Apportionment formula, Acts 1 & 2

July 19, 2017

A couple weeks ago, the Central Election Commission invited me to speak at a public hearing on changing the formula for apportioning legislative seats to the 22 cities and counties. I assume roughly half the people who read that first sentence are already nodding off to sleep. Let’s say that my reaction was exactly the opposite: my heart started pumping faster and my brain started racing. Change the formula! What in the world is going on!?!

I’m not quite sure how to best tell this story, so I guess I’ll tell it in three acts.

Act one: The technical details

Act two: The CEC public hearing and my testimony

Act three: The political machinations

 

Act one: the dry, boring, technical stuff that you need to understand everything else

 

Before the hearing, the CEC sent me an official notification of the meeting in which they laid out the reason for the meeting. The CEC is holding three public hearings in reaction to the demand from legislators, since legislators have raised concern that the current formula violates the constitution. The constitution sets out two criteria for allocating the 73 district seats: 1) every city and county should get at least one seat, and 2) the seats should be allocated according to the population of each city and county (not including the indigenous population, since they are represented by other legislators). The current formula uses a two stage process, and the legislators questioned whether the two stage process violates the second criterion. They suggested returning to an earlier formula which only uses one stage.

The current formula has been used for the three elections since 2008 (7th, 8th, and 9th Terms), so it has been dubbed the “7th Term Formula.” For simplicity, I’m going to label it T7. In T7, you take the national population and divide it by 73 to get a quota (Q1). Using June 2017 population, Q1=315019. There are six cities and counties whose population is less than one full quota, so we first give them each a seat (S1). Then the population of the other 16 cities and counties is summed and divided by 67 to obtain Q2=329837. This Q2 is used to apportion the remaining 67 seats. For every full quota, a city or county gets a seat (S2). If there are remaining seats after all the full quotas are allotted, they go to the cities or counties with the largest remainder (S3).

 

T7 Formula (using June 2017 population)

  Pop S1 Pop S2 R S3 S
total 22996448 6 22099102 60   7 73
Taipei 2673539   2673539 8 34843   8
New Taipei 3927273   3927273 11 299066 1 12
Taoyuan 2096623   2096623 6 117601   6
Taichung 2743103   2743103 8 104407   8
Tainan 1878892   1878892 5 229707 1 6
Kaohsiung 2744102   2744102 8 105406   8
Yilan 440228   440228 1 110391   1
Hsinchu Cn 528325   528325 1 198488 1 2
Miaoli 545096   545096 1 215259 1 2
Changhua 1278821   1278821 3 289310 1 4
Nantou 474319   474319 1 144482 1 2
Yunlin 690196   690196 2 30522   2
Chiayi Cn 507270   507270 1 177433 1 2
Pingtung 773446   773446 2 113772   2
Taitung 141245 1         1
Hualien 237493 1         1
Penghu 102908 1         1
Keelung 362548   362548 1 32711   1
Hsinchu Ci 435321   435321 1 105484   1
Chiayi Ci 268652 1         1
Kinmen 134527 1         1
Lienchiang 12521 1         1

 

The proponents of change want to go back to the formula used in the 1998, 2001, and 2004 elections, which I will label the “T4” formula. In T4, there is only one quota (Q=pop/seats=315019), which is identical to Q1 in the T7 formula. First apportion one seat to every city and county (S1), then if they have enough population for two or more full quotas, give them any additional full quotas (S2). Finally, allot the remaining seats according to the largest remainders (S3).

 

T4 Formula (using June 2017 population)

  Pop S1 S2 R S3 S
Total 22996448 22 46   5 73
Taipei 2673539 1 7 153387   8
New Taipei 3927273 1 11 147045   12
Taoyuan 2096623 1 5 206509   6
Taichung 2743103 1 7 222951 1 9
Tainan 1878892 1 4 303797 1 6
Kaohsiung 2744102 1 7 223950 1 9
Yilan 440228 1 0 125209   1
Hsinchu Cn 528325 1 0 213306 1 2
Miaoli 545096 1 0 230077 1 2
Changhua 1278821 1 3 18745   4
Nantou 474319 1 0 159300   1
Yunlin 690196 1 1 60158   2
Chiayi Cn 507270 1 0 192251   1
Pingtung 773446 1 1 143408   2
Taitung 141245 1       1
Hualien 237493 1       1
Penghu 102908 1       1
Keelung 362548 1 0 47529   1
Hsinchu Ci 435321 1 0 120302   1
Chiayi Ci 268652 1       1
Kinmen 134527 1       1
Lienchiang 12521 1       1

 

As you can see, the difference between the two formulae is not insignificant. In T7, Taichung and Kaohsiung both get 8 seats, while in T4 they get 9. In T7, Nantou and Chiayi County get 2 seats, but T4 only gives them 1 seat each.

To reiterate, the normative argument that proponents of T4 are pushing is that the two quotas (Q1 and Q2) in T7 are somehow violating the requirement that seats be apportioned according to population. Since the constitution and election law say nothing about discarding the populations of the small cities and counties and calculating a new quota, they argue that it is forbidden to do so.

Before we move on, there is one more (quite important detail). We are all using June 2017 data. However, according to the election law, seats are apportioned according to the population 26 months before the end of the term. That means that critical data is the November 2017 population. We still have five months to go. As my friend Donovan Smith keeps pointing out, Taichung is growing faster than Kaohsiung and is about to overtake it as Taiwan’s second largest city. For this exercise, that doesn’t matter. What does matter is that Taoyuan is growing fastest of all. Over the past year, Taoyuan has increased an average of a little more than 3000 people per month. By November, it should have at least 15000 more people. Right now, Taoyuan’s remainder is 206509. With another 15000, it will have a remainder of about 221000, which might even pass Kaohsiung for the fourth largest remainder. The loser will probably be Hsinchu County, which is growing by about 300 people per month. If you add 1500 people to its current remainder of 213306, it will be at about 215000, about 6000 behind by Taoyuan and Kaohsiung. Poor Hsinchu County! For a couple of years, it has been excited that it will get a second seat and now that second seat is about to be ripped away. Worse, nobody seems aware that this is happening. I also have no indication that the Taoyuan politicians are aware a seventh seat might fall into their lap.

From here there are two things that might happen. After the three public hearings, the CEC will assess the public feedback, hold a meeting, and vote on which formula to adopt. For reasons that I will delve into in Act Two, I’d be shocked if they didn’t vote to maintain the current formula (T7). Following that decision, the legislators have a choice. If they are dissatisfied with the CEC’s decision, they can choose to override it by writing the formula into the election law. I suspect most legislators will favor T4, and so this will be the eventual outcome. Those calculations are the subject of Act Three.

 

Act Two: The CEC public hearing

I’ve never been asked to speak at a public hearing before, so I really didn’t know quite what to expect. I was told that everyone would get only five minutes, with the possibility of another round if time permitted. Five minutes is not long, so I had to be concise. I think this is the shortest powerpoint presentation I’ve ever made. Here it is in its entirety.

Slide1

Slide2.PNGSlide3.PNG

Slide4

The first point is simple. Neither of these formulae violate the constitution. The constitution mandates the CEC to allot seats according to population, but there are many ways to do that. The constitution does not specify which formula to use, so the CEC has leeway to use its judgment as to which is the most appropriate. Every person who spoke today agreed on this point. In other words, we all explicitly rejected the current normative argument for changing from T7 to T4.

The second point is that there is, in fact, a solid normative argument to be made in favor of T4. T4 reduces the overall level of malapportionment relative to T7. My hope is that, since the politicians will probably continue to press for T4, I hope they will use this argument instead of the constitutional one. I’d like for all discussions of electoral system fairness to be conducted in terms of disproportionality.

I will say that one of the other scholars, Huang Kai-ping 黃凱苹 (NTU), and the KMT representative, Huang Teh-fu 黃德福 (who is also a political scientist and was one of my teachers two decades ago when I was a MA student at NCCU), disagreed with me about which system was fairer. They focused on comparing the four cities and counties that would change seats, looking at the ratios of voters in each one. For example, Huang Teh-fu, looked at the ratios of the each of the three larger districts to the smallest district among the four, adding up the differences to get a deviation index as follows:

T4 formula

  Pop Seats Ave Ratio Ratio-1
Taichung 2743103 9 304789 1.0000 .0000
Nantou 474319 1 474319 1.5562 .5562
Chiayi Cn 507270 1 507270 1.6643 .6643
Kaohsiung 2744102 9 304900 1.0004 .0004
Sum         1.2209

T7 formula

  Pop Seats Ave Ratio Ratio-1
Taichung 2743103 8 342887 1.4458 .4458
Nantou 474319 2 237159 1.0000 .0000
Chiayi Cn 507270 2 253635 1.0695 .0695
Kaohsiung 2744102 8 343012 1.4463 .4463
Sum         0.9616

As you can see, T4 yields a deviation index of 1.22, much higher than the T7 deviation index of 0.96. Huang’s conclusion was that T7 is fairer.

I have two responses to this. First, a deviation from the mean in Taichung is not equivalent to a deviation of equal magnitude in Nantou. In T7, Taichung has four times as many seats as Nantou, so a deviation of equal magnitude affects four times as many people in Taichung as in Nantou. If you think about the total number of people affected, it is more important that Taichung not be too far over or under whatever the fair number is than Nantou. Second, you should not consider the four cities and counties that will be affected in isolation. Malapportionment is a national level problem, and you have to look at it from a national perspective.

The standard measure used in the academic literature to measure is the Loosemore-Hansby Index, which considers the difference between how many seats each district should have according to its population and how many it actually has.

Taiwan has 79 nominal seats (73 district and 6 indigenous). I made a spreadsheet with 79 rows to calculate this index, but since that is a tad long I’ll just do one line for each county. Taipei has eight seats, so you should imagine eight identical rows for Taipei 1 through Taipei 8. (After the actual districts are drawn, Taipei 1-8 will each have a slightly different population, so each one will deviate slightly differently. This will increase the overall level of malapportionment slightly. For now, we will ignore that part.)

T7 Formula: modified Loosemore-Hansby Index

  S Ave Pop Exp obs Abs(diff)
Taipei 8 334192 0.0142 0.0131 0.0011
New Taipei 12 327273 0.0139 0.0130 0.0009
Taoyuan 6 349437 0.0148 0.0133 0.0015
Taichung 8 342888 0.0146 0.0132 0.0013
Tainan 6 313149 0.0133 0.0129 0.0004
Kaohsiung 8 343013 0.0146 0.0132 0.0013
Yilan 1 440228 0.0187 0.0145 0.0042
Hsinchu Cn 2 264163 0.0112 0.0122 0.0010
Miaoli 2 272548 0.0116 0.0123 0.0008
Changhua 4 319705 0.0136 0.0129 0.0006
Nantou 1 237160 0.0101 0.0119 0.0018
Yunlin 2 345098 0.0147 0.0133 0.0014
Chiayi Cn 1 253635 0.0108 0.0121 0.0013
Pingtung 2 386723 0.0164 0.0138 0.0026
Taitung 1 141245 0.0060 0.0107 0.0047
Hualien 1 237493 0.0101 0.0119 0.0018
Penghu 1 102908 0.0044 0.0102 0.0058
Keelung 1 362548 0.0154 0.0135 0.0019
Hsinchu Ci 1 435321 0.0185 0.0144 0.0041
Chiayi Ci 1 268652 0.0114 0.0123 0.0009
Kinmen 1 134527 0.0057 0.0106 0.0049
Lienchiang 1 12521 0.0005 0.0090 0.0085
Plains Indig 3 86959 0.0037 0.0100 0.0063
Mountain Indig. 3 98382 0.0042 0.0101 0.0059
Sum/2         0.0729

In Taipei, each of the eight districts has 334192 people, which is 1.42% of the total population of Taiwan. Since each Taipei district has 1.42% of the population, each should also have 1.42% of the representatives. What do they actually have? There are 113 legislators and each district gets one, so Taipei 1 has 1/113 of the representation. But wait, what should we do about the 34 party list legislators? We assume that they are perfectly apportioned to each district according to its population and thus do not create any further malapportionment. (This is the “modified” part of the modified Loosemore-Hansby Index.) Thus, Taipei 1’s total representation is (1/113) + (.0142*34/113). Taipei 1 thus has 1.31% of the total legislators, or somewhat power less than its 1.42% of the population would receive under perfect apportionment. The eight districts in Taipei are thus collectively underrepresented by 0.88% of the total legislature.

To get the total malapportionment, you add up the absolute value of all these deviances and divide by two. T7 will thus produce 7.3% malapportionment. What this means is that 7.3% of the seats in the legislature are apportioned to places that would not receive them under perfect apportionment. From a cross-national perspective, 7.3% is fairly high. When you consider that Taiwan is not federal system with an upper house (two factors that tend to be associated with higher malapportionment), Taiwan’s malapportionment problem is actually quite serious. I’ll skip the table for T4, but the answer is that T4 yields 6.7% malapportionment, or a slightly fairer outcome than T7. The difference is not enormous, but neither is it insignificant. If you care about malapportionment, T4 is better.

What’s going on with this? The essence of the problem is that the constitution requires a significant level of malapportionment. Politicians have made value judgments that it is important to overrepresent indigenous voters and voters from sparsely populated cities and counties. The six indigenous seats and six seats from cities and counties that are below the quota account for 6.3% malapportionment. That is, these voters are getting an extra 6.3% of the representation in the legislature beyond what their raw numbers would confer. Lianchiang County is the most extreme; it gets 0.90% of the power with only 0.05% of the population. Since the constitution mandates that these voters be overrepresented by 6.3%, voters in the other 67 districts must be underrepresented by 6.3%. Remember how Taipei is collectively underrepresented by 0.88%? In essence, Taipei is making up for Lianchiang’s 0.85% overrepresentation.

So why is T4 better than T7? In T4, other than the 12 constitutionally mandated overrepresented seats, there are also four other overrepresented districts (two in Miaoli and two in Hsinchu County). This means that in addition to making up the 6.3% deficit, the remaining 63 districts have to further give back an additional 0.4% of the total power. In T7, Nantou and Chiayi County both get two seats, making them overrepresented. This means that the remaining 59 districts now have to spit up 7.3% of the total power. Think about this from the perspective of Taichung. Under T4, Taichung gets 9 seats. Each of those nine seats accounts for 1.29% of the population but only gets 1.27% of the power. Under T7, Taichung gets only eight seats, each of which has 1.46% of the population but only 1.32% of the power. Taichung is already underrepresented under T4, but under T7 it becomes much more underrepresented.

(As noted above, if current population trends hold, T4 would take one seat away from Hsinchu County and give it to Taoyuan. This would reduce the malapportionment to 6.5%, an even fairer outcome. A reduction from 7.3% to 6.5% is not too shabby.)

 

Remember when I said my remarks at the CEC hearing were concise? Suffice it to say I didn’t have time to go into all that detail.

 

My third point was that T4 is vulnerable to a mathematical problem. If the populations are distributed just right, you can actually apportion more full quotas than there are seats. The slide gives an example of how you might apportion 74 seats with full quotas.

Now, this probably won’t happen. You need all the remainders to be pretty small. However, it isn’t impossible. I was able to manipulate the numbers to apportion 75 seats. And then with even further manipulation I got up to 76 seats. And in an extreme case, I got to 77. Theoretically, I think with six cities or counties below the quota, the absolute limit would be 78 seats. So 74 seats is quite possible, even if it isn’t likely. But as the world has seen repeatedly over the past couple years, improbable things happen all the time.

This isn’t necessarily a fatal flaw. As long as the bureaucrats are aware of the problem, they can write a rule for how to deal with it. The obvious fix is that the lowest remainder should lose a seat. However, you have to have this rule clearly spelled out in advance. Otherwise, if you try to tell a city that, even though it has a full quota for each seat, it isn’t getting all those seat, all hell will break loose. Remember, you can’t just compromise and allot 74 seats because the constitution clearly says that there will be exactly 73 district seats.

Fantastic! Now we are aware of the problem, and we can avoid the great constitutional crisis of 2037.

By the way, I suspect this mathematical vulnerability is probably the reason the CEC switched from T4 to T7 a decade ago.

 

My fourth point was a simple, normative point. In electoral systems, stability is a value. You shouldn’t change the rules of the game unless there is some compelling reason to think that the system is significantly unfair. The electoral rules are the playing field, and you shouldn’t modify the playing field for a particular party or area’s short-term interests. This time you lose; next time you might win. Populations grow or shrink in ways that are hard to predict. Further, it is entirely possible that we might have some administrative reform. Ten years ago, there were 25 cities and counties; now there are only 22. Ten years ago, Kaohsiung was a winner because it was still split into two pieces. Kaohsiung City had enough people for 4.7 seats and Kaoshiung County had enough for 3.8. Because they were independent, both numbers were rounded up. If they had been combined, as they were in 2010, Kaohsiung would have only had enough people for 8.5 seats, and this would not have been enough to get the 9th seat. Tainan had exactly the opposite situation. In 2006, Tainan County had the population for 3.4 seats, which Tainan City had enough for 2.4. Both remainders were too small to get an additional seat. After the two were combined in 2010, Tainan had enough people for 5.8 seats, so this time it will get a sixth seat. If you change the formula every time based on your immediate political interests, people will lose faith in the neutrality of the system.

