Slides for my dissertation proposal, presented to my panel on February 22, 2010. These slides have been modified with some comments made during the actual presentation. http://brianbelen.blogspot.com

1. Properties
of
Philippine
Remittances
(An Econometric Analysis)
Dissertation Proposal

2. The aim of this presentation is to convey exactly what it is I want
to do, communicate an idea of what I’ve already done and what
I’m aware needs to be done, and thereby get some guidance so I
know how to proceed from here on out.
(I concede it’s possible the paper doesn’t get this across as well as a presentation might.)
Anything in these red-trimmed grey boxes won’t appear on the slides. These are just
annotations to help read through the presentation since I’m not around to deliver it.

3. This
Presentation
will touch upon:
The Motivation for the research.
A description of the data Treatment.
Initial departures and preliminary Results.
Issues and Directions to explore.

4. The
Motivation
for the research.

5. Remittances
are particularly significant
for the Philippines
and for Filipinos .

6. The Filipino Diaspora and Workers’ Remittances
15
The two sets of bars are on different
scales, but illustrate the growth in OFW
Remittances.
10
5
0
2000 2001 2002 2003 2004 2005 2006 2007
Overseas Filipinos Workers’ Remittances
(in Millions of Filipinos) (in Nominal US$ Billions)
Sources: International Labor Organisation (2009) and Bangko Sentral ng Pilipinas (2008)

7. The Seven Largest Remittance Recipient Countries
India
China
Mexico
Remittances as a
percentage of GDP is
Philippines
much larger in the
Philippines than in any of
France
the largest remittance
recipient economies.
Spain
Germany
0 15 30 45 60
Remittances % of GDP
(in Nominal US$ Billions) (2008 GDP)
Source: The World Bank Migration and Remittances Data (November 2009)

8. “Stylized facts”about remittances
SOME
tions
contrast with findings from individual
country cases.
Observa
e
My argument is essentially that the Philippines is an
abo ut th
interesting case study, as shown in the prior slides. e
lite ratur
S)
Some approaches have yet to be applied TANCE
(ON REMIT
to the Philippine case.
Many studies have approached the topic
from a development perspective.
Some humor Of the approximately 102,000 articles
(haha!) on remittances available on Google Scholar,
not a single one was written by me!

9. Apply new approaches to Philippine Data.
Inquire into the cyclicality and other
properties of remittances to the Philippines.
This was the starting point that led me to explore
different directions for this research.
Approach the topic from a
-
macroeconomic perspective.
Develop the research like an
Wh open-source project.
y
No
t..
.? This idea came up
because I decided
to use R.

10. The
Treatment
applied to the data

11. Data for Overseas Filipino Workers’ (OFW) remittances and other
macroeconomic variables were obtained for 1989Q1 to 2008Q4.
Each time series (v) was assumed to have the following components:
vt = trend t + cyclical t + seasonal t + et
Three techniques were tried to remove
the seasonal component of the data.
Two of these were performed using the R functions stl( ) and decompose( ). The third
involved regression with dummy variables.
Two business cycle filters were considered to remove the
time trend and isolate the cyclical component of the data.
The Hodrick-Prescott Filter was chosen becaused of its use in macroeconomics. The
Christiano Fitzgerald filter was also considered since it is a broader filter.

12. ln(y) (Raw Data) ln(r) (Raw Data)
5.0
7.4
4.5
7.2
4.0
7.0
y
r
3.5
6.8
3.0
6.6
1990 1995 2000 2005 1990 1995 2000 2005
Quarterly Frequency, 1989Q1-2004Q4
1989Q1-2008Q4 Quarterly Frequency, 1989Q1-2004Q4
1989Q1-2008Q4
This is what the raw data looks like...

13. HP Filtered y by Seasonal Adjustment Technique CF Filtered y by Seasonal Adjustment Technique
0.04
0.02
0.02
Growth Rate (Percent)
Growth Rate (Percent)
0.00
0.00
-0.02
-0.02
-0.04
-0.04
by LOESS by LOESS
by Moving Averages by Moving Averages
by Dummy Variables by Dummy Variables
1990 1995 2000 2005 1990 1995 2000 2005
Quarterly Frequency: 1989Q1 to 2008Q4 Quarterly Frequency: 1989Q1 to 2008Q4
Percentages are Normalized to 1 Percentages are Normalized to 1
This is what deseasonalized y (ln GDP) data looks like after filtering...

