https://perspectives.ewb.ca/owenscott

Regardless,the results are in: I have no less than 5 separate intestinal parasites. I have yet to do the research on what these are, or how I got them. They all have long latin-sounding names, and none are particularly familiar. However, one guess I’ll safely hazard: my behaviour is to blame.

Despite working in public health, I’m pretty easy going when it comes to my own exposure. I drink untreated tap-water in Malawi’s cities, and, generally, whatever everyone else is drinking in rural areas. I walk around barefoot in villages, including daily for 4 months when I was living in the village in Chikwawa. (The inspiration for this blog’s title actually, now that I think of it.)

One incident in particular comes to mind. It was the start of mango season, so probably around September of last year. The bus route from Salima to Lilongwe is the best place to buy. During one trip, I got a beautiful grocery bag full for K50 (maybe $0.35) and, having not eaten lunch, I immediately started digging in.

A guy at the back of the bus stops me. “Excuse me. I noticed that you’re eating those mangos without washing them.”

“Yes…” (“I’m on a bus. There’s no tap…”)

“In Malawi we sometimes think it’s important to wash them first.”

“I’m not too worried.” That was my blurted out reply. I’m not too worried. Off the top of my head. The blunt truth.

Stock photography of mangos from earlier in the season.

And that’s the thing. I wasn’t too worried. See, I almost never get sick. Even as I type this, with 5 parasites inside me (or maybe some of them are bacteria, I’m not too sure actually), I feel fine. And I’ve had them since at least November, maybe much longer – no reaction. I went for a run yesterday. Health = more or less ok.

Therein lies the tricky thing with working in health. I pursue a bunch of risky behaviour, get sick, but my body fights it off and I barely notice. In fact, had it not been for the test, I wouldn’t even have noticed at all. Someone else does the same thing, and gets sick. Extrapolate this to a whole community, and we have a serious disconnect between cause and effect. Behaviours that we know are risky, like not washing your hands, improper disposal of faeces, drinking unsafe water, or, yes, eating unwashed mangos on a bus, don’t always lead to negative health effects. And thus the need to change those behaviours isn’t always that obvious. If it was, I think ideas like handwashing would have taken off long ago, and working in public health wouldn’t be even a tenth as challenging…

Great plan, in Canada anyways. And a pretty good one in Malawi. Of all the services here, ATMs are one of the most reliable. Still, as I sat at that ATM in Blantyre, waiting for money to come out, I had a thought: “If this doesn’t work, I’ll be pretty screwed.” I might have enough money to cover my cab fare, but not my accommodation, my trip to Mulanje the next morning, or other minor details like…food – not to mention somehow getting back to Lilongwe.

The trip on a map. Pretty far from home to risk running out of money.

You see, things go wrong in Malawi all the time – way more than in Canada. Buses break down. Electricity goes out. The water stops. 3 weeks ago I was having to walk 15 minutes to a friend’s house to use her shower – my water had just randomly shut off for a few days.

Communication infrastructure is even worse. I have what is, ostensibly, the best internet service in Malawi – 3G wireless internet hooked up by USB to my computer. When it works, it’s great. The other half of the time, I may as well not have it. Makes keeping up with email a little more challenging than it needs to be. And cell phones…oh, cell phones. I remember , last time the network cut out, asking a friend: “what do people do in Canada when the cell network goes down?”

“It doesn’t go down.”

“Oh ya…”

All of this makes life way harder in Malawi. It also makes business harder. Talk to an entrepreneur anywhere in Africa, and see how long it takes them to mention electricity supply as a major challenge. Not so in other parts of the world.

So what do we do? How do people cope with all this unpredictability? Partially through redundancy. Most people in the country, including me, have SIM cards for multiple cell phone networks – one goes down, you use the other one. Most serious businesses have backup generators. Computers are hooked up to uninterruptable power supplies.

But that’s only part of the answer. The bigger part, the real glue that keeps things going here, is best illustrated by a story – another bus adventure.

Flash forward to my trip back to Lilongwe later in the week. I was still buried under work, so wanted to get an early start and not waste much time travelling. I woke up at 5:50am, aiming to be at the bus station for a 7:00am scheduled departure. First stop though: the bank. Travelling eats up money pretty quick, and once again my cash had run dry.

I get to the machine, and put in my card. I go through the motions – same old same old. Pin. Checking Account. Withdrawal amount. A message comes up: “your provider has declined the transaction.”

“Excuse me?”

I try again. Same message. I phone my mom in Canada, seeing if she can get VISA on the line and sort it out. But, as she starts trying, I look at the time – even if she sorts it out quick, I’ll probably still miss my bus.

I look at the remaining wad of cash in my pocket. It looks thicker than I initially thought. I count it out. I scrape the bottom of my bag. I’m almost there. Only short K85 for a bus ticket. A meaningless amount. In Canada, less than 60 cents.