 

You might have noticed that my four points don’t all point in the same direction. Points 1, 3, and 4 favor T7, while Point 2 favors T4. As a scholar, it isn’t my job to make the value judgement about which priority is most important. Rather it is my responsibility to point out whether the system has any serious flaws and what the impact of the various options might be. It’s up to the CEC and the legislators to make the value judgements.

(Don’t worry, I’ll take off my scholarly hat and tell you what I really think in Act Three. However, I’m too tired to write that story now.)

CEC hearing.php

Pension reform

June 30, 2017

As I start this post, the legislature has just passed the third reading of the civil servants pension bill. It now moves onto the bill for teachers, and the legislature has yet to take up the bill for military pensions. Nonetheless, now that the rules for civil servants have been rewritten, the others should follow along those basic lines. There is a lot of cleanup work still left for the legislature, but the basic fights have already been waged.

We all have a basic understanding that the current system needed some adjustment. There was too much money going out and too little coming in, and the system was going to go bankrupt in fairly short order. Even President Ma recognized the need for reform. (He quickly aborted his nascent reform in the face of a backlash from public servants, who constitute one of the KMT’s most important voting blocs.) The retirement benefits were simply too generous. Civil servants could often retire in their early fifties and collect monthly stipends nearly equal to their full salaries. Since benefits were based on their last month’s salary (ie: the highest they had collected in their entire career), that meant that the state was often paying people more in their retirement than it had while they were working AND their retirements might be as long as their working careers had been. This system may have been defensible when the GDP was growing by double digits every year, the birth rate was high, and civil servants earned a relatively low base salary. However, those conditions haven’t described Taiwan for two or three decades. Things had to change.

Pension reform was one of the three or four most important goals for Tsai Ing-wen’s first term; arguably it is the single most important domestic reform item on her agenda. Tsai has taken a lot of criticism over the past year. People who didn’t vote for her (predictably) think she is doing a terrible job, and they point to things like China’s more antagonistic stance toward Taiwan and the resulting drop in group tourism from China. They are also furious about the effort to nationalize the KMT’s ill-gotten party assets, which they see as a witch hunt (the “green terror”). Many people who did vote for Tsai are also somewhat disillusioned. Her support for marriage equality has been less than strident, her cabinet is full of old men (many of whom have ties to previous discredited administrations), some of the government’s economic policies have been presented and implemented clumsily (labor standards law, infrastructure package), the economy isn’t growing at 8% a year, transitional justice hasn’t been achieved yet, and the world isn’t perfect yet. Against this background, achieving pension reform should be a shining star on Tsai’s report card.

In fact, I’d argue that pension reform has almost perfectly embodied Tsai Ing-wen’s vision of consensus democracy. There were a lot of people who wanted the DPP to present their ideal bill and ram it through the legislature. After all, what is a majority for? Instead, Tsai took the process slowly and deliberately. Tsai’s cabinet included Minister Without Portfolio Lee Wan-yi, whose sole job was to oversee pension reform. The government held a national forum on pension reform, and Lee’s committee held several other hearings. These hearings were somewhat contentious and the opposition did not always participate in good faith. Still, most of the important political arguments were presented, and the committee was able to filter through them. One of Tsai’s stated goals at the outset was not to treat public servants as an enemy. As she put it, they were to be seen as partners in the reform rather than objects to be reformed. The Executive Yuan committee ultimately came out with a fairly moderate bill. At about the same time, the Examination Yuan came out with its own bill. The Examination Yuan members have fixed terms, and over half of them are still left over from the Ma era. As might be expected, the Examination Yuan bill was even more modest than the Executive Yuan bill. Transition periods were stretched out over more years and various formulas were adjusted to be somewhat more favorable to public servants. However, the two bills were surprisingly similar. By the time the Examination Yuan was ready to propose its bill it had become clear that some sort of reform was unavoidable, so the Examination Yuan proposed a substantive reform bill. During the first half of 2017, anti-reform forces were trying to arouse public opinion against Tsai. Various veterans, civil servants, and teachers groups held rallies, but these were generally not well attended. Surveys showed that public opinion was solidly in favor of reform, and this did not soften as a result of anti-reform activism. If anything, public opinion solidified in favor of a more aggressive reform. By the time the bills got to the legislature, the anti-reform movement was largely played out. In the legislature, the pro-reform forces took their turn trying to pass a more aggressive bill. Both the DPP and NPP caucuses demanded changes to various formulae and transition periods. They succeeded in some of these demands, and the law that eventually passed was somewhat more aggressive than the Executive Yuan bill. Nonetheless, Tsai stepped in to ensure that the most radical demands would not be adopted.

By the end of the process, the KMT found itself in a quandary. Public servants constitute a core constituency, and the KMT wanted to speak for them. However, public opinion was clearly against them, and the DPP caucus showed no signs of wavering. As the saying goes, there are two ways to resist in the legislature: civil and military (文、武). The “military” method involves physically occupying the speaker’s podium and disrupting the normal parliamentary procedures. The “civil” method involves using dilatory tactics such as introducing hundreds of amendments to stretch out proceedings as long as possible. In general, if you are sure of your position and your support in society, you go for the military option. If you are on shaky ground, the civil option is the best you can do. For months, I expected we were heading for a “military” showdown. However, the KMT will eventually crumbled. The KMT could not agree on an alternative bill, so the caucus was reduced to supporting various bills proposed by individual members. Instead of occupying the podium or offering hundreds of amendments, the KMT opted for a very weak battle plan. They would have several people speak on every clause, thus taking several days to pass the bills. The DPP was relatively happy to oblige, so the legislature has been engaged in marathon sessions all week. (A minor but telling point: When the DPP made a motion to extend yesterday’s meeting until midnight, it passed unanimously. If the KMT were really trying to resist, it would have opposed lengthening the meeting.) I’ve been sick this week, so I watched a fair amount of these debates on the LY channel. The KMT offered two main arguments against the reform. On the one hand, they suggested that the reform unfairly cut civil servants’ pensions too much. On the other hand, since the pension fund is forecast to go bankrupt in about 2049 (as opposed to in about 5-10 years under the current system), this reform doesn’t really solve the financial problem so there is no point in doing it. Note that those two positions are contradictory. If you want a reform that will be permanently sustainable, you are going to have to cut pensions even more.

In the end, Taiwan got a pension reform that both sides were a bit unhappy with, which is probably a pretty good indicator that it is a moderate compromise. Public discussion was allowed to percolate until some arguments were discredited and others emerged as superior. Opposition was marginalized, with the street protesters painting themselves into an ever smaller box. Instead of forming the vanguard of a public movement against reform, the anti-reformers demonstrated themselves to be merely selfishly interested in defending a system that unfairly privileged them. As they got smaller, their appeals got cruder and further discredited their moral position. (Example: a sign referring to President Tsai’s genitalia is not a smart way to make the case that civil servants are being unfairly discriminated against.)

If you had asked President Tsai after her inauguration when she expected to pass pension reform, I suspect she would have replied that it would take about a year. In fact, it has taken just over a year. One year to study the problem, hold public discussions, allow protesters to make their case, for supporters to reaffirm their insistence on this reform, and to pass a new law. Don’t expect the media to come out with glowing editorials praising President Tsai’s leadership. Democracy is messy, and we have been watching a messy and aggravating process unfold for nearly a year. Moreover, we ended up with something of a compromise, and no one loves a compromise. Nonetheless, I suspect this is exactly how President Tsai thinks democracy should work.

KMT party chair election, revisited

June 22, 2017

Wu Den-yi was elected KMT chair about a month ago. At the time, one of the popular theories about his win was that it represented a victory of the Taiwan-oriented local factions over the orthodox Chinese nationalist wing. (Or, if you prefer, the Taiwanese wing defeated the Mainlander wing.) In this line of thought, Wu was inheriting the support previously won by Lee Teng-hui, Wang Jin-pyng, and Huang Min-hui. The unspoken implication was that native Taiwanese Wu would lead the KMT in a more localist direction, perhaps even becoming another Lee Teng-hui.

I’ve never been too enamored with this discourse, but I keep talking with smart people who believe it is more or less what happened. I see Wu as a firm believer in the orthodox KMT catechism. He may not be as extreme as Hung Hsiu-chu, but all of his statements and actions over the past four decades seem to me to indicate someone who is quite comfortable with the direction established by Lien Chan and Ma Ying-jeou. That is, he should be acceptable to both wings of the party. I think what happened in the chair election is that KMT members – who want to return to power – simply chose the strongest leader.

So what if I’m wrong? What if Wu was elected because the local factions mobilized to support him? What would that look like? One notable difference between the KMT chair elections in 2016 and 2017 was that there were about 50% more eligible voters and valid votes in the 2017 election. Many people have speculated that this was the result of local factions signing up new party members in support of Wu. If so, we should see a clear pattern. There should be far more new voters in central and southern Taiwan, where the local factions are strongest. Moreover, if Wu inherited and built on Huang Min-hui’s 2016 support, the increase should be greatest in places where more new people signed up for KMT membership.

Let’s look at the results of the 2016 and 2017 KMT party chair elections. The KMT tallied results for individual ballot boxes, but I can only find the full results aggregated up to the city and county level:

 

2016 KMT party chair election

    陳學聖 李新 黃敏惠 洪秀柱
    Chen Lee Huang Hung
合計 139558 6784 7604 46341 78829
.          
台北市 12802 756 901 2990 8155
新北市 16694 723 916 4131 10924
基隆市 1931 121 136 504 1170
宜蘭縣 2845 139 138 1110 1458
桃園市 10745 1597 787 1698 6663
新竹縣 3378 153 191 1389 1645
新竹市 1944 74 112 485 1273
苗栗縣 5204 216 265 1796 2927
台中市 11238 548 751 3484 6455
彰化縣 8074 249 325 4217 3283
南投縣 4038 159 210 1905 1764
雲林縣 4354 148 188 2627 1391
嘉義縣 3842 47 92 2765 938
嘉義市 2678 27 62 1748 841
台南市 11102 316 561 3895 6330
高雄市 15996 632 1048 4956 9360
屏東縣 6358 197 370 2808 2983
花蓮縣 3420 189 243 795 2193
台東縣 2738 121 117 1315 1185
澎湖縣 1367 86 79 361 841
金門縣 1606 74 23 132 1377
連江縣 445 33 14 65 333
海外黨部 6759 179 75 1165 5340

 

And here is the 2017 election:

  valid Hung Han Pan Hau Chan Wu
  有效票 洪秀柱 韓國瑜 潘維剛 郝龍斌 詹啟賢 吳敦義
合計 272704 53065 16161 2437 44301 12332 144408
.              
台北市 26887 5209 1689 248 6250 1338 12153
新北市 28684 6486 1658 240 4544 984 14772
基隆市 4537 461 217 33 1586 156 2084
宜蘭縣 6055 1244 302 63 749 180 3517
桃園市 18372 4001 998 132 4067 458 8716
新竹縣 7192 955 400 70 1413 346 4008
新竹市 5253 1576 355 78 696 212 2336
苗栗縣 9671 1641 693 100 1579 445 5213
台中市 22588 3934 1121 151 4035 707 12640
彰化縣 18808 2566 889 172 2770 1002 11409
南投縣 8566 879 234 31 577 179 6666
雲林縣 8765 1062 1476 95 1390 288 4454
嘉義縣 5038 898 198 19 524 391 3008
嘉義市 4810 1078 267 63 817 675 1910
台南市 20535 4588 1262 178 3124 1882 9501
高雄市 36623 6657 2239 389 4645 1695 20998
屏東縣 14798 2377 667 108 1418 476 9752
花蓮縣 9645 2681 690 156 1424 318 4376
台東縣 5100 810 255 42 1193 114 2686
澎湖縣 2711 768 124 32 546 302 939
金門縣 2382 747 148 20 448 91 928
連江縣 574 124 53 1 97 26 273
海外黨部 5110 2323 226 16 409 67 2069

 

You will notice right away that the total number of valid votes nearly doubled, increasing by 133,146. At the same time, the number of votes won by the (supposed) representative of local factions (Huang in 2016, Wu in 2017) increased by 98,067. It seems plausible that these two shifts are related.

98,068 divided by 133,146 is .74. A reasonable interpretation is the pre-existing party members voted basically as they had in 2016, but 74% of the new party members voted for Wu. However, once you start looking at individual cities and counties, things start to break down. We expect Wu’s mobilization efforts to be most effective in central and southern Taiwan, where the local factions supposedly went all out to mobilize new party members for Wu. Assuming Wu’s increase came entirely from new members, he only won 8% of the new members in Chiayi City and 20% in Chiayi County. Those results can perhaps be explained away because Huang was from Chiayi, so they might have already mobilized for her in 2016. However, if you accept the hometown effect for Chiayi, you also have to discount the high ratio in Nantou, since that is Wu’s home. Throughout the rest of the region, the ratio does not differ markedly from the national average; if anything it is slightly lower. At any rate, Wu’s supposed share of new voters is lower in all of central and southern Taiwan (excepting Nantou) than in New Taipei (.89) and Taoyuan (.92). These are not the supposed loci of local factions in Taiwan.

    Increase Increase  
    Wu-Huang Valid ratio
合計   98067 133146 0.74
.        
台北市 Taipei 9163 14085 0.65
新北市 New Taipei 10641 11990 0.89
基隆市 Keelung 1580 2606 0.61
宜蘭縣 Yilan 2407 3210 0.75
桃園市 Taoyuan 7018 7627 0.92
新竹縣 Hsinchu Cnty 2619 3814 0.69
新竹市 Hsinchu City 1851 3309 0.56
苗栗縣 Miaoli 3417 4467 0.76
台中市 Taichung 9156 11350 0.81
彰化縣 Changhua 7192 10734 0.67
南投縣 Nantou 4761 4528 1.05
雲林縣 Yunlin 1827 4411 0.41
嘉義縣 Chiayi Cnty 243 1196 0.20
嘉義市 Chiayi City 162 2132 0.08
台南市 Tainan 5606 9433 0.59
高雄市 Kaohsiung 16042 20627 0.78
屏東縣 Pingtung 6944 8440 0.82
花蓮縣 Hualien 3581 6225 0.58
台東縣 Taitung 1371 2362 0.58
澎湖縣 Penghu 578 1344 0.43
金門縣 Kinmen 796 776 1.03
連江縣 Lienchiang 208 129 1.61
海外黨部 Overseas 904 -1649 -0.55

 

Maybe I’m thinking of this wrong. Maybe the point is that the growth in new KMT voters was much higher in central and southern Taiwan. The valid votes grew by 95% from 2016 to 2017. In 2016, Huang Min-hui won 33.2% of the votes, while Wu Den-yi won 53.0% in 2017, for an increase of 19.7%. If it was mobilization, these two numbers should move together. For example, valid votes increased by 129% while Wu beat Huang by 26.4%. Both of these numbers are larger than the national average, and Kaohisung is in the south. The problem is that we don’t see similar numbers throughout the rest of center and south. For example, in Changhau valid votes increased substantially, by 133%. However, Wu only bested Huang by 8.4%. All those extra voters didn’t seem to be going to Wu. In Tainan, valid votes only grew by 85% and Wu only outperformed Huang by 11.2%. In fact, some of Wu’s best areas were in the north. Wu outperformed Huang by 31.6% in Taoyuan and 26.8% in New Taipei, but neither one of these places had a particularly large increase in new voters. If you stare really hard and long at this table, you might convince yourself that you see a pattern. However, you are probably hallucinating. The correlation between the two columns is 0.05, just about as close to zero as you will ever see.

    % increase Vote share
    Valid votes Wu-Huang
合計   95 19.7
.      
台北市 Taipei 110 21.8
新北市 New Taipei 72 26.8
基隆市 Keelung 135 19.8
宜蘭縣 Yilan 113 19.1
桃園市 Taoyuan 71 31.6
新竹縣 Hsinchu Cnty 113 14.6
新竹市 Hsinchu City 170 19.5
苗栗縣 Miaoli 86 19.4
台中市 Taichung 101 25.0
彰化縣 Changhua 133 8.4
南投縣 Nantou 112 30.6
雲林縣 Yunlin 101 -9.5
嘉義縣 Chiayi Cnty 31 -12.3
嘉義市 Chiayi City 80 -25.6
台南市 Tainan 85 11.2
高雄市 Kaohsiung 129 26.4
屏東縣 Pingtung 133 21.7
花蓮縣 Hualien 182 22.1
台東縣 Taitung 86 4.6
澎湖縣 Penghu 98 8.2
金門縣 Kinmen 48 30.7
連江縣 Lienchiang 29 33.0
海外黨部 Overseas -24 23.3

In the end, there just isn’t any compelling evidence for the idea that local factions elected Wu chair by mobilizing tons of new voters for him. Heck, there isn’t evidence that anyone mobilized new voters for Wu.

I think the increase in new KMT voters is related to party morale, not to the KMT party chair election. Morale was at a nadir in the aftermath of the 2016 wipeout, and lots of party members let their membership lapse. As morale has recovered (slightly), some of those party members have drifted back (and paid their dues). The turnout rate was also markedly higher this time. However, the number of eligible voters and valid votes are far below the levels of 2005, when the winner was widely expected to become the next president.

