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New paper: "Milking the Data"

Tariq Khokhar's picture
Quick: how much milk did you drink last year?
 
If you can answer that accurately, you’re either taking the “quantified self” thing a bit far, or you may have been reading some of our research.
 
A new paper co-authored by our colleges on the Living Standards Measurement Study (LSMS) team compares different methods for estimating how much milk is being taken from livestock for human consumption.
 
Alberto wrote about this research last year and the work has been published in Food Policy under an open access license. I think the findings are super-interesting - the authors are trying to understand how to accurately find out from individuals “how much milk did you collect from your animals this year?”
 
Simply asking that question isn’t likely to get you an accurate answer, but if you had to rely on questions in a survey, which questions would you pick? The study compares the answers provided by different survey “recall methods” in Niger against benchmark data gathered by actually measuring the volume of milk taken (weighing it in a jug... ) one day every 2-weeks over the course of a year.

Bribery and limited access to banking are challenges for Afghan private firms

Arvind Jain's picture

The World Bank Group’s Enterprise Surveys benchmark the business environment based on actual experiences of firms. In a new blog series we kicked off last week, we’re sharing these findings from recently analyzed surveys conducted through extensive face-to-face interviews with managers and owners of firms in several countries.
 
In this post we focus on Afghanistan. We’ve conducted a survey with 410 firms across five regions and four business sectors—manufacturing, construction, retail, and services.

The International Monetary Fund (IMF) has noted that considerable political and security uncertainties have posed challenges for Afghanistan. Furthermore, the financial sector has been vulnerable with eight out of 15 banks classified as weak in late 2014. Within this context, the Afghanistan Enterprise Surveys (ES) shed light on several interesting findings:

Corruption is a challenge

According to the Afghanistan Enterprise Survey, firms face almost a 50 percent chance of having to pay a bribe if they applied for an electricity connection, tried to obtain permits, or met with government officials for tax purposes (“Bribery incidence”).  This is more than double of what private firms in landlocked developing countries experience on average.
 

Chart: People in Fragile States Receive Higher Average Remittances

Tariq Khokhar's picture
Also available in: 中文 | Français | Español

Remittances – money sent back home by emigres – amount to a larger financial flow than development aid. Since 2000, remittance inflows per capita to fragile states have been higher than those to other developing countries.

Read more in the OECD's States of Fragility 2015 and access data on migration and remittances and data on population and remittances from World Development Indicators. Note the aggregations and data used in the chart above are made available by the OECD at: http://dx.doi.org/10.1787/888933185242

Access to finance is biggest challenge for firms in Namibia

Joshua Wimpey's picture

The private sector continues to be a critical driver of job creation and economic growth. However, several factors can undermine the private sector and, if left unaddressed, may impede development.  Through extensive face-to-face interviews with managers and owners of firms, the World Bank Group's Enterprise Surveys benchmark the business environment based on actual experiences of firms. A series of blogs, starting today, share the findings from recently analyzed surveys conducted in several countries.

The Namibia Enterprise Surveys consisted of 580 interviews with firms across three regions and three business sectors – manufacturing, retail, and other services. So what are some key highlights from the surveys?

Exports take on average 8 days to clear through customs but varies according to firm size
In 2013, it took a firm in Namibia about eight days to clear exports through customs, which is considerably more than the two days it took in 2006. Despite this increase, the average time to clear direct exports through customs is still about the same as in the upper middle income countries (8 days) and lower than the Sub-Saharan Africa regional average (10 days). Moreover, there is a wide variation across firm size. For a small firm, it takes about 17 days on average to clear exports through customs, compared to around six days for medium-sized firms and about two days for large firms.

Clearing imports, in contrast, through customs is considerably faster in Namibia (five days) than the average for upper middle income countries (11 days) and Sub-Saharan Africa average (17 days).


 

New time series of global subnational population estimates launched

Dereje Ketema Wolde's picture

We've just launched a new, pilot global subnational population database featuring time series population estimates for 75 countries at the first-level administrative divisions (provinces, states, or regions). The database has time series data that spans 15 years (2000-2014), with total population numbers for each area and the shares relative to total national population estimates.

