Heading to DRC this Monday and US F1-VISA.

A few hours ago I got myself a plane to Kigali: flying out from Amsterdam this Monday (yes, the day after tomorrow), and arriving Tuesday afternoon. Upon arrival I hope to get myself a bus and head to the Rwanda-Congo border that same day. I'll only be in the Congo for 3 days, I leave again on Friday afternoon (plane leaves early Saturday morning from Kigali). It's a short trip to work together with Search for Common Ground (95% of the time), to give a presentation on how to conduct evaluations, and to meet my team that has been in the field over the last months. Because it's so short I will only stay in Bukavu. No field for me this time. It's a pity, otherwise I could have used the following sleeping back (h/t Simon):

 

One reason to leave quickly again is because on August 6 I have an appointment at the US Embassy in Amsterdam for my F1-visa -- after 5 years my current visa expired. I already filled out the online application, which included the following questions:

1. Are you coming to the United States to engage in prostitution or unlawful commercialized vice or have you been engaged in prostitution or procuring prostitutes within the past 10 years?
2. Have you ever been involved in, or do you seek to engage in, money laundering?
3. Have you ever committed or conspired to commit a human trafficking offense in the United States or outside the United States?
4. Are you the spouse, son, or daughter of an individual who has committed or conspired to commit a human trafficking offense in the United States or outside the United States and have you within the last five years, knowingly benefited from the trafficking activities? 
5. Do you seek to engage in terrorist activities while in the United States or have you ever engaged in terrorist activities?
6. Have you ever been directly involved in the establishment or enforcement of population controls forcing a woman to undergo an abortion against her free choice or a man or a woman to undergo sterilization against his or her free will?

:)

From Brussels to... Congo?

Yesterday morning 5am. Alarm went off. 530am: on my way to Rotterdam Centraal, and from there to Brussels to get a single-entry, one-month visa for the Congo (I know. Again!? More on this later). Depositing the necessary material is between 9-12pm, and the pick-up is at 430pm. Thus a lot of time in Brussels. Of course I had my laptop with me and the grandiose plan to work. Well that didn't work out at all with me being in a city that I haven't explored a lot yet and the sun shining. So I hired a bike for a day [Genious system btw. They have bikes positioned throughout the city. It costed me $1.60 for a day], and spent a day playing the tourist.

Brussels is a great city.

Brussels' Grote Markt

The Worst Mistake in the History of the Human Race.

Agriculture.

...

To science we owe dramatic changes in our smug self-image. Astronomy taught us that our earth isn't the center of the universe but merely one of billions of heavenly bodies. From biology we learned that we weren't specially created by God but evolved along with millions of other species. Now archaeology is demolishing another sacred belief: that human history over the past million years has been a long tale of progress. In particular, recent discoveries suggest that the adoption of agriculture, supposedly our most decisive step toward a better life, was in many ways a catastrophe from which we have never recovered. With agriculture came the gross social and sexual inequality, the disease and despotism, that curse our existence. 

...

One straight forward example of what paleopathologists have learned from skeletons concerns historical changes in height. Skeletons from Greece and Turkey show that the average height of hunger-gatherers toward the end of the ice ages was a generous 5' 9'' for men, 5' 5'' for women. With the adoption of agriculture, height crashed, and by 3000 B. C. had reached a low of only 5' 3'' for men, 5' for women. By classical times heights were very slowly on the rise again, but modern Greeks and Turks have still not regained the average height of their distant ancestors. 

....

There are at least three sets of reasons to explain the findings that agriculture was bad for health. First, hunter-gatherers enjoyed a varied diet, while early fanners obtained most of their food from one or a few starchy crops. The farmers gained cheap calories at the cost of poor nutrition, (today just three high-carbohydrate plants -- wheat, rice, and corn -- provide the bulk of the calories consumed by the human species, yet each one is deficient in certain vitamins or amino acids essential to life.) Second, because of dependence on a limited number of crops, farmers ran the risk of starvation if one crop failed. Finally, the mere fact that agriculture encouraged people to clump together in crowded societies, many of which then carried on trade with other crowded societies, led to the spread of parasites and infectious disease. (Some archaeologists think it was the crowding, rather than agriculture, that promoted disease, but this is a chicken-and-egg argument, because crowding encourages agriculture and vice versa.) Epidemics couldn't take hold when populations were scattered in small bands that constantly shifted camp. Tuberculosis and diarrheal disease had to await the rise of farming, measles and bubonic plague the appearnce of large cities. 

