Typical “Mas Bro” Usage

“Mas Bro” is a uniquely Indonesian construction. It combines “bro” (as in, well, bro, brostep, etc.) with the Javanese-inspired term of address “Mas,” used to refer to a young man. So it’s redundantly redundant. I can’t quite tell, as I’m not fluent enough in bahasa gaul, but I think using it even has the bro-y connotation that bro (or “brah”) have.

Ada apa mas bro? = S’up brah?

And, because Indonesian slang is endlessly playful, the way that you address a young woman is to replace the Mas with Mbak, its female equivalent. So, Mbak Bro.

I also had to pick up a Bintang. You know, as a control beer.

ASIDE: Apparently that last post attracted a lot of comments! Note the Australian commenter David Horovitz holding forth that some lousy “craft” brewery in Bali produces a beer that’s so complex that an unsophisticate like me just can’t understand it. This is why the developed world doesn’t take Australians seriously.

But I digress. For my last post, a review of two Stark beers. And as before, the disclaimer that I don’t know how to taste and rate beer applies.

Stark Wheat

Medium head that dissipates quickly. Golden-to-orangish color (at least when viewed through my hotel room coffee mug). Citrus-y nose, with some light hops, not unlike a Sam Adam’s Summer. Very forward citrus on the initial taste, verging toward sour in the middle. Lingering finish, with yeasty notes appearing. Overall: a mass-market style wheat beer, lacking something in the subtlety department but not unpleasant. Would be a nice change from the adjunct lagers that dominate the Indonesian market.

Stark Dark Wheat

Bigger head than the Wheat, but still doesn’t linger. Brownish color in the mug. Same citrus on the nose, but also hints of nut and malt—the exact same aroma as characterizes Ithaca Butt Brown. Initial taste is citrus. Nutty flavors emerge later, and the finish is mostly sour. Overall: Like its lighter cousin, an interesting alternative to Bintang and Anker, but probably less drinkable over the course of a long evening.

Is this the future of academic research and publishing? Some might think so. In fact, the idea of producing new scientific research in real time, in a publisher-free, open-access format, for immediate consumption by close network of fellows scholars is the epitome of what research ought to be like in the internet age. No waiting for an editorial decision. No territorial gatekeepers at the reviewer stage. Real time feedback on each step of the process. The possibility of dialog with critics.

Maybe so, but as a natural contrarian, I’m skeptical that these changes are on the horizon. And now I have one data point. So here are some notes and reflections on this process of “long form research blogging”. Of course, I welcome further thoughts from readers in the comments.

Different than Scholar-Blogging

Over the past ten years we have seen a rapid growth of “scholar-blogs,” particularly in political science and economics. These are blogs which publicize new research, either working papers or new publications, alongside shorter commentaries on current events from an informed disciplinary perspective. The list of these is endless, and they are great (my regular reads include the Monkey Cage, Blattman, Consider the Evidence, Economist’s View, Phil Arena, Dart-Throwing Chimp, Bellemare, Understanding Society, IPE@UNC, and probably 50 more that I am forgetting). But rather than platforms for publishing, these scholar-blogs are more like dissemination mechanisms for existing work.

Producing new research, published as a series of blog posts, is much more rare. My posts have not been about summarizing research produced elsewhere, but rather about producing something new, here, in pieces. The closest parallel I can see is Sides and Vavrecks’ The Gamble, which we saw unfold in real time during the 2012 presidential campaign. But even they had a contract with Princeton University Press before they started.

The observation here is that while I see lots of scholar-bloggers these days, their blogs are not yet widely used to publish new research.

The Audience

Full disclosure: throughout these posts, I have violated rule number 1 of writing. That is, I have not thought particularly hard about who my intended audience is. Is it political scientists? Asianists? Malaysians citizens?

Part of the reason for this is the blog’s history. My wife (girlfriend at the time) and I started it nine years ago as a way to keep our parents informed about our daily doings overseas. In the early years, most posts were about how neat it was to see an orangutan eat a banana, things like that. I have only recently come to focus on a wider audience, and to understand the professional function of blogging. (And the personal still creeps in: my next blog post will be a review of two new craft beers from Bali.)

That said, I think that the very nature of long form research blogging also makes it possible to neglect one’s audience. Because there’s no editor or reviewer, there’s no acute pressure or incentive to target the writing. If I were writing for a journal, I would spend much less time on explaining what exactly is happening in Malaysia, and would be much more sensitive to what other scholars had written and where my exact contribution lies. Which brings me to my next point…

Interaction, Citation, Attribution

One of the main supposed benefits of open access, real time publishing is instant feedback and interaction. I have had some good interactions in the comments and with other academic bloggers, for sure. These interactions have been substantive. But to be honest, I have not had nearly the level of deep and continuous feedback that would represent a new mode of academic knowledge production. It’s basically the same level of commentary as if I were to share a new working paper.