  Valid votes Eligible voters turnout
2005 518324 1033854 50.2
2016 139558 337351 41.6
2017 272704 476147 58.1

At any rate, I think the evidence suggests that Wu Den-yi was elected by a fairly broad base of support within the KMT rather than by any specific group such as local factions or Taiwan nationalists. Admittedly, there is a limit to what we can see with crude data like this, so maybe it is best to state my conclusion in the negative. I don’t see any clear evidence for the local faction mobilization thesis.

 

 

 

KMT party chair election

June 5, 2017

(I’ve been working on this post on and off for a couple of months now. Rather than revise it again, I’m just going to post it.)

 

The votes are in and Wu Den-yi has been elected the next KMT party chair, so I guess it is just about time for me to write up my election preview.

 

Here are the results:

吳敦義 Wu Den-yi 144408 52.2%
洪秀柱 Hung Hsiu-chu 53063 19.2%
郝龍斌 Hau Lung-pin 44301 16.0%
韓國瑜 Han Kuo-yu 16141 5.8%
詹啟賢 Chan Chi-hsien (Steve) 12332 4.5%
潘維剛 Pan Wei-kang 2437 0.9%

That is roughly twice as many votes (and KMT party members) than the last KMT party chair election. However, before you get too excited about a certain member mobilizing new members, remember that this is actually quite a bit fewer votes (and party members than the 2005 party chair election when over a million people were eligible to vote and over half a million votes were cast.

I was out of the country when the accusations of vote buying exploded, so I mostly missed that. However, I did watch both debates on Youtube, and I learned quite a bit from those forums about how each candidate was presenting him or herself. I’ll discuss the candidates in reverse order of their finish.

 

Pan Wei-kang

Pan was elected to the legislature in 1992 and has spent most of the last 25 years in the legislature, often also serving on the KMT central committee. For someone who has been at the center of national politics for so long, I was somewhat surprised by how little I knew about her. She is a second generation politician, and she comes out of the Huang Fu-hsing (military) system. However, I can’t remember hearing her speak very many times, and I never thought of her as particularly extreme. As such, I was a bit taken aback when she came out in the first debate breathing fire, demanding state reparations for the current wave of Green Terror against the KMT. She seemed determined to displace Hung Hsiu-chu as the candidate of the reactionary nostaligists. She toned down the rhetoric a bit in the second debate, but she managed to redefine herself in my eyes.

I don’t know what Pan was doing in the race. She never seemed to matter, and she never carved out a distinct niche for herself.

 

Steve Chan Chi-hsien

Chan was a complete mystery to me when this contest started. He had served as Economics Minister, but I don’t pay a whole lot of attention to governing. I’m into politics. I had heard his name bandied about as a possible running mate for the KMT presidential candidate, but that came to nothing. In retrospect, the high moment for Chan’s party chair campaign might have been when they announced the final results of the signature drives. All six candidates easily passed the minimum threshold, but Chan somewhat surprisingly finished second, edging out Hung and Hao. This turned out not to be predictive of the voting results though, as Chan actually got fewer votes than signatures.

In the second debate, Chan mentioned that his mother had been a Changhua county councilor and his brother had been Yuanlin town mayor. This was news to me, and I’m supposed to know these sorts of things. However, there was a reason I had never heard of them: they were elected back in the dark ages. Chan’s older brother was elected mayor in 1973, and we don’t have systematic records from town elections that far back. In fact, Chan comes from an elite local family with several prominent doctors. A bit of googling revealed that he is distantly related in some way to most Taichung and Changhua local faction families and even a few opposition politicians. However, the family’s electoral activities were decades ago and the old nework is almost certainly long gone today.

In the debates, Chan was the embodiment of a bureaucrat. He exuded as little charisma as possible and gave me the impression that he understood all of the details of problems without necessarily grasping the big picture. He spoke of visiting grassroots party members as if they were some abstract idea. People who routinely interact with ordinary voters don’t talk about those interactions as if they require some special effort. Someone told me that Steve Chan is close to Lien Chan. I don’t know if that is true, but they have very similar styles.

 

Han Kuo-yu

Five of the candidates sounded rather similar. Han sounded completely different. During both debates, Han didn’t talk about things like the 1992 Consensus, KMT party assets, or other partisan topics. Instead, he talked about the difficulties of everyday life for lower income and less educated people. Good jobs are scarce, drug use is common, things are too expensive, and life is generally hard. Notably, he did not blame all of these woes solely on President Tsai and the DPP. He was complaining about the effects of President Ma’s policies just as much. His discourse was limited to expressing the pain felt by the lower class. He did not bother to offer any solutions, not even Trump-esque claims that everything could be easily fixed if only someone really wanted to. This was a campaign aimed at the people who know the system is rigged against them and will continue to be rigged against them. It was also aimed at young men, especially the types who might drive a truck or join a gang. This may not have been the best strategy for a KMT party chair race, since I would wager that KMT members are less likely than the general population to be young, unemployed, financially struggling, or to feel that the system is rigged against them. Nevertheless, Han didn’t do terribly. I wonder how many candidates will pick up this campaign strategy for the city and county councilor elections next year.

 

Hau Lung-pin

Hau Lung-pin had exactly one remarkable idea. He stressed repeatedly that if he were elected chair, he would not personally run for president in 2020. Instead, he would ask Hon Hai boss Terry Gou to be the KMT candidate. Let’s think about this for a minute. There are a few reasons that this might be a good idea. 1) The KMT doesn’t exactly have a stable of qualified, charismatic candidates foaming to challenge President Tsai in 2020. Everyone is flawed, and no one is terribly popular. 2) The conventional approach failed dramatically in 2016, so the KMT needs to try something new. 3) Public opinion surveys show that Gou is more popular than any current KMT politician. 4) Donald Trump just showed that the USA was willing to vote for a business tycoon with no political experience, so maybe Taiwanese voters will follow suit. There is the small matter that Gou is currently busy running Hon Hai and may not have the time or desire to run for or serve as president. Nonetheless, Gou didn’t shoot the idea down, and there have been a few discreet trial balloons hinting that he might be willing. Rich people think they can do anything, that their immense wealth proves their superior wisdom and vision. Gou’s availability may not be the fundamental flaw in Hau’s plan.

There are two basic problems with Hau’s plan. The first is that Gou would probably bomb miserably as a presidential candidate. Gou has reasonably good poll numbers now, but the public hasn’t thought carefully about Gou as a potential president. He is a very successful business leader – with a far more impressive record than Donald Trump – and the public evaluates him mostly as a business leader. Once he becomes a politician, the media scrutiny will intensify and become much more critical. The halo surrounding Eric Chu in 2014 melted away in only a few months under the harsh spotlight of national politics in 2015. Gou’s current good (not great) polling numbers are almost irrelevant; six months after entering the political fray the public will think of him in a completely different light.

What about Gou’s fantastic business record? (Unlike Trump) Gou has built an enormous, world-class company. Hon Hai is one of the pillars of Taiwan’s economy, and it employs over half a million people around the world. Gou is good at business. Unfortunately, his business talent might not translate into a political appeal. For one thing, Taiwan does not have the traditional reverence for free enterprise that America does. Especially for Republicans, you often hear voters say that they prefer a person who understands business. As the chair of General Motors once said, the business of the US government is business. There is a significant slice of the electorate that sees pro-business policies as a moral appeal. Taiwanese voters are different. Among traditionalists, Confucianism views commerce with a skeptically. Politics and agriculture are honorable and create a better world; people in commerce are not much better than parasites and must be carefully regulated and restrained by the state. Contemporary mainstream Taiwanese society views business more favorably, especially since the Taiwan economic miracle was built on exports by small and medium sized business. Still, there is nothing like the American or British reverence for the invisible hand of the market. Not many people believe that an unregulated economy would produce a better society. Moreover, there is a growing worry about the increasing gap between rich and poor, and business tycoons may not be the ones preoccupied with addressing these concerns. Suffice it to say, Trump’s victory in the USA doesn’t necessarily mean that a business leader in Taiwan will do well in Taiwan. (Also, there is the strong possibility that by the time 2020 arrives, Trump’s disastrous presidency will be widely seen as evidence that business leaders do not make good politicians.)

Trump’s international dealings were never more than a side note among the perpetual storm of astounding news swirling around his campaign. For Terry Gou, it is unthinkable that his ties in China would not be at the center of his campaign. Hon Hai is the single biggest private employer in China. One way to interpret that is that Hon Hai has some leverage over the Chinese economy. Another interpretation is that China has enormous leverage over Hon Hai’s (and Terry Gou’s personal) fortunes. When China demands that Hon Hai does something, Hon Hai has little choice but to comply. It is not much of an exaggeration to say that the prime duty of Taiwan’s president is to defy China. There is a fundamental conflict of interest on the overriding question in Taiwanese politics. Gou’s loyalties would be continually questioned, and he would have no way to reassure the dubious public. Moreover, it isn’t like Gou is a strident democrat. The Gou Doctrine (“You can’t eat democracy.”) might find sympathy in a society that takes democracy for granted, like the USA. In Taiwan, democracy is what keeps Taiwan from being absorbed by a voracious China. (Note: Many people believe Taiwan’s economy keeps it independent. Hong Kongers wish that were true.)

I don’t care what the current polls say. I can’t see any way that Terry Gou wouldn’t be a disaster as a presidential candidate. Hau Lung-pin bet his political career on a terrible idea.

The second problem with Hau’s plan to ask Gou to be the presidential candidate is that it shows that Hau fundamentally misunderstands the nature of power in Taiwanese politics. Quite simply, power flows from the presidency. This is not unique to Taiwan. When there is an elected president with significant power, parties organize themselves to capture that big prize. Parties are presidentialized. Regardless of who holds the formal position of party leader, the de facto leader of the party is the incumbent president, the presidential candidate, or the person who could potentially become the presidential candidate. By promising not to run for the KMT’s presidential nomination in 2020, Hau basically ensured that he would never wield any power. His campaign appeal boiled down to, “Elect me as your leader so that I can refuse to be your leader.” Not only is this an illogical appeal, we’ve just seen how badly it works in practice. Eric Chu tried being a neutral referee in early 2015 when the entire party was practically begging him to run for president. That didn’t work out well for either Chu or the KMT.

To recap, Hau made a terrible choice in choosing to outsource the KMT presidential nomination, and he made another terrible choice by selecting Gou as his target. He deserved his humiliating third place finish with a pathetic 16% of the vote.

Where does Hau go from here? He probably won’t leave politics simply because the KMT has so little talent at the top levels. However, I think he is probably spent as a serious political force. It has been a very bad few years for him. As a two-term mayor, he was not on the ballot in 2014 and so was spared that humiliation. Nonetheless, his satisfaction ratings were routinely among the lowest of all the mayors and magistrates. It certainly isn’t good for your reputation when the other party wins your formerly unwinnable city after your eight years of lackluster performance in office. In early 2015, when the KMT was casting around desperately for a presidential candidate, Hau was one of those who boldly decided to sit on his hands and watch Hung Hsiu-chu’s rise. He bears a share of responsibility for the damage she inflicted on the party. Instead of running for president, Hau managed to secure the KMT nomination for Keelung City, one of the few safe KMT seats remaining. His calculation seemed to be that he did not want to sacrifice himself in the coming DPP tidal wave. Someone else could do that. He would position himself as leader of the KMT legislative caucus, which would be the de facto center of KMT power after the election. He would be able to pick up the pieces and lead the party back from defeat. There was one flaw in that plan: he lost the Keelung election. It wasn’t that there weren’t enough votes. The winning DPP candidate only got 41%. The problem was that MKT and PFP candidates combined to siphon off 24% of the votes, leaving him with only 36%. He was not able to unify the blue voters around his candidacy. The same thing happened in the KMT chair election. He did not lose because the general electorate rejected him. He lost because KMT party members – supposedly the group most enthusiastically supporting him – looked at him and collectively mumbled, “meh.”

 

Hung Hsiu-chu

I don’t have a lot to say about Hung that hasn’t been said many times over the past two years. She is far too extreme for the Taiwanese electorate. She was a disaster as a presidential candidate and party chair, and if it had elected her to another four years as party leader the KMT would have been sentencing itself to political oblivion. This wasn’t working, and even most of the KMT members who like what Hung stands for could see that the party needs to go in a different direction if it ever wants to return to power.

 

Wu Den-yi

It wasn’t a surprise that Wu won the race. He acted like the front-runner and the other candidates and the media treated him like the front-runner throughout the campaign. His first-round victory was perhaps a surprise, though. I had thought that he would get somewhere around 45% and need a second round to dispatch Hung or Hau. Instead, he won 52% and beat the second place candidate by 33%. In a race with five candidates getting significant numbers of votes, 52% is a fairly impressive result.

Wu’s strategy can be summed up quite simply: Let’s party like it’s 2011! In this view, there was nothing wrong with the party that won the 2008 election and was re-elected in 2012. Everything was going well until the party got derailed during Ma’s second term. The KMT flubbed things like the gas and electricity pricing and the capital gains tax. They failed miserably at political communication, and the population came to believe that nuclear power was dangerous and that the Services Trade Agreement would somehow risk Taiwan’s political sovereignty while transferring enormous wealth to the rich elite. The KMT failed most disastrously by shifting away from the 1992 Consensus under Hung Hsiu-chu. The task at hand is simply to return to that winning strategy. That means the entire package. For example, the KMT has to rebuild its ties with the local factions, assuring them that they are still a critical component of the KMT coalition. It also means returning to the greater ambiguity of the 2008 campaign, in which Ma repeatedly promised “no unification, no independence, no war.” In subsequent years, the KMT seemed to forget the “no unification” part of that formula. However, this does not mean that Wu Den-yi is a modern-day version of Lee Teng-hui, secretly scheming to lead the KMT and Taiwan toward independence. Wu is a deeply conservative person who believes in traditional values and deference to authority. He is well-schooled in the Church of Sun Yat-sen, and there is very little evidence he is not a sincere and committed believer. Lin Yang-kang and Wu Po-hsiung are much better models for Wu than Lee Teng-hui. Wu firmly supports returning to the 1992 Consensus, including the part about insisting that there is only One China. The Ma presidency was built on the premise that Taiwan’s economy should be further integrated into the larger Chinese economy for both economic and political purposes. Economically, Ma believed that integration would lead to faster economic growth for Taiwan. Politically, Ma saw an economic appeal as a way to win votes from a public skeptical of the glorious history of the Republic of China. The message was, “Don’t worry so much about China. We won’t take any steps toward unification. Instead, we will use them to make ourselves rich.” Of course, this strategy depended on negotiating a better economic relationship, and China would not negotiate with Taiwan unless Taiwan accepted the One China principle. The ambiguity that Ma was so proud of involved telling China, “Look, One China! Don’t worry about independence!” while simultaneously telling Taiwanese, “Look, each side with its own interpretation! Don’t worry about unification!” This delicate balancing act arguably produced two election victories before, in Wu’s interpretation, the KMT blundered by walking away from it. Wu promises to resume the friendly (but still arm’s length) relationship with China by reaffirming and adhering to the One China principle.

Is Wu correct to think he can simply put the old band back together? I have some doubts. For one thing, China in 2017 (and 2020) is not the China of 2005 or even 2012. Today’s China is much less deferential to the international order and much more aggressive about pursuing its international interests. In 2005 the world was still talking about the peaceful rise of China, and it was marginally plausible that Taiwan could have an exclusively economic relationship with China (win-win!). These days, China looks far more predatory and menacing. Further, in 2005 the two economies were more complementary, matching Taiwanese capital and technology with Chinese labor. Today, the two compete directly in many critical sectors. Finally, the Chinese economy is no longer growing at miraculous rates; it is now entering phase of relatively slower growth.

A second and more important point is that Taiwan of 2017 (and 2020) is no longer the Taiwan of 2005 and 2012. Identity has shifted. I assume that my readers are all familiar with the NCCU Election Study Center trends on national identity. Prior to 2008, more people held a Chinese identity (either exclusive or dual) than an exclusive Taiwanese identity. After 2008, that has no longer been the case. Nowadays, exclusive Taiwanese identity outpaces Chinese identity by a large margin (58.2% to 37.7% in the most recent data point). This is partly due to generational replacement, partly because some people have changed their minds, and partly because the language of political discourse has changed and Taiwanese are simply less likely to use the term “Chinese” to refer to themselves regardless of their political stance. Nonetheless, a KMT promising One China will face a far more skeptical electorate in 2020 than in 2008.

The third problem for this strategy is that the 1992 Consensus is no longer the same thing. In 2008, no one knew how the 1992 Consensus would work in practice. If you wanted to project optimistic or pessimistic visions on it, you could. Now we have eight years of experience. By the last few years, China was increasingly unhappy with Taiwan’s reluctance to take more concrete steps toward unification, and the Taiwanese electorate was increasingly unhappy with the continual degrading prostrations and erosion of sovereignty necessary to keep the official channels open. Ma’s implicit promise to voters, “Don’t worry about the political implications; this is just going to be pure economics,” was increasingly far-fetched. Instead of the wink-wink-nudge-nudge promise that the 1992 Consensus would allow Taiwan to enjoy both political sovereignty and close economic relations, it became increasingly apparent that the two were, in fact, inseparable. Accepting One China would have political consequences. In Taiwanese politics, whenever one issue (in this case economic strategy) clashes with the China cleavage, the China cleavage subsumes and absorbs the other one. I don’t think Wu can simply ignore eight years of history and pull them back apart.

