Dissertation Proposal

(An Econometric Analysis)Remittances

PropertiesofPhilippine

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.

PresentationThis

will touch upon:

The Motivation for the research.

A description of the data Treatment.

Initial departures and preliminary Results.

Issues and Directions to explore.

MotivationThe

for the research.

are particularly significant Remittances

Philippines for the

and for Filipinos .

The Filipino Diaspora and Workers’ Remittances

0

5

10

15

2000 2001 2002 2003 2004 2005 2006 2007Overseas Filipinos Workers’ Remittances(in Millions of Filipinos) (in Nominal US$ Billions)

Sources: International Labor Organisation (2009) and Bangko Sentral ng Pilipinas (2008)

The two sets of bars are on different scales, but illustrate the growth in OFW Remittances.

The Seven Largest Remittance Recipient Countries

Germany

Spain

France

Philippines

Mexico

China

India

0 15 30 45 60

Remittances % of GDP

Source: The World Bank Migration and Remittances Data (November 2009)

(in Nominal US$ Billions) (2008 GDP)

Remittances as a percentage of GDP is much larger in the Philippines than in any of the largest remittance recipient economies.

SOME

Observations

about the

literature

(ON REMITTANCES)

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!

“Stylized facts”about remittances contrast with findings from individual country cases.

Some approaches have yet to be applied to the Philippine case.

My argument is essentially that the Philippines is an interesting case study, as shown in the prior slides.

Some humor (haha!)

Why Not...?

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 open-source project.-

This was the starting point that led me to explore different directions for this research.

This idea came up because I decided to use R.

TreatmentThe

applied to the data

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:

v = trend + cyclical + seasonal + et t t t t

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.

Two of these were performed using the R functions stl( ) and decompose( ). The third involved regression with dummy variables.

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.

ln(y) (Raw Data)

Quarterly Frequency, 1989Q1-2004Q4

y

1990 1995 2000 2005

6.6

6.8

7.0

7.2

7.4

ln(r) (Raw Data)

Quarterly Frequency, 1989Q1-2004Q4

r

1990 1995 2000 2005

3.0

3.5

4.0

4.5

5.0

1989Q1-2008Q4 1989Q1-2008Q4

This is what the raw data looks like...

Percentages are Normalized to 1

Quarterly Frequency: 1989Q1 to 2008Q4

Gro

wth

Ra

te (

Pe

rce

nt)

1990 1995 2000 2005

-0.04

-0.02

0.00

0.02

0.04

by LOESS

by Moving Averages

by Dummy Variables

HP Filtered y by Seasonal Adjustment Technique

Percentages are Normalized to 1

Quarterly Frequency: 1989Q1 to 2008Q4

Gro

wth

Ra

te (

Pe

rce

nt)

1990 1995 2000 2005

-0.04

-0.02

0.00

0.02

by LOESS

by Moving Averages

by Dummy Variables

CF Filtered y by Seasonal Adjustment Technique

This is what deseasonalized y (ln GDP) data looks like after filtering...

Percentages are Normalized to 1

Quarterly Frequency: 1989Q1 to 2008Q4

Gro

wth

Ra

te (

Pe

rce

nt)

1990 1995 2000 2005

-0.4

-0.2

0.0

0.2

0.4

0.6

by LOESS

by Moving Averages

by Dummy Variables

HP-Filtered r by Seasonal Adjustment Technique

Percentages are Normalized to 1

Quarterly Frequency: 1989Q1 to 2008Q4

Gro

wth

Ra

te (

Pe

rce

nt)

1990 1995 2000 2005

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

by LOESS

by Moving Averages

by Dummy Variables

CF Filtered r by Seasonal Adjustment Technique

While deseasonalized r (ln remittance) looks like this after filtering.

ResultsPreliminary

from work on the topic thus far

In keeping with the literature, the idea was to develop regression models using variables relevant to remittance sending:

GDP Past remittance Exchange Rater = f ( , , )

I would apply and extend this model subsequently to consider different cases.

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.

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.

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

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.

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.

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

...and to Australian remittances to the Philippines.

Perhaps the different remittance-sending behavior described by the models is due to the type of OFW

deployed in each particular country.

Conjecture:

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.

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).

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”.)

IRFS USING HP-FILTERED DATA

xy$xhp.ya

0.00

0.05

0.10

0.15

xy$x

hp.ra

0.00

0.05

0.10

0.15

0 1 2 3 4 5 6 7 8 9 10

Orthogonal Impulse Response from hp.ra

95 % Bootstrap CI, 1000 runs

xy$x

hp.ya

-0.02

0.00

0.02

xy$x

hp.ra

-0.02

0.00

0.02

0 1 2 3 4 5 6 7 8 9 10

Orthogonal Impulse Response from hp.ya

95 % Bootstrap CI, 1000 runs

IRFS USING CF-FILTERED DATA

xy$x

cf.ya

0.00

0.01

0.02

xy$x

cf.ra

0.00

0.01

0.02

0 1 2 3 4 5 6 7 8 9 10

Orthogonal Impulse Response from cf.ya

95 % Bootstrap CI, 1000 runs

xy$xcf.ya

-0.02

0.00

0.02

0.04

0.06

xy$x

cf.ra

-0.02

0.00

0.02

0.04

0.06

0 1 2 3 4 5 6 7 8 9 10

Orthogonal Impulse Response from cf.ra

95 % Bootstrap CI, 1000 runs

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.

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

DirectionsIssues

to explore

and

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?

Resolved: The problem, apparently, is that an HP-Filter leaves more noise compared to a CF-Filter. 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 are also different models that can be applied to examine this question more thoroughly.

This can be a key section of the dissertation, probably following the work of Vargas-Silva.

Brian L. Belen

bbelen@gmail.com

brianbelen.blogspot.com

@brianbelen

END OF PRESENTATIONT H A N K Y O U