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R Programming for Public Policy Analysis

Early in 2019 I posted a short ‘listicle’ with some of the key reasons I think Python and/or R should become essential tools in a modern policy analyst’s toolkit.

The full article is here, but the headline points in the article were; R programming’s use across disciplines fitting in well with multidisciplinary policy analysis teams; the greater reproducibility/transparency written code provides; and the practical advantages that can come from automating repetitive bits of policy analysis (such as reporting results of policy analysis across multiple scenarios).

While the article didn’t end in me getting a book deal, it did result in me receiving a surprising number of messages from people that were just as passionate as I am about the potential of R in the public policy world. At the same time, I also had a number of people asking me to put my money where my mouth is by showing them how it’s useful by teaching them.

So after being offered a space by the Microsoft Reactor in Sydney, I took up their challenge. Throwing together a course based on what I thought would be most useful based from my experience as a consultant/economist/policy analyst.

Running for an hour a week over four weeks it covered the basics of automating tasks, undertaking exploratory analysis, visualizing data and generating summary statistics in the context of answering questions as a policy advisor.

The course went well. So well in fact, that the most common request from participants in course evaluations were for future courses to be longer. I also found:

  • The grammar of the Tidyverse made learning the basics much faster: I learned base R via an online series of courses and managed to learn the core principles of the Tidyverse in a little more than a day. For policy analysts/consultants it also made more sense, thanks to Tidyverse’s more intuitive grammar.
  • People didn’t need a background in statistics to be able to quickly pick up the basics: the course was equivalent to a little over a full day of material and covered a lot of ground but everyone kept up. From past courses I’ve seen this isn’t guaranteed, so it was a pleasant surprise.
  • Practice was preferred to theory: I wasn’t a straight A student, so I get this. But everything was picked up quicker if it was made relevant to the daily lives of participants rather than being draped in a purely theoretical framework.
  • Pipes are confusing: This is contentious, but I remember feeling this way when I first started to learn R. I love pipes now, but people in my sessions preferred nested formulas. Trying to introduce it so early was just distracting.
  • People loved data viz with ggplot: However, this was more because of ggplot’s ability to quickly segment and visualize data (such as through applying facets to demographic classifications) than quality of what it could produce. This makes sense given a large part of a policy analyst’s work is about exploratory analysis that is used to inform written recommendations, rather than being presented.

So where to from here? Well, outside of shamelessly rebranding my 2019 article for 2020, I’ve been convinced to develop a longer and more widely accessible online version of the free course to satisfy the demands from those that wanted to join but couldn’t due to time constraints or being in the wrong city/country:

Which is the second reason I wanted to write this up, as if you’re a fellow R/Python programmer in the policy/consulting space I’d love to hear from you to get your thoughts about what you think is useful. So if that’s you, feel free to drop me a line either via LinkedIn, Twitter or the contact form here.

And if you or someone you know is interested in signing up for the first run of the online crash course in R, you can do so via program4policy.com

7 Reasons for policy professionals to get into R programming in 2019

Note: A version of this article was also published via LinkedIn here and on Medium here. 

With the rise of ‘Big Data’, ‘Machine Learning’ and the ‘Data Scientist’ has come an explosion in the popularity of using open-source programming tools for data analysis.

This article provides a short summary of some of the evidence of these tools overtaking commercial alternatives and why, if you work with data, adding an open programming language, like R or Python, to your professional repertoire is likely to be a worthwhile career investment for 2019 and beyond.

Like most faithful public policy wonks, I’ve spent more hours than I can count dragging numbers across a screen to understand, analyse or predict whatever segment of the world I have data on.

Exploring where the money was flowing in the world’s youngest democracy; analysing which government program was delivering the biggest impact; or predicting which roads were likely to disappear first as a result of climate change.

New policy questions, new approaches to answer them and a fresh set of data.

Yet, every silver-lining has a cloud. And in my experience with data it’s often the need to scale a new learning curve to adhere to legacy systems and fulfil an organizational fetish for using their statistical software of choice.

Excel, SAS, SPSS, Eviews, Minitab, Stata and the list goes on.

Which is why I’ve decided this article needed to be written:

Because not only am I tired of talking to fellow analytical wonks about why they’re limiting themselves by only being able to work on data with spreadsheets, but also that there are distinct professional advantages to unshackling yourself from the professional tyranny of proprietary tools:

  1. Open-Source Statistics is Becoming the Global Standard

Firstly, if you haven’t been watching, the world is increasingly full of data. So much data, that the world is chasing after nerds to analyse it. As a result, the demand for a ‘jack of all trades’ data person, or “data scientist” has been outstripping that of a more vanilla-flavoured ‘statistician’:

% Job Advertisements with term “data scientist” vs. “statistician”

(Credit: Bob Muenchen – r4stats.com)

And although you might not have aspirations to work in what the Harvard Business Review called the ‘Sexiest Job of the 21st Century’ the data gold rush has had implications far beyond the sex appeal of nerds.

For one, online communities like Stackoverflow, Kaggle and Data for Democracy have flourished. Providing practical avenues for learning how to do some science with data and driving demand for tools that make applying this science accessible to everyone, like R and Python.

So much, that some of the best evidence, suggests that not only is demand for quants with R and Python skills booming, but the practical use of open-source statistical tools like R and Python are starting to eclipse their proprietary relatives:

Statistical software by Google Scholar Hits:

(More credit to Bob Muenchen – r4stats.com)

Of course, I’m not here to conclusively make the point that a particular piece of software is a ‘silver bullet’. Only that something has happened in the world of data that the quantitatively inclined shouldn’t ignore: Not only are R and Python becoming programming languages for the masses, but they’re increasingly proving themselves as powerful complements to more traditional business analysis tools like Excel and SAS.