What's new about this?
The common data source of population estimates for most countries is a census, often conducted every 10 years or so. Many countries publish annual estimates between census years, but few publish similar population estimates for subnational regions. This database aims to provide intercensal estimates using a standard methodology.

Attention governments: Big Data is a game changer for businesses

Alla Morrison's picture
Also available in: 中文


When I speak about big data with government leaders in our client countries around the world, I often find that many have some awareness of big data, but for many, that's where the story ends. Most are not sure how it is going to affect them or what they should do. Most leaders are largely unaware that the impact of big data is likely to be broad and deep. What governments do (or fail to do) will likely shape up the competitiveness of their countries' businesses for the next generation.  

In countries further along on its adoption curve, big data has already started to transform not only the information technology sector but almost every business in every industry. Incorporation of big data today is analogous in many ways to the transformative effect of electricity on industries in the 19th century. While electricity production and distribution became an industry in itself, it also led businesses in all sectors to redesign their processes to take advantage of this new resource, leading to unprecedented productivity gains of the Second Industrial Revolution. It isn't surprising therefore that at the recent World Economic Forum in Davos, there was much talk about the global economy being on a brink of a Fourth Industrial Revolution, fueled by big data enabled innovations. Governments in emerging economies cannot afford to be left out of this conversation. 

In this blog I hope to show how big data, as a new resource – one that is abundant and rapidly growing – is transforming the business environment and changing the way companies compete with each other. I will also offer suggestions for actions and policies that governments can initiate to position their economies for the advent of the so-called Big Data Revolution, and show that if they don't, they risk losing market share to more digital data-savvy competitors. Finally, I will share a new tool: Open Data for Business (OD4B) Assessment and Engagement Tool, that the World Bank has launched to help governments lay the foundation for the use of one type of big data – open government data – by the private sector.

Chart: Women Earn More in Male-Dominated Jobs

Tariq Khokhar's picture
Also available in: العربية | Français | 中文 | Español

A recent study in Uganda found that women in female-dominated sectors earned less than half what men did in male-dominated sectors. But women who "crossed over" to male-dominated sectors such as metalwork and carpentry earned almost as much as men. Read more about "Breaking The Gender Earnings Gap" 
 

Where do women most lag men in access to financial institutions?

Masako Hiraga's picture

Where do women most lag men in access to financial institutions?

 


Globally, an average of 65% of men and 58% of women over the age of 15 have an account at a financial institution. However, beneath this 7 percentage point global difference, there are many countries where the gender gaps are much wider. Find our more in the Gender Data Portal and the Global Findex data portal.

 

Why are Indigenous Peoples more likely to be poor?

Oscar Calvo-González's picture
Also available in: Español | Portuguese

Indigenous Peoples face poverty rates that are on average twice as high as for the rest of Latin Americans. This fact is probably not a surprise to most readers of this blog. More intriguing, however, are three additional findings from recent work on the topic.

First, until recently, we did not have as robust quantitative evidence of such poverty gaps as that found in the recent World Bank report Indigenous Latin America in the Twenty-First Century. In fact, not all countries in the region have data on poverty by ethnicity and fewer still have the micro-data needed to understand the stumbling blocks that Indigenous Peoples face on the path out of poverty.

Second, the gap between the poverty rate of Indigenous Peoples and the rest of the population is not getting smaller. In some countries the gap remains stagnant and in others it is actually widening. Why are Indigenous Peoples benefiting less from growth and more likely to be poor? One way to explore these issues is to disentangle how much of the poverty gap between Indigenous and non-Indigenous populations can be explained by factors such as that indigenous peoples tend to live in rural areas, have lower education, etc. The results of such analysis bring us to my final point, illustrated in the chart below.

Source: SEDLAC (World Bank and CEDLAS). Note: the bars represent the percentage of people living on less than US$4 per day 2005 PPP for indigenous peoples and the rest of the population. The poverty rates are calculated using late-2000s weighted average for Bolivia, Ecuador, Guatemala, Mexico and Peru.
*Variables include characteristics of the head of the household (education, age, and gender), family composition (number of non-working members), geographical characteristics (country of residence, rural status) and employment characteristics of the head (sector of employment and occupation).

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