thesis final draft

ELECTION REFORM EFFECTS ON POLICY TARGETING 1

Election Reform Effects on Policy Targeting: Voter Responses to Rice Subsidies in Thailand

Michael A. Campbell

University of Colorado Denver

Author Note

Master’s thesis project presented to the University of Colorado Denver Economics

Department on December 16, 2016.


Abstract

Electoral reform from Thailand’s 1997 Constitution brought about changes in voters’ response to

populist policies. This study provides a measure of some (possibly unintended) results from

election reform. The study shows that rice farmer voters have greater sensitivity to rice prices

after 1997. Because of their large share of the population, rice farmers are a common target for

populist party policies. These policies appear to have resulted in a decrease in voter share to the

(Thai) Democrat Party, Thailand’s largest right-wing party. These subsidy policies may have

contributed to the rise of populist parties. Electoral reform also resulted in increased polarization

on issues, which may have contributed to political turmoil since 2006. With only four valid

elections since 1997, results are not yet robust to linear time trends or lagged results; however,

the main results cited rely on statistically significant, weather-based instrumental variable

techniques.

Keywords: Elections, voting, institutional reform, election reform, vote buying, voter behavior

Author Acknowledgments: The author would like to express sincere thanks to his research

advisers (Daniel Rees, PhD; Brian Duncan, PhD; and Hani Mansour, PhD) and referee James

Reeves at the University of Colorado Denver, as well as Brent Soderborg and other reviewers. In

addition, the author wishes to provide special thanks to Allen D. Hicken, Ph.D., Associate

Professor of the University of Michigan, Department of Political Science, for compilation of

Thailand election information.


Election Reform Effects on Policy Targeting: Voter Responses to Rice Subsidies in Thailand

Normally higher income results in right-wing parties gaining vote share, but not always.

In Thailand, the right-wing party has more difficulty. Thailand’s successful populist parties target

rice farmers, the country’s largest occupation. Changes in farmers’ income often occur because

of subsidies crafted by populist parties. Farmers value these subsidies. This study uses an

instrumental variables technique to exploit random variation in nearby countries’ weather to

explain these effects. This technique illustrates that increases in rice prices correspond to

declines in the country’s main right wing party vote share. Rice price increases also correlate

with a lower probability of this party gaining a seat. The study shows that these effects mostly

happened after election reform from the 1997 Constitution.

This paper focuses on Thailand’s right-wing Democrat Party (Phak Prachathipat or พรรค

ประชาธปัิตย)์. The Democrat Party is Thailand’s oldest active political party, founded in 1946 as a

conservative, royalist party (Wikipedia.org, 2016). The Democrat Party was also the opposition

party of the last freely elected parliament from 2011 elections. This party’s main political

opponent, the Pheu Thai Party and its predecessors, have a shorter history (since 1998), and

provide less information for analysis as a result. These parties also tend to field accusations of

vote buying and corruption more frequently (such as was the case in 2007 elections); however,

this may be due to bias in election oversight groups. The Democrat Party tends to favor

conservative, pro-market policies. For this reason, this party tends to suffer from subsidy policies

toward rice farmers, especially as Thai voters have focused more on party platforms after


reforms brought about in the 1997 Constitution. These events provide an interesting background

to study the effects of rice prices on vote share.

Thailand

Economy and Agriculture

The Kingdom of Thailand is an emerging market country in Southeast Asia. Thailand

borders Myanmar, Laos, China, Malaysia, and Cambodia. Approximately 42 percent of its labor

force works in agriculture, most of which is related to rice production; however, agriculture only

constitutes around 12 percent of GDP (The Global Rice Science Partnership, 2013). Figure 1

provides a map of rice production in Thailand. Most rice production occurs in the North and

Central plains. Historically the Chao Phraya River Delta (Central Thailand near Bangkok)

provided the most fertile land for rice production. This area tends to have the largest and most

developed irrigation systems. There are also significant concentrations of rice farmers in the

Northeast Korat Plateau, although these are generally smaller farms. Rice farming is less

prevalent in Southern Thailand, which focuses more on rubber plantations and tourism. Every

province in Thailand grows rice to some extent.

Rice prices are a function of weather, as well as global commodity prices and other

factors. Figure 2 shows the nominal international rice export price in dollars. The figure

highlights the major events affecting large changes in rice prices. As the figure shows, most of

these shocks are commodity related, although some, such as the drought in Japan in the early

90s, are due to weather. Without well-developed irrigation and drainage systems, floods and

increases in temperature can devastate rice crops.


Political Setting

Democracy has a tumultuous history in Thailand. Since 1932, when the country abolished

absolute monarchy, there have been 25 general elections, but 19 coups d'état, 12 of which were

successful (Brown, 2014). Many of the coups were transitions from one military government to

another. The military largely controlled politics for most of Thai history from 1932 to 1992, with

several House of Representative elections during interim periods. Many of these interim

elections were due to popular pressure to return to democracy.

In 2006, after more than a decade of relative stability, the army executed another coup

against then Prime Minister Thaksin Shinawatra on accusations of corruption. By May 2007, the

Constitutional Tribunal dissolved his Thai Rak Thai (“TRT”) party on corruption allegations

(Wikipedia.org, 2016). Since then, the country has made multiple interim constitutions and had

several regime changes. After military rule from 2006-2007, the People’s Power Party (“PPP”),

the successor to Thaksin’s TRT, ruled the country after House of Representative elections in

2007. PPP’s rule ended after the Constitutional Court dissolved the party in December 2008, on

vote buying allegations (Wikipedia.org, 2016). The current successor party is the Pheu Thai

Party (“PTP”).