....

Besides malnutrition, starvation, and epidemic diseases, farming helped bring another curse upon humanity: deep class divisions. Hunter-gatherers have little or no stored food, and no concentrated food sources, like an orchard or a herd of cows: they live off the wild plants and animals they obtain each day. Therefore, there can be no kings, no class of social parasites who grow fat on food seized from others. Only in a farming population could a healthy, non-producing elite set itself above the disease-ridden masses. Skeletons from Greek tombs at Mycenae c. 1500 B. C. suggest that royals enjoyed a better diet than commoners, since the royal skeletons were two or three inches taller and had better teeth (on the average, one instead of six cavities or missing teeth). Among Chilean mummies from c. A. D. 1000, the elite were distinguished not only by ornaments and gold hair clips but also by a fourfold lower rate of bone lesions caused by disease.

...

From an 1987 article by Jarod Diamond. Not sure how it fits in with his "Guns, Germs and Steel".

Their Brussels, Our Netherlands. Wrong!

On 12 September 2012 we'll have, yet again, elections in the Netherlands. Today I read the program of the Partij Voor de Vrijheid (PVV) - indeed, the infamous Dutch right-wing party of Geert Wilders. The people that read this blog know that I'm against almost all that this party stands for: anti-Islam, anti-immigration, anti-development aid, anti-Europe, etc.



"Party for Freedom. Their Brussels, our Netherlands"

Their program can be found here (only in Dutch). It's quite painful to read. It's very populistic. An important topic again is "Islam = Evil". But this time the over-arching theme is "Europe = Evil". In brief, the party program read like: Let's-all-put-our-heads-in-the-sand-build-a-big-fence-around-the-Netherlands-and-not-acknowledge-what-has-happened-in-the-world-over-the-last-decades.

This is not a party for the future.

How to Stop Atrocities?

Easy. Just present these slides.

Verandering van Spijs Doet Eten.

In line with the Dutch saying (literally translated): "Change of food makes one eat"[1], I just changed the layout of my blog. Also, it was about time after 3 years. It has a bit more serious appearance. I do not think this will lead to more serious posts though. :)

[1] Originally from "Varietas delectat" (variety delights). 

Just Post It.

I argue that journals should require authors to post the raw data supporting their published results. I illustrate some of the benefits of doing so by describing two cases of fraud I identified exclusively through statistical analysis of reported means and standard deviations. Analyses of the raw data provided important confirmation of the initial suspicions, ruling out benign explanations (e.g., reporting errors; unusual distributions), identifying additional signs of fabrication, and also ruling out one of the suspected fraudster’s explanations for his anomalous results. If we want to reduce fraud, we need to require authors to post their raw data.

 

A new working paper by Uri Simonsohn: "Just Post it: The Lesson from Two Cases of Fabricated Data Detected by Statistics Alone". Please find a link to the paper here. 

Bla bla bla.

The previous post reminded me of the training Raul and I did in the Congo in the summer of 2010 (more here). During the training the students used a wide variety of ways to start a sentence. Anyone who has worked in the DRC knows about this: there are often a lot of words necessary before getting to the point. You're ready? Here we go: 

• "Je  voudrais donner un petit clarification… XXXX"
•  "Merci pour me donner le parole… XXXX"
•  "J'ai un petit preoccupation… XXXX"
•  "Je voudrais savoir dans le cadre de … XXXX"
•  "Je suis dans un confusion total… XXXX"
•  "Ca me complique un peu... XXXX"
•  "Je voudrais me assure un chose… XXXX"
•  "Je me nager dans l'eau"
•  "C'est un engagement pikante"
•  "C'est un question que a demande beaucoup de tactique… XXXX"
•  "C'est un question que necessite des problemes… XXXX"
•  "Je veux simplement eclaircir la nuit"
•  "Cette question n'a pas de raison d'etre"'
•  "Le question risque de avoir un petit probleme au  niveau… XXXX"

Genious!

