POST-CRISIS DEVELOPMENTS IN ARMENIA

8/22/2019 POST-CRISIS DEVELOPMENTS IN ARMENIA

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VILEN V. KHACHATRYAN

HARUTYUN T. TERZYAN

ANNA R. MAKARYAN

POST-CRISIS

DEVELOPMENTS

IN ARMENIA

Yerevan, Armenia

Gitutyun Publishing House

National Academy of Sciences of the Republic of Armenia

2013


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UDC 338.124.4 Approved for publishing by the decree of theScientific Council of M. Kotanyan Institute of

Economics of the National Academy ofSciences of the Republic of Armenia

Khachatryan V.V. et al.

Post-Crisis Developments in Armenia / Khachatryan, V.V.; Terzyan,H.T.; Makaryan, A.R., Yerevan, Armenia: Gitutyun Publishing Houseof the NAS of the RA, 2013, 106p.

Acknowledgments

This research has been implemented in the scope of CRRC-Armenia Research Fellowship Program, financed by the CarnegieCorporation of New York.

The authors are grateful to Dr. Aleksandr Grigoryan and Dr.Tatul Manaseryan for reviewing the text; Ms. Armine Hakobyan forher assistance to the Research; and Peter Jones, CRRC-Armenia Inter-

national Fellow, and Dr. Vahe Khojayan for proofreading the text.

This book attempts to investigate how the global financial crisisimpacted the structure of the Armenian economy and the significanceof GDP components, as well as the channels (crisis social impactincome transmission channels) that significantly affected households(HHs). The findings presented in this book could be used by policymakers in designing economic development and social protection

and labor policies, for planning and implementing various measuresand programs, and by scholars interested in post-crisis social andeconomic developments.

ISBN 978-5-8080-1014-7 Khachatryan, V.V.; Terzyan, H.T.; Makaryan, A.R., 2013

Caucasus Research Resource Centers (CRRC) - Armenia, 2013

UDC 338.124.4


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Abstract: In this book we attempt to investigate how the global financialcrisis impacted the structure of the Armenian economy and the significanceof GDP components, as well as identify the channels (crisis social impact

income transmission channels) that significantly affected households(HHs). The authors classify the industries causing significant changesin GDP growth rates over 3 periods (quarter first 2001 to quarter 22008; 2001-2009; and 2001-2011) by estimating the GDP productionmethod equation using real growth rates. Manufacturing, agriculture,and construction all proved to be significant in explaining GDP growthover all the periods. We found that the large percentage of GDP that isconcentrated in these industries, combined with their high productivity,are the main reasons they have such a significant impact on real GDPgrowth. By estimating GDP by expenditure the components equationusing real growth rates over the same periods, we show that HH finalconsumption causes major changes in GDP growth rates after the crisis.The authors investigate the role (rank) various income sources HHsreport have on total HH income over three years (2008-2010) forvarious stratums by estimating generalized ordered probabilisticequations and relying on CRRC Data Initiative/Caucasus Barometerdatasets of 2008, 2009, and 2010. In addition, by surveying 384 HHs in

Yerevan in November 2011, we identify income sources that causedsignificant changes in HH income over four years (2008-2011), andprovide a short snapshot of their social impact (nutrition, debts, andsocial capital) on Yerevan HHs. The results presented in this book couldbe used by policy makers in designing economic development policies,social protection and labor (SP&L) policies, and various measures andprograms.

Key Words: Armenia, the global financial crisis, post-crisisdevelopments, shifts in the structure of economy, householdwelfare, the role of income sources, transmission income channelsand mechanisms, crisis social impact.


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Contents

Introduction ........................................................................... 5

I. Post-Crisis Developments: Shifts in the Structure ofEconomy, and Expenditure ................................................ 7

1.1 Shifts in the Structure of Economy .............................. 7

1.1.1 Model Specifications ....................................... 8

1.1.2 Estimation Results .......................................... 9

1.1.3 Industry Response ......................................... 17

1.1.4 Forecasts for 2012 ......................................... 25

1.2 Shifts in Expenditure by GDP Components ................... 25

1.2.1 Model Specifications ....................................... 26

1.2.2 Estimation Results .......................................... 28

II. Post-Crisis Developments: Impact on Households ................ 33

2.1 Sensitivity of Armenian Households to Changes inRanks of Various Income Sources Before and

After the Crisis ........................................................... 332.1.1 Framework ................................................... 33

2.1.2 Sources of Income ........................................ 34

2.1.3 Changes in the Ranks of Income Sources ........ 35

2.1.4 Changes in Total Income of Households .......... 38

2.1.5 Model Specifications and Variables Used ......... 43

2.1.6 Estimation Results ......................................... 45

2.2 Impact on Yerevan Households ................................... 61

2.2.1 Nutrition ....................................................... 62

2.2.2 Debts and Loans ........................................... 66

2.2.3 Human Capital .............................................. 69

Conclusions and Policy Recommendations ................................ 72

References............................................................................. 77

List of Abbreviations ............................................................... 80

Appendices ............................................................................ 81


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Introduction

In low-income countries with underdeveloped financialmarkets and limited capital flows, the currency crises are orderlydevaluations, which are current account events (Bleaney, 2005:1).VAM Food Security Analysis of the World Food Programme(2009), Public Forum Armenia (2008), Canagarajah (2010), and theUNDP (2010) identified various transmission mechanisms throughwhich the Global Financial Crisis had its negative economic

impact on Armenia. The majority of the stated channels throughwhich the Global Financial Crisis spread to Armenia were relatedto the current account events (remittances, export/commodityprice decline, tourism receipts, foreign assistance/aid flows), thuscausing a depreciation of the Armenian dram (AMD). Rogoff andReinhart (2009:6) define a currency crash as an annual depreciationin excess of 15 percent 1, hence the crisis Armenia experienced in 2009was a currency crisis 2.

A crisis can be a struggle for some industries/enterprises,households and the government to sustain the current pace ofdevelopment and/or growth. During a crisis, companies reconsidertheir strategies; households adopt different coping strategies thatcould affect their long-term welfare; the government reconsiderscertain policies (building automatic stabilizers, and etc.). Post-crisisdevelopments identify the setbacks (areas of concern) that thepolicymakers must address to build the resilience of the economy.Shifts in the structure of the economy stress the significant role ofthose industries that affect the real economic growth of Armenia

1 Although the significant devaluation of Armenian dram against US dollar (about 22%)

happened on March 3rd, 2009, the developments started in 2008 that resulted in currency

crush in 2009. However, we defined the start period of the currency crisis in Armenian the

third quarter of 2008, since the Lehman Shock happened on September 15th, 2008, resulting

in negative developments worldwide, but 2009 is viewed as the year of crisis for the

purpose of this research.2 The annual average depreciation of the Armenian Dram against the US dollar was about

20% in 2009 over the previous period.


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to be able to estimate GDP contraction that could be caused bythe decline of the respective industries hit by different crisistransmission mechanisms (shocks). The significant role of income

sources (ranks) in explaining the changes in HH income would becrucial in identifying the social impact transmission channels ofthe crisis that households are vulnerable to, thus urging them torespond to crisis by adopting such practices that would deterioratethe state of human development. Identification of significantindustries and sources of income will assist policy makers indesigning and implementing various economic development andsocial protection and labor (SP&L) strategies, policies or various

programs, as well as crisis response measures if Armenia facestransmitted crisis (contagion) in the midterm.

Part I investigates the shifts that happened in the economicstructure: (1) how the real growth rates of certain industries stoppedplaying significant role in explaining changes in real GDP growthrates, and how the real growth rates of other industries emergedto do so; and (2) the role of GDP expenditure components (thegovernment and consumer final expenditures, gross fixed capital

formation, net exports) in explaining changes in the real growthrates of GDP over 3 periods. Part II identifies the role of incomesources (crisis social impact transmission channels) in explainingthe changes in overall household incomes nationwide, in ruralcommunities, in urban towns/cities and in Yerevan (the capital)from 2008 to 2010 and in 2011 (for Yerevan only). Based on thesurvey result, the authors provide a short snapshot of the crisissocial impact on Yerevan households, focusing on social capital,

nutrition, and debts and loans.


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I. Post-Crisis Developments: Shifts in the

Structure of Economy, and Expenditure

1.1. Shifts in the Structure of Economy

Gupta et al. (2003:13) 3 find that a crisis preceded by a higheroutput growth rate experiences a bigger contraction during the crisisperiod, and after the crisis growth is higher when the bigger the

tradable goods sector (2003:16). Armenia was experiencing double-digit GDP growth rates from 2002 to 2007 (CAGR: 13.1%). Overthe same period the compound annual growth rate of theconstruction industry was 28.4%, while the share of constructionin GDP nearly doubled reaching 24.5% in 2007 (12.6% in 2002).Hence, according to Gupta et al. (2003), Armenia in the thirdquarter of 2008 when the Global Financial Crisis emerged couldhave anticipated a bigger contraction and slow recovery if theeconomy had experienced the transmitted currency crisis ororderly devaluation, since prior to 2008 the output growth wasespecially high and was mainly attributed to the non-tradedsector of the economy (construction). In 2008, the GDP growthslowed (6.9% year over year (y/y)), and experienced a biggercontraction in 2009 (-14.1% y/y), while the contraction of theconstruction industry was much bigger (-41.6% y/y). In 2011,the share of construction in GDP reached 12% comparing to25.3% in 2008. Obviously, the structure of GDP began to change

starting in 2008 (see Appendix C, Tables C1, C2), however, werethe industries with changes in shares (% of GDP) still significantlyaffecting the changes in GDP growth rates? Because of this, wehave attempted to test the following hypothesis:

3 According to the regression results based on the sample of 195 crisis episodes in 91developing and emerging markets.


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Hypothesis 1: Have various industries of economy started contributingto the changes in the real GDP growth differently from the third quarterof 2008 when the Global Financial Crisis emerged?

We have attempted to identify which industries were significantlyaffecting the change in the real GDP growth rates attributed to thechanges in real growth rates of various industries of economy priorto crisis (2001:1-2008:8), and what changed from 2008:2 onwards.

1.1.1. Model Specifications

Our model is defined as:Real GDP growth rate= f (real growth rate of agriculture, hunting

and forestry (A)*; real growth rate of fishing (B); real growth rate ofmining and quarrying (C); real growth rate of manufacturing (D); realgrowth rate of electricity, gas and water supply (E); real growth rate ofconstruction (F); real growth rate of wholesale and retail trade; repair ofmotor vehicles, motorcycles and personal and household goods (G); realgrowth rate of hotels and restaurants (H); real growth rate of transport

and communication (I); real growth rate of financial intermediation (J);real growth rate of real estate, renting and business activities (K); realgrowth rate of public administration (L); real growth rate of education(M); real growth rate of health and social work (N); real growth rate ofother community, social and personal service activities (O)).

Note: Each capital letter corresponds to the respective industryof economy according to the NACE 1.1 Revision of EconomicActivity Classification.

The dataset contains 44 observations covering the period2001:1-2011:4 4 (see Appendix A, Tables A1, A2, A3, A4). Weestimated the same equation three times for three different sampleperiods to identify the shifts in GDP structure. The sample periodof the first estimation is from the first quarter of 2001 to the

4 We understand that we had to cover a longer period than the sample period. The choice of

the sample period was due to lack of data, since the National Statistical Service of the

Republic of Armenia (NSS) provides quarterly industry level data (according to the NACE1.1 revision of economic activity classification) starting from 2001.


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second quarter of 2008. The second estimation sample period isfrom the first quarter of 2001 to the fourth quarter of 2009, andthe third estimation sample period covers period 2001-2011: the

first quarter of 2001 to the fourth quarter of 2011.By testing variables for normality distribution, and existence

of multicolinearity and finding no evidence of violation of OLSassumption, we estimate the following equation:

GDPgrt=0+1Agrt+2Bgrt+3Cgrt+4Dgrt+5Egrt+6Fgrt+7Ggrt+8Hgrt+9Igrt+10Jgrt +11Kgrt+ 12Lgrt+13Mgrt+14Ngrt+15Ogrt+t (1),

where:

GDPgrt is the real GDP growth rate in period t.

Agrt, Bgrt, Cgrt,...Ogrt are growth rates of respective industriescorresponding to the NACE 1.1 Revision of economicactivity classification in period t5.

0, 1,2,, 15aremodel unknown parameters.

t is the error term in period t.

1.1.2. Estimation Results

Period 1: Prior to Crisis

Prior to the crisis, the changes in the real growth rates of 5industries (manufacturing; electricity, gas and water supply;construction; agriculture, hunting and forestry; and real estate,renting and business activities) of the economy could explain

positive statistically significant changes in the real growth rates ofGDP, while real growth of education sector could cause statisticallysignificant decline in real GDP growth rates (see Estimation #1,Table 1). The above-mentioned industries were the main economicgrowth drivers prior to the crisis and were the source of

5 In order to estimate GDP production method equation we used real growth rates of the

industries due to lack of data, since the National Statistical Service of the Republic of

Armenia provides quarterly industry level real data (according to the NACE 1.1 revision ofeconomic activity classification) starting from 2005.


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employment for 62.7% of the employed in 2007 6. The role of theonly service-oriented industry among the statistically significantindustries that had positive impact on GDP growth rates could be

attributed to the fact, that it consists of several sub-sectors, andthe economy was experiencing the real estate boom prior to crisis.In 2007, these industries accounted for about 60% of GDP. Theremaining service-oriented industries (sectors excluding education)and mining and quarrying were experiencing positive growth,but did not contribute to the changes in real GDP growth rates.Therefore, the governments anti-crisis measures, stimulatingprograms or interventions to increase the supply and/or boost

the demand, focused on these industries of the economy couldhave prevented further GDP contraction both during the crisisand its aftermath.

According to estimation results (Estimation #1) 10 percentage-point (p.p.) change in real growth rates of manufacturing industrycould cause 1.5 p.p. change in real GDP growth rates, on average(ceteris paribus). It was predicted that the 10 p.p. change in realgrowth rates of electricity, gas and water supply industry, on

average, would cause 1.06 p.p. change in real GDP growth rates.10 p.p. change in real growth rates of the construction sector

of the economy could, on average cause 1.46 p.p. change in realGDP growth rates. And 10 p.p. change in real growth rates of realestate, renting and business activities would cause 1.2 p.p. change(on average) in real GDP growth rates, while the largest changein GDP growth rate could have been caused by 10 p.p. changes inagriculture (2.18 p.p.). It was surprising to find that the real

growth of the education sector could cause statistically significantnegative impact on the GDP growth rates (see Estimate #1). Sinceover Estimation periods #2 and #3, the industry proved to beinsignificant we do not focus here on explaining the reasons whythe education sector could have had a negative impact on GDPgrowth rates, which could be attributed to low degrees of freedom(Estimation #1), etc.

6 The sources of all data are various reports and datasets released/published by NationalStatistical Service of the Republic of Armenia, if not referred otherwise.


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Table 1. Estimated Models for different periods(Method: Ordinary Least Squares (OLS))

Estimation 1:

2001:1-2008:2

Estimation 2:

2001:1-2009:4

Estimation 3:

2001:1-2011:4Agrt 0.218

(3.25)***0.156

(2.32)***0.205

(4.44)***

Bgrt 0.003(0.29)

0.005(0.56)

0.004(0.44)

Cgrt 0.015(0.43)

0.030(1.14)

0.046(1.94)*

Dgrt 0.151(2.38)**

0.189(3.59)***

0.172(4.01)***

Egrt 0.106(1.89)*

0.122(2.17)**

0.064(1.65)

Fgrt 0.146(4.89)***

0.202(7.27)***

0.217(9.49)***

Ggrt 0.001(0.15)

0.121(1.95)*

0.151(2.63)**

Hgrt 0.009(0.51)

0.004(0.21)

0.011(0.59)

Igrt 0.009(0.10)

0.144(2.45)**

0.154(3.47)***

Jgrt 0.029(1.11)

0.034(1.83)*

0.038(2.26)**

Kgrt 0.121(2.89)**

0.096(2.09)**

0.051(1.43)

Lgrt 0.010(0.38)

0.0001(0.00)

-0.022(-0.75)

Mgrt -0.092(-1.97)*

-0.005(-0.97)

-0.004(-0.75)

Ngrt 0.006

(0.34)

-0.004

(-0.19)

-0.002

(-0.09)Ogrt -0.024

(-0.90)-0.037(-1.43)

-0.013(-0.61)

Constant 31.95(1.56)

-2.44(-0.22)

-5.76(-0.60)

Observations 30 36 44

R-squared 0.85 0.97 0.96

Adjusted R-squared 0.69 0.94 0.93

Durbin-Watson stat 1.99 2.00 2.05

Note: t statistics values in parentheses*significant at 10%; **significant at 5%; ***significant at 1%.


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Looking Forward

Using the one-step ahead static forecasting method, we triedto forecast GDP growth rates for the period 2008:3-2009:4 to

understand if Armenia could have anticipated GDP contractionceteris paribus (see Table 2). Based on forecasted GDP growthrates, Armenia could have faced contraction starting from thefourth quarter of 2008 throughout 2009. Therefore, the crisis thatwas transmitted to Armenia could have merely intensified theGDP contraction.

Table 2. Forecasted GDP Growth Rates for the period 2008:3-2009:4

(Method: One-step ahead static forecast)

Quarter Forecasted Growth Rates Actual Growth Rates

2008:3 112.8 115.4

2008:4 93.7 94.1

2009:1 91.6 93.7

2009:2 84.2 81.4

2009:3 82.5 80.3

2009:4 93.3 92.2

Source: NSS online database (April 8, 2012); authorsown calculations.

Periods 2 and 3: Crisis and Post-Crisis periods

Based on the results of Estimation #2 and Estimation #3, themodel-predicted GDP growth rates did not differ much from theactual GDP growth rates (see Table 3), demonstrating thatstatistically significant industries over two periods (2001-2009

and 2001-2011) accurately explained the changes in GDP growthrates and predict actual rates at each period of time.

Table 3.Model-Predicted vs. Actual GDP growth rates (%)

2009 (Estimation #2) 2010 (Estimation #3) 2011 (Estimation #3)

Predicted -12.5 2.2 5.0

Actual -14.1 2.1 4.6

Source: NSS, 2012; authorsown calculations.


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Three industries (agriculture, manufacturing, and construction)proved to be consistent in explaining the statistically significantchanges in GDP growth rates attributed to the respective changes

in industry growth rates over three estimation periods (2001:1-2008:2; 2001:1-2009:4; and 2001:1-2011:4). Three industries (wholesaleand retail trade; repair of motor vehicles, motorcycles and personaland household goods; transport and communication; and financialintermediation) began to be statistically significant in explainingchanges in real GDP growth rates from the second estimationperiod onwards. Real estate, renting and business activities,electricity, gas and water supply no longer explained statistically

significant changes in GDP growth rates over the third estimationperiod (2001:1-2011:4), while the mining and quarrying industryemerged to significantly impact GDP growth rates over the thirdestimation period. The education sector disappeared from the listof statistically significant industries both over the second andthird estimation periods.

Overall, during the 2001-2011 period, the industries withshare close to 10% of GDP or higher (agriculture, manufacturing,

construction 7, and trade (major industries)), the most productiveindustries (financial intermediation, and mining and quarryingwith productivity 8 over 8 million drams per employee in 2010(see Appendix D, Table D1), and the transport and communicationindustry with a share of over 6% of GDP (2011) proved to bestatistically significant in explaining changes in the GDP growthrate (see Table 4). Hence, the productivity or the share in GDP,matters in contributing to the real GDP growth. The industries

with a higher share in GDP contribute to higher GDP growthrates, while the contribution of the most productive two industriesthat were reporting high growth rates, was relatively low (financialintermediation and mining and quarrying).

7 Construction was the second productive industry after mining and quarrying and followed

by financial intermediation. For the notion of productivity and competitiveness see Porter

et al., 2003.8 Industry value added in constant AMDs (2005=100) divided by number of employees.


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Table 4. Industry characteristics that explain the changes in real GDP growth rates

IndustryIndustry share in GDPThreshold value: of 6%

and higher

Industry ProductivityThreshold value: higher than 5 million in constant AMDs

(2005=100)

Mining and quarrying 6% (12.7% in 2011)8.53 million, the second productive industry in 2010(-58% decrease over 2006)

Financial intermediation 6% (13.5% in 2011) 3.16 million (2010), and reporting 16.8% increase over 2006

Agriculture, hunting and forestry >6% (20.4% in 2011) Less than million AMDs per employee in 2006-2010

Source: NSS, various publications; and authors own calculations.

9 Since NSS releases annual average employment data only once a year and in December, therefore productivity data of 2010 are reported.

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On the other hand, the emergence of the transport andcommunication industry as a statistically significant industry couldbe attributed to the following: 6% of GDP could be a threshold

value (this assumption could be tested by incorporating moreobservations into the model). Although the share of wholesaleand retail trade; repair of motor vehicles, motorcycles and personaland household goods industry over 2001-2008 was higher than10% of GDP over the first estimation period it proved to beinsignificant. This could be attributed to lower degrees of freedom(see estimation #1). Further by adding more observations theindustry proved to have statistically significant role in explaining

the changes in GDP growth rates over the next 2 estimationperiods. The same was true in the case of the transport andcommunication industry.

The share of the electricity, gas and water supply industrybegan to shrink. Therefore, over the third estimation period itproved to be insignificant and reported productivity decline in2006-2010 (see Appendix D, Table D1). Although the real estate,renting and business activities industry was the fourth productive

industry in 2010, it had nearly half the productivity of constructionand financial intermediation industries and about 3 times lessthan that of the mining and quarrying industry. Furthermore,with the gradual decline in the share of the industry over 2001-2005, the industry share reached 5% of GDP in 2011. This cannotbe considered a key factor in making the industry statisticallysignificant, since the productivity of the real estate, renting andbusiness activities industry (4th in 2010) was much lower than

that of the three most productive industries. Based on thisevidence, we could make another assumption that the industriesreporting productivity of about 8 million AMD 10 per employeeor higher (or at least higher than 5 million, since there is huge gapbetween the most productive ones and the productivities ofremaining industries over 2006-2010) would turn into statisticallysignificant ones contributing to real changes in GDP growth rates.

10 See footnote 8.


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On the other hand, the number of industries and theindustries themselves changed (Estimation #3) which could haveaffected the GDP growth rates over the first period (Estimation

#1), stating the fact that diversification of the economy startedtaking place as a result of the crisis. However, the vulnerability ofthe Armenian economy to external shocks and/or currentaccount events and weather calamities (in the case of Agriculture,poor harvest could cause serious GDP contraction) has increased.This increase in vulnerability is due to the increase of the share ofstatistically significant industries in GDP, the growth of whichdepends on developments of the global markets (see table 5) to

various degrees. If, in 2007, the statistically significant industriescomprised 62% of GDP, in 2009 the share of the significantindustries increased by 13.8 p.p. reaching 75.8%, while in 2010and 2011 the share was close to 70%.

Table 5. The role of statistically significant industries in GDP(share in GDP, %)

Industry 2007 2009 2010 2011

Agriculture, hunting and forestry 18.2 16.7 17.2 20.4Mining and quarrying 2.5 2.9

Manufacturing 9.4 8.7 9.5 9.7

Electricity, gas and water supply 3.3 3.1

Construction 24.5 18.6 17.1 12.7

Wholesale and retail trade; repair of motor vehicles,motorcycles and personal and household goods

12.7 13 13.5

Transport and communication 7.2 6.8 6.3

Financial intermediation 3.9 3.6 4

Real estate, renting and business activities 3.8 4.9Education 2.8

Share of statistically significant industries in GDP 62.0 75.8 69.7 69.5

Source: NSS on-line database (April 2012).

It was interesting to note that industries with major governmentownership, including public administration, proved to be statisticallyinsignificant in explaining changes in real GDP growth rates. This

could mean that the increase of government spending on education,


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health, and social issues could just improve long-term humandevelopment accumulation (WB 2011: xxiii). However, these industriesthat produced more and more value added did not cause

statistically significant changes in GDP growth rates. On the otherhand, if the government increases spending and invests in long-term human development accumulation, then better educated andtrained, healthy employees working for the industries that havestatistically significant impact on changes in real GDP growthrates, could support higher growth rates. But, if we look at theindustries that played a statistically significant role in explainingchanges in real GDP growth rates from 2001 to 2011 (agriculture;

mining and quarrying; manufacturing; construction; trade; financialintermediation; transport and communication) that employed73.7% of the employed in 2010 (see Appendix G, Table G2), wenotice that these industries do not require very sophisticated,specific skills and abilities the majority of employees need topossess and/or obtain in order to be hired by these industries.Therefore, the increase in spending on healthcare will allowArmenia to have a healthy labor force, but increased education

spending needs to be more targeted, depending on the prioritiesof the government. If the main strategy is to build a competitiveand knowledge-based innovative economy targeted investmentsin education and other programs would pay off in economicdevelopment over the longer-run, hence stressing the strategicchoices the government would face.

1.1.3. Industry Response

Construction

Between 2001 and 2009, a percentage point change in thegrowth rate of the construction industry could cause 0.20 p.p.change in GDP growth rates (Estimation #2), and over 2001-2011it comprised 0.217 p.p. compared to 0.146 p.p. over the period2001:1-2008:2. It is interesting to note that although the share of

the construction industry in GDP declined over 2009-2011, the


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percentage point contribution to changes in real GDP growthrates increased. Based on Estimation #3 results, 10 p.p. decline ofthe industry growth rate would cause on average 2.17 p.p.

decline in GDP growth rate while over the period of 2001:1-2008:2(Estimation #1), the same decline would comprise 1.46 p.p.. Thesechanges in coefficients could pinpoint the inertia the economystill has, and the higher economic growth can be achieved if theindustry experiences positive growth rates. On the other hand, thegovernment faces two choices: either to build knowledge-based and/ordiversified economy that would ensure higher economic growth rates inthe longer-term, or to increase the spending and/or increase gross fixed

capital formation thus experiencing higher growth rates in the midtermand or short-term.

Agriculture, hunting and forestry

Based on the results of Estimation #3, a percentage pointchange in the growth rate of the industry could cause 0.205 p.p.change in GDP growth rates. Hence, the -15.8% contraction of theindustry in 2010 alone could have caused -3.24 p.p. GDP growthrate decline. And in 2010 the GDP growth could have reached5.25%, if the industry had reported a 0% growth, other thingsbeing equal. The consistency of the coefficient values before andafter the crisis along with the rising share of the industry in GDP,stresses the important role of agriculture as the main employmentand/or income source for rural community households (45.3 ofthe employed were engaged in agriculture in 2010 (see AppendixG, Table G2)). Hence the government programs (i.e. subsidies)that were initiated in 2011 11 coupled with better weathercondition could have supported higher growth rate of agriculturein 2011. Same type of interventions are expected to support higherindustry growth rate in 2012 as well. These programs could becrucial, since the rural communities proved to be very sensitive toincome decline received from sales of livestock and crops in 2009and 2010, (see Part II for detailed analysis of the estimation

11 2011- Government decree No 349 (March 31, 2011) the amendment to the decree No1802(December 23, 2011); 2012- Government decrees Nos. 75 & 88 (February 2, 2012).


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results, and see Appendix G, Tables G5, G6) 12. In 2009, if therural households depended on sales of livestock and crops as themajor source of income, decline in HH income could have been

reported with the probability of 57%, on average, while in 2010 theprobability was 43.3%. Higher GDP growth rates could be ensuredby large-scale investments in agriculture, government interventions,etc. in the short-run. However, the long-run strategy requireswell-designed programs and initiatives in line with overall govern-ment economic development strategy, and priorities.

Manufacturing

After the crisis, the impact of the industry on real GDP growthrates increased. If over the period 2001:1-2008:2 (Estimation #1)1.0 p.p., increase in the growth rate of manufacturing industrycould cause 0.15 p.p. change in GDP growth rate, over the thirdestimation period, it would have caused 0.172 p.p. change in GDPgrowth rate, reflecting the importance that the industry gainedafter the crisis. Upon experiencing contraction of 7.1% in 2009, theindustry reported solid growth in 2010-2011 (see Appendix C,

Tables C1, C2). Over the same period the share of the industryincreased by 1.0 p.p. comprising 9.7 p.p. in 2011. Although themanufacturing industry managed to recover in shorter period,the sectors performance varied, reflecting the developments thathappened in Armenia and worldwide during the crisis year andafterwards. In 2009, all sectors reported contraction except threesectors (tobacco products (output real growth: 17.6% y/y); rubberand plastic products (output real growth: 16.7% y/y); basic

metals manufacturing (output real growth: about 21% y/y)13

.The basic metals manufacturing experienced growth in 2010 and

12 We used CRRC DI/Caucasus Barometer datasets of 2008, 2009 and 2010 to estimate our

regression equations (Generalized Ordered Probit regression equations to identify the

ranks of various sources of income that causes significant changes in HH income over

previous period and probabilities HHs would report the particular choice at (change in

income)).13 Growth rates are calculated according to NACE 1.1 Revision of economic activity

classification (Source: NSS various publications).


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2011 at 6.4% (y/y) and at 9.9% (y/y) 14 respectively (output realgrowth). In 2010, the beverages sector experienced 31.6% realgrowth of output (y/y) that continued in 2011 as well (19.7%

y/y), reflecting an increase in demand in the main destinationmarket (Russia) and trends in other markets (particularly China).In 2011, the output of beverages (13.6%) and basic metals (24.0%)accounted for 37.6% of the output of manufacturing industry inArmenia 15. These two sectors mainly produce export-orienteditems making them vulnerable to global shocks.

Mining and quarrying

Over 2009-2011, the share of the industry in GDP reached2.9% (1.7% in 2009) experiencing high growth rates annually. Theindustry started playing a statistically significant role in explainingchanges in the real GDP growth rates over 2001:1-2011:4(Estimation #3). A percentage point change in the industrygrowth rate would cause 0.06 p.p. change in GDP growth rate.The industry emerged over the stated period as a significantindustry that could impact real GDP growth rates, while real

estate, renting and business activities and electricity, gas andwater supply industries stopped doing so (see Estimate #3), theshare of which in GDP exceeded the share of mining andquarrying industry (respectively 5% and 3.4% in 2011, the samepattern was observed in 2010 and 2009 as well). It would beinteresting to trace if the growth rates of mining and quarryingindustry start declining gradually or dramatically (decline inproductivity) would it cause statistically significant changes in

the real GDP growth rates? The answer could be given only byincorporating more observations into the model and based on theestimation results to make judgments and generalizations.Overall, the industrys role in GDP increased, hence increasingthe vulnerability of the industry towards transmitted shocks.Even if the industry begins to increase annually by 20%, it would

14 2010 and 2011 growth rates are calculated according NACE 2 revision of economic

activity classification (Source: NSS, 2012).15 Source:NSS, 2012.


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contribute to a 1.3 p.p. increase in GDP growth rates, on average,much less than the manufacturing industry would contribute tothe GDP growth rate reporting the same growth rate (3.4 p.p.).

Electricity, gas and water supply

Over 2001-2011 (Estimation #3) the industry proved to be

playing insignificant role in explaining the changes in real GDP

growth rates, reflecting the developments in other industries and

households response to crisis, since that directly impacted the

output growth rates of the industry. The industrys real value

added in 2010 was less than the value added reported in 2008 and

2009. The electricity consumption by Armenian householdsincreased by 4% (y/y) in 2010, exports more than tripled (3.16

times), while the overall production of electricity (kilowatt hours)

increased by 14.5% (y/y) 16. Hence the contraction of the electricity,

gas and water supply industry was due to the decline in gas

consumption (cubic meters), attributed to either a mild winter or

negative crisis coping mechanisms (either substitution or decline

in consumption) or combination thereof. The gas sector declinedby 13.3% y/y in 2010 17. Therefore, the economic growth will

impact the growth of the industry either at declining or increasing

rates, depending on GDP growth rates and the industries that drive

that growth. If we assume, that the consumption of gas, electricity,

water by households did not grow much or at least remain

unchanged 18, the main growth will be led by various industries.

Hence, we do believe, that the industry wont play statistically

significant role in explaining the changes in GDP growth rates inthe long-run ceteris paribus.

Financial Intermediation

The industry emerged as a statistically significant industry

the real growth rates of which affected the real GDP growth rates

16 Source:NSS, 2011.17 Source:NSS, 2011.18 Birth rates and migration remain at the same level.


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over 2001:1-2009:4, and proved to do so over 2001:1-2011:4. The

share of the industry in GDP doubled over the 10-year period and

reached 4% in 2011 compared to 1.9% in 2001. 1.0 p.p. change in

the industry growth rate causes 0.038 p.p. change in real GDPgrowth rate (Estimation #3). The lending to the private enterprises

over 4 years increased by 508.5 billion AMD and at the end of

2011 amounted to 688.7 billion AMD; the lending to households

increased by 255.3 billion AMD reaching 478.7 billion AMD, with

no serious decline in interest rates charged 19. The new pension

reform would enable the industry to attract substantial financial

resources (deposits by the mandatory pension funds) that could

be channeled into different industries of the economy, thus

increasing both the share in GDP and the impact it would cause

on GDP growth rates, and resulting in decrease in interest rates

charged. Increasing lending portfolio will make the industry

vulnerable to banking and other type of economic/financial crises,

thus requiring tight supervision by respective authorities to

prevent future financial crises.

Wholesale and retail trade; repair of motor vehicles,motorcycles and personal and household goods (or Trade)

From 2002 to 2008 the share of this industry in GDP did not

change much (see Appendix C, Tables C1, C2), while in 2009 it

increased by 1.1 p.p. and reached 12.7% of GDP, with a -4.8%

contraction in 2009 (y/y). Over the 2010-2011 period, the industry

experienced growth (respectively 4.8% (y/y) and 4.1% (y/y)). The

share of the industry in 2011 amounted to 13.5% of GDP.Compared to the Estimation #2 results, the industry coefficient

changed (Estimation #3) and 1.0 p.p. change in the real growth

rate of the industry would cause 0.15 p.p. change in GDP growth

rate. However, the rising level of indebtedness (as mentioned

above) could put the future growth of the industry into a trap if

the increase in the turnover of private businesses and respective

19 Source:CBA Online database, 2012.


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growth in HH income is not high enough to be translated into

higher household income to repay debts, increase real consumption,

and higher profits for companies to repay loans and invest.

Therefore, it would be hard to predict what direction the growthof the industry will have in the midterm (3-5 years) and in the

long run.

Transport and communication

Between 2003 and 2008, the industry was reporting high

growth rates (see Appendix C, Tables C1, C2). Compared to 2009,

the share of the industry in GDP declined by 0.9 p.p. reaching

6.3%, while in 2010, it reported positive growth at 4.9% y/y, and

a slight contraction (-0.2% y/y) in 2011, highlighting the fact that

other industries were growing faster than the industry itself.

According to the Estimation #3 results, the industry plays a

statistically significant role in explaining the changes in the real

GDP growth rates, hence 10 p.p. increase in the growth rates of

the industry would, on average, cause 1.54 p.p. increase in real

GDP growth rate (vs. 1.43 p.p. over the second estimation period).Although the industrys share in GDP was lower than the

respective shares of each of the four major industries (agriculture,

trade, manufacturing and construction) in 2009-2011 and higher

than the shares of newcomers (financial intermediation and mining

and quarrying industries), the industry coefficient is pretty close

to the coefficient of trade (0.15).

On the other hand, the industry supports the operationalefficiency of the companies/ industries/households (logistics; efficient

use of ICTs (information and communication technology) and/or

telecom services; and more productive labor-force), hence translating

positive developments/gains of the industry into higher rates of

return in other industries. The industry itself depends on the

growth of other industries as well (especially transport sector).

Therefore, the growth of the transport and communication

industry would be mainly driven by growth of other industries


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and increase of household incomes. If we decompose the industry

into the main two sectors of the industry: transport and commu-

nication, we could notice that in terms of freights shipped (in

thousand tons), the industry in 2008-2010 reported growth afterthe decline in 2008 (y/y), then experienced decline in 2011 (8% y/y),

while freight turnover (in million tons/km) increased by 6.7%

y/y 20. This could mean that the crisis affected the strategy of the

companies in terms of diversification. If we take into account the

fact that the sector is sensitive to the developments in trade

(imported items) and export-oriented sectors of economy, the

future growth will depend on the rising income level of households

and revenues of export-oriented companies, and investment decisions

of enterprises. In 2010, the number of Internet subscribers increased

1.82 times over 2009 (2.72 times over 2008). The total revenues of

companies received from providing internet services increased by

37.2% y/y in 2011 (at compatible prices) 21, while real revenues

received from providing fixed-line and mobile services declined.

Hence, the growth of the transport and communication industry

would be mainly driven by the transport sub-sector led by growthof other industries of the economy, and new services provided by

telecom companies/internet service providers (ISP) and increased

internet penetration rates in the longer-run along with increase of

the incomes of households.

Since education stopped affecting the changes in real GDP

growth rates over 2001-2009 and 2001-2011 (Estimations #2 and

#3), we do not address the developments in the industry overEstimation #2 and # 3 periods. We dont discuss the response of

the real estate, renting and business activities industry either,

since it did not prove to significantly change real GDP growth

rates over 2001-2009 and 2001-2011.

20 Source:NSS, various publications.21 Source:NSS, various publications.


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1.1.4. Forecasts for 2012

For all quarters of 2012, positive GDP growth rates are forecasted

(based on the one-step ahead static forecasting method), ceterisparibus (see Table 6). If Europe experiences one or multiple debt

crises and the negative impact of the crises/crisis is transmitted

into Armenia by various transmission mechanisms and/or shocks

experienced by other countries, the actual GDP growth rates

might not be negative due to the lag period after which the crisis

will seriously hit the economy. Therefore, the Armenian economy

would be negatively impacted in 2013 if the crisis is transmitted

in the last quarter of 2012.

Table 6.Forecasted GDP Growth Rates for the period 2012:1-2012:4

Method: One-step ahead static forecast 2012:1 2012:2 2012:3 2012:4

Forecasted GDP growth rates 106.8 108.3 116.8 107.2

Actual GDP growth rates 105.6 106.6 N/A N/A

Source: NSS, 2012; and authors own calculations.

1.2. Shifts in Expenditure by GDP Components

Although the main objective was to trace the shifts observed

in the structure of GDP before and after the crisis, in terms of

the role statistically significant industries played in explaining

changes in real GDP growth rates, we attempted to estimate the

changes that could cause changes in real GDP growth rates ifdifferent components reported decline and/or growth. We

mainly tried to identify the equations that described the shifts in

expenditure by major GDP components for three different

periods 22 (see Appendix B, Tables B1, B2, B3, B4) as described in

the previous section.

22 The choice of the sample period (2001:2011) was due to lack of data, since the NSS of theRepublic of Armenia reports growth rates starting from 2001.


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1.2.1. Model Specifications

Our model is defined as:

Real GDP growth rate= f (real growth rate of household final consumptionexpenditure; real growth rate of government final consumption expenditure;real growth rate of gross fixed capital formation; real growth rate of netexports of goods and services) 23.

By testing variables for normality distribution, and findingthe evidence of multicolinearity and respectively violation of OLSassumptions, we made the following transformations to identifythe equation that describes the pre-crisis period the best (2002:22008:2). First we estimated the following equation:

GDPgrt=0+1Cgrt+2Ggrt+3Kgrt+4NXgrt+t (2),

where:

GDPgrt is the real GDP growth rate in period t.

Cgrt is the real growth rate of household final consumptionexpenditure in period t.

Ggrt is the real growth rate of government final consumptionexpenditure in period t.

Kgrt is the real growth rate of gross fixed capital formation(GFCF) in period t.

NXgrtis the real growth rate of net exports of goods and servicesin period t.

0, 1,2,, 4 aremodel unknown parameters.

t is the error term in period t.

According to Magnus et al., (2004) the predicted values canbe considered good estimators of actual values, so we then usedthe predicted values of the real GDP growth rates to estimate thefollowing regression equation for the pre-crisis period, by

23 We used real growth rates of GDP components due to lack of data, since the National

Statistical Service of the Republic of Armenia provides quarterly real data of GDP

components starting from 2005, and in case of several other variables that could have beenincluded in the model, real values of those variables could be calculated starting from 2003.


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incorporating values of the real growth rate of gross fixed capitalformation lagged 5 periods instead of actual values of theexplanatory variable in period t, and values of predicted real GDP

growth rates lagged 4 periods:GDPgrestt=0+1Cgrt+2Ggrt+3Kgrt-5+4NXgrt+5GDPgrestt-4+t (3),

where:

GDPgrestt is the estimated value of the real GDP growth rate inperiod t.

GDPgrestt-4 is the estimated value of the real GDP growth ratelagged 4 periods.

Kgrt-5 is the real growth rate of gross fixed capital formationlagged 5 periods.

For the second period 2001:1-2009:4, by finding strong evidenceof multicolinearity among the real growth rates of governmentfinal consumption expenditure and the real growth rate of netexports of goods and services, we estimated first the followingregression equation:

NXgrt= 0+1Ggrt+ t (4),

Then we used the predicted values of the real growth rates ofnet exports of goods and services to estimate the followingregression equation for the period 2001:1-2009:4:

GDPgrt=0+1Cgrt+2Kgrt+3NXpgrestt +t (5),

where:

NXpgrestt is the estimated value of the real growth rate of netexports of goods and services in period t.

For the third estimation period 2001:2-2011:4 we found strongevidence of multicolinearity among the real growth rates of grossfixed capital formation and the real growth rates of net exports ofgoods and services we estimated first the following regressionequation:


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Kgrt= 0+1Kgrt-1+ 1NXgrt+t (6),

where:

Kgrt-1 is the real growth rate of gross fixed capital formationlagged one period.

Then we used the predicted values of the real growth rates ofgross fixed capital formation to estimate the following regressionequation for the period 2001:2-2011:4.

GDPgrt=0+1Cgrt+2Kpgrestt+3Ggrt +t (7),

where:

Kpgrestt is the estimated value of the real growth rate of grossfixed capital formation in period t.


Prior to Crisis

Period 1: 2002:2 2008:2Estimation # 4

GDPgrestt = 68.8 + 0.152*Cgrt+0.062*Ggrt+0.014*Kgrt-5-0.0008NXgrt+0.165*GDPgrestt-4(7.16)*** (7.22)*** (6.41)*** (1.94)* (-2.12)** (1.99)*

Sample: 2002:2-2008:2; Observations: 25R-squared: 0.87; Adjusted R-squared: 0.86; Durbin-Watson stat: 1.72Note: value of t statistics in parentheses*significant at 10%; **significant at 5%; ***significant at 1%.

Prior to the crisis, the large changes in GDP growth rates, onaverage, were caused by the changes in the real growth rates ofhousehold final consumption expenditure, a major GDP component(see Appendix E, Tables E1, E2), while the largest changes in GDPgrowth rates were caused by the changes in the real GDP growthrates lagged 4 periods (see Estimation #4). 1.0 p.p. change in thereal consumption by the HHs in period t was causing 0.152 p.p.

change in the real GDP growth rate in the same period, and the1.0 p.p. change in GDP lagged 4 periods, was causing 0.165 p.p.


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change in the real GDP growth rates. Real growth rates ofgovernment final consumption expenditure had positive impacton GDP growth rates. 1.0 p.p. change would cause 0.062 p.p.

change in growth rate of the real GDP. 1.0 p.p. change in the realgrowth rates of gross fixed capital formation lagged 5 periodswould cause, on average, 0.014 p.p. change in GDP growth rates.As it was expected, the real growth rates of the net exports ofgoods and services had a negative impact on changes in the realgrowth rates of GDP. Since the impact was minor, large changesin the net exports of goods and services could have caused asmall reduction in the real GDP growth rates. In 2007, 37.3 p.p.

the growth in the net exports of goods and services caused, onaverage, 0.03 p.p. reduction in real GDP growth rate. Hence, overthe period 2002:2-2008:2 the increase in imports had a negligiblenegative impact on the real GDP growth rates.

Crisis and Post-crisis Developments

Period 2: 2001:1-2009:4

Estimation # 5

NXgrt = 54.01+0.84*Ggrt(1.12) (3.42)***

Sample: 2001:1-2009:4; Observations: 36R-squared: 0.46; Adjusted R-squared: 0.43; Durbin-Watson stat: 1.95Note: value of t statistics in parentheses***significant at 1%.

Over the period 2001:1-2009:4, changes in the real growthrates of government final consumption expenditure explainedabout 46% 24 of variation in the real growth rate of net exports ofgoods and services, and 1.0 p.p. real change was causing 0.84 p.p.change in the real growth rate of net exports (see Estimation #5),

24 We deliberately skipped omitted variables test, since we do admit that the other variables

could have explained variation in the real growth rate of net exports of goods and services

in period t as well, since our purpose is to explain variation by changes in GDP

expenditure components, taking into account the fact of the lack of quarterly real data ofother variables that could have been included in the model.


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other things being equal. The relatively high impact of the realchanges in the growth rates of the government final consumptionexpenditure on the real growth rates of the net exports of goods

and services, other things being equal, could be explained by thefollowing: while meeting the individual and collective needs ofpopulation, mostly imported items (medical supplies, etc.) werepurchased, and this new demand caused by the growing spendingof the government and resulting in an increase in the real importsled to the growth of net exports of goods and services in period t(while exports reported growth as well). In 2001-2008, economicgrowth was translated into higher revenues available to spend by

the government, thus meeting more individual or collective needs(although due to the crisis, the share of the government finalconsumption expenditure in real GDP increased by 3.1 p.p. (y/y)in 2009, the expenditure reported negative growth at -1.9% (y/y).However, with other GDP expenditure components reportingdecline, this expenditure component could offset some negativeimpact of other components in 2009, with -1.9% decline in realgovernment final consumption expenditure. The same trend that

had been observed prior to the crisis continued as well.Estimation # 6

GDPgrt = 32.9 + 0.38*Cgrt+0.32*Kgrt -0.010*NXestgrt(2.54)** (2.68)** (7.75)*** (-1.79)*


Over the period 2001:1-2009:4 the role of real household finalconsumption expenditure increased (see Estimations #6 and #5)in explaining changes in the real GDP growth rates, other thingsbeing equal. 1.0 p.p. change in the final consumption expenditureby households was causing 0.38 p.p. change in GDP growth rate(0.152 p.p. over 2001:1-2008:2). Hence, other things being equal,4.5 p.p. decline in real final consumption spending by the

households, on average, could have caused 1.71 p.p. decline of the


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real GDP growth rate in 2009. Over the same period, the changesin real growth rate of gross fixed capital formation in period twere causing positive changes in real GDP growth rates in the

same period t in comparison to pre-crisis period, when the changesin real growth rates of gross fixed capital formation lagged 5periods were causing, on average, changes in the real growth rateof GDP in period t. One of possible explanations could be that theinvestments in fixed capital prior to crises required some time(lagged 5 periods) to become productive and cause real changesin GDP, while in 2009 changes in GFCF were mainly attributed togovernment interventions (providing loans for purchasing

equipment, infrastructure projects, etc.), thus causing positivechanges in GDP growth rates in period t. Conversely, the negativeimpact of the changes in real growth rate of net exports of goodsand services increased. 1.0 p.p. change in the real growth rate ofthe net exports was causing -0.1 p.p. decline in the real GDPgrowth rate.

Period 3: 2001:2-2011:4

Estimation #7Kgrt = 25.15 + 0.52*Kgrt-1+ 0.27*NXgrt

(2.04)** (4.05)*** (2.51)**

Sample: 2001:2-2011:4; Observations: 43R-squared: 0.57; Adjusted R-squared: 0.55; Durbin-Watson stat: 2.08Note: value of t statistics in parentheses**significant at 5%; ***significant at 1%.

Over the second period, the real growth rate of governmentfinal consumption expenditure in period t was causing significantgrowth in the real growth rate of net exports of goods andservices in period t, and over the third period, changes in the realgrowth rates of net exports in period t were causing changes inthe real growth rate of gross fixed capital formation in period t(see estimation #7). 1.0 p.p. change in the real growth rate of netexports of goods and services in period t was causing a 0.28 p.p.

change in the real growth rate of gross fixed capital formation inperiod t. Crisis could be viewed as an opportunity to invest in the


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future growth (government interventions aimed at purchasingmachinery and equipment). On the other hand, the governmentinvestment in infrastructure, etc. was also boosting GFCF and the

growth in the real net imports was translated into new importeditems channeled into infrastructure and other projects to boostreal growth of GFCF. Over 2001:1-2011:4 the real growth rate ofgross fixed capital formation was also explained by its growthlagged 1 period (1.0 p.p. change in period t-1 was causing 0.52p.p. change in period t).

Estimation #8

GDPgrt= 15.25 + 0.52*Cgrt+0.22*Kestgrt + 0.11*Ggrt(0.34) (3.27)*** (4.89)*** (1.71)*


In comparison to the pre-crisis period, the role of real house-hold final consumption expenditure increased, and emerged as the

main driver (taking into account the share of the expenditure com-ponent in GDP) 25 of the real economic growth (if other expenditurecomponents do not report substantial real growth). If prior tocrisis 1.0 p.p. change in the real household final consumptionexpenditure had caused 0.152 p.p. change in the real growth rateof GDP, then over period 2001:2-2011:4, 1.0 p.p. change was causing0.52 p.p. change in the real GDP growth rate. The real growth rateof government final consumption expenditure in period t over the

third period was causing 0.11 p.p. change in the real GDP growthrate in (0.062 p.p. change prior to crisis). The same trend wasobserved with regard to 1.0 p.p. change in the real growth rate ofgross fixed capital formation in period t causing 0.22 p.p. changein the real GDP growth rate over the third period.

25 In 2010 the share comprised 81.8%, and in 2011 the share was equal to 82.5 (See AppendixE, Tables E3, E4).


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II. Post-Crisis Developments:

Impact on Households

Crises have in common a disruptive impacton households welfare and social environment

Marzo and Mori (2012:6)

2.1. Sensitivity of Armenian Households to

Changes in Ranks of Various IncomeSources Before and After the Crisis

2.1.1. Framework

According to Marzo and Mori (2012), the nature of a particularcrisis and the transmission channels associated with it determinethe crisis impacts on households. Pernia and Knowles (1998:1)

have identified six major transmission channels through whichanycrisis can exert adverse social impacts. For Eastern Europe andCentral Asia, the World Bank (2011:5) has identified four channelsthat affect the welfare of the households. As for Armenia, crisissocial impact exerting transmission channels were addressed bythe World Bank (2010) and the VAM Food Security Analysis ofthe World Food Programme (2009). The transmission channels,by nature, were associated with the sources of income and/or

cash, and prices and/or exchange rate adjustments as main crisistransmission channels that exert the negative impact on thehouseholds as discussed by these authors. The price channel affectsthe purchasing power of the same amount earned/received bythe households, in the terms of less quantity of goods andservices purchased, while other channels directly reduce theamount (disposable income) available for purchasing these items.In cases where households have multiple income and/or cash

sources, each of them might differently contribute to the overall


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changes in household incomes, thus affecting the welfare of aparticular household. If crisis had no impact on the disposable(increase or at least maintain the same level) income available to a

particular household (HH), it could be assumed that the householdwould not respond to crisis in terms of adopting negative copingmechanisms and/or strategies, unless the negative impact of theprice channel is significant enough to cancel out positive gains(increase in incomes) or reduce the purchasing power of thedisposable income. Changes in overall household incomerespectively depend on how each source of income is seriouslyaffected. Therefore, our primary goal is to identify the role of income

sources (rank): HHs with these sources report significant changesin HH income by estimating generalized ordered probabilisticequations and then calculating the probabilities with which changesin HH income are reported over 3 (2008, 2009, 2010) and 4 (in caseof Yerevan, including 2011) periods.

2.1.2. Sources of Income

For the purpose of this research we have identified sevenincome sources that Armenian HHs could earn or receive:

1. informal transfers (received by relatives or friends residingin Armenia) 26;

2. formal transfers (benefits and pensions received from thegovernment, including benefits on maternity leave, studentsstipends, unemployment benefits, etc.);

3.

formal and informal salaries (salary received as a registeredor non-registered employee, self-employment (domestic labormarket));

4. remittances (received from abroad by members and non-members of a given household (overseas labor markets));

26 Informal transfers received by HHs are income received by others either locally (earned or

formal transfers) or from overseas, therefore we view it as a separate income source, atransmission channel for the purpose of this research.


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5. sales of agricultural goods and livestock (primarily receivedby rural residents) 27;

6. rents (received from the lease of property, etc. (real estatemarket));

7. other 28.Although loans and debts are considered sources of cash and

viewed as a channel (credit is considered crisis social impacttransmission channel by Pernia et al., 1998) 29, they could also beviewed as a negative coping strategy or mechanism in responseto crisis. Due to its dual nature we dont address its role in our

main analysis, but present the role of loans and debts for Yerevanhouseholds (based on survey data analyzed) in Section 2.2.

2.1.3. Changes in the Ranks of Income Sources

Each income sources had its importance and rank (relative toother sources) in the total income of a particular household. Therank of each source of income could change either due todecrease or increase in income received from the given source ordue to new sources of income that boost the total householdincome (see Table 7). Regardless of the reasons for changes inranks of income in our model, the role of the ranks of a particularincome source in explaining changes in household income is ourprimary focus, and we leave the reasons why changes in incomeoccurred for the detailed future research, although we brieflyaddress them and explain in details in case of some sources.

27 This source of income is viewed as a separate channel due to its important role in rural

communities for the purpose of this research, although the largest share of employed are

engaged in agriculture.28 All other sources of income are classified as other income sources into a single category.

This source of income does not include sales of property and dividends and interests earned.29 Constricting access to credit for investment or consumption (Pernia et al., 1998).


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Table 7. Changes in the Ranks of Income Sources

Sources ofIncome

Increase in Ranks Decrease in Ranks

informaltransfers

increase in income received(more relatives/friends/institutionssupporting HHs, or increase in theamount provided)

loss/reduction in income received fromother sources

decrease in income received(fewer relatives/friends/ institutions supporting HHs ordecrease in the amount provided)

increase in income received from other sources relative to theamount received from the given source

new sources of income comprising larger share of HHs income formaltransfers

increase in income received (either increasein number of HH members contributingto total HH income (employed, pension/benefits recipients, migrant workers) orincrease in the amount, or both)

loss/reduction in the amount of incomereceived from other sources

decrease in the amount of income received (reduction innumber of HH members contributing to total HH income orreduced amounts, or both)


new sources of income comprising larger share of HHs income formal andinformalsalaries

remittancessales ofagriculturalgoods andlivestock

increase in income received (morelivestock/crops sold or less quantitiessold at higher prices)


decrease in income received (less livestock/crops sold ormore quantities sold at lower prices; or draught and/orweather conditions)


new sources of income comprising larger share of HHs income rents increase in rents paid by the tenants


decrease in rents paid by the tenants increase in income received from other sources relative to the

amount received from the given source new sources of income comprising larger share of HHs income

36


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Role of Income Sources for Armenian Households

Both in Yerevan, urban towns, and rural communities, income

received from hired employment comprised the largest share ofper capita average monthly household income (see Appendix G,Tables G3, G4) in 2008-2011. Although more than 50% 30 of ruralhouseholds reported sales of agricultural products as the incomesource in 2008-2010 31 (see Appendix F, Tables F1, F2), the averageper household capita amount received was below the amountreceived from paid employment. Hence, in general, thosehouseholds that relied on paid employment (with several

members being employed) could ensure more monthly incomeon average, than sales of agricultural products and livestockcould provide in rural communities. The second source in termsof share in the monetary household income was formal transfersin 2008-2011, since about 50% of households were relying on thissource of income in 2008-2010. In urban settlements in 2008, theincome received from self-employed activities (per household capitamonthly average) was about equal to the amounts of informal

transfers and remittances received both from Armenia andabroad (per household capita monthly average). Since self-employment for the purpose of this research is considered earnedincome and is classified as formal and informal salaries, thedevelopments in the labor market are crucial in explainingchanges in total income of households and the respective changesin ranks, especially in urban settlements. In 2009-2010, the role ofother sources of income increased, pinpointing the fact the negative

crisis coping mechanisms were still in place especially in 2010.

30 On the other hand, agricultural products and livestock are the source of the non-monetary

income for the rural households and the source of food not purchased. The percentage

states the share of households engaged in the direct sales of crops and livestock.31 Source:CRRC DI/Caucasus Barometer Datasets of 2008, 2009, 2010.


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2.1.4. Changes in Total Income of Households

Although the nominal per household capita average monthly

income32

was increasing in 2007-2011, the real income33

reporteda 10.7% (y/y) increase in 2008, a tiny increase in 2009 (0.9% y/y),and in 2009-2011, the compound annual growth was 2.32%, whilein 2011 the increase was at 0.6% (y/y). The positive increasepinpoints the fact that, on average, households income increasecancelled out the CPI inflation. However, different sources ofincome reported different patterns of growth, hence determiningthe changes in income of individual households depending on

various sources. It is interesting to note that although average perhousehold capita monthly income was growing in 2008-2010 34,the share of households reporting a decline in their income grew(see Appendix F, Table F3). In 2008, only 25.2% of householdsreported a decline in their total HH income over 2007, while29.2% reported an increase. In 2010, 50.8% percent of householdsreported a decrease in their HH income over 2009 (48.4% over2008), while an increase was reported by 18.4% (8.2% in 2008). In

2010, in rural communities, both real and nominal per householdcapita monthly average income increased, which could mean twothings: those who reported increase in income managed to receivefrom major sources such an income that exceeded the previousyears income tremendously, thus resulting in an increase inaverage per capita income; and the NSS of the Republic of Armeniain calculating monthly average per household capita incomeincludes other sources of income (use of savings, etc.), which

increased from 2008 to 2010, while in 2011 the income from thissource decreased (see Appendix G, Tables G3, G4, G5, G6). Inurban towns, a decrease in household income was reported by18.8% of surveyed households in 2008, while 62.1% of themreported that their household income nearly remained the same

32 Hereafter in this section by income we mean per household capita average monthly income,

if not mentioned otherwise.33 CPI adjusted (2005=100).34 CRRC DI/Caucasus Barometer Questionnaire excluded the question on income changes

over last 12 months (NYRRLPR), therefore data for 2011 are not available.


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compared to 2007. In 2010, 34.1% of urban households reported adecline in their income (36.3% in 2009), while 48.1% of them didnot experience significant 35 household income changes over 2009

(46.4% over 2008). Yerevan households were more resilient thanhouseholds in urban towns and rural communities. In 2008,60.7% 36 of households reported no significant change inhousehold total income over 2007, while in 2010, 54.6% 37 did(56.8% in 2009).

Changes in Formal and Informal Salaries

The major source of income, real paid income, increased in

2008 by 8.7% (y/y) with the compound annual average growth at1.27% in 2008-2011, while in 2011 the growth was at 0.58% (y/y).The same trend was observed in rural settlements, where thecrisis seriously affected the labor market and resulted in an 11%(y/y) decrease in per household capita real average monthly paidincome. After reporting growth of income at 11.1% (y/y) in 2010,in 2011, the growth rate was -7.2% (y/y). If we assume that thosewho were employed in public/state institutions (polyclinics/

ambulatories, schools, local self-government bodies, etc.) did notexperience a decline in nominal income in 2009, the labor marketchannel seriously affected those who were employed in theprivate sector in rural communities. Although the nominal paidincome in rural settlements reported a tiny decline of 2 AMD(8,717) in 2011, the real paid income could not enable thehouseholds receiving this source of income to buy the samequantity of goods and services at the same price in 2010. It is

interesting to note that, although in urban settlements, in 2008-2011, per household capita average monthly real income frompaid employment reported an increase (a small increase in 2009-2010) and Yerevan households were more resilient to changes inthe labor market channel in terms of huge decrease in share ofhouseholds reporting substantial decline in income from this

35 The amount of total income nearly remained the same over previous 12 months.36 62.9% in 2008 (Source: Authors dataset).37 61.5% in 2009; 55.4 in 2010; and 54.6% in 2011 (Source: Authors dataset).


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source over 2008-2011 (see Appendix F, Table F6), average monthlyreal wages in Yerevan declined by 1.7% (y/y) in 2010 and by 2.9%(y/y) in 2011 (see Appendix G, Table G1). Income received from

self-employed activities (nominal and real) both in urban andrural settlements decreased in 2009-2010, reporting an increase in2011. In urban settlements, the amount of self-employed incomein 2011 exceeded that of 2008, thus reporting real growth as well.In 2011 the real growth of this source of income was 41.7% (y/y).In urban settlements real average self-employed income slightlydeclined in 2011 over the previous year.

Changes in Government Benefits and PensionsNominal per household capita average monthly income

received from the government (benefits and pensions) reported astable increase from 2008 to 2011 in Armenia (see Appendix G,Tables G3, G4). However, the increase was not enough to nullifythe CPI inflation in 2010, thus causing a decline in real incomereceived from the government. After reporting a 3.1% (y/y)decrease in real amount received as pensions and/or benefits in

2010, the real amount increased in 2011 by 2.5% (y/y). The valueof 2011 was less than the real value of 2009 (see Appendix G,Tables G5, G6). Although the government was increasing thenominal amounts paid to the individuals and/or households, theincreased sums were not consuming the increase in prices ofgoods and serviced purchased owing to CPI Inflation. The trendof 2010 was observed both in rural and urban settlements, whilein 2011, in urban settlements the real sums received on average

increased by 3.8% (y/y). This could be explained by two things:the average household size in rural communities is greater thanthe average size in urban settlements and those who becamerecipients of state benefits and/or pensions on average spentmore years in the labor force resulting in greater amounts received;and/or those eliminated from benefits/pensions receivers listwere rural residents and/or households.


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Changes in Sales of Crops and Livestock

In rural communities, developments in 2009 owing to thenegative impact of the GFC and especially 2010 38 played a crucial

role in explaining changes of amounts received from sales oflivestock and crops. Both nominal and real amounts receivedfrom this source per household capita declined in 2009, reportinga sharp decline of 37.2% (y/y) in real income during 2010 (seeAppendix G, Tables G3, G4, G5, G6). Although in 2011, incomereceived from this source exceeded the amount received in 2008(4,778AMD comparing to 4,670AMD) and experienced 52.6%(y/y) growth over 2010, the purchasing power of the value reduced

(3389AMD received from this source comparing to 3991AMD). In2011, the real amount increased by 41.7% (y/y). In general, thoserelying on this source of income could have faced difficulties ifthey had not found access to other sources of income, hence makingabout 50% of households with this income source sensitive to thechanges in income ranks in 2009-2010.

Changes in Informal Transfers and Remittances

The nominal amount of informal transfers and remittancesreceived from Armenia and abroad per household capita (averagemonthly) were growing over 2008-2011 both in rural and urbansettlements. However, the real values of the transfers andremittances reported slightly different patterns in these two typesof settlements (see Appendix G, Tables G3, G4, G5, G6). A slightdecrease was reported in 2009 over 2008 in both settlements, thensignificant increase in 2010, while in 2011 in rural communitiesthe real amounts received, on average, both exceed the amount of2010 and were the largest in four-year period (2542 AMD (2011)comparing to 1667 AMD (2008)), while in urban settlements a realdecrease of 9.8% was reported in 2011. It is interesting to note thatthe real decline started in 2008 in rural communities thatcontinued up to 2010. Overall, the share of households in the

38 Financial difficulties to cultivate crops owing to higher prices of imported fertilizers and

seeds, etc., debts and/or loans repaying problems causing livestock to be slaughtered(UNWFP, 2010); and natural calamities in 2010 (Source: Ministry of Agriculture, 2011).


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entire sample that stated remittances and transfers received frommigrant workers and relatives/friends living abroad nearlyremained the same 39 in 2008-2010 (see Appendix F, Tables F1, F2).

Changes in Other Sources of Income

On average, both in rural and urban settlements, the relianceof this type of income increased in 2009-2010 due to the negativeimpact of the crisis and the natural disasters of 2010 in both ruraland urban settlements. Although in 2011 both real and nominalvalues of per household capita average monthly other incomedecreased in rural and urban settlements still the values were

higher than in 2008 but less that the values of 2009.

Overall, based on analysis we could have expected that in2008, the particular type of settlement would not explain changesin total household income. From 2009 to 2010, in rural communities,the households were, in general, sensitive to amounts receivedfrom the sales of crops and livestock, explaining the major changesin total household income. Therefore, this stratum could haveemerged as a statistically significant one. In 2009, urban settlementshouseholds showed some resilience because of the reliance ofother types of income and the increasing role of the governmentsbenefits and pensions, in general, to cancel out the negativedevelopments in the labor market (especially from self-employedactivities). Therefore, the role of other sources of income could havebeen stressed. In general, in 2010, the reliance on other types ofincome sources increased that might mean that urban settlementshouseholds were more sensitive to income changes, and this typeof settlement could have had a significant role in explaining changesin HH income. Therefore, we would rely on our econometricmodels to justify our identified patterns, and identify the role ofvarious sources (ranks of sources) in reporting statistically sig-nificant changes in total household income, and the type ofsettlement to do so.

39 16.9% in 2009; 17.1% in 2010; and 15.9% in 2011.


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2.1.5. Model Specifications and Variables Used

P (NYRRLPR=j) = F (11INCRAAB1, 12INCRAAB2,

13INCRAAB3, 14INCRAAB4,21INCRARM 1, 22INCRARM 2,23INCRARM 3, 24INCRARM 4,31INCRAAG 1, 32INCRAAG 2,33INCRAAG 3, 34INCRAAG 4,41INCRASL 1, 42INCRASL 2,43INCRASL 3, 44INCRASL4,51INCRAGO1, 52INCRAGO2,53INCRAGO3, 54INCRAGO 4,61INCRARE 1, 62INCRARE2,63INCRARE 3, 64INCRARE4,71INCRAOT 1, 72INCRAOT2,73INCRAOT3, 74INCRAOT4,75INCRAOT5, 81STRATUM1,82STRATUM2, 83STRATUM3, j)

where:

P (NYRRLPR=j) is the probability of reporting the jth choice ofoverall HH income change over previous year in the ithhousehold (j=1 (if HH reported total income decrease overprevious year, excluding income from sales of property orvehicles), 2 (if HH reported no substantial change in total incomeover previous year, excluding income from sales of propertyor vehicles), 3 (if HH reported total income increase overprevious year, excluding income from sales of property or

vehicles)).INCRAAB1, INCRAAB2, INCRAAB3, INCRAAB4 are ranks of

money from family members, relatives, or friends livingelsewhere in Armenia (dummy variables scored 1 if for the ithhousehold the respective source of income is ranked 1, 2, 3, 4and 0 otherwise).

INCRARM1, INCRARM2, INCRARM3, INCRARM4 are ranks ofmoney from family members, relatives, or friends living inanother country (dummy variables scored 1 if for the ith


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household the respective source of income is ranked 1, 2, 3, 4and 0 otherwise).

INCRAAG1, INCRAAG2, INCRAAG3, INCRAAG4 are ranks of

cash sales from agricultural products (dummy variables scored1 if for the ith household the respective source of income isranked 1, 2, 3, 4 and 0 otherwise).

INCRASL1, INCRASL2, INCRASL3, INCRASL4 are ranks ofaggregated earned income (salary) of all household members,except sales of agricultural products (dummy variables scored1 if for the ith household the respective source of income isranked 1, 2, 3, 4 and 0 otherwise).

INCRAGO1, INCRAGO2, INCRAGO3, INCRAGO4 are ranks ofpensions and government benefits (dummy variables scored 1if for the ith household the respective source of income isranked 1, 2, 3, 4 and 0 otherwise).

INCRARE1, INCRARE2, INCRARE3, INCRARE4 are ranks of Incomefrom renting of property, vehicles, or appliances (dummyvariables scored 1 if for the ith household the respective source

of income is ranked 1, 2, 3, 4 and 0 otherwise).INCRAOT1, INCRAOT2, INCRAOT3, INCRAOT4, INCRAOT5 are

ranks of other sources of income (dummy variables scored 1 iffor the ith household the respective source of income is ranked1, 2, 3, 4, 5 and 0 otherwise).

STRATUM1 is a dummy variable scored 1, if the respectivehousehold is Yerevan resident, otherwise 0; STRATUM2 is adummy variable scored 1, if the respective household is urban

town resident, otherwise 0; STRATUM3 is a dummy variablescored 1, if the respective household is rural community resident,otherwise 0 40.

jis the error term.

40 Sources: CRRC DI/Caucasus Barometer Datasets and questionnaires of 2008, 2009, 2010

and survey results conducted by the authors in November 2011 in Yerevan. In our

questionnaire, we incorporated the same questions the data of which were used for other

models to estimate Yerevan equations and ensure consistency. For sample description, seeSection 2.2 and Appendix I, Tables I1 and I2.


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Prior to the Crisis (2008)

First, we identified the sources of income were causingstatistically significant changes in total household income. Thenwe left only those sources that were at most significant at 25%and then estimated the regression equations again to calculate therespective probabilities of ranks of various income sources causingrespective changes in household income, other things being equal.

Table 8.Estimation results for the generalized ordered probabilisticregression equations, 2008 41, 42

Model1 (Sample2008) NYRRLPRt Model2 (Yerevan2008) NYRRLPRt

INCRAAB1 -0.39 INCRAAB3 1.54(-2.48)*** (2.07)**

INCRARE2 -0.66 INCRARM2 -0.54(-1.61) (-2.14)**

INCRARM1 -0.23 INCRAGO2 -0.14(-2.28)** (-1.11)

INCRASL1 -0.38 Akaike info criterion 1.82(-6.56)*** Included observations: 377

Akaike info criterion 1.92Included observations: 1772

Note: value of z statistics in parentheses*significant at 10%; **significant at 5%; ***significant at 1%.

Table 9.Results of Estimated Models in terms of Probabilities, 2008

Variables

Sample

Variables

Yerevan

Probabilities Probabilities

P (j=1) P (j=2) P (j=3) P (j=1) P (j =2) P (j =3)

INCRAAB1 0.261 0.574 0.424 INCRAAB3 0.363 0.565 0.072

INCRARM1 0.211 0.582 0.414 INCRARM2 0.008 0.266 0.726

INCRASL1 0.264 0.573 0.421

41 Source:CRRC DI/Caucasus Barometer Dataset of 2008.42 The stratums that proved to be insignificant are not reported in this section, rather than are

presented in Appendix H, Tables H1 and H2.


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As expected, no particular type of settlement could cause

significant changes in household income. The sample households

were sensitive to the changes in informal transfers, remittances

from abroad, and salaries (see Table 8), ranked as the first sourceof income 43. If a household depended on paid salaries as a first

source of income in 2008 due to several things: another household

member had found a job, or the total su

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