The Democrat Party tends to favor the royalist elite class, government workers, and the

military who paved the way for it to be in power. As previously noted, it tends to support

conservative, non-interventionist economic positions.

It is important to note that this party has never won a majority of legislative seats through

democratic elections (it won the most seats in 1948, 1976, and September 1992). The Democratic

Party led the government from 2008 until elections in mid-2011.


PTP and its predecessors are populist parties. The party’s most prominent member is

Yingluck Shinawatra, the youngest sister of Thaksin (who is said to direct the party from exile).

PTP draws its support largely from poorer citizens in the rural Northeast (Isaan) region. This area

has heavy concentrations of rice farmers, which PTP took advantage of in its rice-pledging

scheme. In this scheme, the government agreed to buy rice directly from farmers at a price that

was approximately 50 percent above the prevailing market prices of the time. The party believed

that by buying directly from farmers and constraining the export market, it could increase the

future export price.

This was a classic and unfortunate example of the failure of poorly considered farm

subsidy policies. The scheme had several disadvantages. These included storage logistics (with

potential for corruption), deficit spending by the government, damage to the existing private

export market, lack of quality controls, and increased exports from competing countries. The

problems that occurred were numerous. Thailand lost its place as the leading exporter of rice, and

the government did not have enough funds to pay money owed to farmers. The government was

unable to borrow funds from the country’s private banks to fund this scheme. This scheme lost

roughly $22 billion (approximately equal to the government’s investment budget for one year)

and had widespread accusations of corruption (Warr, 2014). At the time of this writing, the

current military-backed government is also contemplating a rice subsidy program, which Ms.

Yingluck alleges is the same type of rice scheme her party proposed in 2011.

Thailand attempted to hold fresh elections in February 2014, which would likely have

resulted in continued PTP control of the government; however, the Democrat Party boycotted

this election (citing corruption). Mass protests and conflicts disrupted the voting process in 69 of

375 districts, making the election invalid. The Election Commission of Thailand attempted to


have additional elections in the disrupted provinces. This event caused the election to take longer

than the one-day requirement of the constitution. As a result, the Constitutional Court declared

the election invalid. Because of these events, the army again took power in a bloodless coups

d'état in May 2014 led by Army Commander in Chief Prayut Chan-ocha. Prayut formed an

interim government that has held power ever since, although it promises to eventually hold

democratic elections.

Voting Changes

It is important to note that voting systems changed a few times during the study period.

Prior to 1996, Thailand employed a block voting system where voters received multiple votes per

seat in a “first past the post” election. This change provided opportunities for voters to give some

of their “surplus votes” to candidates offering money for votes.

Thailand enacted a new constitution in 1997, with several noted reforms. As noted by

(Hicken, How Effective Are Institutional Reforms?, 2007, pp. 153-157), Thailand implemented

the constitution to shift the focus of elections away from individual-centric elections to ideology-

based voting. As noted in (Hicken, How Do Rules and Institutions Encourage Vote Buying?,

2007, pp. 47-60), candidate-centered campaigns (as compared to issue-centric campaigns) are

much more susceptible to vote buying. By contrast, elections where issues are more central to a

campaign tend to have lower prevalence of vote buying. This goal appears to have been

successful, as evidenced by increased polarization in Thai politics. This study also shows a sharp

change in the voting behavior in relation to rice prices.

After the introduction of the 1997 constitution, citizens provided separate votes for

candidates at the local level and for parties at a national level (i.e., a “mixed member” system),

but with only one vote for a candidate and one for a national party. Vote buying campaigns are


harder to organize nationally than for localized elections. This change of incentive makes

campaigning based on policies a lower-cost alternative to local vote-buying campaigns. As a

result, voters are more likely to vote based on policy platforms, such as the rice-pledging scheme

in national lists like the party list, and not based on local vote buying relationships.

Due to additional political changes, voting systems changed again in 2007 and 2011. In

2007, the country returned to a block voting system, with up to three seats per district. For each

district-level election, this meant that up to three candidates for each party could run within each

district. For purposes of this study, I collapsed multiple candidates in 2007 within a party and

district into one observation. Collapsing the study allows for consistency with the other years. In

2011 the country returned to a single-seat, national election system with party list voting.

Rice-Pledging Schemes

It is important to understand how the policies of the Pheu Thai Party, and its predecessors

could complicate the income effects on voting. Because these parties targeted rice farmers

specifically (rather than citizens as a whole), farmers could attribute gains in rice prices to

populist party policies. These schemes tend to have more effect on domestic market prices than

international prices. Countries intervene in rice markets mostly to stabilize domestic prices.

Adam John (2013) provides econometric analysis showing that domestic interventions in

Thailand tend to have only temporary effects on world prices. Conversely, shocks to the world

price (outside of Thailand) tend to have slow, but long-term effects on domestic prices in

Thailand.

Figure 3 highlights the historical negative correlation in real local harvest prices to

Democrat vote share. As is also apparent in Figure 3, the correlation is less obvious for export

prices, for which domestic Thai subsidy policies have a less-pronounced effect.


Vote Buying

Any understanding of voting behavior in Thailand is incomplete without understanding

this facet of Thai politics. I discuss vote buying for clarification on some possible mechanisms of

the relationship between income and vote behavior, especially before 1997. Vote buying is also a

confounding factor to studying elections. Use of robust identification strategies, such as the

instrumental variable approach I use, can mitigate this effect.