14. HP-Filtered r by Seasonal Adjustment Technique CF Filtered r by Seasonal Adjustment Technique
0.4
0.6
0.3
0.4
0.2
Growth Rate (Percent)
Growth Rate (Percent)
0.2
0.1
0.0
0.0
-0.1
-0.2
-0.2
by LOESS by LOESS
-0.4
by Moving Averages by Moving Averages
by Dummy Variables by Dummy Variables
-0.3
1990 1995 2000 2005 1990 1995 2000 2005
Quarterly Frequency: 1989Q1 to 2008Q4 Quarterly Frequency: 1989Q1 to 2008Q4
Percentages are Normalized to 1 Percentages are Normalized to 1
While deseasonalized r (ln remittance) looks like this after filtering.

15. Preliminary
Results thus far
from work on the topic

16. First, the research explores the cyclicality of
workers’ remittances to the Philippines.
Based on the literature, cyclicality unearths questions of whether
remittances are sent out of altruistic or profit-seeking motives.
In keeping with the literature, the idea was to develop
regression models using variables relevant to remittance sending:
r = f( GDP , Past remittance , Exchange Rate )
I would apply and extend this model
subsequently to consider different cases.

17. Applied to Philippine remittances in general, the model yielded
different results depending on the filtering technique.
Thus, I proceeded to consider whether it would be useful to
disaggregate remittance by source country.

18. Remittances from the US appeared to indicate that all variables
were statistically significant – if the CF-Filtered data were used.

19. For Japanese remittances, the real effective exchange rate was a
significant regressor regardless of the filtering method, though the
relationship (sign) varied for regressors using CF-Filtered data.

20. Remittances from the UK appeared to behave consistently (based on the
model) regardless of filtering method employed. However, regressors
did show different relationships compared to US and JP cases.

21. Applying the model to the case of Canadian
remittances to the Philippines...

22. ...and to Australian remittances to the Philippines.

23. Conjecture:
Perhaps the different remittance-sending behavior
described by the models is due to the type of OFW
deployed in each particular country.
Because of the different results, I looked up worker deployment in
the different countries, and found that there is normally a particular
type of worker who finds employment in a specific country.

24. In the parlance of the literature, it appeared that the more skilled the type of worker,
the more remittances fit a profit-seeking profile (that is, procyclical with the home
country economy).
In contrast, the remittance-sending behavior of less skilled workers might be described
along the lines of altruism (that is, counter-cyclical with the home country economy).

25. Vector autoregressions were employed with the end in view
of understanding remittances’ macroeconomic impact.
A lag length of one was used to prevent overfitting using VARs.
Impulse-response functions were also employed to
model the effect of shocks to the variables.
Initially, this was the favored approach to the research, since VARs
allow one to model the interrelationship between variables via IRFs.
(Not to mention much of what has already been said about VARs and “macroeconomics without theory”.)

26. IRFS USING HP-FILTERED DATA
Orthogonal Impulse Response from hp.ya Orthogonal Impulse Response from hp.ra
0.15
0.02
0.10
hp.ya
hp.ya
0.00
0.05
-0.02
0.15 0.00
xy$x xy$x
0.02
0.10
hp.ra
hp.ra
0.00
0.05
-0.02
0.00
0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

27. IRFS USING CF-FILTERED DATA
Orthogonal Impulse Response from cf.ya Orthogonal Impulse Response from cf.ra
0.06
0.02
0.04
0.01
cf.ya
cf.ya
0.02
0.00
0.00
0.06 -0.02
0.02
xy$x xy$x
0.04
0.01
cf.ra
cf.ra
0.02
0.00
0.00
-0.02
0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

28. In the simple bivariate case, it seemed that a shock to remittance
had little effect on the economy.
On the other hand, the model was unable to consistently show
what effect a shock to the economy would have on remittance
receipts (seeing as the IRFs differed depending on filtering
technique used).
Thinking that this could be attributed to the simplicity of the
model, I decided to try out a more complicated model.

29. Unfortunately, I made a mistake in specifying this model: y appears
in both the regressor and regressand (c+i+g+x-m). (Oops!)

30. Issuesand
Directions
to explore

31. Resolved: The problem, apparently,
is that an HP-Filter leaves more
Why are there different results between noise compared to a CF-Filter.
different filtering techniques? However, a Double HP-Filter more
closely approximates a CF-Filtered
series.
Underlying question: what happens
if the remittances are valued in
different currencies (i.e. host
country/dollar/peso)? Note: there
Can the exchange rate effects be
are also different models that can accounted for in the analysis?
be applied to examine this
question more thoroughly.
How to handle structural breaks
in the data?
This can be a key section of the How do remittances measure up
dissertation, probably following against other inflows like FDI?
the work of Vargas-Silva.
What other macroeconomic variables
would be relevant to the analysis?

32. END OF PRESENTATION
T H A N K Y O U
Brian L. Belen
bbelen@gmail.com
brianbelen.blogspot.com
@brianbelen