Still, less than 60 cents is a significant part of a bus ticket seller’s daily salary – there’s no way I can ask her to eat my shortfall out-of-pocket. And I know that she’s not allowed to give discounts. What do I do?

Not being one to give up without a fight, I come up with a plan. I have K456 in “talk time” on my phone. (On a good day) you can send talk time from person to person via the cell network. So I figure I just go to the bus station, and start asking people if they want to double their money – K200 in talk time for K85 in cash. It’s a good deal, and I’m sure it’ll work.

What I forget though, is that I’m in Malawi. The first person I ask just gives me a funny look (probably at least in part because I was speaking in Chichewa), smiles, and then hands me the money I need. I try to get her phone number to pay her back, and she declines with a laugh. That’s life here.

And that is the answer. The glue that holds things together. People. Each other. When you can’t always rely on services, when the world’s not always that predictable, you turn a little more to the people around you.

Since coming to Malawi I’ve been helped out of a lot of jams by friends and strangers. And I’ve helped out a lot of other people too. It’s not just me. Everyone’s doing it. It’s what keeps the country going. When you can’t rely on the fact that, day in day out, action x will lead to result y, what other choice do you have…

Of course in the last half century times have changed. We now have a much more balanced view of development. The Human Development Index has brought to the fore the importance of non-economic indicators like literacy and life expectancy. The environmental movement has mainstreamed the significance of sustainability. And radical ideas like Bhutan’s Gross National Happiness have shown us that completely different ways of looking at development are possible.

As positive as these recent trends are, one fear I have is that the importance of economic development is quickly becoming underappreciated, especially in western “development studies” programs.

Nothing brought this home for me more vividly than, last month, when Malawi published projections for the income from this year’s tobacco harvest. Tobacco production is, by far, Malawi’s most important industry. It generates roughly 60% of export earnings – the foreign exchange from which, in turn, buoys the rest of the economy.

Tobacco is not just a big industry on paper, its very very noticeable here. In the central region, many smallholder farmers grow at least some tobacco, at times it feels it’s almost as common a sight in rural areas as maize. There are also huge tobacco estates scattered around the country, which employ hundreds of people each. And many better-off people, including a good number of my co-workers and friends, grow tobacco on a semi-commercial basis as a side business on family land. Come harvest season, tobacco is everywhere.

A small field of tobacco growing in Tenje Village, Ntchisi District, Malawi

That’s why I was so shocked by what I read last month: national export earnings for tobacco this year were expected to be somewhere around $380 million. $380 million?!!?! From the country’s largest export industry? From the crop I see everywhere, being grown, dried, graded, baled, and loaded on to trucks? From the industry everyone talks about when they talk about Malawi’s economy? $380 million…

Reading this in the local paper was one of the first times I think I really realized what economic development means.  Malawi’s leading industry, its source of 60% of export revenues, earned $380 million last year. That’s about on par with the United Kingdom’s 2009 earnings for the manufacturing of watches and clocks ($349 million). It’s lower than the UK’s 2009 export earnings from the manufacture of “corrugated paper and containers” ($435 million).

It’s less than the export earnings from the UK’s wine industry (I didn’t even know the UK had a wine industry), which came in at $504 million in 2009. It’s closest UK competitor seems to be the export earnings from the “quarrying of sand and clay” ($385 million), which is remarkable since sand and clay are low value, high weight products, which don’t generally lend themselves to export. And, of course, Malawi’s tobacco earnings pale in comparison to earnings from the UK’s ubiquitous and omnipresent “manufacture of tubes” industry, which brought $2,092 million in 2009.

All of this really puts into perspective for me the real challenge of economic development. Any economy which would generally be considered “developed”, like the UK’s, has dozens and dozens of obscure industries (seriously…UK wine?) which earn more annually than the flagship industry in Malawi. While Malawi’s economy rises and falls with annual tobacco earnings, people take little notice of the earnings from “manufacture of knitted and crocheted pullovers” ($654 million for the UK in 2009), “manufacture of toys and games” ($1,161 million) or any of the other diverse and unexpected industries that keep developed economies moving. And yet many of these industries absolutely dwarf Malawi’s tobacco crop in terms of export earnings.

Looking at this, I have to think that, while the pursuit of human development and quality of life should be the real goals of growth in Malawi, a lot of economic growth is also going to be needed to make these nobler goals possible. A lot. I knew that Malawi, like many developed countries, is dependent on one major export industry – I just didn’t realize how small that industry really was. I think that putting things into perspective can really help to appreciate the scale of the challenge, and maybe restore to the goal of economic development, e.g. the development of new industries and manufactures, a bit more well-deserved prominence in the greater development debate. Human development is important, but things like health and literacy won’t come freely – there needs to be a diverse and vibrant economy in place as well to support them. $380 million per year from your largest industry just isn’t going to cut it…

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Note: All UK economic stats were taken from the UK Office for National Statistics online site. Of course I initially wanted to use Canadian export statistics, but Stats Canada saw fit to try to charge me $81 for the privilege. Transparency at its finest…

• help district government staff quickly visualize data about water coverage in their district, without relying on expensive software or extensive training
• build enthusiasm for data management and evidenced-based decision making so we can segway into more serious training on data analysis and planning
• motivate district staff to fund/organize their own data collection surveys

The goal of all of this is to help improve planning of water infrastructure, making sure that new wells go to the communities with the greatest need for clean water, and that aid money is used as efficiently as possible. Right now there are serious inefficiencies in the way money spent on clean water supply is used, and better management of information is one way of addressing this. In effect this is all about aid effectiveness.

