Caucasus Research Resource Centers (CRRC) Armenia

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

NELLI GASPARYAN Yerevan – 2011

1 Table of Contents Table of Contents...... 2 I. INTRODUCTION...... 3 II. LITERATURE REVIEW...... 5 III. THE DATA...... 5 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

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 2 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.”

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 3 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.”

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 4 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.

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 5 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.”

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 6 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:

P(y  Poor | X )  (0   j X j ) (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.”

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 7 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.

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 8 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.

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 9 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 -.3894 -.3477 (.1297)*** (.0644)*** (.0607)*** members -.1463 -.0814 (.0149)*** (.0126)*** water source  own system .3688 (.1733)**

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 10  water ven door -.3050 (.1135)***  rain water .3962 (.2101)*  centralized .3543 (.4525)** Housing  apartment -.1991 (.0562)***  temp lodging -.2998 (.1182)** elders -.2095 headage>=63 (.0874)** mediumage -.1236 31<=headage<=62 (.0524)** educhigh .4797 .3138 .3136 (.0827)*** (.0553)*** (.0695)*** female -.2825 -.1006 (.0872)*** (.0596)*** constant 1.8806 2.016 1.351 (.4596)*** (.1941)*** (.0717)*** Difference in predicted 13.28% 12.72% 9.86% probabilities being non poor for all age group number of observations 1342 3072 3445

Table-2 Dependent Variable: poor=0 if Household is Poor, =1 if Non Poor; Year 2008 Regression model Pov_Yerevan Pov_ Urban Pov_Rural Regressor socialassist -.4934 -.2123 (.0651)*** (.0588)*** members -.0200 -.0856 .0253 (.0104)* (.0086)*** (.0080)*** water source  spring water, wells .0902 (.1041)***  own system .4089 (.1991)**  water ven door .0751

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 11 (.1183)***  centralized water supply -.3936 (.1812)*** socasist_elders .2284 -.1297 (.0904)** (.0636)** elders -.0704 -.2534 .0770 headage>=63 (.1081)*** (.0356)*** (.1238)*** mediumage -.5761 .5959 31<=headage<=62 (.1050)*** (.1218)*** head_maried -.1457 (.0596)** educhigh .6803 .1995 -.5146 (.0459)*** (.0426)*** (.0471)*** female .3579 -.3338 (.0836)*** (.0599)*** constant -.7324 .1391 -1.2805 (.132)*** (.0850)** (.1590)*** Difference in predicted 10.3% probabilities being non poor for all age groups Difference in predicted 0.55% 4.89% probabilities being non poor for elders Difference in predicted 6.87% probabilities being non poor for medium age and young number of observations 1344 3072 3456

Table-3 Dependent Variable: poor=0 if Household is Poor, =1 if Non Poor; Year 2009 Regression model Pov_Yerevan Pov_ Urban Pov_Rural Regressor socialassist -.5939 .8011 (.0777)*** (.3452)** members -.1184 -.0150 (.0089)*** (.0082)*** water source  spring water, wells .3758 (.1007)***

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 12  own system .2530 (.1415)*** socasist_elders -.5181 (.3523)*** socasist_mediumage -.1938 -.4024 (.0634)*** (.3506)*** elders -.5547 -.4960 1.0645 headage>=63 (.1089)*** (.0982)*** (.1496)*** mediumage -.5171 -.1993 .9049 31<=headage<=62 (.1059)*** (.0968)** (.1487)*** head_maried .2203 (.0432)*** educhigh .6965 -.3838 (.0465)*** (.0471)*** female .1449 -.1841 (.0436)*** (.0358)*** constant -.8027 .3479 -1.8054 (.1045)*** (.1183)*** (.1633)***

Difference in predicted 11.17% probabilities being non poor all Difference in predicted 4.28% probabilities being non poor for elders Difference in predicted 6.04% 7.23% probabilities being non poor for medium age Difference in predicted -3.1% probabilities being non poor for young number of observations 1344 3072 3456

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

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 13 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

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 14 5 2 . 2 . 5 1 . 1 . 5 0 . 0 20 40 60 80 100 perc grurban grural gyerevan

Source: NSS-Armenia Graph 2 -Growth Incidence Curve for 2008-2009 1 . 5 0 . 0 5 0 . - 1 . - 0 20 40 60 80 100 perc grurban grural gyerevan

Source: NSS-Armenia

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 15 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

D:\Docs\2018-05-05\0d5adf8636e1d9a84d3e57a037eaa2bd.doc 6-六月-18 Page 16 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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