As in several developing countries, vote buying is rampant, with as many as one-third of

voters in Thailand targeted for vote buying (Phongpaichit, Treerat, Chaiyapong, & Baker, 2000).

It is common knowledge that both sides engage in vote buying. The country has a “neo-feudal”

culture of patronage, where poor farmers and villagers provide support in exchange for

assistance from wealthy patrons. Perception of corruption depends on the amounts involved.

Petty amounts are termed “gifts of good will,” moderate payments for business purposes are

titled “bribery,” and only very large amounts extracted using political power are referred to as

“corruption” (Phongpaichit, Treerat, Chaiyapong, & Baker, 2000).

Vote canvassers distribute cash, bus tickets to home provinces, discount coupons for

gasoline, payment of fees and health care bills, and other forms of benefits to village headmen to

buy votes. There is one (likely fictitious) story of a politician who distributed slippers, one side

before the election, and the other after they were successfully elected. Reporting vote buying can

also be dangerous, as political murders are common (Fuller, 2007).

Literature Review

The consensus on the income effect on voting is that as incomes increase, voters’

preferences shift toward right-wing parties. Brooks & Brady (1999) found that increases in post-

war income have resulted in increased voting for Republican candidates in US presidential


elections. This study suggests that all else equal, poorer voters are more likely to vote for policies

of redistributing income. The data in this study indicates that this is through the mediating role of

changes in voters’ policy evaluations. The study refers to voters’ changes in attitudes regarding

desired government policies. Controlling for policy evaluation, income itself did not have a

significant effect.

Existing literature is quick to point out that many other dimensions complicate the

relationship between income voting. Generally, religion and social issues play the largest

intervening role. For example, poor voters may favor right-wing parties due to social issues, even

when a left-wing party’s redistribution policies would be more favorable.

A study by Huber & Stanig (2007) presented a 28-country study on factors that contribute

to the likelihood of poor citizens voting for right-wing parties. The authors found that countries

that more likely to have poor populations support right-wing parties are “ethnically

heterogeneous, rich, low in urbanization, low in party-system polarization, and that have no

parties that are at once left wing on redistribution and right wing on issues related to individual

liberty.” Thailand is not ethnically heterogeneous, is relatively poor, generally moderate in

overall urbanization (although around one sixth of the country lives in the Bangkok area), high in

party-system polarization (and conflict), but does not typically campaign on social issues of

individual liberty. Most of this would indicate that the income effect in Thailand would likely be

weak.

Neither (Brooks & Brady, 1999) nor (Huber & Stanig, 2007) presented a convincing

randomization strategy. As a result, there is likely bias from correlation of income and other

factors, including religion. Brooks & Brady use various models with and without controls for

voters’ stated preferences and income measurement to support this conclusion. This study does


not rely on a randomization strategy. The Huber & Stanig paper used a two-stage, generalized

least squares method to estimate its regressions. Their main specification used the probability of

poor voters as a dependent variable in a probit model. They then used and re-weighted the

coefficients in a second-stage regression as discussed by (Huber, Kernell, & Leoni, 2005). They

did not lay out an argument that variables are exclusive to the first stage, which would also be

required in an instrumental variable model. These papers do not provide convincing logical

arguments that the specifications eliminate the endogeneity between income and other variables.

This study contributes to the existing literature in several ways. First, it provides an

exogenous measure of income using standard econometric tools. These include instrumental

variable and difference-in-difference techniques. Second, it provides a study of Thailand, which

other studies generally omitted from their analyses, citing difficulty of studying the country. The

difficulties of studying Thailand is because corruption. The econometric techniques used in this

study provides mitigation to this problem.

Methodology

Dube & Vargas / Crost & Felter Studies

This paper employs the techniques of (Dube & Vargas, 2013), henceforth D&V, and

(Crost & Felter, 2016), henceforth C&F. The D&V paper is the seminal work on this technique,

and the C&F paper refines this technique for stronger arguments of exclusivity. Both papers rely

on rain and temperature not affecting their variables of interest through any other mechanism

other than through the commodity prices (i.e., the exclusivity assumption).

In D&V, the authors exploit changes in coffee and oil prices to determine the effect of

each on violent conflict in Colombia. They employed an instrumental variables technique

measuring the effect of rainfall, temperature, and other factors on the price of each commodity.


In the second stage regression, they measure the effect that these changes in price have on the

number of violent incidents in each municipality.

The authors identified two effects, the opportunity cost effect and the rapacity effect. The

opportunity cost effect is the substitution effect that wage has on conflict violent activities.

Increased wages increase the relative appeal of work, relative to violent activities. The

conflicting rapacity effect is the effect more productive industries have on the returns to violent

activities, including extortion. D&V show that for changes in the price of coffee, the opportunity

cost effect prevails, while changes in oil prices tend to exhibit a more powerful rapacity effect.

The C&F paper further refines this technique in a study of the effect of export crops (e.g.,

bananas) on violent conflict in the Philippines. Rather than measuring weather variables at the

local level, it relies on rainfall in Ecuador to determine the price of bananas exogenously. While

local rainfall may not affect voting behavior outside of its effect on rice prices, this provides

additional credibility to the technique. The C&F paper also examines the effect on violence

further by level of insurgent control.

The study finds that for bananas, the rapacity effect dominates; however, the effect

depends on the level of existing insurgent control. In areas with high insurgent control (i.e.,

where insurgents have an established presence), violent incidents decrease due to strengthening

of insurgents’ position. In low- or medium-control areas, the number of violent incidents

increase. This effect is because conflict tends to focus on areas of moderate control where

marginal changes in conflict affect insurgent control the most.