My initial post on the software generated a lot of interest. This afternoon I have been working on some user manuals for potential international sharing of the software, and so given the interest people have already expressed, I figured I’d share one of them on this blog. The following set of instructions show how the software can be easily customized for setting up maps in new districts (or even countries).

(As always, acknowledgement to Edward Ng, from the University of Waterloo, as the software’s original author.)

Getting a Base Map

In order to set up a new map in [the software] you will need to get a base map from somewhere, as a .jpg or other image file. For instance, you can obtain a base map by taking a screen capture from GIS software, by finding an existing image file of a map, or even by taking a photo of a hard-copy map using a digital camera.

A screencapture of Karonga District, Malawi, taken using ArcView GIS

Setting Up the Map in Excel

Step 1: Right-Click on the “Map” tab and choose “Unprotect Sheet…”

Step 2: Insert your .jpg base map and resize it to fit nicely into the white rectangle.

Step 3: Click on the “Water Coverage” button and re-locate the legend as necessary by dragging and dropping it. Click on the “Waterpoint Functionality” button and do the same.

Step 4: Click on the “Water Coverage” button and change the words “[District Name]” on the title to the appropriate name. Click on the “Waterpoint Functionality” button and do the same thing.

Step 5: Click on the “Insert” tab, choose “Shapes” and then select the “Freeform” shape.

Step 6: Use the “Freeform” shape to trace a neat outline of each subdivision of the district.

When completed you should have a map of the district with all subdivisions outlined.

Step 7: Select all of the district shapes you’ve just drawn, right-click on them, and select “Send Backwards”. Continue to do this until all shapes are behind your original base map.

Step 8: Trace all of the roads on the map with red lines. Group the lines together and name the shape “Roads”. (This allows the “Roads” checkbox to work.)

Step 9: Trace all of the rivers on the map, group them together, and name the shape “Rivers”.

Step 10: Use the “Freeform” tool to draw outlines of all protected areas, national parks, and lakes. Colour them in accordingly.

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Step 11: Delete your original .jpg base map. You should be left with a full outline map of the district.

Step 12: Select each district subdivision shape, and change its default name to its real name. (This will allow the software to assign the shape its proper colour based on that subdivision’s water coverage and waterpoint functionality rates.) Note: It can help to turn off the “Roads” and “Rivers” checkboxes first.

Step 13: Once each of the subdivisions has been renamed, select all of the “freeform” shapes on the map and group them together. Name the group “AllMap”. (This allows the “Copy Map” button to work.)

Step 14: From the “Insert” tab choose “Shapes” and then “Textbox”. Use “Textboxes” to label each subdivision in the district, and any other relevant features.

Step 15: Group all of the Textboxes together and name the shape “Labels”

Step 16: Right-click on the “Map” tab and choose “Protect Sheet”.

Step 17: Click on the “Legend” tab. Choose a colour scheme for the map. Click “Update”. The legend on the Map should now have changed. (Note: you can also change the values associated with your colour scheme.)

Step 18: Click on the “Database” tab. Enter some sample data into the Database in order to test the map.

Step 19: Click on the “Pivot Table” tab. Click the “Update” button. A summary of the data should appear on the Pivot Table.

Step 20: Click on the “Map” tab. The Map should now be coloured in based on the data you entered and the colour scheme you selected.

And that’s all there is too it. Generally we start by setting up a map for district government staff, and focus training more on basic data management skills and evidence-based decision making. Once staff become more proficient at managing/analyzing data, we can also train them to set up/modify the map as well. So far this software has been really well received by district government staff, as it works completely offline (e.g. requires no internet connection and all the data is stored on their computers) while also allowing staff to create maps with a fraction of the training required for conventional software.

 An example of mapping output from our software. (Note: in this case using fabricated data).

As our approach with districts has been getting increasing attention, we’ve been getting requests from different stakeholders to add more and more functionality to the software. Requests have ranged from maps showing political constituencies, to more data on specific waterpoints, to information on community mobilization.

A couple weeks ago, while attending a major conference on water/sanitation mapping (my first such opportunity), a new realization finally crystallized for me. There are a lot of software packages already out there for database management and mapping. Creating your own can be very useful in a specific context. However, at some point of desired software functionality, it’s probably better to stop trying to reinvent the wheel, and just use what’s already on the shelf. Being a visual person by nature, this realization led me to the following graph:

Technological Inflection Points in Waterpoint Monitoring – Shown Graphically

What this is trying to show is that custom software can be great, up to a point, but if you want to do something really complex, then eventually you just have to bite the bullet and use the real stuff.