2. R is for Renaissance Wo(Man)

For those watching the news, you’ll no doubt have heard of the great battle being waged between the R and Python languages that has tragically left the internet strewn with the blood of programmers and their pocket protectors.

But I’m going to goosestep right over the issue as in my opinion much of what I say for R, is increasingly applicable to Python.

For those of you unfamiliar with R, in essence it’s a programming language made to use computers to do stuff with numbers.

Enter: “10*10” and it will tell you ‘100’ 

Enter: “print(‘Sup?’)” and the computer will speak to you like that kid loitering on your lawn.

Developed around 25 years ago, the idea behind R was in essence to develop a simpler, more open and extendible programming language for statisticians. Something which allowed you greater power and flexibility than a ‘point and click’ interface, but that was quicker than punch cards or manually keying in 1s and 0s to tell the computer what to do.

The result: R – A free statistical tool whose sustained growth has helped create one of the most flexible statistical tools in existence.

So much growth in fact, that in 2014 enough new functionality was added to R by the community that “R added more functions/procs than the SAS Institute has written in its entire history.” And while it’s not the quantity of your software packages that counts, the speed of development is impressive and a good indication of the likely future trajectory of R’s functionality. Particularly as many heavy hitters including the likes of Microsoft, IBM and Google are already using R and making their own contributions to the ecosystem:  

Using R for Analytics – Get in Before George Clooney Does:

Image source. Also, see here

Not only that, but with much of this growth being driven by user contributions, it is also a great reminder of the active and supportive community you have access to as an R and Python user. Making it easier to get help, access free resources and find example code to steal base your analysis on.

3. R is Data and Discipline Agnostic

(Source: xkcd)

One of the first things that motivated me to learn R, was the observation that many of the most interesting questions I encountered went unanswered because they crossed disciplines, involved obscure analytical techniques, or were locked away in a long-forgotten format. It therefore seemed logical to me that if I could become a data analytics “MacGyver”, I’d have greater opportunities to work on interesting problems.

Which is how I discovered R. You see, as somebody that is interested in almost everything, R’s adoption by such a diverse range of fields made it nearly impossible to overlook. With extensions being freely available to work with a wide variety of data formats (proprietary or otherwise) and apply a range of nerdy methods, R made a lot of sense.

I think it was Richard Branson that once said “If somebody offers you a problem but you are not sure you can do it, say yes. R probably has a package for it” (!):

Then R (and increasingly Python) has you covered.

Yet there is perhaps a subtler reason adopting R made sense and that’s the simple fact that by being ‘discipline agnostic’ it’s well-suited for multidisciplinary teams, applied multi-potentialites and anyone uncertain about exactly where their career might take them.

4. R Helps Avoid Fitting the Problem to the Tool

As an economist, I love a good echo chamber. Not only does everybody speak my language and get my jokes, but my diagnosis of the problem is always spot-on. Unfortunately, thanks to errors of others, I’m aware that such cosy teams of specialists, isn’t always a good idea – with homogeneous specialist teams risking developing solutions which aren’t fit for purpose by too narrowly defining a problem and misunderstanding the scope of the system it’s embedded in.

(Source: chainsawsuit.com)

While good organizations are doing their best to address this, creating teams that are multidisciplinary and have more diverse networks can be a useful means to protect against these risks while also driving better performance. Which of course stands to be another useful advantage of using more general statistical tools with a diverse user base like R: as you can more fluidly collaborate across disciplines while being better able to pick the right technique for your problem, reducing the risk that everything look like a nail, merely because you have a hammer.  5. Programming Encourages Reproducibility

Yet programming languages also hold an additional advantage to more typical ‘point and click’ interfaces for conducting analysis – transparency and reproducibility.  

For instance, because software like R encourages you to write down each step in your analysis, your work is more likely to be ‘reproducible’ than had it been done using more traditional ‘point and click solutions. This is because you’re encouraged to record each step needed to achieve the final result making it easier for your colleagues to understand what the hell you’re doing and increasing the likelihood you’ll be able to reproduce the results when you need to (or somebody else will).

In addition to this being practically useful for tracing your journey down the data-analysis-maze, for analytical teams it can also serve as a means for encouraging collaboration by allowing to more easily understand your work and replicate your results. Assisting with organizational knowledge retention and providing an incentive for ensuring analysis is accurate by often making it easier to spot errors before they impact your analysis or soil your reputation.

Finally, while the use of scripting isn’t unique to open-source programming languages, by being free, R and Python comes with an additional advantage that in the instance you decide to release your analysis, the potential audience is likely to be greater and more diverse than had it been written using propriety software. Which is why in a world of the “Open Government Partnership” open-source programming languages makes a lot of sense, providing a means of easing the transition towards government publicly releasing government policy models.

6. R Helps Make Bytes Beautiful  

As data-driven-everything becomes all the rage, making data pretty is becoming an increasingly important skill. R is great at this, with virtually unlimited options for unleashing your creativity on the world and communicating your results to the masses. Bar graphs, scatter diagrams, histograms and heat maps. Easy.

Just not pie graphs. They’re terrible.  