My guess is that Wu will be fairly successful at holding the broader KMT coalition together. I don’t expect a spate of new splinter parties from the blue side, at least not in the next year and a half. However, I think Wu is overestimating the number of voters who are waiting to be pulled back into the KMT coalition. In 2012, 54% voted for Ma or Soong. In 2016, only 44% voted for Chu or Soong. Wu might consolidate that vote, but his plan to return to the good old days of 2011 doesn’t seem to me to hold much promise of expanding it much. Wu Den-yi is betting otherwise. I guess we’ll see.

Is marriage equality a cleavage?

May 12, 2017

Last week, my colleague Wu Yu-shan gave a stimulating talk about changes in political cleavage structures around the world. Most of the talk was about the rise of pre-material cleavages (ie: nationalism) in western industrial democracies, but he also had something to say about Taiwan. He believes that we are seeing the rise of materialist (ie: a left-right cleavage) and post-materialist (ie: marriage equality and environmentalism) cleavages in addition to the old nationalism cleavage. In Taiwan’s political science world, Wu is the major voice staking out this position. The opposite view, that national identity is still basically the only cleavage that matters, has most recently and forcefully been voiced by Chris Achen and T.Y. Wang in their forthcoming edited volume, The Taiwan Voter. It is hard to overstate the importance of this debate. Depending on whether you believe Taiwan has one or multiple important political cleavages, you might come to different conclusions on many of the most central questions facing Taiwan today. Does the KMT need to change its position on China, or is returning to the 1992 Consensus a viable option? Will the NPP be able to encroach on the DPP’s pool of voters? Will it be able to appeal to voters that the DPP cannot? Did the 2016 election mark a fundamental break with the past, or is it merely a deviance from a well-established pattern? Should President Tsai push for marriage equality? Why isn’t President Tsai aggressively pushing for admission to the United Nations under the name “Taiwan”? This question of one or many cleavages gets right to the heart of our understanding of how Taiwanese politics work.

During his talk, Wu presented a fascinating graph, taken from a story on Commonwealth Magazine’s website. In this post, I want to explore what we should and maybe should not learn from this graph.

CW UI ME plot.jpg

This graph plots legislators’ positions in the political space along two dimensions. The X axis is the Independence-Unification dimension (with independence on the left), while the Y axis is support or opposition to marriage equality (with support at the top).

I don’t understand exactly what the authors did to produce this graph, but I’ll do my best to explain the methodology. The authors looked at Facebook data from each legislator. They used the two party chairs as anchors, examining people who followed both the party chair and the legislator. (Note: I don’t understand exactly how they used these overlapping followers. However, they presented this part in detail, as if they believed it was the most important thing for us to know.) They examined the “likes” on various posts and put that data into a factor analysis model. The purpose of factor analysis is to condense many variables into a smaller number. If you start with n variables, the model calculates a matrix to multiply each variable by to produce n new variables that are completely uncorrelated to each other. However, these n variables are not equally useful. Some have a lot of explanatory power, while others have almost none. Typically, we throw all the variables that account for less than 1/n of the total variance in the data. They have kept two dimensions, though they did not report how much explanatory power each one had or how many variables cleared the 1/n threshold. The final challenge in factor analysis is naming the new variables. Remember, the algorithm has simply produced new variables that are orthogonal to each other; it doesn’t care what went into them. The researcher typically looks at the coefficients that were multiplied with the original variables and decides on a name. Factor analysis has the veneer of cold, objective data analysis, but interpreting it is actually highly subjective. At any rate, I’m going to assume that the authors made reasonable assumptions and inferences in handling the data. For example, I’m going to assume that the dimensions are appropriately labeled. I’m also going to mostly ignore the possibility that Facebook likes and followers don’t necessarily mirror a legislator’s own positions or even the preferences of that legislator’s constituents.

What are we supposed to see in this graph?

I suspect the first thing people will notice is the position of the two party chairs. Tsai Ing-wen is fairly distant from her party median on both dimensions. On the IU axis, she is in the center of the political spectrum. This looks reasonable; most of us think of her as a moderate on identity and nationalism. The Y axis suggests she is also a bit out of touch with the rest of her party on marriage equality. She is noticeably higher on the plot, suggesting she is a stronger supporter of marriage equality than the average DPP legislator. I think this also fits in with the conventional wisdom. There are a few DPP legislators who are more stridently in favor of marriage equality than Tsai, but there are also a lot of hesitant legislators terrified of angering their socially conservative constituents. So Tsai is moderate on China and somewhat progressive on marriage equality. Hung Hsiu-chu’s position is rather more surprising. Hung is widely known as an extremist on national identity questions. Yet here she is smack dab in the center of the KMT caucus. Further, she has made several statements that indicate she is more pro- marriage equality than the average KMT legislator, yet here she is, again, right in the middle of the KMT caucus. These data suggest that Hung Hsiu-chu is not an extremist. She is actually a nearly perfect representation of the average KMT legislator!

CW1.png

The second thing people might notice is how lonely Jason Hsu looks up at the top of the graph. He is the only KMT legislator firmly in the pro- marriage equality camp. Reporters love to interview him on this topic, and this gives the impression that there is a significant wing favoring marriage equality in the KMT. Nope. Not according to this plot.

CW2.png

Third, there is a relationship between the two dimensions. In the DPP, there seems to be a tradeoff. Extreme nationalists tend to be social conservatives, while social progressives tend to be moderate on identity. Why does someone choose to be in the DPP? It is one or the other. I don’t know why it isn’t both, but it doesn’t seem to work that way. The same relationship also exists to a lesser extent in the KMT. Social progressives are slightly more moderate on identity.

CW3.png

Fourth, the NPP is all located in roughly the same position (though Hsu Yung-ming is slightly less progressive and more nationalist than the other four). It is socially progressive but moderate on nationalism. I think this will surprise many people. The common perception is that the NPP is extreme on both dimensions. Here it simply looks like an extension of the progressive wing of the DPP.

CW4.png

I think those are the obvious things we are supposed to see. What are some of the less obvious things?

First, this is a two dimensional plot, giving the impression that there are two equally important cleavages in Taiwan. However, the second dimension isn’t necessary. A vertical line perfectly separates the blue and green camps.

CW5.png

The authors did not report the eigenvalues of the two factors, which indicate how much of the variance each factor accounts for. We don’t know that the second value was at least 1/n or that the first dimension wasn’t several times as powerful as the first. Maybe instead of a square box, this graph should have been flattened into a short and wide rectangle like this to give a better sense of the actual political space:

CW7.png

If you think about the plot this way, one of the takeaways is the extent to which the DPP has captured the middle ground and the KMT has been pushed back into the far right. I’ll bet the KMT held much more of the middle ground in 2008.

Second, look at that cluster of DPP legislators in the top half of the graph. Notice anything about them? How about if I list all the DPP legislators higher than the top KMT legislator (roughly from top down):

尤美女 You Mei-nu, party list

鄭麗君 Cheng Li-chun, party list

林靜儀 Lin Ching-yi, party list

蔡培慧 Tsai Pei-hui, party list

林淑芬 Lin Shu-fen, New Taipei 2

鍾孔炤 Chung Kung-chao, party list

段宜康 Tuan Yi-kang, party list

邱泰源 Chiu Tai-yuan, party list

吳焜裕 Wu Kun-yu, party list

陳曼麗 Chen Man-li, party list

Kolas Yotaka, party list

蔡英文 Tsai Ing-wen, president and party chair

余宛如 Yu Wan-ju, party list

何欣純 Ho Hsin-chun, Taichung 7

蘇嘉全 Su Chia-chuan, party list

施義芳 Shih Yi-fang, party list

徐國勇 Hsu Kuo-yung, party list

吳思瑤 Wu Si-yao, Taipei 1

That’s 14 party list legislators (of 22 total) and 3 district legislators (of 51). Lin Shu-fen is the only district legislator occupying a clearly pro- marriage equality position. This radically changes the way I look at this chart.

CW6.png

For one thing, as the party chair, Tsai Ing-wen had the final say on the composition of the party list. She seems to have packed it with social progressives. So while she might be somewhat out of favor with gay rights activists for her current tepid stance, most of the strong voices in favor of gay rights in the legislature are there because she put them there.

From another point of view, if you only consider district legislators – the ones who actually go out and win votes – the DPP and the KMT don’t look all that different. The two big parties both cover roughly the same portion of the Y axis. The DPP may be slightly more progressive, but the difference isn’t all that great.

Ignoring the DPP list legislators also makes the NPP stand out. They now occupy a distinctive space on the political spectrum (assuming the second dimension is important). They are basically the only politicians who take a clear pro- marriage equality position before the voters.

One way to think about this is that elected politicians are socially conservative, and this social conservatism probably reflects a cold strategic judgement that full marriage equality is too radical for the electorate to swallow. A different way to think about it is that Lin Shu-fen, Huang Kuo-chang, Hung Tzu-yung, and Freddy Lim all won district elections while occupying this part of the political space, so maybe there wasn’t a marriage equality penalty in 2016. It certainly didn’t seem to hurt the other major politician in the top half of the chart, Tsai Ing-wen. It could be the case that (a) there are plenty of socially progressive voters, or (b) the second dimension simply doesn’t matter. Of course, it could also be the case that the cleavage simply hadn’t fully emerged in 2016.

Still, that vertical line perfectly dividing the space is a major problem for the idea that the second dimension matters. I’ll be more open to the idea when that line needs to be drawn at a 60 degree slope. To me, it looks as though there is still one dominant cleavage line in Taiwanese politics, and it isn’t marriage equality. However, this debate is far from settled.

2016 Taipei LY races revisited

May 11, 2017

It has been a while since I posted anything on this blog, but I actually have been mulling over a topic for a few months now. I want to write about the KMT chair election. Nope, just kidding. I want to write about the past rather than speculate about the future. I want to revisit the 2016 legislative elections. More specifically, I want to take another look at the DPP’s choice to cooperate with non-DPP candidates, especially in Taipei City. Did that strategy work well? Did DPP voters obediently vote for their party’s electoral ally? Did the non-DPP candidates bring in other voters that the DPP wouldn’t normally win? Should the DPP (or KMT) try this again in the future? I won’t provide definitive answers to any of these questions, but maybe we can unearth some suggestive evidence.

 

Caution: This section is about methodology. It is extremely boring. I suppose you could skip it, but I will judge you and think you are a bad person who probably believes all the fake news on the internet. If you can’t stand to read boring stuff, you aren’t educated. Also, what are you doing reading my blog.

Normally the way to attack this topic would be to look at individual-level vote choices from survey data. If we wanted to look at national-level vote choices, that is exactly what I would do. I’d go to the TEDS survey data and put together a table of how people voted in the presidential and district elections. Imagine if there were only two presidential candidates and two legislative candidates:

  LY: DPP LY: KMT
Prez: DPP p 1-p
Prez: KMT 1-q q

Of the people who voted for the DPP presidential candidate, some percentage p also voted for the DPP legislative candidate, while the rest (1-p) defected to the KMT legislative candidate. Similarly, q of the KMT presidential voters were loyal while 1-q defected. We have two fairly large post-election surveys from TEDS for the 2016 election (one face-to-face, one telephone), so we could put together fairly good estimates for p and q.

However, I’m interested in Taipei 4, where the DPP supported PFP city councilor Huang Shan-shan. I’m also interested in Taipei 8, where the DPP supported independent (and former KMT and New Party) city councilor Lee Ching-yuan. For any given legislative district, we only have about 50 or 60 respondents from the two surveys combined. That isn’t enough to produce even a really bad estimate. National surveys simply aren’t much help; we would need a dedicated post-election survey for each legislative district. Unfortunately, I don’t have such data, and I am not aware that any such surveys were ever conducted. We will have to look elsewhere for evidence.

Instead of surveys, I look to the aggregate election results. The CEC has provided electoral returns from each precinct. In 2016, Taiwan had 15,582 precincts, so the average legislative district had 200 to 300 data points. The problem is that these are aggregated data, not individual-level data. These reports do not tell us how individual voters voted, which is what we really want to know.

This problem, which is known as ecological inference, has a long and not very distinguished history in political science. W.S. Robinson identified the basic problem in 1950. Aggregate data from the American states showed that states with higher levels of foreign born residents also had higher levels of literacy. In other words, the ecological inference should be that foreign-born residents had higher levels of literacy than native-born residents. However, it was well known that the opposite was actually true. The ecological inference was not just off by a little; the basic relationship was backward! From this, social scientists have learned to beware of what is called the ecological fallacy: inferring individual-level relationships from aggregate data can go horribly wrong.

Nonetheless, there are many instances in which all we have to work with is aggregate data. About twenty years ago, two of political science’s leading methodologists, Gary King (Harvard) and Chris Achen (then at Michigan, now at Princeton) published books about this problem. King’s book made the bigger splash. King put together a software program that would take the aggregate data, run thousands of simulations, and provide estimates for p and q. Unfortunately, there were a few limitations. King’s method worked well in a 2×2 case, but much less well with 2×3 or larger tables. His program might be able to manage 3×3, but as the number of rows or columns increased, the program often simply couldn’t converge to a solution. Second, his program didn’t handle covariates well. Imagine you wanted to control for the percentage of Hakka voters. His program could usually handle one covariate. However, if you wanted to control for the percentages of Hakkas, university graduates, farmers, and people who had moved into the district within the past five years, the program was likely to crash. I tried using King’s program a few times in grad school and gave up in frustration. (Another problem: Chinese language fonts make the interface go bananas, so it is hard for me to navigate.) That was probably fortunate for me. Not a whole lot of articles using his method were ever published, and I suspect many were rejected by reviewers who couldn’t be sure that they weren’t still running up against the ecological fallacy. At any rate, I thought I’d download the program and try some models for this blog post, but his software won’t run on my computer. Apparently, Gary King hasn’t bothered to update his software since 2003, which I take as a pretty good indication that he doesn’t consider it to have been a smashing success. But don’t feel too sorry for him, he’s got lots of other triumphs. Recently he has made headlines analyzing government funded posts on Chinese social media.

Unlike King, Chris Achen didn’t make any grandiose claims to have definitively solved the ecological inference problem. Achen’s book, co-authored with W. Phillips Shively (Minnesota), explored the old warhorse of ecological inference, the ecological regression, which is what I will use in this blog post. If you’ve taken any basic social science methodology course, you have probably studied ordinary least squares (OLS) regression. If you have a dependent variable Y and two independent variables X1 and X2, the normal equation is:

Y=b0 + b1X1 + b2X2 + e, where e is an error term (that we will henceforth ignore).

The normal model has a constant, b0. However, in an ecological regression, there is no constant. Effectively, the regression line is forced to go through the origin. This makes the equation:

Y = b1X1 + b2X2

In the above table, Y is the DPP’s district LY vote share, X1 is the DPP’s presidential vote share, and X2 is the KMT’s presidential vote share. B1 is an estimate of p, the percentage of DPP presidential voters who remained loyal and voted for the DPP legislative candidate. B2 is an estimate of (1-q), the percentage of KMT presidential voters who defected to the DPP in the legislative race.

Easy! Except it isn’t. Theoretically, b1 and b2 should never be less than zero or greater than one. Unfortunately, that happens quite frequently. You get results saying that 107% of DPP presidential voters voted for the DPP legislative candidate, and negative 11% of KMT presidential voters defected to the DPP legislative candidate. That doesn’t make any sense at all.

Achen and Shively suggest putting a squared term in the model to get a slightly less biased estimate. Unfortunately, this also makes it harder to interpret. Since the benefit is small and I’m looking for something quick and dirty, I’m going to just live with biased estimates.

Fortunately, Achen and Shively do tell us something about how the estimates are biased. First, loyalty rates are overestimated and defection rates are underrated. Second, if one election is more polarized than the other, estimates will commonly exceed the 0-1 range. Third, if there is a uniform swing, every ecological regression will have at least one estimate outside the 0-1 range (Achen and Shively 1995, 85). Let’s not worry too much about the second and third points, which simply suggest that it is nearly impossible to avoid having some estimates outside the 0-1 range. Unlike surveys, getting more data will not necessarily result in a better estimate. However, the first point is very important to keep in mind. The results I show you will probably overestimate the prevalence of straight-ticket voting for the KMT and DPP. When you see an estimate for straight-ticket voting, you should mentally insert “at most” in front of that number.

Achen and Shively have not solved the problem of the ecological fallacy. Strictly speaking, ecological regressions should be seen as evidence of aggregate-level trends rather than individual-level trends. As in most empirical inquiries, the best way to avoid falling victim to an unwarranted inference is a thorough knowledge of the facts and a robust theory explaining how the pieces fit together. It is usually a mistake to think the facts can speak for themselves, and this is more true than normal in the case of ecological inference.