Fun Things With Data Entry.

As you know from the previous post I'm entering data. Part of this data is information about the lab-in-the-field experiments I am conducting in Eastern Congo. Normally for such experiments the players are explicitly selected so that they are not acquainted with each other, or this is assumed, or the players play the games anonymously. Researchers do this so as to single out a particular mechanism that impacts a player's strategic behavior. Now of course the real-life strategic behavior of individuals is very much influenced by the context and whom one is interacting with. What is important are often things different than only 'observables'; people make decisions towards others based on things such as frequency of interaction and shared experiences. "We are member of the same church", "He is family", et cetera. In contrast to most lab-in-the-field experiments, our games in Eastern Congo take the player's natural context -- his underlying, latent network so to say -- at heart. To do so we take polaroid pictures of each of the 18 players and have them make strategic decisions toward each of the other 17 players. From the information that this gives we can construct trust and benevolence networks (see here for more information). We then ask to each of the 18 players questions about each of the other 17 players, such as "Are you family of this person?", "Are you friends with this person?", etc. This provides us the latent-network information -- information that is not observable. From combining these two we can thus learn what is important for levels of trust and benevolence. Maybe being member of the same church is important for levels of trust. Maybe having been displaced together is crucially important for levels of benevolence. Et cetera.

Eustache at work: showing the picture of
one of the players, to another player.

So what is important for people to contribute to others? I have not yet combined the network information with the trust and benevolence networks for two reasons. First, I want to pre-register the hypotheses before looking at the data so that I cannot cherry-pick my results. Together with collegue Neelan we are about to finish a paper in which we present our hypothesis and present results based on fake data. We expect to register this soon. Second, I have only entered data for 2 villages up to now. However, some interesting things I already would like to share via this blogpost. After each game we do an in-depth debriefing with each player to understand why the players played as they did. I've now filled out only two villages but this already gives a potential 2*18*17 = 612 reasons.


So what are reasons for people to contribute to each other? [Do note that you should take this with a grain of salt because this is what people say; it is thus not necessarily the truth. People for example might be unwilling to say "I don't contribute to him because he/she is from a different ethnic group". Indeed this is exactly the reason in the first place why we conduct lab-in-the-field experiments, and the data from the network questions will shed light on this in due time. However, this might already be very informative.] Common reasons not to give are "He/she is arrogant", "He/she is hypocritical", "Not from my generation", etc. Often-mentioned reasons to give include "he gives me advice", "we are both migrants", etc. Now of course the great thing of just asking why a person contributes is that one also receives more exotic reasons for (non)contribution. Hereby a small selection (I translated this from French), note that I added whatever is in square brackets:

• "Once he paid the school fees for my son."
• "When I'm alone at home, she will collect firewood for me."
• "He always buys drinks (alchohol), but without giving anything to me."
• "We were caught by the Interhamwe [a rebel group in Eastern Congo] but he freed me."
• "She assists my child by giving food in my absence."
• "He is a miner and therefore his family is not social."
• "He reminds me of my father who died. And I therefore like him."
• "My daughter married his son."
• "He gave me food when I came from the forest and was famished."
• "If my house would catch fire he would help."
• "He put me in prison."
• "I need the points to buy a pen."
• "He is ANR [The Congolese security service]."
• "He tells bad stories about me to the other villagers."
• "He never pays his debts. He owes me a lot of money."
• "He is a sorcerer, and I am sure he placed a spell on me."
• "He does't salut me when we pass on the street"

As expected, it is likely that latent (unobservable, but measurable) networks are going to be very important to understand the trust and benevolence networks in Congolese villages. Also, this reminded me of a great reply from one of our enumerators for the evaluation I'm conducting with Macartan and Raul in Eastern Congo. The question was "Where was the person in 2006?". Below is the raw input by the enumerators into their PDA. We make use of codes: "-8" for example means "not applicable" and therefore appears very often. However, some enumerators are more complete and write things such as "-8, was not born yet". Two replies are specifically great. I highlighted them in yellow below. Translated in English the person in 2006 was "Still in the belly of his/her mother". Brilliant! (h/t Raul).

