IV. RESEARCH METHOD
4.1 Scope of Study
This research was conducted in Indonesia focusing on factors that influence Indonesian cocoa export to the European Union.
4.2 Types of Data and Sources
The type of data used in this research is secondary data time series time series and cross section as much as 14 years, start from 1998 until 2011. The data
obtained from several agencies such as: Statistics of Indonesia, Agricultural Ministry, ICCO, Global Trade Atlas Navigator and other institutions. Data used
are export volume of cocoa, the distance between countries, Gross Domestic Product and population.
Table 5. Sources of Data
Data Unit
Sources of Data Export Value of Indonesian Cocoa
US Global Trade Atlas
Navigator Exchange Rate of Rupiah towards Dollar
RpEUR OANDA
GDP PPP of European Union US
World Bank GDP PPP of Indonesia
US World Bank
Population of European Union Million People
Million People
FAOSTAT Population of European Union Million
People Million
People FAOSTAT
Export Tax Financial Ministry of
Indonesia
4.3 Data Analysis Methods
Data processing was conducted by using stata which use panel data Analysis with gravity model panel data. We often find problem regarding to data
availability. Sometimes time series data provided are short and sometimes cross section data provided are limited. In Econometrics this problem can be solved by
using pooled data in order to get efficient estimation. A panel data set, while having both a cross sectional and a time series
dimension, differs in some important respects from independently pooled cross section. To collect panel data, sometimes called longitudinal data, we follow or
attempt to follow the same individuals, families, firms, cities, states, or across time. For example a panel data set on individual wages, hours, education and
other factors is collected by randomly selecting people from a population at a given point in time. Then, these same people are interviewed at several
subsequent points in time. This gives us data on wages, hours, education, and so on, for the same group of people in different years.
Panel data sets are fairly easy to collect for school districts, cities, counties, states, and countries, and policy analysis is greatly enhanced by using
panel data sets. Hsiao 2003 lists several benefits from using panel data. 1.
Controlling for individual heterogeneity. 2.
Panel data give more informative data, more variability, less collinearity among the variables, more degrees of freedom and more efficiency.
3. Panel data are better able to study the dynamics of adjustment.
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4. Panel data are also well suited to study the duration of economic states like
unemployment and poverty, and if these panels are long enough, they can shed light on the speed of adjustments to economic policy changes.
5. Panel data are better able to identify and measure effects that are simply not detectable in pure cross section or pure time-series data.
6. Panel data models allow us to construct and test more complicated behavioral models than purely cross-section or time series data.
7. Micro panel data gathered on individuals, firms and household may be more accurately measured than similar variables measured at the macro level.
Biases resulting from aggregation over firms or individuals may be reduced or eliminated.
8. Macro panel data on the other hand have a longer times and unlike the problem of nonstandard distribution typical of unit roots tests in time series
analysis. There are three model which can be estimated in panel data. These are
Pooled Least Square, Fixed Effects and Random Effects.
4.3.1 Pooled Least Square
Pooled least square use panel data by using cross section, time series and pooling. Every observation each period has regression. We can know N
Quantity of unit cross section and T period of time. From all of the observations N.T, we can write function
Y
it
= α + X
it
βj + ε
it
for i,j = 1,2,…, N and t = 1,2,…,T
where: Y
it
= endogenous variable X
it
= exogenous variable α
= intercept β
= slope i
= individual i t
= period year t ε
= error N
= Quantity of unit cross section T
= Quantity of time period The simplest approach to estimate this function is ignoring cross section
and time series dimension from panel data and estimating by ordinary least square which is determined by pool data.
In this method, model assume that variable’s intercept is the same, then this model also assume that coefficient slope from two variables is identical for all
unit cross section. This is strict assumption. Although PLS method pooled least square is relatively easy, but model possibly distort the real relationship between
Y and X in unit of cross section. Pooled Least Square models are consistent if the dependent variables are
not correlated to the error. Pooled models also produce an unbiased estimator if the unit effects α
i
are uncorrelated with the independent variable x. But, commonly α
i
is correlated to the x, therefore pooled and pa tend to produce a bias estimator of β Clark and Linzer, 2012. Fixed effects and random effects model
could solve this problem. Clark and Linzer 2012 stated that the fixed effects
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model will produce unbiased estimates of β, but those estimates need a high variability on the sample.
4.3.2 Fixed Effect Model
Fixed effect model is model which considers eliminated variables can change intercept of cross section and time series. Dummy variables can be added
to the model to make intercept changes possible. Afterward model is estimated by using Ordinary Least Square OLS
Y
it
= α
i
D
i
+ β X
it
+ ε
it
Where: Y
it
= endogenous variable X
it
= exogenous variable α
= intercept D
= dummy variable β
= slope i
= individual i t
= period year t ε
= error
4.3.3 Random Effect Model
Additional of dummy in fixed effect can reduce quantity of degree of freedom. This condition will also reduce efficiency of estimated parameter.
Random effect model can be used to solve this problem. In this model, different parameter between individual and time is included to error. Random effect model
can be explained by this equation:
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