But R’s visualization tools don’t finish at your desk, with the ‘Shiny’ package allowing you to take your pie graphs to the bigtime by publishing interactive dashboards for the web. Boss asking you to redo a graph 20 times each day? Outsource your work to the web by automating it through a dashboard and send them a link while you sip cocktails at the beach.

7. R and Python are free, but the Cost of Ignoring the Trend Towards Open-Source Statistics Won’t Be

Finally, R and Python are free, meaning not only can you install it wherever you want, but that you can take it with you throughout your career:

  • Statistics lecturers prescribing you textbooks that are trying to get you hooked on expensive software that likely won’t exist when you graduate?  Tell them it’s not 1999 and send them a link to this.
  • Working for a not-for-profit organization that needs statistical software but can’t afford the costs of proprietary software? Install R and show them how to install Swirl’s free interactive lessons.
  • Want to install it at home? No problem. You can even give a copy to your cat.
  • Got a promotion and been gifted a new team of statisticians?  Swap the Christmas bonuses for the give the gift that keeps giving: R!

But I’m not here to tell you R (or Python) are perfect. Afterall, there are good reasons some companies are reluctant to switch their analysis to R or Python. Nor am I interested in convincing you that it can, or should, replace every proprietary tool you’re using. As I’m an avid spreadsheeter and programs like Excel have distinct advantages.

Rather, I’d like to suggest that for all the immediate costs involved in learning an open-source programming language, whether it be R or Python, the long-term benefits are more than likely to surpass them.

(Source)

Not only that, but as a new generation of data scientists continue to push for the use of open-source tools, it’s reasonable to expect R and Python will become as pervasive a business tool as the spreadsheet and as important to your career as laughing at your boss’ terrible jokes.  

Interested in learning R? Check out this link here for a range of free resources.

You can also read my review of the online specialization I took to scale the R learning curve here.

A Review of John Hopkins University’s Online Data Science Specialization (Coursera.org)

So for all those loyal subscribers out there (hey mum!) you might wonder what the hell happened to my constant stream of insightful, relevant and handsome blog posts.

Well, I’d have to say you’re thinking about me a little too much – I’d suggest committing yourself to a hobby like me.

Perhaps regular blogging?

Surviving Nay Pyi Taw

In truth, Myanmar has also kept me pretty busy. That is until recently, when I was handed a steaming pile of free time as a result of moving to the traffic-free social desert that is Nay Pi Taw, Myanmar.

And how would any sane person use this time?

Well you’d be best to ask them. As for me, I decided to enroll in a six month dose of data science administered by John Hopkins University (JHU).

So consider this your warning, as that’s where this blog is going.

But to make escape easy here’s a link to YouTube trending and for those of you with a short attention span I’ve also created a TLDR (short) version at the end.

Cyanide and Happiness

Rationalizing Self-Harm

That’s right. I know what you’re thinking – why would anyone volunteer to learn about data?!

Well you see, I was a curious child and time has turned me into a curious man-child, as a result, a surprising amount of my career has been defined by being asked difficult questions by difficult people.

While this has meant that I’ve been able to do a lot of interesting work, it has also made it increasingly apparent that many interesting problems go unsolved because people aren’t sure how to approach data.

Dilbert Comics

Which is where this niche blog post begins, as it was from this observation that I decided it would makes sense to arm myself with a statistical tool that:

  1. Is capable – allowing it to be applied to a range of data-related geekery;
  2. Is portable and cheap – allowing it to be easily adopted regardless of an organization’s size and financial resources;
  3. Can work with data in a variety of formats – making it easier to transport analysis to/from a wide variety of sources; and
  4. Is useful across disciplines – making it suited across fields and in multidisciplinary teams/organizations.

In essence, I was looking for the ‘spork’ of data science software. Which is apt because like a spork, R can do a lot of things but is a little awkward and unwieldy.

However, unlike a spork R is popular.

So popular, that it’s a global standard in the data world. But not so popular that you’re going to get invited to more parties :(.

Which brings me to another disadvantage of R – it’s known for being a little unwieldy:Basic Analysis of Workshop Data

Don’t get me wrong, I’m not claiming your learning experience will see as many deaths as the figure above. But it’s best to go in expecting that learning R is more like walking on lego than cake.

Which is why I chose to do JHU’s Data Science specialization. As if I’m going to be walking on lego I’d prefer to do it quickly and with more decorum than a monkey with a typewriter.

So, the choice was made and a high standard set: Don’t be a monkey.

An Overview

Now for those of you with a short attention span, remember I’ve include a short summary at the end of this post but in essence JHU’s Data Science specialization is made up of nine courses which can be roughly divided up into two main ‘flavors’:

  • The basics of working with R – such as writing scripts, using GitHub, importing/exploring data, and generating statistics; and
  • The actual reason we want to work with R – such as creating interactive visualizations on the web, creating catterplots, running regression models and encouraging your computer to become sentient via machine learning.

Once completing these nine courses students are then provided with the option to complete the final ‘Capstone’ course which is meant to provide an opportunity to apply your skills on a real-world problem.

setwd(“internet”)

So in the spirit of [insert closest holiday here] and to spoil the ending, let me just say that completing the specialization was worthwhile. It covers a range of useful topics, is delivered by world-class lecturers and forces you to apply what you learn. The course also fulfilled my embarrassing desire to apply some science to data, which is essentially the only way to learn R, via R-ing (?).

For instance, the quizzes and programming assignments give you messy data, complicated problems and ask you to use R to present analysis in a digestible format. As a result, if you legitimately complete the courses you’ll come out having learned a lot.