My study will refer to the opportunity cost effect and the rapacity effect in different

context. Income can affect voting behavior through increasing the opportunity cost of voting,

making voting less likely. Conversely, increased income could also afford rice farmers increased


ability to use time for non-work activities, such as voting. It is important to note, however, that

this effect may not be significant. Rice farmers do not tend to have significant time constraints

outside of planting and harvesting seasons. Thailand also holds elections on Sundays to avoid

disrupting work. Increasing the income of rice farmers can also provide more resources to

political parties that target these groups (through more money available for political

contributions). Increased income could provide more resources for marketing policies to rice

farmers, or through providing additional resources for vote buying or national campaigning. This

affect corresponds most closely to the rapacity effect discussed for violent conflict.

Potential Issues and Mitigation

Rice production in Thailand presents some possible issues with endogeneity. Until 2012,

Thailand was the highest exporter of rice. Because of the importance of rice producer

constituencies, government policy has tended to favor these groups since 1981. Schemes similar

to the 2011 scheme were common, although at much lower scales. Implementing this scheme

often involved purchasing and storing rice (John, 2013). When Thailand intentionally increases

its export volumes, this would tend to depress prices, with the opposite effect when it restricts

exports. Because prices tend to move in the opposite direction of production, this will temper the

observed effect of government interventions.

The most recent “valid” elections in Thailand (2011) had some effects on world prices

between the time of the announcement of the election date (May 9, 2011) to the election (July 3,

2011). Excluding this year did not result in changes to the negative effect on local harvest prices

and election outcomes after 1997.

The instrumental variable techniques of this equation, which focus on competing rice-

producing countries, mitigate concerns of endogeneity. It is unlikely that rainfall and temperature


in other countries significantly affect voting behavior in Thailand in any other way than through

rice prices. Like the C&F paper, this study only uses weather measures for countries that do not

border the country of interest (although they reside in the same large continent). I used weather

data of India, Bangladesh, the Philippines, Japan, and Indonesia. While it does not have a

physical border with Vietnam, I excluded this country to avoid correlation with the rainfall in

Thailand, due to its geographic proximity. Results are not generally dependent on the countries

used in the first stage analysis.

Another factor that could convolute the relationship between rice prices and vote share is

reverse causality. Electing parties that oppose the Democrat party, such as the PTP allow these

parties to deliver on campaign promises, such as rice subsidies. Past wins of populist parties

would cause an increase in the local harvest price, in addition to any direct effects of prices on

election outcomes.

In theory, a valid instrumental variable approach can mitigate this. If, in fact, the

instruments only explain the variation caused by uncorrelated shocks to income (such as

rainfall), it will exclude effects of reverse causality.

Empirical Approach

My paper will modify the D&V and C&F techniques to measure the effects of local and

export rice price changes (in the prior year) on right-wing vote share. It is the first use of this

technique to measure political outcomes, of which I am aware. My first stage takes the following

form:

(1) Intensit y j∗Pric e t=α 0+ Intensit y j∗f 1 ( Rc ,t ,T c ,t )+γ j+λ t+υ j , t

Intensit y j is the average output of rice in the province from 1968-2011.

Pric et is the price of rice in a given year (t).


Rc ,t and T c ,t are vectors rainfall and temperature measures for competing rice

exporting countries, not physically bordering Thailand.

γ j and λ t are province and time fixed effects.

(2) PV Sijt=β0+β1 [Intensit y j∗Pric et−1 ]+γ j+λt +ϵ ijt

PV Sijt is the party vote shore in a given election district (i).

In this form, I analyze the effect of production values of the closest harvest.

o For elections held in the first half of the year (most fall in this category), I

use the prior-year harvest.

o For elections held during the latter half of the year, I used the rice harvest

of the current year. Using this assumes that rice farmers base election

decisions on their knowledge of the current year and the first (minor)

harvest of the year. This also allows for separate analysis of each election

in 1992 (the first 1992 election was in March and the second was in

September).

The second stage is a difference-in-difference specification that controls for differences

between provinces (through province fixed effects) as well as common time trends affecting all

provinces. In its basic specification, it does not directly control for any time trends that differ

between provinces. I add linear time trends to this specification to control for this. The effect of

linear time trends is ambiguous, likely due to over specification of the regression.

Use of the intensity measure is consistent with C&F. This intensity measure allows the

treatment effect to vary linearly by intensity of rice production in each province. It also provides

a mechanism to avoid perfect collinearity between time fixed effects and rice prices in the first

stage regression (since country weather data has perfect collinearity with time fixed effects). For


easier interpretation, I normalized the intensity measure by dividing average rice production for

each province by the number of provinces. An average rice-producing province would have an

intensity measure of 1.0, while a province that produces twice the amount of an average province

would have an intensity measure of 2.0.

Until 2012, Thailand was the world’s leading exporter of rice. Rice subsidies similar to

the 2011 scheme were common, although at much lower scales. Implementing the subsidies

often involved purchasing and storing rice (John, 2013). When Thailand intentionally increases

its export volumes, this would tend to depress global prices, with the opposite effect when it

restricts exports.

Data

This study focused on House of Representatives general election constituency (district)

results by candidate. In this study, most variables have a province-year unit of observation, but

election results are at a sub-province (district) level. For this reason, I cluster results at the

province level. I did not analyze voting data from national party lists, but focused on the district-

level local voting for consistency with 1969. This consistency allowed me to see the change

between pre-1997 and post-1997 elections.

