Caucasus Research Resource Centers (CRRC) Armeniacrrc.am/hosting/file/_static_content/fellows/fellowship12... · 2019. 9. 14. · 1 Caucasus Research Resource Centers (CRRC) – Armenia

1

Caucasus Research Resource Centers (CRRC) – Armenia

A program of Eurasia Partnership Foundation

This research has been implemented in the scope of CRRC-Armenia Research Fellowship

Program, financed by the Carnegie Corporation of New York.

________________________________________________

Grants to Support Social Science and

Policy- Oriented Research # C10-1014

Growth and Poverty Dynamics in Armenia

By

Nelli Gasparyan

Yerevan – 2011

C:\Users\Valeria\Desktop\Growth and poverty-Nelli Gasparyan.doc 8-Jun-15 Page 2

Table of Contents

Table of Contents ..................................................................................................................... 2

I. INTRODUCTION ................................................................................................................. 3

II. LITERATURE REVIEW .................................................................................................... 5

III. THE DATA ........................................................................................................................ 6

IV. MODEL AND ESTIMATION ........................................................................................... 7

V. EMPIRICAL RESULTS FROM THE ESTIMATED BINARY CHOICE PROBIT

MODEL .................................................................................................................................... 8

A. Estimated Result for 2007 ............................................................................................ 9

B. Estimated Result for 2008 ............................................................................................ 9

C. Estimated Result for 2009 .......................................................................................... 10

VI. RESULTS FROM THE GROWTH INCIDENCE CURVE ............................................ 14

VII. CONCLUSION ............................................................................................................... 17


I. INTRODUCTION

Since the declaration of its independence in 1990, the economy of the Republic of Armenia

(hereinafter referred to as “RoA”) is facing continuing barriers resulting in low level of

income growth and widespread poverty, especially in rural areas of the republic. The

economic situation in the country became better only in 2004 and according to the

Governmental Program of 2004-2008 1the poverty level was reduced in 2004-2008 due to

relative increase in real per capita income. Two-digit annual growth rate was mainly

attributed to construction sector growth, which ensured almost 40% of GDP growth in 2008.

According to the Governmental Program of 2004-2008 share of poor people in Armenia fell

by 32.1%. However, currently the poverty remains an issue in Armenia: in 2008 27.6% and

in 2009 34.1% of the population considered as poor. Poverty continues to be considerably

high in rural 34.9% and urban 33.7% areas, excluding Yerevan, where poverty rate is the

lowest. This increasing dynamics in poverty was due to 2008-2009 global financial crises.

After the mentioned strong economic growth patterns of 2002-2007, in 2009 the Armenian

economy experienced 14.15 % downturn out of which 10.7% accounted from construction

sector. As a result, poverty has started to increase and it increases at a faster rate in rural

areas2.

The poverty issues are considered priority of the Government of Armenia and in the RoA

Government Program of 2008-2012, the poverty reduction is indicated to be the first

priority3. To relieve population from falling into poverty the Government of Armenia has

made adjustment in the 2009 State Budget by increasing pension and social assistance

transfers. In particular, the poverty reduction social transfers were increased by 13.8 % in

2009 State Budget. It should be mentioned, that the weight of social transfers in GDP

observed in 2008 was 0.8%, and it reached 1.4% in 2009. The “Social Snapshot and Poverty

in Armenia 2009” Statistical analytical annual report of the RoA National Statistical Service

(NSS) emphasized the importance of the RoA government expediters directed to the poverty

reduction. The same report indicated that the poverty could have been much more severe in

2009 in the absence of poverty reduction social support4. Besides this, the Armenian

Government in conjunction with Millennium Challenge Account-Armenia State Non-

Commercial Organization (hereinafter referred to as “MCA –Armenia”) is implementing

1 “Government-programs.pdf,” 13.

2 “99461648.pdf (application/pdf Object),” 223.

3 “Government-programs.pdf,” 13.

4 “poverty_2010a_1.pdf.”


projects directed to the reduction of poverty. The five year poverty reduction program (2007-

2012) aims to reduce rural poverty by improving irrigation infrastructure, roads and e.t.c.

The MCA and the Government are implementing those projects by using different strategy

plans and policies5.

Given the importance of this issue at hand, it is vitally important to know how effective these

projects in targeting poverty are. The present research paper assesses the effect of the poverty

reduction social assistance programs implemented by the Armenian Government and MCA-

Armenia for 2007-2009 on poverty reduction in different regions of Armenia.