Although it’s hard to compare online courses with those offered by a traditional university, I’d probably say that JHU’s Data Science specialization might be something close to a four-course graduate certificate. This is based both on the level it’s pitched at, the workload and the fact the entire specialization took me a little over six months with a background in statistics (although your experience may vary).

It’s also relatively inexpensive when compared with the more traditional alternatives at a little under $300 USD or around five percent of the cost of a comparable program.

This is Fine

Yet all is not well in the world of the JHU Data Science series.

Gunshow comics.

You see, although I’m glad I did the course, it was not without shortcomings.

Firstly, I was originally attracted to the course as it appeared to cover an impressive array of topics. Yet courses were only a month long which meant subjects often had to sacrifice depth and/or place unrealistic learning outcomes on the students. Unfortunately, the JHU Data Science Specialization often chose both by skimming through essential topics then grading students on them.

Take the Statistical Inference course, which tries to quickly illustrate how to respect the rules of the God of numbers and explain why we care about infinity, even when we’re unlikely to get there anytime soon. While interesting, a frolic through discussions in the message boards made it pretty clear that the ‘vomiting equations onto a PowerPoint presentation’ wasn’t a particularly effective teaching approach.

A similar story could also be told of the regression course which gently introduces learners to the concept of linear regression before abruptly lobbing a grenade of generalized-linear models, probits, logits and something to do with a hockey stick.

This I found to be particularly unfortunate as regression analysis is a useful tool for so many types of analysis. It’s also conceptually useful, as it reminds budding statisticians that there isn’t usually a ‘silver bullet’ explanation for what’s driving something and usually your conclusion relies as heavily on statistical assumptions as it does the data.

More generally, when you’re applying statistics in the real-world, abstract concepts aren’t particularly helpful until you’ve internalized them – something I suspect for most mortals would require more time than the course allowed.

The Sound of One-Hand Clapping

War and Peas

Of course whether the course did include other mortals is an open question, with discussion boards mainly filled with generic ‘please mark my assignment’ requests from past sessions of the course. Although this might have been a natural consequence of the field not attracting social superstars (myself excluded of course…), even for a mixed-gender game of dungeons and dragons human-to-human interaction was low.

Relative to other online courses I’ve done, this led to a much poorer learning experience. This was both because you weren’t able to rely on the hive-mind when you had a problem and as it meant you didn’t get the benefit of understanding how others are applying what they learn outside of the course.

Assessment Structure

Given online courses can have thousands of students, quizzes and ‘peer-graded’ assignments tend to be the backbone of the assessment structure in the world of MOOCs. In JHU’s case, online quizzes were typically run each week while peer-grading (where students mark each other) was used for major projects.

For those unfamiliar with ‘peer-grading’ basically you submit your assignment, mark five of your peers and receive a grade based on the most common score given by five students that have marked your submission. Generally, it can work quite well and I’m a big fan – you see how others have approached a problem, get a sense of where you stand relative to your peers and hopefully receive useful feedback to improve your work.

Alas, in the JHU specialization it wasn’t always done well, with much of the feedback I received being minimal. Although I suspect this is in part due to me having attained perfection, I’d also say that this is a result of:

  • The courses being run within a short timeframe – discouraging students from assigning more time and thought to marking;
  • The marking criteria sometimes not providing much scope to differentiate adequate assignments from the exceptional;
  • The age of the course meaning that the internet is now awash with past assignments, making plagiarism easier for the lazy; and
  • The system not encouraging quality feedback – such as by rewarding those that give good feedback by assigning them markers that are likely to give good feedback in return.

PHDComics.com

The Capstone

Finally, there’s the final project or ‘Capstone’ which was described as “a project drawn from real-world problems and will be conducted with industry, government, and academic partners.”

I of course assume that was a typo as a more apt description was “A project randomly drawn from a real-world problem largely unsuited to the R language, principally unrelated to the other courses in the specialization and unlikely to be useful at any point in the near future.’

In the words of one reviewer “Of all the offerings in the specialization, this one felt like it was thrown together in less than hour.” And while this might seem unfair, this thought definitely crossed my mind as I was cobbling together an interactive predictive text application that will unlikely be useful to anyone unless they’re looking to generate gibberish.

A disappointing end given the effort that was required to get there.

Two-parts contentment. One part complaining.

But again, the specialization wasn’t all bad. Far from it.

For instance, while the regression modelling, machine learning and statistical inference courses could definitely be better structured and longer, my experience is that teaching these topics is harder than learning them. I also imagine this is all the more difficult when you’re teaching a classroom of 100+ whiny nerds.

I’d also say that some of the potentially boring topics were well done.

For instance, although both ‘Getting and Cleaning Data’ and ‘Exploratory Data Analysis’ could have been more tightly focused, I came out of both courses with a much better appreciation of what’s possible. The courses also made me remember why I was doing the course in the first place as it demonstrated why R is so useful.

Finally, while the final lecture for ‘Reproducible Research’ appeared to be from a different subject altogether, the course was one my favorites. This is for one as it explained what the hell the ‘knit’ button in R Studio does, but also as it covered the how/why of making research reproducible in R – something that is rarely achieved in economics.

While at first glance this might appear as a solely academic issue, as an applied economist I can see many times during my career that the tools would have been tremendously useful for naturally building in reproducibility and transparency into my team’s work as it:

  1. Makes collaboration easier;
  2. Allows the analysis to be quickly repeated with new assumptions and/or data; and
  3. It provides a more reliable way of recording what was done for archival purposes.