 

[Digression: I first met Chris Achen about 20 years ago, when he spent a few months as a visitor at the Election Study Center. In fact, I have a vague memory of him teaching me how to do an ecological regression to estimate the incidence of split ticket voting in the 1997 Chiayi City mayoral election and the concurrent LY by-election. Chris is a wonderfully sunny and generous person; we quickly dubbed him 阿陳 (A-Chen). He has regularly returned to Taiwan over the past two decades, and he has trained several prominent Taiwanese political scientists. You might be most familiar with Hsu Yung-ming, who is now a New Power Party legislator. He is also one of the driving forces behind The Taiwan Voter, which will be the authoritative work on voting behavior in Taiwan for the next generation and the go-to source for anyone wishing to understand identity politics. (It will be published by University of Michigan Press later this year and is co-edited by Chris Achen and T.Y. Wang.) Chris is an awesome dude; everyone loves Chris.]

 

To recap the methodology section: I’m using ecological regressions to infer individual-level voting decisions from aggregate level data. This risks falling afoul of the ecological fallacy, and no reputable academic journal would publish such dodgy research. Nonetheless, the results are quite suggestive, and I believe they have some value. Moreover, we have fairly strong suspicions of how votes might have been distributed; we will want to see evidence that the results are more or less “reasonable.” Finally, remember that these results will overestimate straight-ticket voting and underestimate defections. When two people split their tickets in opposite directions, in the aggregate data it looks like they have both voted straight-ticket. There is a lot more churning under the surface. Proceed with caution. On the other hand, I wouldn’t have spent all this time working on this post if I didn’t think I wasn’t learning anything.

 

Substantive Results:

For each legislative district, I attempted to learn how the district candidates’ votes were produced by trying to figure out how the party list vote and the presidential vote translated into the district votes. For each important district candidate, I thus ran two regressions. The cases were precinct-level vote percentages. (Indigenous voters do not vote in normal legislative districts, so I only used precincts in which the number of eligible voters in the district election was at least 90% of the eligible voters in the presidential election.) For both regressions, the dependent variable was the vote share of the district candidate. In the first model, the independent variables were the vote shares of the party lists. However, the nine smallest parties kept producing crazy results, so I lumped them all together. Sorry. Next time win more votes. In the second model, the independent variables were the vote shares of the three presidential candidates. As you will see, the results of the two models generally tended to be consistent with each other, which should provide a small measure of confidence in the results.

 

Taipei 1

Taipei 1 is a good place to start since it was the only district in Taipei in which a KMT candidate faced a DPP candidate. The presidential and party list votes showed that this formerly blue district had turned light green:

Tsai 55.3 DPP 42.5
Chu 34.8 TSU 2.1
Soong 9.9 NPP 6.2
    KMT 27.2
    PFP 5.1
    New 6.5
    MKT 2.1
    F&H 2.2
    G/S 3.2
    Nine 3.0

There were five total candidates, but two got negligible votes and the third candidate got just a hair over 5%. It was fairly close to a straight KMT-DPP contest.

丁守中 Ting KMT 82649 43.77%  
吳思瑤 Wu DPP 95951 50.81% *
黃清原 Huang 台灣獨立黨 379 0.20%  
王靜亞 Wang MKT 9480 5.02%  
吳忠錚 Wu 健保免費連線 348 0.18%  

The overall totals are fairly similar across the three elections. However, this will not necessarily imply massive straight-ticket voting. If there are variations from precinct to precinct, the ecological regressions might show significant split-ticket voting. This could result if a candidate has a geographical stronghold, has done large and effective amounts of constituency service, or has some other cross-party appeal.

So what do the ecological regressions tell us? I ran regressions for the top three candidates:

  DPP KMT MKT
DPP .985 .008 .008
TSU 1.050 .061 -.139
NPP .712 .102 .173
KMT -.085 1.071 .016
PFP .031 .713 .240
New .138 .928 -.063
MKT .015 -.107 1.074
F&H .023 .961 -.010
G/S .827 .147 -.003
Nine .327 .531 .116
.      
Tsai .949 .033 .016
Chu -.041 1.050 -.009
Soong -.026 .550 .446

These results show a fairly straightforward party to party fight. Of the voters who voted for the DPP list, 98.5% also voted for Wu Si-yao. Likewise, these results show that 107.1% of the people who voted for the KMT list voted for Ting Shou-chung. Technically speaking that isn’t possible, so we should probably state the results much more imprecisely: Almost all of the DPP and KMT list voters also voted for that party’s district candidate. Supporters of the two smaller parties in the green camp, the TSU and NPP, mostly followed suit, though the number is a bit lower for the NPP. On the blue side, New Party list voters mostly supported Ting. However, the fourth blue camp party, the MKT, had its own district candidate. The regression results show that MKT list voters overwhelmingly voted for the MKT district candidate. The PFP and MKT allied on the presidential ticket. However, in the district race, PFP voters mostly supported the KMT district candidate rather than the MKT candidate. Of the two non-aligned parties, the Green/SDP Alliance voters mostly opted for Wu, while the Faith & Hope League overwhelmingly supported Ting.

I was particularly heartened to see that latter result. F&H is led by conservative Christians whose main demand is to stop marriage equality. Ting Shou-chung has been one of the strongest voices against marriage equality. It makes perfect sense to see overwhelming F&H support for Ting, though the regression models, which know nothing of the fight over marriage equality, would not have been predisposed to produce this result. Ecological regressions CAN impart knowledge!

Looking at the district race from the presidential perspective, the story is roughly the same. Almost all Tsai voters supported Wu; almost all Chu voters opted for Ting; and Soong voters split roughly half and half between Ting and the MKT candidate.

Remember, these coefficients overestimate party loyalty, so there was probably a bit more split-ticket voting than these results suggest. Still, Taipei 1 was pretty darn straightforward. I couldn’t have asked for a better example to set the stage for the other seven Taipei districts.

 

Taipei 2

Taipei 2 is the DPP’s strongest district in Taipei, and it is the only one that the DPP won in 2012. This year, the KMT basically gave up on this district and did not nominate its own candidate. Instead, it nominated a New Party city councilor. Nominating a candidate from a more extreme party seems a somewhat dubious strategy to appeal to the median voter in a green-leaning district. For this post, an obvious question is whether KMT supporters and other more moderate blue camp supporters all lined up behind the New Party candidate.

Here are the presidential and party list results:

Tsai 61.6 DPP 48.6
Chu 28.7 TSU 2.6
Soong 9.7 NPP 6.3
    KMT 23.4
    PFP 5.5
    New 4.9
    MKT 1.4
    F&H 1.8
    G/S 2.7
    Nine 2.8

The district race had seven candidates, but the top two candidates took over 95% of the total votes:

王銘宗 Wang IND 1342 0.74%  
陳民乾 Chen 台灣獨立黨 865 0.47%  
吳俊德 Wu F&H 3550 1.96%  
林幸蓉 Lin 健保免費連線 1561 0.86%  
潘懷宗 Pan New 65967 36.42%  
姚文智 Yao DPP 107366 59.29% *
陳建斌 Chen 自由台灣黨 433 0.23%  

The ecological regressions:

  DPP New Faith
DPP 1.012 -.014 -.004
TSU 1.284 -.532 .067
NPP .681 .209 .045
KMT -.056 1.058 -.010
PFP .228 .512 .037
New -.062 1.033 .029
MKT .236 .328 .237
F&H .030 .510 .622
G/S .457 .574 .083
Nine .446 .551 -.044
.      
Tsai .979 .000 -.002
Chu -.094 1.055 .050
Soong .178 .629 .068

The regression coefficients for party lists are a little far out of the bounds, especially for some for some of the smaller parties. -53.2% of TSU voters supposedly voted for Pan Huai-tzong, and the coefficients for the three equations don’t add up to anything near one for the NPP, PFP, or MKT. Maybe we shouldn’t take these results too seriously. Instead, let’s look at the regression coefficients for the presidential race. With only a 3×3 table, these bottom coefficients place fewer demands on the data. The bottom set of coefficients show another fairly straightforward green-blue contest, in which both sides mostly stayed loyal. The major exception is with Soong voters, of whom 17.8% voted for Yao Wen-chih. So KMT supporters did not seem to defect, but a fair proportion of Soong voters did. I would be cautious about attributing this to an extremist New Party candidate, though. As we go through different districts in the country, you will see that coefficients for Soong voters vary significantly from district to district, and 17.8% is a bit high but not terribly extreme.

 

Taipei 3

Now we are getting to the messier and more interesting districts. In Taipei 3, Wayne Chiang Wan-an defeated the KMT incumbent Lo Shu-lei in the primary, thus avenging his father’s own defeat at Lo’s hands in the primary four years prior. The DPP made a mess of this district. It originally nominated city councilor Liang Wen-chieh, but the self-appointed moral authority Lin Yi-hsiung protested and Liang was forced to withdraw. For months, it was not clear who the main green camp candidate would be. The DPP finally agreed to throw its support behind Billy Pan, a physician running as an independent who styled himself as a second Ko Wen-je. (Ko, however, did not seem so interested in supporting Pan.) This was a contentious decision in the DPP. Pan was strongly supported by the Hsieh faction, but he had less support from the rest of the party. Many green camp voters were also attracted to Social Democrat Lee Yan-jung. However, she ran a lackluster campaign. Until the last few weeks, she seemed more interested in her ideals than in actually winning votes, leading her to do things such as print fewer leaflets (such nasty and unenvironmental waste!).

The district, which had previously been solidly (though not overwhelmingly) blue turned a light shade of green. Here are the presidential and party list results:

Tsai 52.1 DPP 39.7
Chu 37.5 TSU 2.1
Soong 10.4 NPP 6.0
    KMT 28.3
    PFP 5.7
    New 8.4
    MKT 1.2
    F&H 2.2
    G/S 3.5
    Nine 2.9

The district race had ten candidates, but only three mattered:

高士恩 Kao 大愛憲改聯盟 541 0.28%  
潘建志 Pan IND 73797 38.41%  
林新凱 Lin 台灣獨立黨 794 0.41%  
趙燕傑 Chao IND 352 0.18%  
邱正浩 Chiu 和平鴿聯盟黨 450 0.23%  
陳科引 Chen IND 1448 0.75%  
李晏榕 Lee G/S 23706 12.34%  
李成嶽 Lee 軍公教聯盟黨 721 0.37%  
黃麗香 Huang IND 607 0.31%  
蔣萬安 Chiang KMT 89673 46.68% *

Chiang Wan-an won the district, putting the KMT’s first family back into the political fray. However, he did not get a majority; the combined totals of Pan and Lee would have defeated Chiang. Was Chiang’s victory due to the green camp’s failure to concentrate its support on one candidate?

  IND KMT G/S
DPP .867 .000 .125
TSU 1.041 -.080 .002
NPP .466 .155 .360
KMT .097 .878 .010
PFP -.362 .915 .236
New -.061 1.214 -.089
MKT -.376 .570 .587
F&H -.004 .612 .386
G/S -.024 .264 .792
Nine -.212 .932 .019
.      
Tsai .783 .020 .181
Chu -.049 1.023 .044
Soong -.052 .702 .123

The ecological regressions suggest that green camp supporters were not enthralled with Pan. Only 87% of DPP list voters supported Pan. 87% may sounds high, but in normal districts this figure was generally close to 100%. 87% is actually a disaster. Pan’s support was even lower among NPP voters, who gave almost as much support to Lee as to Pan. This ambivalent green camp support for the designated DPP ally is also evident in the presidential candidate regressions, where only 78% of Tsai voters supported Pan. In short, Pan failed to consolidate the green camp vote.

But wait, there’s more. Pan was an independent. When Ko Wen-je ran for mayor, we heard endless arguments that blue camp voters might be able to defect to Ko because he was not a DPP member. Did Pan eat into the blue camp vote? Or was Chiang able to consolidate all the blue votes?

The evidence here is mixed. The top set of regressions have some numbers that suggest there may have been significant numbers of voters who crossed the normal boundaries. Chiang only has 88% of KMT list voters, while nearly 10% of them voted for Pan. However, the bottom set of ecological regressions seems to indicate that Pan got almost no support from Chu or Soong voters. My guess is that the bottom numbers are probably closer to the actual result.

Both sets of regressions indicate that the blue camp lost some votes to the Social Democratic candidate. In the party list regressions, Lee took 24% of the PFP votes and 59% of the MKT votes. In the presidential regressions, she won 12% of Soong’s votes.

Overall, it looks to me as though Lee took votes from both camps, but she took significantly more from the green side. In the alternate world in which the DPP nominated Liang Wen-chieh and he consolidated almost all of the green camp support, it would have been a much closer race. The Chiang family had better remember to send a Christmas card to Lin Yi-hsiung.

 

Taipei 4

Taipei 4 might be the most interesting race of all; the rest of this post was mostly a by-product of a crazy idea that maybe I could figure out what happened in Taipei 4. In past years, this district had been nearly hopeless for the DPP. Several DPP city councilors were eager to run, but the party leaders refused to nominate any of them. Instead, Tsai Ing-wen doggedly insisted on cooperating with PFP city councilor Huang Shan-shan. I will not delve into the various strategic angles of this decision at this time. For this post, we simply note that it was a bit controversial for the DPP to collaborate with a PFP stalwart, even as the PFP chair ran against Tsai in the presidential race. Many traditional DPP supporters were unhappy at being instructed to vote for Huang Shan-shan. Without a major “true green” candidate in the race, minor party candidates volunteered to fill the void. TSU, NPP, and Social Democrat candidates all picked up significant numbers of votes. The top two candidates finished within 2% of each other, so a minor shift from any of the three minor candidates to Huang would have delivered the seat to her. Of course, as a PFP candidate, Huang presumably tapped into pools of support that normal green candidates could never hope to win.

At the national level, in 2016 the district shifted from a deep blue district to a tossup district.

Tsai 51.3 DPP 38.0
Chu 37.3 TSU 1.8
Soong 11.5 NPP 6.8
    KMT 28.9
    PFP 6.2
    New 7.8
    MKT 1.5
    F&H 2.4
    G/S 3.3
    Nine 3.3

The district race was extremely fragmented, with five relevant candidates:

何偉 Ho IND 2497 1.16%  
陳尚志 Chen G/S 10278 4.78%  
黃珊珊 Huang PFP 85600 39.86%  
李岳峰 Lee 和平鴿聯盟黨 251 0.11%  
陳兆銘 Chen 台灣獨立黨 568 0.26%  
李彥秀 Lee KMT 89612 41.73% *
蕭亞譚 Hsiao TSU 13648 6.35%  
林少馳 Lin NPP 12246 5.70%  

Here are the ecological regressions:

  PFP KMT TSU NPP G/S
DPP .627 .121 .128 .077 .033
TSU -.080 .760 .270 -.074 .123
NPP .399 .081 .082 .228 .142
KMT -.124 1.106 -.021 .005 .043
PFP .721 .377 -.041 .009 -.056
New .736 .288 .010 -.008 -.048
MKT .048 .047 .187 .353 .134
F&H .750 .035 .202 -.131 .099
G/S 1.152 -.929 .134 .219 .397
Nine .339 .538 -.001 .082 .001
.          
Tsai .501 .200 .131 .091 .061
Chu .202 .739 .004 .007 .050
Soong .573 .359 -.048 .065 -.019

There is exactly one coefficient in this table that looks normal. 111% of KMT voters supported Lee Yen-hsiu. Ok, that isn’t quite logical, but the idea the almost all of the KMT party list voters also voted for the KMT district candidate is the most normal thing about this table. Lee didn’t get a lot of the other blue camp votes, though. Nearly three-fourths of PFP and New Party voters opted for Huang. On the other hand, Lee did get 12% of DPP list voters. In addition to that 12%, a further quarter of DPP list voters plumped for one of the three minor party candidates, leaving a mere 63% for Huang. A very, very large number of DPP voters were unwilling to support a PFP nominee, regardless of Tsai Ing-wen’s entreaties.

It’s important to note that there are a lot of really strange results on this table that are probably simply wrong. If we take the numbers literally, most TSU list voters supported the KMT candidate (even though there was a TSU candidate in the race); the models can’t figure out what happened with nearly half of the MKT voters; and negative 92% the Green/Social Democrat voters supported Lee Yen-hsiu. We are probably demanding too much of the data.

Nonetheless, the regressions from the presidential election largely echo those from the party list election. Tsai’s voters were fragmented in the legislative race, with half voting for Huang and the other half splitting their votes among the other four candidates. Three fourths of Chu’s voters supported Lee, but 20% opted for Huang. Among Soong voters, more supported Huang than Lee.

What can we conclude? The DPP strategy partly succeeded and partly failed. Their ally took quite a few blue votes. If Huang had been able to add all the green votes to her blue votes, she would have won. However, the green camp clearly did not unite behind Huang. Hordes of green voters could not stomach supporting a candidate from the other side of the political spectrum. Judged narrowly by the district election outcome, the DPP’s alliance strategy was a failure. Not only did it not win, it also angered and frustrated a large number of sympathizers.

 

Taipei 5

Taipei 5 was one of those districts that the DPP erroneously deemed as “difficult.” It had lost by a lot in 2008, and by a fair amount in 2012. Nonetheless, this was a district that the DPP could hope to compete in with a mildly favorable partisan swing. In the tidal wave of 2016, this district turned a clear green.

