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159
Appendix
Table K1: The estimation of the effects of on-farm storage intervention using various estimation
methods
Coefficient on ∆on-farm storage quantity under the intervention (∆𝑂 𝑆𝑖𝑦 )
Estimation methods
2SLS
(1)
Dependent variable
Rice Price level (observations = 247)
Panel A: harvesting period
∆Log (𝑃𝑖 0𝑦 )
-0.0004
[0.0050]
∆Log (𝑃𝑖 𝑦 )
0.0131**
[0.0055]
∆Log (𝑃𝑖 2𝑦 )
0.0114*
[0.0066]
Panel B: Non-harvesting period
∆Log (𝑃𝑖 𝑦 )
0.0067
[0.0051]
∆Log (𝑃𝑖2𝑦 )
0.0085***
[0.0031]
∆Log (𝑃𝑖3𝑦 )
0.0054*
[0.0028]
∆Log (𝑃𝑖4𝑦 )
0.0116***
[0.0032]
∆Log (𝑃𝑖5𝑦 )
0.0086***
[0.0022]
∆Log (𝑃𝑖6𝑦 )
0.0136***
[0.0047]
∆Log (𝑃𝑖7𝑦 )
0.0164***
[0.0058]
∆Log (𝑃𝑖8𝑦 )
0.0115***
[0.0039]
∆Log (𝑃𝑖9𝑦 )
0.0083**
[0.0034]
Rice price volatility (observations = 247)
∆𝑃𝑣𝑜𝑙𝑖𝑦
-0.0024
[0.0021]
2-Step GMM
LIML
Fuller (4)-LIML
(2)
(3)
(4)
-0.0005
[0.0050]
0.0140**
[0.0055]
0.0142***
[0.0038]
0.0006
[0.0069]
0.0138**
[0.0058]
0.0116*
[0.0067]
-0.0004
[0.0050]
0.0109**
[0.0046]
0.0098*
[0.0051]
0.0093**
[0.0045]
0.0080***
[0.0025]
0.0036*
[0.0020]
0.0105***
[0.0031]
0.0078***
[0.0020]
0.0097**
[0.0043]
0.0147**
[0.0058]
0.0111***
[0.0039]
0.0066**
[0.0033]
0.0069
[0.0052]
0.0085***
[0.0031]
0.0055*
[0.0029]
0.0131***
[0.0037]
0.0088***
[0.0023]
0.0184**
[0.0075]
0.0243*
[0.0126]
0.0207
[0.0130]
0.0118**
[0.0052]
0.0056
[0.0040]
0.0064**
[0.0026]
0.0043**
[0.0021]
0.0095***
[0.0027]
0.0072***
[0.0018]
0.0137***
[0.0047]
0.0177***
[0.0067]
0.0138**
[0.0055]
0.0082**
[0.0034]
-0.0038**
[0.0018]
-0.0025
[0.0026]
-0.0024
[0.0019]
Note: The figures in brackets below the estimates are the robust standard errors. To save space,
controls for year fixed effects are not shown. *, **, *** indicate significance at the 0.1, 0.05,
0.01 levels, respectively.
160
Table K2: 2SLS estimates of the effects of on-farm storage intervention using the alternative
specification
Coefficient on ∆on-farm storage quantity under the intervention (∆𝑂 𝑆𝑖𝑦 )
2SLS
(1)
Over.
test
(2)
Dependent variables
Rice Price level (observations = 247)
Panel A: harvesting period
ΔLog (𝑃𝑖 0𝑦 )
-0.0013
0.052
[0.0051]
ΔLog (𝑃𝑖 𝑦 )
0.0126**
0.176
[0.0055]
ΔLog (𝑃𝑖 2𝑦 )
0.0111
0.638
[0.0068]
Panel B: Non-harvesting period
ΔLog (𝑃𝑖 𝑦 )
0.0062
0.335
[0.0052]
ΔLog (𝑃𝑖2𝑦 )
0.0085***
0.815
[0.0032]
ΔLog (𝑃𝑖3𝑦 )
0.0054*
0.319
[0.0030]
ΔLog (𝑃𝑖4𝑦 )
0.0119***
0.114
[0.0033]
ΔLog (𝑃𝑖5𝑦 )
0.0087***
0.281
[0.0022]
ΔLog (𝑃𝑖6𝑦 )
0.0144***
0.051
[0.0048]
ΔLog (𝑃𝑖7𝑦 )
0.0173***
0.029
[0.0059]
ΔLog (𝑃𝑖8𝑦 )
0.0123***
0.029
[0.0041]
ΔLog (𝑃𝑖9𝑦 )
0.0093**
0.081
[0.0036]
Rice price volatility (observations = 247)
Δ𝑃𝑣𝑜𝑙𝑖𝑦
-0.0022
0.258
[0.0022]
AR1
test
(3)
AR2
test
(4)
Adj. Rsquared
(5)
p-value
∆𝑂 𝑆𝑖𝑦
(6)
p-value
∆𝑂 𝑆𝑖𝑦
bootstrap (7)
0.004
0.020
0.897
0.803
0.829
0.021
0.063
0.897
0.022
0.093
0.014
0.003
0.898
0.100
0.430
0.029
0.021
0.900
0.229
0.486
0.011
0.591
0.924
0.007
0.062
0.002
0.454
0.954
0.068
0.040
0.000
0.162
0.960
0.000
0.025
0.001
0.005
0.969
0.000
0.026
0.000
0.003
0.941
0.003
0.074
0.002
0.011
0.908
0.004
0.025
0.000
0.133
0.899
0.003
0.022
0.000
0.512
0.858
0.011
0.071
0.002
0.075
0.900
0.333
0.492
Note: First stage F-statistic equals 30.65. The figures in brackets below the estimates are the
robust standard errors. To save space, controls for year fixed effects are not shown. *, **, ***
indicate significance at the 0.1, 0.05, 0.01 levels, respectively.