This analysis may be used to identify the factors driving the changes in household

consumption behaviours over three reference periods. This study is based on the annual

surveys done by RA NSS. Starting from 2007 the sample size hade been expanded to cover

all regions and rural areas of Armenia. Binary choice probit model is used to assess the

dynamics of poverty determinants and effectiveness of above mentioned poverty reduction

policies.

Besides, this study examines to what extent different income or consumption groups of the

population benefit form economic growth. For this purpose this research paper uses graphical

technique to look into the interrelationship between economic growth and poverty in

different regions in Armenia over three years i.e. 2007-2009.

The reminder of the research paper is organized as follows: Chapter II provides review of

related literature; Chapter III focuses on data sets description; Chapter IV includes discuses

model description and its estimation; Chapter V discuses the empirical results and finally

chapter VI concludes.

5 “MCA-annual report.pdf.”


II. LITERATURE REVIEW

In economic literature topics related to poverty issues are discussed by different authors in

many different ways. Almost all studies concentrate on poverty issues along with growth and

inequality.

The literature of cross-country and individual country growth studies emphasis the fact that

growth has a strong and positive correlation with poverty reduction. The authors arrived to

this finding using both macro or micro based concepts of growth. The main questions that

are discussed in papers are the following: What is poverty? Is poverty the same as hunger?

What is the best measure of poverty? How to measure it? What is the relationship between

poverty and growth and etc?

To identify the interrelationship between poverty and growth Dollar and Kraay (2000) used

cross-country regression based on 80 countries’ cross-section data. The regression results

suggested that poor population as well as the whole economy equally benefited from growth.

One of the findings of the study was to stress the importance of proper methodological

choice. In particular, it was showed that regression methodologies should not be the same for

cross-country analyze, and for individual country analyze. Since, cross-country regression

results give an average picture of poverty for the selected countries. To make deeper analyze

on growth and poverty relationship one should relay on household level analysis. Moreover,

the regression methodology should not be the same that was used for cross-country level

analyze. In the economic literature there are several studies supported this finding Deininger

and Okidi (2002)

Andy McKay(2005) prepared 14 country’s case studies to look at the growth-poverty

dynamics. The case studies used set of analytical tools to examine the meaning of pro-poor

growth. The topic was discussed through following steps. First, building disaggregated

picture of growth. Second, examining distributional and poverty affect on growth, and the

third interpreting the links between growth, inequality and poverty reduction. Each of this

section in their turns used specific techniques to look at the relationship between growth

inequality and poverty reduction. Some of the techniques are widely used in discussing

poverty-growth topics. Growth incidence curve fro income is one of the techniques to

examine this relationship. Case studies for 6 countries look at the distributional pattern of

income growth, highlighting The importance of the absolute and relative concepts of pro-

poor growth. It could be the case when country experienced pro-poor growth in absolute

sense but not in relative sense. The analyses based on absolute concept of pro-poor growth

derived from the shape of GIC. When it is above zero then has poverty fallen, and when it is

bellow then poverty has increased in absolute sense. Relative concept of pro-poor growth is

based on both shape and position of the GIC. When economy grows and the poorer people

benefited more from growth than the richer then growth is pro-poor in relative sense. In the

case of Indonesia, growth was pro-poor in relative sense, but it was not pro-poor in absolute

sense since growth was negative over 1996-2002.

Despite the fact that poverty still an issue in Armenia, there is little research done on this and

related topics. Some of studies concentrate on poverty dynamics and its determinant in

Armenia but the most comprehensive analyses are statistical-analytical reports prepared by

NSS- Armenia. Almost all analytical tools are used to examine the effect of growth on

poverty reduction. To assess how effectively growth helps to reduce poverty the growth and

inequality elasticities of poverty estimation technique was applied. The idea behind of this is

to estimate how poverty changes in response of one percent increase in average income or

consumption. In general, absolute poverty falls if average income or consumption increases.


Two time periods 2004-2008 and 2008-2009 were selected for above mentioned analyze.

This choice was based on macroeconomic situation in Armenia. Firs period was associated

with the strong economic growth and second period the economy went down. Based on the

2008-2009 estimates 1% of economic negative growth had increased general poverty

incidence by 1.78%. It was shown that in good times when economy expanded poor were

benefited less than rich population. The consequences of the global financial crisis in 2008-

2009 on poverty incidence were alarming. As a result, poor became even poorer but the rich

population became richer. Thus, poverty and inequality increased both in relative and in

absolute sense.

