How Many Tetri Are in a Lari? The Importance of Municipal Statistics for Good Governance
Framing 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 potential 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 current 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, 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 it 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 this chart. 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 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 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 the 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 would require 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. 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. Likewise, citizens 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 does not know how many tetri are in a lari.
Each government of Georgia has had a wide range of successes; but how do the public see these successes from Shevardnadze’s time to the present?
When Eduard Shevardnadze’s government is mentioned in Georgia today, it tends to be connected with the dark times Georgia experienced in the 1990s. Yet, his government also saw the introduction of the Georgian Lari, resulting in a stable exchange rate. The United National Movement is credited with fighting petty corruption, and oversaw a period of relatively high economic growth, while at the same time failing to avoid the disastrous 2008 war with Russia. The Georgian Dream government too is seen as having had some success, for example, in reducing the prison population, from what was among the highest in the world. At the same time, incidents like the Gavrilov Nights and issues around election integrity are often cited as failures.
While each Georgian government has had a range of successes, as described in another post published today, they have each had their own spectacular failures.
From Shevardnadze’s failure to establish state power outside Tbilisi, to the human rights abuses under the UNM and Gavrilov’s Nights under Georgian Dream, every government has had significant shortcomings.
While these are some of the most memorable, little research has been conducted on what the public thinks are the largest failings of each government. Data released on Tuesday from a CRRC Georgia survey conducted in partnership with the Levan Mikeladze Foundation and Carnegie Europe provides a picture of the public’s views of the largest successes and failures of government.