Citation and attribution is a related issues. I find it easier to organize citations via academic publications in a references section than via hyperlinks. Pingbacks are a nice feature, I suppose, but not enough.

Perhaps the conclusion is that academics are busy people. And, as a result, the format or the technology of the academic paper is not what stands in the way of deeper real time interaction.

Style

Rereading my past posts, the writing is verbose, and at times very pedantic. Some of that can be attributed to massive jetlag of the Ithaca-Jakarta trip, but not all of it. Another driver is surely the unclear audience (see above), which left me explaining things that a professional audience would understand, while also resulting in digressions where I established my bona fides on important technical points. Another problem was simply the speed at which I was writing, which made my usual strategy of “leave it in the desk drawer for a month, then revise” impossible.

If you think that great writing takes time, then any publication platform designed to speed up the writing process to something close to real time is bad news.

Professional Benefit

Finally, the real question for a career-minded scholar (which is everyone): what is the professional benefit of long form research blogging? The ideal outcome would be an increase the audience of people who read and cite my work, but I have no idea if that will happen. Maybe I am just getting new twitter followers in Petaling Jaya and Sibu.

Another question is on the future of these posts. Can I revise these posts into a paper, then submit it to a journal? Or by publishing them online, have I made it impossible for me to produce a “real” publication—one with a CV entry, and that Google Scholar can find—because that would be self-plagiarism? Here, I don’t yet know. I may actually have to contact some journal editors to see what they think.

Here’s the point: My analyses of GE13 are not anything like the core of my professional identity. That’s why it doesn’t bother me too much if they cannot be published in a real journal, and that’s why I embarked on this experiment on long form research blogging. So long as that is true, I am skeptical that long form research blogging will ever transform academic research and publishing.

In this post, I take a closer look at what the data say. Along the way, we will review some principles of causal explanation in the social sciences, to highlight what explanations such as mine are meant to do, and how we think about competing explanations for social phenomena. To preview: I do not believe that ethnicity and urban/rural are competing ways of looking at the results of the GE13 parliamentary races, and I can show that they both “matter” (in a sense that I will make precise later). But if we adopt the view that they must be competing interpretations, so that evidence in favor of one entails evidence against another, then ethnicity wins. Full stop.

Before proceeding, some caveats are in order.

1. The data that I am using is still the preliminary data that was released the day after GE13. I have not yet scraped the official data from undi.info or any official website. If the numbers have changed in any appreciable way, my results might not hold.
2. There are so many criticisms of the electoral process, including phantom voters, ballot box stuffing, and other shenanigans, that we should probably wonder if the official vote totals can be trusted. While it’s not true that the two-coalition vote shares are as random as darts on a dartboard, I take the possibility that the data are “cooked,” or at least gently simmered in places, very seriously. If anyone reading this has access to the voting-station level returns (I’m looking at you, poli sci PhDs who are now part of Malaysia’s parliament!) I am very happy to explore some election forensics with you.
3. I have focused only on parliamentary races, even though I know that state assembly races may exhibit different patterns. I invite anyone who is seriously interested in engaging with these arguments, and who thinks that state elections are fundamentally different, to collect those data and analyze them.

Keeping those caveats in mind, there are far more analysts of Malaysian politics than there are social scientists who try to use Malaysian data to analyze GE13 systematically. Showing how ethnicity matters means different things depending on your perspective, so it’s worth being explicit about what I have in mind.

Competing Explanations, Competing Hypotheses?

My analyses show that ethnicity predicts vote choice. This does not mean that only ethnicity predicts vote choice, or that ethnicity always predicts vote choice, in which case there are no other possible factors that could explain why any Malaysian votes for or against a BN candidate. It should go without saying that I also don’t have any patience for the perspective that we can make ethnic politics in Malaysia go away by studiously avoiding any discussion of ethnicity, or by asserting that we must adopt some alternative perspective.

Social scientists will understand this as an “effects of causes” approach rather than a “causes of effects” approach. This is very much mainstream political science. My analyses so far have only been able to show that an independent variable (ethnicity) explains a dependent variable (two-coalition vote shares). My analysis is rather indifferent to the fact that other scholars examine the other things that may explain vote choice in GE13.