For instance, our software application works by allowing district staff to manage their own database of rural water access (in Microsoft Excel, with which they’re usually familiar), produce summary tables breaking down raw data into useful statistics (happens automatically using a Pivot Table linked to their database), and produce maps at the click of a button using a custom VBA macro. We keep it obsessively simple, and generally it works well.

 An example of statistical summary information produced automatically using our system.

The advantage of this approach is that we can train someone in the basics of database management, information analysis, and map production in only a few hours, often less. This lets the user quickly move on from struggling to manage the database to using it to make improved decisions. Training someone from scratch in the equivalent skills using a more traditional ArcView/MS Access approach would take days, if not weeks.

However, if we wanted to help someone manage a database with ten times as many indicators, calculate 15 different custom statistics, generate 10 different types of map, update in real-time while automatically analyzing trends, etc., then it would probably better to bite the bullet and do it using conventional software approaches. It wouldn’t be easy, and it may not be possible, but it’s easier than trying to create a bloated custom application which tries to make extremely complex tasks simple and user friendly, and ends up collapsing under it’s own weight.

The point I’m making is similar to my last post. Custom waterpoint monitoring software applications can fill a niche. I think our application is doing a very good job of that at the district level in Malawi. But it’s unrealistic to expect them to do everything. Understanding where the inflection points are is key to understanding which approach makes sense.

“What’s Important”

There are a lot of things people want to know about water access. As a result, most monitoring systems end up pretty bloated. Sit in a room full of water-sector people, and ask what data should be collected. Expect to be overwhelmed. The following diagram shows some common requests.

Figure 1 – A (small) selection of commonly requested waterpoint monitoring indicators.

“What’s Possible”

This is where it gets harder. You could make a great water access monitoring system by attaching radio-signal emitting flow-rate censors to every tap and boreholes in the country. Would it work though? Could you afford it? Of course not. And what’s true for radio-signal emitting flow-rate censors (a technology I may have just invented for the sake of this post) is also true of much more commonly requested indicators.

Figure 2 – Some of the reasons why not everything that’s important is possible.

Next Step – Know What You Want to Do

“What’s Possible” changes immensely depending on what you’re trying to do. If you’re trying to do a one-time survey for policy/research purposes, a lot is possible. In a one-time survey, with enough money, you can collect almost any data.

If you’re an NGO trying to monitor your own programs, a lot is usually possible as well. NGOs typically have relatively few waterpoints that they’re trying to monitor, and have a lot more money to play with than government.

However, if you’re working with government, trying to help them set up their own monitoring systems, you probably want to be careful. Local level governments (which is who are usually responsible for monitoring ) are generally subject to a lot of constraints. Money, incentives, staffing levels, technical capacity – all of these things can dramatically limit what you can do with a monitoring system.

Gratuitous Photo 1: The Temple of Apollo which housed the Delphic Oracle – “Know Thyself” (source: Wikipedia)

A lot of unrealistic monitoring system implementation is attempted because people confuse what they’re trying to do. So know yourself, know what you’re trying to do. If you want a lot of data, do a survey – but don’t try to take a survey-amount of data and cram it into a government monitoring system. It usually doesn’t work.

Monitoring at the Intersection

Generally, I think the concept that “the proof is in the pudding” works well here. A very good way to guarantee from the beginning that a government waterpoint monitoring system is at least possible is to have government staff at the helm from the beginning. This means relying on them to do the work, and to fund it. If they can’t implement the system initially, with your technical support, then it’s unlikely to be sustainable anyways.

Think about it – if the system is failing even while still enjoying your technical support, than the challenges probably aren’t technical; more likely the issue is that people aren’t incentivized to do the work, they can’t afford to fund the system, or they just don’t care about it. Either way, something is fundamentally off. But if you fund it and do the work, hoping government will take over after, you may only learn the truth once it’s too late. So have government fund the system, and do the work from the beginning.

Many of the challenges with sustaining waterpoint monitoring systems in Africa come from the  lack of appreciation for “what’s possible”. Developing a monitoring system based solely on what you think is important is like developing a product without thinking about whether your customers want it or can afford it – it’s an incomplete strategy. But it happens all to often. Here’s to hoping that this approach will eventually change.

“In addition, we had a school garden, whereby you plant some vegetables, rice, but after installing that Playpump, we failed to get that garden, and now it’s dry.”

“So because of the Playpump, now you no longer have a garden at your school?”

“Yes. We have water problems.”

Oh dear.

The following is an interview Duncan and I recorded with the Head Teacher, Deputy Head Teacher, and another teacher at Mikolongo (Chikonde) School, Chikhwawa District, Malawi. We shot this video after filming the now (slightly) famous Playpump vs. AfriDev video posted on this blog last March. The Playpump at this school was installed by UK-based NGO One Difference, as indicated on their website here. One Difference uses money raised through the sale of a bottled water product, One Water, to support the installation of Playpumps in rural Africa. Check out this link to see some of their reasoning behind supporting Playpumps.