While this might still sound somewhat abstract, in the world of economic policy it’s not uncommon to be asked to repeat several iterations of politically sensitive analysis in a short-time frame.

Get it right and you can keep your job.

Make a mistake and you might just make history.

Summary()

So what would I say to someone thinking of making the arduous journey to complete the specialization?

Well, firstly although parts of the specialization were disappointing, it’s a great overall program and I’ve learned much of what I was hoping to. I understand R, have a better sense for when meaningful insights can be gained from data outside of economics and have a better feel for how analysis can be made interactive and accessible to a wider audience.

The course is also a bargain, costing less than five percent of the tuition of a comparable 6-month course at University.

XKCD

Of course, it also seems that the JHU Data Science specialization has been largely abandoned, with the world of online data science courses becoming more competitive in the meantime, with Harvard, Berkley, Microsoft and the University of Michigan all providing their own data science specializations both in R and Python.

As such, while I’m glad I endured through the 10-course JHU data science extravaganza, if I was going to do it now I’d be inclined to go with one of the competitors.

This is both because I would place my bets on the competing options having learned from the strengths of the JHU course, while dropping its weaknesses. But also, because unless JHU updates their specialization its prestige and its power to signal the recipient’s determination will diminish over time.

Of course, in the world of online learning it doesn’t have to be all or nothing – pick one, pick two or decide to prioritize your social life by picking none of them, whatever you choose it’s a great time to conquer your fear of data.

I’m looking at you Darren.

 

TLDR Version:

Good course, glad I did it but would recommend checking out the alternatives from Harvard, Berkley, Microsoft and the University of Michigan.

The Good:

  1. Getting and Cleaning Data­­­­­ – Learn how to get data into R and make it useful for analysis.
  2. Exploratory Data Analysis – Make graphs with different plotting systems and be given a brief and unsatisfying crash course on PCA.
  3. Reproducible Research – Learn what the ‘create R markdown’ document option means in Rstudio and the philosophy of reproducible research.
  4. Regression Models – Be gently introduced to linear regression through a series of intuitive lectures before being rushed through the more complicated logistic and poison regression in the final week.
  5. Practical Machine Learning – Learn the basics of machine learning models.
  6. Developing Data Products – Briefly learn about some of the coolest parts of R such as creating interactive dashboards for the web.

The Bad

  1. R Programming – Learn the essentials of R and lose sleep while writing functions needed to complete the assignments. Bonus: Watch a large proportion of the class drop out.
  2. Statistical Inference – Be quickly rushed through essential statistical concepts with insufficient explanation. Bonus: Watch a large proportion of the class drop out.
  3. Capstone Project – Be given minimal instructions about solving a problem which will likely be useful for 0.5 per cent of R users during their career.

The Neutral

  1. The Data Scientist’s Toolbox – Install R, Rstudio and set up a github account.

Op-ed: Public Financial Management Reforms and Fiscal Decentralization in Myanmar

My latest op-ed was published in  Monday’s edition of the Myanmar Times.

The article provides a brief summary of Myanmar’s democratic and economic reforms as they relate to the country’s management of their public finances. A summary of the article and a link to the full piece is provided below.

Catalysing transition through public financial management reform

By Giles Dickenson-Jones and Matthew Arnold

Public financial management reforms are central to Myanmar’s entire transition.  Improvements to social services like garbage collection, investment in new roads and bridges, and raising standards of health and education are all premised on the government being able to raise more revenue and then effectively spend it achieving policy goals. In order for the National League for Democracy government to achieve its goals for economic and political reform, it is therefore a critical area for prioritisation.

See here for the full article.

 

New Research Report: Intergovernmental Fiscal Relations in Myanmar

Another blog post and another research report focusing on Myanmar’s taxation system:

Intergovernmental Fiscal Relations in Myanmar: Current Processes and Future Priorities in Public Financial Management Reform

This was my final research report developed at Myanmar’s Centre for Economic and Social Development.

The paper ‘Intergovernmental Fiscal Relations in Myanmar’ takes a look at how Myanmar’s State, Region and Union governments relate to each other as part of budget and planning processes.

Although it is targeted at a more general audience, it has been developed in the interest of providing greater clarity around the informal and formal processes that inform public budget processes and fiscal decentralization in Myanmar.

As somebody who started his career in government, I think perhaps one strengths of this report was that many of the initial findings were tested at the drafting stage as part of an interactive workshop the team held in Naypyitaw.

Open Myanmar Initiative’s Budget Explorer Launched – Budget Data Visualization in R

Yet another quiet couple of months on the blogging front can be explained by me feverishly working on a number of projects as I reach my 2 year anniversary in Myanmar. The latest of these has been the launching of the Open Myanmar Initiative’s Budget Dashboard, which is now available online here:

http://mmbudgets.info/

The website, which I helped develop using the open-source R language and the free Shiny library provides the first user-friendly interface for exploring Myanmar’s budgets both at the Union level and across all 14 States and Regions.

Although there is still a long way to go before citizens become genuinely engaged with the budget process, I think this is a significant first step in the right direction and will allow interested citizens, researchers and businesses to more easily examine where public money is spent, so a conversation about where it should be spent can be had. I’m also encouraged to see public finances have been included in the National League for Democracy’s economic polices.