This voting data comes from the Office of the Election Commission of Thailand. Allen D.

Hicken, Associate Professor of Political Science at the University of Michigan, compiled election

data from 1969 to 2011. Data from 1969 to 1996 is publicly available from the Constituency-

Level Elections Archive (CLEA) (2016). Dr. Hicken also provided separate election data from

2001 to 2011 that I compiled with the CLEA data.

The elections I use since 1997 include 2001, 2005, 2007, and 2011. Consistent with Dr.

Hicken’s recommendation, I did not consider either the 2006 election or the 2014 election.


Neither election has valid detail on voter preferences. In both 2006 and 2014, opposition parties

(led by the Democrat Party) decided to boycott the elections.

This study focuses on the first round of voting (before disqualifications for vote buying or

corruption). Additional research opportunities remain for studying the second round of voting.

Rice production and pricing information comes from the International Rice Research

Institute World Rice Statistics database (2016). This database provides detail of production by

province, local prices, and world (i.e., export prices) from 1950 to 2012.

Weather information for rice-producing countries comes from The World Bank Group –

Climate Change Knowledge Portal from 1900 to 2012. I also collected detailed rainfall and

temperature data by province from the National Statistical Office of Thailand (2016) - (generally

from 2002 to 2015). This source provided similar results as the external country specification.

Please refer to Table 1 for descriptive statistics of some of the data used in this regression.

I separate data before and after the implementation of the 1997 constitution because this is a key

point of analysis further below. The results of this study indicates that the constitutional reform

resulted in voters focusing more on the issues of each party, rather than individual-centric

campaigns and individual vote buying transactions.

Discussion of Results

Overall Observations

Results in Table 2 show an overview of voter reactions to changes in rice prices. The

table presents results for both local harvest prices (paid in Thai Baht) and export prices (in US

dollars). This table illustrates the sensitivity of voters to changes in rice prices. It illustrates the

size of price shocks that lead to a one percent increase (decrease) in the Democrat Party’s vote

share in House of Representative voting districts. Each coefficient represents the increase


(decrease) of rice prices to result in a one percent increase in vote share for the Democrat Party

(assuming an average intensity measure of 1.0). A province with an intensity of 2.0 would

experience a two percent increase in vote share for the same dollar change. If a province had an

intensity of 0.5, the dollar change indicated in the table would be half a percent. A smaller dollar

coefficient corresponds to increased sensitivity. Results for real, local Thai prices converted to

dollars (for convenience of interpretation) at the most recent official 2010 purchasing power

parity (“PPP”) equivalent exchange rate of 16.55 Baht per dollar. It is important to note that

historical exchange rates are closer to 30 Baht per dollar. The difference is due to the lower cost

of living in Thailand. The PPP measure is less volatile and more representative of the real value

received by local Thai rice farmers than foreign exchange rates. Inflation adjustments for baht

and dollar measures come from 2012 prices. Results in Thai Baht are stable in reduced form

(Column 1) and instrumental variable (Column 2) specification.

To control for any correlation between local Thai weather and other countries, I added a

control in Column 3 for local rice production in each province. This uses an intensity

specification based on average production of all provinces. This tends to lower the magnitude,

but not change the sign.

The relationship is ambiguous when attempting to control for lagged relationships in the

independent variable (Column 4) of Table 2. Because this variable has significant

autocorrelation, I added two one-year lags of the independent variable. The amount of lags

comes from a time-series correlogram analysis of this variable. It is also important to note that

the falsification test generally (but not always) passes after three years or more.

The issue of autocorrelation could mean that the relationships between vote share and

price is not confined to only one year of voter observations, but voter experience over multiple


periods, all captured in the current price. It is important, however, to note that lagged coefficients

(not shown in the table) are generally significant and negative. This fact means that rice prices

are capturing the changes in vote share, but not only due to the effects of one-year changes.

I also attempted to test for differing time trends for each province. The results for this

analysis was also ambiguous. These non-significant results may be due to model over-

specification.

Results for export values are mostly positive. These results would be consistent with an

overall income effect shifting voting preferences toward right-wing perspectives. The export

price is the price faced by exporters in the country, but the farmers do not directly face this price,

especially due to rice-pledging subsidy programs. Its effect, which appears positive overall, is

weaker than the direct effect of changes to the local price. This result likely constitutes the

typical income effect on voting discussed in the Literature Review section.

Table 3 illustrates this methodology on another variable: the change in probability of

winning a seat. The table uses OLS and probit estimates for reduced form estimates. Consistent

with other tables, most results (even the controlled specification, the lagged specifications, and

the linear time trend specification) point to a negative relationship between local prices and the

probability of winning a seat.

Post-1997 Analysis

To analyze the change in voter behavior from 1997 to 2011, I add a post-1997 dummy

variable to the instrumental variable regressions, summarized in Table 4. This variable measures

the marginal contribution of rice prices to changes in voting share since 1997 (compared to pre-

1997 data). Columns (1) through (5) show the estimated effect prior to 1997. These columns do

not have a clear consensus but are positive overall, suggesting mostly a typical income effect.


The remaining columns indicate the overall effect post 1997. Many of the statistics in

Columns (6) to (8) show significant, negative differences from the results from Columns (1)

through (3). This table provides some evidence that local rice prices and rice subsidies have

begun to play a more significant role in Thai politics. The current government contemplation of a

rice scheme also provides anecdotal evidence of this. Overall, the instrumental variable

regression (the main specification of this paper) suggest a negative relationship overall; however,

other results (lagged variable and linear time trend specifications) do not support this conclusion.