III. THE DATA

The main data source for this analysis is the Annual “Integrated Leaving Condition Surveys”

(ILCS) conducted by National Statistical Service (NSS) of Armenia. In addition, for some

statistical inference I also used Annul Household Surveys conducted by Caucasus Research

Resource Center (CRRC)6. This data is relevant for the purposes of my study in terms of

similarity of data collection and its coverage. Since January 2007 (the data is available from

2002) the ILCS’ data can be used to asses the impact of the program co-funded with State

Budget of Armenia and MCA aiming reduction of rural poverty by improving rural roads

and irrigation infrastructure, agriculture sector and providing with financial assistance to

farmers. In 2007 the ILCS’ data was adjusted to asses the impact of the above-mentioned

program. Besides, the data is usable to asses the effect of social assistance programs

(pensions, family benefits programs for poverty reduction, subsidies mainly in rural areas)

implemented by the government of Armenia in 2004-2009. Since 2007 these annual surveys

have collected information from 7,872 households on their individual characteristics, income

sources, expenditures, age as well as sex and educational level of household heads, and other

individual characteristics.7

Although ILCS of NSS contains data on both expenditure and income of households, in my

analysis I will asses the household welfare as a response variable with help of real per capita

household expenditure. Choosing consumption as an indicator of the welfare is reasoned

with the fact, that the estimations on income level face many difficulties (Fofack, 2000). One

of the difficulties is that households tend to not declare, or under-declare their incomes,

which brings to biased reported aggregated income. The under-reporting income level is

more severe in developing countries with the high level of shadow economy. In such cases,

estimation approach on the income level is not reliable. Moreover, to a certain extent,

Armenia is not exclusion. In addition, ILCS data contains a broader measure of consumption

aggregates from 2004, which allowed me to do a deeper analysis on poverty. From 2004-

2008 by ILCS poverty measurement based on the minimum food basket, which is used to

calculate poverty lines (extreme line-food, total line-food and non food consumption)

adjusted for inflation over time and over space. In 2008 in response to changes of

consumption structure a new minimum food basket was defined, which also had been

corrected for average annual inflation rate. In the cases, when household consumption level

is below the poverty line, he/she is considered a poor. For household to be extremely poor,

the consumption should be below food poverty line. (Social Snapshot and Poverty in

Armenia 2009, page 3).

6 See “CRRC Data Initiative - CRRC.”

7For more details, see “Statistical data bases / Armenian Statistical Service of Republic of Armenia.”


IV. MODEL AND ESTIMATION

The logistic for the model choice and its estimation for the study is the following:

A. I have started with the investigation of the key determinants of poverty for 2007-2009

through the binary choice probit model. This was followed with the evaluation of the

impact of various policy changes on poverty reduction with the use of the same

model. Specifically, my objective is to estimate the effect of social assistance

program on poverty reduction in different regions in Armenia over three reference

periods. The binary choice probit model is expressed as follows:

)()|( 0 jj XXPooryP (1)

where is the cumulative standard normal distribution function, X is a vector of explanatory

variables:

The model parameters are estimated using maximum likelihood estimation (MLE) method.

The method chooses the unknown values of parameters such that maximize the likelihood

function. For further details on probit models and MLE, see Greene (2010)8.

The y variable takes on two unique values, y=0 for household classified as a poor (whose

real consumption are bellow total poverty line) and y=1 for non poor household.

The probit coefficients do not have direct interpretation. The model should be interpreted by

computing predicted probabilities of y=1. Indeed some conclusion can be made from the

estimated probit regression looking at the sign of the estimated coefficient. In case when it is

positive then increase in explanatory variable associated with decreasing welfare. The

negative sign of the estimated coefficient suggest increasing welfare with increasing X.

B. Second, is to explain the relationship between distributional pattern of economic

growth and poverty reduction. A growth incidence curve (GIC) which is a graphical

technique to look at this compares two points in time. Depicting the survey data into

percentiles groups of population and looking at the dynamics of household’ well

being of the same percentile groups changes over time and over space. It is important

to mention that the averages with same percentile groups have been compared. For

the detailed methodological explanation of GIC, see Andy McKay (2005)9.

8 Greene and Hensher, Modeling Ordered Choices.

9 “growthpoverty-tools.pdf.”


V. EMPIRICAL RESULTS FROM THE ESTIMATED BINARY CHOICE PROBIT MODEL

The most significant determinants of poverty used in this model are identified through a

stepwise procedure. These determinants which are the key factors affecting poverty remain

unaltered for the three reference periods i.e. 2007, 2008 and 2009. The model regression

results allow taking a closer look on a statistical evidence of being poor conditional on

spatial location of household over time.