The budget dashboard is part of the Asia Foundation’s support of an open budget process in Myanmar in partnership with the Open Myanmar Initiative (OMI). OMI’s Budget explorer was developed by Ewan Keith, Loren Velasquez and Giles Dickenson-Jones with the help of Statistics Without Borders.

More information about the project is available here.

* Postscript: As an update, the original budget dashboard described in this post has since been taken over (and greatly improved!) by the locally based budget and parliamentary transparency organization ‘The Ananda’. The link has been updated to reflect this.

New Article Published – State and Region Public Finances in Myanmar

Hey all,

So no doubt you would have all noticed I have been rather silent lately on the ye olde interweb. Although there is of course no excuse for this, it’s predominantly a result of having been working rather intensely on a piece of research looking at Myanmar’s public finance system:

This paper focuses on understanding the role of state and region governments in relation to Myanmar’s public finances. This has been done to take stock of existing research, better understand the composition of subnational finances, and attempt to address whether, at this point in the fiscal decentralization process, state and region governments have sufficient resources to fulfil their constitutionally delegated responsibilities. Recognizing the complex and varied factors relevant to addressing these questions, a range of qualitative and quantitative approaches were employed, including semi-structured interviews of stakeholders, consultation with sector experts and analysis of published budget and socioeconomic data.

 

For those of you who are interested, you can download a copy here.

Day trip to Dala (Yangon, Myanmar)

When I recount my time in the Philippines I often remember how living in the concrete jungle that is Manila felt somewhat claustrophobic. Although this was for a range of reasons, it is perhaps unsurprising given Manila has the highest population density in the world.

In fact, when comparing where I lived then (Manila), with where I live now (Yangon), it is pretty why this is no longer a problem with Manila’s population density 6 times that of Yangon. Consequently it is possible for everybody’s inner-hermit to find some solitude.

Unfortunately, if there were a party of inner hermits, mine would still be the one hiding behind a curtain in the corner. Which is why, he was so excited to hear about my weekend plan: a day trip to somewhere even quieter than Yangon; Dala.

Dala is a township on the outskirts of Yangon, on the south of the Yangon River. Although it is relatively close to the urban hub of Yangon (1.5 km away), it is only accessible by ferry which seems to have made all the difference to how urbanised it is.

Ferry to Dala

The first step to getting across the river required that we meandered to Pansodan Jetty, directly opposite the Strand hotel.

I say meander as the area attached to the jetty terminal also functions like most markets in Yangon, serving the hundreds of locals who commute from Dala to Yangon each day (during the day the ferry leaves every 20 minutes).

Heading past the many stalls towards the Yangon river, eventually you come to the ferry terminal where there will no doubt be a line in the door and ample crowd waiting inside.

Upon arriving, we were pulled aside and pointed into the manager’s office to buy our tickets. Unfortunately, I couldn’t convince him of being a local no matter how convincingly I wore my longyi and spoke broken Burmese. Unfortunately this meant I couldn’t get a 100 kyat local ticket, rather having to pay the tourist price of 4,400 kyats for a return ticket (or 4 USD if you have dollars on you).

And although I was sure to make a point about how outrageous this exorbitant $4 foreigner fee was, it was to no avail. Besides, there really isn’t much to complain about with it actually being a pretty quick and comfortable trip with it taking around 20 minutes and there being an ample number of traders willing to sell you cigarettes, coconut and cowboy hats.

Of course, in Myanmar it pays to be careful so if you decide to purchase a cowboy hat please consult this chart to ensure you live to tell the tale.

Except for the trader selling snacks and cigarettes t’s a pretty standard ferry ride over to Dala

Unfortunately it’s illegal for foreigners to take these boats across.


It’s easy to forget how big the boat is until you see the masses of people exiting the boat.

Arriving in Dala

As you might expect, taking the ferry in itself is a pretty worthwhile in and of itself, albeit a cushy one. Still, it’s a great opportunity to see how day to day commerce takes place with many of those living in Dala, working (or selling their goods) in Yangon (did you know they transport chickens in bundles?!).

Although it seems the ferry is predominately populated with locals, there are apparently enough tourists to foster a generous number of traders and tour guides who operate at the Dala jetty terminal, so prepare be swamped.

Now while when it comes to the town itself, you could walk around yourself I wouldn’t recommend this as everything is quite spread out. Given this, I’d say you’re best to hire a tri-shaw, the going rate which seems to be around 1500 kyats per hour, with a full tour taking around 2 to 3 hrs.

This is of course unless you happen to be me, who may have paid a bit more than as a consequence of the driver telling me that I’m “handsome like a movie star”. My mum was right.

 

Cruising Around Dala

There are three main sights that tourists typically come to see while in Dala. The Pagoda, Fishing Village and Bamboo Village, however, truth be told it’s a pretty worthwhile experience just for the purposes of seeing just life in Dala, which, as you’d expect, is similar to other rural communities in Myanmar.

Shwe Sayan Pagoda, Dala

Let’s face it. If you’re not seeing a pagoda a day when touring Myanmar, you’re doing something wrong.

Dala is of course no exception, with the township having a surprisingly well maintained pagoda. Although it is seemingly like any other pagoda in Yangon a number of things make it a bit different.

Firstly, there seemed to be around 20 children who hang around the thing during the day, climbing the stupa and mobbing hapless foreigners when the opportunity arises.

Secondly, the colours used have much more variety than typical pagodas in and around Yangon. However, perhaps the most significant difference is that this pagoda includes a now deceased monk who it is said predicted cyclone Naga.