Enjoy the video. As usual, we’re not documentary film makers, but we are talking to the right people. A written transcript of the interview is provided below. (Also, BIG thanks to Josh Workman from the Vancouver City Chapter of EWB for editing and subtitling!)

Interview with Teachers at Mikolongo School, Chikhwawa District, Malawi, March, 2010..

0:00 Can you start by telling us your names and your positions?

0:06 I’m Mr. Nkupuki Deputy Head Teacher, Mikorongo School.

0:13 I’m Mr. Mzunga, Head Teacher, Mikorongo School.

0:18 I’m Justin, Teacher, Section 10, at this school.

0:25 And when was this PlayPump installed?

0:30 It was installed last year, in June.

0:36 And what was the process to install? Did the school request to have a Playpump?

0:42 At first we had a borehole.

0:47 And then what happened?

0:49 And the borehole it was alright, and all of a sudden, they came, a certain organization, to replace that borehole with the Playpump.

0:60 Did they ask you before they replaced it?

1:03 No, they didn’t ask us. Just come and say that we are going to replace this borehole with this Playpump because we are…they said the government invited these Playpumps from South Africa, so we want to try these here in Malawi.

1:26 And at this school, which one do you prefer: the Playpump, or the borehole that you had before?

1:31 We prefer the borehole we had before.

1:34 Why is that?

1:35 It was very fast. We had no any difficult when we were playing with that borehole, than this Playpump.

1:49 In addition the Playpump is also difficult to, anylike, to draw some water than this borehole, because they can’t draw that Playpump to draw some water.

2:02 And the kids, do they play on the Playpump?

2:05 Yes.

2:07 Is it enough to fill the tank?

2:10 Since it was installed, they have never filled the tank.

2:21 Do you have any questions Duncan?

2:23 Did you have any problems with the borehole?

2:25 No, we had no problems with the borehole. Everything was alright. Because they said “we want to try this technology” and our school was picked as a pilot project.

2:36 Oh, as a pilot project?

2:37 Ya.

2:38 And then will they come back to check on the Playpump?

2:41 No. No, they failed. They said once we get problems we should phone them, but one day we had a pipe that was broken, so we phoned them…they didn’t come.

2:52 How long ago was this? How long ago did that happen?

3:00 Four months.

3:04 And what did you do with the broken pipe?

3:07 We tried our best to use our…to use our initiative?

3:13 Oh, so you just fixed it yourself?

3:15 Ya.

3:17 Do you have anywhere else around the school where you can get water?

3:20 No, we don’t have.

3:24 So if you could have this Playpump removed and have a new AfriDev handpump back, which would you prefer?

3:30 Ya, we can prefer that new handpump, rather than this one. This one, ah, it’s too much boring [tiring for community members to use]. It’s not working properly. Because we have the enrolment and the community, we have more people than we get little water from that Playpump. Then, in order to make the system goes well, to get water properly, than we need a very good pump that can manage to supply water to the number of people around the school grounds.

4:08 In addition, we had a school garden, whereby you plant some vegetables, rice, but after installing that Playpump, we failed to get that garden, and now it’s dry.

4:28 So because of the Playpump, now you no longer have a garden at your school?

4:30 Yes. We have water problems.

4:34 I’m very sorry.

4:36 So if you could send one message to the people who installed that Playpump, what would you say?

4:41 Ah, they don’t answer.

4:44 Ah, in addition, especially we should say: if the message can go to them, then I think they should come, and remove that Playpump, and replace our old one. Maybe we can stay a little bit better than that one.

5:01 Sure.

5:03 If even another organization may come and replace that one with a certain system, maybe an electrical pump, or a solar pump it can be a very good thing rather than that one. Because they come here, they said: “this pump is a very good one”, talking about that one, Playpump, to us not knowing that it is very boring [slow to use]. They realized very early that, ah, this pump cannot assist us. Then we may wish other organizations to come and remove that one.

5:39 Ok, that one is best for only children. They just play there, and draw some water to drink for them. But ??? and community, that one is boring. If they can give us another borehole, and leave that pump – no problem – because our learners or pupils will go to play there. They enjoy it. They enjoy it but to us, we have got a problem with water. We spend hours and hours waiting for some people to draw some water.

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The World by Numbers – Introduction

A lot of data is freely available online about almost every country in the world. I decided to compile some of this data and do some basic analyses. I was interested in correlations between different parameters that might suggest factors that could influence whether a particular country is more or less successful, as well as in finding links between parameters that seem unrelated at first.

This is a rough, first-cut type of analysis. I didn’t use any statistical techniques more rigorous than simple correlations. However, I was pleasantly surprised and excited to see just how much information could be gleaned from such a broad-strokes approach.