This result may be partially due to the regular income effect, as well as limited data availability

for the linear time trend and lagged variable specifications.

Heterogeneous Effects

Rice prices can have conflicting effects on different voters. For example, low-intensity

provinces (such as Bangkok and Southern provinces) do not have as widespread of an income

effect when the price of rice changes. In these provinces, rice may be more of a consumption

good than a production good. Increases in rice price could serve to lower disposable income in

these provinces. Consistent with this logic, the income effect of rice prices would be highest in

the high-intensity provinces. These provinces also tend to be the poorer provinces, and a

marginal change in income would have a greater effect here.

Results from Table 5 show that the effects do indeed vary by intensity. The income effect

does appear strongest in high-intensity areas. The effect is so strong in these areas that the

income effect outweighs any additional negative results from the so-called rapacity effect. This

could also be due to differing time trends in the high intensity areas. Low-intensity areas have

ambiguous, but also (insignificant) negative effects. Rice does not play as large of a role in

incomes for these provinces. Most of these provinces are central urban areas or tourist locations


in the South. Bangkok, for example, has an intensity of just below 0.5. Low-intensity provinces

also tend to have a higher vote share for the Democrat party. The negative effect is strongest in

medium-intensity areas. This result could be due to mechanisms discussed previously. Possible

explanations include greater freedom to vote and income increases that provide additional

opportunities for party support.

First-stage Results

Ideal rice growing conditions (for typical varieties) include a temperature between 20°

and 27° Celsius. Ideal planting temperature for rice is at least 25° Celsius (77° Fahrenheit).

Rainfall should ideally be between 1,750 mm and 3,000 mm, but typically needs to be at least

1,150 mm (Chand, 2016). Some varieties (such as Japanese rice) are resilient to lower

temperatures.

A country-by-country analysis of first stage regressions on nominal export prices

generally provided logical results. Flooding in Asia often leads damage to (or complete failure

of) the crop. Likewise, excess temperature in the latter-end of the year can lead to drought and

insect penetration. In more temperate Asian countries, above average temperatures during

planting time result in better rice crops. First stage regressions show that temperature has the

most significant effect on rice prices overall. Where models have too many variables to run (due

to collinearity), I exclude rainfall measures and leave in temperature measures. Unfortunately,

the data was not sufficiently large to allow me to exploit six-month measures of temperature or

rainfall. Instead, I relied on full-year measures.

Countries do have some correlation of rainfall and temperature. Please see Table 6 for

correlation coefficients.


Weather variables provided a strong instrument for this analysis, but interpretation of

individual coefficients is tricky. Interpretation is especially true in multi-country models, and in

an intensity-interacted specification. Table 7 presents sample first stage regression estimates.

Changing the rice price measure (from local to international prices) also results in some

inconsistency in coefficient signs. In addition, having the same intensity measure on both sides

of the equation complicates interpretation of significance measures.

It is possible that the strong significance in relation to prices in the Philippines is because

Thailand and the Philippines grow a similar variety of rice (jasmine rice). Higher planting

temperatures result in better early growth. Lack of rainfall in the Philippines is not a significant

concern, due to the proximity to the ocean. For this reason, increased temperature would have a

positive effect on rice production (resulting in lower rice prices).

As a check of robustness, I tested a first-stage specification without intensity interactions,

and found strong results (significant coefficients and F-tests above 10), both with and without

fixed effects. These results were also robust to inclusion or exclusion of countries.

Future Periods Falsification Test

A test of rice prices against vote shares show statistically significant results for local

prices up to 15 years in the future in Table 8. These results represent a failure of an important

falsification test.

This implies that the instrumental variables may not have been successful in removing

reverse causality between voting results and rice. To be consistent with best-practice econometric

techniques, this study included time fixed effects in the first stage regression. These fixed effects

likely include variation from the actual rice prices each year, nullifying the instrument. We can

see that rice prices are negatively associated with democrat vote share.


Local Price Specification

As possible mitigation for falsification test failure of the intensity-based model, I

prepared an alternate specification. This specification uses actual local production by each

province and year as well as rainfall and temperature variables specific to each province and

year. This information is generally available only 2003 and later, which covers the 2005, 2007,

and 2011 elections. This alternate data set provides much more precision in analyzing local data.

It also does not require the use of an intensity factor. For this reason, it calculates an average

effect for all provinces that does not have to be multiplied by a province intensity factor for

interpretation. I contend that previous-year rainfall in a province is unlikely to affect vote share

through any other mechanism than rice prices. I present the alternate specification as Table 9.

The table shows significant, but less sensitive vote share relative to Table 2. Table 10 also shows

that this specification does not generally fail the falsification tests, with some exceptions.

Conclusion

These regressions provide some, but not complete econometric evidence of the impact

that rice policies have on the shares of Thailand’s main opposition party. Reduced form and

instrumental variable evidence points to negative relationship between prices and vote share, as

well as the probability of winning a seat. These results do not necessarily reflect single-year

changes in rice prices. This study also does not rule out the possibility of differing voting trends

between provinces. In addition, this effect is concentrated at medium-intensity provinces.

Positive relationships in higher-intensity (poorer) provinces suggest that the income effect

dominates there, but that other effects dominate medium-intensity provinces. This study also

does not completely rule out reverse causality where vote share results in changes of rice prices.

The local specification, however, shows negative association without failing the falsification test.


The observed effect may simply be the effect of rice subsidies of the PTP and its predecessors on

rice prices. Future information and study can clarify these relationships further.