But let me also be clear: ethnicity is an essential, fundamental factor in Malaysian politics. So I am transgressing the norms of mainstream comparative politics, and I am trying to make the case that ethnicity is something like a “master variable”—this is not a technical term—in Malaysian politics. Not the master variable, but one master variable. That is why it is so important that I show the amazingly tight quadratic fit between Malay population and BN vote share.

Taken together, the methods that I have used to study vote share are about showing the effects of causes. But I have discussed the results as if they are also making a broad point, about the causes of effects: why, in the broadest terms, GE13 turned out the way that it did.

So with that in mind, how do we analyze two different theories of what district characteristics explain BN vote share? It depends on the purpose of the analysis. If we want to show the effects of causes, we should find a measure of the urban/rural split across districts, and then see if that variable also explains BN share. We can also examine that measure next to our measure of ethnicity to see our rural variable explains any of the additional variation in BN vote share that ethnicity does not explain. This is one common interpretation of what multiple regression does: when viewed as a way to illustrate causal relationships (instead of just as a way to summarize partial correlations), multiple regression assumes that one set of outcomes can have multiple causes.

There is much less agreement about how to formally compare or adjudicate among different causes of effects. For some, the entire endeavor is ill-posed: what does it mean to assert that some explanation is the cause of some effect? One way to do this is to compare the extent to which two independent variables explain the variation in a dependent variable—in this case, do rural/urban differences explain more about the electoral results than ethnicity does? The problem that arises is that both variables may be pretty good at explaining variation.

Even so, there are various kinds of model selection procedures which can be used to select which model does “better” according to some metric. Recently, Imai and Tingley (2012) provided a very different way to think about this problem. We have two theories of what determines BN votes at the district level. These two theories imply two different hypotheses. The hypotheses are non-nested: one is not a subset of the other, which is what makes them mutually exclusive. Imai and Tingley propose that we can compare any set of theories with the proportion of the cases being analyzed that are “statistically significantly consistent” with one theory versus the other. Finite mixture models, as they discuss, are one way to calculate this.

This problem still assumes that the two models really do compete with one another, which remains a theoretical assumption rather than an empirical result. We will return to this at the end of this post. For now, let’s now turn to the data.

Results

I have described the data on ethnicity previously. How should we measure whether a district is urban or rural? I would argue that we should not think of districts as either urban or rural, but rather as varying along an urban-rural continuum. And a natural indicator for the extent to which a district is urban or rural is its size. I have calculated each district’s area using the maps that my research assistant Rachel Greenberg has created. (I’m not sure what exact distance metric this uses, but that does not matter because the areas are all calculated with the same map using a common metric.) Taking the natural log of the district’s area yields a nice continuous measure of how rural the district is.

Below are the results are six simple regressions, using data from the peninsula. The dependent variable in each is the BN two-coalition vote share, and Models 1-3 differ from Models 4-6 in that the latter include fixed effects to control for any particularities of party politics in each state.

Table 1: Peninsular Districts

Standard errors in parentheses. + p < 0.01, * p < 0.001.

The results show not only the extraordinary explanatory power of ethnicity, but also that how rural a district is is also a strong predictor of BN vote share. But the most important results are in Models 3 and 6. They show that ethnicity remains a very strong predictor of BN vote share even when accounting for urban/rural cleavages. My previous findings about ethnicity cannot be reduced to an urban/rural cleavage.

These results also show that, indeed, rural districts were more supportive of the BN, even accounting for districts’ ethnic makeup. This gives important credence to scholars who focus on the urban/rural split in GE13.

In this an all previous analyses, I have focused exclusively on districts in peninsular Malaysia. However, I can tell a similar story when including all districts. First, the graphical presentations:


Now, identical models as before, except for I also include a dummy for whether the election is in the peninsula or not. None of these results change if that is omitted.

Table 2: All Districts

Standard errors in parentheses. + p < 0.01, * p < 0.001.

The results are unchanged from before. Both ethnicity and urban/rural differences are very strong predictors of the BN vote share.

Competing Theories Revisited

Despite these findings, it is possible to still maintain that explanations based on ethnicity versus urbanization really are mutually exclusive, and we have to choose one. So, we must compare Models 1-2, and 4-5, ignoring the fact that Models 3 and 6 exist.

The very simplest way to compare models is to compare the adjusted R2, or the percentage of the total variation in the dependent variable that is explained by the independent variables (with a penalty applied for more complex models which might be overfit the data). It is worth pausing to emphasize that comparing R2 is very bad statistical practice, especially from an effects-of-causes perspective. (This is an obligatory shout-out to my intro to methods teacher.) That said, we see that in both Table 1 and Table 2, adjusted R2 is higher for Model 1 than Model 2, and Model 4 than Model 5.

If it is a head-to-head contest between ethnicity and urbanization, score one for ethnicity.