The most important variables including in the model are listed in Table-1 (see enclosed

Annex). The first three variables are direct measures of the effect of social assistance as well

as its interaction with different age groups of households. The next two categories of

variables summarize the household ownership of asset and amenities. The first of these is the

type of water consumption, to household and the second one is the type of housing. The next

one is the household characteristics such us marital status, sex, education and literacy level

of household heads, as well as three age groups of household heads. The choice of age group

interval derived from data distribution and the retired age in Armenia during the reference

periods.

The probit regression results for 2007-2009 are provided correspondingly in Tables 1-3

below. As I already mentioned, my first objective is to estimate the effect of poverty

reduction social assistance programs implemented by the Government of Armenia on the

probability of being poor. The same model estimation results allowed me to make some

inference on the effectiveness of poverty reduction program directed to rural areas of

Armenia. This program is implemented by RA-MCA and as an indicator I incorporated into

the model public water consumption sources.

It is worth mentioning, that probit estimates reported in Tables 1-3 below do not have direct

interpretation. It is due to nonlinear nature of the model. Because of nonlinearity, the sample

averages are chosen to compute the difference of predicted probabilities for those household

who get social assistance and those who do not get. The final rows in each table reports these

estimated differences for rural, urban areas and for Yerevan separately.


A. Estimated Result for 2007

For 2007 social assistance has expected sign for all regions. However, the effect is large for

Yerevan. For those who do not get social assistance the probability of getting into poverty is

estimated to be 13.28%. For urban and rural parts the estimated differences are

correspondingly 9.86% and 12.72%. And these effects do not significantly differ for different

age groups. With increasing number of household adult members the probability of being

poor in urban and rural areas is decreased.

Almost all water sources used in rural areas were inefficient in terms of helping to overcome

poverty. The indicator, that I am mostly interested in, is centralized water supply. As I

mentioned above, this indicator stands for poverty reduction policy effect. For 2007 it did not

have an expected effect. The explanation may be that the project started in 2007 and at least

one year is required for having a desirable effect. Let us look at this from another side. The

project started from January 2007 and some construction works were made in order to

provide rural household with centralized water. In spring cultivation season started and

centralized water had been provided later and hence household used other water sources to

meet their needs.

In Table-1 it is shown, that housing type only maters in reduction of poverty in urban areas,

particularly those who have an apartment and temporarily lodging. Those household, whose

head belongs to elders and medium age groups are more likely to get into poverty in rural

areas. In other regions, the effect is not significant and differs in sign magnitude.

Unfortunately, the household whose head have high education are more likely to fall into

poverty, than those who do not have high education. The effect of this phenomenon is

broader in Yerevan. The reason could be that those students, who come from rural and urban

parts of the country after graduating from their college or university, prefer to stay in the

capital. Hence, educated labour force concentrates in Yerevan, which brings to the

difficulties with entering into the labour market.

B. Estimated Result for 2008

Let us to look how these determinants affect household well being in 2008.


Social assistance has negative sign, but the way it affects poverty status of household slightly

differs from the estimated results of 2007. For Yerevan, the difference in probabilities is the

same for all age groups, which is on average 10.30%. Based on the estimated coefficients

there is 0.55 % probabilities to fall into poverty for those whose head belongs to the elders

from urban parts of Armenia. The difference in probabilities for other age group household is

6.87%. In rural parts the program has expected effect, but only elders significantly benefited

from it securing 4.89% probability for not falling into poverty. The interesting finding is that

centralized water supply has already expected effect on household poverty status in rural

areas. Other types of water source are inefficient, as it was in previous year.

C. Estimated Result for 2009

For 2009 the picture is as follows:

For Yerevan for all three years, we have the difference in probabilities, which does not differ

across age groups. The social assistance program equally affects all age group household.

For 2009, the difference in predicted probabilities is 11.17%. Thus, household, who get

social assistance, has 11.17% more chance to overcome poverty. For household whose head’

age varies between 31 and 62 and who get social assistance has 6.04 % more likelihood to

get out of poverty in urban parts of Armenia. For rural parts the predicted probabilities are

4.28% for elders, 7.23% for medium age group household. The results for young group

household somehow differ from all findings discussed in this paper. Poverty reduction social

program has negative effect on young age group in terms of overcoming poverty. The

estimated coefficient suggests that there is a 3.1% more probability to fall into poverty for

those, who get social assistance. Although centralized water supply had negative coefficient,

it was insignificant and was dropped from the further analysis.

