1. The Causes of Differing District Development in Rural PNG
Colin Filer, Jon Fraenkel, Terence Wood
Hi my name is Terence Wood I’m here to talk about the causes of differing levels of development in rural PNG.
I’m going to start by introducing an interesting fact: when we look at important aspects of development, like education, poverty, and under 5 mortality there is an incredible amount of variation between different rural districts in PNG.
Having introduced this fact I’m going to offer some explanations as to why the variation exists based on development economics and econometric analysis of data from PNG.
Before I go any further I want to emphasis 3 points:
This analysis is looking at rural areas and small towns. For data reasons Lae and Port Moresby are excluded.
All my findings today are preliminary. There is a lot more work to be done.
I am working as part of a team. And I am deeply indebted to my fellow team members Jon Fraenkel and Colin Filer. If I say anything stupid today it is not their fault. If I say anything intelligent it is thanks to them.

2. Some parts of rural PNG are more developed than others
Let’s start by looking at what I mean when I say there’s a big difference in development outcomes between different parts of rural PNG.
You can see this in under 5 mortality data from late 1990s. Each of the lines on this chart is a district in PNG. And the reason why I am showing you this chart is to demonstrate just how much difference there is in under 5 mortality across the country.
In Menyamya in Morobe under 5 mortality in the late 1990s was 226/1000. Almost 1 in 4 children died in their first few years of life. In Kundiawa-Gembogl in Chimbu on the other hand, under 5 mortality was nearly 10 times less at only 26 per That’s a huge variation.
Under 5 mortality (per 1000),

3. Under 5 mortality (per 1000), 1995-99
Sometimes even remarkable variation within provinces
Remarkably, there is even significant variation within individual provinces. So here we’re looking at under 5 mortality data from the late 1990s again. And we’ve focused in on Madang province.
What we see here is that in Middle Ramu district the under 5 mortality rate is almost 3 times higher than it is in Madang. It is more than twice that which it is in Sumkar. These three districts are all adjacent to each other.
The same dramatic variation can also be found when we look at development statistics in other areas such as education and poverty.
Under 5 mortality (per 1000),

4. What explains the variation?
It’s an interesting practical question:
If we can learn the secret of successful district’s success, perhaps we can adopt in other places?
It’s an interesting theoretical question (what causes development?):
Natural endowments (logs, mines)?
Environment more broadly?
Colonial history?
Pre-colonial/social institutions (cooperation)?
Ethno-linguistic fragmentation (cooperation)?
But it’s hard to research: need data!
The research question for me and my colleagues is: what explains the variation.
Potentially it’s an interesting practical question: we may be able to learn from successes to help improve areas that are not performing that well.
Today though, I want to talk about it as a more academic question, because there’s a huge literature in development economics on why development differs between different countries:
Specifically, economists have looked at differences between countries to see whether things such as being rich in natural resources helps or harms development?
Whether an unfavourable geography harms development?
Whether countries’ colonial histories have an impact on development?
Whether social factors that have origins in pre-colonial times have an effect on development, perhaps through their effect on cooperation? In the case of PNG the key factor is the difference between Austronesian cultures and non-Austronesian cultures. Some authors have claimed that more hierarchical Austronesian cultures are better able to organise themselves in ways that are better for development.
Whether high ethnic diversity in the form many different languages spoken in an area makes cooperation harder, causes more competition and prevents development.
These explanations have all been studied a lot when looking at differences between countries. Because there is so much variation within PNG it makes sense to look at them within Papua New Guinea.
The only problem is that it’s very hard to get the data you need. The only reason we have any data is thanks to Colin Filer, who has made an amazing effort to use old data sources and to bring them together. Even so, the quality of the data we have is still a limitation. One problem, for example, is that all our development data comes from the late 1990s and early 2000s. We’re trying to get more recent, reliable data.

5. Resource endowments & colonial history don’t matter much
Method
OLS regressions on rural districts, province fixed effects as robustness tests
Preliminary findings
Enrolment
Mortality
Poverty
Number of languages
-0.54**
1.53*
0.01***
Is Austronesian
9.60**
-14.99
-0.08*
Years colonised
-0.10
-0.00
Government or church mission
-4.05
7.84
-0.01
Logging?
1.25
-2.35
-0.07*
Mining?
1.10
-4.45
0.06*
Coastal?
8.41
-3.53
0.08
% bad road access
-15.38**
53.35*
0.18***
% on poor quality land
-1.91
3.78
-0.05
% in town
35.69
-69.39
-0.09
Population 2000
Observations 82 83 R2
0.44
0.37
0.48
Resource endowments & colonial history don’t matter much
Access matters; poor land doesn’t
But at least thanks to Colin we do have some data. And so with this information I have conducted analysis. Technically, for those econometricians in the audience, my basic analysis involved multiple regressions using Ordinary Least Squares. The unit of analysis – that is the unit I gathered data for and compared – were rural districts.
I ran three sets of regressions: in the first I looked at variation in school enrolment as the dependent variable; in the second I looked at variation under 5 mortality; in the third I looked at variation in estimated poverty.
Importantly, all of the regressions were multiple regressions, this means I was able to separate the effects of individual influences on development from the effect of other influences.
Also, most of my findings were robust to adding provincial level fixed effects into the regression equation.
The result is a complicated looking table. I don’t have time to go into it in detail, but I’m happy to talk more in question time. The main points I want to make now are that: resource endowments – logging and mining – don’t seem to have much of an impact on development on average. That is also true with colonial history. Similarly, the quality of the land in a district does not seem to effect development much. On the other hand access, how easy it is to travel within the district makes a big difference.

6. Rural districts with more languages have (a lot) lower development outcomes
Most interestingly though, I think is the fact that there is a strong negative relationship between the number of languages spoken in a district and development measures.
For example, my analysis shows that, on average, taking other factors into account, in a district where only one language was spoken historically, school enrolment rates in 2000 would be almost 60 per cent.
On the other hand, on average, taking other factors into account, in a district where 40 languages were spoken historically, school enrolment rates would typically be just a bit over 35 per cent.
In rural areas, hi levels of linguistic diversity that have their origins in pre-colonial times, and the challenges for cooperation that this ethnic diversity brings, appear to hinder modern development.

7. (Not as robust) but Austronesian areas appear to have higher development outcomes
Also, although the finding was not as robust, development levels are higher in parts of the country that were settled by Austronesians. If this finding is right, it suggests variations in pre-colonial culture, and its impact on cooperation, has an impact in the present on differing levels of development in PNG.

8. History, land quality, logs and mines don’t matter as much.
Tentative findings
Social factors/cooperation (languages, Austronesian) have a big impact on district development
Access matters too.
History, land quality, logs and mines don’t matter as much.
But we need better data
To summarise: social factors such as numbers of languages and social practices associated with Austronesian culture have an important impact on development.
Geography is important too, but only through the impact it has on how accessible different parts of a district are.
On the other hand colonial history, land quality, logs and mines don’t appear to matter nearly as much in influencing rural development.
These are my provisional findings for you today. They are interesting, I hope, but they are only provisional: an important message is that we won’t know anything for certain until we have better data. The good news is that various different groups of people are working on this at present.