Read on to see what I found. The first section contains my observations including some tables and graphs, along with some suggested interpretations (which are tentative, since correlations don’t indicate whether there is a causal relationship or which way it might work). Next, I discuss some of the limitations of the work done. For those who want to dig deeper I present the methods I used, including descriptions of the variables, in the next section. I finish with some suggested take-away ideas and related links.

Observations

Table 1 shows correlations between the different variables I compiled. Cells are colour-coded to make the stronger correlations stand out. Some of these correlations are quite intuitive, such as higher GDP correlating with higher education spending and electricity usage. The correlations between things like civil rights, political liberties, and freedom of the press indicate that rights and freedoms tend to come as a package deal. One connection that I wasn’t expecting was the relatively strong correlation between property rights and perceived corruption. However, it made sense when I thought about it; in a country where corruption is commonplace, it is easy to imagine an authority figure groundlessly expropriating someone’s land, or demanding a share of imported/exported goods. These two variables of property rights and corruption perception also had a strong link to GDP. In turn, GDP had the strongest correlation to net migration rate; it seems that economic factors are the strongest driver behind global migration.

Table 1 – Correlations between different variables. 

Table 2 shows the results of a linear regression analysis. I made net migration the dependent variable since it seemed like the best proxy for how desirable a country is to live in (I looked at it as people voting with their feet). I only included some of the factors from the whole set as independent variables; GDP was removed because I didn’t want it to dominate the regression to the point of overshadowing other factors and then I tried to pick a subset of factors that weren’t highly correlated with each other (i.e. redundant). Here, security of property rights was the biggest determinant of migration rate, followed by civil liberties (but only property rights were significant at a 95% confidence level).

Table 2 – Linear regression analysis.

I also put together some graphs: Fig. 1 shows GDP and the Gini index, which measures [in]equality; fig. 2 shows GDP with the corruption perception index; and fig. 3 shows the civil liberties score with the net migration rate. In fig. 1, a very weak trend appears, while figure 2 has a much stronger relationship. In fig. 3 it is hard to see any trend as civil liberties scores are discrete. However, a closer examination will reveal that the median net migration rate appears to be negative except for countries with the strongest civil liberties (score of 1). Some of the most prominent outliers on these graphs are Persian Gulf states, where I presume their oil revenue can give them a pass on equality, corruption, and civil liberties that would not be tolerated in states not swimming in cash.

 Figure 1 – Gini Index and GDP 

Figure 2 – GDP and Corruption Perception Index 

Figure 3 – Net Migration and Civil Liberties 

The remaining figures were made during a second round of analysis, after adding a few parameters and normalizing a couple of others by population. Fig. 4 made me very disappointed. It shows that Official Development Aid is not being allocated to the regions of most desperate need as far as water is concerned. That’s not to say that the countries receiving lots of aid have no need; "access to improved water supply" includes things as basic as public standpipes, so this aid could be used to upgrade to household plumbing, for example. However, countries with no access at all appear left out of ODA allocations. Note that there are a number of countries receiving even higher amounts of ODA for water that do not appear on this graph since they didn’t have data available for improved water access, or have populations under 1 million. I thought fig. 5, which compares cell phone use with access to improved sanitation, was very interesting. It appears that increased access to sanitation and mobile communication go quite well together except for some spread at the top. The point in the top left is Cuba, which makes a lot of sense considering the government there has heavily promoted public health but is wary of dissidents. Fig. 6 shows a loose relationship between press freedom (recall that a high press freedom mark indicates more attacks or barriers against press freedom in a country) and internet access. It appears that, while a lack of internet access does not imply poor freedom of the press, good press freedom and high levels of internet access certainly go together. From this analysis, we can only speculate which way a causal relationship might run. Finally, I’m sure that no one will be surprised by the link shown between GDP and electricity use in fig. 7.

 Figure 4 – Official Development Assistance for Water and Access to Improved Water Supply

Figure 5 – Access to Improved Sanitation and Cell Phone Usage

Figure 6 – Press Freedom and Internet Usage

Figure 7 – Electricity Consumption and GDP Per Capita

I know lots of readers of this blog have in-the-field experience in development. I’d love to hear comments about ways in which these broad trends concur or conflict with your experience.

Discussion of Strengths and Weaknesses

Of course this analysis is very rough, so I want to make its limitations clear. As this is just a blog post, readers are referred to the original sources and to encyclopaedias for full descriptions of how each parameter is defined and determined, and the associated limitations. I will just highlight some of the key limitations here.