Tsai 53.4 DPP 41.3
Chu 36.3 TSU 2.2
Soong 10.4 NPP 5.9
    KMT 28.3
    PFP 5.7
    New 7.1
    MKT 1.4
    F&H 1.9
    G/S 2.8
    Nine 3.3

The district race was essentially a two man race, featuring two of the most different people imaginable. On the on hand, the KMT featured a long-term incumbent, Lin Yu-fang. Lin was straight-laced, professional, conservative, and a committed Chinese nationalist. Facing him was Freddy, the death-metal rocker. In a result that almost certainly still bewilders Lin Yu-fang, Freddy won:

林郁方 Lin KMT 76079 45.58%  
李家幸 Lee 台灣獨立黨 885 0.53%  
黃福卿 Huang IND 587 0.35%  
洪顯政 Hung 大愛憲改聯盟 478 0.28%  
龔偉綸 Kung IND 1710 1.02%  
林昶佐 Lim NPP 82650 49.52% *
尤瑞敏 You 樹黨 4506 2.69%  

How did Freddy win? Did the NPP open up some new constituency, or was it simply a DPP campaign clad in yellow? The ecological regressions:

  NPP KMT Tree
DPP .971 -.014 .018
TSU .845 .242 -.130
NPP .953 .048 .057
KMT -.038 1.021 .023
PFP .116 .765 .042
New -.043 1.037 .008
MKT -.261 .935 .163
F&H .308 .682 -.037
G/S .749 .046 .146
Nine .064 .611 .123
.      
Tsai .936 .019 .020
Chu -.056 1.030 .021
Soong .152 .699 .085

For the most part, this looks like a classic blue-green contest. Lin Yu-fang got almost all of the KMT, New Party, and MKT votes, while Freddy soaked up almost all of the DPP, TSU, and NPP votes. The only number that might be a bit surprising is the 11% of PFP voters who opted for Freddy. In doing this exercise, I’ve noticed again and again that there seems to be a minority of both NPP and PFP voters who will support the other one. During the campaign, I also remember social movement activists who seemed to differentiate between the PFP and the other blue parties. I suspect that there was a pool of voters that liked both the PFP and NPP (and perhaps also the Social Democrats). If so, it would make sense that this pool was prone to split its support among those parties.

 

Taipei 6

This is the forgotten race of Taipei. Taipei 6 has always been a deep-blue stronghold, and the DPP was fully justified in looking for an ally here. They opted to support Fan Yun, the well-known sociologist and activist. Unfortunately, she turned out to be an uninspired candidate. It turns out that elections and social movements are completely different animals.

Tsai 47.0 DPP 34.2
Chu 42.9 TSU 2.0
Soong 10.1 NPP 5.5
    KMT 30.3
    PFP 5.2
    New 11.1
    MKT 1.6
    F&H 2.9
    G/S 3.9
    Nine 3.2

This is the most highly educated district in Taiwan, and, perhaps not coincidentally, it always draws large numbers of useless candidates. This time there were twelve candidates, but only two got more than 4%. That isn’t to say this was a classic one-on-one race; the two top candidates combined for only 81% of the votes.

陳家宏 Chen 樹黨 1986 1.23%  
范雲 Fan G/S 56766 35.35%  
龎維良 Pang IND 2963 1.84%  
趙衍慶 Chao IND 3212 2.00%  
林珍妤 Lin 台灣獨立黨 1328 0.82%  
周芳如 Chou IND 4120 2.56%  
蔣慰慈 Chiang IND 271 0.16%  
鄭村棋 Cheng 人民民主陣線 4927 3.06%  
曾獻瑩 Tseng Faith 4826 3.00%  
蔣乃辛 Chiang KMT 74015 46.09% *
古文發 Ku 大愛憲改聯盟 184 0.11%  
吳旭智 Wu MKT 5962 3.71%  

Chiang Nai-hsin, the KMT incumbent, was held under 50%, but he won by a comfortable margin since Fan Yun only managed 35%. Did she fail to consolidate all the potential green camp voters? Was her poor showing the result of the same process as in Taipei 4, where DPP voters refused to obediently follow their party leaders’ instructions? Here are the ecological regressions for the top five candidates:

  G/S KMT MKT Faith Cheng
DPP .857 -.038 .008 .018 .046
TSU .733 .180 .012 .105 -.050
NPP .542 .337 -.175 -.046 .061
KMT -.066 .998 .066 -.019 -.006
PFP -.017 .744 .026 .007 .153
New .136 .828 .006 .012 .086
MKT -1.172 .662 1.312 -.014 -.002
F&H .367 -.074 -.028 .842 -.002
G/S .963 -.034 -.045 .005 -.052
Nine -.287 .361 .124 .122 -.027
.          
Tsai .788 .017 .004 .032 .034
Chu .018 .871 .036 .036 .027
Soong -.238 .782 .195 -.006 .029

These results indicate that both of the two main candidates had some difficulties consolidating potential votes, though it was nowhere near the scale of Taipei 4. Chiang was somewhat more successful, winning almost all of the KMT votes and 87% of Chu’s voters. Most DPP voters went along with Fan, but not all. 86% voted for her, and 79% of Tsai’s supporters also did so. Perhaps the most interesting numbers are from the NPP supporters. Since the NPP and Social Democrats were both born out of the Sunflower Movement, I expected that NPP voters would enthusiastically support Fan Yun. The regression coefficients say otherwise, indicating that she only won 54% of NPP list voters.

 

Taipei 7

Taipei 7 nearly turned out to be one of the biggest shocks of election night. This had always been a deep, deep blue district. I questioned the DPP’s decision not to nominate its own candidate in many districts, but not this one. Defeating incumbent Alex Fai with an ordinary DPP candidate seemed rather hopeless to me, and the DPP needed to try something new. It turned to Yang Shih-chiu, an erstwhile KMT city councilor. Yang had dropped out of the KMT to run as an independent, but he had longstanding blue credentials. The DPP’s hope was that Yang could keep some of his blue voters and that they could deliver all of their green voters. This was a reasonable strategy, though I had very little reason to believe it would succeed.

Shockingly, this deep blue district experienced such a big partisan swing that that the district seat came into play.

Tsai 49.6 DPP 37.2
Chu 39.6 TSU 1.9
Soong 10.8 NPP 6.0
    KMT 29.9
    PFP 5.6
    New 8.9
    MKT 1.5
    F&H 2.2
    G/S 3.4
    Nine 3.4

The district race turned out to be very close. This was even more surprising since there was a G/S candidate available to soak up any protest votes from green camp supporters who couldn’t stomach voting for Yang:

林文傑 Lin IND 1063 0.64%  
呂欣潔 Lu G/S 17747 10.73%  
詹益正 Chan IND 625 0.37%  
蘇承英 Su 和平鴿聯盟黨 689 0.41%  
范揚律 Fan 大愛憲改聯盟 231 0.13%  
林芷芬 Lin 台灣獨立黨 588 0.35%  
費鴻泰 Fai KMT 74455 45.04% *
楊實秋 Yang IND 69882 42.28%  

Here are the ecological regressions for the top three:

  IND KMT G/S
DPP .832 -.015 .161
TSU 1.162 -.004 -.152
NPP .971 -.230 .368
KMT -.072 1.062 -.019
PFP .160 .544 .201
New .258 .845 -.046
MKT .306 .636 -.027
F&H .612 .496 -.044
G/S .138 .100 .686
Nine -.008 .678 .145
.      
Tsai .789 .009 .182
Chu .024 .985 -.007
Soong .200 .518 .186

The picture these numbers paint is fairly clear. Fai consolidated almost all of the KMT votes, but Yang ate away at rest of the blue camp vote pool. If DPP voters had unified behind Yang, he would have won. However, they were unable to do this, and about 16% of DPP voters protested by voting for the G/S candidate. Probably the very same qualities that enabled Yang to win a number of blue votes were also the qualities that prevented him from getting all the green votes.

A quick note: Alex Fai has been a prominent opponent of marriage equality, so you might expect him to win all the Faith & Hope votes. However, Yang Shih-chiu is a Christian, something I know because most of his campaign ads featured a cross somewhere in the logo. It is thus reasonable to see that they split the F&H vote.

 

Taipei 8

Taipei 8 was almost exactly the same story as Taipei 7. This was a hopeless district for the DPP, so they cooperated with a KMT city councilor who had left the party to run for legislator. In this case, they cooperated with independent Lee Ching-yuan, who had previously been a member of the KMT, PFP, and New Party. As in Taipei 7, there was also a G/S candidate ready to soak up protest votes.

The big difference between Taipei 7 and Taipei 8 is that the latter is even deeper blue. There would be no election night suspense in this district. Even with the 2016 DPP tidal wave, this remained a solidly blue district.

Tsai 44.2 DPP 31.5
Chu 44.3 TSU 1.7
Soong 11.4 NPP 6.0
    KMT 32.1
    PFP 5.9
    New 10.4
    MKT 1.4
    F&H 3.2
    G/S 4.0
    Nine 3.8

The KMT incumbent easily won the district race:

李慶元 Lee IND 60459 35.79%  
賴樹聲 Lai IND 1071 0.63%  
苗博雅 Miao G/S 21084 12.48%  
賴士葆 Lai KMT 83931 49.69% *
陳如聖 Chen 台灣工黨 808 0.47%  
方景鈞 Fang IND 1540 0.91%  

The ecological regressions:

  IND KMT G/S
DPP .956 -.054 .077
TSU .029 .302 .615
NPP -.036 .420 .540
KMT .124 .921 -.045
PFP .584 .370 .021
New -.372 1.220 .120
MKT -.037 .650 .362
F&H .015 .867 .113
G/S .586 -.437 .884
Nine -.004 .530 .366
.      
Tsai .828 -.022 .171
Chu -.088 1.020 .066
Soong .274 .477 .169

There are some differences from Taipei 7, but I think the overall story is roughly the same. In these models, the top set of regressions don’t seem quite right to me. I’m a little skeptical that almost zero TSU or NPP supporters voted for Lee Ching-yuan, but 30-40% of them voted for the KMT candidate. I think it may be wiser to look to the bottom set of results. Nearly all the Chu voters stayed loyal to the KMT, but roughly a quarter of Soong voters shifted to Lee. However, Lee could not win all the Tsai voters, as 17% of them defected to the G/S candidate.

 

Conclusions

We observed several patterns. (1) When the KMT or DPP nominated its own candidate, that candidate was generally able to consolidate the entire KMT or DPP vote as well as most of the allied small party vote. (2) When the KMT or DPP supported a candidate from an allied party (New Party in Taipei 2, NPP in Taipei 5), there wasn’t much difference from the cases in which they nominated their own candidates. (3) When the DPP supported an independent who had formerly been part of the blue camp, that candidate was able to win some blue votes. However, roughly a fifth of DPP voters refused to go along with the strategy, thus negating those gains. (4) When the DPP supported Huang Shan-shan, who was still a member of a party actively competing with and usually opposed to the DPP, there was a widespread rebellion within the party. Only about half of Tsai’s voters voted for Huang. The losses from unhappy green voters probably outweighed the gains from luring over blue voters.

Please remember that ecological regressions are a very shaky method. These results are not definitive in any way, and there is a possibility that they are entirely wrong. I don’t think they are generally misleading, but I might be wrong.

 

Protests against pension reform

March 22, 2017

This morning I was downtown, and I walked by the legislature. There is a group of people opposed to the DPP’s proposed pension reform who have been protesting outside it for a few weeks now. They label themselves the 800 heroes, and have claimed that if President Tsai persists they will turn into 8000 or maybe even 80,000. However, when I walked by I think there may have been closer to 8 than 80, much less 800. Well, it was lunchtime, so maybe they were busy.

The legislature has beefed up security. The government clearly doesn’t want to allow another occupation of the legislature by protesters.

Photo 22-03-2017, 11 27 40 AM

Anyway, they have lots of signs and banners up identifying their members and stating their views. Let’s look at some of the photos.

Photo 22-03-2017, 11 24 24 AM

This one identifies them as members of various graduating classes from the military academy.

Photo 22-03-2017, 11 25 54 AM

Here they are complaining that their benefits that they were promised are being taken away. This is supposedly the heart of the controversy.

Photo 22-03-2017, 11 24 10 AM

This sign says they are both against unfair pension reform and “mobbing.” Does that mean that they don’t believe protesters should be allowed to affect public decisions? No? Maybe only the “good” protesters should be heard.

Photo 22-03-2017, 11 29 15 AM

“Pension reform; first change the ministers and legislators.” Actually, the Ma administration tried pension reform, but it went nowhere. So just over a year ago, we changed the the ministers and legislators. Public opinion doesn’t seem to be clamoring to change them again just yet.

The sign on the right says, “Oppose Tsai Ing-wen’s cultural revolution-style struggle.”

Wait, what?  Did I miss something? While I was looking the other way, did Tsai become tremendously charismatic, institute a cult of personality, mobilize mobs of students to hold a massive demonstration demanding that she sweep away the regular institutions of government and the conservative members of the DPP in order to impose a pension reform? Are those students organizing themselves into paramilitary bands, arresting opponents, and holding struggle sessions?  Did I miss that? Well, what exactly do they think happened in the Cultural Revolution? Do they really think it was about pension reform or getting a majority of votes in the national legislature?

Photo 22-03-2017, 11 24 46 AM

Um, maybe a good place to start would be by studying some Chinese history so you don’t make a fool of yourself when you invoke the Cultural Revolution.

Somehow, we don’t seem to be talking about pension reform any more.

Photo 22-03-2017, 11 24 59 AM

Is that what they mean by reviving Chinese culture? Institutionalizing inequality? Ok, maybe that little bit of snarkiness was unfair, but what the heck is this sign asking for? Every democratic constitution in the world sets out formal equality of all citizens as a fundamental principle. Opposing equality is like opposing families or prosperity; you aren’t going to get very far if that is your appeal.

I guess I’m just a bit confused by these protesters. Maybe they are revealing a bit too much about themselves.

Weighted population density

March 15, 2017

A few months ago, as I was lying in bed unable to sleep, I found myself on the Wikipedia page for Bihar, a state in northern India. Now, I’ve never been to India, and if I have the good fortune to travel there at some time in the future, I can’t imagine the first place I will go is Bihar. For me, Bihar is an intellectual construct rather that a real place. Having said that, let me introduce you to (my imagined) Bihar. Bihar lies along the Ganges River, so I’m imagining a mostly flat river valley. It has a population of 103 million, which is enormous. (Locals probably aren’t impressed, because neighboring Uttar Pradesh has twice as many people.) Bihar has a population density of 1,100 people per square kilometer, which is pretty high. In fact, that figure is roughly equal to that of (neighboring) Bangladesh, the densest country in the world other than city-states such as Singapore. But what really woke me up is that the biggest city in Bihar only has 2 million people, and the second largest city only as about half a million people. There are about a dozen cities in the 150,000-400,000 range, and everything else is smaller. Depending on how you define urban, only 5-10 million of those 103 million people in Bihar are urban. That leaves a lot of rural residents. To put it another way, that population density is probably a pretty accurate figure of what you might find. Pick a random square kilometer in Bihar, and you will probably find a mostly rural, agricultural place with 600-1000 people living in it.

Sometimes it is hard to imagine what population density means, and it is easier if we scale it down to more familiar terms. In the USA, we would use a football field; Europeans would use a soccer field, but since this is a Taiwan-centric blog we must use a baseball field. Fair territory in the average major league baseball field is roughly 2.5 acres, or almost exactly 0.01 km2. So all we have to do is take two zeros off all those population density figures and imagine them on a baseball field. If we guess that Bihar’s rural population density is 800 people/km2, then imagine a baseball field with eight residents. They are farmers, so most of the land will be planted in rice and vegetables, and there might be a few chickens or a cow wandering about. Eight people will have one or two houses. Don’t forget to leave a bit of land for communal properties, such as a temple. There will also be one or two small roads or paths cutting through the outfield. I don’t know if they would have irrigation channels, and maybe they have a small fish pond. But imagine baseball field after baseball field, each supporting six to ten people, almost all engaged in some sort of agriculture.

I tried imagining Bihar in Taiwanese terms. Take one Kaohsiung City (minus the mountainous areas), add in a handful of Chiayis (city plus county minus mountains), add 25 copies of Yunlin, and then add 75 more Yunlins without Douliu (the biggest city). It’s still probably too urban, but that’s about as close as we get to dense and rural in Taiwan.

Of course, this post is not about Bihar; my imagined Bihar is just a new angle from which I can think about Taiwan. Taiwan’s official population density is 650 people per square kilometer, which I a bit lower than Bihar. Among non-city states, Taiwan is second only to Bangladesh. However, as you are certainly aware, Taiwan’s population is not distributed evenly. If you randomly choose a square kilometer of land, chances are you will hit a mountain with fewer than 5 people/km2. Throw your dart a few more times, and you’ll hit a place like rural Yunlin, with 200 people/km2. Keep throwing it and you might hit a town center. Then the number will jump to 2000-5000 people/km2. And once in a while, you’ll hit a metro city center, where you might find 50,000 people in your square kilometer. Taiwan and Bihar have similar population densities, but Taiwan’s number is a lie. It tells you almost nothing about how people actually live.

