SOCIAL PROTECTION & JOBS
DISCUSSION PAPER
No. 2203 | MARCH 2022
Cash in the City:
The Case of Port-au-Prince
Olivia D’Aoust, Julius Gunneman,
Karishma V. Patel and Caroline Tassot
CA RI BBE A N RE GI O N A L R E S I L I E N C E BU I L D I N G FAC I L I T Y
EUROPEAN UNION
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Abstract retro geometric background: © iStock.com/marigold_88 Project 41595
Cash in the City: The Case of Port-au-Prince
Olivia D’Aoust, Julius Gunneman, Karishma V. Patel and Caroline Tassot 1
JEL: D81, R12, R23, I32, Q56
Keywords: Targeting, social assistance, vulnerability, urban inequality
Abstract: Following the 2010 devastating earthquake and subsequent cholera epidemic, Port-au-
Prince’s residents have been increasingly affected by food insecurity, socio-economic unrest
including periods of complete lock-down, and gang violence. In light of the insecurity which limits
the possibilities to collect the necessary information to target the vulnerable residents of Port-
au-Prince, this paper aims at providing meaningful evidence to inform the remote targeting and
delivery of a potential social assistance program. Putting together household and geospatial data,
we compute a composite vulnerability indicator for the metropolitan area, offering a first
snapshot of inequality and vulnerability within the city, and discuss the results’ implications for
social protection programming.
1
This activity was supported by European Union in the framework of the EU Caribbean Regional Resilience Building
Facility, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR). We thank Paula Restrepo
Cadavid, Cornelia Tesliuc, Giovanni Toglia and Pascal Jaupart, as well as the participants of the March 3rd workshop,
representing the Ministry of Social Affairs and Labor, World Food Programme, Inter-American Development Bank
and European Union for helpful comments. All remaining errors are ours. The findings, interpretations, and
conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of
the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of
the Executive Directors of the World Bank or the governments they represent.
Contents
I. Vulnerability in Haiti and Port-au-Prince ......................................................................... 3
II. Programming social assistance in Port-au-Prince ............................................................ 6
III. Port au Prince’s hotspots of vulnerability ..................................................................... 8
Defining Port au Prince Metropolitan Area......................................................................................8
Data.............................................................................................................................................12
A Composite Urban Vulnerability Indicator (CUVI).........................................................................14
IV. Implications of the CUVI in terms of the vulnerability of the population ..................... 19
V. Discussion..................................................................................................................... 23
Limitations and caveats ................................................................................................................23
Implications for social assistance ..................................................................................................24
VI. Conclusion................................................................................................................. 26
Appendix ............................................................................................................................. 30
Appendix A. Population estimates for Port-au-Prince ....................................................................30
Appendix B. Distribution of the survey-based components: grid vs household ...............................30
Appendix C. CUVI Component I: HDVI equivalent indicator............................................................31
Appendix D. CUVI Component II: Asset index ................................................................................35
Appendix E. CUVI Component III: Dwelling index...........................................................................36
Appendix E. CUVI Component IV: Food consumption score data ....................................................37
Appendix F. Details on other data sources for PAPMA ...................................................................39
I. Vulnerability in Haiti and Port-au-Prince
Progress in terms of social and physical vulnerability in Haiti has been hampered over the last
decade following a devastating earthquake in 2010, a series of devastating natural disasters as
well as socio-economic unrest. The latest official data indicate that 58.5 percent of the
population was considered poor, or living at or below $1.90 per day in 2012. 2 More recent World
Bank estimates indicate a marginal increase, with nearly 60 percent of the population being poor
in 2020.3 With a Gross Domestic Product (GDP) per capita of $1,149.50 and a Human
Development Index ranking of 170 out of 189 countries in 2020, Haiti is the poorest country in
the Latin America and the Caribbean region and among the poorest and most unequal countries
in the world.