• These statistics are for countries as a whole and say nothing about internal variations. Just looking at Canada, we can see that while we have one of the highest per capita GDPs in the world, we still have poverty, notably on First Nations’ reserves.
• Each of the parameters that I’ve used here has its own limitations resulting from how it is calculated and how it is defined. By necessity, a single number is at best a proxy for what we really want to know about. For example, GDP ignores grey- and black-market economic activity.
• Correlations do not show a causal relationship. In the graphs, I’ve placed the variables on the axes in a way that seems plausible to me, but I have no statistical evidence that the axes couldn’t be flipped.
• In the regression, I assumed that net migration rate could be a proxy for how desirable a certain country is to live in. Of course not everyone has the capacity to "vote with their feet". And the magnitude of the migration rate will be influenced not only by how desirable a certain move is, but also how easy. For example, moving from Mexico to the US is economically favourable and the border is somewhat porous, so millions of people have made the move. In contrast, I’m sure escaping from North Korea would be nice for many of its citizens, but they face a high risk of death (extending even to their families) for making the attempt.
• Some of the water data was incomplete, especially in Europe, which could skew these analyses.

Methods

Sources and Descriptions of Variables

Here I list each parameter I included in this study and briefly describe it. The sources are the CIA World FactBook (WFB), Freedom House International (FH), Transparency International (TI), Economic Freedom Network—a consortium of free-market think-tanks such as the Fraser and Cato Institutes (EFN), Reporters Without Borders (RSF), and the World’s Water Report (WW). When I describe a variable as subjective, that is to differentiate ones like Freedom House scores that are ranked by expert opinions from ones with a definite numerical value such as electricity usage.

• Net Migration—(WFB) the population gain/loss from {immigration – emigration}, per thousand people
• Population—(WFB) the total polulation
• Education Spending—(WFB) reported as a percentage of GDP, but I converted to a per capita value, as that made more sense in my view
• Electricity Consumption—(WFB) total annual electricity consumption in kilowatt-hours; I normalized it to a per capita figure for this analysis
• Gini Index—(WFB) a measure of [in]equality of income within a country, where 0 indicates perfect equality (everyone has the same income) and 100 indicates perfect inequality (where all the income is controlled by one person).
• GDP—(WFB) the per capita purchasing power parity GDP
• Political Rights—(FH) an index of political rights ranging from most free=1 to least free=7; subjective
• Civil Liberties—(FH) an index of civil liberties ranging from most free=1 to least free=7; subjective
• Press Freedom Mark—(RSF) a mark indicating the number and severity of press freedom violations in the past year; higher mark=more violations; subjective
• Corruption Perception Index—(TI) an index of the perceived government corruption in a certain country; higher score=perceived to be less corrupt; subjective
• Security of Property Rights—(EFN) a score of how secure property rights are in a certain country; higher=more secure; subjective
• Freedom to Trade Internationally—(EFN) a score of how much freedom there is to trade internationally; higher=more freedom; subjective
• Business Regulation—(EFN) a score of how good business regulation is a certain country, from the perspective of free-market think-tanks; higher="better" regulation (it looks like less red tape is considered good, but complete lack of regulation is not favoured as far as I can tell); subjective
• Withdrawal of Water—(WW) annual per capita use of water in cubic meters; excludes rainfall, I think; this will be heavily influenced by the presence of water-thirsty crops and industries more than by domestic consumption in most cases, and waste will also play a role
• Access to Improved Water Supply—(WW) the percentage of the population in a country that has access to improved water supply; the threshold for "improved" is set very low, so even things like a protected spring or rainwater collection will qualify
• Access to Improved Sanitation—(WW) the percentage of the population in a country that has access to improved sanitation
• Annual ODA for Water—(WW) the average annual Official Development Assistance (per capita, in USD) a country receives that is targetted at water and sanitation projects; it excludes things like flood control and power supply, as well as private aid
• Cell Phone Users—(WFB) the number of cell phone users in a country; I normalized it by population from the total figure given in the World FactBook
• Internet Users—(WFB) the number of internet users in a country; I normalized it by population from the total figure given in the World FactBook

Data Manipulation

Once I downloaded the data from each source (except to my chagrin, I typed it in from the World’s Water Report, because I have the book, and then I found that they have it on their website) I then imported it into Excel if necessary and cleaned up html artifacts and things like that. I also had to change the decimal separator to a period in the Press Freedom Index, since it comes from a French-based organization. One of the key steps I had to do when compiling the data was to make sure the country names were consistent. South Korea, for example, was listed as "South Korea", "Korea, South", "Korea, Republic of", and "Korea Rep.". Once all the names were reconciled, Excel’s lookup functions pulled everything into a nice, aligned table. From there I could do the analyses shown above. For some of them, I had to remove countries that didn’t have complete records available. I should also mention that countries with populations less than 1 million were excluded entirely.

Take Away Ideas

This analysis doesn’t prove anything, as I was only checking for correlations. However, it is suggestive of some things, such as the link between [a lack of] corruption, property rights, and GDP; and the role that free communication has in securing other freedoms. The emergence of expected correlations, such as the ones between civil rights and political freedoms, or between water and sanitation access, or between internet and cell phone usage, suggest that this analysis probably has some validity. Finally, I’d like everyone to realize that there is a wealth of data freely available and interesting patterns can show up if we take the time to look.