Stop for a second and think about that baseball field again, this time with the population density of a city center. As many as 500 people might live in fair territory. But wait, city centers typically dedicate one third to one half of the land area for roads. There will probably be one high traffic two-lane road cutting straight through the middle of the field with a lot of alleys all over the rest of it. Let’s assume that 40% of fair territory is covered in pavement for roads. A high proportion of road space is occupied by parked cars. 500 people might have 100 cars to park, though perhaps 20 of those will park underground. Then think about businesses. In Taiwan, almost all city centers mix residences and businesses together. Some of the land will be used for banks, noodle shops, beauty parlors, motorcycle repair shops, half a 7-11 and a fourth of a Family Mart, some fraction of an elementary school, a post office, a traditional market, and so on. Let’s just assume that almost no one lives in the infield. After all that, we have to figure out where the 500 people will live. I don’t think we can do this with those gleaming new luxury apartments. Those types of buildings tend occupy quite a bit of space, since they often have courtyards and other green space on the ground level. Besides, since the new units are often bought up by real estate speculators, many of the are empty. There are two viable routes to getting our 500 people. On the one hand, we could put up one of those monstrous behemoths, with 15 floors and eight units on each floor. Most of these 120 units will be full, though the ground floor might be devoted to shops. If we assume an average of three people live in each unit (in reality, the average is probably lower), that gets us to about 330 people. We’ll need one and a half of these monsters, so one will go take up all of right and most of center field, and the other will straddle the left field foul line (and be shared with the neighboring baseball field). One advantage of a huge complex is that it doesn’t require as many roads. There are certainly not lots of little alleys crisscrossing the complex. There should be a fair amount of outfield grass left over. The other possibility is to crisscross the entire outfield with a maze of alleys and fill in the spaces with lots of small buildings. Most people in Taiwan still live in the workhorse of Taiwan city centers, the ramshackle four floor ugly cement block with no elevator and an illegal addition on the fifth floor. These units tend to be about 30 ping, or 900 square feet. Assuming three people per unit and a few vacant units, you will need to to cram about 35 of these five floor buildings into the outfield. I’m guessing you will need three arcs with eight, twelve, and sixteen buildings, respectively. Either way, I think it is safe to say the 500 people living in this baseball field experience everyday life differently than the eight people on the baseball field in Bihar.

As a political scientist, I’ve been frustrated by population density for decades. We haven’t uncovered a lot of strong patterns relating to urban and rural voters, and I’m convinced that one of the reasons is that we are terrible at measuring population density, which is one of the key components of almost every operationalization of urbanization. To give an example, the population density of Xindian District 新店區 in New Taipei City is 2507 people/km2. That’s not very different from places such as Miaoli City (2373) or Shalu District 沙鹿區 (2257) on the outer edge of the Taichung metro area. Miaoli and Shalu are populated, but I’ll eat my hat if they are anywhere near as urbanized as Xindian. Xindian is part of the core Taipei metro area; that’s a goddamn city! You probably know why these places have similar population densities. Much of Miaoli and Shalu are mostly flat, while most of Xindian is mountainous. Good luck trying to quantify that, though.

What I’ve always wanted to do is look at population density by tsun and li 村里, the unit below townships. (For brevity, I’m going to simply call these “li.”) My big idea is to produce a weighted density, with each li weighted by its population. To illustrate, consider weighting Taoyuan by each district. Taoyuan City officially has a population density of 1760 people/km2, but that figure is pulled down by Fuxing District, which accounts for over a fourth of the total land area. If you weight by where people live, the experience of the 20.2% of the population living in close quarters in Taoyuan District becomes the most important element, not the experience of the 0.5% who live in Fuxing District. I think Taoyuan City’s weighted population density of 5110 people/km2 is a much better reflection of how people actually live than the raw figure of 1760. Of course, I’d like to go a step further and weight by li instead of by district.

District Pop Area density share subtotal
All 2147763 1219.98 1760.5   5110.8
桃園區 434243 34.80 12476.6 0.202 2522.6
中壢區 396453 77.88 5090.5 0.185 939.6
大溪區 94102 105.52 891.8 0.044 39.1
楊梅區 163959 86.55 1894.3 0.076 144.6
蘆竹區 158802 75.71 2097.5 0.074 155.1
大園區 87158 87.39 997.3 0.041 40.5
龜山區 152817 71.93 2124.5 0.071 151.2
八德區 192922 33.69 5726.7 0.090 514.4
龍潭區 120201 75.24 1597.6 0.056 89.4
平鎮區 221587 47.80 4636.2 0.103 478.3
新屋區 48772 84.90 574.5 0.023 13.0
觀音區 65555 87.79 746.7 0.031 22.8
復興區 11192 350.78 31.9 0.005 0.2

 

Is this a reasonable method? If I were interested in trees or watersheds, it would not be. However, I am interested in how people experience everyday life and how their patterns of living shape political attitudes. From that perspective, it is entirely appropriate to put more emphasis on the heavily populated areas and less emphasis on sparsely populated ones. I also think that a li is a pretty good unit for this. A li is big enough to cover a person’s immediate neighborhood. In comparison, a district is often so big that a many parts of it are irrelevant to a given person’s everyday experience. Philosophically, I think this is a pretty good measure. Mathematically, it has a serious problem. The weighted population density depends heavily on how you cut up a given territory. Cutting Taoyuan into one piece (ie: not cutting it at all) gives a weighted density of 1760. Cutting it into the thirteen pieces defined by districts yields a weighted density of 5110. If I cut it into several hundred smaller pieces, I can get a weighted density of over 10,000. If the government redraws li boundaries, as it sometimes does, the number can go up or down. Unlike district boundaries, li boundaries are commonly redrawn whenever the population grows or shrinks dramatically. Nonetheless, I think this imperfect measure is still an improvement over raw population density. As an analyst, I cannot manipulate the numbers at will; I am limited by the government’s decisions to draw the boundaries in one particular way.

 

This measurement was only a pipe dream until a few weeks ago when I stumbled upon the mother lode. I found a spreadsheet with the population and area of every li in Taiwan. I don’t have any idea who put these data together or why they put them up on the internet. I am simply grateful. There is no date on the spreadsheet, but judging from the population figures, I estimate these data are from some time in 1990. That’s right, I can tell you all about population density 27 years ago. What about today? Finding population data for each li is the easy part. The hard part is finding the area of each li. In many townships the li boundaries have not changed in 27 years, so I can simply copy from the old spreadsheet. However, the places with no changes are places with very little population growth. Nearly every urban district has had an adjustment in li boundaries at some point over the past 27 years. So I started searching online, district by district. Taipei City was the best. In Taipei, every district posts an annual document of official statistics on its website, including the area of each li in the district. Outside of Taipei, it was rare to find such good fortune. Once in a while, I’d find a place like Xizhi, which posted the statistical abstract but left that one critical column in that one critical table completely blank. If I couldn’t find an official document with the area of each li, I tried looking for an official document published by DGBAS stating how the boundaries had been changed. In most cases, the document stated that a certain li had been split off from another li or had been folded into another li. In those cases, I could combine the areas or populations of the two concerned lis. When districts had added lis (most of the changes), I was essentially trying to undo the changes and return to the 1990 boundaries. This could make for some enormous lis. For example, in 1990 Zhongpu Li 中埔里 in Taoyuan District (then Taoyuan City) had a population of 9368. By 2016 it had been split into nine lis with a combined population of 58371. Since I only have the 1990 area, my 2016 spreadsheet collapses them all into one case. After all this, there were still a few places where the li boundaries had changed but I could not find an official record of those changes. For these last few infuriating places (looking at you, Xizhi, you jerkface), I used the eyeball test comparing maps from 1991 to maps from 2015 to see where the new li had come from. I have 2015 maps on my GIS program; the 1991 maps came from the TPGIS system, an invaluable resource put together by my colleague Chi Huang. (Go spend some time on it. It’s fun! The current version is a bit slow, but version 2.0 is coming out soon.) To my sadness, I was never able to find area stats for li in Kinmen or Lienchiang Counties. Those weren’t in the 1990 spreadsheet (probably since they were still under martial law), and there were no stats on their websites. Sorry Kinmen and Matsu, I have to ignore your existence in this post. If all this sounds like a tedious and time-consuming task, well maybe my idea of entertainment is different than yours.

 

Before I show you township-level maps, let’s look at the city and county level numbers. These are the data for 2016. Density is the regular calculation of population density (population/area). The regular numbers make Taiwan look a lot like Bihar. However, once the numbers are weighted by where people actually live, the picture looks a lot different. The typical person lives in an area with a population density of 14893. This is a decidedly urban society.

The difference in individual cities and counties is striking. Taipei City is ringed by mountains, and once we downweight all those areas, we find the typical person lives in a place with 31000 people/km2, not 10000. The difference in most other places is even more dramatic. New Taipei now looks just as dense as Taipei. Among the six metro areas, only Tainan has a weighted density of less than 10000. Of course, Tainan has a lot of rural agricultural areas – the old Tainan County was significantly bigger than the old Tainan City – so maybe it isn’t surprising that the weighted density for Tainan is a notch lower. From decades ago, I always felt that Nantou was more urban than Miaoli, Chiayi, or Yunlin, but the numbers said it wasn’t. Weighted density says that my gut was right all along. Most people in Nantou live in the downtown areas of the four biggest towns rather than in the vast mountainous areas, so the weighted density is 17.9 times the conventional population density. The smallest difference is in Yunlin, where the weighted density is only 2.93 times the conventional population density. Yunlin is, not surprisingly, mostly flat, rural, and agricultural. Changhua is even flatter, but it has a bigger urban area than Yunlin so its weighted density is inflated a bit more (3.86 times). Flat cities are similar. Hsinchu and Chiayi Cities are mostly flat, and their population is spread out fairly evenly throughout the entire territories. The weighted densities are only 3.04 and 2.18 times the regular densities, respectively. Finally, there is the east coast, where much of the population is concentrated in the Hualien and Taitung urban areas. In Hualien, the weighted density is nearly 50 times higher than the regular density. All in all, weighted population density paints Taiwan in an entirely different light than conventional population density.

  Pop16 Area Density16 Weighted16
National 2695704 35963.49 650.4 14893.0
Taipei 2695704 268.11 10054.5 31166.2
Kaohsiung 2779371 2947.94 942.8 14939.6
New Taipei 3975564 2022.35 1965.8 31274.2
Taichung 2761424 2227.10 1239.9 11518.2
Tainan 1886033 2194.29 859.5 8179.5
Taoyuan 2148606 1216.35 1766.4 10089.7
Yilan 457538 2115.68 216.3 4019.5
Hsinchu County 547481 1421.44 385.2 4326.3
Miaoli 559189 1817.81 307.6 2940.0
Changhua 1287146 1076.37 1195.8 4620.6
Nantou 505163 4106.50 123.0 2201.8
Yunlin 694873 1291.64 538.0 1577.3
Chiayi County 515320 1901.67 271.0 1378.6
Pingtung 835792 2777.54 300.9 3229.3
Taitung 220802 3510.37 62.9 2343.9
Hualien 330911 4627.38 71.5 3529.9
Penghu 103263 123.08 839.0 5021.2
Keelung City 372100 131.94 2820.2 11369.1
Hsinchu City 437337 104.46 4186.6 12737.7
Chiayi City 269874 70.51 3827.6 8341.2

 

What does this look like on a map? I thought you’d never ask. Compare a regular population density map with weighted population density. The urban areas suddenly look much larger and much denser, especially in the north.

dense1.png

dense2.pngWeighted population density is always greater than population density. Theoretically, it could be equal, but in practice there is always some inflation. The question is simply how much inflation there will be. If the original area (city, town, county, country) has the same population density everywhere, then the weighted density will not be much greater than the conventional density. If there is a large amount of variance, though, the two numbers might be very different. For example, take the southeastern corner of the Taichung metro area. This is a map of population density by li in three districts: East, Dali, and Taiping. (East is upper left; Dali is lower left; Taiping is on the right.) Years ago, when I used to live in that area, I often wondered why people thought of them so differently. Taiping and Dali didn’t see any less citified than East to me. The official population densities insist that these are differences. East and Dali are cities, though hardly dense ones (8119, 7282, respectively). Taiping is suburb, perhaps maybe not even a part of the urban area (1542). On the map you can see the problem with this. Two-thirds of Taiping’s land is mountainous with fewer than 100 people/km2. The city part, however, is real city. If anything, it looks a bit more dense than neighboring East. Weighed density tells us that these two places are roughly the same. East, which has roughly the same density everywhere, is barely inflated at all (9966). Taiping is inflated significantly, to 10940. Like Taiping, Dali has quite a bit of variance in its density. Unlike Taiping, though, the sparsely populated areas only make up a small part of Dali’s overall territory. Its weighed density (18099) tells us that it is significantly more urbanized than either East or Taiping. Compared to the raw density, the weighted density for Taiping has been inflated 7.09 times; for Dali, 2.49 times; and for East, only 1.23 times.

dense3.png

Remember that I started this with data from 1990? Hey, let’s look at the data from 1990! Here’s a map of each town’s weighted density back then. You might notice that there is an arrow pointing to an orange town in south-central Taiwan. That town is Beigang 北港鎮. I e or less expected to see all the other dense places, but Beigang took me by surprise. If you had told me there would be one fairly dense place between northern Changhua and Chiayi City, I might have guessed Douliu 斗六市, Erlin 二林鎮, Huwei 虎尾鎮, and Xiluo 西螺鎮 before I guessed Beigang. Normal population density doesn’t indicate anything special about Beigang. The conventional population density in 1990 was only 1197, but the weighted density was 12020, an order of magnitude higher. How does that happen? Look at the picture of Beigang (1990 on left; 2016 on right). A whole heaping chunk of the population was jammed into a tiny urban area. That dense little area adds up to almost exactly one square kilometer, and in 1990 it held about 22,000 people. The rest of the town had about 27000 people in 40 square kilometers. Most city cores taper off gradually; Beigang goes from dense city straight to rural countryside with almost no transition. I confess I don’t remember much about the town except for the market right near the spectacular temple. I have no idea if there is some natural barrier that separates the city center from the rest of the town. The discovery that Beigang was a dense (if tiny) city makes me wonder about the birth of the Tangwai in Yunlin. Su Tung-chi 蘇東啟 started from Beigang in the early 1960s, and now it seems possible that he was a product of urban discontent, similar to Kao Yu-shu 高玉樹, Hsu Shih-hsien 許世賢, Huang Hsin-chieh 黃信介, Ho Chun-mu 何春木 and many of the others. (Former Yunlin county magistrate and current legislator Su Chih-fen 蘇治芬 is Su Tung-chi’s daughter. After he was arrested, her mother Su Hung Yueh-chiao 蘇洪月嬌 and elder sister Su Chih-yang 蘇治洋 were elected to the provincial assembly.)

dense13.png

dense4.png

You’ll notice that Beigang doesn’t stick out on the 2016 map. By 2016, its weighted density had plunged to only 7606 due to two trends that I’ll turn to now.

First, let’s turn away from population density momentarily to consider raw population growth. From 1990 to 2016, the overall population grew from 20.5 million to 23.4 million, or just about 15%. This map shows the ratio of 2016 to 1990 population, so anything below 1.15 is below the national average.

dense5.png

All the growing areas – the yellow, orange, and red areas – are in the north and the major urban areas. The rural areas, especially on the east coast and in southern Taiwan, are a sad, sad sea of blue and darker blue. These places have been losing population for the past 25 years, some of them at an alarming rate. There are two exceptions. Many of the majority indigenous townships are holding steady, or at least losing population more slowly than the nearby Han townships. My guess is that this reflects higher birthrates among indigenous people. The other is that single bright orange dot on the west coast, about halfway between Taichung and Tainan. That town is Mailiao 麥寮鄉, home to the huge Formosa Plastics plant. Mailiao has grown 42% while all the nearby towns have shrunk by about 20%. My guess is that an overwhelming majority of my readers have negative attitudes toward huge petrochemical facilities like the one in Mailiao. For you guys, this dot of orange in an ocean of unhappy blue is just something to keep in mind.