Table-1

Dependent Variable: poor=0 if Household is Poor, =1 if Non Poor; Year 2007

Regression model Pov_Yerevan Pov_ Urban Pov_Rural

Repressors

socialassist -.6156

(.1297)***

-.3894

(.0644)***

-.3477

(.0607)***

members -.1463

(.0149)***

-.0814

(.0126)***


water source

own system .3688

(.1733)**

water ven door -.3050

(.1135)***

rain water .3962

(.2101)*

centralized .3543

(.4525)**

Housing

apartment -.1991

(.0562)***

temp lodging -.2998

(.1182)**

elders

headage>=63

-.2095

(.0874)**

mediumage

31<=headage<=62

-.1236

(.0524)**

educhigh .4797

(.0827)***

.3138

(.0553)***

.3136

(.0695)***

female -.2825

(.0872)***

-.1006

(.0596)***

constant 1.8806

(.4596)***

2.016

(.1941)***

1.351

(.0717)***

Difference in predicted

probabilities being non poor for

all age group

13.28% 12.72% 9.86%

number of observations 1342 3072 3445

Table-2



Regressor


socialassist -.4934

(.0651)***

-.2123

(.0588)***

members -.0200

(.0104)*

-.0856

(.0086)***

.0253

(.0080)***

water source

spring water, wells .0902

(.1041)***

own system .4089

(.1991)**

water ven door

.0751

(.1183)***

centralized water supply -.3936

(.1812)***

socasist_elders .2284

(.0904)**

-.1297

(.0636)**

elders

headage>=63

-.0704

(.1081)***

-.2534

(.0356)***

.0770

(.1238)***

mediumage

31<=headage<=62

-.5761

(.1050)***

.5959

(.1218)***

head_maried -.1457

(.0596)**

educhigh .6803

(.0459)***

.1995

(.0426)***

-.5146

(.0471)***

female .3579

(.0836)***

-.3338

(.0599)***

constant -.7324

(.132)***

.1391

(.0850)**

-1.2805

(.1590)***


probabilities being non poor for all

age groups

10.3%



elders

0.55% 4.89%

Difference in predicted 6.87%



medium age and young


Table-3



Regressor

socialassist -.5939

(.0777)***

.8011

(.3452)**

members -.1184

(.0089)***

-.0150

(.0082)***

water source

spring water, wells .3758

(.1007)***

own system .2530

(.1415)***

socasist_elders -.5181

(.3523)***

socasist_mediumage -.1938

(.0634)***

-.4024

(.3506)***

elders

headage>=63

-.5547

(.1089)***

-.4960

(.0982)***

1.0645

(.1496)***

mediumage

31<=headage<=62

-.5171

(.1059)***

-.1993

(.0968)**

.9049

(.1487)***

head_maried .2203

(.0432)***

educhigh .6965

(.0465)***

-.3838

(.0471)***

female .1449

(.0436)***

-.1841

(.0358)***

constant -.8027 .3479 -1.8054


(.1045)*** (.1183)*** (.1633)***


probabilities being non poor all

11.17%


probabilities being non poor

for elders

4.28%



for medium age

6.04% 7.23%



for young

-3.1%


VI. RESULTS FROM THE GROWTH INCIDENCE CURVE

To explain how different groups of the population get along with time and how poverty

changes over time let us analyze the graphs below. In horizontal axis different percentile

groups of population are represented. It has been calculated based on real per capita

expenditure. The lowest percentile accounts for the poorest household and the highest

percentile for richest one. The vertical axes represent the annual real per capita expenditure

change between two corresponding percentile groups from two years i.e. 2007-2008 and

2008-2009. Graph-1 below represents the growth incidence curve for 2007-2008 for urban

real areas and for Yerevan separately. Correspondingly, the second Graph represents the

growth incidence curve for 2008-2009. The analysis whether growth has been pro-poor is

based on the relative concepts of GIC. The growth is pro-poor in relative sense when

inequality between poor and non-poor household falls.

Looking at the downward decreasing shape and position of the curves one can conclude that

almost all percentile groups’ expenditure has increased over 2007-2008 period. Thus, the

expenditure level has increased in almost all percentiles groups and all regions. However, in


urban and rural areas it increased at faster rate than in Yerevan, especially in lower percentile

groups. Only the household below 50th

percentile groups benefited from the growth more

significantly, suggesting that poverty has fallen over 2007-2008 periods. One of more

important insights is that in rural and urban areas inequality has also decreased over this

period. For Yerevan the distributional pattern of growth completely differs. The curve is

strictly downward sloping till the highest percentile groups and from then it changes its

trend. Despite the fact that the expenditure has increased for all percentile groups only the

richest have benefitted from economic activities more. From one hand, poverty has not

increased in Yerevan, but in the other hand inequality became more sever.