 

Fisheman Village, Dala

The Fisherman’s village is located along the banks of Dala river. Many of the fishermen who work along the Yangon River live with their families in huts along the shore. Perhaps for me the highlight of this was the fact that they were building and repairing a number of their boats on the shore, a feat all the more impressive to me given that I have trouble cooking oatmeal without setting myself on fire.

Some Final Thoughts

I have to admit I’ve still got a long list of sites to see in Yangon, I think my half day in Dala was without a doubt the best touristy thing I’ve done in Yangon. It’s also an unbelievable convenient way to get out of the concrete jungle for a breather. Although I don’t mean to suggest it’s going to be as relaxing as lying beside the pool, martini in hand, it is a beautiful side of Yangon to see.

Living and working in Yangon, Myanmar

Living and working in Yangon

This post is the first in a series focusing on Yangon, Myanmar. It is predominantly meant to provide an additional perspective on the logistics of living and working in Yangon. Recognizing that my knowledge and perspective on the city will change I’ll be updating it over time.

2016 Update: So it has been a little over two years since I wrote this post and it has turned out to be surprisingly popular post resulting in me often meeting people who are like ‘hey I read your blog’. But it’s not just my new found fame which has changed since writing this, so has Yangon. So to account for this I’ve gone back to my original post and made some additions/changes where necessary.

For anyone who has skimmed my blog you’ll know that not unlike Leonardo da Vinci I’m a man of many passions, including (but not limited to) economics and travel. Fortunately, I’ve recently been able to combine both these passions in my new role as an economist in Yangon, Myanmar.

Of course given the logistical challenge of moving countries, few people take the decision lightly. For me, this was made all the more difficult as I tend to only make hard choices after doing hard research, a task made particularly difficult given the dearth of information on living in Yangon. So here I am, writing a post covering Yangon 101, so if this doesn’t sound like something you’re not interested in you should probably stop reading somewhere around here.

Introduction

At the outset, I probably don’t need to tell you where Myanmar is. But to be safe, it’s a country in South Asia bordered by India, China and Thailand (among others).

 

Weather

Being much closer to the equator and at an altitude somewhere near sea-level Yangon can be hot. Of course this is not universally true with some areas in the north of Myanmar even experiencing snow, but in any case you probably aren’t going to need warm clothes.

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2016 Update: It’s worth mentioning the heat is really a big deal over here. Obviously this is going to subjective as I notice locals in arctic gear when I’m almost fainting from heat exhaustion, but be warned it can get very hot and uncomfortable. Power outages also seem to occur more frequently (or perhaps just more painfully) during the hottest part of the year, which means air conditioners and fans can stop operating when you least expect it.

Although you might be able to avoid this by finding an apartment with a generator, I personally still don’t think the extra cost of renting such a place is justified, but that will depend on you.

Traffic

Traffic in Yangon is bad, but not much worse than many other populated cities in Asia. For me it seems somewhere close to the traffic in Phnom Penh, Cambodia. On a practical level what this might mean is a 5 km trip takes 30-40 minutes instead of 10 minutes during heavy traffic.

Yangon is not the ‘wild west’: there are enough shops/restaurants and cinemas to make your stay more than comfortable. Although you shouldn’t expect wine, caviar and toast points, there are enough shops, restaurants and cinemas to make an unadventurous expat happy.

2016 Update: Actually, now you probably can find caviar and toast points (not that I’ve looked). But overall the food scene has improved a lot in two years, there are now relatively decent Mexican, Japanese and Vietnamese restaurants there (you can see a good sample via Yangon Door 2 Door). There are also more and more decent coffee shops emerging, particularly around the expat-rich Yawmingyi area.

A Note on Food

Although this has been the death of my diet, there is actually a food delivery service in Yangon called ‘Yangon Door2Door’. Although I haven’t tested out much on their menu, they have a pretty decent range of food they’ll deliver to your door (including pizza, etc).

Internet in Yangon

The internet in Yangon is slow. Although I’ve heard this complaint a lot since being here, I personally think it’s perfectly manageable provided you’re not expecting to watch cat videos on your phone. I use it mainly for reading the news, calling friends via Skype and Viber and checking my email. It’s also not too hard to find cafes with free WiFi.

2016 Update: Internet in Yangon can still be expensive, but it has come leaps and bounds with a number of providers providing internet more cheaply than when I arrived. I probably spend around $30 USD a month on mobile internet for around 8gb. Not great, but definitely an improvement. More and more cafes have wifi connections too if you really want to save money.

Language

Although you can generally get around okay with English, I’d recommend you try and learn some Burmese to make life a bit easier and to show respect. You’ll likely be laughed at countless times for your miss-pronunciation (I certainly have been), but making mistakes is all part of the learning process. I’ve also found that most of the locals appreciate you making the effort and will be happy to help you learn.

Accommodation in Yangon

For those of you looking to move to Yangon for the long-haul, there are cheap accommodation options available, although less and less so as foreign aid and investment flows in. In the limited time I’ve been here, I’ve inspected around ten different places and done a fair chunk of research, but like everything here take it with a grain of salt.

Firstly, rental fees seem to operate like a Dutch auction in that the owners seem to start at the top and you have to bargain them down. Often I get the feeling that landlords will ask for more rent than they think a place is worth and hope that you won’t bargain. This is of course anecdotal, but the amount of rent a landlord asks in my (limited) experience can have little to do with the quality of the place, so shop around. This is probably why they say that ‘everything is negotiable’ when it comes to real-estate in Yangon.

