Zimbabwe ZW: Deposit Accounts: per 1000 Adults: Commercial Banks data was reported at 244.070 Number in 2016. This records an increase from the previous number of 79.939 Number for 2015. Zimbabwe ZW: Deposit Accounts: per 1000 Adults: Commercial Banks data is updated yearly, averaging 150.678 Number from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 492.515 Number in 2004 and a record low of 74.763 Number in 2011. Zimbabwe ZW: Deposit Accounts: per 1000 Adults: Commercial Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Banking Indicators. Depositors with commercial banks are the reported number of deposit account holders at commercial banks and other resident banks functioning as commercial banks that are resident nonfinancial corporations (public and private) and households. For many countries data cover the total number of deposit accounts due to lack of information on account holders. The major types of deposits are checking accounts, savings accounts, and time deposits.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

Zimbabwe ZW: Loan Accounts: per 1000 Adults: Commercial Banks data was reported at 218.827 Number in 2016. This records an increase from the previous number of 34.298 Number for 2015. Zimbabwe ZW: Loan Accounts: per 1000 Adults: Commercial Banks data is updated yearly, averaging 11.225 Number from Dec 2004 (Median) to 2016, with 13 observations. The data reached an all-time high of 218.827 Number in 2016 and a record low of 1.421 Number in 2008. Zimbabwe ZW: Loan Accounts: per 1000 Adults: Commercial Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Banking Indicators. Borrowers from commercial banks are the reported number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks and other banks functioning as commercial banks. For many countries data cover the total number of loan accounts due to lack of information on loan account holders.; ; International Monetary Fund, Financial Access Survey.; Median; Country-specific metadata can be found on the IMF’s FAS website at http://fas.imf.org.

Graph and download economic data for Internet users for Zimbabwe (ITNETUSERP2ZWE) from 1990 to 2023 about Zimbabwe, internet, and persons.

Zimbabwe ZW: Internet Users: Individuals: % of Population data was reported at 23.120 % in 2016. This records an increase from the previous number of 22.743 % for 2015. Zimbabwe ZW: Internet Users: Individuals: % of Population data is updated yearly, averaging 2.250 % from Dec 1990 (Median) to 2016, with 24 observations. The data reached an all-time high of 23.120 % in 2016 and a record low of 0.000 % in 1990. Zimbabwe ZW: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

Zimbabwe ZW: GDP: Gross Value Added at Factor Cost: Financial Intermediary Services Indirectly Measured data was reported at 103.451 USD mn in 2017. This records an increase from the previous number of 102.350 USD mn for 2016. Zimbabwe ZW: GDP: Gross Value Added at Factor Cost: Financial Intermediary Services Indirectly Measured data is updated yearly, averaging 76.605 USD mn from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 103.451 USD mn in 2017 and a record low of 22.138 USD mn in 2009. Zimbabwe ZW: GDP: Gross Value Added at Factor Cost: Financial Intermediary Services Indirectly Measured data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Gross Domestic Product: Nominal. Financial intermediation services indirectly measured (FISIM) is an indirect measure of the value of financial intermediation services (i.e. output) provided but for which financial institutions do not charge explicitly as compared to explicit bank charges. Although the 1993 SNA recommends that the FISIM are allocated as intermediate and final consumption to the users, many countries still make a global (negative) adjustment to the sum of gross value added.; ; World Bank national accounts data, and OECD National Accounts data files.; ;

Abstract

The program has three components: (i) results-based contracting; (ii) management and capacity building; and (iii) monitoring.

Under the first component, a portion of financing received by health facilities depends on the quantity and quality of services, with a focus on maternal and child health. User fees have also been abolished on a package of services in districts, with the aim of improving access to care.

The impact evaluation was designed to inform several policy questions including the effects of the RBF pilot program on the utilization and quality of maternal and child health services as well as its effects on health system functioning. The impact evaluation comprised quantitative and qualitative approaches. The evaluation investigated the impact of RBF over a broad range of targeted and non-incentivized services related to maternal and child health services.

Geographic coverage

National

Analysis unit

• Household
• Facility

Universe

The 32 districts were purposively sampled from a universe of 64 districts in Zimbabwe and then pair-matched on predetermined, observable characteristics.

Kind of data

Sample survey data [ssd]

Sampling procedure

The process evaluation applied a retrospective study design and a theory-based evaluation approach that made use of sequential mixed methods. The retrospective design allowed for classification of observations according to the outcomes of interest and retrospectively assessing their exposure and interaction with specific study factors, e.g., contextual factors and intervention design factors. This is facilitated by the theory-based evaluation approach, which examines the interaction between the context, the actors, and the intervention, and then attempts to explain how this interaction works to produce the outcomes of the intervention by interrogating the intervention’s formal theory of change. The theory-driven approach sought to explore the influence of contextual factors on interventions and its outcomes through tracking and validating the program impact pathways.

The DHE (District Health Executives) team members, facility managers, health workers, HCCs and health facility catchment communities within World Bank funded RBF districts constituted the sampling frame from which respondents were purposively drawn to participate in a qualitative inquiry. A multistage sampling approach was used to select the Province, Districts, Facilities and Community Members with each using Purposive Sampling although each had varying “purposes” or specific reasons for selection. The cascade sampling first selected three provinces from the eight rural provinces in which RBF operated. The criteria for selection was based on geographic spread to ensure representation from each geo-region. Then within each of the three selected provinces, one or two districts were selected based on their identification as cases of interest by the project implementing entity. A total of four districts were selected.

Finally, the third stage of sampling involved the selection of one high- and one low-performing facility from each selected district. Of note is that the facilities were in part selected based on performance as defined by their actual earnings relative to expected earnings. The classification of performance therefore entailed initially assessing facility performance using quantitative methods and then proceeding to obtain primary qualitative data. The research team collected primary data through in-depth interviews, focus group discussions, and group interviews. The basic principles of analyzing qualitative data were applied. In particular, the processing of data for each facility made use of a desktop matrix analysis of themes drawn from both the conceptual framework and others emerging from transcripts. A comparison of these qualitative data across facilities enabled the research team to identify trends across facilities and to interpret the findings.

Mode of data collection

Face-to-face [f2f]

Research instrument

Zimbabwe's Health Results-Based Financing Impact Evaluation (Household Survey) 2014 has two structured questionnaires (available in English and downloadable under the "Resources" tab):

1. Woman Questionnaire a. Cover page b. Table of contents c. Consent d. Knowledge on maternal health e. Reproduction f. Contraception g. Trust in health services h. HIV/AIDS and other i. Pregnancy and postnatal care j. Maternal mental health k. Interviewer's Observations

2. Household Questionnaire a. Cover page b. Table of contents c. Consent form d. HH roster e. Economic activities f. HH characteristics g. Health status and utilization h. Growth monitoring i. Child immunization, health and nutrition j. Weight, height and MUAC measurement k. Interviewer's observation

Zimbabwe ZW: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data was reported at 60.000 NA in 2023. This stayed constant from the previous number of 60.000 NA for 2022. Zimbabwe ZW: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data is updated yearly, averaging 47.500 NA from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 60.000 NA in 2023 and a record low of 35.000 NA in 2017. Zimbabwe ZW: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Governance: Policy and Institutions. The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Key information about Zimbabwe Number of Subscriber Mobile

• Zimbabwe Number of Subscriber Mobile was reported at 15,000,000.000 Person in Dec 2023
• This records an increase from the previous number of 14,300,000.000 Person for Dec 2022
• Zimbabwe Number of Subscriber Mobile data is updated yearly, averaging 12,367.000 Person from Dec 1960 to 2023, with 52 observations
• The data reached an all-time high of 15,000,000.000 Person in 2023 and a record low of 0.000 Person in 1996
• Zimbabwe Number of Subscriber Mobile data remains active status in CEIC and is reported by World Bank
• The data is categorized under World Trend Plus’s Association: Telecommunication Sector – Table ZW.World Bank.WDI: Telecommunication

Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.;International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database;Sum;Please cite the International Telecommunication Union for third-party use of these data.

Zimbabwe ZW: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 87.963 NA in 2022. This stayed constant from the previous number of 87.963 NA for 2021. Zimbabwe ZW: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 49.650 NA from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 87.963 NA in 2022 and a record low of 43.019 NA in 2005. Zimbabwe ZW: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;