 ************************************************
  ************************************************

tab qf019_location_2006_1

   QF019_LOCATION_2006_1 |      Freq.     Percent        Cum.
-------------------------+-----------------------------------
    n'etaitpas encore ne |          1        0.00        0.00
                      -8 |          2        0.00        0.01
                      +8 |          1        0.00        0.01
                     --8 |          2        0.00        0.01
                      -0 |          1        0.00        0.02
                      -5 |          1        0.00        0.02
                      -7 |          1        0.00        0.02
                      -8 |      3,508        7.89        7.91
       -8   PAS  VIVANTE |          1        0.00        7.92
     -8  PAS  ENCORE  NE |          1        0.00        7.92
                     -8. |          2        0.00        7.92
                      -9 |         10        0.02        7.95
                     .-8 |         30        0.07        8.01
                    ..-8 |          1        0.00        8.02
                       0 |         31        0.07        8.09
                    1720 |          1        0.00        8.09
                      50 |          4        0.01        8.10
                    7010 |         45        0.10        8.20
                    7020 |         10        0.02        8.22
                    7030 |          1        0.00        8.22
                    7052 |          3        0.01        8.23
                    7053 |          3        0.01        8.24
                    7054 |          4        0.01        8.25
                    7065 |          1        0.00        8.25
                    7071 |          6        0.01        8.26
                   70710 |          1        0.00        8.26
                    7073 |          9        0.02        8.28
                    7074 |          8        0.02        8.30
                    7075 |          1        0.00        8.30
                    7076 |          8        0.02        8.32
                    7090 |         10        0.02        8.34
                    7094 |          4        0.01        8.35
                       8 |          2        0.00        8.36
                     A-8 |          2        0.00        8.36
                      A0 |          3        0.01        8.37
                      A1 |     37,165       83.64       92.01
                    A1-8 |          1        0.00       92.01
                     A1. |          8        0.02       92.03
                   A1... |          1        0.00       92.03
                     A1… |          1        0.00       92.03
                      A2 |      1,962        4.42       96.45
                      A3 |        401        0.90       97.35
                      A4 |         94        0.21       97.56
                      A5 |         41        0.09       97.66
                      A6 |        740        1.67       99.32
                      A7 |         55        0.12       99.44
Dans  leventre  desamere |          1        0.00       99.45
      N'etait encore nee |          1        0.00       99.45
  N'etait pas  encore ne |          1        0.00       99.45
   N'etait pas encore ne |          5        0.01       99.46
  N'etait pas encore nee |          1        0.00       99.46
   N'etait pas encore né |          1        0.00       99.47
       N'était encore né |          1        0.00       99.47
   N'était pas encore né |          3        0.01       99.48
                     NON |          4        0.01       99.48
                       O |          1        0.00       99.49
     PAS  NE  EN  2007   |          1        0.00       99.49
           PAS ENCORE NE |          2        0.00       99.49
                   Pa ne |          2        0.00       99.50
                 Pas  ne |          1        0.00       99.50
              Pas encore |          1        0.00       99.50
           Pas encore ne |          5        0.01       99.51
                  Pas ne |        159        0.36       99.87
                  Pas né |          1        0.00       99.87
                   Pasne |          1        0.00       99.88
                  Ventre |          1        0.00       99.88
                      _8 |          1        0.00       99.88
                  pas ne |         49        0.11       99.99
                  pas né |          1        0.00       99.99
                   pasne |          3        0.01      100.00
-------------------------+-----------------------------------
                   Total |     44,435      100.00

  ************************************************

  ************************************************

Data from the Congo.

One of the things I'm doing this summer is data entry. At the moment I've data in from 15 villages, another 11 are still to come (the enumerator teams are still in the field). This data has been collected over the last seven months (non-stop work by the teams) and includes data from the village mapping we did in each of these villages. That is, we conducted a survey with ALL the households in these villages (more here). Moreover, we also did networked lab-in-the-field experiments in each of these villages (more here). On average each of the piles on the picture below contains 200 sheets: around 150 for the household surveys (each household is a sheet), and another 50 for the games. The mapping survey and manual can be found here and here, respectively. The player survey can be found here. If you count well, though, the picture below has 17 piles. In addition to the data from the 15 villages I collected two additional things:

1. When in the Congo last December-March2012 I got to know the chairman of the committee responsible for the displaced people in the territory of Kalehe. In brief, they keep lists of what types of migrants are in which villages. I got access to this list and spend a day photo-copying it.
2. For my dissertation I look mainly at rural-to-rural migration. It is very well possible that migration to larger cities is very different. To learn about this I hired an enumeration team that kept track of in-migration to Bukavu (I'll write a short other blogpost on this). So that is yet another pile of data.
    

