between rich and low-income countries.
When it comes to academic performance, girls often do as well as, or better than, boys in science and mathematics.
In primary schools, there are no gender differences in science achievement in more than half of the 47 countries where performance is measured (Figure 1). Girls score higher than boys in 26 percent of the countries. The difference in achievement is almost three-times higher when girls score more than boys compared to when boys score more than girls. Results for mathematics achievement are similar. There are no gender differences in about half of the countries with data, but boys score better than girls in 37 percent of the countries.
Figure 1: Primary-school girls perform as well as boys in science and mathematics
A new guidebook published by the World Bank and the UNESCO Institute for Statistics (UIS) casts light on how to measure the heavy burden of education spending that falls on the world’s families. Measuring Household Expenditure on Education: A Guidebook for Designing Household Survey Questionnaires will help countries report on SDG 4 indicator 4.5.4: education expenditure per student by level of education and source of funding. The guidebook also aims to ensure proper representation of education expenditures in consumption-based poverty and inequality measures, and enable more micro-econometric research on resource allocation in households.
The burden of education spending by families
We already know that the burden on families can be heavy. UIS data released in 2017 found that families in low-income countries pay more for their children’s education: households in many developing countries spend a far greater share of average GDP per capita on education than those in developed countries. Household spending on secondary education amounts to 20-25% of average GDP per person in Benin, Chad, Côte d’Ivoire, Guinea, and Niger, and more than 30% in Togo. In stark contrast, the share does not exceed 5% in almost all high-income countries.
The data also reveal that families—including the poorest—are providing much of the world’s education spending. For example, households provide about one-quarter of education expenditure in Viet Nam, one-third in Côte d’Ivoire, half in Nepal, and more than half in Uganda.
Improving the capacity of national statistical systems (NSSs) has long been a part of the global development agenda. The NSSs play an important role in modern economies. They provide stakeholders, ranging from policy makers to stock market analysts and the general public, with the data on the country’s socioeconomic developments. At the international level, monitoring global initiatives such as the Sustainable Development Goals (SDGs) requires high-quality data that are produced consistently across different national statistical systems.
In 2004, the World Bank developed the Statistical Capacity Index (SCI) to measure progress in statistical capacity building. The SCI was based on publicly available data and was designed to assess a country’s statistical capacity in an internationally comparable and cost-effective manner. Several international and national agencies have adopted the SCI for measuring progress in statistical capacity building and related investments (United Nations, 2016).
In 2015, leaders of 193 countries formed an ambitious plan to guide global development action for the next 15 years by agreeing on a set of Sustainable Development Goals (SDGs). Four years after their launch, the World Bank’s expertise in development data and its large repository of development indicators has played an important role in helping track progress made towards the achievement of the SDGs.
How does SDG monitoring work and how is the World Bank involved?
To monitor the 17 goals and 169 associated targets, a framework of 230+ indicators was developed by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs), a group of UN Member States with international agencies as observers. Different international agencies were assigned as “custodians” of the SDG targets. In this capacity, the custodian agencies work with national statistical offices to develop methodologies for indicators to measure progress on the SDGs. The agencies also work with countries to compile data for SDG indicators, which they submit to the UN Statistics Global SDG database.
The World Bank participates in IAEG-SDGs as an observer and is a custodian or co-custodian (with other agencies) for 20 indicators, and is involved in the development and monitoring of an additional 22 indicators. Altogether, the World Bank is formally engaged with the monitoring of 42 of the 230+ indicators. The indicators cover a wider range of topics in which the World Bank has expertise, including poverty and inequality, social protection, gender equality, financial access, remittances, health, energy, infrastructure, and so on.
Globally, 56 percent of children live in countries with Human Capital Index (HCI) scores below 0.5. As these countries gear up to improve their human capital outcomes, it is vital to set a target that is ambitious enough to prompt action and realistic enough to be achieved. One way to get at this is to examine the historical rate of progress that countries demonstrated to be possible.
Using time-series data between 2000 and 2017, we estimated countries' progress in the health components of HCI (fraction of children not stunted, child survival and adult survival) using a non-linear regression model.  Our measure of progress is the fraction of gap to the frontier that is eliminated every year- the frontier being 100 percent child and adult survival, and no stunting.,
We address the following two questions:
What is the typical progress in the health components of HCI observed globally?
This round called for ideas that had an established proof of concept that benefited local decision-making. We were looking for projects that fostered synergies, and collaborations that took advantage of the relative strengths and responsibilities of official and non-official actors in the data ecosystem.
In 2004, my colleague Zurab Sajaia and I submitted a maximum likelihood routine to the Stata SSC archive. The program was quickly propelled by the Stata user community to the top 10 most downloaded Stata files; it is still in use now. While experimenting with similar algorithms to develop test procedures (five years after the program’s release), we uncovered an error in the routine. Hundreds, if not thousands, of econometricians had used our program and looked at our code, but no one raised any concerns.
Open Source Software (OSS) is quickly gaining popularity in the corporate world as a practical alternative to costly proprietary software. 78% of companies are now using OSS extensively and open source components are found in more than half of all proprietary software. The rationale is simple: OSS lowers development costs, decreases time to market, increases developer productivity, and accelerates innovation.
We’re living in a time of disruptive technologies evolving at an exponential pace. Today, you can enjoy an Impossible Burger (meat industry disrupted) delivered by Caviar (food delivery disrupted) to your AirBnB (hotel industry disrupted) while you’re on FaceTime (telecommunication industry disrupted) urging your teenager to get back to lessons on Khan Academy (education industry disrupted). And all the while, you’re leaving a trail of digital data points.