161
Table K3: 2SLS estimates of the effects of on-farm storage intervention without Surin and
Nakhonratchasima samples
Coefficient on ∆on-farm storage quantity under the intervention (∆𝑂 𝑆𝑖𝑦 )
2SLS
(1)
Over.
test
(2)
Dependent variables
Rice Price level (observations = 221)
Panel A: harvesting period
ΔLog (𝑃𝑖 0𝑦 )
0.0038
0.097
[0.0054]
ΔLog (𝑃𝑖 𝑦 )
0.0132***
0.191
[0.0045]
ΔLog (𝑃𝑖 2𝑦 )
0.0074**
0.275
[0.0033]
Panel B: Non-harvesting period
ΔLog (𝑃𝑖 𝑦 )
0.0032
0.125
[0.0025]
ΔLog (𝑃𝑖2𝑦 )
0.0079***
0.797
[0.0025]
ΔLog (𝑃𝑖3𝑦 )
0.0065***
0.158
[0.0020]
ΔLog (𝑃𝑖4𝑦 )
0.0117***
0.194
[0.0031]
ΔLog (𝑃𝑖5𝑦 )
0.0086***
0.435
[0.0021]
ΔLog (𝑃𝑖6𝑦 )
0.0101*
0.096
[0.0052]
ΔLog (𝑃𝑖7𝑦 )
0.0100
0.033
[0.0065]
ΔLog (𝑃𝑖8𝑦 )
0.0072
0.037
[0.0044]
ΔLog (𝑃𝑖9𝑦 )
0.0042
0.194
[0.0036]
Rice price volatility (observations = 221)
Δ𝑃𝑣𝑜𝑙𝑖𝑦
-0.0037*
0.692
[0.0020]
AR1
test
(3)
AR2
test
(4)
Adj. Rsquared
(5)
p-value
∆𝑂 𝑆𝑖𝑦
(6)
p-value
∆𝑂 𝑆𝑖𝑦
bootstrap (7)
0.009
0.080
0.8895
0.481
0.663
0.013
0.110
0.8972
0.003
0.045
0.007
0.006
0.9035
0.026
0.043
0.033
0.061
0.8946
0.269
0.212
0.013
0.522
0.9241
0.002
0.052
0.003
0.624
0.9542
0.001
0.037
0.001
0.084
0.9633
0.000
0.041
0.002
0.005
0.9700
0.000
0.018
0.000
0.002
0.9454
0.054
0.259
0.004
0.007
0.9190
0.126
0.141
0.001
0.203
0.9045
0.104
0.114
0.001
0.987
0.8559
0.244
0.269
0.003
0.117
0.8933
0.073
0.278
Note: First stage F-statistic equals 85.52. The figures in brackets below the estimates are the
robust standard errors.
To save space, controls for year fixed effects are not shown. *, **,
*** indicate significance at the 0.1, 0.05, 0.01 levels, respectively.
162
Chapter 5 General conclusion and avenues for further research
My doctoral research aims to deepen our understanding about the effect of policy
interventions that aim to solve farmers’ low-income problems on the functioning of agricultural
markets. Specifically, I evaluate three agricultural policy interventions in Thailand, including
price support policy, promoting farmer organizations, and supporting on-farm storage. These
interventions have been implemented over a decade in many developing countries. This
dissertation used data from several sources for empirical analysis. In chapter 2 and 4, I used
provincial-level data collected from several government agencies. In contrast, in chapter 3, I
used individual-level field survey data collected from two provinces in Northeast Thailand. In
this section, I first summarize the results from the dissertations. I then discuss implications for
policy and evaluation. Lastly, I discuss avenue for future research.