Data from 15 villages in South Kivu carefully
kept in my bedroom.

I decided (at least for now) not to hire somebody to do the entry. There are three reasons for this. First, I don't trust somebody else to do it. It is very easy to make a mistake. Moreover, the data has also more qualitative components to it, and it's in French. Secondly, by doing it myself I hope to get a 'feel' for the data. Finally, it allows me to spot mistakes quicker, and immediately take up contact with the enumerator teams. Actually, the above is not really true. I did found a great RA who is currently also filling out data: my mom. She rocks!

No Comment.

From Crowdsourcing to Crowdseeding.

I just returned from a workshop at the Free University of Berlin. Together with fellow-authors a book is put together about "ICT in Areas of Limited Statehood". The book deals with how new technology (phones, satellites, mapping, etc.) can be used and enable us to learn about and solve the world's big problems -- especially developmental ones. This was the second time we met as a group (last time was about a year ago: post here) so the feeling-uncomfortable-around-each-other was gone, and we had a really good time, also socially. It is also a particularly great group, which includes some of the big names when it comes to ICT, and some super-impressive people that work on the implementation side (the key people of Ushahidi, Russia Helpmap, and Map Kibera).

I'm writing a chapter based on our experiences with Voix des Kivus: our cellphone-based system that we implemented in Eastern Congo to learn about local level events (more here). To obtain high-quality (representative) data in real-time we implemented Voix des Kivus with a crowd-seeding system in contrast to the-know-so-often-used-term crowd-sourcing? Why?

Under crowd-sourcing, in contrast to outsourcing, a task is not given to a specific group of people but to an undefined public. This has two major benefit: 1. it's relatively cheaper to collect large amounts of data, and 2. One can make use of the "wisdom of the crowd": the idea is that a group of people is smarter than one.

I presented Voix des Kivus on the second day of the workshop so during day 1 I was thinking in what way crowd-seeding is different from crowd-sourcing, and how to convincingly bring this across. You're ready? Please have a look at the picture below. A+B+C+D is the whole population of people (for example all the people in South Kivu, Congo where Voix des Kivus works). "Knowledge" stands for people knowing about the task send into the crowd. "Means" stands for whether or not the people can undertake the task. In the Congo the idea was to make use of the Congolese crowd. However, given the conditions in Eastern Congo a large part of the population won't know about the Voix des Kivus project. As result we're confined to the first row in the table only. Moreover, in the Congo few people have cell-phones and even less have also phonecredit and thus we are left also only with the left column. That is, crowd-sourcing would make use of the subset of the population: only A. In the Congo this is a very small part of the whole population (A+B+C+D).

Under crowd-seeding we select the reporters and visit them, make them aware the project (in our case Voix des Kivus), and we give them phones with phonecredit (we 'seed' phones into the crowd). As a result, crowd-seeding increases the crowd from A to A+B+C+D. That's the first benefit: a larger population. Moreover, Voix des Kivus took a random subset of A+B+C+D to be reporter: as a result i that the data that we receive is representative -- again in contrast to a crowd-sourcing system where its likely a particular type of people send in information (indeed a forthcoming article in Political Analysis by Adam Berinsky, Gregory Huber and Gabriel Lenz investigating the crowd-workers of Amazon's Mechanical Turk finds exactly that). Finally, under a crowd-seeding system one knows the people that provide information. One builds up a long-term relationship with the information providers and as a result the incentives to lie decrease (important in the Congo where people might want to overstate suffering and hardship in order to receive aid) and it makes it more difficult for the bad guys to hack the system and provide misinformation.

The picture above is made by Patrick Meier (friend, founder of Ushahidi, etc.). He wrote about the presentation here.

