November 2018 Georgia Indebtedness Pov ertyNote Nov 5€¦ · Indebtedness_Pov ertyNote_Nov 5 Analysis Based on Integrated Household Survey Natsuko Kiso Nozaki, Alan Fuchs Tarlovsky,

November 2018

Georgia

Indebtedness_Pov

ertyNote_Nov 5 Analysis Based on Integrated

Household Survey

Natsuko Kiso Nozaki, Alan Fuchs Tarlovsky, and Cesar A. Cancho POVERTY AND EQUITY GP, ECA

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GEORGIA INDEBTEDNESS_POVERTYNOTE_NOV 5

Contents Overview ....................................................................................................................................................... 2

Macroeconomic Evidence ............................................................................................................................. 4

Poverty and Prevalence of Borrowing ........................................................................................................ 10

Indebtedness and Its Impact on Household Wellbeing – Regression Analysis .......................................... 16

Conclusion .................................................................................................................................................. 20

References ................................................................................................................................................... 23

Appendix. .................................................................................................................................................... 26

Appendix 1. Loan-related variables in IHS ............................................................................................ 28

Appendix 2. Interest Rates ...................................................................................................................... 28

Appendix 3. Literature Review, Model Specification, Estimation Strategy and Data ............................ 29

Summary Statistics – Selected Variables .................................................................................................... 38

2


Overview

There is considerable public concern about the level of household indebtedness in Georgia.

The new regulation expected to come into force on November 1, 2018 addresses this concern by enforcing

the responsible credit framework targeting the commercial banks 1 . A recent study by the Finance,

Competitiveness & Innovation (FCI) Group named Borrowing by Individuals: Capacity, Risks and Policy

Implications, Summary Note also emphasizes the over indebtedness of individual borrowers which -if the

issue is generalized and representative at the national level- can be a potential source of vulnerabilities that

could trigger macroeconomic financial distress. Without the institutional mechanisms in the event of

financial distress, the adverse consequences of over-indebtedness on household welfare as well as the

overall macroeconomic implications may be severe for Georgia, compared to more advanced countries.

The objective of this note is twofold. First, the note presents micro-level evidence using the

nationally representative household survey to understand households’ borrowing patterns with

supporting evidence from perceptions surveys. The high level of indebtedness of households to bank

loans, especially among the poor and vulnerable, may harm economically and socially their drive for

escaping poverty. Household profiling is based on quantitative measures complemented by analysis using

a set of subjective measures represented at the national level.

Second, the note examines plausible causal effects of over-indebtedness on household’s

welfare. Much of the solid empirical evidence illustrating the causal relationship between financial

development and poverty reduction is at the macro-level given the limitations of nonexperimental data.

Doubts have been raised about the welfare impact of bank loans at the micro-level. With excessive debt,

there is a risk for poor and vulnerable households to be caught in a spiral of debt and high interest rates that

could lead them to poverty traps. Taking advantage of the survey instrument that enables to address the

issue of selection bias, the note provides preliminary results on the impact of bank credit on well-being at

the household level.

Findings are indicative of the financial distress illustrated in the report prepared by the FCI Group.

The main messages are:

1. Georgia has seen significant increase in households’ bank borrowing, causing public concern

about its economic and social impact. Focusing on the formal banking sector, share of borrowing

households has almost doubled from 2011 to 2016, with largest increase in the share of poor

households. Estimates from the national representative survey show that over 40 percent of all

households uses some type of financial services in 2016, with majority borrowing only from formal

commercial banks. Moreover, between 2011 and 2016, share of poor households in the bottom quintile

increased the most (by 3.2 percentage points) followed by those in the second lowest quintile (1.2

percentage points) among the borrowing households in contrast to a drop in its share among the richest

quintile (negative 4.4 percentage points). Macroeconomic indicator also shows that the credit

developments in recent years between 2014 and 2017 have been driven by households as opposed to

corporate sector.

2. Banks have become increasingly the main source of loan provision for the households as opposed

to informal lenders. However, public trust in banks has fallen drastically to its lowest in 2017

since the financial meltdown in 2008 – only 26 percent of the population had trust in 2017,

1 Caucasus Business Week, October 18, 2018. http://cbw.ge/banking/commercial-banks-against-national-bank-new-

regulations-to-take-effect-in-november/ .

http://cbw.ge/banking/commercial-banks-against-national-bank-new-regulations-to-take-effect-in-november/

http://cbw.ge/banking/commercial-banks-against-national-bank-new-regulations-to-take-effect-in-november/

3


corresponding to less than half the share in 2008. 2 This level of trust is also low from international

perspective. The declining trend of public trust in banking after the global financial crisis in 2008 is

commonly found in many other countries, but they tend to rise or stabilize at around year 2011/2012.

Interestingly, in Georgia, that is not the case – the perception toward banks has continued to deteriorate

since 2010 with larger share of individuals expressing distrust towards banks. The trend is accentuated

when asked about the National Bank in particular – percentage of individuals rating National Bank as

“favorable” has dropped drastically from 67 percent in 2011 to 21 percent in 2017, only slightly

increasing to 22 percent in 2018.3 International comparison based on Life in Transition Survey (LiTS)

also shows that the level of trust towards banks in Georgia is among the countries with relatively high

rates of “distrusts” at 48 percent of all population.

3. Indebtedness, as measured by the ratio of unpaid debt to household total income, has no

significant impact, and if any, will increase the household’s likelihood of being in poverty. By

isolating causality from mere correlation based on more sophisticated econometric methodology

compared to the naïve OLS estimates, we show that: (1) increasing bank loans do not increase the

household welfare in terms of per capita consumption, (2) higher indebtedness, measured either by the

ratio of borrowing amount or unpaid debt over total household income, have negative (but insignificant)

impact on household’s per capita consumption, and that (3) we cannot reject the hypothesis that the

higher indebtedness increases the household’s likelihood of being in poverty or in vulnerable status.

Results confirm that descriptive statistics and naïve OLS estimates seem to be biased and overestimate

the impact of borrowing from banks.

4. Given the dramatic increase in household debt and indication of increasing debt stress, there is

an urgent need to gather basic facts from the demand and supply side of the financial market.

Only with systematic observations of credit market and dynamics we would be able to reach concrete

policy implications tailored to Georgian context. Efforts are needed to validate the magnitude of over-

indebtedness and irresponsible lending at the national level. International comparison and variation of

financial development within Georgia would also be essential in designing regulatory and policy

interventions without being overly restrictive.

This paper is structured as follows. Section 2 provides macroeconomic indicators and findings from

perception survey as the background evidence. Section 3 illustrates the prevalence of borrowing among the

households and identifies type of households that borrow from different sources. Section 4 shows results

from the causal impact analysis of bank loans on household welfare. Section 5 concludes with directions

for future research.

2 2012 Edelman Trust Barometer: U.S. Financial Services and Banking Industries.

https://www.slideshare.net/EdelmanInsights/2012-edelman-trust-barometer-us-financial-services-and-banking-

industries . 3 Opinion poll conducted by Baltic Surveys/The Gallup Organization (2018).

https://www.slideshare.net/EdelmanInsights/2012-edelman-trust-barometer-us-financial-services-and-banking-industries


4


Macroeconomic Evidence

There is considerable public concern about household indebtedness in Georgia. In Georgia, it is

estimated that between 3 – 5 percent of households could have moved below poverty line due to financial

conditions.4 Taking on debt can increase consumption beyond what one’s income can support, it can smooth

consumption in face of shocks and it can represent an investment in the future. However, over indebtedness

may result in significant financial distress, ultimately capturing households in poverty traps. Indebtedness

may thus signal irresponsible spending, a lack of self-control, or low level of financial literacy. To address

increasing household indebtedness, the National Bank of Georgia (NBG) has established a cap on loans to

households without verifiable income (25 percent of banks’ regulatory capital), awaiting upcoming

legislation to promote responsible lending.5

The level of household debt in Georgia has been rising steadily over the years until 2016 and declined

slightly in 2017. According to the World Development Indicators, credit to households and other sectors

reached 62.05 percent of GDP in 2016 compared to 35.52 percent of GDP in 2011. More specifically, IMF

reports that household debt had reached 34 percent of GDP at end-2017, which had doubled in the last five

years.6 The rate for Georgia is still significantly low compared to Euro Area estimates7 and close to the

average of all ECA countries excluding high income, but higher than countries such as Albania, Armenia

and Azerbaijan (Figure 1).

Figure 1: Trend in Loans by Households and Other Sectors* – Cross Country Comparison

4 June 5, 2018 REZONANSI: “MORE 5% OF POPULATION BECAME IMPOVERISHED!” 5 Programs are underway to enhance financial education and sensitive households on risks associated with financial

imprudence, over-indebtedness, and FX borrowing (IMF Georgia Article IV, June 2018). 6 Ibid. 7 Ibid.

5


Source: World Development Indicators (as of September 14, 2018).

Note: * Includes gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial

corporations, state and local governments, and social security funds.

Credit growth is driven by households (Figure 2), and its magnitude is proven empirically to be an

important factor for economic growth and poverty reduction. A study shows that the relation between

financial depth (as defined as private credit as a share of GDP) and poverty is not only causal and

statistically significant but also sizeable. Even after controlling for other variables, almost 30 percent of the

cross-country variation in changing poverty rates can be attributed to cross-country variation in financial

development.8 Although the level of household debt and the size of non-performing loans (NPL) in Georgia

are still at the reasonable level compared to its peers and developed countries (Figure 3), the size and stock

of household debt may trigger concerns over financial distress in the medium to long terms.

Figure 2: Share of Household Debt Figure 3: Household Debt – Cross Country Comparison

8 Interesting example The World Bank, 2008.

35.52

38.17

43.56

49.1754.77

62.05

Georgia,

58.84

Albania

Armenia

Azerbaijan

Europe & Central Asia

(excluding high income) Russian Federation

Turkey

0

10

20

30

40

50

60

70

80

2011 2012 2013 2014 2015 2016 2017

% o

f G

DP

Credit to Households and Other Sectors* (as % of GDP)

6


Source: IMF Article IV, June 2018. Source: Non- performing loans from World Development Indicators (as of

September 14, 2018) and households outstanding loans from Financial

Access Survey, IMF (as of September 16, 2018).

http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C .

Banks had become increasingly the main source of loan provision as against informal lenders.

However, trust in banks has fallen drastically to its lowest in 2017 since the financial meltdown in

2008 – only 26 percent of the population had trust in 2017, corresponding to less than half the share

in 2008 (Error! Reference source not found., left). The declining trend of public trust in banking after the

global financial crisis in 2008 is commonly found in many other countries, but they tend to rise or stabilize

at around year 2011/2012.9 Interestingly, in Georgia, that is not the case – the perception toward banks has

continued to deteriorate since 2010 with larger share of individuals expressing distrust towards banks. The

trend is accentuated when asked about the National Bank in particular. Opinion poll conducted by Baltic

Surveys/The Gallup Organization in 201810 shows that the National Bank was the least trusted institutions

with highest share of individuals rating “unfavorable” (67 percent), second only to Political Parties (68

percent “unfavorable”).

Figure 4: Poor Public Perception of Banks in Georgia

9 2012 Edelman Trust Barometer: U.S. Financial Services and Banking Industries.

https://www.slideshare.net/EdelmanInsights/2012-edelman-trust-barometer-us-financial-services-and-banking-

industries . 10 Public Opinion Survey: Residents of Georgia (April 10-22, 2018). The sample consists of 1500 residents of

Georgia, representative of the general population by age, gender, region and size/type of settlement. For details, see

http://www.iri.org/sites/default/files/2018-5-29_georgia_poll_presentation.pdf .

25.96

3.45

0

5

10

15

20

25

30

35

0

5

10

15

20

25

30

35

40

45

MD

A

UKR BLR

ME

X

KAZ

AZE

ALB

PER

RU

S

UV

K

DEU

HU

N

RO

U

TUR

ARM SR

B

BG

R

FRA

LTU

MK

D

LVA

SVN

USA JPN

MN

E

GEO BIH

CZE

IRL

SV

K

HR

V

POL

EST

ISL

% T

ota

l Gro

ss L

oa

ns

% o

f G

DP

Households Outstanding Loans with Commercial Banks and Bank Non-Performing Loans to Total Gross Loans (2016)

Outstanding Loans with Commercial Banks (HHs) as % of GDP Bank nonperforming loans to total gross loans (%)

http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C



http://www.iri.org/sites/default/files/2018-5-29_georgia_poll_presentation.pdf

7


Source: The Caucasus Research Resource Centers. Caucasus Barometer, 2008 – 2017 Georgia. Retrieved through ODA

- http://caucasusbarometer.org on October 24, 2018 (left) and Baltic Surveys/The Gallup Organization, 2018 (right).

Trust in banks and financial system in Georgia is also low by international standard. The Life in

Transition Survey (LiTS III) allows international comparison on the level of trust towards banks and the

financial system. Trust varies significantly across regions, and Georgia is among the countries with

relatively high rates of “distrusts” (48 percent).

Figure 5: International Comparison of Perception Towards Bank – Selected Countries

Source: Author’s estimation using LiTS III (2014).

One of the causes for poor public perception of commercial banks may be the lack of debt relief policy

measures such as debt counselling, restructuring and personal insolvency framework as addressed

by FCI. FCI’s Individual Indebtedness Survey (IIS) reveals severe debt pressures among households with

over-indebtedness. Given the choice-based sampling frame adopted by the IIS, the sample does not provide

national representation of borrowing households in Georgia. Yet, IIS is a valuable source of information

5.62

6.48 8.65

4.89

14.74

10.667.3

16.96

30.45

16.54

25.0119.38

22.8920.91

16.54 14.3727.32

23.93

48.13

42.77

11.01

12.72 11.14

16.8

11.68

17.5521.79

16.86

5.03

20.2

12.9820.55

17.1122.45

28.8733.6

23.76 35.54

21.11

28.7

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% o

f P

op

ula

tio

n

Trust - banks and the financial system

Not applicable Don't know Complete distrust Some distrust Neither trust nor distrust Some trust Complete trust

8


that can help assess the type and degree of financial distress, households’ tendency for over-indebtedness

and its implication to debt traps.11

Excess indebtedness is a legitimate concern given its potential economic and social impact. However,

it is also important to assess the magnitude of the problem by assessing households’ borrowing

behavior and prevalence of indebtedness at the national level. If over-indebtedness associated with

severe debt pressure is truly widespread across nation, then establishing debt resolution processes may be

one of the urgent policy measures to maintain stable financial system. This note addresses this concern by

using nationally representative household survey to examine the prevalence of borrowing and how it varies

with observed characteristics at the national level. The note also tries to examine the causal impact of

indebtedness on household welfare by addressing issues of endogeneity.

Box 1: Survey Overview and Potential Bias of the Estimates

This note reveals that the IHS-based estimates differ substantially from the ones from the Individual Indebtedness

Survey. Among others, the difference comes from sample design, sample size, unit of collection, and the objectives

in conducting the surveys which is described briefly below.

Data Description of Georgia IHS

The data used for the analysis is the series of Georgia Integrated Household Survey (IHS) from 2011 to 2016

collected by the National Statistics Office of Georgia (Geostat), unless otherwise noted. The IHS is a nationally

representative household survey, whose stratification is based on 2002 census. It collects information on household

and individual’s socio demographic characteristics, as well as consumption using a 7-day diary, expenditures in the

last three months, and income from labor, social assistance, private transfers, and agricultural activities. It’s major

focus is to allow for distributional analysis on multiple topics based on income, consumption and wealth.

The survey estimates are made representative not only at the national level but also at the regional level, as

well as for urban and rural areas. Because regions with small number of population (Racha-Lechkhumi and Kvemo

Svaneti) were joined to an adjacent region, and two regions not under the control of the central government of

Georgia were omitted (Tskhinvali and Abkhazia AR), households are divided into 10 regions as specified in the

main report.

The sample is composed of roughly 11,000 observations per year comprising around 3000 households

interviewed four times throughout the year (one per quarter) to correct for seasonal bias. Households are replaced

by another randomly selected households from the same cluster after one cycle (household rotation). The survey is

structured as a rotating panel where households are visited in four consecutive quarters. Attrition rates are available

from the Geostat and in general, they range in levels acceptable for this type of surveys.

Drawbacks in the IHS sample design are the ones common to most household surveys. Most importantly,

although sample households are representative geographically based on stratified sampling, they are not necessarily

representative of households’ financial characteristics, which is the focus of this study. Ideally, if sufficient

information were available, the sample would use a design that minimized the expected sampling error for a

weighted combination of financial variables, where weights may reflect the relative importance of the variables of

interest.12 It is unclear whether the sample households overrepresents or underrepresents the households’ borrowing

behavior and financial position, as its comparison with national account shows mixed results (Table 1).

Data Description of Georgia Individual Indebtedness Survey

11 A. Prigozhina, et al., 2018. 12 Kennickell and McManus, 1993.

9


Individual Indebtedness Survey (IIS) used for the note, “Borrowing by Individuals: Capacity, Risks and Policy

Implication, Summary Note13” used choice-based sampling frame and are collected at the much smaller scale. It is

focused on individual’s borrowing behavior and has advantage in allowing in-depth analysis on capacity of

individual borrowers to manage their debt repayments and the characteristics of households with and without debt

by type of loans. About 4000 residents throughout Georgia were interviewed during October 2017 – January 2018

by Caucasus Research Resource Center (CRRC) under the World Bank Financial Deepening and Inclusion Project.

Out of 4000 residents, 3500 had current outstanding loans and about 500 had no current borrowing.

Micro-level data on financial access and usage is limited and only few surveys focus on this topic. This

survey is thus an important effort in improving our understanding of households’ indebtedness. These are the

only way to get detailed information on who uses which financial services from which types of institutions,

including informal ones.

However, major concern of using the survey is the possible bias introduced through choice-based sampling and

limited sample size. Samples were formed conditional on four choices: (1) currently have at least one loan from a

commercial bank but have no current loans from other financial sources; (2) individuals who currently have at least

one loan from any non-bank source in addition to commercial banks; (3) currently have at least one loan from a

non-bank but have no current loans from banks; and (4) individuals who currently have no loans. This entails over-

sampling of households with loans and the distribution of these three types of borrowers in the population is

unknown. Without correcting for weights that is validated against administrative data, the sample is likely to over-

represent certain types of borrowers. 14

It is important to note that both studies suffer from their own limitations – over-representatives of certain

types of borrowers in case of Indebtedness Survey, and potential under-representativeness of borrowers in case of

IHS since typical surveys fail to capture the subtle distributional properties at the very top of the distribution.

However, given that the sampling frame of IHS is based on census to assure representativeness at three levels

(national, regional and urban/rural) and designed to correct for seasonal bias with equal number of observations

for each quarter throughout a year, estimates based on IHS is expected to be more reliable with bias smaller in

magnitude.

Consistency between National Account and IHS Estimates

The comparison of survey data with data derived from administrative sources is a familiar approach in the

scientific literature.

Table 1 shows the ratio of households’ consumption, income, and amount borrowed from banks reported in

IHS against the data reported in national accounts (available from National Bank of Georgia and Geostat, as of

September 13, 2018).

Table 1: Comparison of Estimates by Data Source (2016)

13 Finance, Competitiveness & Innovation, The World Bank, 2018. 14 For details, see Methodological Report by Caucasus Research Resource Center, 2017.

(in millions, Lari)

Intergrated Household

Survey (IHS) External Source

Consumption* (in millions, Lari) 8710.0 21272.3 0.409

Income* (in millions, Lari) 11418.7 32340.8 0.353

Loan from commercial banks* (in millions, Lari) 555.7 2385.0 0.233

Number of Borrowing Households/Individuals

from Commercial Banks** (in millions) 0.2 1.4 0.116

Data Source

Ratio

(=Survey/External)

10


*External Sources for consumption, income and loan from commercial banks are reported figures from National Bank of

Georgia and Geostat, as of September 13, 2018.

**External Source for number of borrowers is Financial Access Survey (FAS), IMF (as of September 16, 2018).

http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C. IHS reports number of households while FAS refers to number of individuals.

The level of discrepancies between figures from survey and external source in consumption and income are not

surprising and common in other countries. For example, in Armenia, household expenditures in the survey

accounted for 37 percent of that in the national accounts and income around 40 percent15.

Table also shows that IHS captures 23.3 of the households’ loan from commercial banks against the loan

amount reported in external source. This ratio (i.e., total from household survey against total from external source)

is lower than the ratio for consumption and income. Larger downward bias in loan amount, compared to those in

consumption and income, is somewhat expected as household survey often fails to capture households at the top

end of the wealth distribution and positive correlation of loan amount and household wealth is anticipated in the

population.16

Number of borrowers captured in the survey is also low compared to that reported in external source (0.12).

The downward bias may be due to the difference in unit of analysis (being household in the survey and individual

in the external source), or non-observation bias (due to omission of wealthy households as mentioned above), or

mis- or under-reporting of borrowing behavior.

However, IHS estimates on prevalence of borrowing do resonate with those in the Caucasus Barometer, which

is representative of all population of ages 18 and over. The survey is collected annually about socio-economic

issues and political attitudes in the three South Caucasus countries: Armenia, Azerbaijan and Georgia. The project

started in 2004 and data is available since 2008 by the Caucasus Research Resource Centers (CRRC) (Annex).

Source: Inchauste and Lustig, eds., 2017.

Note: “NA” refers to national account.

Final consumption expenditures of households include expenditures for purchasing consumer goods and services and also

other consumption of goods and services in kind, produced for own use (available by quarter and annual). National account on

“commercial bank loans (excluding interbank loans) to households by loan purpose” includes other items such as “business

loans for large enterprises,” “business loans for SME,” “lombard loans,” and “other loans.”

Poverty and Prevalence of Borrowing

Financial development has a pronounced impact on changes in relative and absolute poverty with

disproportionate impact on the poor.17 But much remains to be learned about the channels through

which financial development affects income inequality and poverty reduction. Cross country studies

show that greater financial development induces the incomes of the poor to grow faster than average per

capita GDP growth, which lowers income inequality.18 This impact may come from direct access of the

poor to credit or indirectly through better financial access for nonpoor entrepreneurial households. 19

Relative importance of these channels on growth and poverty reduction may differ by country and needs

more in-depth research at the household level to derive effective policy implications.

15 Inchauste and Lustig, eds., 2017. 16 A study on savings behavior in Austria reports that underestimation of deposits due to undercoverage of most

affluent tail of the distribution can be relatively minor (Andreasch and Lindner, 2014). 17 Beck, et al., 2007. 18 Ibid. 19 The World Bank, 2009.

http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C

11


Analysis of financial access and indebtedness at the household level has been scarce. Most of the

empirical evidence has been at the country level. Having established the importance of financial

development at the macro-level, the next task is to go beyond the national level and focus on the level of

households and firms. One of the focuses of this note is to explore whether there is a risk for household

well-being to be worsened through increased debt burden from bank loans. This question is legitimate as

debt may be viewed as a welfare enhancing mechanism as well as potential channel to poverty trap when

used imprudently and excessively without institutional mechanisms for households to deal with debt

distress.

This section illustrates the pattern and levels of financial exposure of poor and non-poor households

in Georgia to formal and informal credits. This will contribute to the literature by providing evidence

of household indebtedness in Georgia at the micro level. By using the IHS, a nationally representative

survey, the section highlights the trend and extent to which households rely on different financial sources.

Over 40 percent of all households uses some type of financial services and the share has been

increasing over time for the poor and the non-poor (Figure 6), figure consistent with the estimates

from Caucasus Barometer (Appendix). These are households that either borrowed and/or repaid back to

the financial organizations within the past 3 months of the interview.20 Survey identifies two sources of

loans – (1) banks or other financial organizations, and (2) private persons. Without further details, (2)

private persons can include any informal sources, such as professional moneylenders, pawnbrokers,

tradespeople, and associations of acquaintances. Following analysis would thus interpret (1) as formal

banking sector and (2) as informal credit institutions.

Figure 6: Prevalence of Borrowing – 2011 – 2016 Trend

20 Households that had borrowed and/or repaid back only to banks are categorized as “bank only” borrowers.

Similarly for “private only” borrowers.

12


Source: Author’s calculation using Georgia IHS.

Note: Poor households are defined using per adult equivalent consumption aggregates and national poverty lines (125.9 and

137.13 GEL for years 2011 and 2016 respectively).

“Bank Only” refers to households that borrowed/repaid only to formal banks, and “Private Only” refers to those that only

borrowed/repaid to private source.

Commercial banks had become the major source of credit over time for both poor and non-poor

(Figure 7). The share of poor households borrowing from banks had increased significantly over time,

reversing the relative importance of formal vs. informal source since 2011. This is true for the households

in the richest quintile as well as for those in the poorest quintile.

Figure 7: Prevalence of Borrowing, by Quintile - Trend


36.43%

45.85%

33.07%

41.85%

0%

10%

20%

30%

40%

50%

2011 2012 2013 2014 2015 2016

% o

f H

ou

se

ho

lds

Share of Households Borrowing/Repaying with Any Type of Credit Source

Non Poor Poor

19.35%

10.97%

22.56%

15.02%

26.50%

17.12%

29.09%

19.64%

32.25%

23.77%

33.01%

24.57%

12.65%

18.50%

12.28%

18.89%

9.69%

15.73%

8.74%

16.00%

6.90%

11.22%

7.29%

11.51%

4.43%3.60%

6.19% 5.90%6.03%

8.37%5.50%

5.59%5.34%

5.64%

5.55%

5.77%

0%

10%

20%

30%

40%

50%

NonPoor

Poor NonPoor

Poor NonPoor

Poor NonPoor

Poor NonPoor

Poor NonPoor

Poor

2011 2012 2013 2014 2015 2016

% o

f H

ou

seh

old

s

Share of Credit Source

Bank Only Private Only Both

0%

10%

20%

30%

40%

50%

2011 2016 2011 2016 2011 2016

Any Source Bank Only Private Only

% o

f H

ou

seh

old

s

Share of Households that Borrowed, by Quintile

Poorest quintile Richest quintile

13


While formal and informal finance coexists, they are used as substitutes and not as compliments by

households. Interestingly, share of households that borrow from both sources is small (Figure 6).21 This is

understandable if credit contracts differ substantially between these two sectors and thus there is only very

limited inter-sector competition. Greater importance of informal private sources among the poor households

and vice versa among the non-poor reflects typical market failure stemming from imperfect information,

moral hazard as well as lack of collateral to prevent moral hazard.22

Focusing on the formal banking sector, the share of poor and vulnerable households in the bottom

two quintiles increased the most, with increase driven by the growing share of borrowers in the

bottom quintile. Poor are not over-represented among the bank borrowers (Appendix), but the increase in

the share had been the highest among the households in the bottom quintile (3.18 percent points) followed

by those in the second lowest quintile (1.18 pp) in contrast to the drop in its share among the richest quintile

(negative 4.37 pp).

Figure 8: Distribution of Households among Borrowers by Source


Note: Poor households are defined using per adult equivalent consumption aggregates and national poverty lines (125.9 and

137.13 GEL for years 2011 and 2016 respectively).

“Bank Borrowers” refers to households that borrowed only from banks, and “Private Borrowers” refers to those that only

borrowed from private source.

Borrowers are unevenly distributed throughout the regions, with largest share of borrowers in Tbilisi

in the formal credit market. The role of informal finance is diminishing and continues to serve rural

households. Figure 9 shows that borrowers are unevenly distributed throughout the regions. Most

concentrated region is Tbilisi followed by Imeriti for the formal banking, where the share had remained

stable over time. Although smaller in magnitude, the share of Samegreb has increased. These two regions

were identified as location with large growth potential in tourism, industry, and trade, and the World Bank

also supports multiple regional development projects.23

21 There is a huge variation in the pattern of borrowing across countries. Share of borrowers getting credit from both

formal and informal sources varies from 70 percent in India (Das-Gupta et al., 1989) to 13 percent in rural Thailand

(Giné, 2011) 22 Literature suggests that formal and informal finance coexist in markets with weak legal institutions and low levels

of income (Madestam, 2014). 23 The World Bank, 2015.

80.97 85.92

62.2474.2

19.03 14.08

37.7625.8

0%

20%

40%

60%

80%

100%

2011 2016 2011 2016

Bank Borrowers Private Borrowers

% o

f H

ou

se

ho

lds

Poverty

Non Poor Poor

9.41 12.5923.2 22.93

15.1516.33

20.22 19.98

33.38 29.0119.14 16.98

0%

20%

40%

60%

80%

100%

2011 2016 2011 2016


% o

f H

ou

seh

old

s

Quintile

Quintile Lowest 1 Quintile 2

Quintile 3 Quintile 4

Quintile Highest 5

14


Figure 9: Regional Distribution


Note: “Bank Borrowers” refers to households that borrowed only from banks, and “Private Borrowers” refers to those that

only borrowed from private source.

Lower regional borrowing rate is uncorrelated with regional poverty rate. Instead, for the formal

sector lending, there is a positive correlation between drop in poverty rate and increase in borrowing

rate between 2011 and 2016 (Figure 10). Negative correlation is observed for the informal banking.

From the supply side of credit, this indicates the strategic placement decision of formal banks based on

market potential and profitability. From the demand side, households seem to increasingly switch

borrowing channels from informal to formal source.

Figure 10: Correlation of Decline in Poverty Rates in Increase in Borrowing Rates

27.33 27.2

16.56

Tbi lisi, 12.6

2.75 2.495.89

Samtskhe-Javakheti, 14.07

10.1813.51

11.3

Samegrelo, 5.65

18.09 18.3119.08

Imereti, 26.84

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2011 2016 2011 2016


Regional Distribution of Borrowers

Kakheti Tbilisi Shida Kartli Kvemo Kartli

Samtskhe-Javakheti Ajara Guria Samegrelo

Imereti Mtskheta-Mtianeti

27.33 27.216.56 12.6

31.87 29.24

21.1323.11

40.8 43.56

62.31 64.29

0

20

40

60

80

100

2011 2016 2011 2016

Bank Borrowers PrivateBorrowers

% o

f H

ou

seh

old

s

Urban/Rural

Tbilisi Rest Urban Rural

15



Note: Size of the bubbles reflect the relative size of the population across regions.

Borrowing also varies by household type and its pattern remains constant over time with huge

parallel shift – upward shift for formal banking and downward shift for informal banking. In addition

to geographic variation, Figure 11 illustrates the borrowing rates by household type. Interestingly, the

patterns have shifted parallelly between 2011 and 2016 – households of all type increased borrowing from

formal banks and ceased from informal credits. For formal credit, higher rates are visible among the

following groups: larger households; households with educated heads; households whose heads are

married/living together; multiple member households; and families with children. Young households –

characterized either by having young head or with lower share of elderlies within a household – are groups

associated with higher borrowing rates. These are mostly not surprising and can be explained by need for

consumption smoothing in face of shocks or need for investment in human capital. Households with low

educated head may be one of the groups excluded from the formal credit market, while elderlies may be

associated with lower demand for credit (due to universal coverage and reasonable generosity of old-age

pension).

Figure 11: Share of Borrowing Households by Demographic Type (2011 and 2016)

Kakheti

Tbi lisiShida Kartli

Kvemo Kartl i

Samtskhe-Javakheti

AjaraGuria

Samegrelo

Imereti

Mtskheta-Mtianeti

y = 0.4802x + 0.0789R² = 0.201

0%

5%

10%

15%

20%

25%

30%

0% 5% 10% 15% 20% 25%

Incr

ease

in B

orr

ow

ing

Ra

te (

% o

f H

Hs)

Reduction in Poverty Rates (% of pop)

Changes in Poverty and Borrowing Rates, Bank Only(2011 - 2016)

d_borrowed_bankonly Linear (d_borrowed_bankonly)

Kakheti

Tbi lisi

Shida KartliKvemo Kartl i

Samtskhe-Javakheti

Ajara

Guria

Samegrelo

Imereti

Mtskheta-Mtianeti

y = -0.7555x + 0.0127

R² = 0.3069

-30%

-20%

-10%

0%

10%

20%

0% 5% 10% 15% 20% 25%

Incr

ea

se i

n B

orr

ow

ing

Ra

te (

% o

f H

Hs)

Reduction in Poverty Rates (% of pop)

Changes in Poverty and Borrowing Rates, Private Only(2011 - 2016)

d_borrowed_privonly Linear (d_borrowed_privonly)

16



Note: Differences are significant at 10 % significance level or lower for all the categories among bank only borrowers.

Differences are significant for selected classification for informal borrowers (share of 66+, age of head, single/multiple).

Indebtedness and Its Impact on Household Wellbeing – Regression Analysis

The objective of this section is to estimate the causal relationship between bank borrowing and

household’s welfare. There is a growing concern over households’ indebtedness and its effect on

household welfare in Georgia. Taking on debt can increase consumption beyond what one’s income can

support, it can smooth consumption in face of shocks and it can represent an investment in the future.

However, over indebtedness may result in significant financial distress, forcing households to be caught in

poverty trap.24 By drawing on lessons from the empirical literature on microcredit, the note tries to estimate

the causal impact of bank loans on household welfare. Policy implications – whether and how Government

should promote or repress financial intermediation – will be discussed at the end.

Credible evidence on whether bank loans can reduce poverty remains limited. The main reason for

this is the nonrandom nature of the borrowing practice. From the demand side, there is a concern for

self-selection bias which comes from unobserved household attributes (such as endowments of

entrepreneurial ability, innate health, and productivity). If household’s decision to borrowing is based on

unobservable attributes that simultaneously affect outcome, then estimates of the effect of bank loans will

be biased. Market imperfections – such as moral hazard and adverse selection that arise from serious

information asymmetries and enforcement problems – may lead to an unequal distribution of credit in favor

of the wealthy households.

There is also an endogeneity with respect to bank’s spatial distribution, or, placement bias, from the

supply side. Banks are expected to make strategic placement decisions based on specific features of

markets depending on their motivation – either areas with vibrant market potential for profitability or

relatively poor areas because of social concerns. Selection bias can go in either direction.

Drawing from the literature on microcredit and project evaluation, this paper uses interest rate

averaged over sample households in each location-year-season group as instrumental variables (IV)

24 Implications of indebtedness are described, for example, in Mannah-Blankson

0%

10%

20%

30%

40%

50%

HH

size

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ize

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oget

her

Div

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d/S

epar

ated

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(>=3

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n (0

-15)

HH

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h Ch

ildre

n (0

-15)

HH

wit

h C

hild

ren

& O

lder

Hea

dH

H w

ith

Child

ren

& Y

oun

g H

ead

HH size Gender Education ofHead

LF status of Head Marital Status ofHead

Age ofHead

Share of66+

withinHH

Single Childrenand Age of

Head

% o

f H

ou

seh

old

sPrevalence of Borrowing - Bank Only

Bank Only 2011 Bank Only 2016

0%

10%

20%

30%

HH

size

<=5

Larg

e H

H S

ize

(>=

6)

Mal

e H

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ec

Spec

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ec

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No

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gle

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Div

orce

d/Se

para

ted

Wid

ow/e

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er H

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(>=3

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(15

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-15)

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n (0

-15)

HH

wit

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ren

& O

lde

r H

ea

d

HH

wit

h C

hild

ren

& Y

ou

ng

Hea

d

HH size Gender Education of Head LF status of Head Marital Status ofHead

Age ofHead

Share of66+

withinHH

Single Childrenand Age of

Head

% o

f H

ou

seh

old

s

Prevalence of Borrowing - Private Only

Private Only 2011 Private Only 2016

17


to address the classis issues of endogeneity when using nonexperimental data to evaluate the effect of

bank loans on outcomes such as household welfare. To measure the effect of bank loan on household

welfare, we estimate a restricted welfare equation that conditions household’s per capita consumption

welfare on the household’s decision to take loans from the bank. Taking up the loan cannot be treated as

exogenous because households that apply and succeeded in obtaining loans may systematically differ from

those that do not apply for or applied but denied bank loans. Thus, the model comprises two stages in which

IV is used to estimate the first stage in modelling the decision to take the bank loan. The price of bank loans

– the average interest rate of the area in which household reside in specific quarter in a given year – is used

as an identifying instrument. By taking the average of the reported interest rates by the borrowing

households within each group, we can partial out the portion of interest rate that may be correlated with

household’s attributes known to lenders but unknown to researchers and treat it as exogenous to the

wellbeing of households. Details of the model and estimation strategy as well as literature review on the

methodologies are described in the Appendix.

Focusing on the formal banks, share of borrowing households has almost doubled from 2011 to 2016.

Table 1 presents the percentage of households that had borrowed from formal banks and the average per

capita consumption aggregate and its logarithm. As shown earlier, percentage of households taking bank

loans has increased steadily over the years by 2 – 3 percentage points from 2011 to 2015 slowing down to

less than 1 percentage points from 2015 to 2016.

Table 2: Weighted Mean and Standard Error of Per Capita Consumption Aggregates


Note: All values are weighted except for number of observations in the third column.

Descriptive statistics show that borrowing households consistently have higher per capita

consumption than non-borrowing households (Table 2). Table 2 provides some indication for the

relationship between household wealth and bank borrowing - average per capita consumption and its

logarithm are higher for borrowers across all years considered. The gaps are all statistically significant, with

null hypothesis that these mean differences are equal is rejected at the 0.00 significance level. However,

there are many possible factors generating these gaps. For example, borrowers are better off than non-

borrowers because banks strategically select wealthy households; households that chose to borrow may

also be different from those that chose not to borrow in their attributes including their entrepreneurial

abilities and prospects for the future. Combinations of demand and supply side factors are at play. To

disentangle causation from correlation, we turn to regression analysis addressing selection biases from both

demand and supply sides.

Year Household Type # Obs

% HH

Borrowing

Mean Per Cap

Consumption

Standard

Error

Mean Log Per

Cap

Consumption

Standard

Error

Non Borrower 9395 2321.97 23.38 7.46 0.008

Borrower 1811 3027.56 227.30 7.69 0.016

Non Borrower 9070 2513.48 25.26 7.55 0.008

Borrower 2195 2832.21 49.74 7.70 0.015

Non Borrower 8589 2707.78 26.15 7.65 0.008

Borrower 2502 3422.97 65.09 7.87 0.014

Non Borrower 8315 2854.79 28.53 7.71 0.008

Borrower 2843 3403.80 62.40 7.89 0.013

Non Borrower 7844 2898.92 29.75 7.73 0.008

Borrower 3155 3103.23 42.02 7.82 0.012

Non Borrower 7639 2908.77 28.03 7.73 0.008

Borrower 3219 3166.14 47.34 7.84 0.012

16.9

20.64

24.38

27.24

30.72

31.49

2011

2012

2013

2014

2015

2016

18


Empirical Results

Estimates show that there is no impact of bank loans on household’s well-being. Furthermore, size of

debt has negative impact on household’s wellbeing, if any. First, we estimate the impact of bank

borrowing on logarithm of per capita consumption.25 The first two columns in Table 3 report coefficients

from the OLS regressions controlling for household attributes as well as area-, seasonal- and year-specific

unobservables. Specification [2] also includes proximate of household’s cognitive ability expected to

capture attributes such as entrepreneurial ability, self-confidence, and aspirations for the future as an

attempt to minimize the selection bias. The naïve OLS estimates show that households with bank loans

consume 12.5 percent more than households without the loan (specification [1]) and 9.3 percent more when

controlling for the household’s cognitive skills (specification [2]). However, once we take into account the

selection bias by using IV methodology, the impact disappears – columns [3] and [4] show that the

coefficient becomes highly insignificant.26 Specification [4] includes debt level as regressors as well, which

indicates that the magnitude of debt relative to household income may have negative effect on household

consumption, although they are statistically insignificant.

Table 3: Model Results, Estimates of the Effect of Bank Loans on Log (Per Capita Consumption Aggregate)


Note: In specifications 2,3,4,5, bank loan dummy is treated as endogenous. Sample are restricted to households in years 2013-

2016 in specifications [2]-[4] due to availability of perception questionnaire. Perception variables are jointly significant at

0.00 significance level.

Moreover, estimates suggest that we cannot reject the hypothesis that higher indebtedness would

worsen the household welfare. Instead of using dummy for borrowing from the bank, this model uses

amount of unpaid debt to the banks (measured as borrowed amount minus repaid amount over household’s

total income) as the variable of interest. Here, log of per capita household consumption is regressed on the

ratio of unpaid debt to household’s total income in the past 3 months. 27 From the results reported in Table

25 A description of the independent variables with their mean and standard errors are reported in Table A1 in the appendix. 26 Endogeneity test is rejected with p-value of 0.00, suggesting that significant positive impact estimated in the OLS regression is

biased as expected. However, test results also indicate that the weak identification test cannot be rejected (for example, with F-

statistic equal to 0.38 in specification [4] and 0.378 in the last specification [5]). Weak instrument may be the result of

measurement error and how missings were treated in the dataset. Specifically, for households that did not borrow from the bank

in the past 3 months, value for this variable is missing. The average interest rates reported by the households in the same year –

region – location classification was assigned to the non-borrowers and borrowers with missing interest rates. 27 Because actual amount borrowed from banks and spent on repayment are available only for households that borrowed within

the past 3 months, households defined as “borrowers” are restricted compared to the first model where we defined households

that either borrowed or repaid in the past 3 months as “borrowers.”

[1] [2] [3] [4]

OLS OLS IV IV

Added HH Perception on Financial

state during the next 12 months and

Perception on Income Needed to Meet

HH's Need (GEL) as regressors

Same as [2] and used

IV

Same as [3] but added

relative size of debt to

total HH income as

regressors

Dummy=1 if borrowed/repaid to Bank

ONLY in the past 3 months 0.125*** 0.0930*** 13.20 12.51

(0.00640) (0.00731) (27.72) (23.98)

Degree of Indebtedness (Reference Level = 0)

Debt ( <50% of HH Total Income) -5.688

(11.45)

Debt ( 50 - 100 % of HH Total Income) -5.390

(10.97)

Debt (>= 100% of HH Total Income) -5.085

(10.49)

Number of observations 49,252 31,855 31,855 31,854

19


4, we find that higher unpaid debt ratio has positive correlation with per capita consumption when the debt

ratio is treated as exogenous (specification [1]) or when it is treated as endogenous but without controlling

for the set of household’s attributes that are assumed to be correlated with household’s entrepreneurship

and cognitive skills ([2]). However, once these household attributes are taken into account (specifications

[3], [4], [5]), the impact turns negative although statistically insignificant. Results again indicate that there

is tendency for better off households to borrow more, which lead to overestimate the impact of borrowing.

Table 4: Model Results, Estimates of the Effect of Unpaid Debt (in GEL) on Log (Per Capita Consumption Aggregate)


Indebtedness, as measured by the ratio of unpaid debt to household total income, has no impact, and

if any, will increase the household’s likelihood of being in poverty. Table 5 and Table 6 show estimates

from additional analyses by regressing household’s poverty status on the size of unpaid debt to banks.

Unpaid debt, or indebtedness, is measured by borrowed amount minus repaid amount over household’s

total income, all in the past 3 months. Estimates are all insignificant, but specifications controlling for

additional household characteristics ([3], [4], [5]) consistently show positive sign – that percent increase in

the ratio of unpaid debt over total income would increase the likelihood for households to be in

poor/vulnerable status.

Table 5: Model Results, Estimates of the Impact of Unpaid Debt on Poverty Status (1 if per adult equivalent is less than

national poverty line, 0 otherwise)


Table 6: Estimates of the Impact of Unpaid Debt on Likelihood of being in Bottom 40% (1 if HHs belong to the bottom 40%, 0

otherwise)

[1] [2] [3] [4] [5]

OLS IV IV IV IV

Added HH perception

on financial state during

the next 12 months as

regressors

Added GEL per month

needed to meet HH

need as regressors

Added two HH perceptions

as regressors

Ratio of Unpaid Debt to HH Income (=

(Borrowed - Repaid to Banks in the past 3

months) / Total Income in the past 3 months) 0.00904*** 4.228 -14.55 -8.814 -11.35

(0.00179) (14.54) (67.69) (26.43) (43.58)

Number of observations 49,251 49,251 31,854 31,854 31,854

[1] [2] [3] [4] [5]

OLS IV IV IV IV

Added HH perception



regressors

Added GEL per month

needed to meet HH

need as regressors


as regressors



months) / Total Income in the past 3 months) -4.88e-05 -1.105 5.573 3.425 4.350

(0.00116) (3.893) (25.93) (10.29) (16.72)


20



Conclusion

The note contributes to the literature by revealing the pattern of households’ borrowing behavior

and estimating causal impact of bank loan on household welfare at the micro level. However, there

are important limitations to the study that need further analyses.

First, Integrated Household Survey – nationally representative survey used in the note – does not

capture all debt from all possible sources. Moreover, impacts are restricted to marginal borrowers

and not inframarginal borrowers who borrowed before the reference period defined in the

questionnaire (past 3 months). This is a strength in the sense that marginal borrowers are the focus of

much theory, practice and policy. But it is a weakness in the sense that impacts on inframarginal borrowers

are key to understanding the overall impact of bank loans, and especially if credit market is potentially

saturated. Thus, more innovation is needed in combining data from different sources – from credit bureaus

and focus surveys with nationally representative household data - to disentangle the relationship between

poverty and indebtedness and to assess longer-term impact.

Second, it does not answer to the question on when and why households get into debt or too much

debt. By questionnaire design, the analysis falls short of capturing the magnitude of indebtedness beyond

past 3 months or the use of existing loans, which prevents us from pinning down the causes of possible

negative impact on household welfare. Only by using better data, we can test various hypothesis and identify

sources of struggle and distress that may possibly worsen the household welfare. How to define “too much

debt”, or over-indebtedness, is also a topic that may be revisited. 28

Third, the analysis is capable of providing additional possible policy measures that may influence

households’ borrowing behavior without providing concreate policy recommendations until further

data and assessments become available. Assistance to the financial sector and support for household debt

management have already been proposed by FCI and policies have been put in place or underway. However,

to have the overall picture, accurate assessments of market penetration, irresponsible lending practices,

over-indebtedness, households’ borrowing sensitivities to credit contracts, are among the few that needs to

be identified from supply side and demand side data.

Loan pricing is one of the measures that can be effective if done right based on extensive empirical

research. Given the dramatic increase in household-level credit and indication of increasing debt

stress, there is an urgent need to gather basic facts from the demand side, such as estimates of

households’ loan demand curve with respect to interest rate (in other words, households’ price

elasticity of demand for bank credit). Only with information on households’ sensitivity to interest rates,

28 For example, D’Alessio and Iezzi (2013) and Banerjee (2013).

[1] [2] [3] [4] [5]

OLS IV IV IV IV

Added HH perception



regressors

Added GEL per month

needed to meet HH

need as regressors


as regressors



months) / Total Income in the past 3 months) -0.00369*** -2.022 7.524 4.583 5.844

(0.00129) (6.997) (35.02) (13.76) (22.46)


21


policy makers can effectively design optimal rates in targeted markets. If there is heterogeneity in the price

elasticities, loan pricing can be used for targeting certain group of households. Loan maturity is also

considered as effective policy parameter that affects demand for credit.29

Market-based regulations, such as compulsory affordability assessments, establishment of credible

credit bureaus, and mechanisms to address adverse incentive are priorities to assess lending

environment from the supply side. From the supply side, there is a need to validate the magnitude of

reckless lending practices and if there is a sign of vicious cycle where increasingly irresponsible lending

leading to over-indebtedness of households at the national level. South Caucasus Barometer showed

deteriorating public perception towards banks, which may be an early indication of potential debt stress. As

increased debt stress can result in social unrest and political repercussions30, actions should be taken to

accurately assess the supply side risks and implement monitoring mechanisms at an early stage.

In addition to recognizing Georgia’s level of financial development by international standard,

assessing variation of financial development within Georgia – with higher market saturation in

particular geographic areas or with particular population groups – would also be a key in

implementing any policy measures (Box 2). Obtaining a systematic view of credit market and dynamics

within would be essential in designing regulatory and policy interventions tailored to Georgian context.

Overly restrictive or uniformly prescriptive regulatory environment should also be avoided if Georgia’s

financial development is identified as the expansion stage of credit market cycle.

Box 2: Variation of Accessibility within Georgia

As stated in the Access to Finance and Development: Theory and Measurement31, improving access and building

inclusive financial system is a goal that is relevant to economies at all levels of development. There is also empirical

evidence that show positive correlation between financial depth and poverty reduction - that better developed

financial systems experience faster drops in income inequality and faster reduction in poverty. 32 In many

developing countries, however, less than half of the population has access to formal financial services and in most

of Africa less than one in five households has access33.

Figure 12 provides a crude indication of geographic access or lack of physical barriers to access for selected

countries. First, it is clear that geographic access varies greatly across countries. Focusing on the branches of

commercial banks (Figure 12, left), density of branches relative to the population shows that among the peers,

Georgia has high rate of 32.7 per 100,000 population, which is equivalent to US at 32.6. On the contrary, Georgia

fairs low in terms of geographic distance (branches per 1,000 km2). Combined, indicators illustrate that branches

are not distributed equally across country but are clustered in cities and some large towns.34 As indicators may also

reflect the inclusiveness of the financial system, there are high variability in borrowing pattern across regions and

urban/rural as will be presented in the main text.

Financial services are provided also by the informal sector, such as credit unions and financial cooperatives (Figure

12, right). This channel appears to be extremely uncommon in Georgia as shown earlier in the main text. High

penetration of credit union in Poland can be explained by the fact that the country is a member of European Network

of Credit Unions, and Germany as the country to first establish the credit union in the 1850s.35 These figures

suggest that in Georgia, other, perhaps more informal intermediaries may be the important financial source.

29 Karlan, D. S. and J. Zinman (2008). 30 Davel, G. (2013). 31 Ibid. 32 Back, T., et al., 2009, 2007 and the World Bank, 2008. 33 Ibid. 34 Better measure would be the average distance from the household to the branch, but these data are available for

very few countries. 35 http://www.creditunionnetwork.eu/

22


Figure 12: Accessibility to Financial Services – Cross Country Comparison

Source: Financial Access Survey, IMF (as of September 16, 2018). http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-

598B5463A34C

0 5 10 15 20 25 30 35

HUNTURGEOALB

ARMROUCZE

DEUPOL

DEUHUNTURALBCZE

ARMROUPOLGEO

per

1,00

0 km

2pe

r 10

0,00

0 ad

ult

s

Bra

nch

es o

f co

mm

erci

al b

anks

Accessibility of Commercial Banks

0 5 10 15 20 25 30 35

GEOCZE

ROUTUR

HUNPOLDEU

CZE

GEOROUTUR

HUNDEUPOL

pe

r 1

,00

0 k

m2

pe

r 1

00

,00

0 a

du

lts

Bra

nch

es

of

cre

dit

un

ion

s an

d f

ina

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al

coo

pe

rati

ves

Accessibility of Credit Unions and Financial Cooperatives

23


References

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26


Appendix.

Figure 13: Poverty Rates – National and Regional

Source: Author’s calculation using Georgia IHS (left) and from World Bank 2018.

Note: Poverty rates are calculated using national consumption aggregates and national poverty lines of 125.9 and 137.13 GEL

(per adult equivalent per month) for years 2011 and 2016 respectively.

Figure 14: Non-Borrowers and All Borrowers (either from Bank or Private)


Note: Poverty rates are calculated using national consumption aggregates and national poverty lines of 125.9 and 137.13 GEL

for years 2011 and 2016 respectively. Analysis at the household level.

Appendix Figure 1: Households and Individuals with Bank Account or a Bank Card

32.50%

21.28%

29.31%

18.05%

0%5%

10%15%20%25%30%35%

Year2011

Year2016

Year2011

Year2016

Individual Level Household Level

% o

f P

op

ula

tio

n/H

ou

seh

old

s

National Poverty Rates

28.49%

17.59%

32.39%

20.60%

0%

5%

10%

15%

20%

25%

30%

35%

2011 2012 2013 2014 2015 2016

% o

f H

ou

seh

old

s

Share of Poor Households

Non-Borrowing HHs Borrowing HHs

27


Source: The Caucasus Research Resource Centers. Caucasus Barometer, Armenia and Georgia 2015 (left) and 2011 – 2017

Georgia (right). Retrieved through ODA - http://caucasusbarometer.org on October 24, 2018.

Appendix Figure 2: Households and Individuals with Debts

Source: The Caucasus Research Resource Centers. Caucasus Barometer, 2008 – 2010 Georgia (left) and 2011 – 2017 Georgia

(right). Retrieved through ODA - http://caucasusbarometer.org on October 25, 2018.

34

43 4146 46

66

57 5853 53

0

10

20

30

40

50

60

70

2011 2012 2013 2015 2017

DEBTPERS: Do you have any personal debts? (%)

Yes No DK/RA

28


Appendix 1. Loan-related variables in IHS

The survey questionnaires focused in the study are as follows from module labelled, “Shinda05”:

Q1_3: Please specify, how much did you borrow from a private person during the past three months

(GEL). (Write 0 if you did not borrow).

Q1_5: Please specify, how much credit did you obtain from the banks or other financial

organizations during the past three months (GEL). (Write 0 if you did not obtain any credit.)

Q1_9: Please specify, how much GEL did you spend on repayment of the debt to a private person

during the past three months. (Write 0 if you did not spend anything.)

Q1_10: Please specify, how much GEL did you spend on repayment of the bank credit during the

past three months. (Write 0 if you did not spend anything.)

Q1_5a: If you obtain a bank credit, please specify the interest rate. (Annual)

Appendix 2. Interest Rates

Figures are provided here to show the sample variation of interest rates used for the IV. Figure 15 shows

the distribution of interest rates reported by households on bank loans by year (response to question

Q1_5a).

Figure 15: Annual Interest Rates Reported by Households


For households with missing interest rates and for those who did not borrow, average interest rate within

the given year, region and urban/rural was assigned. The distribution of the average rates is shown below.

29



Appendix 3. Literature Review, Model Specification, Estimation Strategy and Data

Literature Review

At the macro-level, there is a large empirical body of literature that identifies positive correlation

between financial development, economic growth and poverty reduction using cross-country data.36

While theory provides conflicting views about the impacts of financial development on the economic

growth, inequality and poverty reduction37, there are ample empirical evidence demonstrating a strong,

positive link between financial development and economic growth at the macro level.38 Some studies

even show that the level of financial development is a good predictor of future economic development.39

More recent studies examine the causal relationship between financial development and poverty

reduction at the macro level, addressing the endogeneity associated with financial development.

Causality at the macro level using panels are reported in Boukhatem (2016), Uddin et al. (2014), cross-

sectional datasets in Rewilak (2017).

Macro-level evidence also exists in identifying causal impact of microfinance on poverty reduction.

The positive impact of microfinance has been reported in studies such as Burgess and Pande (2005),

Lopatta and Tchikov (2017), Miled and Rejeb (2015).

36 See for example, Levine (1997), The World Bank (2009), Beck, T. et al. (2009), Donou-Adonsou and Sylwester

(2016), Lombardi et al. (2017). 37 See Levine (1997) for a comprehensive review on theoretical and empirical analyses. 38 Seminal book by McKinnon (1973) studies the relationship between the financial system and economic

development in Argentina, Brazil, Chile, Germany, Korea, Indonesia, and Taiwan in the post-World War II period. 39 King and Levine (1993), for example, studies 80 countries over the period 1960-1989.

30


At the micro-level, empirical analyses have struggled to identify causal impact of microfinance on

poverty reduction. This is due to the well-known selection biases that can come from both the demand-

side and supply-side as described in Banerjee et al. (2015). Attempts were made for example by Pitt,

Rosenzweig, and Gibbons (1993), Pitt and Khandker (1998), McKernan (2002), Townsend (2011), Breza

and Kinnon (2018), and critical assessment of the methodology by Morduch (1998), Chemin (2008),

Chowdhury (2009), Duvendack and Palmer-Jones (2010), Roodman and Morduch (2013) among others.

Given the limits in using nonexperimental data to evaluate causal impact of microfinance at the

micro-level, researchers increasingly turned to randomized controlled trials. Recent studies include

six studies published in American Economic Journal: Applied Economics (2015)40, and Banerjee et al.

(2017), Coleman (1999). Quasi-experimental analyses based on treatment and control groups are also

conducted in countries such as India, Thailand and Malaysia.41

Acknowledging the virtue of randomization, this paper relies on instrumental variable (IV)

approach to address the causality. Due to the non-randomized nature of bank loans in our setting and

cross-sectional nature of the dataset, we use the interest rate each household reports in the survey as the

IV for taking up the bank loan. The analysis draws on the methodology adopted in the microfinance

literature.

The objective is to estimate the causal relationship between bank borrowing and poverty. There is a

growing concern over households’ indebtedness and its effect on household welfare in Georgia. Taking on

debt can increase consumption beyond what one’s income can support, it can smooth consumption in face

of shocks and it can represent an investment in the future. However, over indebtedness may result in

significant financial distress, forcing households to be caught in poverty trap.42 By drawing on lessons from

the empirical literature on microcredit, the note tries to address the causal impact of bank loans on household

welfare. Policy implications – whether and how Government should promote or repress financial

intermediation – will be discussed at the end.

Model

For the implementation of IV method, the first-stage equation for taking bank loans 𝐿𝑖𝑗 is,

𝐿𝑖𝑗 = 𝑋𝑖𝑗𝛽𝐿 + 𝑍𝑖𝑗 𝜋 + 𝜇𝑗𝐿 + 𝜖𝑖𝑗

𝐿 , (1)

where 𝐿𝑖𝑗 is a dummy for taking bank loans such that 𝐿𝑖𝑗 = 1 if a household i in area j either has borrowed

or repaid to banks (or both) in the past 3 months of the survey and 𝐿𝑖𝑗 = 0 otherwise, 43 𝑋𝑖𝑗 is a vector of

household characteristics (e.g., age, education, marital status, and labor market status of the household head

and household’s demographic features), 𝑍𝑖𝑗 is a set of household or village characteristics distinct from the

X’s in that they affect 𝐿𝑖𝑗 but not on other household behaviors conditional on 𝐿𝑖𝑗, 𝛽𝐿 and 𝜋 are unknown

parameters, 𝜇𝑗𝐿 is an unmeasured determinant of 𝐿𝑖𝑗 that is fixed within an area j, and 𝜖𝑖𝑗

𝐿 is a nonsystematic

40 Banerjee et al. (2015). 41 Coleman (1999) for Thailand, Samer et al. (2015) for Malaysia, 42 Implications of indebtedness are described, for example, in Mannah-Blankson 43 Ideally, 𝐿𝑖𝑗 would measure the stock amount of bank credit. However, dataset only has the amount of “flows”, i.e.,

the amount of bank loans that occurred in the past 3 months. which would highly underestimate the effect of

borrowing. In the later analysis, the model will be restricted to households that had borrowed within the past 3

months allowing the estimation of the impact of indebtedness (current bank loan less amounts repaid) on household

welfare status.

31


error term that reflects unmeasured determinants that vary over households such that 𝐸(𝜖𝑖𝑗𝐿 |𝑋𝑖𝑗 , 𝑍𝑖𝑗 , 𝜇𝑗

𝐿) =

0.

The conditional demand for outcome 𝑦𝑖𝑗 (such as household’s welfare) conditional on 𝐿𝑖𝑗 -whether the

household has a bank loan (or had borrowed from the bank in the past) is,

𝑦𝑖𝑗 = 𝑋𝑖𝑗𝛽𝑦 + 𝐿𝑖𝑗 𝛿 + 𝜇𝑗𝑦

+ 𝜖𝑖𝑗𝑦

, (2)

where 𝑦𝑖𝑗 is the continuous variable measuring household’s per capita consumption aggregates,44 𝛽𝑦 and 𝛿

are unknown parameters, 𝜇𝑗𝑦

is an unmeasured determinant of 𝑦𝑖𝑗 that is fixed within an area, and 𝜖𝑖𝑗𝑦

is a

nonsystematic error reflecting unmeasured determinants of 𝑦𝑖𝑗 that vary over households such that

𝐸(𝜖𝑖𝑗𝑦

|𝑋𝑖𝑗, 𝐿𝑖𝑗 , 𝜇𝑗𝑦

) = 0. The estimation issue arises as a result of the possible correlation of 𝜇𝑗𝐿 with 𝜇𝑗

𝑦and

of 𝜖𝑖𝑗𝐿 with 𝜖𝑖𝑗

𝑦. Econometric estimation that does not take these correlations into account may yield biased

estimates of the parameters in equation (2) due to endogeneity of taking bank loans, 𝐿𝑖𝑗 .

In the model set above, the exogenous regressors 𝑍𝑖𝑗 in equation (1) are the identifying instruments.

We apply the approach motivated by demand theory – that is, to use the price of the endogenous variable

as an identifying instrument. In our case, the most obvious measure of the price of bank loan is the interest

rate charged, which is available in the dataset. There is sufficient level of variation across the sample as

each household reports interest rate charged from the banks. Admittingly, using reported interest rate does

not entirely address the issue of endogeneity as it is likely that some of the variation in interest rates may

reflect unmeasured household attributes unknown to researchers but known to the lender and likely to be

part of the error term, 𝜖𝑖𝑗𝐿 , which violates the exclusion restriction of IV. Unfortunately, other variables that

gives exogenous variation to obtaining bank loans that can justifiably be used as an IV are difficult to find.

Without panel data on households before and after the availability of bank loans, interest rate is the best

candidate for our instrument to estimate 𝛿. However, we have tried to address this issue by taking the

average of reported interest rates within the area in which the household resides (urban/rural within a

specific region) in a specific quarter within a given year. By using the average of interest rates within the

specific location – season – year group, we can treat the interest rate as exogenous to household’s wellbeing

but correlated with the take-up of loans.

To control for other household specific attributes that might affect both outcome (household welfare) and

decision to borrow, included in 𝑋𝑖𝑗 ’s are household’s subjective views on well-being45 : household’s

perception on the changes in financial status in the past 12 months, and income needed in order to meet the

basic needs (in GEL). With the exception of question on income needed for basic needs, these are

categorical variables classified into 5 to 6 categories.

To control for area-specific unobservables, we use area-specific fixed-effects (FE) technique that

treats the area-specific errors 𝜇𝑗𝐿 and 𝜇𝑗

𝑦 as parameters to be estimated and thus control for area-specific

44 When both the behavioral outcome (in our case, borrowing from banks) and the outcome variable are measured as binary

indicators, identification of the behavioral impact is generally not possible (Pitt and Khandker, 1991). Thus, in our model

specification, the outcome variable 𝑦𝑖𝑗 is continuous. For robustness check when restricting the sample to borrowing households

within the past 3 months to focus on recent flow of borrowing, we may relax this restriction and use dichotomous indicator as the

outcome variable in the second stage. 45 Other perception variables were dropped due to weak statistical power (e.g., household’s perception on own economic status

given their income levels, expectations about the financial conditions in the next 12 months, and perception of the housing

condition).

32


unobservables.46 In our case, areas are defined by urban/rural divide within each of the nine regions which

is expected to control for nonrandom placement of bank branches. Due to the lack of village-level survey

or any variable at the village-level, no other village-level attributes are controlled for in the model

specification. Year and quarter dummies are included to control for year trend and seasonal bias.

Data

Data used for the analysis is the Georgia Integrated Household Survey (IHS) from 2011 to 2016 as described

in Box 2. For some specifications, years are restricted due to the availability of perception questions or use

restricted sample for robustness check.

Dependent variable is the logarithm of per capita consumption aggregate temporally and spatially adjusted.

As described earlier, our variable of interest 𝐿𝑖𝑗 is a dummy variable equal to one if the household either

has borrowed from the bank in the past 3 months and/or has repaid back to the bank in the past 3 months,

and zero otherwise. The reason for adopting this definition is that, there are quite a number of households

that had not borrowed but has reported to have been repaying back during the specified period (past 3

months from the survey date). Because actual amount borrowed and spent on repaying are available for

households that borrowed/repaid in the past 3 months, subsample of households is used in the later analysis

when estimating the effect of indebtedness.

Weighted Means and Standard Errors of Variables used for Regression Analysis – Household Attributes

Table A1: Weighted Means and Standard Errors of Variables

Household Attributes

46 Fixed-effect estimation with limited dependent variables raises the issue of consistency, which may become an

issue in the later section when we use household’s poverty status as the dependent variable. However, Heckman

(1981) provides Monte Carlo evidence that with eight or more observations per fixed-effects unit, the inconsistency

issue becomes relatively inconsequential. In our sample, as we use region-urban/rural as the fixed-effect unit with

more than eight observations in each, this would not become an issue.

33


Regression Results

N mean se(mean) N mean se(mean) N mean se(mean) N mean se(mean) N mean se(mean) N mean se(mean)

Age of Head 11206 59.67508 0.143129 11265 59.4418 0.142925 11091 59.42879 0.141119 11158 60.10503 0.138953 10999 60.08219 0.142577 10858 60.11582 0.139497

Age of Head Squared 11206 3790.661 16.72592 11265 3763.422 16.75217 11091 3752.634 16.49088 11158 3828.032 16.34006 10999 3833.438 16.79729 10858 3825.183 16.44114

HH Size 11206 3.617313 0.018542 11265 3.611596 0.017828 11091 3.598659 0.017946 11158 3.602532 0.018042 10999 3.593326 0.018037 10858 3.542118 0.01785

Dummy Female HH head 11206 0.352638 0.004514 11265 0.346275 0.004483 11091 0.331822 0.004471 11158 0.334463 0.004467 10999 0.330326 0.004485 10858 0.337364 0.004538

Share of 0-6 y.o. within HH 11206 0.057532 0.001107 11265 0.063975 0.001157 11091 0.062187 0.001166 11158 0.061142 0.001161 10999 0.06336 0.001201 10858 0.064534 0.001228

Share of 7-15 y.o. within HH 11206 0.077349 0.001364 11265 0.076189 0.001332 11091 0.07407 0.001327 11158 0.078196 0.001364 10999 0.07677 0.001375 10858 0.074652 0.001351

Share of 66+ y.o. within HH 11206 0.231759 0.003264 11265 0.220503 0.003175 11091 0.21574 0.003147 11158 0.225208 0.003168 10999 0.225853 0.003206 10858 0.219755 0.003171

Dummy Own House 11200 0.916231 0.002618 11256 0.9213 0.002538 11091 0.933507 0.002366 11158 0.936886 0.002302 10999 0.931385 0.002411 10858 0.944437 0.002198

Living Space (in m2) 10382 73.76712 0.423923 10111 77.46922 0.442137 9876 78.44082 0.456775 9731 80.77607 0.453114 9685 83.37673 0.482721 9610 84.49064 0.51069

Whole Space (in m2) 10378 113.4254 0.672571 10105 117.9554 0.693822 9839 122.4599 0.745511 9766 126.1546 0.778555 9705 126.2781 0.761612 9607 125.2065 0.770879

Cultivation Area (in hectar) 11206 0.300721 0.008994 11265 0.353874 0.016531 11091 0.361385 0.010581 11158 0.383121 0.009664 10999 0.388766 0.013973 10858 0.392542 0.012074

Dummy HH has

car/motorcycle/truck/tractor 10937 0.265596 0.004223 11123 0.300055 0.004346 11049 0.30873 0.004395 11115 0.333664 0.004473 10952 0.375643 0.004628 10829 0.388045 0.004683

Dummy HH has TV/PC 10937 0.965597 0.001743 11123 0.970854 0.001595 11049 0.975742 0.001464 11115 0.984606 0.001168 10952 0.985249 0.001152 10829 0.985458 0.00115

Dummy HH has heating device

(heater/ac/gas elec stove) 10937 0.674946 0.004479 11123 0.748284 0.004115 11049 0.826935 0.003599 11115 0.864174 0.00325 10952 0.893973 0.002942 10829 0.916905 0.002653

Dummy HH has basic electronics

(fridge/washing mach/vacuum) 10937 0.711976 0.00433 11123 0.773727 0.003968 11049 0.821247 0.003645 11115 0.859799 0.003293 10952 0.89991 0.002868 10829 0.920974 0.002593

Number of mobile phone per HH 10937 1.35813 0.011688 11123 1.547518 0.011361 11049 1.754752 0.012046 11115 1.952687 0.012579 10952 2.131029 0.01308 10829 2.138687 0.012562

Quantity of cow/bull/baffalo/cattle 11206 0.746494 0.01437 11265 0.797531 0.015652 11091 0.842863 0.016903 11158 0.000877 0.000828 10999 1.06575 0.028653 10858 1.042387 0.029578

Quantity of donkey/horse 11206 0.034067 0.001962 11265 0.035238 0.002126 11091 0.02967 0.001805 11158 0 0 10999 0.034175 0.00233 10858 0.044863 0.003889

Quantitiy of poultry 11206 5.0461 0.088788 11265 4.589345 0.080846 11091 5.049869 0.090971 11158 0.005818 0.002533 10999 5.776831 0.109149 10858 5.311808 0.097032

Quantitiy of other

(pig/sheep/goat/rabit/bee) 11206 0.629364 0.036368 11265 0.568698 0.04376 11091 0.784241 0.077307 11158 0.001162 0.000756 10999 2.813378 1.05981 10858 1.976237 0.727561

Dummy =1 if HH received retirement

pension in past 3 mon 9873 0.55524 0.005002 10181 0.535786 0.004943 9774 0.550475 0.005032 9736 0.588787 0.004987 9116 0.623623 0.005075 8849 0.630358 0.005132

Dummy =1 if HH received assistance

for socially vul fam in past 3 mon 9873 0.147972 0.003574 10181 0.141347 0.003453 9774 0.153992 0.003651 9736 0.135621 0.00347 9116 0.126108 0.003477 8849 0.117833 0.003428

Incomplete 5-12 686 522 705 436 332 259

General Sec 5,050 5,082 4,424 5,158 5,308 5,208

Special Sec 2,655 2,783 2,782 2,837 2,709 2,635

Tertiary 2,487 2,574 2,498 2,535 2,550 2,660

None/<primary 328 304 463 192 99 93

Employee 2,619 2,829 2,647 2,650 2,717 2,728

Self Employed 4,653 4,640 4,768 4,898 4,745 4,658

Unemployed 394 454 423 343 321 355

Retired 2,533 2,373 2,322 2,350 2,348 2,209

OLF 777 765 930 749 725 741

Married 6,834 6,898 6,989 6,813 6,690 6,639

Single 634 550 528 584 623 593

Living together 80 90 153 132 149

Divorced/Separated 339 347 358 337 332 387

Widow/er 3,319 3,380 3,216 3,271 3,222 3,090

belongs to the household only 9,444 9,487 9,620 10,003 9,972 9,768

share 63 49 29 25 40 56

does not have 1,698 1,729 1,442 1,130 987 1,034

belongs to the household only 5,835 6,059 6,275 6,697 7,077 7,282

share 103 83 55 41 54 83

does not have 5,267 5,123 4,761 4,420 3,868 3,493

Tbilisi 2,018 1,919 1,882 1,952 1,926 1,869

Rest Urban 2,394 2,486 2,379 2,417 2,390 2,359

Rural 6,794 6,860 6,830 6,789 6,683 6,630

Kakheti 1,421 1,384 1,365 1,357 1,334 1,351

Tbilisi 2,018 1,919 1,882 1,952 1,926 1,869

Shida Kartli 887 876 886 866 849 846

Kvemo Kartli 1,203 1,204 1,183 1,225 1,224 1,234

Samtskhe-Javakheti 687 695 669 693 675 670

Ajara 862 891 855 864 845 838

Guria 660 706 694 672 676 672

Samegrelo 1,070 1,037 1,070 1,076 1,067 1,045

Imereti 1,851 1,972 1,916 1,894 1,854 1,788

Mtskheta-Mtianeti 547 581 571 559 549 545

1 2,740 2,855 2,748 2,793 2,748 2,712

2 2,789 2,832 2,792 2,794 2,741 2,742

3 2,807 2,796 2,765 2,804 2,764 2,691

4 2,870 2,782 2,786 2,767 2,746 2,713

1.Good 48 70 45 45

2. Middle 1,124 1,113 1,110 1,051

3. Satisfactory 3,459 3,800 3,910 3,908

4. Bad 4,828 4,786 4,731 4,678

5. Very Bad 1,632 1,389 1,203 1,176

1. Has worsened very much 1,302 729 1,269 1,817

2. Has slightly worsened 2,124 2,148 2,729 2,793

3. Has not changed 6,475 6,814 6,122 5,581

4. Has slightly improved 1,163 1,415 818 615

5. Has improved very much 27 52 60 52

1. Will be worsened very much 192 183 322 407

2. Will be worsened slightly 359 358 700 694

3. Won't be changed 2,088 2,467 2,758 2,736

4. Will be improved slightly 1,911 1,462 987 882

5. Will be improved very much 227 207 168 122

6. Don't know 6,314 6,481 6,064 6,017

1. Well-repaired 409 366 338 369

2. Does not need any repairing at this stage 975 1,045 1,159 1,202

3. Needs cosmetic repairing 3,651 3,971 4,087 4,021

4. Needs overall repairing 5,566 5,334 5,058 4,954

5. If not immediately repaired, it will be ruined. 490 442 357 312

< 500 1,942 1,476 1,234 976

500 <= GEL < 1000 4,348 4,427 4,207 3,894

1000 <= GEL < 2000 3,642 3,975 4,030 4,245

2000 <= GEL < 3000 892 947 1,033 1,240

3000 <= GEL < 20000 267 333 495 503

2016

GEL per month

needed to meet HH

need

2011 2012 2013 2014 2015

HH's Perception on

Economic Status

HH's Perception:

how has the

financial state

changed during the

last 12 months?

HH's Perception:

Expectation on your

financial state during

the next 12 months?

HH's Perception:

Housing condition

Education of HH

head

LF Status of HH

head

Marital Status of

HH head

Kitchen condition

Bath condition

Location

HH Demographics

Assets

Social Benefit

Region

Quarter

34


Table A2: Regression Results – Impact of Having had Bank Loans on Log (Per Capita Consumption

Aggregate)

35


(1) (2) (3) (4) (5)

VARIABLES Log of gallT in 2011 PPP Log of gallT in 2011 PPP Log of gallT in 2011 PPP Log of gallT in 2011 PPP Log of gallT in 2011 PPP

Dummy=1 if borrowed/repaid to Bank ONLY in the past 3 months 0.125*** -1.866 7.754 8.913 8.801

(0.00640) (1.268) (11.12) (14.63) (14.31)

age_head -0.0148*** -0.0199*** 0.0116 0.0121 0.0113

(0.00131) (0.00397) (0.0386) (0.0434) (0.0411)

age_head2 0.000131*** 0.000142*** 4.08e-05 5.91e-05 6.36e-05

(1.14e-05) (2.08e-05) (0.000144) (0.000129) (0.000119)

Edu attainment of HH head = 2, General Sec -0.0172 -0.0127 -0.0217 -0.0206 -0.0167

(0.0140) (0.0242) (0.110) (0.126) (0.122)

Edu attainment of HH head = 3, Special Sec 0.0145 0.0620 -0.199 -0.214 -0.205

(0.0148) (0.0395) (0.355) (0.417) (0.398)

Edu attainment of HH head = 4, Tertiary 0.152*** 0.176*** 0.0522 0.101 0.109

(0.0152) (0.0303) (0.198) (0.142) (0.134)

Edu attainment of HH head = 5, None/<primary 0.0716*** 0.0623 0.0150 0.0359 0.0328

(0.0223) (0.0388) (0.198) (0.203) (0.203)

LF status of HH head = 2, Self Employed -0.0597*** -0.232** 0.638 0.685 0.672

(0.00743) (0.110) (1.046) (1.235) (1.195)

LF status of HH head = 3, Unemployed -0.306*** -0.522*** 0.538 0.608 0.596

(0.0139) (0.140) (1.219) (1.476) (1.435)

LF status of HH head = 4, Retired -0.202*** -0.345*** 0.476 0.512 0.497

(0.00929) (0.0924) (0.997) (1.155) (1.110)

LF status of HH head = 6, OLF -0.177*** -0.404*** 0.773 0.859 0.845

(0.0108) (0.146) (1.393) (1.700) (1.657)

Marital status of HH head = 2, Single 0.0790*** 0.0111 0.450 0.458 0.454

(0.0131) (0.0488) (0.557) (0.626) (0.612)

Marital status of HH head = 3, Living together -0.0693** -0.0330 0.00198 -0.0688 -0.0667

(0.0319) (0.0595) (0.265) (0.249) (0.246)

Marital status of HH head = 4, Divorced/Separated -0.0940*** -0.127*** 0.275 0.296 0.289

(0.0159) (0.0346) (0.552) (0.646) (0.625)

Marital status of HH head = 5, Widow/er -0.0326*** -0.0178 0.0263 0.0158 0.0172

(0.00927) (0.0185) (0.101) (0.100) (0.101)

Household size -0.172*** -0.133*** -0.321 -0.303 -0.301*

(0.00232) (0.0250) (0.212) (0.185) (0.181)

Dummy=1 if Head & Female -0.00172 -0.00346 -0.0829 -0.0913 -0.0950

(0.00842) (0.0145) (0.116) (0.143) (0.150)

share of 0-6 y.o. -0.122*** -0.145*** -0.108 -0.156 -0.148

(0.0272) (0.0491) (0.195) (0.266) (0.248)

share of 7-15 y.o. -0.101*** -0.187*** 0.231 0.216 0.224

(0.0217) (0.0660) (0.525) (0.522) (0.545)

share of 66+ y.o. 0.0956*** 0.0108 0.377 0.354 0.345

(0.0124) (0.0581) (0.435) (0.436) (0.414)

ownhouse -0.0525*** -0.0713*** 0.0301 0.0350 0.0196

(0.0115) (0.0231) (0.161) (0.185) (0.156)

living space (space occupied by the household, in m2) 0.000831*** -9.07e-05 0.00660 0.00746 0.00738

(0.000111) (0.000617) (0.00816) (0.0107) (0.0105)

whole space (space occupied by the household, in m2) 0.000277*** 0.000868** -0.00314 -0.00356 -0.00352

(6.96e-05) (0.000395) (0.00474) (0.00604) (0.00598)

kitchen (how comfortable is the apartment (choose one answer per each)) -0.0310*** -0.0437*** 0.0284 0.0411 0.0368

(0.00505) (0.0119) (0.0897) (0.124) (0.115)

bath (how comfortable is the apartment (choose one answer per each)) -0.0425*** -0.0529*** -0.0212 -0.0257 -0.0262

(0.00362) (0.00908) (0.0419) (0.0415) (0.0395)

what is the overall area used for cultivation (including leased land)? in hectar 0.0115*** 0.0127*** 0.0273 0.0258 0.0275

(0.00193) (0.00340) (0.0200) (0.0197) (0.0219)

HH has car/motorcycle/truck/tractor 0.245*** 0.319*** -0.00618 0.0113 0.0197

(0.00634) (0.0484) (0.394) (0.380) (0.358)

HH has TV/PC 0.119*** 0.147*** 0.136 0.153 0.159

(0.0172) (0.0345) (0.141) (0.167) (0.171)

HH has heating device (heater/ac/gas elec stove) 0.0512*** 0.0971*** -0.0736 -0.0881 -0.0775

(0.00761) (0.0320) (0.188) (0.232) (0.211)

HH has basic electronics (fridge/washing mach/vacuum) 0.140*** 0.178*** -0.0540 -0.0583 -0.0552

(0.00789) (0.0277) (0.275) (0.315) (0.305)

Number of mobile phone per HH 0.0990*** 0.182*** -0.245 -0.254 -0.248

(0.00289) (0.0530) (0.490) (0.540) (0.524)

Quantity of cow/bull/baffalo/cattle 0.0297*** 0.0190*** 0.0500 0.0524 0.0534

(0.00162) (0.00736) (0.0433) (0.0516) (0.0537)

Quantity of donkey/horse -0.0105 0.0257 -0.192 -0.215 -0.214

(0.0106) (0.0294) (0.305) (0.384) (0.377)

Quantitiy of poultry 0.00375*** 0.00604*** -0.00379 -0.00544 -0.00532

(0.000324) (0.00156) (0.00970) (0.0145) (0.0141)

Quantitiy of other (pig/sheep/goat/rabit/bee) -1.53e-05 -3.39e-06 -6.83e-05 -9.03e-05 -9.41e-05

(8.85e-05) (0.000153) (0.000525) (0.000615) (0.000610)

Dummy =1 if HH received retirement pension in past 3 mon 0.0429*** 0.0435*** 0.0289 0.0351 0.0355

(0.00774) (0.0133) (0.0697) (0.0722) (0.0720)

Dummy =1 if HH received assistance for socially vul fam in past 3 mon -0.159*** -0.255*** 0.297 0.348 0.344

(0.00848) (0.0632) (0.650) (0.818) (0.806)

quarter = 2 -0.0756*** -0.0733*** -0.0590 -0.0498 -0.0507

(0.00714) (0.0124) (0.0520) (0.0670) (0.0652)

quarter = 3 -0.0617*** -0.0485*** -0.180 -0.192 -0.192

(0.00730) (0.0151) (0.159) (0.200) (0.199)

quarter = 4 -0.00911 0.0221 -0.136 -0.140 -0.141

(0.00727) (0.0235) (0.162) (0.186) (0.187)

Survey year = 2014 0.153*** 0.293*** -0.155 -0.170 -0.174

(0.00940) (0.0905) (0.306) (0.368) (0.375)

Survey year = 2015 0.0340*** 0.211* -0.410 -0.421 -0.432

(0.00942) (0.114) (0.534) (0.594) (0.623)

Survey year = 2016 0.0436*** 0.235* -0.459 -0.457 -0.473

(0.00948) (0.123) (0.619) (0.655) (0.697)

Urban (1) or rural (0) = 1, Urban -0.0150* 0.0683 -0.422 -0.442 -0.442

(0.00781) (0.0547) (0.628) (0.728) (0.721)

Region number = 1, Tbilisi 0.177*** 0.0847 0.686 0.925 0.918

(0.0116) (0.0619) (0.718) (1.344) (1.315)

Region number = 2, Shida Kartli 0.0535*** 0.368* -1.128 -1.292 -1.281

(0.0141) (0.202) (1.717) (2.221) (2.188)

Region number = 3, Kvemo Kartli -0.0447*** -0.0222 0.0306 0.101 0.0903

(0.0127) (0.0261) (0.0982) (0.243) (0.211)

Region number = 5, Samtskhe-Javakheti 0.274*** 0.145* 1.019 1.158 1.162

(0.0148) (0.0858) (1.089) (1.492) (1.493)

Region number = 7, Ajara 0.108*** 0.0686** 0.473 0.521 0.501

(0.0131) (0.0337) (0.527) (0.668) (0.625)

Region number = 8, Guria 0.0685*** 0.0203 0.396 0.369 0.377

(0.0157) (0.0409) (0.503) (0.491) (0.504)

Region number = 9, Samegrelo 0.104*** 0.370** -1.066 -1.174 -1.158

(0.0115) (0.170) (1.707) (2.080) (2.037)

Region number = 10, Imereti 0.150*** 0.204*** 0.0247 0.0432 0.0501

(0.0103) (0.0389) (0.208) (0.182) (0.169)

Region number = 11, Mtskheta-Mtianeti -0.0853*** -0.0752** -0.124 -0.0849 -0.0686

(0.0185) (0.0325) (0.130) (0.172) (0.191)

Redefined expectation = 2, Same 0.0918 0.0972

(0.0779) (0.0910)

Redefined expectation = 3, Improved slightly/very much -0.0942 -0.142

(0.351) (0.482)

Redefined expectation = 4, Don't know 0.0245 0.0117

(0.120) (0.156)

Survey year = 2012 0.0339*** 0.0699**

(0.00879) (0.0275)

Survey year = 2013 0.0932*** 0.182***

(0.00898) (0.0583)

hhpercp_need = 2 -0.161 -0.164

(0.302) (0.306)

hhpercp_need = 3 -0.530 -0.531

(1.147) (1.142)

hhpercp_need = 4 -0.779 -0.777

(1.792) (1.777)

hhpercp_need = 5 -1.092 -1.081

(2.585) (2.547)

Constant 8.080*** 8.446*** 6.007* 5.831 5.882

(0.0460) (0.246) (3.077) (3.943) (3.647)

Observations 49,252 49,252 31,855 31,855 31,855

R-squared 0.369 -0.871 -20.955 -27.821 -27.101

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

36


Table A3: Regression Results – Impact of Unpaid Debt on Log (Per Capita Consumption Aggregate)

37


(1) (2) (3) (4) (5)

VARIABLES Log of gallT in 2011 PPP Log of gallT in 2011 PPP Log of gallT in 2011 PPP Log of gallT in 2011 PPP Log of gallT in 2011 PPP

indebt_bank 0.00903*** 0.378* -2.624 -2.403 -2.522

(0.00179) (0.204) (2.784) (2.346) (2.582)

age_head -0.0152*** -0.0177*** -0.00740 -0.00703 -0.00601

(0.00131) (0.00226) (0.0103) (0.00892) (0.00982)

age_head2 0.000132*** 0.000154*** 7.09e-05 6.69e-05 5.86e-05

(1.15e-05) (1.97e-05) (8.20e-05) (7.15e-05) (7.83e-05)

Edu attainment of HH head = 2, General Sec -0.0164 0.00401 0.0291 0.0296 0.0259

(0.0140) (0.0222) (0.0822) (0.0753) (0.0782)

Edu attainment of HH head = 3, Special Sec 0.0179 0.0361 0.0584 0.0474 0.0451

(0.0148) (0.0226) (0.0844) (0.0770) (0.0802)

Edu attainment of HH head = 4, Tertiary 0.154*** 0.176*** 0.232** 0.200** 0.197*

(0.0153) (0.0240) (0.108) (0.0977) (0.102)

Edu attainment of HH head = 5, None/<primary 0.0716*** 0.0967*** 0.0709 0.0543 0.0600

(0.0224) (0.0335) (0.125) (0.116) (0.121)

LF status of HH head = 2, Self Employed -0.0708*** -0.0851*** -0.0242 -0.00643 0.00496

(0.00743) (0.0128) (0.0804) (0.0690) (0.0785)

LF status of HH head = 3, Unemployed -0.319*** -0.309*** 0.0477 0.0338 0.0595

(0.0140) (0.0198) (0.386) (0.322) (0.358)

LF status of HH head = 4, Retired -0.212*** -0.226*** -0.133 -0.115 -0.101

(0.00931) (0.0150) (0.103) (0.0860) (0.0972)

LF status of HH head = 6, OLF -0.191*** -0.184*** -0.117 -0.0947 -0.0874

(0.0108) (0.0153) (0.102) (0.0934) (0.102)

Marital status of HH head = 2, Single 0.0752*** 0.0801*** 0.165 0.176 0.180

(0.0132) (0.0181) (0.124) (0.111) (0.119)

Marital status of HH head = 3, Living together -0.0673** -0.0791* -0.0697 -0.0311 -0.0286

(0.0320) (0.0441) (0.165) (0.152) (0.160)

Marital status of HH head = 4, Divorced/Separated -0.0964*** -0.114*** -0.0119 -0.00909 -0.000228

(0.0159) (0.0239) (0.126) (0.109) (0.119)

Marital status of HH head = 5, Widow/er -0.0316*** -0.0346*** 0.00708 0.00830 0.00863

(0.00930) (0.0128) (0.0605) (0.0550) (0.0580)

Household size -0.170*** -0.171*** -0.177*** -0.196*** -0.195***

(0.00232) (0.00325) (0.0130) (0.0127) (0.0132)

Dummy=1 if Head & Female -0.00196 -0.00317 -0.0584 -0.0544 -0.0530

(0.00845) (0.0115) (0.0645) (0.0560) (0.0589)

share of 0-6 y.o. -0.123*** -0.106*** -0.207 -0.169 -0.190

(0.0273) (0.0383) (0.191) (0.165) (0.180)

share of 7-15 y.o. -0.106*** -0.0921*** 0.0174 0.0462 0.0355

(0.0218) (0.0307) (0.185) (0.164) (0.172)

share of 66+ y.o. 0.0903*** 0.0905*** 0.0505 0.0756 0.0760

(0.0125) (0.0170) (0.0720) (0.0634) (0.0669)

ownhouse -0.0535*** -0.0466*** 0.00844 0.00543 0.0175

(0.0115) (0.0162) (0.101) (0.0872) (0.0963)

living space (space occupied by the household, in m2) 0.000776*** 0.000894*** 0.000136 0.000329 0.000192

(0.000112) (0.000166) (0.00102) (0.000823) (0.000922)

whole space (space occupied by the household, in m2) 0.000314*** 0.000285*** 0.000315 0.000225 0.000281

(6.98e-05) (9.65e-05) (0.000391) (0.000343) (0.000368)

kitchen (how comfortable is the apartment (choose one answer per each))-0.0316*** -0.0307*** -0.0441 -0.0444 -0.0436

(0.00507) (0.00693) (0.0339) (0.0306) (0.0322)

bath (how comfortable is the apartment (choose one answer per each))-0.0431*** -0.0427*** -0.0500** -0.0476*** -0.0461**

(0.00363) (0.00495) (0.0200) (0.0180) (0.0187)

what is the overall area used for cultivation (including leased land)? in hectar0.0116*** 0.0107*** -0.00333 0.000692 -0.00179

(0.00194) (0.00269) (0.0247) (0.0207) (0.0233)

HH has car/motorcycle/truck/tractor 0.250*** 0.245*** 0.357*** 0.325*** 0.323***

(0.00636) (0.00908) (0.101) (0.0873) (0.0926)

HH has TV/PC 0.121*** 0.115*** 0.162 0.162 0.153

(0.0173) (0.0238) (0.112) (0.102) (0.106)

HH has heating device (heater/ac/gas elec stove) 0.0541*** 0.0580*** 0.0416 0.0414 0.0368

(0.00764) (0.0106) (0.0458) (0.0417) (0.0440)

HH has basic electronics (fridge/washing mach/vacuum) 0.142*** 0.143*** 0.157*** 0.149*** 0.147***

(0.00792) (0.0108) (0.0523) (0.0468) (0.0485)

Number of mobile phone per HH 0.104*** 0.108*** 0.0627 0.0456 0.0419

(0.00288) (0.00447) (0.0384) (0.0319) (0.0360)

Quantity of cow/bull/baffalo/cattle 0.0290*** 0.0291*** 0.0225*** 0.0246*** 0.0233***

(0.00163) (0.00222) (0.00810) (0.00771) (0.00787)

Quantity of donkey/horse -0.00815 -0.00472 -0.00235 -0.00243 -0.00335

(0.0106) (0.0146) (0.0558) (0.0509) (0.0534)

Quantitiy of poultry 0.00389*** 0.00401*** 0.00283* 0.00327** 0.00321**

(0.000325) (0.000448) (0.00167) (0.00154) (0.00161)

Quantitiy of other (pig/sheep/goat/rabit/bee) -1.43e-05 -4.34e-06 -4.59e-05 -3.97e-05 -3.69e-05

(8.89e-05) (0.000121) (0.000383) (0.000353) (0.000369)

Dummy =1 if HH received retirement pension in past 3 mon 0.0429*** 0.0439*** 0.0479 0.0420 0.0439

(0.00776) (0.0106) (0.0426) (0.0403) (0.0418)

Dummy =1 if HH received assistance for socially vul fam in past 3 mon-0.165*** -0.158*** -0.167*** -0.157*** -0.160***

(0.00851) (0.0122) (0.0471) (0.0424) (0.0448)

quarter = 2 -0.0754*** -0.0784*** -0.00872 -0.0168 -0.0134

(0.00717) (0.00992) (0.0724) (0.0624) (0.0680)

quarter = 3 -0.0608*** -0.0565*** -0.0764** -0.0779** -0.0767**

(0.00732) (0.0103) (0.0380) (0.0350) (0.0366)

quarter = 4 -0.00721 -0.00958 -0.0334 -0.0386 -0.0367

(0.00730) (0.0100) (0.0383) (0.0354) (0.0369)

Survey year = 2014 0.162*** 0.171*** -0.00530 -0.00694 -0.00437

(0.00943) (0.0137) (0.0752) (0.0674) (0.0704)

Survey year = 2015 0.0454*** 0.0569*** -0.126 -0.150 -0.134

(0.00943) (0.0143) (0.0997) (0.0938) (0.0933)

Survey year = 2016 0.0560*** 0.0725*** -0.159 -0.192 -0.173

(0.00949) (0.0158) (0.143) (0.134) (0.136)

Urban (1) or rural (0) = 1, Urban -0.00972 -0.00404 -0.0244 -0.0385 -0.0386

(0.00783) (0.0111) (0.0583) (0.0529) (0.0552)

Region number = 1, Tbilisi 0.172*** 0.201*** -0.00959 -0.0748 -0.0884

(0.0116) (0.0228) (0.220) (0.188) (0.214)

Region number = 2, Shida Kartli 0.0736*** 0.0890*** -0.00998 -0.0158 -0.00998

(0.0141) (0.0210) (0.113) (0.102) (0.106)

Region number = 3, Kvemo Kartli -0.0424*** -0.00948 -0.0723 -0.106 -0.0999

(0.0127) (0.0252) (0.105) (0.0942) (0.102)

Region number = 5, Samtskhe-Javakheti 0.266*** 0.283*** 0.230*** 0.228*** 0.214***

(0.0149) (0.0223) (0.0852) (0.0753) (0.0820)

Region number = 7, Ajara 0.106*** 0.133*** 0.0717 0.0831 0.0851

(0.0132) (0.0235) (0.0803) (0.0725) (0.0761)

Region number = 8, Guria 0.0662*** 0.0913*** -0.0770 -0.0216 -0.0450

(0.0157) (0.0256) (0.162) (0.125) (0.148)

Region number = 9, Samegrelo 0.121*** 0.138*** 0.0653 0.0402 0.0338

(0.0115) (0.0183) (0.0860) (0.0752) (0.0841)

Region number = 10, Imereti 0.154*** 0.166*** 0.0717 0.0606 0.0537

(0.0103) (0.0157) (0.109) (0.0938) (0.103)

Region number = 11, Mtskheta-Mtianeti -0.0840*** -0.0580** -0.180 -0.187* -0.203*

(0.0186) (0.0291) (0.114) (0.101) (0.110)

Redefined expectation = 2, Same 0.131* 0.126*

(0.0774) (0.0718)

Redefined expectation = 3, Improved slightly/very much 0.268* 0.269**

(0.145) (0.136)

Redefined expectation = 4, Don't know 0.123** 0.121**

(0.0622) (0.0582)

Survey year = 2012 0.0370*** 0.0689***

(0.00883) (0.0214)

Survey year = 2013 0.0988*** 0.0978***

(0.00900) (0.0123)

hhpercp_need = 2 0.0377 0.0406

(0.0490) (0.0520)

hhpercp_need = 3 0.184*** 0.191***

(0.0529) (0.0568)

hhpercp_need = 4 0.426*** 0.438***

(0.129) (0.144)

hhpercp_need = 5 0.420*** 0.421***

(0.1000) (0.106)

Constant 8.102*** 8.114*** 7.915*** 8.041*** 7.872***

(0.0462) (0.0633) (0.349) (0.291) (0.349)

Observations 49,251 49,251 31,854 31,854 31,854

R-squared 0.364 -0.183 -11.023 -9.192 -10.144

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

38


Summary Statistics – Selected Variables

N mean cv p25 p50 p75

Year 11207 2011 0 2011 2011 2011

Urban (urban(1) rural(0)) 11207 0.613367 0.793979 0 1 1

HH Size 11207 3.636299 0.544829 2 4 5

Head Gender (female (1) male (0)) 0

Single HH (single(1) multiple(0)) 0

Head Age 0

Head Age (15-29 (1) older (0)) 0

% of elderlies (age>66) within HH (>avg (1) <avg(0)) 0

Single & Elderly (age=66+) (1), Single & Non-Elderly (age<66) (0) 0

Family with Children (0-15) (1), Family without Children (0-15)(0) 0

Fam with Children & Young Head (1), Fam with Children & Older Head (0) 0

Borrowed from Bank and Private 11207 0.013384 8.58603 0 0 0

Borrowed from Private only 11207 0.160882 2.283904 0 0 0

Borrowed from Bank only 11207 0.049076 4.402056 0 0 0

Non-Borrower 11207 0.776657 0.536278 1 1 1

Borrowed from Bank/Private 11207 0.223343 1.864869 0 0 0

HH Total Income in GEL (current, unadjusted monthly avg) 11207 587.8053 1.103211 225.3457 414.6895 741.0118

GEL borrowed from Private, Past 3 months (HH) (unadjusted, current) 11207 74.00241 6.10625 0 0 0

GEL borrowed from Bank, Past 3 months (HH) (unadjusted, current) 11207 94.74761 8.138532 0 0 0

GEL repaid to Private, Past 3 months (HH) (unadjusted, current) 11207 19.12278 11.07638 0 0 0

GEL repaid to Bank, Past 3 months (HH) (unadjusted, current) 11207 90.77059 5.292404 0 0 0

% borrowed from Private over total income (GEL) 11170 0.099535 12.16713 0 0 0

% borrowed from Bank over total income (GEL) 11170 0.082372 18.81228 0 0 0

% repaid to Private over total income (GEL) 11170 0.010904 9.977361 0 0 0

% repaid to Bank over total income (GEL) 11170 0.058505 13.02907 0 0 0

% unpaid to private over total income (GEL) 11170 0.088631 13.69015 0 0 0

% unpaid to bank over total income (GEL) 11170 0.023867 66.17639 0 0 0

39



Year 11265 2012 0 2012 2012 2012


HH Size 11265 3.631869 0.530146 2 3 5



Head Age 0

Head Age (15-29 (1) older (0)) 0








Non-Borrower 11265 0.761385 0.559843 1 1 1










% repaid to Bank over total income (GEL) 11245 0.098494 18.84143 0 0 0


% unpaid to bank over total income (GEL) 11245 -0.01111 -246.741 0 0 0


Year 11092 2013 0 2013 2013 2013


HH Size 11092 3.596827 0.532857 2 3 5



Head Age 0

Head Age (15-29 (1) older (0)) 0








Non-Borrower 11092 0.777587 0.534841 1 1 1










% repaid to Bank over total income (GEL) 11082 0.06607 7.305763 0 0 0.03661



40



Year 11160 2014 0 2014 2014 2014


HH Size 11160 3.540233 0.535407 2 3 5



Head Age 0

Head Age (15-29 (1) older (0)) 0








Non-Borrower 11160 0.805376 0.491607 1 1 1














Year 10999 2015 0 2015 2015 2015


HH Size 10999 3.466679 0.543464 2 3 5



Head Age 0

Head Age (15-29 (1) older (0)) 0








Non-Borrower 10999 0.828803 0.454509 1 1 1












% unpaid to bank over total income (GEL) 10995 -0.0065 -76.4116 -0.03821 0 0

41



Year 10858 2016 0 2016 2016 2016


HH Size 10858 3.514551 0.538899 2 3 5

Head Gender (female (1) male (0)) 10858 0.328237 1.430652 0 0 1

Single HH (single(1) multiple(0)) 10858 0.14662 2.412652 0 0 0

Head Age 10858 60.87843 0.23407 51 61 72

Head Age (15-29 (1) older (0)) 10858 0.020538 6.906155 0 0 0

% of elderlies (age>66) within HH (>avg (1) <avg(0)) 10858 0.34546 1.376543 0 0 1

Single & Elderly (age=66+) (1), Single & Non-Elderly (age<66) (0) 1592 0.585427 0.841784 0 1 1

Family with Children (0-15) (1), Family without Children (0-15)(0) 10858 0.390035 1.250606 0 0 1

Fam with Children & Young Head (1), Fam with Children & Older Head (0) 4235 0.027155 5.986196 0 0 0




Non-Borrower 10858 0.836434 0.442233 1 1 1


HH Total Income in GEL (current, unadjusted monthly avg) 10858 867.7884 0.951383 371.6847 653.1402 1100










% unpaid to bank over total income (GEL) 10840 -0.0278 -15.9617 -0.05573 0 0

Documents

November 2018 Georgia Indebtedness Pov ertyNote Nov 5€¦ · Indebtedness_Pov ertyNote_Nov 5 Analysis Based on Integrated Household Survey Natsuko Kiso Nozaki, Alan Fuchs Tarlovsky,