References

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Tables

Table 1

Summary Statistics

Variable Overall 1969-1996 Post 1996Democrat Vote Share 0.2530 0.2374 0.2815

(0.21) (0.18) (0.24)Probability of Democrat Seat 0.2791 0.2860 0.2666

(0.45) (0.45) (0.44)Nominal Rice Prices (Export) 272.64$ 239.84$ 348.51$

(127.76) (99.17) (151.98)Nominal Rice Prices (Local - Baht) ฿4,176.06 ฿2,591.36 ฿7,246.44

(2,699.55) (1,145.75) (2,137.72)Total Rice Production (tons) 20,443 18,542 30,267

(7,122) (5,891) (4,280)Thailand Temperature C° 26.22 26.18 26.51

(0.32) (0.29) (0.34) Thailand Rainfall - mm 1,655 1,641 1,740

(130.24) (121.07) (153.96)Means, with standard deviation in parentheses


Table 2

Overall Results

The Value of Rice Production (t-1) and Vote Share (Value to Increase Vote Share 1%)(1) (2) (3) (4) (5)

Harvest Value (Local - $PPP) -$62.42*** -$62.48*** -$135.79* -$984.13 $113.37**(15.65) (14.64) (80.25) (5,818.24) (54.45)

Export Value (Export - Dollar) $284.90** $304.88 $1672.24 $255.75** -$121.21**(137.17) (216.57) (7,186.72) (114.47) (49.66)

Province Fixed Effects? Yes Yes Yes Yes YesTime Fixed Effects? Yes Yes Yes Yes YesInstrumental Variables? No Yes Yes Yes YesLocal Production Controls? No No Yes No NoLagged Production? No No No Yes NoLinear Time Trends? No No No No YesChange in price required to increase voting share by (one percentage point, multiplied by province intensity factor). Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 3

Probability of Obtaining Seat

The Value of Rice Production (t-1) and Seat Probability(1) (2) (3) (4) (5)

Seat (OLS) - Local -$42.55** -$46.48** -$61.10 -$99.88 $81.66(20.26) (23.24) (42.81) (147.92) (86.07)

Seat (Probit) - Local -$9.97**(4.61)

Seat (OLS) - Export $16.78 -$47.58 -$14.15 -$26.62 -$2.71***(19.02) (234.90) (24.79) (88.42) (1.04)

Seat (Probit) - Export $3.33(3.08)

Province Fixed Effects? Yes Yes Yes Yes YesTime Fixed Effects? Yes Yes Yes Yes YesInstrumental Variables? No Yes Yes Yes YesLocal Production Controls? No No Yes No NoLagged Production? No No No Yes NoLinear Time Trends? No No No No Yes

Change in price required to raise probability of obtaining a seat by (one percentage point, multiplied by province intensity factor). Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 4

Results by Period

The Value of Rice Production (t-1) and Vote Share1969 to 1996 Difference (post 1996 minus 1969-1996) 1997 to 2011 (Total Effect)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)Harvest Value (Local) $85.35*** $16.88*** $13.11*** $254.96 $23.42*** -$53.95*** -$29.48*** -$29.05*** -$242.67 $85.35 -$146.66*** $39.49*** $23.88 -$5035.48 $18.38***

(29.78) (3.57) (3.38) (438.92) (6.90) (11.99) (5.64) (5.78) (211.49) (75.34) Export Value (Dollar) $2141.33 -$1451.38 NM $452.49 -$63.33 -$50.51*** -$57.08*** -$45.93*** -$47.15*** $67.48 -$51.73*** -$54.92*** -$45.89 -$52.63*** -$1030.93*

(7,382.31) (5,160.93) NM (501.63) (41.91) (10.89) (13.88) (12.49) (10.09) (86.60)

Province Fixed Effects? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesTime Fixed Effects? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesInstrumental Variables? No Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes YesLocal Production Controls? No No Yes No No No No Yes No No No No Yes No NoLagged Production? No No No Yes No No No No Yes No No No No Yes NoLinear Time Trends? No No No No Yes No No No No Yes No No No No YesChange in price required to increase voting share by (one percentage point, multiplied by province intensity factor). Standard errors, clustered at the province level, are in parentheses. Significance for columns 9-12 measured as joint test of significance. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 5

Heterogeneous Effects by Production Intensity

The Value of Rice Production (t-1) and Vote Share by Production IntensityLow Medium High

Harvest Value (Local) -$306.73 -$30.21*** $96.22*(3,487.69) (6.89) (50.87)

Number of Provinces 27 36 13Number of Observations 842 1141 725Change in price required to increase voting share by (one percentage point, multiplied by province intensity factor) for various production intensities, using instrumental variable regressions. Low intensity covers intensities less than 0.5. Medium intensity is between 0.5 and 2.0. High intensity is greater than 2.0. Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 6

Weather Correlation Coefficients

Rain Correlations Thailand Bangladesh India Japan Indonesia Philippines Vietnam Lao PDR MyanmarThailand 1.00 Bangladesh 0.01 1.00 India 0.21 0.26 1.00 Japan 0.07 (0.05) (0.10) 1.00 Indonesia 0.33 0.28 0.47 (0.12) 1.00 Philippines 0.47 0.10 0.16 (0.09) 0.49 1.00 Vietnam 0.47 (0.04) (0.07) (0.06) 0.03 0.39 1.00 Lao PDR 0.60 (0.07) (0.10) (0.07) 0.10 0.39 0.80 1.00 Myanmar 0.49 0.49 0.17 (0.09) 0.20 0.20 0.07 0.13 1.00