The country has a higher number of natural disasters per square kilometer than the average of
the Caribbean countries, and is prone to hurricanes, cyclones, torrential rains, flooding, and
earthquakes. On January 12th, 2010, the metropolitan area of Port-au-Prince was severely hit by
a 7.0 magnitude earthquake whose epicenter was located just 25 km southwest of the capital
city. This event was the most destructive event any country has experienced in modern times
when measured in terms of the number of people killed as a percentage of the country’s
population,4 with up to 250,000 dead or missing, and 1.5 million homeless. 5 The same year, a
cholera epidemic hit Haiti, including Port-au-Prince, sickening 820,000 people and killing nearly
10,000. 6 Haiti has since endured a notable series of hurricanes and earthquakes over the last
2
Enquête sur les Conditions de Vie des Ménages après Séisme 2012. Institut Haitien de Statistique et Informatique
(IHSI).
3
World Bank. (2021). Haiti Overview, April 26, 2021. Available at:
https://www.worldbank.org/en/country/haiti/overview
4
Cavallo, E.A., Powell, A., and Becerra, O. (2010). Estimating the Direct Economic Damage of the Earthquake in Haiti.
IADB.
5
Lozano-Gracia, Nancy; Garcia Lozano, Marisa (2017). Haitian Cities: Actions for Today with an Eye on Tomorrow.
World Bank, Washington, DC.
6
Haiti cholera outbreak prompts fresh UN aid plea. BBC News. 12 November 2010. Retrieved September, 13 2020:
https://www.bbc.com/news/world-latin-america-11743629
3
decade, including the most recent earthquake on August 14, 2021, killing 2,248 people and
injuring 12,000 people in the Southern region of Haiti. 7
The prevalence of acute food insecurity and malnutrition have worsened in recent years. In 2016,
the World Food Programme found that 30 percent of households in Port-au-Prince were food
insecure, 8 while the latest estimates in 2021 indicated that 46 percent of the population was
facing acute food insecurity across Haiti. 9 Escalating security issues have also contributed to
inconsistencies and disruptions in supplies reaching markets in Port-au-Prince. 10
The population in Haiti has been rapidly urbanizing: in 1990, 29 percent of the population lived
in urban areas. In 2020, that figure had risen to 57 percent, and it is projected to reach 75 percent
by 2050 (Figure 1), one of the highest rates of change in the world. The country’s urban
population has increased by 5.2 percent annually throughout the second half of the 20th century,
due to factors such as faulty agricultural policies and overexploitation of land deteriorating the
rural economy and fueling a massive migration into urban areas of peasants seeking security,
opportunities, and access to services. Cité Soleil 11 is considered one of the largest informal
settlements in the Northern Hemisphere, with a population of 265,072 inhabitants reported in
2015 12 and unofficial estimates ranging from 200,000 to 400,000. At a relatively advanced stage
of urbanization in 2020, urban population growth has decreased to 2.5 percent growth in the last
20 years.
7
https://www.bernama.com/en/news.php?id=2000560
8
https://reliefweb.int/sites/reliefweb.int/files/resources/wfp286374.pdf
9
https://www.ipcinfo.org/ipc-country-analysis/details-map/en/c/1152816/
10
https://fews.net/central-america-and-caribbean/haiti/food-security-outlook/june-2021
11
https://gho.unocha.org/haiti
12
Institut Haïtien de Statistique et d’Informatique (IHSI). 2015.Population de 18 ans et plus ménages et densités
estimés en 2015.
4
Figure 1. After 20 years of rapid urban population growth, urbanization is slowing down in
Haiti 13
12 100
10.5 90
Millions
Urban share of the population
10 80
Urban population
percent
70
8
57.1 60
56
6 50
(%)
40
4 30
2 20
10
0 0
1950 1970 1990 2010 2030 2050
Urban population Urbanization (%)
Source: World Urbanization Prospects, 2018
With an estimated 24 percent of the Haitian population (circa 2.6 million) and 51 percent of all
urban population residing in the Port-au-Prince Metropolitan Area (thereafter referred to as
PAPMA),14 the capital city represents the largest population hub in the country. Port-au-Prince is
predicted to reach a population of about 5 million by 2050. 15 The area is marked by very high
density-levels, reaching as high as 32,500 people per sq. km., much higher density than the center
of African cities with similar levels of per capita income. 16 Port-au-Prince is also one of the largest
cities in the world to exist without a central sewerage system.