5.1 Summary of results
In chapter 2, I address two research questions. First, how much oligopsony power do
processors or intermediaries in the Thai Jasmine rice market have and exercise over farmers?
Second, what are the market and welfare effects of price support policy in the presence of
oligopsony? To answer the first question, I develop a rice market model consisting of rice
supply and demand equations based on the NEIO framework. To answer the second question,
I develop an imperfect competition model to evaluate the welfare effects of the Paddy Pledging
Program (PPP), a price support policy in Thailand. Using 15-year data, 15 provincial-level with
225 observations, I find that intermediaries in the Thai Jasmine rice market have oligopsony
power. The estimates of oligopsony power parameter (1 = highest level of oligopsony power)
range from -0.39 to 0.65. I also find that intermediaries exercise oligopsony power over farmers.
The estimated oligopsony price distortion ranges from -33% to 55%.
163
Using the above-estimated parameters to simulate the Thai Jasmine rice market under the
paddy pledging program, I find that the price support policy increases the farm gate price by
8.4% and reduces the consumer price by 6.35%. As a result, the program increases consumer
surplus and farmer surplus by $10.6 million and $38.8 million, respectively. However, I find
that the program is inefficient. It imposes a deadweight loss to society of about $34.9 million
per year. Nevertheless, the program can be efficient by setting an optimal support price where
the government does not have to buy rice from farmers. Next, I consider the income
redistribution effect of the program. The program is effective in income redistribution because
every public dollar spent on the program returns $1.10 in income redistribution. My findings
challenge generally accepted “wisdom” regarding price support policy in agricultural markets.
The perceived wisdom regarding this policy is that it benefits farmers, hurts consumers, and
always imposes a deadweight loss on society. Therefore, the government should eliminate the
price support policy. However, my findings show that the price support policy can benefit both
farmers and consumers in an imperfect competition market and can be designed to increase
social welfare.
In chapter 3, I test the hypothesis that nonparticipating farmers or farmers who sell
rice to private intermediaries in the areas where there is direct competition between marketing
cooperatives and private intermediaries (treated areas) are likely to receive a higher price than
those who sell rice in other areas (comparison areas). To test this hypothesis, I use language
spoken at home as an instrument. Using data from randomly selected 360 households from 36
villages in treated and comparison areas, I find that nonparticipating farmers in treated areas
receive 10.9% higher prices from private intermediaries than those who sell rice in comparison
areas. This finding provides support for the view that the presence of marketing cooperatives
can significantly force private intermediaries to competitively raise prices paid to farmers.
Therefore, promoting farmer organizations' role in the rice value chains can generate a spillover
164
effect or indirect effect.
In chapter 4, I address two research questions. First, does the change in local supply
caused by on-farm storage interventions affect equilibrium market prices? Second, is this
change in supply able to stabilize price inter-seasonally? To answer these questions, I use 4year and 5-year lagged on-farm storage quantity as instrumental variables. I find that an
increase in the on-farm storage quantity under the intervention by 20,000 tons, which is equal
to 20,000 tons decrease in local supply in the markets, causes the farm gate price in November,
February, March, April, and September to increase by 1.31%, 0.85%, 0.54%, 1.16%, and 0.83%,
respectively. Using these estimated values to calculate the welfare benefits, I find that
nonparticipating farmers gain considerable welfare benefits from on-farm storage intervention.
For example, the local supply change caused by the intervention increases the farm gate price
in November in Surin province by 7.46% or approximately $24.24 per ton. If nonparticipating
farmers in Surin sell all of their surplus paddy this month, the aggregate welfare benefits to
nonparticipating farmers in Surin will be $19.32 million. In contrast, I find that the increase in
on-farm storage quantity under the intervention does not significantly reduce price volatility.
Overall, chapter 4 shows that allowing farmers to store grains by offering them the harvesttime cash loan can affect the equilibrium market price. Hence, supporting on-farm storage can
increase farm gate prices.
165
5.2 Implications for policy and evaluation
My dissertation provides 3 crucial evidence and 7 policy implications for agri-food
policy debates regarding the welfare effect of price support policy in the presence of market
power, the role of farmer organizations in agricultural development and agricultural markets,
and the welfare implications of on-farm storage interventions when delivered on a massive
scale.
