Slides for my dissertation proposal, presented to my panel on February 22, 2010. http://brianbelen.blogspot.com

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

2. 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.

3. The
Motivation
for the research.

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

5. The Filipino Diaspora and Workers’ Remittances
15
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)

6. The Seven Largest Remittance Recipient Countries
India
China
Mexico
Philippines
France
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)

7. “Stylized facts”about remittances
SOME
tions
contrast with findings from individual
country cases.
Ob serva
e
abo ut th
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.
Of the approximately 102,000 articles
on remittances available on Google Scholar,
not a single one was written by me!

8. Apply new approaches to Philippine Data.
Inquire into the cyclicality and other
properties of remittances to the Philippines.
Approach the topic from a
-
macroeconomic perspective.
Develop the research like an
Wh open-source project.
y
No
t..
.?

9. The
Treatment
applied to the data

10. 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 business cycle filters were considered to remove the
time trend and isolate the cyclical component of the data.

11. 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

12. 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

13. 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

14. Preliminary
Results thus far
from work on the topic

15. First, the research explores the cyclicality of
workers’ remittances to the Philippines.
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 )

22. Conjecture:
Perhaps the different remittance-sending behavior
described by the models is due to the type of OFW
deployed in each particular country.

24. 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.

25. 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

26. 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. Issuesand
Directions
to explore

29. Why are there different results between
different filtering techniques?
Can the exchange rate effects be
accounted for in the analysis?
How to handle structural breaks
in the data?
How do remittances measure up
against other inflows like FDI?
What other macroeconomic variables
would be relevant to the analysis?

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