Temperature Correlations Thailand Bangladesh India Japan Indonesia Philippines Vietnam Lao PDR MyanmarThailand 1.00 Bangladesh 0.36 1.00 India 0.46 0.74 1.00 Japan 0.36 0.53 0.54 1.00 Indonesia 0.55 0.43 0.46 0.43 1.00 Philippines 0.61 0.60 0.64 0.62 0.68 1.00 Vietnam 0.84 0.50 0.58 0.48 0.64 0.69 1.00 Lao PDR 0.88 0.52 0.51 0.46 0.59 0.69 0.94 1.00 Myanmar 0.70 0.65 0.53 0.31 0.57 0.54 0.71 0.75 1.00


Table 7

First Stage Regression Sample Estimates

Sample First-stage Regressions Linear Regression St. Dev. From MeansLocal Values Export Values Local Values Export Values

Japan Rain -0.01 0.18*** -1.76 29.81***(0.01) (0.02) (1.97) (3.49)

Phillippines Rain 0.13*** -0.20*** 42.77*** -65.97***(0.01) (0.01) (2.40) (4.15)

India Rain 0.49*** 0.16*** 45.04*** 14.41***(0.03) (0.05) (2.51) (4.56)

Indonesia Rain -0.09*** 0.31*** -24.34*** 87.49***(0.01) (0.02) (2.86) (4.72)

Japan Temperature 57.70*** -100.57*** 30.21*** -52.65***(4.71) (8.55) (2.46) (4.48)

Phillippines Temperature -54.80*** -226.25*** -18.09*** -74.69***(15.81) (24.09) (5.22) (7.95)

India Temperature -14.04** 21.76 -4.67** 7.24(6.72) (13.59) (2.24) (7.24)

Indonesia Temperature 17.05 -332.96*** 4.79 -93.64***(22.17) (26.15) (6.24) (7.35)

Constant 199.02 7112.04*** 167.01*** 187.62***(200.78) (180.99) (16.47) (32.72)

Regression estimates on price x intensity production values (in PPP dollars), based on continuous measures of rainfall (in mm) and temperature (in degrees Celsius). Standard errors are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 8

Falsification Results

Falsification Test - Future Prices vs. Current Vote Share(t+1) (t+2) (t+3) (t+4) (t+5) (t+10) (t+15)

Harvest Value (Local - $PPP) -$34.33*** -$57.55*** -$33.95*** -$34.93*** -$51.65*** -$65.18** -$402.84(7.45) (11.73) (6.71) (7.77) (13.86) (28.90) (738.54)

Export Value (Export - Dollar) $144.30* $141.64** $1655.63 $632.91 $448.43 -$826.45 -$32.87***(86.00) (58.58) (12,472.04) (1,510.17) (609.30) 826.45 (8.93)

Change in price required to increase voting share by (one percentage point, multiplied by province intensity factor) using instrumental variable regressions (future rainfall and future prices). Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 9

Overall Results – Local Specification

The Value of Rice Production (t-1) and Vote Share (Value to Increase Vote Share 1%)(1) (2) (3) (4)

Harvest Value (Local - $PPP) -$154.15*** -$168.79* $32.66 $14.88(50.73) (95.24) (28.95) (157.34)

Export Value (Export - Dollar) -$1028.81 -$196.46* $246.31 $97.09(2,021.63) (104.21) (6,631.44) (146.01)

Province Fixed Effects? Yes Yes Yes YesTime Fixed Effects? Yes Yes Yes YesInstrumental Variables? No Yes Yes YesLagged Production? No No Yes NoLinear Time Trends? No No No YesChange in price required to increase voting share by one percentage point. Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

The Value of Rice Production (t-1) and Seat Probability(1) (2) (3) (4)

Seat (OLS) - Local -$136.09* -$25.93** $3.80 $0.52(76.63) (12.91) (2.14) (4.89)

Seat (Probit) - Local -$31.15*(18.46)

Seat (OLS) - Export $19.24 -$1.92** $0.06 -$1.10(21.57) (0.87) (0.05) (1.78)

Seat (Probit) - Export $3.78(3.41)

Province Fixed Effects? Yes Yes Yes YesTime Fixed Effects? Yes Yes Yes YesInstrumental Variables? No Yes Yes YesLagged Production? No No Yes NoLinear Time Trends? No No No Yes

Change in price required to raise probability of obtaining a seat by one percentage point. Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Table 10

Falsification Results – Local Specification

Falsification Test - Future Prices vs. Current Vote Share (Local IV)(t+1) (t+2) (t+3) (t+4) (t+5) (t+10) (t+15)

Harvest Value (Local - $PPP) -$17.88* -$726.27 -$25.93 -$18.65 -$10.87** -$11.29 $42.55(10.05) (7,655.52) (19.26) (12.78) (4.59) (7.47) (32.06)

Export Value (Export - Dollar) -$34.49 -$1828.15 -$61.61 -$30.36 -$20.34** -$15.38 -$10.14***(27.39) (149,226.79) (43.66) (21.11) (8.39) (13.15) (3.80)

Change in price required to increase voting share by one percentage point using local instrumental variable regressions (future rainfall and future prices). Standard errors, clustered at the province level, are in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


Figures

Figure 1. Map of rice-producing areas of Thailand. Each dot represents 20,000 hectare

Source: ricepedia.org, Global Rice Science Partnership.


Figure 2. Analysis of nominal price changes in rice.


Figure 3. Analysis of real rice harvest prices and export prices

thesis final draft

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