More sophisticated model selection procedures for non-nested hypotheses include comparisons of Information Criteria, the J test, and the Cox-Pesaran test. In all cases above, the Aikike Information Criterion and the Bayes Information Criterion are lower in Model 1 than 2 and 4 than 5.

Score one more for ethnicity.

The J test and Cox-Pesaran tests, interestingly, fail in this case because the tests reject both models. This can happen when both models fit the data well, as is this case here. No points awarded, and a big question mark raised once again over whether the these hypotheses really are competing with one another.

Finally, the mixture model approach of Imai and Tingley (2012). I will focus here on the results from Table 2, Models 4 and 5. The technical details are here: most useful for our purposes are two quantities. First, the mean of the estimated prior probabilities that each observation is consistent with Model 4 or Model 5. Second, the number of observations that are statistically significantly consistent with Model 4 or 5. The results are in Table 3.

Table 3: Mixture Model Results

Clearly, more of the district vote totals are consistent with an explanation based on ethnicity than on urban/rural cleavages.

A final point for ethnicity.

To summarize, all of the evidence I have presented points to three conclusions.

1. Both ethnicity and urbanization are very good predictors of the pattern of BN vote shares, not just in the peninsula but throughout Malaysia.
2. Even though ethnicity and urbanization overlap, and even though that overlap has historical origins which explain why both matter for the evolution of the BN and its electoral base, the data do not allow us to conclude that one variable encompasses the other.
3. Nevertheless, if we force our analysis to adjudicate between ethnicity and urbanization as competing explanations for BN vote shares, ethnicity wins. Every model, every time.

Ed is not alone in emphasizing the urban/rural divide rather than my ethnic politics angle. Khairudin Aljunied, writing over at New Mandala, finds that “While such carrot and stick tactics worked well with the rural folk – at least for this year’s elections – and have found expression in terms of BN’s victory in rural parts of Malaysia, Malays in urban areas showed opposing reactions.” Lynette O, commenting on my post election report at the Monkey Cage, writes “If you put in rural/urban dummy, and age, I suspect the ethnic factor is not as significant/not significant anymore.”

So we have here a narrative that sheds a different light on the election than I have. It’s about an urban/rural split rather than a Malay/non-Malay split. In this first of two posts, I want to give credence to the importance of rural votes for the BN, but insist that ethnic politics is absolutely central to our understanding of what happened in GE13. The discussion in this post reviews the issues, and the next post will take us to the data.

The first thing to note is that the variables “rural” and “Malay” covary: rural areas tend to be more Malay than urban areas do. This is true even when broadening the focus to include East Malaysia, as I showed in my Post-Election report over at the Monkey Cage.

This means that we cannot simply look at rural areas and their tendency to vote BN, and conclude that they do so because they are rural rather than because they are predominantly Malay. The same thing is also true in the reverse, of course.

Relatedly, these two variable covary for reasons that are equally important to the origins of Malaysian party politics: the perceived social and economic hierarchy in colonial Malaya featured a largely (but not exclusively) urban Chinese population and a largely rural Malay population. The fact that the Malays were largely rural, and hence “backward,” was considered part of the justification for why they needed a party like UMNO that would advocate in favor of their interests. Conceptually, then, it doesn’t make sense to separate UMNO’s rural focus from its Malay focus. They were one and the same, and one justified the other.

Third, this dynamic has not much changed. If you are going to campaign for Malay votes in a rural district, you need to emphasize rural issues. In rural areas, therefore, rural issues happen to also be Malay issues. We must be careful not to ignore the pull of ethnicity when party named the United Malays National Organisation, with a long and widely known history of Malay chauvinism, campaigns for Malay votes in overwhelmingly Malay areas without emphasizing that history.

Fourth, none of this is to ignore the other resources that UMNO and the BN have in rural areas. These are finely tuned machines with deep reach into rural communities. UMNO’s machine, in particular, is especially effective in rural areas. But of course, these are also Malay areas.

Fifth, although I have no data to back this up, I sense a tendency in the commentary on GE13 to think that it is somehow more politically correct to focus on rural/urban issues rather than ethnicity. I disagree. Urban chauvinism is, to me, no better or worse than Chinese chauvinism. Eric Thompson has a nice discussion of urban chauvinism in the context of GE13.

In sum, it is conceptually difficult to separate Malay issues from rural issues in Malaysian politics. For historical reasons, the two are deeply interrelated. The fact that they are so deeply interrelated means that they are also hard to disentangle empirically. In a followup post, I will delve more deeply into how analysts ought to go about doing that.

