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.