remittance and financial development
TRANSCRIPT
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Cam Nguyen
Karla La Rosa
Graduate Seminar Prof Veitch
December 1, 2006
Does Remittance have a positive impact on Financial Development?
The two main capital inflows in developing countries are foreign direct
investments (FDI) and remittances. For the past two decades the supply of remittances
has been increasing in developing countries and has become a steady flow of income for
the families of the migrant workers (see appendix Graph 1 and Graph 2). Remittance in
developing countries has been seen as source of income, sometimes primary or
supplemental income, for families, especially in rural areas. Remittances may also be
sometimes vital for developing countries since it does help the economy of a developing
nation by the additional cash flow received. According to previous papers and studies,
remittances can help alleviate poverty in developing countries. As such, research in the
topic of remittances expanded due to the rapid increase in remittance income to
remittance receiving countries (RRC). Most remittance receiving countries are
developing countries. People from these countries migrate to seek for better job
opportunities and higher compensation. Many of the papers we found focused on the
effects of remittances on household consumption, education and poverty alleviation.
The continuous increase of remittances in developing countries and its positive
contribution to growth and poverty alleviation led to a number of interesting studies;
studies that directly link remittances to poverty alleviation, studies that indirectly link
remittances to growth and studies that discusses the relationship because remittances and
financial development. The main question that we pose in this research is -- do
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topic is fairly new and have yielded mixed results- some research say remittance has a
positive impact some say negative on growth via financial development, current
accounts, etc.
Reviewing past research in regards to remittances which start as early as in the
70’s until now, one can see that the idea and sentiments of remittance have changed
throughout the years. Research in the topic of remittances expanded in the 80’s due to
rapid increase in the remittance income to remitting receiving countries (RRC). The
initial feelings about remittances in the 70’s and early 80’s seem to be that remittances are
not beneficial. But in the later part of the 80’s and to the present, another voice started
emerging and viewed remittances in a different light- a positive one. In the recent years,
many research in this field focus on empirical evidence rather than theoretical arguments
like that of the 70’s and early 80’s.
There are positive and negative effects of everything- including remittances.
Many who deem remittances as a negative solution to poverty seem to adopt similar
reasoning/arguments. They argue that:
Remittances increase dependency, contribute to economic
and political instability and development distortion, and
lead to economic decline that overshadows a temporary
advantage for a fortunate few.1
Aside from the arguments stated above, many also believe that remittances offer little
contribution to development since the amount of capital being remitted will most likely
1 Keely, Charles B. and Tran, Bao Nga. “Remittance from Labor Migration: Evaluations, Performance, and
Implications”. International Migration Review, Vol.23, No.3. Autumn 1989. pp.500-525
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undocumented workers seek jobs abroad. This study was very useful in helping us
understand the underpinnings of remittances.
As mentioned earlier, empirical research on the effects of remittances on growth
started early in this decade and continue on so. These empirical researches tried to
explain how remittance affects growth by way of another channel whether it is current
account or financial development. Adams and Page (2003) in a published paper by the
World Bank, International Migration, Remittances, and Poverty in Developing Countries,
tried to explain the impact of international migration and remittances on poverty using a
74 country cross section dataset. The authors looked at how GDP per capita, survey
mean income per capita, Gini coefficient, remittance as a share of country GDP affects
poverty. Poverty was measured as a headcount of people living less than 1USD per day,
the poverty gap and the poverty gap squared. In each of these regressions, the remittance
variable came up to be statistically significant. The authors found that remittances had a
statistically significant impact on reducing poverty- “10% increase in share of remittance
in a country GDP will lead to a 1.6% decline in the share of people living on less than
1USD per person per day”.
We wish to explore this topic in a different light by looking at it in a different
perspective. Mundaca’s study of remittances looked at it in a perspective wherein a
developed financial market can lead to more remittances. The paper by Aggarwal, Kunt
and Peria provides a model that will allow us to measure the effect of remittances in the
financial sector. Remittances can contribute to the improvement of financial sectors such
as the banking industry through the increase of deposits or the amount of credit provided
to private sectors (Aggarwal, Demirguc-Kunt, Peria, 2006 ). The increase in credit
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provided can be attributed to the increase in loanable funds which can be increased
through remittance deposits. Currently, remittance income is mainly utilized to buy
necessary and discretionary items, pay off debts, purchase of non-productive assets such
as land and homes. In the paper by Charles Stahl, he notes that the propensity to save is
higher for remittance receiving households than that of non-receiving households – this
increase of savings rate can have a long run positive effect on the economy if we follow
the Solow Model. As you can see an increase in the savings rate does not only promote
financial development but can also promote growth. The paper by King and Levine
further discusses how financial development is correlated to growth by way of several
different types of financial indicators. Aggarwal, Demirguc-Kunt, and Peria uses two of
the 4 different financial indicators suggested by King and Levine to create a financial
development model that will help us map the relationship between remittances and
financial development and in turn affecting growth as suggested by the paper from King
and Levine. Furthermore, the paper by Aggarwal uses balance of payments data on
remittances of 99 countries that were categorized as low to middle developing economics
from 1975-2003. The primary sources of data for these remittances were obtained from
the International Monetary Fund (IMF) Balance of Payments Statistics Yearbook.
Data
In our paper we will be working with the same data but decided to start and end on
a later period from 1980-2004. Also we will not conduct our study on the 99 countries but
on the top ten countries and also thirty one other countries that are similar to the top ten
remittance receiving countries that are currently receiving the highest inflow of
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remittances. These forty-one countries will be chosen with same criteria as the selection
criteria in the Aggarwal, Demirguc-Kunt, and Peria paper- all countries are categorized as
low to middle developing economies. However, not all of the countries that are chosen in
our paper are highly dependent on remittances. We would like to see if there were any
biases during the selection of countries in the Aggarwal, Demirguc-Kunt, and Peria paper
since most of the 99 countries in their model are dependent on remittances. If there are
biases, then the current model only applies to countries that are dependent on remittances
and nothing else.
Although data is readily available and easy to attain, we are aware of the fact that
data on remittances are not as accurate because of there are migrant workers who remit
there money through unofficial sectors. These unofficial sectors do not provide records of
remittances remitted by migrant workers and because of that there is no way to measure it.
These remittances are left unaccounted for and many believe that the range for unaccounted
or unrecorded remittances range from 20 to 200 percent (Aggarwal, Demirguc-Kunt, Peria,
2006). Data used in our model is obtained from the International Monetary Fund (IMF),
United Nations Conference on Trade and Development (UNCTAD) and the World Bank
(WB) websites and statistical yearbooks. The dataset includes countries from South
America, Central America, the Middle East, Africa, Southeast Asia, Central Asia, and
Europe.
Methodology
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We will use balance of payment data on remittance flows from 31 countries
categorized as low to middle developing economies over the period of 1981-2005 to study
the impact of remittances on financial development. The countries of interest are the top
ten remittance receiving countries are the following: India, Mexico, Egypt, Philippines,
Turkey, Morocco, Poland, Jordan, Bangladesh, and Brazil. The 31 other countries have
similar economic backgrounds as the top ten remittance receiving countries- all categorized
as low to middle developing economies by the World Bank. Increases in the level of
deposits to banks will be used to measure financial development in our panel dataset
because when big projects are implemented, individuals will need loans which will be
finance by banks; hence, if there are increases in the level of deposits to banks, more loans
are available for usage. King and Levine suggested that the level of demand deposits is a
good indicator for financial development since there is a relationship between this financial
indicator and economic growth. To examine the relationship between financial
development and remittances by running regressions on the following model:
FDi,t =β0 + β1 Rem/GDP + β2 Ln(GDP) + β3 GDP/Cap + β4 Inflation +
β5Flows/GDP + β6 Exports/GDP + β7 Interest Rate + μ
The difference between our model and the Aggarwal, Demirguc-Kunt, and Peria is that
we included an interest rate variable because we believe that changes in interest rates will
affect financial development. A standard measure of financial development, FD,
according to the literature by King and Levine is the ratio of bank deposit to GDP. Data
that are used to construct this ratio are obtained on the IMF statistics website. Rem/GDP
is the ratio of remittances to GDP. Remittance data is obtained from the IMF World
Economic Outlook and the UN Statistics Handbook under workers’ remittances which are
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the current transfers made by migrant workers working abroad. If our theory that
remittances does have a positive impact on financial development, we will observe that
Rem/GDP will be a statistically significant positive value. Log of GDP and GDP per
capita are included in this model because they allow us to take into account for the
country size and the level of economic development. Data for the Log of GDP and GDP
per capita are obtained on the UN statistics division website. We can expect that these
two variables will have a positive impact on financial development: an increase in these
two variables will cause an increase in financial development. To account for inflation,
we will look at the GDP deflator during this time period. GDP deflator data was obtained
from the UN Statistics Handbook. According to Smith, Levine, and Boyd, 2001, inflation
affects individuals’ decision-making choices that also influences savings rate in real
assets. We would expect that this variable will have a negative affect on financial
development. Another variable that influences financial development is capital inflows to
GDP, Flows/GDP. The ratio of Flows to GDP was constructed by data that was obtained
from the UN Statistics Handbook. We expect Flows/GDP to have a positive effect on
financial development. The level of export to GDP is also important to helping us
understand financial development. The ratio of export to GDP was constructed by data
that was obtained on UN statistic division website under trade and development. We
expect this variable to have a positive affect on financial development because as exports
increase, firms will have an incentive to expand due to the raising demand, thus helping
financial development. We would also include interest rate as the final independent
variable in our model because interest rate does effect individuals’ decision on saving.
Interest rate data was obtained on the IMF statistics website. Since there were missing
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data for the lending rate and discount rate, we made an interest rate variable as the
average percent change in the discount rate and lending rate. We expect this value to be
positive also.
A correlation matrix (on appendix, Correlation Matrix) for the variables showed
that among the seven independent variables, ln_gdp is positively correlated to per capita
gdp by .3082 and ln_gdp is negatively correlated to rem_gdp by -.4572 and to exp_gdp
by -.2801. Given this, ln_gdp seemed to be the variable that is highly correlated with
rem_gdp, per capita gdp and exp_gdp if compared among the other explanatory variables
in the model. But these values are not strong enough evidence to state that
multicollinearity can be a problem in this model. Also, we can not simply drop ln_gdp in
our model because we believe that this variable is relevant, has explanatory power and
must be included in the model. Omitting an important variable can result to specification
bias which is a more serious problem than multicollinearity.
Our model has data from 41 countries over 20 years so there may be some
characteristics in each individual country that persist over time which are unobservable in
our model. For that reason, we will need to run a fixed effect or random effect regression
to address this underpinning issue, time invariant errors, with our data. We will need to
perform a Hausman Test to see which regression, fixed or random, best fits our dataset.
When running the Hausman Test, the Null Hypothesis is that the Fixed Effects and
Random Effects Results are equal and the alternative hypothesis is that the fixed effects
and the random effect results are not similar.
We would first run this regression but without the remittance to GDP variable:
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FDi,t =β0 + β1 Rem/GDP + β2 Ln(GDP) + β3 GDP/Cap + β4 Inflation +
β5Flows/GDP + β6 Exports/GDP + β7 Interest Rate + μ
We should expect the signs of this model to be similar to that of the Aggarwal, Demirguc-
Kunt, and Peria model. And then we will add the remittance to GDP variable and
observe the significance of remittances to financial development. Aggarwal, Demirguc-
Kunt, and Peria suggested that remittance is statistically significant in their 99 country
panel dataset so if their model is good, we should see similar results in our regression.
We will then add a remittance lagged variables (one year and two year lag) into our
model to see if that would yield more robust results with the lagged terms. Adding
lagged variables in our model does make economic sense because it usually takes several
quarters to see the affects of remittances on financial development. After that is done, we
will run both the random and fixed effects regressions on our model above.
In addition to the regressions we have mentioned, we will further more split our
dataset into two subsamples: one sample including 21 countries that are highly dependent
on remittances and the other sample including 20 countries that are not so dependent on
remittances. The 41 countries were ranked according to the amount of remittances it
receives and from there the dataset was divided into two subsamples. The top 21
countries were included in the first subsample of highly dependent countries and the
bottom 20 countries in the subsample moderately dependent countries. If the Aggarwal,
Demirguc-Kunt, and Peria model is correct, this means that the model will work for these
two subsamples and should yield similar results. However if it is wrong, then we should
observe deviations of their interpretation of remittances on financial development.
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Empirical Results
The appendix pages in back provide all of the regressions that were used in this
paper. After running both the Fixed Effects and the Random Effects Regression, I ran the
Hausman test to see which model was more appropriate for the dataset. The null
hypothesis of the Hausman Test states the following: H0=βFE= βRE which means that the
difference in coefficients of the Fixed Effects and the Random Effects is not systematic.
The Hausman Test rules out that the Fixed Effects model was more appropriate for the
dataset.
Table 1 reports the fixed effects estimates of the model with and without the
variable ratio of remittances to GDP. Comparing the two models, we can see that
remittance have a positive coefficient and is also statistically significant in the 1% level
of confidence. Furthermore, the R-squared between the two models are quite different-
the model without remittance has an R-Squared of 4.1% whereas the model with
remittance has an R-Squared of 12.5%. This means that the model with remittances helps
explain the variation of financial development better than the model without the ratio of
remittances to GDP. Also, by looking at the model with the ratio of remittance to GDP,
we see that a one percentage point increase in the share of remittances to GDP leads to a
0.132 increase in the ratio of deposits to GDP. An F-test was conducted on these models
to further validate the importance of the inclusion of the remittance variable in the model.
The restricted model is the model without the remittance to GDP variable and the
unrestricted being the model that includes this variable. If we accept the null hypothesis
this indicates that the restricted model is as good as the unrestricted in explaining the
dependent variable. The F-value turned out to be 24.31 is clearly greater than the F
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critical value of 3.86. Hence, we can reject the null and this means that the unrestricted
model is a better model to use.
As we can observe from results of the fixed effects regression on Table 1,
financial development is affected by the country’s size, the level of income in the country,
the size of capital inflows, and the ratio of exports to GDP positively and also statistically
significant ranging from the 10% level of confidence to the 1% level of confidence.
Financial development is negatively affected by inflation as we have expected due to the
fact that inflation affects individuals’ decision-making choices which also influences
savings rate in real assets. Looking more closely at Table 1 we see that the changes in
interest rate, though positive, appear to have no significant effect on financial
development. The Random Effects estimates yielded similar results to the Fixed Effects
results but since the Hausman test ruled that the Fixed Effects model was more
appropriate, we will only consider the results of the Fixed Effects estimate for later
regression. The results of Table 1 are consistent with Aggarwal’s study.
Fixed Effects regressions of the forty-one countries and also the two subsamples,
highly dependent and moderately dependent, are located on Table 2. When comparing
the whole sample regression to the highly dependent sample, we can see that the highly
dependent countries’ fixed effects results are fairly similar to that of the whole sample.
We observe that the ratio of remittances to GDP is positive and statistically significant in
the 1% level of confidence. However, the coefficient of the ratio of remittances to GDP
increased from 0.132 to 0.155 which suggests that a one percentage point increase in the
ratio of remittance to GDP leads to a 0.155 increase in the ratio of deposits to GDP.
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As expected, the signs of most coefficients of the independent variables
explaining financial development did not change for countries that are highly dependent
on remittances. The variable ln(GDP), which helps measure the country’s size, is still
positive in both models but has dropped in significance from 10% level of confidence to
the 20% level of significance. Per capita GDP is still positive and has increased in
significance level to the 20% level of confidence in the highly dependent countries’ fixed
effects results. Inflation variable is negative and dropped its level of significance which
means that inflation does not have a statistically significant effect on financial
development in highly dependent countries. The ratio of flows to GDP remains positive
and statistically significant in the 1% level of confidence. The coefficient for this variable
increased from 0.289 to 0.444- this suggests that for countries that are highly dependent
on remittances, FDI accounts for more of the change in financial development than the
ratio of remittances to GDP. The same changes are also true to the ratio of exports to
GDP. We observe the same behavior, positive effect on financial development and also
statistically significant in the 1% level of confidence. As for interest rate, we noticed that
interest rate positively affected the whole sample and now negatively effects countries
that are highly dependent on remittances- though the sign of the coefficient has changed,
this variable remains insignificant in both cases. The R-squared for the model increased
from 0.125 to 0.155 which indicates that the specified model works better for countries
that are highly dependent on remittances.
When comparing the whole sample results to countries that are moderately
dependent on remittances results, we observe that the ratio of remittances to GDP is
negative and also insignificant. This means that for countries that are moderately
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dependent on remittances, remittances is not a statistically important variable that helps
explain their financial development and also negatively effects their financial
development. This may be caused by how remittances are used in these countries that are
moderately dependent on remittances. Remittances that were bought into their country
through financial intermediaries are often withdrawn out immediately for consumption
usage which does not help financial development. A reason why remittances are
insignificant may be also due to the fact that with these moderately dependent countries,
the amount money that is being remitted is often times sporadic and is not a steady source
of income of families. The results of the other variables are similar to that of the whole
sample results with ln(GDP), GDP per capita, ratio of flows to GDP, ratio of exports to
GDP, and interest rate being positive and the variable inflation being negative. We must
note that ln(GDP) and the ratio of exports to GDP dropped in significance level. The
only two variables that are significant in explaining financial development for countries
that are moderately dependent on remittances are inflation and ratio of flows to GDP. The
R-squared for the model increased from 0.125 to 0.168 when we split the data into two
subsamples.
Table 3 shows the fixed effects results with one time lag. The results of
comparing countries that are highly dependent on remittances to the whole sample are
similar to that of the results with no time lags. We observe that the ratio of remittances to
GDP is positive and statistically significant in the 1% level of confidence- also with an
increase in the coefficient of the ratio of remittances to GDP increased from 0.39 to 0.49
which suggests that a one percentage point increase in the ratio of remittance to GDP
leads to a 0.49 increase in the ratio of deposits to GDP. We observe that one time period
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lagged variable of remittances is also significant but negatively impacts financial
development. The significance of this variable in the whole sample is at the 1% level of
confidence whereas it is in the 5% level of confidence for countries that are highly
dependent on remittances. The net effect of remittances, ratio of remittances to GDP and
the lagged variable, is positive (0.49-0.34=.15). This means that for countries that are
highly dependent on remittances, remittances positively affects financial development.
The signs of most coefficients of the independent variables explaining financial
development did not change for countries that are highly dependent on remittances-
yielded similar results as the model without the lagged variable. The variable ln(GDP),
which helps measure the country’s size, is still positive in both models but has dropped in
significance from 10% level of confidence to the 20% level of significance. Per capita
GDP is still positive and became insignificant. Inflation variable is negative and dropped
its level of significance. The ratio of flows to GDP remains positive and statistically
significant in the 5% level of confidence. The coefficient for this variable increased from
0.246 to 0.396. For the ratio of exports to GDP, we observe a positive effect on financial
development and also statistically significant in the 1% level of confidence. As for
interest rate, we noticed that interest rate positively affected the whole sample and now
negatively effects countries that are highly dependent on remittances- though the sign of
the coefficient has changed, this variable remains insignificant in both cases.
The results when comparing countries that are moderately dependent on
remittances and the whole sample with one time lag are fairly similar to the results when
comparing countries that are highly dependent on remittances to the whole sample with
one time lag. We notice that the ratio of remittances to GDP is positive but insignificant.
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We observe that one time period lagged variable of remittances is also insignificant and
negatively impacts financial development. The net effect of remittances, ratio of
remittances to GDP and the lagged variable, is negative like that of the model without the
time lag (0.081-0.097=-0.016). This means that for countries that are moderately
dependent on remittances, remittances negatively affects financial development even
though this results is insignificant.
We also tried running the same model with two time lags on remittances with the
idea that remittances takes time to affect a countries’ financial development and yielded
similar results as to the model with just one period time lag- Table 4. The main
difference between the one period time lag and the two period time lag models is that the
results for the time lags are insignificant for the whole sample, countries that are highly
dependent on remittances, and also countries that are moderately dependent on
remittances. Besides that main difference, Table 4 had similar results as Table 3 with the
one period time lag.
In addition to splitting the data into two subsamples, I ran the Hausman Test to
see if the variables in both models were significantly different. The null hypothesis of the
Hausman Test is as follows: βHighly Dependent=βModerately Dependent- the differences in coefficients
are NOT systematic. After running the Hausman test, we reject the null hypothesis that
the differences in coefficients are not systematic in the 1% level of confidence- so the
differences in coefficients are systematic. The result of the Hausman Test implies that the
data should be split into two subsamples.
By running all of these models, we notice the underlying theme which carries on
throughout all of these models- remittances are positive and significant for countries that
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are highly dependent on remittances. As for countries that are moderately dependent on
remittances, we observe that remittances are not significant and negative for financial
development. This insignificant should not overshadow the fact that remittances do help
families and therefore is not necessarily bad.
Conclusion
The steady flow of remittances in many low middle income developing countries
have paved way for studies of its effect on growth, poverty alleviation and eventually
financial development. The main question we posed earlier was do remittances lead to
financial development. After further study, research and test conducted on this model it
has generated some interesting results.
Based on the results of this study, remittances do have a significant and positive
impact on financial development. But another interesting result came about upon splitting
the data into two subsamples: one sample including 21 countries that are highly
dependent on remittances and the other sample including 20 countries that are moderately
dependent on remittances, results show that there are indeed differences in the effect of
remittances on financial development. For countries that are highly dependent on
remittances the significance and positive impact of remittances on financial development
is still evident. However for countries that are moderately dependent on remittances, such
an impact does not exist. Remittance is not significant meaning that it does not contribute
to financial development. With these results, our model seemed to work not for all
countries but only for countries that are highly dependent on remittances.
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Furthermore, when adding the lag term to see if past period of remittance
behavior effects financial development, we observed that the one period time lag value is
negative and significant which may be explained through past studies on the usage of
remittance income. Though remittances do provide a steady source of income for
developing economies, the remittance income is used to purchase household/daily goods
for living.
Through this research, we can see that remittances do effect financial
development positively and significantly. And since remittances effects financial
development, remittance should effect growth due to the fact that the financial
development is similar to that of King and Levine’s model and King and Levine showed
the positive relationship between financial development and growth.
There are some policy implications based on this study. Since there are evidence
that remittances can contribute to financial development for countries that are highly
dependent on it, policymakers can propose or implement policies that can improve
remittance collection through formal channels. Policies that can help improve remittance
collection may further contribute to financial development.
Further Research
Based on several studies conducted on the use of remittances, remittances are
used for consumption purposes by the families of the migrant workers. As such, we
believe that a study should be done in a micro level since remittances are used and is
more effective in the micro level rather than the macro level. Also, remittances have
different effects for each country. For countries that are highly dependent on remittances
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it may be vital to their financial development but such an effect may not be evident for
countries that are moderately dependent on it. Hence, a study on remittances on a per
country basis on a regional or municipality level may further capture the effect of
remittances on development. Data for such study may be unavailable since this field has
not been thoroughly explored.