Residents of the Port-au-Prince Metropolitan Area have faced a number of crises in recent years.
2018 witnessed the beginning of demonstrations known as “Peyi Lòk”,17 primarily in Port-au-
Prince, arising from the release of results from a probe initiated by the Superior Court of Accounts
13
Data from the UN’s population division. https://population.un.org/wup/Download/
14
According to the « Institut Haitien de Statistiques et de l’Informatique, Population Totale, de 18 ans et plus,
Ménages et Densités Estimés en 2015 », the metropolitan area (including the cities of Port-au-Prince, Delmas, Cité
Soleil, Tabarre, Carrefour and Pétion-Ville) represented 2,618,894 inhabitants while the overall population included
10,911,819 inhabitants.
15
Port-au-Prince made up 27 percent (and 51 percent) of Haiti’s total (and urban) population. Assuming PaP’s
population growth remains constant, it should reach about 5 million people by 2050.
16
Lozano-Gracia, Nancy, Garcia Lozano, Marisa (2017).
17
‘Peyi Lòk’ refers to a lockdown form of protest whereby businesses, schools, and public transportation are
generally halted, leading to shortages of food, gas, and other necessities.
5
and Administrative Disputes on the use of the Petro-Caribe fund. 18 Tensions further rose with
the terms of most legislators ending in January 2020 without an election to replace them, and
the assassination of the President in July 2021.
To further exacerbate this situation, the fragile country is also becoming a progressively violent
one, and armed gangs are increasingly active, particularly in the Port-au-Prince area. Throughout
2020 violence against civilians in the country rose by nearly 35 percent compared to 2019.19
Violence has been concentrated mostly in the impoverished neighborhoods of Port-au-Prince,
which are divided and controlled by local gang lords. Civilians are often targeted and exploited
by gangs, in particular, through kidnappings for ransom which have spiked since 2019. In 2021,
armed clashes have increased further by about 15 percent compared to 2020 and the monthly
frequency of abductions has nearly doubled, 20 leading Port-au-Prince to recently be
characterized as the “kidnapping capital of the world”.21
In parallel, the COVID pandemic and associated economic downturn have further compounded
the socio-economic crisis, including through closures of businesses and school that had already
been shut down previously during the Peyi Lok.
II. Programming social assistance in Port-au-Prince
The objective of this note is to inform efforts to reduce social and physical vulnerability in Port-
au-Prince in three steps. First, we aim to update estimates of the population living in various
areas of the PAPMA, given that the last census was conducted in 2003. Second, we develop and
apply an urban vulnerability indicator to identify the most vulnerable areas of PAPMA based on
existing data, including the 2019 ENUSAN dataset, 2017 World Bank flood risk data, and Million
Neighborhoods data discussed further in the data section. Third, we discuss implications for
18
Petro-Caribe is an oil alliance involving 18 Caribbean member states and Venezuela. The CSCCA reports can be
found at https://www.cscca.gouv.ht/rapports_petro_caribe.php
19
Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: an armed conflict location and event
dataset: special data feature. Journal of peace research, 47(5), 651-660. Data retrieved in 2021.
20
https://acleddata.com/2021/02/02/ten-conflicts-to-worry-about-in-2021/#1612195820235-14ee80d6-2b08
21
https://www.nytimes.com/2021/10/25/opinion/haiti-kidnapping-gangs.html
6
prioritizing and targeting of potential social assistance to households in selected areas based on
the population estimates and urban vulnerability scores.