Rent is also generally paid upfront and involves one month’s rent as commission. So if you were to rent a place for $500 a month, you’d need to pay $6500 upfront. I’d also strongly, recommend not pre-arranging long-term accommodation before arriving as it’s a sure fire way to pay too much.

If you’re looking to save money consider going with an unfurnished place. The real-estate market is somewhat segmented, with furnished accommodation tending to be significantly more expensive, given it is targeted towards the expatriate community.

For instance, a two bedroom unfurnished place can be found for around $200-$500 US per month, while a similarly sized furnished place might go for closer to $800-$1,000 US and above per month. It also goes without saying that you can spend more if you want, with plenty of condominiums willing to charge you closer to the $2,000 to 4,000 US per month mark.

Given furnishing a place is likely to cost somewhere between $1,000 to $2,000, going for a furnished place is probably reasonably value as long as you’re not paying more than $80 to $160 extra per month in rent.

Although I don’t recommend searching for accommodation online, a friend of mine has had a surprisingly large amount of success using the website below to find places.

http://en.house.com.mm

There is also an excellent blog post from a bunch of Yangon Expats here:

http://lifeinthetropics.org/cost-of-living-for-an-expat-in-myanmar-yangon-in-2014/

Don’t Judge an Apartment by its Cover

Another key point to remember is that what an apartment block looks like on the outside has little to do with how it is inside. When looking at places there have been times when I feel like I’ve past through the door to Narnia as a seemingly dilapidated stairwell has led to an incredibly modern apartment.

2016 Update: I’d say much of what I said before still applies in Yangon except there is more competition and more options available now. Often when people talk about how real estate is becoming more affordable they’ll reference back to how expensive it was in 2014 (when I first wrote this blog).

I’d also note that because the exchange rate has changed so much the figures I’ve given aren’t necessarily accurate. Also, Frontier magazine came up with this more up to date and comprehensive guide to Yangon’s rental market here.

Some additional advice I’d give to people looking into local accommodation is is:

Look around your local area. Monasteries, Mosques, tea shops, restaurants and sometimes night schools can be noisy so if you want a nice quiet area it can be best to avoid these.

Be careful to look for buildings with speakers pointing out of their windows. These are relatively common in Yangon and can potentially play music and speeches from very early in the morning to late at night. This can become more intense and likely during the rainy season (May to October), when the amount of time  you spend indoors is likely to be much greater, so be warned.

Noisy neighbors and thin walls can make a big difference – Unfortunately this is hard to protect against without knowing somebody who already lives in the area, but when you look at places it’s a good idea to ask about the neighbors to understand the likelihood they’ll be noisy. I had a family people move upstairs from me who made noise at 11pm and 3am. While this deprived me of sleep for a long time, this wasn’t because they were obnoxious, but just because I lived in an old building and they had members of the family who worked or went to school in the very early hours of the morning.

Ground floor apartments generally aren’t recommended. They’re sometimes more expensive (for a number of reasons), they can be more likely to flood and subject to more noise from the beloved yangon street dogs and street traffic. I’m sorry to say that in some apartments I’ve lived in, people also throw trash out the window so ground floor apartments have the potential to be closer to this.

Rooftop apartments can be a problem during the rainy season. This is both if the rain hitting the roof is noisy and if the roof isn’t sealed properly. I’ve had friends who have experienced both issues. Still, I also have friends who have sweeping views of Yangon.

Cost of Living

When it comes to other expenses such as food, taxis and entertainment I’ve generally found that you can spend anywhere between $10 to $20 USD per day, with eating out costing around $5 to $10 and taxis between $2 to $3 (depending on how far you’re travelling). As you might expect where your costs end up will depend on where you choose to eat and whether you’re able to bargain, with there being plenty of upmarket places around to blow your budget.

 

 

 

 

 

 

 

 

Some Cultural Tips

This pretty much goes without saying, but coming in to Myanmar with some level of humility and self-awareness is likely to go a long way. Although people are pretty forgiving (thankfully), it’s best not to test people’s patience. It is also pretty easy to avoid offending people, provided you tread lightly and be aware of some of the basics:

Myanmar Etiquette

EDIT: A Note on Furniture 

When it comes to finding stuff in Yangon, often all you need to know is the right street to wander down.

For hand made furniture and an opportunity to have something custom made there is a street in Tamway you can go to.

Although my strategy for getting there involved me saying ‘Tamway Furniture’ to a taxi driver, from memory it’s around Tha Mein Ba Yan Road 

For ready made furniture not made of teak (and pretty much everything else) you can got to Yuzana Plaza, also in Tamwe. This is a lot less rustic than the Tha Mein Ba Yan Road, but there are some things you can get at Yuzana you can’t get from the street of carpenters (bed frames, mattresses, sofas, desks etc).

If you’re looking for something a bit easier to get up the stairs, you can also check out the Rattan Store in Dagon which has basic stuff like chairs, shelves and lamps. They can also custom make furniture if you so desire. To find it, you take a trip east along Bagaya Street (away from the Dagon Centre). The store itself is just past the Myay Ni Gone Mosque and south down Thawtar Street. Actually, I’m not 100% sure if that’s the right street, but if you get to the Mosque you’ll be close!

EDIT: A Note on Nightlife 

Although there is an increasing amount of activities and night life in Yangon, it can still be a challenge on occasions to find activities that don’t involve beer and regret. Luckily, some one has come up with a great solution called ‘Myanmore’ a website and mailing list which summarizes what’s happening in Yangon.