The findings in chapter 2 point out that the policy prescription to deregulate
agricultural markets in developing countries must be undertaken with caution. In an agricultural
market with oligopsony power, government policies can be warranted not only to mitigate
market distortion but also to protect small farmers and consumers from the adverse effects of
market power. In other words, my findings have highlighted the need for market interventions
when the markets function poorly due to a low competition level. In particular, when there is a
market failure, a price support policy can be designed to improve the market's efficiency and
thereby increase farmers’ income and lower consumer prices.
The finding in chapter 3 shows that strengthening the role of farmer organizations in
agricultural markets can benefit not only members but also non-members. Four implications
emerged from this finding. First, evaluating the inclusiveness of marketing cooperatives toward
poor farmers should not be limited to sampling and analyzing participating farmers only.
Second, prior studies that do not control for the spillover effect of marketing cooperatives may
underestimate the benefits of marketing cooperatives. Third, the spillover effect needs to be
incorporated in the future evaluation of the marketing cooperative’s performance. Finally,
policies aiming at enhancing the role of marketing cooperatives in rice value chains should be
aware of and address the free-rider problem to ensure that social welfare is maximized
The results in chapter 4 show that supporting on-farm storage by allowing farmers to
access credit during the harvesting time can increase the local market prices. Hence, the
166
evaluation of the economic impact of on-farm storage interventions or any investments that
will improve farmers’ ability to store needs to include its market-level effect. Moreover, onfarm storage interventions, when delivered at scale, can be used by policymakers as an effective
tool to prevent the falling local farm gate prices due to excess supply at harvest.
Overall, it is possible to raise farmers’ income through existing interventions to some
degree, and the impact assessments of these interventions need to include their spillover effects
and market-level effects.
167
5.3 Avenues for further research
There are at least three avenues for further inquiry for deepening our understanding
about the effect of policy interventions that aim to solve farmers’ low-income problems on the
functioning of agricultural markets. Firstly, we need more empirical evidence on the effects of
policy intervention on consumers. In chapter 2, we show that government policies can increase
consumers' benefits by reducing oligopolistic middlemen's rent. In chapter 3, we show that
cooperative activities have the possibility to increase consumers' benefits by reducing
oligopolistic buyers' rent. Overall, policies and cooperative activities can counter oligopsony
and oligopoly, that is, they can increase farmers' prices and may decrease consumers' prices.
Hence, it is crucial to generate more evidence on the impact of policy interventions on
consumer welfare.
The second avenue for further research is to analyze policy intervention's impact in
other vertically related markets. This is because the agricultural markets are interlinked in
complex ways. Hence, the intervention in one market may affect other vertically related
markets. As an illustration, consider a simple agricultural supply chain:
[Input providers] ➔ [Farmers] ➔ [Intermediaries] ➔ [Consumers]
where farmers buy inputs such as seeds from input providers and then sell their crops to
intermediaries such as traders and processors. And then, intermediaries sell processed crops to
consumers. In this supply chain, there are three vertically related markets: the market between
input providers and farmers, the market between farmers and intermediaries, and the market
between intermediaries and consumers. Although the policy interventions that I evaluate take
place in the market between farmers and intermediaries, it can impact other vertically related
markets as well. For example, the price support policy assessed in chapter 2 may also impact
the market between input providers and farmers. Namely, the increase in the price received by
farmers caused by the price support policy may lead to the rise in land lease fee or fertilizer
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prices. Hence, we should analyze the impact of policy intervention in both the market where
the policy is implemented and other vertically related markets.
The third avenue for future research is to investigate how technology can be used to
solve farmers’ low-income problems. In particular, the widespread adoption of mobile phones
and the internet in rural areas creates the potential for enhancing the competition in agricultural
markets. Mobile phones and the internet can be used to enhance the functioning of agricultural
markets in developing countries in several ways. First, farmers can use mobile phones to speak
to multiple intermediaries to collect price information. This price information may allow
farmers to engage in optimal trade or arbitrage. Namely, a price difference between markets
should induce farmers to reallocate their goods to the market that offers the highest price.
Second, private sectors and governments can use a mobile phone as a platform to deliver market
information to farmers through various mobile technologies such as short messaging service
(SMS). For example, a subscription SMS service can transmit market information to farmers’
phones. Third, private sectors and governments can use the internet kiosk to deliver market
information to farmers. Lastly, private sectors and governments can set up an electronic market
where intermediaries and farmers connect over an electronic network. This electronic market
is likely to increase market competition as it integrates geographically distant markets within a
common platform. By bridging information gaps and connecting buyers with sellers, mobile
phones and the internet are likely to enhance the functioning of agricultural markets in
developing countries. Therefore, we should evaluate the impact of mobile phones and the
internet on the price received by farmers.
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