The Romanian city with the most permanent residents in 2023 was Bucharest, with over 2.14 million inhabitants. Iași was the second largest city, populated by around 392.6 thousand people, followed by Cluj-Napoca and Timișoara.

This statistic shows the biggest cities in Romania in 2021. In 2021, approximately **** million people lived in București, also known as Bucharest, making it the biggest city in Romania.

The city with the highest pollution index in Romania in 2025 was Bucharest, with an index of 75.4, followed by Iasi. Brasov ranked fifth, with a pollution index of 35.7.

Population in the largest city (% of urban population) in Romania was reported at 16.89 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Romania - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Romania RO: Population in Largest City data was reported at 1,830,515.000 Person in 2017. This records a decrease from the previous number of 1,839,695.000 Person for 2016. Romania RO: Population in Largest City data is updated yearly, averaging 1,886,986.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2,060,655.000 Person in 1992 and a record low of 1,002,300.000 Person in 1960. Romania RO: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Population in largest city in Romania was reported at 1767520 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Romania - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Abstract

The survey is the follow-up of the Diagnostic Review on Consumer Protection and Financial Literacy conducted by the World Bank in 2008-2009. The Diagnostic Review in Romania was the fourth in a World Bank-sponsored pilot program to assess consumer protection and financial literacy in developing and middle-income countries.1 The objectives of this Review were three-fold to: (1) refine a set of good practices for assessing consumer protection and financial literacy, including financial literacy; (2) conduct a review of the existing rules and practices in Romania compared to the good practices; and (3) provide recommendations on ways to improve consumer protection and financial literacy in Romania. The Diagnostic Review was prepared at the request of the National Authority for Consumers' Protection (ANPC), whose request was endorsed by the Ministry of Economy and Finance. Support was provided by the National Bank of Romania (BNR), which supervises banks and non-bank credit institutions. Further assistance was given by the supervisory commissions for securities (CNVM), insurance (CSA) and private pensions (CSSPP).

The Diagnostic Review found that the basic foundations needed for consumer protection and financial literacy are in place in Romania but they benefit from further strengthening support. The Review proposes improvements in six areas: consumer awareness, information and disclosure for consumers, professional competence, dispute resolution, financial education and financial literacy surveys.

Consequently, in 2010 the World Bank commissioned a nation-wide survey of the levels of financial literacy. A consultant (sociologist Manuela Sofia Stanculescu) developed the survey methodology (sampling methodology and questionnaire) in line with the Financial Literacy Survey in Russia (the World Bank, 2008) and the baseline survey Financial Capability in the UK (Financial Services Authority, 2005).2 The final form of the questionnaire was agreed with representatives of the National Bank of Romania (BNR), the Romanian Banking Institute (IBR), the National Authority for Consumers' Protection (ANPC), and the Financial Companies Association in Romania (ALB). The Institute for World Economy (Romanian Academy) collected the data in May 2010.

The main objective of this work is the establishment (and later the evaluation) of a well targeted national program of financial education.

Geographic coverage

National

Analysis unit

Household, individual

Universe

Non-institutionalized persons aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the survey is probabilistic, two-stage, stratified, representative at national level with an error of +/- 3% at a 95% confidence level.

The sample is based on two stratification criteria: (i) historical region (8 regions) and (ii) type of locality (7 types depending on the city size, in urban areas, and on the synthetic index of community development,4 in the rural ones).

The sample volume is 2048,5 out of which 148 cases represent a boost of persons aged 16, 17 or those had their 18th birthday after November 2009.6 Respondents were randomly selected from electoral registers corresponding to 185 voting sections (randomly selected), located in 141 localities (77 communes, 63 towns/cities and the capital Bucharest).

The sample includes a slight over-representation of men, rural respondents, and elderly particularly due to the boost of young but also to the fact that people left abroad concentrate among the 25-44 age category. Nevertheless, the sample fairly reproduces the structure (by gender, age categories and area of residence) of the country population 16+ years according to the data for 2009 provided by the National Institute for Statistics. Socio-demographic structure of the sample is presented in table 3 of the survey report.

Demographic data and data regarding the use of financial services were collected for all members of respondents? households. In the respondents? households live 5406 persons overall. This extended sample has also a slight over-representation of rural respondents and an under-representation of children (0-14 years) and persons 25-24 years (most probably young people who left abroad with children).

MORE INFORMATION ON THE SAMPLING METHODOLOGY

Sample volume: 2,200 non-institutionalized persons aged 18 or older. In addition, the sample will be boosted with 180 persons aged 16-18 years old. Overall, at least 2,000 valid questionnaires should be completed during fieldwork.

Type of the sample: Probabilistic, two-stage, stratified, representative at national level, with an error of +/- 2.8% at a 95% confidence level.

Stratification criteria: The sampling scheme is based on two stratification criteria

(a) Historical region (8 regions) (b) Type of locality, with 7 theoretical strata

i. Urban areas - 4 strata 1. very small towns under 30 thou inhabitants 2. small towns 30,001-100 thou inhabitants 3. medium cities 100,001-199 thou inhabitants 4. large cities 200 thou inhabitants or more

ii. Rural areas - 3 strata determined based on the synthetic index of community development 37 1. poor communes (the 30% communes with the lowest level of development within the country) 2. medium developed communes 3. developed communes (the 30% communes with the highest level of development within the country).

Sampling stages: The sampling scheme includes two stages.

Sampling units: There are two sampling units corresponding to the two sampling stages. In the first sampling stage, voting sections are selected and in the second stage, non-institutionalized persons aged 18 years or more.

Selection: Random selection in all sampling stages.

Sampling scheme: In the first stage the sample is distributed proportionally with the volume of population for each of the 56(= 8 x 7) theoretical strata different from zero.

The corresponding number of voting sections for each strata is determined taking into account on the one hand, the volume of each strata sub-sample (= sample size x share of total population in that strata) and, on the other hand, a minimum level of 10 questionnaires for each sampling point. The voting sections which will represent sampling points are then randomly selected based on the exhaustive national list of voting sections (the latest available from the Permanent Electoral Authority).

The sample has 188 sampling points (voting sections) of which 104 are in urban areas, and 84 are in rural localities, including the capital city.

For each sampling point is computed the number of corresponding questionnaires by dividing the strata sub-sample by the number of sampling points of that strata. In the second sampling stage, the electoral registers corresponding to the voting sections (selected as sampling points) are used as sampling frame. Non-institutionalized persons aged 18 or more are randomly selected from the electoral registers based on the mechanical step method.

In those localities where the electoral registers are not available (or the municipality do not grant access), the random route method will be used. All these cases will be specified and explained in the fieldwork report, except for Bucharest, where the random route method will be used for all voting sections, as the rate of replacement from electoral registers is high in all national representative surveys.

The electoral registers include only persons 18 years or more. Accordingly, the sample will include a boost of persons aged 16, 17 or persons that had their 18th birthday after November 2009.39 For each voting section, one person aged 16-18 years will be added. They will be selected based on the random route method.

Mode of data collection

Face-to-face [f2f]

Response rate

The overall response rate of the survey is 95.2%. More detailed information is provided in "Table 2 Response rates and quality of the sampling frame by sampling method (%) " of the survey report.

The Cultural Vitality of Cities in Romania is a study conducted by the National Institute for Cultural Research and Training with data collected for five categories of indicators, for the period 2010-2016 at the level of 46 cities, which we consider the main poles of cultural development/potential for cultural development, except for Bucharest which was not included in the analysis due to the special nature of the level of cultural facilities and services that dominate the rest of the cities in terms of cultural vitality. The cultural vitality of cities speaks about the cultural potential of local communities and highlights the development of cities on a cultural level. Vitality indices reflect cultural infrastructure, cultural participation, budget expenditure allocated to the cultural sector, specialised human resources and creative industries in the most important cities in the country. In the top of cultural vitality there are also appearances of cities with a spectacular evolution, being cases where propulsion in the top was achieved due to a certain category of indicators rather than a general picture of strong elements of vitality. This is the case for cities such as Craiova, Satu Mare, Târgu Jiu, Bistrita, Sfântu Gheorghe. Studies can be found on the INCFC website: https://www.culturadata.ro/publicatii/

In 2019, Brasov was the city with the highest purchasing power index in Romania, reaching *****. Romania's capital, Bucharest, ranked fourth, with a purchasing power index of *****.

Nearly ** percent of respondents stated that health services was the most important issue in their city in 2020, followed by air pollution and road infrastructure. At the same time, ** percent of Romanian respondents complained about the lack of green spaces in their city, and seven percent were concerned about the architectural quality of the buildings in their city.

Romania RO: Population in Largest City: as % of Urban Population data was reported at 17.328 % in 2017. This records an increase from the previous number of 17.324 % for 2016. Romania RO: Population in Largest City: as % of Urban Population data is updated yearly, averaging 16.910 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 18.986 % in 1977 and a record low of 15.918 % in 1960. Romania RO: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

The city in Romania with the highest crime index was Constanta with ****, it was followed by Craiova, with an index value of ****. These values are high, given that the higher the index value, the higher the level of crime. Nevertheless, theses values are way above the overall crime index in Romania, which by 2024 increased to *****.

Nearly four out of ten respondents were of the opinion that real estate prices in the main cities in Romania would stay the same in 2021. However, ** percent of respondents expected real estate prices to decrease in the next 12 months.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Credit Unions and Financial Cooperatives for Romania (ROUFCBODULNUM) from 2008 to 2015 about branches, credit unions, Romania, financial, and depository institutions.

The number of individual dwelling transactions has fluctuated in the past few years, reaching the lowest number in 2019 at 113,800, and the highest peak recorded in 2021–183,000. Bucharest was by far the most busy real estate market, accounting for 35.5 percent of total transactions. Out of all the large cities, only Pitesti and Ploiesti had a positive number of transactions growth compared to 2022—up by 16 percent each.

The purpose of this research is to identify the best time of year for open-air tourism in ten of Romania's most populated cities. The climate conditions were assessed using the enhanced tourism climate index (ETCI) on a temporal scale of 10 days over 61 years (1961-2021) to determine the best times of year for outdoor tourism. For observing any change, we compared the last 10 years (2012-2021) to the entire period of the current study. Daily mean temperature, relative humidity, wind speed, precipitation and sunshine, maximum temperature, and minimum relative humidity were employed. The trend detection methods were the Mann-Kendall test combined with Sen’s slope and the parameters considered for change detection were the frequency of days ranked as good, very good, excellent and ideal for outdoor tourism; the duration of the occurrence period, the first and the last day of occurrence of each class. We found that the most appropriate weather for open-air tourism usually begins in the third 10-day period of April and ends during the second 10-day period of October. This study could become an extremely useful tool for better planning events for tourism and recreation in the short and mid-term.

Romania's industrial and logistics real estate market showed robust growth in 2023, with average rents reaching *** euros per square meter. This figure reflects the increasing demand for industrial space across the country, particularly in major cities like Bucharest and Cluj-Napoca. The capital city, Bucharest, commanded the highest rent at *** euros per square meter, underscoring its position as the primary hub for industrial and logistics operations in Romania. Expanding industrial landscape The industrial sector in Romania has been experiencing significant expansion. By 2024, the country's modern industrial stock reached *** million square m, with ******* square m of new leasable space added, which represents a ** percent increase compared to 2023. Bucharest continued to dominate the market, accounting for ** percent of the new industrial supply. The growth trend is expected to continue, with an additional ******* square m projected to be completed by the end of 2024. Regional centers attractive to investors While Bucharest remains the focal point of industrial and logistics activities, other regions are also seeing development. The Western and North-Western regions collectively held about **** percent of the total stock. Cities like Cluj-Napoca, Timișoara, and Brașov are attracting investments, with rental rates slightly lower than the capital. In terms of available space, over ******* square m were ready for rent in 2023, with Bucharest holding nearly ** percent of this stock. Timișoara followed as the second most available market with ****** square m, indicating growing interest in regional industrial hubs.

Abstract This data repository contains data at municipality data in Romania about population and population change, wages, agregate turnover, sectorial employment, and e,ployees by types of capital (FDI-capital, domestic and public). The data explores the spatial dynamics of the labor market at the subnational level, providing insights into wage moderation and repression in export-led growth regimes in Central and Eastern Europe. The data are used to investigate the spatial concentration of specialized economies within cities and their periurban areas, where the regional labor force is leveraged to moderate wage increases and attract populations in economies heavily reliant on FDI, in Romania. The data and models are used to show that this leads to the formation of ‘enclave economies’, characterized by localized labor regimes shaped by territorial zoning strategies that regulate labor migration and economic zoning of capital. Employing spatial regression with SARAR-SUR, we model population change and concentration to identify different labor regimes in regional enclaves, examining the impact of wages, sectorial employment, and types of capital. Our findings demonstrate that the sub-national distribution of FDI-led growth in Romania primarily revolves around labor-intensive activities and low capitalization costs, rather than urbanization. Additionally, we observe that changes in employment within public services are not significantly associated with population changes, suggesting that the state does not play a major role as a competitor in the labor markets. Furthermore, our analysis captures the specific labor requirements of multinational firms operating in business services and manufacturing, highlighting the negative impact of foreign companies in the business service sector on the population of core cities.

Data Dependent Variable - The dependent variable in our analysis is population change, which we measure using the population ratio of 2012 to 2011 on a natural logarithmic scale due to its mathematical similarity to percent growth. Dual citizens of Romanian descent from Moldova and Ukraine are drawn to the eastern region, particularly border towns in Suceava, Botoșani, Iași, and Vaslui counties. These immigrants often use their Romanian citizenship to migrate within the EU. To account for this regional trend, we substracted from the population the number of emigrants from each locality over the past decade (both in 2011 and 2021)

Independent variables - We model population change at locality level using wages as a pull factor, capital type and economic sector. To assess the impact of personal income on employment, we have utilized personal income tax data to estimate aggregated wages at the local level as provided on the data portal of the Romanian's Ministry of Regional Development and Public Administration. - The Romanian National Institute of Statistics categorizes individuals as employed or working to account for those not receiving wages, including self-employed and contributing family workers. Agricultural workers make up most of the un-waged working population. To measure waged relations within the labor pool, we used the ratio of waged employees to the active age population (16-65 years), which serves as a measure of the size of the labor market at the local level. - We obtained employment data from the National Institute of Statistics, which provided aggregated balance sheets of all companies at the subsidiary level for 2011 and 2021. - We used a dummy variable to distinguish between foreign and domestic companies, and we separately aggregated the number of employees working in local and foreign companies. For the purposes of this study, a company was deemed foreign if it was incorporated in Romania and had 50% or more of its equity shares or share capital owned by a natural or legal person residing outside of Romania. - NACE codes related to manufacturing were used to isolate the companies of interest. To differentiate business services from other service activities, NACE codes related to activities such as information and communication, financial and insurance activities, real estate activities, professional, scientific, and technical activities, and administrative and support service activities were used to filter companies. The aim is to capture the growth of outsourcing in this sector while excluding other service activities such as social services and commerce and logistics. - The first layer of municipalities surrounding the 260 cities in Romania are referred to as periurban area, as they represent the transitional zone between urban and rural environments (Dadashpoor and Ahani, 2019; Stahl, 1969). Out of the 319 cities in Romania, 59 towns are located within the periurban areas adjacent to larger cities in terms of population. This classification creates three typologies of administrative territorial units (3180): core cities (260), periurban localities (1330), and villages (1590). These locality types (core city, periurban localities, and villages) were transformed into dummy variables and utilized as interaction variables. - In Romania, periurban areas are defined by Law no. 246/2022, which focuses on metropolitan areas and involves modifications and additions to certain normative acts. For municipalities, the periurban area includes the first two layers of municipalities surrounding the core city, while for cities, it encompasses only the first layer. However, it's important to note that the majority of population growth occurred in the first layer of municipalities. Due to this, we considered the periurban area for all 260 cities to be equivalent to the first layer of municipalities. - An alternative approach for analysis would have been to use the functional urban area, as defined by Eurostat, which covers the localities within the commuting range of the central city. Nevertheless, this scale is unsuitable for examining population change, as it shows only a negligible percentage change (0.1%) between 2003 and 2020. In contrast, periurban areas exhibited a much more significant population change of 5% during the same period. Consequently, we opted to present our analysis using the concept of periurban areas.

Model specification - We employed two specifications for the independent variables: a cross-sectional specification for 2021 and a first-differencing strategy that measures the difference between municipality-level values in 2011 and 2021. We used both specifications to assess the effect of employment composition across municipalities on population dynamics. The cross-sectional specification assumes that larger employment markets with more employees out of the active age population act as population magnets. However, it is influenced by idiosyncratic factors specific to each municipality. The first-differencing strategy controls for individual-level effects and plays a similar role to a fixed effect for two discontinuous time points, accounting for time-variant factors . It assumes that an increase in employment opportunities at the municipality level generates an overall population increase, irrespective of the size of the labor market out of the active age population. - Despite utilizing all three categories of the locality type simultaneously as a dummy system, multicollinearity did not pose a problem. The interaction term divided the independent variable into spatial components, forming spatial regimes, as described by Anselin and Rey (2014). Furthermore, when the locality type was used as an interaction effect in all models, the continuous variable only covered a portion of the population, specifically those within an economic sector. It did not account for the entire population of employees.

Model selection - We employed a three-stage least squares approach using Seemingly Unrelated Regression (SUR) models. We incorporated Spatial Autoregressive terms and Spatial Autoregressive Disturbances (SARAR). R package spsur (Angulo et al., 2021) to estimate the model, and a row-standardized queen contiguity spatial weights matrix was computed from the geometries of Romanian localities to perform the regressions.

Files - data cross.csv: The data for the cross-sectional specification for 2021 - data diff.csv: The data for the first-differencing strategy that measures the difference between municipality-level values in 2011 and 2021 - model.csv: The data which contain the analysis for preparatory analysis and model selection - model A.R: The code in R with the preparatory analysis and model selection - model B.R: The code in R with the analysis with the two specifications for the independent variables - Vizualization.twbx: The Vizualization in Tableau of the data and the predicted values of the different models - codebook data cross.csv - codebook data diff.csv - codebook model.csv

As of 2023, almost half of Romania's video game developer studios were based in the capital Bucharest (101 studios), followed by Cluj-Napoca with 36 studios. Other major Romanian cities that were active in the gaming sector were Iași, Timișoara, Ilfov and Brașov.

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