I said that there are two trends behind Beigang’s decline. The first is that rural areas, including Beigang, are losing population. The second trend is that city cores are also losing population. The big cities are growing, but this growth is in the ring around the old city core. I like to think of urban growth as a donut. Taichung has the cleanest example. The old city core has lost population, with the densest areas having lost the most. Central District is so tiny that it is hard to see that it is dark blue; its raw population density plunged from 39131 to 21251. East, West, and North Districts are also blue, but they are surrounded by a ring of orange and red. There is a second half ring to the west of slower growth. Then there is a light blue ring around that, and finally a dark blue ring around that one. Metro Taichung is growing, but it is doing so by spreading out rather than by piling more and more people into the city center.

dense6.png

Tainan and Kaohsiung are bounded by the ocean to the west, so they have half donuts rather than full donuts around them. However, the basic pattern is the same.

dense7.png

dense8.png

Northern Taiwan is more complicated. You can see donuts forming around Taipei and Keelung, but they run together a bit. To the west, there is just one huge mess of growth all the way through Taoyuan City to Hsinchu City.

dense9.png

This map of population growth obscures some uneven growth. Consider the difference between Xizhi汐止, just to the east of Taipei City, and Guishan 龜山, on the eastern edge of Taoyuan. Xizhi has doubled in population since 1990, but different areas have grown at different rates. In 1990 (left map) most of the population was concentrated in the old downtown area near the train station. Xizhi has grown everywhere, including in this old downtown area. However, there is basically an entirely new population center in the northwestern border, next to the Donghu area of Neihu (in Taipei City). The city has spread out, but this population center has grown so fast that it is as dense or even denser in 2016 than the old downtown area was in 1990. As a result, the weighted density has grown (from 6471 to 14817) even faster than the raw density (from 1363 to 2772).

dense10.png

Guishan is a different story. In 1990, the population was highly concentrated in the southwest corner, an area adjacent to Taoyuan City (now Taoyuan District). Over the past 25 years, the Taipei metro area has grown out to the edges of New Taipei City. Guishan has gotten some of the overflow. The new growth in Guishan is concentrated on the northern and eastern borders, where Guishan abuts Linkou and Taishan Districts. The new airport MRT line goes through Linkou, which in recent years has been a hot real estate market. Guishan has gotten a bit of the overflow growth from this boom. However, the newer population areas in Guishan are not as dense as the old areas. As a result, even though the overall population has grown by 60%, the weighted density is actually quite a bit lower (from 9024 to 7124). On the map of change in weighted density in northern Taiwan, Guishan is a conspicuous blue patch in a sea of orange.

dense11.png

dense12.png

This leads us to the final and perhaps most surprising result of all. Taiwan has grown by 15% over the past 25 years, and there has been heavy migration from rural areas to the cities. One might expect that this would lead to a more densely populated country. However, because the cities have spread out, with most of the growth coming in the less dense areas, weighted density insists that Taiwan is a less densely populated place today than in 1990. In 1990, the weighted density was 15457 people/km2; in 2016, that figure has fallen to 14893. By this metric, Taipei, New Taipei, Keelung, Chiayi City, Tainan, and Kaohsiung are all less dense than they used to be. The only places with significant increases are Taoyuan, Taichung, and Hsinchu County. There are more people, but the typical person lives in a less concentrated neighborhood. This finding utterly shocked me.

 

Apportionment!

March 9, 2017

A few days ago, Mrs. Garlic looked up from the newspaper and said, “Here’s the story you keep blathering on about.” Liu I-chou 劉義周, head of the Central Election Commission, had said something about the upcoming legislative redistricting. Now, I’ve been chattering about redistricting for months (ok, a few years), so I eagerly picked up the story. My glee quickly turned to horror when I read that Tainan and Hsinchu County would get an additional seat and Kaohsiung and Pingtung would lose a seat each. Um, that’s not what I’ve been telling people for the past few months.

My initial reaction was to contact Dr. Liu, who I know better as a political scientist and my masters thesis advisor, to warn him that he had made a mistake. Hey, I just published one paper on redistricting and another on malapportionment, and I have been through those rules in excruciating detail. If anyone knows the rules, it should be me. However, doubt began to creep in, and I thought maybe I’d better check the rules one more time. So I looked up the documents and found a table (look on p 107) showing exactly how the apportionment had been done.

Well, isn’t this embarrassing. I’ve been doing it wrong. I omitted one step. I shouldn’t have doubted the excellent civil servants at the CEC. I really shouldn’t have doubted Dr. Liu. I guess the student isn’t the master just yet.

Taiwan uses a largest remainders system. You take the total population (minus the indigenous population) and divide by the number of seats to get a quota. In our case, the quota is 22,986,588/73=314,885. (These numbers are from December 2016. The apportionment will be done with August 2017 numbers, but it is highly unlikely that anything will change between now and then.) Every city or county with fewer than 314,885 people automatically gets one seat. There are six such places. Then take the remaining 16 cities and counties and get a new quota. ****This is the step I skipped.**** The new quota is 22088100/67=329673. For each full quota, a city gets one seat. New Taipei City can thus buy 11 full quotas (see column S2). We have now accounted for 66 seats. What about the remaining seven? To apportion those, you take what is left over for each city or county and give the seven largest remainders the last seven seats.

 

City pop S1 Pop2 S2 Remain S3 S
Total 22986588 6 22088100 60   7 73
新北市 3924326   3924326 11 297922 1 12
台北市 2679523   2679523 8 42138   8
桃園市 2077867   2077867 6 99828   6
台中市 2734190   2734190 8 96805   8
台南市 1878508   1878508 5 230142 1 6
高雄市 2745749   2745749 8 108364   8
宜蘭縣 440708   440708 1 111035   1
新竹縣 526274   526274 1 196601 1 2
苗栗縣 547911   547911 1 218238 1 2
彰化縣 1281569   1281569 3 292550 1 4
南投縣 476289   476289 1 146616 1 2
雲林縣 692532   692532 2 33186   2
嘉義縣 509510   509510 1 179837 1 2
屏東縣 776900   776900 2 117554   2
台東縣 141930 1         1
花蓮縣 238432 1         1
澎湖縣 102789 1         1
基隆市 362819   362819 1 33146   1
新竹市 433425   433425 1 103752   1
嘉義市 268826 1         1
金門縣 134109 1         1
連江縣 12402 1         1

 

Why did I take you through all that mess with such an emphasis on my stupid mistake? Hold on, there’s a point to this. But first, let’s see what would have happened in my alternate, error-ridden fantasy world. When you don’t calculate new quota but simply use the original quota (314885) to apportion seats, we get a different result. Pingtung, Nantou, and Chiayi County all lose a seat, and Tainan, Taichung, and Hsinchu County all gain a seat. Also, Kaohsiung gets to keep its 9th seat. The difference with the correct reapportionment is that two small counties (Chiayi and Nantou) would have lost a seat while two large cities (Taichung and Kaohsiung) would have gained a seat. Calculating a new, larger quota favors small counties.

Let’s take a moment to appreciate what could have been. (This still isn’t the big point.) I may have told a few Taichung politicians that they should start preparing for a ninth district. I even started drawing up some maps of what might happen. This is my favorite one. It meets all the formal criteria (all legislative districts are within 15% of the mean population and it doesn’t even need to split any administrative districts) and even a few of the evil political calculations. (Check out what it would do to Yen Kuan-heng!) Of course, if you have any local knowledge of Taichung, you will see in an instant that there is no way in hell this plan would ever be adopted. The deputy speaker, for one, might have some objections. (I promise this post wasn’t just a flimsy pretext to show everyone this picture that I spent a lot of time making and will never be able to use again. Well, maybe a little…)

Taichung 9D plan E

So after sulking for a while over my stupid error, I thought I’d go back and see what would have happened in previous elections if they had used my erroneous apportionment method. This is my idea of fun. Don’t judge me. Guess what I found. THEY CHANGED THE METHOD IN 2008!!!! In 2004, they used my method! Using the new method, Taipei County should have had 27 seats and Taoyuan should have had 14. But Taipei County actually got 28 seats and Taoyuan only had 13. My method yields the actual result.

Why did they change the formula? There were all sorts of little indications that the Chen administration had tried to influence the CEC’s decisions, so maybe the CEC was manipulating things for the DPP’s political advantage! Or maybe the CEC was stuffed full of career bureaucrats sympathetic to the KMT. Maybe it was a KMT plot! There’s only one way to find out. Which side benefited from the change? Who would have done better in 2008 using the original formula?

 

The answer is: no one. The 2008 apportionment would have been exactly the same using the old formula. The change had zero effect. Moreover, it isn’t as though 2008 was an aberration. The two formulae yielded exactly the same results in 1998 and 2001. 2004 was the only year it made a difference, and that difference was modest, to say the least. It’s a big deal if Nantou goes from one seat to two seats – it has doubled its clout. It’s not such a big deal for the largest county to get one more seat and the second largest county to get one less seat. In the old SNTV system, it is impossible to say if that helped the KMT, the DPP, or a small party.

I doubt the CEC made the change in 2006 or 2007 because it could foresee the effects in 2017. A lot has changed in the meantime. It’s hard to predict exactly how fast Hsinchu will grow or how fast Pingtung will lose population. Moreover, they would have had to guess that Kaohsiung, Tainan, and Taichung Cities and Counties would merge. To put it another way, if I were given the opportunity to change the formula now to help one party in 2029, I’m not sure what I would do. Who can even say what the party system will look like then?

So why did they change the rule? My guess is that it was entirely apolitical. Some bureaucrat thought it would be fairer to apportion the last 67 seats according to their population rather than by taking into account the population of the six small counties. That bureaucrat probably had to propose a change, they probably held some meetings in which they discussed fairness and disproportionality, and they eventually rewrote the rule thinking it would probably never matter very much.

Only it has mattered. This year, two rural counties will double their representation. Because every county gets a seat and indigenous voters are given about 2.3 times as many seats as their population would merit, rural and agricultural areas are already overrepresented. This rule change furthers that overrepresentation. Sorry urban residents.

 

Let’s change gears and think like philosophers about fairness. Scratch that, let’s ask a question that economists would love. Is it fairer to have fixed prices or to allow competitive bidding?

Go back up to the table and look at Pingtung and Nantou. Pingtung has 776900 people, while Nantou has 476289. Even though Pingtung has far more people, both counties will get two legislators. Is that fair? Suppose the country only had these two counties. Should Nantou really get equal representation?

The CEC formula essentially uses fixed prices. Our new country, “Pingtou,” has 1253189 people, so a quota is 313297. Pingtung can afford two full quotas, and Nantou can afford one. After paying those prices, Pingtung has a remainder of 150306, while Nantou has a remainder of 162992. Nantou thus gets the last seat.

However, what if they could bid? Nantou could offer 476289 people for one seat or 238144 each for two seats. Pingtung, however, can offer 258966 each for three seats. Since Pingtung can offer more for the fourth seat, maybe it should get three seats and Nantou should only get one. Wait, now Pingtung gets three times as much power even though it has less than twice Nantou’s population? This is clearly unfair, and I’m not just saying that because I used to live in Nantou and my wife used to live in Pingtung.

I don’t have an answer to which system is fairer. Largest remainders systems, like the CEC method, tend to favor smaller areas. The divisor method used above is called the D’Hondt system, and it favors bigger areas. Before you put on your urban hat and decide that the D’Hondt method is clearly more progressive / pro-industry and therefore more desirable, please remember that these methods are most commonly used for allotting seats to party lists in PR elections, not apportioning seats to different regions. Hey Green Party apologist / Faith and Hope League zealot who can’t stand the sellouts in the establishment, now you probably think the largest remainder system, which is good for your crazy fringe party, is the best way to go.

Since I know you are dying to know, if we used the D’Hondt method to apportion the 73 seats, the big cities would do much better. New Taipei would get a 13th seat, and Kaohsiung, Taichung, and Taipei would all get a 9th seat. The mid-sized cities and counties including Taoyuan (6), Tainan (6), Changhua (4), Yunlin (2), and Pingtung (2) would be unaffected. The rest would only get one seat, which is not good news for Hsinchu County, Miaoli, Chiayi County, or Nantou. Power to the (urban) people!

 

As you’ve been reading along, I’d be willing to be that in each scenario, you judged whether something was reasonable or not by whether it helped or hurt your side. Maybe you thought about it intentionally or maybe it was just an involuntary reflex, but I’ll bet you did it. We all do. It’s not an accident that my crazy map of Taichung with nine districts shows how well Tsai Ing-wen did in each of them. When I realized that the CEC had changed the apportionment formula, my heart sank. I can’t tell you how relieved I was to be able to conclude that there was no obvious partisan motive behind that change. Whether or not one system is objectively slightly fairer than another is really beside the point. We have one system right now that wasn’t designed with obvious partisan motives. This year, it might advantage one side or the other. However, it matters that it was not intended to produce this result. It matters a lot. It is better to have a slightly imperfect but nonpoliticized electoral system than to chase perfection and risk politicization. This apportionment system is just fine.

 

Redistricting, on the other hand, is already a problem, and it is probably about to get worse.

 

Note: This post was written at 37000 feet. If it seems a bit loopy, I’m blaming altitude sickness.

Effort to recall Ker

November 30, 2016

Hey, there’s a bit of election news in Taiwan. As part of the current battle over marriage equality, there are efforts to recall DPP floor leader Ker Chien-ming 柯建銘.

[As an aside, I haven’t paid particularly close attention to Taiwanese politics over the past ten months. Rather, I have watched developments in Europe and America, often rapt in horror. We seem to be on the cusp of a fundamental shakeup in the international order, and, in my darkest nightmares, I worry that a democratic implosion is right around the corner. I’m not sure if it is reassuring or terrifying that Taiwan is preoccupied with “normal” political controversies, such as how to schedule vacation days, blissfully unconcerned that the rest of the world looks like it might be about to go up in flames. Is this oasis of calm one of the few sane spots in the world right now, or is it sticking its fingers in its ears and willfully ignoring the looming storm?]

The Taiwan Law Blog speculates that I do not support the efforts to recall Ker Chien-ming. That is correct, even though I support marriage equality. I explained my general dislike of recalls in the post the Taiwan Law Blog links to, and I stand by that reasoning. When the votes are counted, the election should stop. The battle over who occupies the seat should be settled until the next regularly scheduled election.

Recalls have a role, but they should only be used as a last-ditch resort when an elected official has fundamentally violated the implicit contract with the voters. I do not believe Ker Chien-ming has fundamentally violated his contract with his voters. When he ran, I do not remember him ever taking a public stance on marriage equality. His campaign was about representing the DPP and supporting Tsai Ing-wen’s agenda in the legislature. Marriage equality was merely one, very small part of that agenda. No matter what he does on this issue, it is hard to imagine it constituting a fundamental betrayal of his positions.

What do I think would be justifiable grounds to launch a recall? To give one example, I think South Korean President Park has fundamentally violated her contract with the voters. Massive corruption, allowing an unelected and unappointed spiritual advisor to make major decisions, and all the rest of it were clearly not what the Korean voters had in mind when they voted for her.

To go back to Ker’s case, since Ker’s central appeal was being a good party soldier, if he suddenly emerged as an intransigent opponent of Tsai’s agenda and plotted with the KMT to thwart her proposals, a recall would be justifiable. If we confine the hypothetical to the issue of marriage equality, if Ker had made support for marriage equality a central issue in his campaign but then had decided to throw his support behind a separate law that did not grant full equality, I think that would probably still be defensible and not justify a recall. After all, it is eminently defensible to compromise for 50% or 75% of your original goal. If he did all that, and then we further learned that he had accepted a massive bribe from an opponent of marriage equality to change his position, then a recall would probably be justified. In that case, Ker would have ignored his voters’ demands in favor of the briber’s demands. Ker’s current behavior is nowhere near these thresholds, and I hope the recall effort fizzles out.

The Taiwan Law Blog suggests that, instead of trying to recall Ker, perhaps marriage equality activists should campaign for him to lose his spot as the DPP party whip. I think he and many others are making the same mistake that President Ma made when he tried to purge Speaker Wang in 2013. They are imagining that the party floor leader is pursuing his own agenda.

In fact, what successful floor leaders do is to help the party rank-and-file get what they want. Sometimes, this means that the floor leader has to take some public heat in order to shield the backbenchers from criticism. In the American case, the classic example is from budgetary politics. A house member knows that a particular spending item should be cut but it is also very popular back home. The backbencher needs the speaker to arrange the agenda so that he can tell his voters that he fought hard to keep the item in the budget but he just couldn’t overcome opposition from everyone else. Sometimes, the legislator will even single out the speaker for criticism, and a good speaker understands what is happening and facilitates it. In 2013, President Ma blamed Speaker Wang for not pushing the Services Trade Agreement strongly enough. Ma should have realized that Wang was protecting KMT legislators who did not want to defend support for particular clauses to their voters.

In today’s case, Ker is probably protecting DPP legislators as well. Most DPP legislators have publicly come out in support of marriage equality, probably because they cannot afford to alienate progressive activists and voters. They certainly do not want to alienate young people. (Ask Hillary Clinton if alienating young voters has any costs.) However, Taiwanese society has hardly reached a consensus in support of marriage equality. The surveys I have seen suggest that support and opposition are about evenly split. I am a bit skeptical of these support levels. While elites and young people have mostly come to a consensus on gay marriage, I suspect the rest of society has not. To put it simply, I doubt that Taiwan has wrestled with this issue enough yet. To too many people, homosexuality is simply an idea rather than an everyday reality of many friends and family. There are still a lot of moms and dads my age or older who grew up with the unchallenged assumption that homosexuality was weird and/or wrong, and you can’t simply tell them that they have been prejudiced all their lives. They will need some time and a lot of discussion before they come around. Moving too quickly could cause a backlash, and I suspect that many DPP legislators intuitively grasp that not everyone in society is comfortable with rewriting the social rules just yet. If there were actually overwhelming support for marriage equality in the DPP caucus, Ker would make it happen quickly. He hasn’t been re-elected party whip time and time again because he ignores the rank-and-file’s wishes. If he is stalling or pushing some compromise package, it is almost certainly because they are asking him to do it. Moreover, like any good floor leader, he is taking the public criticism so that they won’t have to.

So what do I suggest for marriage equality activists? Ker Chien-ming is not your problem. Your problem is that you haven’t yet thoroughly sold Taiwanese society on the idea of marriage equality. To put it another way, the DPP caucus looks like it would like to change the law, but activists haven’t done enough work changing minds among ordinary voters to make DPP legislators feel comfortable taking this step. Rather than bullying or threatening Ker Chien-ming, activists should be focusing on broader society, explaining why marriage equality is a good idea that everyone can support. The good news is that the marriage equality side has good arguments and, with a lot of discussion and persuasion, should be able to produce a stronger consensus in society. When that happens, resistance in the legislature will melt away.