Employment and income in Georgia: Differences by educational attainment

According to the data of the National Statistics Office of Georgia for 2005-2016, there are approximately 100,000 students in Georgian tertiary educational institutions. Around the world, education generally contributes to increased individual income, and Georgia would not be expected to be an exception in this regard. Still, the role of tertiary education in the professional lives of the population of Georgia has not been studied thoroughly. Based on CRRC’s 2015 Caucasus Barometer survey, this blog post looks at the share of the population that has completed tertiary education, what share of those are employed and in what positions, how much their personal income is, and how the employment situation of those with tertiary education differs from the situation of those who did not obtain a degree.

The answers to the following questions, which used show cards are analyzed in this blog post:

• What is the highest level of education you have achieved to date? 
• A show card listing levels of education was used.
• Which of the following best describes your situation?
• A show card with the following answer options was used:
• Retired and not working;
• Student and not working;
• Housewife and not working;
• Unemployed;
• Working either part-time or full time (even if the respondent is retired / is a student), including seasonal work;
• Self-employed (even if the respondent is retired / is a student), including seasonal work;
• Self-employed (even if the respondent is retired / is a student), including seasonal work;
• Other.
• Which of the following best describes the job you do?
• A show card listing a hierarchy of job types was used.
• Speaking about your personal monetary income last month, after all taxes are paid, to which of the following groups do you belong?
• A show card with income groups was used.

Thirty percent of Georgia’s population reports having completed tertiary education (Bachelor’s, Master’s, Specialist’s or post-graduate degree). As the chart below shows, 29% of those without tertiary education report being employed compared to 49% of those with tertiary education.

Note: Answer options to the question “What is the highest level of education you have achieved to date?” were recoded in the following way: “No primary education”, “Primary education (either complete or incomplete)”, “Incomplete secondary education”, “Completed secondary education”, “Secondary technical education” and “Incomplete higher education”  were combined into “Do not have tertiary education”. Answer options “Completed higher education” and “Post-graduate degree” were combined into “Have tertiary education”.

Answer options to the question “Which of the following best describes your situation?” were recoded in the following way: “Working either part-time or full time (even if retired / a student), including seasonal work”, “Self-employed (even if retired / a student), including seasonal work” were grouped as “Employed”. Those who answered “Disabled and unable to work” and “Other” (2%) were excluded from the analysis. Answer options: “Retired and not working", "Student and not working", "Housewife and not working", and "Unemployed" were grouped as “Unemployed”. Within this group, those who answered “Yes” to the question “Are you currently interested in a job, or not?” were grouped as “Unemployed who are interested in a job”, while those who answered “No” were grouped as “Unemployed who are not interested in a job”.  

Answers “Don’t know” and "Refuse to answer” to either of these questions were also excluded from the analysis. Overall, 4% of cases were excluded. 

As for job positions, most of those with tertiary education who were employed at the time of the survey (28%) were employed as professionals (in the fields of science, healthcare, education, business, law, culture, etc.). On the other hand, most of those without tertiary education who were employed at the time of the survey (18%), reported working in the service sector (e.g., as salespersons, including personal care workers, e.g. baby sitters). 

The higher the income group, the higher is the share of those with tertiary education in it. For example, almost there are almost 2.5 times as many people with tertiary education among those who earned above GEL 600 the month before the survey, compared to those without tertiary education. A Mann-Whitney test shows that the difference between these groups is statistically significant. 

Note: Answer options to the question “Speaking about your personal monetary income last month, after all taxes are paid, to which of the following groups do you belong?” were recoded in the following way: options “GEL 601 to GEL 1000”, “GEL 1001 to GEL 2000”, “GEL 2001 to GEL 3000” and “More than GEL 3000” were grouped as “More than GEL 600”. Answer options “Up to GEL 120” and “GEL 121 to GEL 240” were grouped as “Up to GEL 240”. Those who answered “0”, “Don’t know”, and “Refuse to answer” were excluded from the analysis (36% of cases).

The findings presented in this blog post show that, like in many other countries, tertiary education plays a positive role for employment prospects in Georgia. People with tertiary education are more likely to be employed compared to those who do not have tertiary education. The largest group of those with tertiary education is employed as professionals, while those without tertiary education are most frequently employed as service workers. Importantly, the income of those with tertiary education tends to be higher. In all cases, the differences between those with and without tertiary education are statistically significant.

For more information about the impact of education, see CRRC’s earlier blog posts including Educated parents, educated children? And Connections or education? On the most important factors for getting a good job in Georgia. For more data, check out our Online Data Analysis tool.

Trends in the Data: Changes in the level of trust in social and political institutions in Armenia

According to an earlier CRRC blog post, which looked at the changes in the level of trust in social and political institutions in Georgia from 2011 to 2015, trust in a fair number of institutions in Georgia declined. This post provides a comparable review of the situation in Armenia, using CRRC’s Caucasus Barometer (CB) survey data.

The level of trust in most political institutions CB asked about has declined in Armenia since 2011. The largest decline can be observed in respect to the President. Trust dropped from 36% in 2011 to 16% in 2015. Trust in executive government and parliament also declined between 2011 and 2013, and has stabilized since at a rather low level.

Note: The charts in this blog post only show the share of those who report trusting the respective institution. Answer options “Fully trust” and “Rather trust” were combined.

The survey results also show a slight decline in trust in courts between 2011 and 2015. Trust in the police, educational system and healthcare system remained largely unchanged, while trust in the army increased.

In sum, of the institutions CB asked about, the largest drop in the level of trust is observed was in the President, while trust in the army increased in Armenia. The levels of trust in executive government, parliament, and courts in Armenia have slightly declined since 2011, while the levels of trust in the healthcare system, police and educational system have not changed.