Related Links

• Hans Rosling is much better at this sort of thing than I am. He’s developed Gapminder: A beautiful and intuitive interactive website that lets you look at global data like I’ve shown in many different ways—and they have a lot more data than I compiled for this post.
• Information is Beautiful: this blog from the UK’s Guardian newspaper uses graphics to try to make sense of statistics in the news.
• I also find Wolfram|Alpha to be a very useful site for quickly looking up various statistics and generating graphs on the fly. This site excels at interpreting plain language searches such as "5 poorest countries" or "GDP of Canada vs GDP of China".

The Fine Print

Copyright notices: the data I used did not originate with me, so I’m including the following copyright notices from the sources.

• WFB: Public domain; source = CIA
• FH: copyright Freedom House, Inc.; All rights reserved
• TI: "All content on this website … is the property of Transparency International e.V. unless otherwise noted, and is protected by German, United States, and international copyright laws. Transparency International grants you a limited license to access, use, print, and copy this site for personal, informational, or academic purposes…"; Full notice here.
• EFN: The authors request that this citation appear: "Gwartney, James and Robert Lawson with Herbert Grubel, Jakob de Haan, Jan-Egbert Sturm, and Eelco Zandberg (2009). Economic Freedom of the World: 2009 Annual Report. Vancouver, BC: The Fraser Institute. Data retrieved from www.freetheworld.com."
• RSF: no copyright notice appears on their website.
• WW: copyright Pacific Institute for Studies in Development, Environment, and Security, 2009; All rights reserved; This data was compiled from various agencies (e.g. WHO) which are enumerated in the introductions to each table.

I would be remiss if I didn’t thank Owen for hosting this guest post on his blog. I’m glad for the opportunity to share this analysis with a wider audience, and I look forward to the discussion to follow.

Anyone wishing to get in touch with me (e.g. to request a copy of this spreadsheet) may do so in the comments below or through my LinkedIn profile.

According to the database, the overall waterpoint functionality rate in 2005  in Malawi was 67.7%,. However, this number includes gravity fed standpipes – most organizations using this statistic deal exclusively with handpumps, plus Malawi contains a lot of derelict gravity fed water schemes which were abandoned by the government in the 1980s. The 10,857 gravity fed standpipes in the database have an average functionality rate of 50.5%, which obviously skews the overall functionality rate substantially south. So since we’re usually talking about handpumps, and since most of the standpipes in Malawi failed about 10-20 years ago, it seems unfair to keep them in the sample. Removing them gives an average handpump functionality rate in Malawi of 73.5%.

A seriously non-functional borehole in Chikhwawa District, Malawi (Photo Credit: Lauren Hockin)

Now there’s the issue of age – handpumps aren’t built to last forever. For argument’s sake, take an average design life of about 15 years as a reasonable length of time to expect a new handpump to operate without being replaced or seriously rehabilitated. If you then look at only handpumps constructed between 1990 and 2005, the functionality rate jumps up to 78.1%.

Then there’s a final issue: all of these functionality rates are taken as one-time snapshots, which comes with an inherent bias (call it "snapshot bias"), best illustrated with an imaginary example from London, UK.

Assume for a minute that the functionality rate for kitchen sinks in occupied houses in London is 100% – every house has a kitchen sink which yields water and which the occupant (of the house, not the sink) regularly maintains in order to keep it working. However, if you did a survey of kitchen sinks in London tomorrow, August 1st, at 12:56pm, you wouldn’t find 100% of the sinks working. A certain percentage of the sinks would have broken down that morning and would be sitting broken until the plumber got there later that afternoon. Thus even if every single house in London was making an ongoing and consistently successful effort to keep their kitchen sink working, at any given moment in time you still wouldn’t find 100% functionality.

It looks functional…but it isn’t. (Photo Credit: Lauren Hockin) 

This snapshot bias is likely considerably more pronounced in rural Malawi than in urban London; in rural Malawi communities can often wait weeks, not hours, for a mechanic (plumber) to access spare parts and come to their village to fix their waterpoint. For this reason, many “non-functional” waterpoints actually belong in something like a "non-functional but soon to be repaired" category. How many belong in this category at a given time is difficult to determine statistically, but it’s likely a significant number.

Thus while the "functionality rate of waterpoints" may sit at 67.7% in Malawi, we can estimate that the "functionality rate of handpumps that were installed within the last 15 years" is more like 78.1%, while the "percentage of handpumps, installed within the last 15 years, which are either functional or have repair efforts actively being organized" is likely well into the 80%+ region. And we haven’t even talked about waterpoints that were so poorly built in the first place that they were either impossible to maintain or never really functional at all.

These considerations are sometimes threatening for organizations working on rural waterpoint operation and maintenance because they reduce the perceived severity of the problem, and thus reduce its marketing potential (which is already very low). However, I think taking them seriously is important – it helps us frame the “water infrastructure management” challenge in a more accurate and nuanced manner, bringing into consideration issues like construction quality, technology choice, design life, and speed of repair. In the end I think it’s worth it – if we market the wrong problems, it might not be long before we find ourselves addressing the wrong problems too.