In the next Graph depicts distributional pattern of growth for the 2008-2009 period is shown.

The growth incidence curves vary across all percentile groups and in all regions. But there is

clear negative trend for lower percentile groups. Generally, the curves are sometimes above

and sometimes are below zero. When it is below zero, it implies that expenditure has fallen

for that percentile group. Nevertheless, global financial crises in 2008-2009 hits low

percentile groups harder than higher percentile groups in all regions. As a result, both

poverty and inequality increase in all regions.

Graph 1 -Growth Incidence Curve for 2007-2008

Source: NSS-Armenia

.05

.1

.15

.2

.25

0 20 40 60 80 100 perc

grurban grural gyerevan


Graph 2 -Growth Incidence Curve for 2008-2009

Source: NSS-Armenia

-.1

-.05

0

.05

.1

0 20 40 60 80 100 perc

grurban grural gyerevan


VII. CONCLUSION

This study has identified the determinants of poverty in Armenia during 2007, 2008, and

2009 years. This study has exposed an important understanding on how poverty reduction

policies affect the poverty status of household, and how its effect alters across regions over

three reference periods. From the point of methodological tools, binary choice probit model

has been used in response of this set of questions. Another aim of this paper has been to

analyse the distributional pattern of economic growth and its affect on household poverty

status during the economic expansion and downturn. Graphical tool technique has been used

to answer to these questions. In addition, the latter method is useful in emphasising

importance of poverty reduction policy during economic downturn.

The study has identified the policy change indicators, household characteristics, spatial

location of household, asset ownership structure and other characteristics of household. The

results of the study showed, that the majority of determinants were stable over time and

across regions. Moreover, the main indicators’ relationships with household poverty status

remained unaltered during three reference periods.

Particularly, it should be noted, that social assistance has an expected effect on poverty

reduction in all regions. In 2007 the magnitude and the way it affect the poverty differed

across regions, but does not differ across age groups. In 2008, the social assistance seemed to

help equally all age household groups in Yerevan, but its magnitude on different age group

varied in rural and urban areas. In 2009 the impact of the program on poverty reduction was

significant and to some extent anticipated. Two digit economic downturn and the resulting

objectionable macroeconomic situation mad harder to reveal the population to overcome

poverty. The social assistance program helped to partly resist poverty. In urban areas, only

elders significantly were benefited from the program. In Yerevan, households were equally

benefitted from the program. In rural areas medium age household and elders have benefited

from the program; however those household whose head belonged to younger group are

negatively affected from the program. In general, the social assistance program had a

sufficiently large effect on the poverty reduction in Armenia. Moreover, the importance and

effectiveness of this project is seen from the GIC depicted in Graphs 1-2. Real per capita

expenditure growth as an indicator of well being increases for all percentile groups, but this

increase is more significant for poorer population in urban and rural areas. Based on these

findings one can conclude, that growth was sense pro-poor in relative in urban and rural

areas in 2007-2008. This conclusion is not related to Yerevan, where growth brings to

inequality severity. In the end of 2008 global financial crises wave hit Armenian economy.

The consequence of the crises was attributed in GIC 2008-2009 pathway, expenditure

decline almost in all percentile levels. However, what is clearly seen from the graph is that

poverty in all areas had increased, but the in rural areas inequality between poor and rich

household had decreased.

Centralized water supply, which stands for the effect of the project implemented by MCA-

Armenia in rural areas, positively related to poverty status of household in 2007. This

positive effect was anticipated, since its effect could be observed only one year after its

implementation. However, in 2008 the result of the program was obvious. Although the

program effect on poverty reduction in rural areas remained unaltered in 2009, but it was

insignificant.

The higher education seems to embarrass household to overcome poverty in almost all

regions in Armenia. The household whose head is less educated has more likelihood to get


out into poverty. This should be alarming for policy makers. Facilitating secondary

specialized education in all regions may let all actors of labour market to be better off.

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Caucasus Research Resource Centers (CRRC) Armeniacrrc.am/hosting/file/_static_content/fellows/fellowship12... · 2019. 9. 14. · 1 Caucasus Research Resource Centers (CRRC) – Armenia