Who reported seeing dzveli bitchebi engaged in the elections?

This article first appeared on the Caucasus Data Blog, a joint production of CRRC Georgia and OC Media. It was written by Dustin Gilbreath, Deputy Research Director at CRRC Georgia.

The views presented in this article do not represent the views of the ISFED, CRRC Georgia, or any related entity. 

Opposition parties and some observers reported the engagement of dzveli bitchi, a term roughly equivalent to wise guys or hoods in English, in election-related activities prior to the October 2021 local elections. Data from an ISFED and CRRC Georgia survey suggests that a substantial share of the public also reported seeing the same.

While the survey on the pre-electoral environment saw a large share reporting seeing dzveli bitchebi near election precincts in the year prior to the elections, reported sightings varied significantly based on which political party people support. Even so, some Georgian Dream supporters still reported seeing dzveli bitchebi around election precincts.

One in nine (11%) of Georgia’s adult population reported seeing a dzveli bitchi near a voting precinct in the past year, 83% reported they had not, and 6% either did not know or refused to answer. 

The public was also asked if they thought the participation of dzveli bitchebi in elections was acceptable. Most of the public thought their participation was completely unacceptable (48%) or unacceptable (39%). Only 4% of the public viewed this as acceptable. A further 8% reported they did not know whether it was acceptable or not and 1% refused to answer the question. 

Those that had seen a dzveli bichi around the polling station felt more strongly that this was unacceptable.

A regression analysis suggests that a number of variables predict whether or not someone reported seeing dzveli bitchebi around the election precinct. 

Wealthier households were more likely to report seeing so than people in poorer households.

People in Tbilisi were more likely to report seeing dzveli bitchebi around the election precinct than people in other urban areas, controlling for other factors. 

By far the strongest predictor of whether or not someone reported that they saw dzveli bitchebi near the voting precinct was the party someone supports. Controlling for other factors, a Georgian Dream supporter had a 3% chance of reporting so while an opposition supporter had a 23% chance of reporting the same, a 20 percentage point gap.

The large partisan gap on this issue may suggest that opposition supporters were reporting they saw dzveli bitchebi around election precincts in order to discredit Georgian Dream, knowing that the survey would eventually be public. This could be the case. But, the fact that some Georgian Dream supporters reported the same thing suggests that there were at least some dzveli bitchebi around election precincts in the pre-electoral period.

Dzveli bitchebi tend to be an urban phenomenon. In this regard, one might suggest that the people in rural areas are reporting what they saw on television. This again may be the case. 

A model comparing people who do and do not watch TV in different settlements suggests that people outside Tbilisi that do not watch TV were less likely to claim they saw dzveli bitchebi around polling stations. However, the rates of reporting seeing dzveli bitchebi in Tbilisi are not significantly different for those that do and do not watch TV.

While one in nine in Georgia reported seeing dzveli bitchebi around the electoral precinct prior to the elections this year, the vast majority did not approve of their engagement in elections. 

The data indicates that whether or not all of these reports are true, some of them likely are, given that even Georgian Dream supporters occasionally reported seeing dzveli bitchebi around the election precincts and that the reporting rates are consistent in Tbilisi, where dzveli bitchebi would most likely be, whether or not someone was reporting what they saw on TV on the survey.

Note: The above analysis is based on a logistic regression. The independent variables include gender, age group (18-34, 35-54, 55+), settlement type, education level, an index of durable goods (proxying wealth), ethnicity (ethnic minority or ethnic Georgian), employment status (not working, working in the private sector, working in the public sector) and partisanship. 

Municipal Transparency Ratings

In 2014, CRRC-Georgia requested information from Georgian municipalities through the Ministry of Regional Development and Infrastructure (MRDI). Only 37 of 63 municipalities responded to our request. 31 of these provided some information. Only 17 provided complete information in response to our request. Based on this experience, CRRC-Georgia decided to rate the municipalities’ responses. While a score of 0 means that the municipality didn't respond at all, municipalities received 1 point for at least responding to the letter. Municipalities which received a score of 2 provided us with some of the information requested, while municipalities that scored 3 provided all requested information.
 

Months later, we repeated our endeavor. However, this time, freedom of information requests were submitted directly to the municipalities. This time around, we asked for data in an Excel file. Almost all the municipalities responded, however, the quality of responses varied. 44 municipalities sent Excel files and 39 contained the requested information. An analogous rating system was used to rate the responses to this round of freedom of information requests.
 

Based on these two rounds of our unintentional rating of municipal transparency, we created an index of municipal FOI transparency, summing up the two scores. Zero corresponds to no response for both rounds of requests while six reflects response with full data provided.

How many Tetri are in a Lari? The importance of municipal statistics for good governance

[Note: This post was co-published with Eurasianet and authored by Koba Turmanidze, CRRC-Georgia's Director.] 

The government of Georgia committed itself to collect and publish policy-relevant data in a timely manner under the Open Government Partnership. Yet while most ministries and state agencies are happy to provide national-level statistics, they often fail to break them down to the municipal level. 

As a result, if you think about it in monetary terms, the current system means that officials do not know how many tetri are in a lari. 

Reliable municipal statistics can contribute to good governance in several important ways. First, municipal-level data can let citizens assess the quality of state services they receive compared to other municipalities, or to the national average. Second, municipal-level data can help policy makers improve the targeting of programs, and therefore, spend public money more efficiently. Third, it can help both government and citizens evaluate the successes and failures of municipal governance.

The following scenarios highlight the benefits of comprehensive data on a local level: 

Imagine you want to move from one town to another for a better job. You consider moving with your spouse and a child of school age. However, your spouse does not want to move, arguing that your town has much better public schools than the new one. In such cases, it would help if you could look at municipal-level education data to see if schools in the two towns are similar or different in terms of the student-teacher ratio, exam scores, the success rate on national examinations, etc. 

Imagine you are a civil servant and are working with an investor to build a new factory in a municipality. You want the factory to be built in the municipality where its social impact will be highest. To persuade the investor, you use municipal statistics to demonstrate that the municipality of your choice has high unemployment, yet its labor force is younger and better trained than in comparable municipalities. 

Imagine you are an analyst in a think tank and your task is to advise the government on whether to extend a poverty reduction program or not. The government claims the program helped to reduce poverty by two percentage points nationwide, but you have reasons to suspect that the reduction happened in certain settlements, whereas in others the program had a negligible impact. You look at relevant data on the program and poverty statistics, and conclude that the program’s effect across municipalities was truly unequal. Importantly, it made no difference in the most economically deprived communities. Therefore, you advise the government to redesign the program to improve its impact on the communities with the highest poverty rates, before pouring more money into it. 

Unfortunately, we can only imagine the above. These three scenarios remain purely hypothetical, since, in Georgia, reliable municipal data is rarely available on education, employment and poverty. 

CRRC-Georgia’s repeated interactions with a multitude of state agencies over the past three years have uncovered at least three problems regarding municipal data. First, when municipal data of potentially good quality exists, it is often not processed and made available to the public. Second, if municipal data is accessible, its quality is often questionable. Third, municipal data often does not exist at all, since the responsible agency does not recognize its value, or is unable to collect it due to lack of relevant training. 

Below are three concrete cases that correspond to these three problems: 

Case 1: Until recently, the National Assessment and Examination Center (NAEC) maintained detailed and high-quality data on the results of the Unified National Exams (UNE) for many years. The data allowed one to see which municipality’s and even school’s students were most successful in the exams. Such data was not proactively shared on the Center’s website, but was available upon request. The situation changed when the Center introduced electronic applicant registration in 2011. Under the new system, an applicant’s place of residence and school was no longer recorded. However, the Center could still identify the municipality based on an applicant’s ID number. 

For the 2015 UNE results, NAEC processed the data this way and made the data file available on its website. However, when CRRC-Georgia requested the same data for 2016, the Center turned the request down, arguing it no longer processed data per municipality, and would not do so for our sake. As a result, it is no longer possible to analyze municipal- or school-level performance and map it as we did in this blog post. At the time, this map drew attention to large differences in educational attainment countrywide, including an important fact – that Unified National Exam scores in Upper Ajara were among the worst in the country. In part in response to this fact, AGL, a Norwegian company building a hydro-electric dam in upper Ajara created a tutoring program for students in the region to help them prepare for the exams. If the NAEC withholds such data, it will hinder the ability of interested parties to spot trends and develop remedial policies.

Case 2: The Social Service Agency (SSA) is a leading organization in the country in terms of providing access to comprehensive data on poverty and targeted social assistance at the national and municipal levels. Among other statistics, the Agency reports monthly data on applicants and recipients of social aid. The 2014 census data, however, casts some doubt on whether the SSA poverty statistics are trustworthy. 

The scale of mismatch between the agency’s data and the 2014 census results is evident from the chart below. Using Geostat’s population estimates, the agency calculates the share of the population registered for targeted social assistance in each municipality. The census was conducted in November 2014, so it is possible to re-calculate the share of those registered for targeted social assistance based on the census data and compare it with the agency’s estimates. 

Let’s take the extreme case of Lentekhi. The agency reported that 44 percent of the population applied for targeted social assistance. When the census data is used, the finding is that 89 percent of Lentekhi residents registered for social assistance. Thus, the SSA estimate was 45 percentage points lower than the actual percentage. The chart below plots the differences between the shares of the population registered for targeted social assistance, as calculated and reported by the SSA on the one hand, and updated calculations based on the 2014 census data on the other, for each municipality in November 2014. Overall, the agency underestimated the share of applicants by 14 percentage points, on average. However, publicly available data has yet to be adjusted based on the 2014 census.    

Case 3: In a number of cases municipal statistics do not exist. Measuring the scale of economic activity (level of employment by employment sector, total value added, etc.) in every municipality would require large-scale surveys that are both time-consuming and expensive. Geostat’s periodic Integrated Household Survey cannot provide this information, due to the cost that such a large sample size would entail. 

However, the Revenue Service (RS) under the Ministry of Finance of Georgia could help solve this challenge. Based on taxpayers’ IDs, the agency can provide information about the number of taxpayers, be it individuals or organizations, and amount of taxes collected in each municipality. This information would serve as a good proxy of economic activity by municipality. However, as the RS told CRRC-Georgia, it can only break the data down for regions. Officials claimed that breaking the data down further was not possible. 

Undoubtedly, the collection and analysis of municipal data requires additional resources. However, the three concrete cases highlighted above show that a little increase in awareness regarding municipal data could go a long way toward promoting better municipal governance in important ways. Investors could have clearer insight into the best investment destinations, for one. Civil society groups would also have better ways to assess the successes and failures of government actions. Citizens likewise would have a better idea of where their place of residence stands compared to other parts of the country, or the national average. Government could have better tools to ensure equal access to services in the country, or to achieve efficiencies in the provision of services. 

All of this is possible. Unfortunately, it is not reality. And as a result, the government doesn’t know how many tetri are in a lari.

Electoral Notes- Municipal Elections, 2014

Dustin Gilbreath, a Research Consultant at CRRC-Georgia, has written electoral notes on yesterday's municipal elections which were published on the web-magazine Liberali.The notes discuss the results of the elections, background and significance, changes to electoral legislation, the pre-electoral environment, capital candidates and campaigns, and outlook for the Georgian Dream Coalition and United National Movement. To read the electoral notes, please click here.

Social Capital, Civic Engagement and Local Self- Government in Azerbaijan

An article entitled “Social Capital, Civic Engagement and the Performance of Local Self- Government in Azerbaijan” by Rafail Hasanov, CRRC 2007 Research Fellowship recipient from Azerbaijan, was published in Nationalities Papers.

The research addresses two questions: what is the role of social capital in civic engagement in the municipalities of three regions of Azerbaijan and how are the norms and networks of civic engagement linked to the quality of public life and performance of local government.

The major finding of the research is that while the legislation offers opportunities for the independence of institutions of local government at all levels, in reality, municipalities are subordinate to executive authority and operate on the basis of an uncertain system of laws and rules, resulting in their limited authority, influence and responsibilities.

Another interesting conclusion is that municipalities are more closely linked with citizens in rural areas, where smaller and less dense communities are typical. However, many citizens still mistrust this relatively new local institution. Moreover, the overall involvement of citizens in all sorts of associations and participation in joint NGO-municipality projects, which serve as the basis for functional local government, is low.

Fair and transparent elections, merit-based personnel selection and expanding municipalities' financial resources are the areas that would need the most improvement in Azerbaijan to ensure responsible and effective local self-government, according to the author.

As a way to increase the efficiency of municipalities, the study recommends regular reporting to the population about the work that has been completed by municipalities, frequent meetings with the population and responsiveness to people’s needs, budgetary transparency and increased staff competence. It is also necessary to gradually replace the currently prevailing vertical networks of civil participation with the horizontal form of interaction in communities.

Social Capital, Civic Engagement And The Performance

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For more information, check out the research paper of Rafail Hasanov, or the journal article.