Now moving from a crowd-sourcing system to a crowd-seeding system is not all honky-dory and this is what we learned from our experiences with Voix des Kivus in Eastern Congo. Macartan and I wrote our reflexions up in a short document that we think is important reading material for any person trying to implement a phone-based project to collect information, especially if they are academics and work in security-sensitive areas. Macartan presented this document earlier this year at the University of Bristish Columbia. Please find the document here:

Voix des Kivus: Reflections on a Crowdseeding Approach to Conflict Event Data Gathering 

If you're looking for great enumerators in Eastern Congo?

Chris asking for directions to locals. Enumerators often

spend days getting from one village to the next.

We worked with an excellent group of almost 100 enumerators for about 18 months in South Kivu, Maniema, Tanganyika and Haut Katanga, DRC. These enumerators are:

• Well-trained: Raul and me did this ourselves for months in 2010 (see eg here);
• All-round: They know how to conducts surveys (ours was very complicated: here the design document), list-experiments, general assemblies, focus groups, etc. They also drive motorbikes and use PDAs and GPS devices. 
• Experienced: They worked in some of the world's harshest (especially transport and disease-wise) and insecure conditions -- often being away from larger villages for weeks;
• Trustworthy: After working 18 months closely together with them (often spending nights in a row in the field together), I know them well and trust them.

They are on the look out for new work. Given its Congo, even this extra-ordinary group of people has difficulties finding jobs! They come with our highest recommendation. Please find contact information here. Also feel free to take up contact with me for more information. These guys are really amazing.

Chris conducting a general assembly.

Mapendo conducting a survey.

 

Luc and Edison entering a new village in Maniema.

Venant driving on some lovely Congolese roads.

(FYI: this is actually quite an ok road. Often the

the guys walk with the motorbike next to them)

Power Boyz - Tchuna Baby.

I've clicked on the "repeat button" several times now. "Tchuna Baby" by the Power Boyz is quite a catchy song. I had a quick search for them online but can't find much about them so, given it's African and I think Portuguese, I go for "It's an Angolan band" and "It fits in the wider style of "Kuduro"". The latter is a type of music and dance originally born in Angola in the 1980s. It is characterized as uptempo, energetic, and danceable. European and American electronic music had begun appearing in the market, which attracted Angolan musicians and inspired them to incorporate their own musical styles. Young producers began adding heavy African percussion to these European and American beats, which resulted in what was then called Batida. In the early 90's, Angolan clubs started playing it and the youngsters started to create new dance moves to follow what the DJs were dropping. (thanks Wikipedia!). Well, check out some of those dance-moves here:

 

Crowdsourcing and Peter Diamandis.

This Thursday I fly to Berlin for a two-day workshop with fellow-authors, where we'll present (and discuss) chapters for a book Steven Livingston and Gregor Walter-Drop are putting together. The book deals with how new technology (phones, satellites, mapping, etc.) can be used and enable us to learn about and solve big problems. I'm writing a chapter based on our experiences with Voix des Kivus: our cellphone-based system that we implemented in Eastern Congo to learn about local level events (more here). In brief, the chapter will first discuss the benefits of crowd-sourcing, and will then introduce and discuss crowd-seeding. While crowd-sourcing sources-out tasks to an undefined public, crowd-seeding selects (part of) the crowd and does this randomly. The reason why is so that one obtains representative data -- often crucial when collecting information. The chapter, however, continues by discussing in more detail especially ethical issues that were raised while operating Voix des Kivus -- issues that should be kept in mind when launching such a system. See the following document that Macartan presented at the University of British Columbia earlier this year, for a brief discussion.

While working on the above I came across a TED-talk by Peter Diamandis -- a very impressive person (understatement). He is among others the the Founder and Chairman of the X Prize Foundation; an educational non-profit institute whose mission is to create radical break-throughs for the benefit of humanity. They do this by offering large (monetary) prizes to "the crowd". A fantastic application of crowd-sourcing. The TED talk is about his recent book  "Abundance: The Future Is Better Than You Think". I haven't read it yet, but the talk is promising and -- by using the argument of crowdsourcing -- he makes quite a case for optimism about our future: