Ease of doing business score (0 = lowest performance to 100 = best performance) in Haiti was reported at 40.72 in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - Distance to frontier score (0=lowest performance to 100=frontier) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Haiti HT: Ease of Doing Business Score: 0=Lowest Performance To 100=Best Performance data was reported at 40.725 NA in 2019. This records an increase from the previous number of 37.859 NA for 2018. Haiti HT: Ease of Doing Business Score: 0=Lowest Performance To 100=Best Performance data is updated yearly, averaging 37.747 NA from Dec 2015 (Median) to 2019, with 5 observations. The data reached an all-time high of 40.725 NA in 2019 and a record low of 37.679 NA in 2015. Haiti HT: Ease of Doing Business Score: 0=Lowest Performance To 100=Best Performance data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Haiti – Table HT.World Bank.WDI: Business Environment. The ease of doing business scores benchmark economies with respect to regulatory best practice, showing the proximity to the best regulatory performance on each Doing Business indicator. An economy’s score is indicated on a scale from 0 to 100, where 0 represents the worst regulatory performance and 100 the best regulatory performance.; ; World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme; Unweighted average; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Haiti HT: Distance to Frontier Score: 0=Lowest Performance To 100=Frontier data was reported at 38.240 NA in 2017. This records an increase from the previous number of 38.230 NA for 2016. Haiti HT: Distance to Frontier Score: 0=Lowest Performance To 100=Frontier data is updated yearly, averaging 38.230 NA from Dec 2015 (Median) to 2017, with 3 observations. The data reached an all-time high of 38.240 NA in 2017 and a record low of 38.170 NA in 2015. Haiti HT: Distance to Frontier Score: 0=Lowest Performance To 100=Frontier data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Haiti – Table HT.World Bank.WDI: Business Environment. Distance to frontier score illustrates the distance of an economy to the 'frontier,' which represents the best performance observed on each Doing Business topic across all economies and years included since 2005. An economy's distance to frontier is indicated on a scale from 0 to 100, where 0 represents the lowest performance and 100 the frontier. For example, a score of 75 in 2012 means an economy was 25 percentage points away from the frontier constructed from the best performances across all economies and across time. A score of 80 in 2013 would indicate the economy is improving.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Logistics performance index: Ability to track and trace consignments (1=low to 5=high) in Haiti was reported at 2.1 1=low to 5=high in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - Logistics performance index: Ability to track and trace consignments (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Title: Extractive Liquidity: Finance and Collapse in Fragile States
Keywords: Haiti, institutional collapse, governance substitution, financial sector, education spending, elite containment, V-Dem, property rights, institutional co-production

Description:
This dataset and accompanying research examine an institutional paradox in Haiti: sustained growth in the formal banking sector amid a prolonged decline in rule of law, judicial independence, and anti-corruption enforcement. Drawing on panel data from major Haitian banks (2003–2019), merged with Varieties of Democracy (V-Dem) institutional quality indicators and UNESCO education spending data, the study reveals that capital accumulation in fragile states can persist through informal resilience mechanisms and elite-controlled financial structures.

The findings challenge conventional assumptions in institutional and agency theory by showing how elites strategically withhold or channel capital to reinforce stability without enabling democratic reform. In particular, the study shows that private education spending—while correlated with reduced corruption and greater civic participation—is also associated with declines in rule of law and polyarchy. This pattern suggests a strategy of elite containment rather than transformation.

The dataset includes longitudinal financial statements for Haiti’s five largest banks, matched to governance indicators and education finance variables. The analytical scripts (in Python) and regression outputs provide a replicable foundation for studying governance dynamics in weak-state environments.

This study contributes to international business theory by proposing a multidimensional framework of governance substitution and institutional co-production. It underscores the need for inclusive, bottom-up financial reform and offers comparative policy insights from other fragile states such as Lebanon, Zimbabwe, Afghanistan, Somalia, and Venezuela.

🧪 How to Run This Notebook in Google Colab

This repository contains the code and supporting data used in the project:

Title: Extractive Liquidity: Finance and Collapse in Fragile States
Keywords: Haiti, institutional collapse, governance substitution, V-Dem, Polity, WGI, elite capture, financial institutions

This study investigates the paradox of sustained banking sector growth in Haiti amid institutional breakdown, using data from V-Dem, Polity V, World Bank GDP, and WGI indicators. The notebook performs a filtered, country-level regression analysis of institutional drivers of democracy, using cluster-robust OLS models and multicollinearity diagnostics.

✅ Files Included

• haiti_democracy_regression_vdem_polity.ipynb — Main notebook (can be uploaded to Google Colab)

• VDEM_trimmed.csv — V-Dem dataset

• p5v2018.csv — Polity V dataset

• GDP.csv — World Bank GDP data

• wgidataset.xlsx — World Governance Indicators (WGI) dataset

🧭 Instructions for Running in Google Colab

🔹 Step 1: Open Google Colab

1. Go to https://colab.research.google.com

2. Click “File” > “Upload notebook”

3. Upload the file haiti_democracy_regression_vdem_polity.ipynb

🔹 Step 2: Upload Supporting Files

1. Click the folder icon (📁) in the left sidebar of Colab to open the file browser

2. Click the Upload icon (📤) and upload:

   • VDEM_trimmed.csv

   • p5v2018.csv

   • GDP.csv

   • wgidataset.xlsx

These will be stored in /content/, which matches the file paths already set in the notebook.

🔹 Step 3: Run the Analysis

• Click “Runtime” > “Run all”, or press Ctrl+F9

• The notebook will:

  • Install required packages (pandas, statsmodels, openpyxl)

  • Import and clean datasets

  • Merge V-Dem, Polity, GDP, and WGI data

  • Filter for Haiti

  • Check for multicollinearity using Variance Inflation Factors (VIF)

  • Run a cluster-robust regression of polity2 ~ v2x_polyarchy + v2x_pubcorr

  • Output VIF scores and a full regression summary

🧠 Method Summary

• Dependent variable: polity2 (Polity V)

• Independent variables: v2x_polyarchy, v2x_pubcorr (from V-Dem)

• Filters: Haiti only

• Estimator: OLS with clustered standard errors by year

• Diagnostics: Variance Inflation Factors (VIF)

Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high) in Haiti was reported at 2.3 1=low to 5=high in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 504.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Haiti was 504 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Logistics performance index: Overall (1=low to 5=high) in Haiti was reported at 2.1 1=low to 5=high in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - Logistics performance index: Overall (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Logistics performance index: Efficiency of customs clearance process (1=low to 5=high) in Haiti was reported at 2.1 1=low to 5=high in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - Logistics performance index: Efficiency of customs clearance process (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high) in Haiti was reported at 2.5 1=low to 5=high in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Abstract

This research was conducted in Haiti in October 2019, as part of the of the Enterprise Survey. The objective of the study is to obtain feedback from enterprises in client countries on the state of the private sector. The research is also used to build a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

In Haiti, data from 149 establishments were taken from listings from three sources: UNOPS 2018, UNICEF 2018, and Haiti Business Database 2018. The survey topics include firm characteristics, infrastructure and services, sales and supplies, management practices, degree of competition, innovation, land and permits, crime, finance, mobile money, insurance, business-government relations, labor, business environment, performance, and post-interview information.

Geographic coverage

The entire sample is composed of four communes in Port-au-Prince Arrondissement: - Port-au-Prince commune - Delmas commune - Peiton-Ville commune - Tabarre commune

Because of safety concerns on the ground or during transit, the following communes in Port-au-Prince Arrondissement were excluded from the sample: - Carrefour - Cite Soleil - Gressier - Kenscoff

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

Following the standard methodological approach, the sample is comprised of businesses that employ 5 or more employees and have at least some private ownership. However, departing from the global methodology, some businesses in the professional and support services activities which are not part of the standard Universe of Inference of the ES were included in the Haiti survey.

Kind of data

Sample survey data[ssd]

Sampling procedure

Attempts were made to obtain quality sampling frames. However due to the lack of reliable data, the sample frame was limited to listings of firms from three sources: UNOPS 2018, UNICEF 2018, and Haiti Business Database 2018. In addition, corrections to the sampling frame were made as interviewers were in the field and new businesses were found.

The entire sample is composed of four communes in Port-au-Prince Arrondissement: - Port-au-Prince commune - where fieldwork was determined depending on the day and level of violence on that particular day. - Delmas commune - where fieldwork was determined depending on the day and level of violence on that particular day. - Peiton-Ville commune - which is considered one of the wealthier communes; fieldwork was conducted during the mornings before protesters make their way there. - Tabarre commune - which was comparatively safer when no protests were planned.

The sampling strategy from the onset began by mapping the locations of the firms in the sampling frame and determining the safety situation. As safety windows appeared, interviewers would go to areas and try to screen and conduct the interview in the business. A total of 149 firms were interviewed.

Mode of data collection

Face-to-Face[f2f]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Haiti HT: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 61.694 NA in 2023. This records an increase from the previous number of 61.450 NA for 2022. Haiti HT: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 48.250 NA from Sep 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 71.594 NA in 2021 and a record low of 36.831 NA in 2011. Haiti HT: 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 Haiti – Table HT.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;

Haiti HT: BOP: Current Account: Imports: Service: Transport: % of Service Imports data was reported at 67.221 % in 2016. This records an increase from the previous number of 66.368 % for 2015. Haiti HT: BOP: Current Account: Imports: Service: Transport: % of Service Imports data is updated yearly, averaging 57.548 % from Dec 1971 (Median) to 2016, with 46 observations. The data reached an all-time high of 91.303 % in 1998 and a record low of 39.925 % in 2005. Haiti HT: BOP: Current Account: Imports: Service: Transport: % of Service Imports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Haiti – Table HT.World Bank: Balance of Payments: Current Account. Transport covers all transport services (sea, air, land, internal waterway, pipeline, space and electricity transmission) performed by residents of one economy for those of another and involving the carriage of passengers, the movement of goods (freight), rental of carriers with crew, and related support and auxiliary services. Also included are postal and courier services. Excluded are freight insurance (included in insurance services); goods procured in ports by nonresident carriers (included in goods); maintenance and repairs on transport equipment (included in maintenance and repair services n.i.e.); and repairs of railway facilities, harbors, and airfield facilities (included in construction).; ; International Monetary Fund, Balance of Payments Statistics Yearbook and data files.; Weighted Average; Note: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.

Haiti HT: Nonpregnant and Nonnursing Women Can Do the Same Jobs as Men: 1=Yes; 0=No data was reported at 1.000 NA in 2017. This stayed constant from the previous number of 1.000 NA for 2015. Haiti HT: Nonpregnant and Nonnursing Women Can Do the Same Jobs as Men: 1=Yes; 0=No data is updated yearly, averaging 1.000 NA from Sep 2013 (Median) to 2017, with 3 observations. The data reached an all-time high of 1.000 NA in 2017 and a record low of 1.000 NA in 2017. Haiti HT: Nonpregnant and Nonnursing Women Can Do the Same Jobs as Men: 1=Yes; 0=No data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Haiti – Table HT.World Bank: Policy and Institutions. Non-pregnant and non-nursing women can do the same jobs as men indicates whether there are specific jobs that women explicitly or implicitly cannot perform except in limited circumstances. Both partial and full restrictions on women’s work are counted as restrictions. For example, if women are only allowed to work in certain jobs within the mining industry, e.g., as health care professionals within mines but not as miners, this is a restriction.; ; World Bank: Women, Business and the Law.; ;