Specifically, the problem facing Indonesian officials is survey non-response: firms and individuals contacted in national surveys on the labor force, industry, and so on are increasingly refusing to fill out survey questionnaires, even though in many cases they are required by law to do so. Non-response is reaching 40% in some surveys, up from under 10% twenty years ago. There is a wide agreement that this is at least partially a result of the collapse of the Soeharto regime, under which there was a kind of vague fear that something might happen if you did not comply with government instructions.

Let me be clear: no one at BPS is telling me (or anyone else) that democracy is a bad thing. But non-response bias is a bad thing if you are responsible for producing the sorts of national economic statistics that policymakers employ to make policy. If non-response is random, then the problem is just that it’s harder to separate the signal from the noise; but if non-response is non-random—which could be the case if, say, larger firms don’t care to waste their time on surveys—then surveys could actually be biased in one direction or another.

This phenomenon sheds an interesting new light on existing research on the effects of regime type on data transparency. Hollyer, Rosendorff, and Vreeland have shown that democracies are more transparent with national statistical data than are dictatorships. Yet it is hard to assess the quality of the data that is being produced. The Indonesian case would suggest that at least in some circumstances, certain kinds of dictatorships can have a certain kind of advantage in the production of good data. Call that the authoritarian data advantage.

The answer is, I don’t know. It’s hard to bet against the BN winning the majority of seats, but frankly, I am too far from the action to develop any sophisticated read of the electorate. These posts have largely focused on the background and the context of these elections, not on the nitty-gritty of daily developments which might prove decisive. I can’t tell from here what’s in store for Malaysia, but we’ll learn soon. And I will be doing my best to offer instant analysis of the election results over at New Mandala.

I would urge interested readers to take a look at my most recent article on Malaysia, just published at Contemporary Southeast Asia (gated, an ungated and slightly cheekier older version is here). This article is probably the most misunderstood of all the things I’ve written, as readers insist on reading it as foreclosing the possibility for political change in Malaysia. But it helps to explain my larger approach to political change in Malaysia, and why I think that Bersih (rather than Twitter or Malaysiakini) is the single greatest threat to Malaysia’s established political order.

See also: Index of preview posts.

1. Results by party and ethnicity, GE12: Preview (1)
2. Thoughts on GE13 in New Mandala: Preview (2)
3. Head to head contests in GE12: Preview (3)
4. East Malaysia and the challenge of regionalism: Preview (4)
5. The strategic logic of party nominations in SMD-PR with two coalitions: Preview (5)
6. State elections: Preview (6)
7. How competitive authoritarianism works: Preview (7)
8. Nomination day results: Preview (8)
9. Malaysian Indians in GE13: Preview (9)
10. Final pre-election thoughts: Preview (10)

I argued several years ago that a key part of Malaysia’s 2008 political tsunami was the near complete rejection of the BN by Indian voters. That represents the culmination of decades of neglect, and my favorite sources on this are Sucked Oranges, the academic work of P. Ramasamy, and Cage of Freedom by my Cornell colleague Andrew Willford.

But a persistent structural problem is the fact that Indians are always and everywhere a minority, in every district. The figure below illustrates this perfectly.

Plots like this—ternary plots—can be hard to read if you’ve never seen them before. So let’s walk through this one. For every district in peninsular Malaysia, we know the percent Malay, Chinese, and Indian, the triple (M%, C%, I%), from the data described in my last post. The sum of the three numbers, of course, is 100. The ternary plot just plots the distribution of the triples in two dimensions. An overwhelmingly Malay district (98,1,1) will fall near the top. An overwhelmingly Chinese district (1,98,1) will fall at the bottom left. A mixed district (33.3,33.3,33.3) will fall in the exact middle. A multiethnic district of type (40,40,20) will fall closer to the northwest side.

The plot shows just how dispersed the Indian population in Malaysia: there is no Indian majority district, nor an Indian plurality district. The colored dots indicate which party was nominated in each district, and we do see that MIC candidates are nominated in just those districts where Malays and Chinese are roughly equal in size and Indians are fairly prevalent. But note, in districts where Indians are equally numerous as those few MIC districts, but Malays and Chinese are not so evenly divided (so the district falls closer to the bottom left or top, but along the same southwest-to-northeast diagonal), the UMNO or MCA always contests.

The conclusion that emerges is the size and distribution of the Indian population in Malaysia leaves is structurally incapable of winning more than juts a handful of seats. That is, of course, just so long as coalitions nominate parties based on ethnicity.

Earlier in the series: Preview (1) | Preview (2) | Preview (3) | Preview (4) | Preview (5) | Preview (6) | Preview (7) | Preview (8)