Since 2020, the COVID-19 pandemic and associated economic downturn saw unprecedented,
large-scale social protection responses all over the world rapidly implemented to cope with the
acute vulnerability of large segments of the population.22 As urban areas were the ground zero
of the COVID pandemic, 23 they were initially prioritized by governments in many countries for
support during lockdown periods that resulted in lower earnings and difficulties in accessing food
and services. The Government of Haiti implemented various programs to support its population
via cash transfers and food distributions, with an intention to rely on the social registry SIMAST 24
to identify beneficiary households when feasible. 25
Limited available information on target groups (including their size, needs and localization), lack
of any major prior program, as well as a lack of a delivery chain to implement such a program in
a context of high insecurity have been major challenges in responding at a large scale to the needs
of the population of Port-au-Prince.
The social registry SIMAST 26 is a national-level database containing information on households’
characteristics used to compute the Haitian Deprivation and Vulnerability Indicator (HDVI), a
Proxy Means Test intended to be used by various stakeholders (including the Government, the
UN, NGOs, and other development partners) to identify the most deprived and vulnerable
households using 20 indicators across seven dimensions.27 Since its inception in 2013, data
22
As of December 11, a total of 215 countries or territories have planned or implemented 1,414 social protection
measures. Gentilini, Ugo; Almenfi, Mohamed; Orton, Ian; Dale, Pamela. 2020. Social Protection and Jobs Responses
to COVID-19: A Real-Time Review of Country Measures. World Bank, Washington, DC.
23
Contagious disease is among the demons of density, which have affected cities historically. This is particularly the
case in poor neighborhoods, where density has turned into crowding. Glaeser, E. (2011). Triumph of the city: How
urban spaces make us human. Pan Macmillan; Bhardwaj, G., Esch, T., Lall, S. V., Marconcini, M., Soppelsa, M. E., &
Wahba, S. (2020). Cities, crowding, and the coronavirus: Predicting contagion risk hotspots. World Bank,
Washington, DC.
24
Système d’Information du Ministère des Affaires Sociales et du Travail.
25
Limitations of the SIMAST include limited geographic coverage and outdated information
26
See http://infopage.simast.info/
27
The indicators were selected following a PMT methodology using the 2012 Survey on Living Conditions of
Households after the Earthquake to identify factors contributing to variation in household consumption. The
resulting index is based on a ranking across 4 categories clarified from a continuous score of 0-100 derived from the
indicators.
7
collection for the SIMAST has expanded via census-sweep of entire communes with door-to-door
surveying of all households. However, SIMAST’s methodology harbors many constraints across
Haiti, including inconsistencies in geographic selection and slow and expensive data collection.
These constraints are further compounded in PAPMA due to difficulties associated with slum
structures and lack of access to them, as well as security issues related to the presence and
control of certain Port-au-Prince areas by gangs. This exclusion has de facto impeded two key
uses of the SIMAST in PAPMA: 1) informing program design (by providing information on the
number of households in need and their levels of needs) and 2) targeting assistance to those
vulnerable households.
In light of the compounding crises and increasingly acute vulnerability of the population of
PAPMA, given the current limitations in collecting the necessary SIMAST information to register
inhabitants of Port-au-Prince in the social registry, meaningful evidence is needed to inform
program design and targeting choices based on existing information. In pursuing this objective,
it is important to note that, while SIMAST was unavailable, no new data were collected for the
exercise at hand, which rather builds on using recently conducted household surveys, including
or focusing on PAPMA (or parts of it), covering different dimensions of the potential risks faced
by its inhabitants.
III. Port au Prince’s hotspots of vulnerability
Defining Port au Prince Metropolitan Area
Estimating PAPMA’s population is challenging for two reasons: city boundaries can be misleading,
and no census has been conducted since 2003. The official administrative zones do not represent
what could be considered urban today. 28 Over the last four decades, the metropolitan area has
expanded dramatically (see comparison between 1986 and 2020 in Figure 2). The other
28
See Roberts, M., Blankespoor, B., Deuskar, C., & Stewart, B. (2017). Urbanization and development: Is Latin
America and the Caribbean different from the rest of the world? World Bank Policy Research Working Paper, World
Bank, Washington, DC for a discussion on global definitions of urban areas.
8
challenging dimension is the outdated census data (last conducted almost twenty years ago), and
the lack of surveys representative for official boundaries.
Figure 2. Port-au-Prince from space: 1986 vs. 2020
1986 2020
Source: NASA, 2020 29
Figure 3 is a map of the administrative borders of the official Arrondissement of Port-au-Prince
(in red), which is made of eight communes. 30
Figure 3. Communes of the arrondissement "Ouest"
Source: Lombart et al. 2014.
29
https://earthobservatory.nasa.gov/images/146787/haitis-accidental-city
30
The arrondissement of Port-au-Prince includes the following eight communes: 1. Port-au-Prince, 2. Carrefour, 3.
Pétion-Ville, 4. Delmas, 5. Cité-Soleil, 6. Tabarre, 7. Kenskoff, 8. Gressier. These are further subdivided into 34
communal sections.
9
Starting with existing data, this note brings together existing methods of consistent urban
population estimates and aims at reconciling PAPMA population estimates with anecdotal
evidence. For example, large parts of the commune of Croix-des-Bouquets (split into 10
communal sections) have high average population density levels and are part of today’s
northeastern urban core. Although not officially considered part of Port-au-Prince in most
population estimates, today, those zones are de facto part of greater Port-au-Prince, and should
be included in this exercise.
Existing population estimates for Port-au-Prince vary widely, depending on the methodology
used. There are broadly three main methodologies available: administrative data (based on the
census), satellite imagery, and cellphone records. Although details on the methodology of the
estimate are not available for each source, the differences are likely based on either discrepancy
in the definition of PAPMA, in the assumed growth rates since the last census, sampling strategies
or input data to the modeling. 31
Looking at the series of official sources using administrative data, large differences appear based
on which administrative areas are included in the estimate and which growth rates are applied
to the outdated census (see Appendix). While the Government’s official estimate of the urban
core of Port-au-Prince was 2,618,894 in 2015, once the Croix-des-Bouquets is included, that
estimate reaches 3,009,619 in the same year. More recent population estimates that extrapolate
from the 2015 Government projections range from 2,913,183 (DHS) to 3,625,183 (UNFPA) for
the metropolitan area in 2019.
Cellphone records have been used in Haiti to estimate population of PAPMA, notably for Haiti’s
2017 Urbanization Review, which used individual cell-phone data from Digicel and estimated
population for greater Port-au-Prince to reach approximately 3.5 million people in 2017. 32
31
In particular, global population and built-up datasets have their limitations because they rely on remote sensing
methods from imagery of varying quality, depending on year and location. Smaller built-up areas often go
undetected, though estimates of population are notably more accurate there; in cities, population is often
underestimated. But the growth in computing power, availability of satellite imagery, and expansion of geospatial
analysis tools mean that better and more accurate models will expand and increase the capacity for enhanced
planning and monitoring.
32
Lozano-Gracia and Garcia Lozano (2017).
10
Our delimitation of PAPMA is meant to be as inclusive as possible in considering contiguous
population density, as well as support an operational understanding ahead of targeting public
assistance. We implement a two-phased approach.
First, we use a cutoff as defined by the Global Human Settlement (GHS) Urban Centre Database
(also known as the Degree of Urbanization 33) at the Joint Research Center of the European
Commission, which defines urban areas cities based a certain level of contiguity in population
density and built-up area. Built up area includes elements such as roads and rivers, and other
spatial covariates attracting settlements. Population density is based on the Gridded Population
of the World (GPW) at CIESIN. 34 In 2015, this methodology led to an estimated population of the
metropolitan area of 2,801,925. 35 To match known administrative boundaries, we also include
communes overlapping with defined urban boundaries. The 2020 WorldPop 36 raster data is then
used to update the population of that area. The final delimitation is shown in Figure 4, leading to
a total estimated population of 2,853,235 people in 2020.
33
The degree of urbanization is a common definition of urban and rural areas, departing from national definition
and allows comparison across countries. https://ec.europa.eu/eurostat/web/degree-of-urbanisation/background
34
Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded
Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic
Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW.
35
The degree of urbanization methodology relies on the GHS-POP dataset, which depicts the distribution of the
population, expressed as the number of people in a 250m2 pixel. Residential population estimates are taken from
CIESIN Gridded Population of the World (GPWv4) disaggregated from census or administrative units to grid cell, and
then attributed to the built-up areas. The Urban Center database considers urban centers as contiguous 1-km grid
cells with a density of at least 1,500 inhabitants, and a population of at least 50,000. See Florczyk, A. J., Melchiorri,
M., Corbane, C., Schiavina, M., Maffenini, M., Pesaresi, M., ... & Zanchetta, L. (2019). Description of the GHS urban
centre database 2015. Public Release and https://ghsl.jrc.ec.europa.eu/degurbaDefinitions.php.
36
The WorldPop program provides high resolution (100m x 100m grid), open and contemporary data on human
population distributions, allowing accurate measurement of local population distributions, high resolution maps of
population counts and densities from 2000-2020. Tatem, A. (2017): WorldPop, open data for spatial demography.
Sci Data 4(1). World Pop estimates uses a weighting layer obtained using a Random Forest (RF)-based dasymetric
mapping approach to disaggregate population counts from administrative units into grid cells. Population counts are
modeled relying on the last census, as well as a series of geospatial covariates, such as distance to urban areas,
roads, distance to the coastline, nighttime lights, etc. See
https://www.worldpop.org/tabs/gdata/html/6375/report_prj_2020_HTI.html for more information and Stevens, F.
R., Gaughan, A. E., Linard, C. & Tatem, A. J. Disaggregating Census Data for Population Mapping Using Random
Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10, e0107042 (2015).
11
Data
Although household data for Port-au-Prince is scarce, some relatively recent data is available
across several communes in Port-au-Prince. While violence is an important factor in the life of
the residents of PAPMA there is unfortunately no data suitable to be included as part of the
analysis at this stage.37 The following were chosen as they contain key aspects of vulnerability in
an urban setting such as risk of flooding, crowding or lack of access to services.
The ENUSAN (National Emergency Survey on Food and Nutrition Security 38) was conducted in
August and September 2019. It covers a total of approximately 3,150 households across seven
communes nationwide.39 Its questionnaire includes modules on the household composition,
asset ownership, dwelling characteristics, access to services, food security, households’
livelihood strategies as well as details on recently experienced shocks, amongst other modules.
In the communes of Port-au-Prince, the survey is representative at the IPC zone level, each of the
seven communes in PAPMA having 30 clusters with 15 households, and each household with
individual GPS coordinates. The ENUSAN data are not available across the entirety of the city. As
Figure 5 shows, interviews (blue dots) were concentrated in some areas matching residential land
cover (shaded black) in 2016.
37
Some other datasets, such as the SAMEPA, DHS, or ACLED, could not be used to construct the urban vulnerability
index but were used for robustness checks. In the ACLED (Armed Conflict Location & Event Data) and DHS
(Demographic and Health Survey) data individual observations could not be pinned down precisely enough
geographically within PAMPA, while SAMEPA (Food Security, Livelihoods and Agricultural Production survey) had a
significantly smaller sample and smaller questionnaire than ENUSAN.
38
Enquête Nationale d'Urgence sur la Sécurité Alimentaire et Nutritionnelle
39
Those communes are the communes of the arrondissement of Port-au-Prince - Port au Prince, Carrefour, Pétion-
Ville, Delmas, Cite Soleil, Tabarre – and also the commune of Croix des Bouquets because it has become de facto an
urban area of greater PAPMA.
12
Figure 4. Population Density of PAPMA per 0.005° Area Figure 5. Landcover vs. ENUSAN sampled
household locations
Source: Authors’ calculations using WorldPop 2020 and GHS Source: Authors’ calculations using Port au Prince Land
urban center Port au Prince boundary Cover Classification (2016), Background study for
Lozano-Gracia and Garcia Lozano (2017). 40
To optimize data coverage, the ENUSAN survey-based indicators were calculated at grids
of three different sizes. The smallest has a side length of 0.005 degrees or 555m, the middle
of 0.01 degrees or 1,110m, and the largest of 0.02 or 2,220m (Figure 6). If there were at least 5
households per grid cell at the smallest grid level, the average for those was computed, if not the
next largest grid size was considered.41 All indicators were then mapped to the smallest grid,
even if computed at a larger scale. With this method complete data at the smallest cell is available
for 38 percent of the population, and data is used at larger grid cell level to have a full dataset
reaching 75 percent of the population. While this exercise is not representative from a statistical
point of view, it offers the first snapshot of inequality and vulnerability across different indicators
40
Note: GPS coordinates at the household level. Most interviews were conducted in clusters of about 30 households.
While many interviews were conducted in some concentrated areas, other areas are left out, which complicates
accurate targeting.
41
The smallest has a side length of 0.005 degrees or 555m, the middle of 0.01 degrees or 1,110m, and the largest of
0.02 or 2,220m. If there are at least 5 households per grid cell, the household vulnerability score was averaged at
that grid size. If fewer than 5 households were surveyed at the smallest 555m grid level, the next largest grid of
1110m was used, and so on. If the largest 2,220m wide grid did not include enough observations, we did not
represent the data. If an administrative border of a communal section runs through a grid, that border separates the
grid into separate components in each of which the observations are averaged independently from one another to
avoid operational implications from any extrapolation.
13
within the metropolitan area of Port au Prince. The survey data is further complemented by
geospatial data sources.
Figure 6. Three different grids
Smallest: 555m side length Mid-sized: 1,110m side length Large: 2,220m side length
Source: Authors’ calculations
Flood risk information stems from a World Bank exercise carried out in 2017 to produce a set of
high-resolution flood hazard maps for Haiti. The work relies on soil and land use as well as rainfall
data, using the LiDAR Digital Terrain Model (DTM) to produce flood hazard maps at a 10m
resolution depth grid for three return periods: 5-year, 25-year and 100-year events.
Million Neighborhoods Data offers a characterization of the topology of access networks in cities
based on OpenStreetMap. To measure street access, the analysis uses an index called the k-index
or “block complexity”. The exact interpretation of this value is the number of buildings an
individual would pass from the least accessible building in a street block to the nearest external
street access. 42 When the k-index is 2 or less it means that all buildings have direct access to
streets, while values greater than 2 reflect blocks that are incrementally more inaccessible.
A Composite Urban Vulnerability Indicator (CUVI)
To be consistent with the current targeting approach, the CUVI methodology follows a
framework similar to the HDVI. The CUVI is meant to be understood as a measure of the level of
42
Brelsford, C., Martin, T., Hand, J., & Bettencourt, L. M. (2018). Toward cities without slums: Topology and the
spatial evolution of neighborhoods. Science advances, Brelsford, C., Martin, T., & Bettencourt, L. M. (2019). Optimal
reblocking as a practical tool for neighborhood development. Environment and Planning B: Urban Analytics and City
Science, 46(2), 303-321.
14
vulnerability, as such higher (lower) scores indicate higher (lower) vulnerability and are reflected
in red (green) throughout the analysis. 43 However, to reflect the particularities of the urban
context matter, indicators that capture vulnerabilities specific to cities were added.
The CUVI has six components as shown in Figure 7. Each of the component indicator is normalized
and then weighted equally when combined in a single indicator. While the first component is
closely related to the HDVI, the other five components proxy for other dimensions of vulnerability
that are not covered in the HDVI but are of relevance in PAPMA.
Figure 7. CUVI components
Component 1: The HDVI indicators can be computed across PAPMA including 17 out of the 20
variables normally used for the HDVI.44 Not all are relevant to the urban context, most indicators
only show variation at the very top of the distribution, while the overwhelming majority of the
variation across households stems from only 6 indicators – measures of (i) demographic
vulnerability45, (ii) overcrowding, (iii) inactive labor, (iv) lack of access to water, (v)
43
Grids ranking in the top quintile above 80 percent of all the grids in which households were surveyed (or grids with
the better scores) are the darkest green. The bottom quartile ranking in the bottom 20 percent of grids in which
households were surveyed (or grids with worse relative scores) are the darkest red. Grids in the middle 20 percent
(ranking above the bottom 40 and below 60 percent) are yellow.
44
See table in Appendix. The standard 20 variables include indicators on household demographics, health, education,
labor conditions, food security, resources at home, and living conditions captured through the SIMAST surveys. The
variables that could not be recreated using the alternative data are variable 2.1 on the presence of chronically ill at
home, variable 3.1. on illiteracy, and variable 3.4. on children’s school lag. Other indicators required adjustments
where data was not available in the exact same format. One complication in construction indicator 1 (Household
demographic composition) was that the ENUSAN-SAMEPA data did not include details on intra-household relations.
It was therefore assumed that if there were both a male and a female adult aged 25 or older and 60 or younger, that
the household is not “single-headed”. For indicator 7, no data on education was available at the member level but
only at the household head level, which is why the latter was used instead. Indicator 11 on unemployment was
proxied for based on activity data.
45
This indicator considers the number of children, the number of elderly and if the household is led by a single
parent.
15
unemployment, and (vi) lack of access to lighting are reflected in Figure 8. The HDVI methodology
was applied, and the existing weights were re-scaled such that they sum to one. Details on how
those six deprivation indicators are defined can be found in the Appendix.
Component 2: The second component of the CUVI is an inverted PCA index of asset ownership.
While the HDVI does not include asset ownership—likely because there is not enough variance
in asset ownership in rural areas—this measure offers relevant data on vulnerability across
households in PAPMA. The asset index relies on variables in the ENUSAN dataset on ownership
of 11 assets (see appendix) and is shown on Figure 9.
Component 3: The third component of the CUVI is a PCA dwelling deprivation index (Figure 10).
It measures if a household’s dwelling floor and walls 46 are built with precarious materials.
Component 4: The inverted food consumption score (FCS) is based data on the types of food the
household consumed, and their consumption frequency over the past 7 days. Figure 11 gives a
snapshot of the food insecurity in 2019.47
Component 5: The fifth component is based on a World Bank funded national flood hazard
mapping in 2017 using the freely available flood modelling software HEC-RAS for a 100-year
return period. The flood models were developed by applying rainfall input data collected from
Damien station just north of Port-au-Prince to a LiDAR-based Digital Terrain Model (DTM). Figure
12 gives a snapshot of relative flood hazard across different grid areas in 2017.
Component 6: Figure 13 reflects access to streets measured by “block complexity”, reflecting the
number of buildings an individual would pass from the least accessible building in a street block
to the nearest external street access. 48 The red areas represent slums, which have very limited
access to streets and hence services, while the green areas have the most access. Table 1
46
Roof material has very little variation in PAPMA, less than 1 percent of roofs are made of materials other than
sheet metal or cement.
47
The data was available for two points in time: 2019 (ENUSAN) and 2020 (SAMEPA) but 2019 had a larger sample
size and was therefore chosen.
48
Brelsford, C., Martin, T., Hand, J., & Bettencourt, L. M. (2018). Toward cities without slums: Topology and the
spatial evolution of neighborhoods. Science advances, Brelsford, C., Martin, T., & Bettencourt, L. M. (2019). Optimal
reblocking as a practical tool for neighborhood development. Environment and Planning B: Urban Analytics and City
Science, 46(2), 303-321.
16
summarizes the pairwise correlations across the six components and shows the correlations with
the final CUVI. More descriptive statistics of the CUVI, along with graphs of its distribution, can
be found in the Appendix.
Table 1. Pairwise correlations of CUVI components
Source: Authors’ calculations
Figure 8. CUVI Component 1: Vulnerability Figure 9. CUVI Component 2: Lack of asset ownership
Source: Authors’ calculations based on ENUSAN (2019). Source: Authors’ calculations based on ENUSAN (2019).
Figure 10. CUVI Component 3: Dwelling deprivation Figure 11. CUVI Component 4: Food insecurity
Source: Authors’ calculations based on ENUSAN (2019) