1

An Unintended Curse of Gifts: Worker’s

Remittances and International Competitiveness in

Armenia

By Gregory Loshkajian

Department of Economics

Princeton University

Princeton, NJ, USA

gal2@princeton.edu

Abstract: Quantitative research on the effects of worker’s remittances on

international competitiveness has been performed in a variety of regions and

countries. However, Armenian remittances, which currently constitute

approximately a quarter of the country’s GDP, have been left unstudied.

This research seeks to fill this informational gap. Consistent with the

economic literature, we use a VAR (Vector Autoregressive) model,

combined with instrumentation for higher education rates, to analyze the

effects of remittances on Armenian international competitiveness. We find

that remittances cause an appreciation in Armenian real exchange rate,

which lowers Armenian international competitiveness, and we show that this

appreciation has a direct link with changes in the prices of non-tradable

goods.

Keywords: Armenia, VAR, workers remittances

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I. Introduction

In the past decade, the global movement of migrant workers and remittance flows

from these workers has increased significantly. Currently, about 3% of the global

population is living outside their country of origin (World Bank 2014), which has

attracted the interest of researchers, observers, and policy makers to study the

implications of this phenomenon, mainly through the effects of remittances. In 2014,

estimated global remittances reached approximately $582 billion, $410 billion of which

went to developing countries. In fact, remittance is a major source of foreign exchange

earnings for many developing countries.

A large portion of remittances (approximately 50-200 percent of officially

recorded remittances) is believed to be unrecorded, mainly because remittances are often

sent through unofficial channels (Aggarwal et al. 2006). These remittances tend to come

from developed countries with a large number of job opportunities, such as the United

States and Saudi Arabia, with US $75 billion originating from these two countries in

2009. Perhaps because of this, worker’s remittances have begun to surpass private capital

inflows and foreign aid in recent years, particularly in countries like Armenia.

In 2014, the stock of migrant workers from Armenia was approximately .777

million people in 2014 (World Bank 2014). According to the United Nations Population

Division, in 2012, migrant Armenian workers were mainly employed by neighboring CIS

states, with European Member States acting as residual destinations, which we can see in

table 1. In particular, Russia is a particularly popular location and remittance source: at

827 million in US dollars in remittances, Russia provided approximate 57.1% of

Armenian 2012 remittance outflows. Migrants fit into a wide range of labor classes: in

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2010, 20.5% of Armenian migrant workers were less skilled, 43.7% were semi-skilled,

and 35.8% were skilled. Also, approximately 39 percent of Armenian migrants are

employed in professional positions. The split between male and female migrant workers

is approximately 3 male workers for every one female worker (World Bank 2014).

Due to the diaspora created by the Armenian genocide, Armenia’s migrant stock

has experienced decent growth over the past century. However, the years following

independence from the Soviet Union have seen an even greater increase in Armenian

Table 1. Country-wise migrant stocks and workers remittances (selected countries for 2013)

Country of Employment Migrant Stock 2013 Remittance income 2013

(US$ million)

Australia 1,253 2

Austria 2 727 1

Azerbaijan 3 729 62

Belgium 3 243 2

Canada 2,675 5

Cyprus 1 383 2

Czech Republic 2 234 2

Denmark 666 1

France 18 766 27

Georgia 2,227 24

Germany 10 667 30

Greece 7,779 15

Iran, Islamic Rep. 2 469 2

Ireland 139 0

Italy 1,008 1

Kazakhstan 8 416 11

Latvia 821 5

Lithuania 522 1

Netherlands 782 4

Norway 272 0

Poland 2 256 3

Russian Federation 510 640 827

Slovak Republic 104 0

Spain 10,642 23

Sweden 2,149 2

Switzerland 731 1

Turkey 1 178 1

Turkmenistan 2 609 11

Ukraine 49,862 75

United States 92,671 158

Total 777 313 1,449

Source: World Bank Remittance and United Nations Population Division databases

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migrant stock, fueled by new labor market opportunities in neighboring countries such as

Russia, as well as anger with the lack of employment opportunities in the Armenian

economy (International Monetary Fund 2012). Due to this trend, Armenian emigration

has reached a peak of approximately 19100 people in 2004. However, it is important to

note that, while Armenian migration stock has grown, emigration has experienced a

downward trend in the past 15 years, only experiencing a particularly large jump from

2003 to 2004, with a smooth downward trend afterwards, which we can see in Figure 1.

Interestingly, while Armenian remittances have always been small in number,

ranking 45th

in the world and constituting only .5% of global remittances in 2012, the real

importance of Armenian remittance can be found in its importance to the country’s GDP.

Since the late 1980’s, remittances have played an integral role in Armenian economic

development, to the point where Armenian worker’s remittances reached a peak of 21.0%

of the country’s GDP in 2013. Globally, Armenia is the 9th

largest recipient of

remittances as a percentage of GDP in 2012 (World Bank Prospects 2014). These facts

show that Armenia has a strong economic dependence on remittance inflows.

Figure 1. Migration Data, Armenia 2000-2012 Source: This data was constructed by the author using CIA

World Factbook (2012) data

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Given the large increase of remittance inflows into developing countries such as

Armenia, economic impact of remittances on receiving countries has become a very

important topic for study. In spite of the numerous economic and developmental effects

remittances can provide, some concerns have been raised due to the importance Armenia

places on remittances. Remittances, as a type of net capital inflow, can lower the

international trade competitiveness of a country by causing a Dutch-disease- like

reallocation of resources to the nontradable sector. Such an effect would prove to be

extremely problematic for any country which relies on remittances as heavily as Armenia

does. However, despite the Armenian economy’s massive dependence on remittances,

there have been no quantitative studies examining the effect of worker’s remittances on

international competitiveness at the country specific level for Armenia.

Therefore, survey data from Armenian migrant workers and real exchange rate

data from 1993 was used on a variation of VAR model utilized by Mamta B. Chowdhury

and Fazle Rabbi, as well as an instrumentation of remittances with education, to analyze

whether the established negative effect of workers remittances on international

competitiveness holds when tested within the Armenian economy. Results show that

Armenian real exchange rate increases by 17% when remittances double, which suggests

that there is a small, significant and positive correlation between workers remittances and

exchange rate. Focusing on the relationship between terms of trade and real exchange rate

shows that this shift is driven by a positive relationship between remittances and non-

tradable prices.

The remainder of this paper will proceed as follows. Section II explores the

literature surrounding the effects of remittance inflows and the determinants of real

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exchange rates. Section III discusses the issues related to data gathering and the

construction of variables employed to study the long run effect of remittances on the real

effective exchange rate of Armenia. Section IV presents the empirical model, as well as

the methodology used in this study. Section V shows the econometric results and Section

VI draws conclusions and makes suggestions as to further avenues for research on the

topic and country. Section VII presents references and an appendix with further data.

II. Literature Review

When examining the effects of remittances on an economy, the economist is faced

with a myriad of positive and negative contributions. One of these, shown by Adams

(2005), Ratha et al. (2005) and Aggarwal et al. (2006), is that remittances, as well as

other monetary inflows, can enhance the long run growth potential of developing

countries by reducing poverty and increasing financial development by reducing

constraints on investment. A steady flow in remittance can reduce the volatility in output,

which has positive effects on growth (Kroft and Lloyd-Ellis 2002). However, existing

literature has also shown that remittances could have certain potential negative effects on

the recipient country’s economy. One such effect, shown by Chami et al. in 2003, is that

remittances can introduce a moral hazard problem, i.e. they allow the migrant’s family to

reduce their work effort, which reduces growth. Secondly, and most importantly to our

analysis, there may be a detrimental effect of remittances on external trade

competitiveness, in that the transfer of aid could impact a country’s economic structure

by inducing a “Dutch disease” like effect.

The Dutch Disease, hypothesized by W. Max Corden and J. Peter Neary in 1982,

is, essentially, the idea that there are possible negative consequences in the labor and

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tradable good markets caused by large increases in a country’s income. To explain the

disease, Corden and Neary use the framework of a small open economy producing two

goods at exogenously given world prices, and a third nontraded good, whose price moves

according to domestic supply and demand. The traded goods are themselves separated

into a booming good and a non booming good. The booming good tends to be the

extraction of natural resources, such as oil or gold, whereas the lagging sector would be

manufacturing. Since one good (the natural resource) is booming, the sector experiences

extra revenue and more demand for labor. However, since natural resource industries

tend to employ fewer people, this shift in labor is negligible. The clearer effect comes

from indirect de-industrialization, where the increase in revenue from the booming

tradable sector causes demand for goods and labor to shift to the non-tradable sector at

the expense of the lagging tradable sector. Naturally, this shift in demand will cause the

price of non-tradable goods to rise, but since prices in the traded goods sector are

internationally determined, they cannot change to match the non-tradable goods sector.

This amounts to an increase in the real exchange rate, since this trend will also lead to a

higher demand for local currency.

While the Dutch disease theory is based upon the idea of resource booms, the

concept translates quite well to capital inflows. In such a case, the “Dutch disease” theory

suggests that cash inflows from sources such as foreign aid, foreign direct investment,

and workers’ remittances may cause the economy to reallocate resources towards the

nontradable sector rather than the tradable sector, reducing the relative price of tradables

(Chowdhury 2004). With weaker tradables, the country’s currency becomes less valuable

in comparison with that of the rest of the world, and the real exchange rate decreases in

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value. The potential reallocation effect of remittances was first postulated by Michaely

(1981), and was later confirmed by Neary (1988), who also suggested that remittances

inflows might cause real exchange rates to appreciate.

Recent research on the relationship between remittance and international

competitiveness has focused on identifying the mechanics behind Michaely’s and

Neary’s suggestions, and applying this idea to various countries around the world. Most

studies identify determinants of exchange rate to use as controls and/or examine the

previously established relationship between remittance and international competitiveness

at country and region specific levels. In a panel study of 13 Latin American countries,

Dorantes and Pozo (2004) use government spending, terms of trade, real interest, and

foreign aid as determinants of international competitiveness, finding that doubling

remittances lead to a 22% increase in RER. On the other hand, Bourdet and Falck (2006)

utilize a time series model with controls for government policy, government spending,

and technological progress to argue that, in Cape Verde, remittance increases also lead to

a significant increase in exchange rate, but with an expectation of long run deprecation.

Chowdhury and Rabbi (2013) examine the proposed relationship in Bangladesh

with a slightly different time series model, which also happens to expand its

methodological scope from Dorantes’ and Bourdet’s work. Most importantly for our

purposes, they do so with a set of proxies based on trade and domestic policies, which

eliminates many ambiguities caused by changes within the international and domestic

markets. By using this model, Chowdhury and Rabbi found that increases in remittance

did lead to significant decreases in Bangladesh’s international competitiveness. While the

literature seems to have obtained a clearer picture of the remittances-real effective

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exchange rate relationship through Chowdhury’s model, an application of this effect to a

CIS state is still non-existent. By applying Chowdhury and Rabbi’s model to Armenia my

paper fills this informational void.

III. Data

Before continuing, it would be prudent to mention certain issues relating to data

availability. Due to Armenia’s status as a post-Soviet country, there is very little data

circa 1993, and data post 1993 is very limited. However, most of the necessary data is

thankfully encompassed within development indicators. Therefore, unless otherwise

noted within this section, the analysis will rely on data from the World Development

Indicator database within the World Bank and is annual. The database contains detailed

information about labor composition and trade activity. In order to account for the

Armenian dram’s adjustment to the world market, the analysis is restricted to the time

period encompassing 1995-2013

Table 2 presents descriptive statistics over the sample time frame. Following is a

summary of the relevant variables based on the model devised by Chowdhury et al.

Table 2: Descriptive Statistics for Sample Period: 1995-2013

Variable Obs Mean Std. Dev. Min Max

Real Ex. Rate 19 82.08845 15.05439 52.3621 106.0325

Remittance 19 0.245824 0.1357958 0.092113 0.4374128

Trade Product. 19 10477.24 9983.286 1344.142 32727.51

Money Supply 19 0.182734 0.0858634 0.0770715 0.3616688

Openness 19 0.7106526 0.072725 0.53659 0.86227

Terms of Trade 19 0.4916938 0.1082401 0.347892 0.666421

Tert. Educ.

Rate

19 .3767308 .1012688 . 168175 .510014

Source: Constructed using World Bank (2014), United Nations (2014), OSCE (2011), International

Monetary Fund (2014), and UIS Data Centre (2014) data.

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Exchange Rate: REER= Real Effective Exchange Rate (2010=100)

For exchange rate data, annual real effective exchange rate data from the

International Financial Statistics database provided by the International

Monetary fund is used. It is assumed that the consumer price index from

2010 is the base value. Here, real effective exchange rate serves as the main

indicator for international competitiveness. Figure 2 plots REER over the

sample period. REER exhibits a clear positive trend, with a sharp increase

between 2004 and 2008.

Remittance Fraction: 𝑅𝐹𝑅𝐴𝐶 =𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑟𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒 𝑠𝑒𝑛𝑡 𝑡𝑜 𝐴𝑟𝑚𝑒𝑛𝑖𝑎 𝑜𝑛 𝑡𝑟𝑖𝑝

𝑖𝑛𝑐𝑜𝑚𝑒 𝑜𝑛 𝑠𝑎𝑚𝑒 𝑡𝑟𝑖𝑝

While Armenian remittance data does exist within the World Bank’s

database, it is not utilized here. The main reason for this is that the World

Bank only displays reported remittances, which, as previously noted, are a

small part of actual remittances. Instead, data has been taken from three

surveys given from 2002-2005, 2005-2007 and from 2011 to 2012 by the

Organization for Security and Cooperation in Europe to individual Armenian

migrants on specific labor migration issues. Table 8, found within the

appendix, presents relevant descriptive statistics for surveyed workers.

Examination of these statistics reveals decent spread in socioeconomic

circumstances and time away from Armenia among sampled workers, which

lends strength to my intended proxy.

For the purposes of this paper, values of interest include the amount of

money sent home during a trip and average monthly income in US dollars.

However, this sample has a relatively small 8 year timeframe, and there is a

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Figure 2: Armenian Real Effective Exchange Rate, 1995-2013 Source: Constructed with data from

International Monetary Fund 2014

three year gap between the available timeframes as well. In order to obtain an

acceptably large sample, labor migration data is incorporated in a proxy for

remittances, under the assumption that each migrant pays a constant fraction

of their income as remittance. OSCE data is used to construct a reasonable

estimate for the fraction of remittances paid by each Armenian migrant.

Remittances: 𝑅𝐸𝑀𝐼 =𝑅𝐹𝑅𝐴𝐶∗𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑚𝑖𝑔𝑟𝑎𝑛𝑡 𝑠𝑡𝑜𝑐𝑘∗𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎

𝐺𝐷𝑃 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠

By multiplying the fraction of remittances as a share of an individual’s

income by the migrant stock and GDP per capita, we obtain an estimate of the

total remittances paid by migrant workers, However, since migrant stock is

only taken every five years by the World Bank, the year over year growth rate

of remittances compiled by the World Bank is taken, then applied onto the

existing remittance values to create a set of remittance values for the time

period from 1995 to 2013. Remittances are displayed here as a percentage of

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GDP mainly to conform to prevailing literature (Neary 1988, Edwards 1989,

Chowdhury and Rabbi 2013). Figure 3 plots remittances over the sample

period, showing a positive trend in remittance growth, as well as a spike

between 2004 and 2005. Most importantly the values of remittances shown

here eclipse those reported by the World Bank, which implies that this

remittance variable has captured unreported remittances, at least to some

extent.

Trade Sector Productivity: 𝑇𝑃 =𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑇𝑟𝑎𝑑𝑎𝑏𝑙𝑒 𝑂𝑢𝑡𝑝𝑢𝑡 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠

# 𝑜𝑓 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑑 𝑊𝑜𝑟𝑘𝑒𝑟𝑠 𝑖𝑛 𝑀𝑎𝑛𝑢𝑓𝑎𝑐𝑡𝑢𝑟𝑖𝑛𝑔 𝑆𝑒𝑐𝑡𝑜𝑟

The literature notes that separating the effect of technological progress

from the effect of remittance inflows can be a challenge. This issue comes

from the Balassa-Samuelson effect, which suggests that as a country develops,

its exchange rate naturally appreciates (Balassa 1964, Samuelson 1964). The

Figure 3: Armenian Workers Remittances 1995-2013 Source: Constructed with data from

the World Bank (2014), United Nations (2014) and OSCE (2011)

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Balassa-Samuelson effect is important enough that some economists, such as

Bourdet et al, create particular controls for technology within their own

regressions to avoid this problem. In this analysis, the most commonly

accepted measure of technological progress; trade sector productivity, will be

utilized as defined by the above formula. Tradable output (assumed here to be

manufacturing output) and employment was taken from the United Nations

Industrial Development Organization, since the World Bank did not have this

data.

Terms of Trade: 𝑇𝑂𝑇 =𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑒𝑥𝑝𝑜𝑟𝑡𝑠 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠

𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑖𝑚𝑝𝑜𝑟𝑡𝑠 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠

Terms of trade serves as a control for the price of the tradable sector, in

that it provides a measure of the value of exports. Depending on the prices of

exports and imports (both of which are indexed at 2000=100), terms of trade

can lead to an income or substitution effect appreciation or depreciation of the

REER, which makes it a helpful control variable. However, by definition,

terms of trade is very similar to real effective exchange rate, lacking only the

price index for non-tradable items. This could lead to a high correlation

between the two variables.

Openness ratio: 𝑂 =𝐸𝑥𝑝𝑜𝑟𝑡𝑠 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠+𝐼𝑚𝑝𝑜𝑟𝑡𝑠 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠

𝐺𝑁𝑃 𝑖𝑛 𝑈𝑆 𝑑𝑜𝑙𝑙𝑎𝑟𝑠

The ratio of openness is another proxy for trade policy used often in the

literature (Edwards et al 1999), in particular, it measures how easily goods

and money flow from one country to another. Given that the main explanatory

variable involves the international flow of money from one country to another,

openness is an especially useful control.

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Money Supply: 𝑀𝑆 =𝑀2 𝑖𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝐺𝐷𝑃

𝐺𝐷𝑃 𝑖𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝐶𝑈

Money supply, the ratio of M2 to GDP, has a positive effect on real

effective exchange rate. With a higher money supply, consumers can purchase

more goods within the tradable and nontradable sectors, leading to the

previously mentioned reallocation effect. M2, in this case, is obtained from

the International Financial Statistics database of the International Monetary

Fund. Unlike the other variables, money supply will be listed in LCU values

of GDP and M2, because these values are not given in US dollars by the

International Monetary Fund.

Tertiary Enrollment Rate: 𝑇𝐸𝑅 =𝑇𝑜𝑡𝑎𝑙 𝑡𝑒𝑟𝑡𝑖𝑎𝑟𝑦 𝑒𝑛𝑟𝑜𝑙𝑙𝑚𝑒𝑛𝑡

𝐴𝑙𝑙 𝑠𝑡𝑢𝑑𝑒𝑛𝑡𝑠 𝑤𝑖𝑡ℎ𝑖𝑛 𝑡𝑒𝑟𝑡𝑖𝑎𝑟𝑦 𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝 ∗ 100

This variable represents enrollment ratio for tertiary school education,

which is calculated by dividing the number of students enrolled in tertiary

level education (regardless of age) by the population of the age group which

corresponds to tertiary education. It is assumed that people enrolled in tertiary

education will have completed secondary school education, as secondary

completion is a usual requirement for admission into college. This variable

will serve as a way to describe education, for the purposes of instrumentation

on remittance, since many of Armenia’s problems in education center around

secondary school completion rate and college enrollment. This data was

constructed using information from the World Bank databases.

IV. Methodology

When looking at Figures 1 and 2, it is clear that Armenian workers’ remittances

and real effective exchange rate have both experienced a positive increasing trend from

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1995 to 2013. With this information, in mind it seems reasonable to assume that there

some positive correlation between the two. In order to prove this, a VAR model based on

Chowdhury and Rabbi’s model, mentioned in Section II is utilized.

As mentioned previously, the effect of workers remittances on the real exchange

rate of Armenia has previously been specified by Chowdhury and Rabbi. This analysis

will build off of this model. The Chowdhury-Rabbi model is written as follows:

REERt = α + β1lREMIt+ βi Xt + εt (1)

where t is the sample time period, and REMI is the key explanatory variable referring to

the ratio of remittances to GDP. Xi corresponds to a vector which encompasses several

control variables: government expenditure (GOV), terms of trade (TOT), technological

progress (TP), openness to trade (O) nominal money supply (MS), and nominal

devaluation (NDV). All reported standard errors in this paper are robust to

heteroskedasticity. By separating the Xi vector into its separate components, and

transforming each series by taking its natural log, we obtain the full empirical model for

real exchange rate, as described by Chowdhury and Rabbi:

ln(REERt) = α + β1ln(REMIt)+ β2ln(TPt) + β3 ln(GOVt) + β4ln(TOTt) + β5ln(Ot)

+ β6 ln(MSt) + β7ln(NDt) + εt (2)

In effect, the Chowdhury-Rabbi model, estimates a vector autoregression (VAR)

model from 1995-2013 of the key relationship between remittances and the exchange

rate, while including standard control variables. However, there are five main differences

between this my model, and my analysis. First, I do not include Chowdury’s time dummy

variable because it was Bangladesh specific, which makes the variable useless for the

purposes of this paper. Instead, a dummy variable for the year 2004, γ, is introduced. This

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year saw a number of socio-economic and political changes in the Armenian economy,

including the beginning of a large spike in remittances and emigration, the establishment

of credit registries and a private credit bureau, and mass protest against the newly elected

president. Robert Kocharyan. Table 3 shows the results of a Chow test for structural

break at 2004, revealing a clear break in the model at this point. γ takes the value of 0

between 1995 and 2003, and 1 from 2004 onwards.

Second, real effective exchange rate is defined in terms of the real effective

exchange rates, in lieu of Chowdhury’s constructed formula. This decision is motivated

by my access to the necessary information from the IMF and the fact Armenia’s

exchange rates are all based upon cross rates with the United States dollar as the base

currency. Third, other than within the following tests, none of these variables will be

lagged, in order to maintain a decently sized sample period of study, and based on the

recommendation of AIC and BIC criteria on the full regression. Fourth, the model takes a

single openness value instead of separating openness by capital markets and good

markets since the Armenian capital market is not nearly as established as Bangladesh’s is,

so its effect on RER is very minimal. Finally, GOV and ND are recognized as weak

controls for remittances, (even by Chowdhury et al.) so these variables will be ignored to

preserve efficiency in the analysis.

Regression analysis produces efficient estimates if the variables are stationary.

Therefore, as a prerequisite of the analysis, the presence of persistent trends in data is

tested using Augmented Dickey Fuller (ADF) tests. Results of the tests with a constant

and constant and trend are reported in Table 4. It is found that all variables used in this

study are non-stationary. However, some time-series, such as real exchange rate,

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Table 3: Chow Break-Point Test: 2004 F-statistic: 7.74 Prob. F of Regression (5,11): 92.54

Prob. > F-statistic: .0047 Prob. Chi-Square(7): 0.000

Adjusted- R2: .92016

Note: Null Hypothesis: No breaks at specified breakpoints. Varying regressors: Equation Sample: 1995–

2013. Performed with 2 lags best on AIC testing

remittances, trade productivity, and money supply, are linearly stationary, and the others

are stationary of their first differences. Linearly stationary variables have been detrended,

and difference-stationary have been first-differenced to create stationary series for

efficient comparison. Note, however, that by differencing some of these series, there is

now one less data point to work with, and would be examining annual growth rates in

these particular cases.

Because there are 18 years to work with, which constitutes a very small number

of observations, not all of the control variables identified in equation 2 are used in any

one given regression. The regression in equation 2 will therefore be split into multiple

regressions for examination. As shown in table 4, a close glance at the correlations

between the explanatory variable and control variables reveals that the natural logs of MS

and TP are highly correlated with the natural log of remittances, and very decently

correlated with the other controls. Further testing, using variance inflation factor tests

(assuming a maximum acceptable VIF value of 10), reveals that while MS and TP are

highly collinear with each other, the two variables are not nearly as problematic when

placed in separate regressions. Therefore, to avoid multicollinearity with the major

independent variable, these two variables will be placed within separate regressions.

TOT, by definition, is very similar to real effective exchange rate, and will be left out of

this particular regression. Therefore, the new model, as a set of equations, is specified

18

Table 4: Augmented Dickey Fuller Unit Root Tests: F-statistics

Variables ADF without trend ADF with trend

(transformation)

Real Exchange Rate -2.420 -2.843** (TS)

Remittance -0.928 -1.821* (TS)

Trade Product. -0.059 -1.844* (TS)

Money Supply -0.374 -3.284** (TS)

Openness -2.500 -4.147** (DS)

Terms of Trade -1.453 -3.309* (DS)

Tertiary Educ. Rate -2.120 -4.314** (DS)

Note: All variables are natural logs. Null Hypothesis: Unit root exists *, ** and *** indicate No Unit Root

at 10%, 5% and 1%, respectively. Performed with 2 lags based on AIC tests of all series.

below. Note that none of the variables are lagged, based on the results of joint AIC and

BIC testing:

ln(REERt) = α+ β1ln(REMIt)+ β2ln(TPt)+ β3∆ln(Ot)+ γ + εt (3)

ln(REERt) = α+ β1ln(REMIt)+ β2ln(MSt)+ β3∆ln(Ot) + γ+ εt (4)

On another note, while TOT was previously excluded from the equation, it still

has a purpose within the overall analysis. Adding TOT as a regressor removes the effects

of the movement of goods prices from the real exchange tradable rate, leaving only the

change in non-tradable prices. Therefore, by using the two previous regressors, the rise in

nontradable prices, which is assumed to drive REER upward in the first place, can also be

examined. Thus I will also test the following regressions:

ln(REERt) = α+ β1ln(REMIt)+ β2ln(TPt)+ β3∆ln(TOTt)+ β4∆ln(Ot)+ γ+εt (5)

ln(REERt) = α+ β1ln(REMIt)+ β2ln(MSt)+ β3∆ln(TOTt)+ β4∆ln(Ot)+ γ+εt (6)

While this model is sound and is based on the discussion within the literature, the

literature also mentions a problematic endogeneity between remittances and real

exchange rate. In other word, the level of remittances experienced at any one point in

time is not exogenous. Rather, remittances may be responding to macroeconomic

conditions in the remitter’s country of origin. (Dorantes and Pozo 2004, Aggarwal et al

2005). With this in mind, we will also test instrumental variable regressions.

19

Table 5: Independent Variable Correlation Matrix LNREMI LNTP LNOPEN LNTOT LNMS

LNREMI 1.0000

LNTP .9252* 1.0000

LNOPEN -.4970 -.5779 1.0000

LNTOT .1890 .2120 .3003 1.0000

LNMS* .8103* .9524 -.6127 .2806 1.0000

*Assuming multicollinearity when |r| > .7, there is multicollinearity around the major independent variable,

LNREMI refers to the natural log of REMI (remittances as a share of GDP) The other variables in this

matrix follow the same convention.

Current discussion in the literature regarding instrumentation of the effect of remittance

on real exchange tends to focus on the relationship between education attainment and

remittance level. According to this theory, in countries with lower education rates,

migrant workers will be more likely to send remittances back to their homes, in order to

finance education for members of their households. Currently, observed data seems to

confirm this suggestion (Cox and Ureta 2003). Furthermore, there is no proven

relationship between real exchange rate and education.

The Cox-Ureta theory on the possible instrument effects of education on

remittance seems to be enough motivation to use an education indicator which captures

the education status for children in Armenia, such as primary schooling enrollment rates.

In fact, instruments such as this are popular in the remittance, exchange rate and

migration literature (Cox and Ureta 2003, Dorantes and Pozo 2004). However, Armenian

primary education rates are very high, approaching 99% (World Bank 2014), so

instrumenting with children’s education would not encompass education-based

remittance spending. Therefore, tertiary enrollment rates will be used as an instrument

for remittances.

Tertiary school enrollment rate is a useful variable to instrument for Armenian

education because Armenian citizens face two problems with access to tertiary education:

20

a low secondary school completion rate due to high dropout rate, as well as a high price

to university education. At the same time, education is highly valued in Armenian

culture, so it stands to reason that workers would remit for the same purposes which Cox

and Ureta propose. (World Bank 2014). The first stage regression for this instrumentation

is estimated as follows:

ln(REMIt) = α + β1ln∆(TERt)+ βi ln(Xt)+ γ + εt (7)

where TER represents tertiary enrollment rate. All other variables are as stated above.

The instrumental regression will also experience similar treatment as previously shown in

equations 3-6, where the controls are arranged in such a way as to preserve efficiency and

avoid multicollinearity.

V. Results

A. VAR Estimation

Table 6, shown below, presents the results of alternative versions of equation 2 of

the time series regression, shown by equations 3-6. Column 1 shows results for the

simple regression of real exchange rate on worker’s remittances. Columns 2 through 5

correspond to equations 3-6 shown within the econometric framework, where equations 2

and 3 represent the estimated effect of remittances on real exchange rate, and equations 4

and 5 represent the effect of remittances on nontradable prices.

Results of this regression mostly imply positive correlation between real effective

exchange rate and workers remittances. Without controls, the coefficient on workers

remittances is .061, implying a 6.1% increase in real exchange rate when remittances

double. When controls are added, the effect of the coefficient decreases in Column 2, to

about -9.1%, and increases in Column 3 to 9.6%, which would imply that money supply

21

Table 6: VAR estimates for real exchange rate on remittances 1996-2013 (1) (2) (3) (4) (5)

Regressor Real Exchange

Rate

Real Exchange

Rate

Real Exchange

Rate

Real Exchange

Rate

Real Exchange

Rate

Remittance 0.061 -0.091 0.095 -0.091 0.069

(0.061) (0.072) (0.10) (0.079) (0.13)

Dummy -0.0062 -0.034 -0.029 -0.034 -0.034

(0.047) (0.035) (0.039) (0.038) (0.042)

TP 0.35**

0.35**

(0.13) (0.14)

∆Openness -0.22 -0.22 -0.22 -0.13

(0.18) (0.23) (0.24) (0.24)

Money

Supply

0.19 0.23

(0.43) (0.38)

∆Terms of

Trade

0.0040 -0.18

(0.21) (0.27)

Intercept 0.0033 0.029 0.024 0.029 0.030

(0.036) (0.030) (0.025) (0.034) (0.032)

N 19 18 18 18 18

R2 0.037 0.425 0.170 0.425 0.217

adj. R2 -0.083 0.248 -0.085 0.185 -0.109

All regressors are in logarithmic form

Standard error in parentheses * p < 0.10,

** p < 0.05,

*** p < 0.01

has a clear negative bias effect, and that trade productivity has positively influenced

remittance. With only this regression to look at, it would be most reasonable to argue that

the observed effect of remittances is actually motivated by the natural progress of

Armenian growth. All other coefficients have their expected positive or negative effect

on real exchange rate.

Looking at columns 4 and 5, the same effect as shown from columns 2 and 3

appears. Due to this, it seems quite difficult to determine the effect of remittances of

nontradable prices. However, in Column 5, results indicate that doubling remittances

increases nontradable prices by 6.9%, which falls in line with the theoretical expectation.

However, it is clear that money supply and trade productivity influence these results in

22

the same way as they had for Columns 2 and 3. Unfortunately, none of these coefficients

are particularly precise or significant, so it would be difficult to draw concrete

conclusions from this particular model. With this fact in mind, we turn to the instrumental

variable analysis.

B. IV Analysis

Results from instrumenting remittance with tertiary enrollment rate are shown in

Table 6, which can be found below. As in the previous section, column 1 shows results

for the simple regression of real exchange rate on worker’s remittances. Columns 2

Table 7: IV estimates (instrumenting for workers remittances with tertiary enrollment)

1996-2013 Regressors (1) (2) (3) (4) (5)

Real Exchange

Rate

Real Exchange

Rate

Real Exchange

Rate

Real Exchange

Rate

Real Exchange

Rate

Remittance 0.19 0.17* 0.27* 0.20* 0.23

(0.13) (0.16) (0.19) (0.18) (0.28)

Dummy -0.065 -0.072 -0.063 -0.074 -0.060

(0.053) (0.049) (0.059) (0.048) (0.061)

TP 0.081 0.074

(0.16) (0.17)

∆Openness -0.028 -0.039 -0.038 -0.029

(0.21) (0.26) (0.21) (0.25)

Money Supply 0.49 0.46

(0.44) (0.51)

∆TOT 0.056 -0.081

(0.21) (0.31)

Intercept 0.046 0.051 0.045 0.051 0.044

(0.035) (0.034) (0.040) (0.035) (0.039)

First Stage

Fstatistic

1.56

(.23)

5.94

(.03)

1.77

(.18)

5.63

(.04)

1.82

(.17)

N 18 18 18 18 18

R2 0.004 0.062 < 0 < 0 0.059

adj. R2 0.129 0.227 < 0 < 0 0.333

All regressors are in logarithmic form

Standard error in parentheses (Prob > F. for F.stat) * p < 0.10,

** p < 0.05,

*** p < 0.01

23

through 5 correspond to equations 3-6 shown in the previous section. First stage

regressions can be found in Table 9, located in the appendix. While OLS regressions

from A. were hard to draw conclusions from, instrumental variable analysis, unlike OLS

regression, provides much more promising results.

According to the IV results table, the simple relationship between remittances and

exchange rate shows that real exchange rate will increase by 19% when remittances

increase. When controls are introduced in column 2, this result decreases to 17%, but

becomes much more significant. Such a result is comparable to Dorantes and Pozo’s

findings of a 22% increase in real exchange rate, as well as Chowdhury and Rabbi’s 14%

appreciation due to remittances Looking at non-tradable prices, Column 4 shows a

significant 20% increase in prices when remittances double. This result, along with the

results from column 2, seem most useful, both in their high adjusted R-squared values

and in the fact that they account for the Balassa-Samuelson effect, which has had a very

large and significant effect in each regression.

These results are further supported by the fact that tertiary education rate seems to

be a particularly useful instrument. Each column in Table 9 shows that remittances

increase by approximately 100 percent when tertiary enrollment doubles, which supports

the theoretical thrust behind the instrumentation. Secondly, the first stage tests for the

education rate instrument for Columns 2 and 4, our main columns of interest, show an F-

statistic equaling approximately 6. While this statistic fails the rule-of-thumb test, it is

large enough to not be considered extremely weak. Therefore, the instrumentation model

appears to be reasonably sound, if not exceptionally strong.

24

VI. Conclusion

This paper has studied whether remittance flows have an adverse effect on the

international competitiveness of Armenia. The results given show strong evidence that

although migrant workers work hard to save a major portion of their income to help their

families and improve their standard of living, their work creates challenges for the

Armenian tradable sector, and by extent, the international competitiveness of the country,

. In some ways, the model used in this paper serves as an improvement over

Chowdhury’s variable. While its data scope is severely decreased, it is also much simpler

econometrically, which makes it easier to infer policy implications by eliminating

confounding variables. Secondly, though its use of terms of trade, the model

distinguishes between the change of remittances on non-tradables and tradables, which

makes it easier to infer the cause of the change in real effective exchange rate.

Regardless, there are two potential pitfalls in these results, mainly stemming from

the proxies used, and the available data. While utilizing international migrant stock as a

proxy for remittance is a widely accepted transformation, and works decently well in this

case (proxy remittance values eclipse reported Armenian remittance values between 2010

and 2012), it still falls short in many ways. Since migrant population growth rate was

needed to create in migrant stock values (and by extension, remittance values), there is a

spike in remittances which might not exist I had access to better data, even though it is

justified by the existence of a structural break in all the series. Regardless, as it stands,

remittance is not as accurate as it should be.

Following from this point, there was little data to work with throughout this

analysis. Due to this lack of data, the aforementioned proxy of proxies was used to

25

construct the main explanatory variable. While this process allows the remittance variable

to capture some of the effects of unreported remittances on the Armenian economy, the

reliability of the original assumptions can be called into question, particularly because the

survey data gathered only took short-term migration into effect. At the same time, data is

also limited by time period. It is also possible that with a wider range of data to work

with from the beginning, then there would have been fewer problems with collinearity,

and the model could have measured the relationship between international

competitiveness and worker’s remittances more efficiently. Therefore, if more inroads are

to be made within this topic, then more attention needs to be paid to obtaining

development and migration indicators for Armenia, as well as other CIS states.

In spite of the positive socio-economic effects of remittances in Armenia, this

study concludes that the inflow of remittances is having adverse effects on the trade

competitiveness of the country. Therefore, the Armenian government should design

policies which create greater openness in goods and capital markets, allocate a portion of

the government budget for capital expenditure on the tradable sector, and divert valuable

remittance flows to priority investment areas through formal financial channels to

improve the international trade competitiveness of the country.

26

VII. References:

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Guatemala.’’ Policy Research Working Paper Series no. 3532, Washington, DC:

The World Bank.

Aggarwal, R., A. Demirguc-Kunt, and M. Peria. 2006. ‘‘Do Workers’ Remittances

Promote Financial Development?’’ World Bank Policy Research Working Paper

no. 3957, Washington, DC: The World Bank.

Amuedo-Dorantes, C., and S. Pozo. 2004. ‘‘Workers Remittances and the Real

Exchange Rate: A Paradox of Gifts.’’ World Development 32 (8): 1407–17.

Balassa, Bela, “The Purchasing Power Parity Doctrine: A Reappraisal,” Journal of

Political Economy 72 (1964):584–96.

Bourdet, Yves, and Hans Falck. "Emigrants' remittances and Dutch disease in Cape

Verde." International Economic Journal 20, no. 3 (2006): 267-284.

Chami, Ralph, Connel Fullenkamp, and Samir Jahjah. "Are immigrant remittance flows a

source of capital for development?." (2003): 1-48.

Chowdhury, M. 2004. Resources Booms and Macroeconomic Adjustments in

Developing Countries. Aldershot, UK and Burlington, VT: Ashgate.

Chowdhury M. & F. Rabbi. 2013, “Workers' remittances and Dutch Disease in

Bangladesh.” The Journal of International Trade & Economic Development: An

International and Comparative Review, 23:4, 455-475.

Corden, W. Max, and J. Peter Neary. "Booming sector and de-industrialisation in a small

open economy." The economic journal (1982): 825-848

Cox Edwards, Alejandra and Manuelita Ureta, 2003. “International Migration,

Remittances, and Schooling: Evidence from El Salvador.” Journal of

Development Economics 72, 429–61.

Datastream International. (October 15, 2014). In International Financial Statistics

[Online]. Available: Datastream International/Economics.

Edwards, S. 1989. Real Exchange Rates, Devaluation, and Adjustment: Exchange

Rate Policy in Developing Countries. Cambridge, MA: MIT Press

Edwards, S., and M. Savastano. 1999. ‘‘Exchange Rates in Emerging Economies:

What Do We know? What Do We Need to Know?’’ NBER Working Paper no.

7228. NBER: Cambridge, MA.

27

Ghazaryan, Armine, and Guillermo Tolosa. "Remittances in Armenia: Dynamic Patterns

and Drivers." International Monetary Fund. (2012).

Kroft, K. and Lloyd-Ellis, H. (2002) Further cross-country evidence on the link between

growth, volatility and business cycles, Working Paper, Queens University,

Kingston, ON.

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United Nations Industrial Development Organization

Michaely, M. 1981. ‘‘Foreign Aid, Economic Structure and Dependence.’’ Journal of

Development Economics 9: 313–30.

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Migration from Armenia and Returnees Surveys." [Online] (November 17th

2014)

Available. The Caucasus Research Resource Center

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28

Table 8: Descriptive Statistics for OSCE Survey 2002-2011 Variable Obs Mean Std. Dev. Min Max

Fraction of income remitted 1070 0.462288 0.294802 0 1

Average Monthly Income* 1070 663.83 1389.98 0 41675.78

Time abroad** 1070 20.85084 20.37758 .3 100

Total Paid Remittances* 1070 6924.364 10944.97 0 81454.59

*in US Dollars **in Months

Source: Constructed by the author using surveys given to Armenian migrant workers by the OSCE

29

Table 9: First Stage OLS Regression Estimates for IV Analysis Regressor (1) (2) (3) (4) (5)

Remittances Remittances Remittances Remittances Remittances

Tertiary Enrollment 1.00 0.86**

0.78 1.03* 0.66

(0.80) (0.35) (0.62) (0.49) (0.64)

Dummy 0.34**

0.20**

0.25* 0.23

** 0.23

(0.14) (0.078) (0.13) (0.094) (0.15)

TP 0.94***

1.08***

(0.25) (0.20)

∆Openness -0.79* -1.10

** -1.02

* -0.90

(0.44) (0.50) (0.54) (0.69)

Money Supply -1.43* -1.34

(0.77) (0.76)

∆Terms of Trade 0.43 -0.30

(0.48) (0.66)

Intercept -0.24* -0.16

*** -0.19

* -0.19

** -0.17

(0.13) (0.036) (0.11) (0.063) (0.14)

N 18 18 18 18 18

R2 0.350 0.742 0.609 0.755 0.617

adj. R2 0.263 0.662 0.489 0.653 0.458

All regressors are logarithmic

Standard errors in parentheses * p < 0.10,

** p < 0.05,

*** p < 0.01