To learn more about trust in institutions in the South Caucasus, take a look at the data using our Online Data Analysis tool.

Trends in the Data: Declining trust in the banks in Georgia

The last few years have been turbulent for Georgia’s national currency, the Lari (GEL), the value of which started to decline in November 2014. While in October 2014 one US dollar traded for GEL 1.75, since February 2015 to date, the exchange rate has fluctuated between GEL 2 and 2.5 per dollar. Needless to say, the depreciation of the Lari has been widely covered by the media, and although it had numerous causes, a number of organizations and people were blamed for the devaluation. With this background in mind, this blog post looks at how reported trust in banks has changed in recent years in Georgia, using CRRC’s Caucasus Barometer (CB) survey data.

In 2015, for the first time since CB started asking the population about their trust in banks, more people in Georgia reported distrusting than trusting them. The decline in trust, however, started well before the GEL began to depreciate. While 27% reported trusting banks in October 2015, 53% did in October 2008.

Note: The original five-point scale was recoded into a three-point scale for this chart. Answer options “Fully trust” and “Trust” were combined into the category ‘Trust,’, while “Fully distrust” and “Distrust” were combined into ‘Distrust.’ “Neither trust nor distrust” was not recoded. The Caucasus Barometer survey was not conducted in 2014.

As is generally the case with trust in social and political institutions in Georgia, the population of rural settlements report less distrust in banks than residents of urban settlements. Nonetheless, since 2008, distrust in the banks in rural settlements has nearly tripled, from 11% in 2008 to 30% in 2015. In the capital, distrust has almost doubled during the same period.
 

Although there has been a decline in trust in the banks in recent years, this decline started before the devaluation of the Lari began in 2014. While the devaluation likely contributed to the decline in trust, the fact that trust began declining earlier shows that there is more to the story than the devaluation.
Given that the banking system, and trust in it, is crucial to the effective functioning of a country’s economy, the government of Georgia and banks themselves should consider efforts aimed at building trust in the banking sector.

What factors are at play in declining trust in the banks in Georgia? Join the conversation on the CRRC-Georgia Facebook page here, and to explore more data on Georgia and the South Caucasus, visit our online data analysis tool (ODA).

Trends in the data: A majority of the population of Georgia now uses the internet

Internet use is on the rise worldwide, and while internet penetration is increasing the world over, in some countries still relatively small shares of the population use it. While only about a third of the population of Georgia reported using the internet at least occasionally in 2009, today, slightly over half of the population is online. This blog post looks at the trends in internet usage in Georgia from 2009 to 2015 by age and settlement type, using the CRRC’s Caucasus Barometer survey (CB) data.

Until 2012, a larger share of the population had reported never using the internet or not knowing what it was than reported using it. In 2013, roughly equal shares reported using and not using the internet. On CB 2015, a majority (57%) of the population of Georgia reported using the internet at least occasionally.

Note: The original question asked: “How often do you use the internet?” For this blog post, answer options “Every day”, “At least once a week”, “At least once a month”, and “Less often” were combined into ‘Yes’. Answer options “Never” and “I don’t know what the internet is” were combined into ‘No’. 

Throughout this period, as one might expect, a larger share of the younger generation has used the internet than the older generation, with 87% of 18-35 year olds reporting using the internet at least occasionally in 2015, compared to only 19% of people aged 56 and older. Interestingly, the largest increase is among those between the ages of 36 and 55, with only 24% of this age group using the internet in 2009 compared with 63% in 2015.

Note: The chart above presents only the shares of those who reported using the internet at least occasionally. 

As is well known, internet usage is lowest in Georgia’s rural settlements. In 2015, slightly less than half of the rural population (43%) reported using the internet. In contrast, roughly seven in ten residents of urban settlements use the internet. Urban settlements outside the capital have seen the largest increase in internet use between 2009 and 2015, with the share of internet users increasing more than 2.5 times in this period, while the share of internet users has more than doubled in rural settlements.

Note: The chart above presents only the shares of those who reported using the internet at least occasionally. 

Currently, a majority of the population of Georgia use the internet at least occasionally. As one might expect, more young people use the internet than older people, and they use it more frequently. While slightly less than half of the rural population uses the internet, this share is steadily increasing. If this growth continues, a majority of the rural population of the country will soon be online as well.

To explore the data yourself, try our online data analysis tool.

Making Votes Count: Statistical Anomalies in Election Statistics

[Note: In order to help monitor the fidelity of the October 2016 parliamentary election results, CRRC-Georgia will carry out quantitative analysis of election-related statistics using methods from the field of election forensics within the auspices of the Detecting Election Fraud through Data Analysis (DEFDA) project. The Project is funded by the Embassy of the United States of America in Georgia, however, none of the views expressed in the following blog post represent the views of the US Embassy in Georgia or any related US Government entity.]

On Friday, August 19th, CRRC-Georgia presented and published a pre-analysis report for the Detecting Election Fraud through Data Analysis project, which contained analysis of the new electoral boundaries set up following the 2015 constitutional court ruling that the previous boundaries were unconstitutional.

The report also demonstrated how the methods of statistical analysis that CRRC-Georgia will use to monitor the 2016 elections work in practice. To do so, we used precinct level data from the 2012 party list elections. Specifically, CRRC-Georgia carried out two types of statistical analyses:

• Logical checks of official election returns, which test whether there were data entry errors when the vote was being recorded and collated; 
• Tests for statistical anomalies in the official electoral returns, which may suggest electoral malfeasance. 

While Monday’s blog shows the logical checks that CRRC will apply to the final CEC vote records, today we discuss the tests used to identify statistical anomalies in vote counts.

Election Forensics: Detecting statistical anomalies in voting data

Direct observation of polling stations is the best method available to ensure the accuracy of the vote, however, election observers cannot be everywhere all the time. Given this fact, the field of election forensics, a subfield of political science, has developed a number of statistical tests to look for statistical anomalies in election returns, which may suggest suspicious election-related activity. Although a number of rather complicated statistics exist, we focus on a number of simpler tests. Specifically, we use tests based on the distribution of the second digit in the number of votes cast, the final digit in the number of votes cast, and the distribution of turnout within an electoral district.

Second digit tests are based on Benford’s law. Benford’s law provides the expected probability of the first digit being any digit one through nine in a number with multiple digits. Although one might expect this number to be equally likely to be any number, in fact 1 is more likely than 2, 2 more likely than 3, etc. Using Benford’s Law, accountants test various documents for anomalies that may suggest issues in documents. This law also applies to the second digit in a number, which researchers have found is more suitable for testing election results. A similar logic is applied to elections as in accounting, and in this blog, we specifically test whether the skew, kurtosis, and the average of the second digit and its distribution follow the expected distribution or not. Instances of non-conformity to Benford’s law may suggest electoral malfeasance.

Besides second digit tests, a number of tests have been proposed for the last digit in vote counts. Here, the expected distribution of digits is much more intuitive, and one expects each digit, zero through nine, to be approximately 10% of the total distribution. Based on this distribution, we test the mean of the last digit and of the mean of the count of zeros and fives in the final digits of votes.

In order to test whether the above noted digit tests in fact indicate potential issues or whether the difference between the observed and expected values was a chance variation, we use a statistical method called bootstrapping. This method lets us to estimate 99% confidence intervals. In the present case, the confidence intervals provide a range within which the result could have fallen by chance. If the range covered does not include the expected value for a given test statistic, we conclude with 99% confidence that the number is different not by chance alone.

Finally, voter turnout is expected to have a relatively normal distribution with a single mode. Based on this expectation, we test whether voter turnout in each electoral district has a single mode or multiple modes using what statisticians refer to as a dip test.

Before reporting the test results, it is worth noting several important caveats when interpreting these tests:

• Test results are probabilistic, which means that they say the distribution is highly unlikely (would occur 1% of the time in the present case), rather than impossible to occur in the absence of issues. For the tests, we calculated 99% confidence intervals. With 99% confidence intervals and having conducted 444 tests on the 2012 proportional election results, statistically we would expect between four and five tests to be set off in the absence of issues due to chance alone. 
• The lack of a test being set off does not necessarily mean a problem occurred, but it does suggest the need for further examination; 

In total, 11 districts show statistical anomalies in the test results, and a total of 15 tests report suspicious results. Results are presented in Table 5. In the rows with district names and numbers, the actual test values are reported. In the row below the district name, 99% confidence intervals are reported. Red cells in the table indicate the presence of a statistical anomaly.

Rustavi’s electoral returns set off three statistical tests. Given that we have no reason to expect specific voting patterns in Rustavi compared to other areas in the country that did not set off suspicious tests, this suggests that there may have been electoral malfeasance in Rustavi in 2012. Reviews of election monitoring reports, however, did not suggest electoral malfeasance. This test may be picking up on undetected electoral malfeasance from 2012 in Rustavi. Although unlikely, these three tests could have also been set off by chance.

In Kobuleti, two tests were also set off. In Kobuleti, we would not expect a particularly distinctive voting pattern. Hence, there is a relatively strong reason to believe that electoral malfeasance may have occurred in Kobuleti in the 2012 elections. This contention is supported by election monitoring reports, which reported issues in Kobuleti.

In Bolnisi, two tests were set off. Complaints were filed in Bolnisi on election day, and the test may have been set off by these issues. However, given Bolnisi’s relatively high ethnic minority population and distinctive voting pattern, the tests could have been set off by this rather than malfeasance.

Eight other districts had single positive tests for electoral malfeasance, including Vake, Saburtalo, Kareli, Akhaltsikhe, Adigeni, Vani, Senaki, and Martvili. A review of the OSCE and GYLA election monitoring reports suggest that issues may have occurred in at least half of these districts. Although these positive tests could have occurred by chance alone, the four districts in which a test was set off and observers did not report malfeasance in may also suggest unreported problems in the 2012 elections.

This blog post has described the methods CRRC-Georgia will use to detect statistical anomalies in election returns. For more on the methods CRRC-Georgia will use to monitor the elections, see our pre-analysis report, here, and take a look at Monday’s blog post on logical inconsistencies in election records.

Making Votes Count: Logical Inconsistencies in Voting Records

In order to help monitor the fidelity of the October 2016 parliamentary election results, CRRC-Georgia will carry out quantitative analysis of election-related statistics using methods from the field of election forensics within the auspices of the Detecting Election Fraud through Data Analysis (DEFDA) project. The Project is funded by the Embassy of the United States of America in Georgia, however, none of the views expressed in the following blog posts represent the views of the US Embassy in Georgia or any related US Government entity.

On Friday, August 19th, CRRC-Georgia presented and published a pre-analysis report for the project, which contained analysis of the new electoral boundaries set up following the 2015 constitutional court ruling that the previous boundaries were unconstitutional. The report also demonstrated how the methods of statistical analysis that CRRC-Georgia will use to monitor the 2016 elections work in practice. To do so, we used precinct level data from the 2012 party list elections. Specifically, CRRC-Georgia carried out two types of statistical analyses:

• Logical checks of official election returns, which test whether there were data entry errors when the vote was being recorded and collated; 
• Tests for statistical anomalies in the official electoral returns, which may suggest electoral malfeasance. 

While today’s blog shows the logical checks that CRRC will apply to the final CEC vote records, tomorrow we will discuss the tests used to identify statistical anomalies in vote counts.

Logical inconsistencies in voting records
For the 2016 elections we will carry out two types of checks of the logical consistency of votes. Specifically, we will check:

• Whether there are more or less votes and invalid ballots than signatures recorded on voter rolls;
• Whether turnout increases over the course of the day.

Voter signatures - Votes recorded - invalid ballots ≠ 0
Taken together, the number of signatures recorded for ballots minus the number of votes recorded minus the number of invalid ballots should equal zero. However, in the 2012 parliamentary proportional list elections this was not the case in approximately 25% of precincts. From the 3,680 precincts which had ten votes or more:

• 936 precincts had more or less signatures than votes and invalid ballots (25% of all precincts); 
• Of these, 918 had more signatures registered than votes recorded for a party or ballots registered as invalid combined; 
• 18 precincts had fewer signatures than votes registered for a party and invalid ballots combined.

These phenomena likely have numerous causes. While some are problematic, others are benign.

To start with the 918 cases of fewer votes registered for a party or invalid ballots than signatures recorded, the severity of the issue varies widely. In order to provide some sense of the gravity of the issue, we have grouped precincts by the number of extra signatures into three categories: unlikely to be problematic (1-9 extra signatures), potentially problematic (10-49 extra signatures), and suspicious (50 or more extra signatures). Table 1 presents the number of precincts that fall into each category:

	Unlikely to be problematic	Potentially Problematic	Suspicious
# of Precincts	816 (89%)	56 (6%)	46 (5%)
Count foreign	0	4	42

Notably, of the 46 suspicious cases, 42 are in foreign precincts. With foreign precincts, we strongly suspect that there was a data entry error as discussed in more depth in our report. Among domestic precincts, there are four suspicious precincts with more than 50 extra signatures. In Marneuli’s 22nd precinct, there were 51 extra signatures. In Khashuri’s 32nd precinct, there were 63 extra signatures. In Gori’s 63rd precinct, there were 71 extra signatures, and in Bolnisi’s 62nd precinct, there were 87 extra signatures.

Potential causes for this situation include voters coming to polling stations, and:

• Signing the voter list and leaving without voting;
• Voting only in the majoritarian race rather than in both the proportional and majoritarian races;
• Additionally, Precinct Electoral Commissions may have inaccurately recorded votes, invalid ballots, and/or signature counts.

In 18 cases, there were less signatures on voter rolls than ballots declared invalid and votes recorded. In 17 of the 18 cases there were 10 votes or less that were without a signature. However, in Gori there were 196. This may stem from a recording error, since there was a very high number of invalid ballots (221), or this may stem from another issue. Generally however, the causes of there being more votes and invalid ballots than signature recorded, the causes are less benign. They include:

• Precinct electoral commissions may have incorrectly counted or reported vote statistics;
• Voters were allowed to vote without signing the voter list;
• Ballot box stuffing occurred.

Declining turnout
Another clear logical inconsistency in the official statistics on the 2012 elections is that the number of votes in several precincts declined between 12PM and 5PM, as well as in one district between 5PM and 8PM. That is to say, according to the official record, fewer people had voted at 5PM, in total, compared to five hours earlier at 12PM in these districts.

District	Saburtalo	Nadzaladevi	Dmanisi	Dmanisi	Akhalkalaki	Mestia	Kobuleti
Precinct	63	44	23	30	48	25	14
Votes between 12PM and 5PM	-1	-159	-19	-58	-40	-43	-210

This is likely to be caused by a reporting error, with precinct officials recording the number of votes between these hours rather than the total number of votes at 5PM.

Conclusions
While each of the above logical inconsistencies in recording the vote is clearly an issue, which could imply malfeasance, we strongly suspect that the vast majority of cases described above stem from recording and data entry errors. While, we do not suspect malfeasance in any particular case, and do not believe that recording issues affected the outcome of the 2012 elections, the illogical recording of the vote is a serious issue.

In Georgia, elections and the outcomes of elections are regularly contested, with accusations of all sorts following the results. If Georgian voters see that the voting records have logical inconsistencies in them, this could undermine citizens’ confidence in the accuracy of the vote, and thus the legitimacy of election results.

Based on this, we recommend that the Central Election Commission, District Election Commissions, and Precinct Election Commissions check for logical inconsistencies in election protocols on election day and explain logical inconsistencies in a public and transparent manner if they do occur. Particular emphasis in trainings should be placed on how to fill out voter protocols.

In Thursday’s blog, we show how we will carry out tests for electoral malfeasance in the 2016 elections using tests from the field of election forensics. In the meantime, check out our full report or this visualization of the issues which Jumpstart Georgia created.

What can CRRC’s Caucasus Barometer survey tell us about internal migration in Georgia?

According to existing estimates, the stock of internal migrants is much larger than the stock of international migrants worldwide. In Georgia, however, internal migration is largely overlooked and there is very little data available on the number and distribution of internal migrants. The National Statistics Office of Georgia (Geostat) regularly collects data on internal migration in the country via an Integrated Household Survey. The Public Service Development Agency, on the other hand, is in charge of population registration by place of residence. Several indicators of internal migration in Georgia can also be found in CRRC’s Caucasus Barometer (CB) survey. This blog post discusses one such indicator: whether, at the time of interview, adults in Georgia lived in the same settlement where they were born. Results of the latest, 2015 wave of CB are presented in this blog post.

About half of the population of Georgia (49%) reported having been born in a settlement where they lived and were interviewed at the time of the survey. An extremely small share (under 3%) reported being born outside the country. The rest are internal migrants – a quarter (25%) were born in another settlement, but in the same region of the country, and almost a quarter (23%) in a settlement in another region of the country. Thus, although we do not know the details of their migration (e.g., at what age, or for what reason they migrated), we can estimate that roughly half of the population of the country (48%) are internal migrants. Importantly, this CB finding is in line with Geostat’s estimates, according to which the share of internal migrants constituted 54% in 2014.

Differences between internal migrants and non-migrants by settlement type, age and, especially, gender are quite striking. Unsurprisingly, the share of internal migrants is highest in the capital, with 65% of Tbilisi’s population born elsewhere in Georgia. Villages, on the other hand, house the highest share of non-migrants. Nonetheless, 41% of the rural population was not born in the villages where they resided at the time of the survey.

When it comes to age differences, the share of internal migrants is highest among the oldest segment of the population (58% of those over 60 years old), and gradually decreases with age. Although differences by age groups are statistically significant, these differences are not particularly large.

While 62% of females report not being born in the settlement where they currently live, roughly half as many men (34%) report the same. This may be related to the tradition of women moving to their husbands’ households after marriage. Thus, in the absolute majority of cases when spouses are from different settlements, it would be a wife moving to another settlement, rather than a husband.

Although it might have been expected that those with higher education would more likely be internal migrants than those with lower levels of education, CB data does not suggest any significant differences between internal migrants and non-migrants in Georgia by level of education.
Preliminary analysis of CB 2015 data lets us estimate that about half of the population of Georgia can be considered internal migrants. Most strikingly, internal migrants and non-migrants differ by gender, with women being internal migrants about twice as often as men. There are relatively small differences between internal migrants and non-migrants by age and settlement type.

CRRC’s Caucasus Barometer data can be explored at our online data analysis platform.

Trends in the Data: Public support for democracy is slowly waning in Georgia (Part 2)

Analysis of survey findings from the last few years, presented in the first part of this blog post, shows that public support for democracy is declining in Georgia. Since 2012, the share of the population who would prefer democracy over any other kind of government dropped from 68% to 47%. As public support for democracy is indispensable to democratic consolidation, it is important to know how and why support for democracy is changing. This blog post describes a number of tendencies that might be related to the declining public support for democracy in Georgia, using the CRRC’s Caucasus Barometer (CB) survey data.

One reason for the declining support for democracy may be related to the worsening of the public’s assessment of domestic political developments in the country. Specifically, while nearly half the population agreed with the statement that “[domestic] politics is going in the right direction” in 2011-12, only 15% did so in 2015. On the other hand, the share of the population that agreed with the statement that “[domestic] politics is going in the wrong direction” quintupled between 2012 and 2015. The share of those who agreed with the statement that “[domestic] politics does not change at all” also increased.

Note: A show card was used for this question. Answer options "Politics is definitely going in the wrong direction" and "Politics is going mainly in the wrong direction" were combined into ‘[Domestic] politics is going in the wrong direction’ on the chart above. Answer options "Politics is going mainly in the right direction" and "Politics is definitely going in the right direction" were combined into ‘[Domestic] politics is going the right direction’. “[Domestic] politics does not change at all” was not recoded. Options ‘Don’t know’ and ‘Refuse to answer’ are not shown on the above chart. CB was not carried out in 2014.

Worsening assessments of domestic political developments might also be related to an increased perception that the government treats people unfairly. The share of those who did not agree with the statement that “people like yourself are treated fairly by the [present] government” more than doubled between 2013 and 2015.

Note: Answer options "Completely agree" and "Somewhat agree" were combined into ‘Agree,’ and options "Somewhat disagree" and "Completely disagree" were combined into ‘Disagree’.

Declining trust towards important political institutions such as parliament, executive government, the president, local government and the court system, also discussed in a recent blog post, might be considered a logical continuation of the tendencies mentioned above. For instance, since 2011 the shares of those who said that they trusted executive government, parliament and local government nearly halved, while the shares of those who report distrusting these institutions increased. Although trust towards the president and court system increased in 2015 compared with 2013, it is still lower than in 2011. This is a serious problem, because trust in political institutions is crucial for the maintenance and consolidation of democracy.

Note: Answer options "Fully trust" and "Rather trust" were combined into ‘Trust’, and options "Rather distrust" and "Fully distrust" were combined into ‘Distrust’. Options “Neither trust nor distrust”, ”Don’t know” and ”Refuse to answer” are not shown on the chart.

Trust in political institutions not only can strengthen democracy, but can also make governance more effective and cost-efficient. Declining trust towards major political institutions could impede Georgia’s stable development. Thus, it is of high importance to regularly monitor changes and analyze their causes. Since the causes of social change are usually complex, further, more focused research is needed on the issues highlighted in this blog post, with the eventual goal of improving the performance of major political institutions, leading to the population’s increased trust in these institutions. 

As the first part of this blog post showed, although the share of the population who supports democratic governance in Georgia declined by a third in recent years, reported support for democratic values strengthened. These findings, taken together, suggest that declining public support for democracy is unlikely to be caused by the weakening of democratic values. Thus, the causes for the decline likely lay elsewhere. The findings presented in this and in the first part of this blog post also suggest that the population’s knowledge and understanding of what democracy means are different from the predominant conception of liberal democracy. Therefore, increasing public awareness of democracy will be important for its consolidation in Georgia. 

This blog post has shown that fewer people report that domestic politics in Georgia is going in the right direction and that more people report the government treats people unfairly. Not surprisingly, trust in political institutions also declined. Taken together, these findings suggest that declining public support for democracy may be related to the public’s worsening assessments of the development of the country’s domestic political situation over the course of the past few years.

To find out more about public opinion in Georgia, visit CRRC’s online data analysis tool. 

A New CRRC-Georgia/PROLoG Report: Legal Professionals’ Views on the Legal System

CRRC-Georgia’s report The Judicial System in Georgia: Views of Legal Professionals was published on 11 July, 2016. The report details the results of a baseline study for the USAID-funded project Promoting Rule of Law in Georgia (PROLoG) implemented by East-West Management Institute (EWMI). The study evaluated how the following aspects of the justice system are seen by judges, prosecutors and private, NGO and Legal Aid Service (LAS) lawyers: 

1. Whether there is an effective balance between parties in law and practice;
2. Whether citizens, including minorities and vulnerable groups can benefit from the protection the justice system offers; 
3. The quality of legal education in Georgia;
4. How different judicial institutions perform.

During the survey, 310 lawyers (of which 204 private lawyers, 49 NGO lawyers and 57 LAS lawyers), 108 judges and 102 prosecutors were interviewed. In addition, semi-structured interviews were conducted with seven judges and seven prosecutors and four focus-groups with private, NGO and LAS lawyers. 

Overall, prosecutors and judges tended to report the most positive views. The assessments of lawyers, and in particular NGO lawyers, were generally less positive compared to the assessments of prosecutors and judges. 

CRRC-Georgia’s researcher Mariam Kobaladze presents the main findings of the PROLoG report at the Frontline Georgia Club in Tbilisi on 11 July 2016. Photo by Mariam Sikharulidze. 

The main findings of the report are presented below:

Balance between Parties in Law and in Practice

• With regard to civil and administrative law, the majority of all legal professionals assess that the balance between parties is similar both in law and in practice, but more lawyers say that there is equality of arms neither in law nor in practice; 
• With regard to criminal law, the majority of judges and prosecutors assess the balance between the parties as similar both in law and in practice, but fewer than half of lawyers agree with this assessment.

Ability of Citizens to Benefit from the Justice System

• NGO lawyers reported more frequently that the LGBT community and religious minorities are the groups the courts and Prosecutor’s Office do not treat fairly or equally, compared to other social groups. However, the majority of other legal professionals thought all groups were treated mainly or fully fairly/equally;
• Compared to other legal professionals, NGO lawyers less frequently think that representatives of all social groups are treated fairly or equally by the courts and the Prosecutor’s Office;
• Many legal professionals consider the police less effective than other legal institutions. Even some prosecutors, who, overall, reported very positive assessments of justice institutions, called for police reform. 

Quality of Legal Education  

• Prosecutors were, again, the most positive about the quality of legal education in Georgia, with slightly more than half giving a positive assessment. The majority of judges and lawyers did not provide positive evaluations of legal education, either theoretical or practical.

Performance of Justice System Institutions

• The majority of legal professionals considered the performance of most justice system institutions as largely transparent, but lawyers (private, NGO and LAS) assessed the transparency and performance of these institutions harsher than judges and prosecutors; 
• Almost all legal professionals agree that the lack of speedy trials is an impediment to the judicial process that is mainly caused by a shortage of judges and lack of alternative dispute resolution outside of courts;
• The high caseload in the courts was seen as a major issue that has negative effects on the speed, quality and, in the case of the Supreme Court, uniformity of legal decisions. 

The full report of the baseline study is available here. 

Civic engagement in Georgia

Participation in various forms of social life can help people solve important social problems without the government’s involvement, which will eventually contribute to the formation of civil society. Different characteristics, including gender, age and settlement type, influence people’s participation in these activities. The results of CRRC’s 2015 Caucasus Barometer survey allow us to find out which of the 12 activities included in the questionnaire, the population of Georgia was engaged in most during the six months prior to fieldwork and how this engagement differs by gender, age and settlement type.

A majority of Georgia’s population (58%) reports having helped friends or neighbors with household chores or childcare during the last six months, while a very small share (6%) answered that they have signed a petition or written a letter, or made a phone call to a TV/radio program (5%). A rather small share reports having attended a public meeting to discuss issues that are important for the community (17%).

 Note: Only the share of those answering “Yes” is presented in the charts throughout this blog.

The finding that a majority of the population reports helping friends or neighbors with household chores is unsurprising, given that Georgia is a country where trust, cooperation and compassion between members of primary social groups (such as family, friends, etc.) is especially strong. As CRRC’s 2011 report “An Assessment of Social Capital in Georgia" pointed out, bonding (within group) social capital is rather strong in Georgia, while bridging social capital, which links representatives of different social groups, is weaker.

People living in different types of settlements report different levels of participation in these activities, and differences are especially apparent between those living in the capital and rural settlements. For example, 62% of the Tbilisi population donated money to a church or a mosque during the last six months, while 52% of people living in rural settlements reported the same. A smaller share of the rural population reported making a contribution to a non-religious charity, including donations by sms and giving money to beggars, compared to the urban population. However, a larger share of people living in rural settlements helped friends or neighbors than the share that did so in Tbilisi or other urban settlements. As for volunteering, there are no visible differences between people living in different settlement types.

Note: Only activities in which at least 20% of the population reported having been engaged in are presented in the chart above and throughout the remainder of this blog.

Involvement in these activities is significantly lower among people who are older than 56, while people between the ages of 18-35 and 36-55 participate at similar rates.

Interestingly, a larger share of men compared to women helped someone to resolve a dispute, volunteered, and helped a friend or neighbor with household chores. The only activity where women’s involvement is higher than men’s is donating money to a church or mosque.

This blog post has looked at the Georgian population’s involvement in different activities, and how this involvement varies by age, sex and settlement type. To see more data from the Caucasus Barometer survey, visit CRRC’s online data analysis tool.

Environmental issues in Georgia: a concern for all?

[Note: This post was first published on Friday, July 15th at the Clarion.]

By Sacha Bepoldin

The United Nations Research Institute for Social Development (UNRISD) has highlighted that “Environmental decline adversely affects the health, well-being and livelihood opportunities of the individuals affected by pollution or natural resource depletion. Soil erosion, deforestation, the loss or depletion of animal and plant species limit the productive opportunities of vast numbers of people.” In Georgia, according to the 2009 National Report on the State of the Environment of Georgia and the 2012-2016 National Environmental Action Programme, the increasing number of natural disasters, chemical pollution of soils and progressing desertification, mainly in Shida Kartli, Kvemo Kartli and parts of Kakheti, are clear signs of man-made pollution. Both rural and urban inhabitants of Georgia are affected by environmental problems, albeit of a different nature. As public opinion data shows, people assess the importance of environmental problems differently. This blog post examines the salience of pollution as an issue for the settlement where people live and the relative importance of this problem compared to other issues, using the CRRC/NDI August 2015 survey.

One of the questions asked during the survey was, “Speaking of public goods in general, what are the three most important issues in your settlement?” Pollution was the fourth most frequently mentioned issue countrywide, after roads, water supply and gas supply. There are, however, differences in the frequency of naming these issues by settlement type. While pollution was the main issue in the capital, named by 44% of Tbilisi residents, it was named by only 7% of the rural population.

Note: The sum of answers does not add up to 100%, since respondents could name up to three issues. The charts in this blog post display only the five most frequently named issues at the national level.

At the national level, improvement of the water supply and roads are the public’s highest priorities in terms of budget spending, while pollution again ranks fourth. Pollution represents the highest priority for the population of Tbilisi, but only 1% of the rural population thinks spending on pollution should be a budgetary priority.

The rural population likely underestimates the importance of pollution and environmental issues in general. At the same time, a study conducted by the University of Gothenburg highlighted that degrading agricultural practices affect 35% of farmland in Georgia, which is already scarce due to the mountainous landscape of the country. As agriculture, according to official sources, is the main employment sector in the country, such practices threaten the lifestyle and economic opportunities of a large share of the population. Given the disconnect between lack of concern over this issue in rural settlements, on the one hand, and the likelihood that it affects the rural population, on the other hand, a communication campaign focused on environmental protection, especially in rural settlements, could help prevent further environmental problems.

To look through the data in more depth, visit CRRC’s Online Data Analysis platform.

Who should Georgia’s closest economic partners be?

Reports on Georgia’s shifting public opinion of Russia and the West have been widely discussed on this blog and elsewhere. Focusing specifically on economic aspects, the Georgian population thinks both Russia and the EU have a greater influence on the Georgian economy than they should, although this perception is not necessarily based on the country’s actual economic relations with either Russia or the EU. More diverse economic partnerships and the population’s awareness of these partnerships could decrease this perceived influence. Notably, there are signs that many regional economic powers are happy to increase their trade with Georgia: with Iran looking to triple or quadruple trade with Georgia and China investing in Georgia as part of the new Silk Road initiative, new players are stepping into the Georgian economy. Iran and China aside, Turkey has long seen Georgia as a market for Turkish goods and a transit corridor to trade partners in Central Asia, especially now that diplomatic and trade ties between Russia and Turkey have deteriorated. This blog post looks at the Georgian population’s attitudes towards economic relations with several countries, using CRRC-Georgia’s survey on Knowledge and Attitudes towards the EU in Georgia (EU survey), which was carried out for Europe Foundation in 2015.

When asked to choose which countries or unions Georgia should have the closest economic cooperation with, people most often name Russia, the EU, and Turkey. The answers are, however, rather different by settlement type, as well as between ethnic minority and majority populations.

Note: While answering this question, respondents were asked to choose three countries/unions from a list of 14 provided on a show card. 

The choice of Russia for preferred economic partner is notable considering Georgia’s current level of trade with the country. While the share of exports to Russia has increased over the last three years, since Russia lifted the 2006 embargo on Georgian exports in 2013 and 2014, it still only accounts for 7% of exports from Georgia, and 8% of Georgia’s imports come from Russia. As expected, younger, urban dwellers tend to mention the EU as Georgia’s preferred economic partner more often, while older rural dwellers tend to mention Russia. Ethnic minorities mention Russia or Turkey more often than the EU. Compared to the population of the rest of the country, those living in Tbilisi are more likely to mention the EU and new economic partners like China.

The EU and Turkey, Georgia’s largest economic partners, are far more involved in the Georgian market, yet overall, fewer answered they would prefer them as the closest economic partner compared to Russia. While trade with the EU has doubled since 2009, fewer people mentioned the EU as a partner that Georgia should have the closest economic cooperation with in 2015 (47%) than in 2013 (60%). Ironically, the drop in 2015 coincides with the introduction of the Deep and Comprehensive Free Trade Agreement (DCFTA) in 2014, which aims at increasing trade between the EU and Georgia.

In the same survey, a similar question was asked about countries or unions that Georgia should have closest political cooperation with. There is a strong statistical correlation between the answers to questions about political and economic cooperation, with Spearman’s correlation coefficients ranging from .623 to .754 when tested for Russia, the EU, Turkey, China, and Iran.

The majority of Georgia’s population still prefers Russia as an economic partner regardless of Georgia’s growing trade ties elsewhere. There may be a number of possible explanations for this finding, two of which seem quite reasonable. On the one hand, the population is likely not entirely aware of the diversity of Georgia’s trade relations and concomitant economic interests. On the other hand, attitudes towards economic partnership may be influenced by political attitudes.

To explore the data more, take a look at our Online Data Analysis platform.

How Georgia became an upper-middle income country

A year ago, on this blog, we took a look at how Georgia had likely become an upper-middle income country because of the 2014 census. On July 1st, the World Bank announced that Georgia had indeed changed income categories, moving from lower-middle income to upper-middle income. Given the change, we thought it would be worth re-posting the blog post from last year discussing how Georgia became an upper-middle income country:

An interesting implication of the 2014 census: Georgia is likely an upper middle income country

While Georgia has yet to be officially declared an upper middle income country by the World Bank, as a result of the 2014 census, it’s likely to be labeled one after the final census results are published in April of 2016. Interestingly, Georgia likely became one in 2013. Why is this the case and what are the implications? Let’s take a look using the 2014 preliminary census data and a population model developed by Ilia State University’s Giorgi Tsuladze published in a 2014 United Nations Population Fund (UNPF) and International School of Economics at Tbilisi State University (ISET) report.

The 2002 census was way off

In 2002, the Georgian government carried out a population census and found that there were 4.37 million Georgians. This number though was and is widely considered to be suspect. According to the 2014 UNPF report (and notably, Geostat employees at the time), the main problem with the 2002 census was its method of counting the migrant population. Specifically, the 2002 population count included 114,000 migrants who may have been permanently settled abroad rather than temporarily. This number may have been even higher considering that an estimated one million Georgians left the country between 1990 and 2002. Their family members who were interviewed for the census may have been reluctant to report that their relatives had gone abroad and instead reported them as temporary migrants or still in the country for a variety of reasons.

Not only was the census off, but so too were the civil registries which count birth and death registration. Between censuses, governments update population counts based on birth and death registrations, but because many births in Georgia happened and to a certain extent still happen outside of hospitals, births are not always registered. Also important are the lack of death registrations.

Recognizing these problems, Giorgi Tsuladze, a Professor at Ilia State University, made a downward adjustment of the population figure from the 2002 census, an upward adjustment to the birth rate, and a decrease in the estimate of the average life expectancy to estimate the population. In turn, his estimates of the population are quite close to what the preliminary 2014 census results tell us about the Georgian population – there are about 3.7 million people in Georgia (excluding South Ossetia and Abkhazia).

	Geostat population estimate (thou.)	Tsuladze population estimate (thou.)
2002	4,372	4,001
2003	4,343	3,966
2004	4,315	3,931
2005	4,322	3,899
2006	4,401	3,869
2007	4,395	3,839
2008	4,382	3,814
2009	4.385	3,797
2010	4,436	3,790
2011	4.469	3,786
2012	4,498	3,777
2013	4,484	3,768

Source: Tsuladze, G.; N. Maglaperidze and A. Vadachkoria. 2002. Demographic Overview of Georgia. Tbilisi, UNFPA. Cited in
Hakkert, Ralph, Gulnara Kadyrkulova, Nata Avaliani, Eduard Jongstra, Lasha Labadze, Maka Chitanava, and Nino Doghonadze. Population Situation Analysis (PSA) 2014. Rep. Tbilisi: United Nations Population Fund, 2015. Print.

Income classifications

The second important part of this story is understanding how countries are classified into income groups. The World Bank classifies countries by Gross National Income per capita (slightly different than Gross Domestic Product per capita – see here for exact definitions).

Countries with a per capita GNI of less than $1,045 are considered low income countries. Countries with greater than $1045, but less than $4,125 GNI/capita are classified as lower-middle income countries. Countries below $12,736 but above $4,125 GNI/capita are considered upper middle income countries, and countries above the $12,736 mark are considered to be upper income countries.

Since, a country’s income classification is based on the size of its population, and as we saw above, Georgia’s official population size was inflated by a fairly sizable margin for the past twelve years, Georgia’s GNI per capita (as well as GDP per capita) was underestimated.

Georgia probably moved from the lower-middle income to the upper-middle income grouping in 2013 when GNI per capita moved from from $3914 in 2012 to $4240 in 2013 (based on Tsuladze’s population estimates). In 2014, using the preliminary census data, Georgia’s GNI was $4489/capita. Hence Georgia has very likely moved income groups, barring a major miscount of the preliminary census data of roughly 330,000 people.

Why does this matter?

Well, it is good and bad news for Georgia.

To start with the bad, aid is sometimes distributed based on a country’s economic status. There are many other important factors at play (see here for a discussion of the subject), but nonetheless it is often considered in aid decisions. Hence, Georgia may expect lower levels of aid in the coming years as its per capita economic statistics are adjusted upward following the finalization of the 2014 census in 2016.

When it comes to the good news for the country, Georgians are doing better than the numbers suggested. This does not change the facts on the ground and widespread poverty experienced in Georgia, but in the long run it can lead to a number of benefits. For instance, foreign private capital flows may increase as the country may be perceived as a more enticing investment environment, having moved to a higher income category.

The upward adjustment will also be important for Georgia’s Euro-Atlantic integration prospects. One of the key factors which the EU has identified as a barrier to prospective membership for countries in its neighborhood is low income levels, and as Georgia’s income level gradually increases, it will make Georgia a more attractive partner country. Notably, the lower population also means that per capita income is increasing at a higher rate than previously thought. In the short term, it may also help ease fears over migrant flight from Georgia (which is likely an inhibiting factor at present for Georgia in the EU visa liberalization process). It is important to note that if income inequality persists in Georgia, economic growth is unlikely to deter migrants from attempting to make their way to the EU, though a fuller treatment of this subject is beyond the scope of this post.

On the grand scheme of things, the adjustment is good as well. While not necessarily good for Georgia, countries in more dire straits may receive more aid that would have been aimed at Georgia. Better decisions about what kind of aid the country receives may also result from the more accurate data and income categorization.

Although we should not expect to see the official income categorization change to upper-middle until after Geostat has published the final census numbers and adjusted its population estimates for 2002-2014, it should occur in the next few years.

To take a look through the preliminary 2014 census results, take a look here, and for the estimates of the population size which this blog is based on as well as other interesting data and analysis on Georgia’s demographic situation, take a look at the UNPF/ISET report, here. Notably, Georgia is not the first and surely not the last country to have a major economic indicator readjustment based on something besides economic growth. Ghana and Nigeria both have had large changes to their economic indicators in recent years caused by how GDP was calculated rather than growth with interesting implications. Listen to this Planet Money story to find out more.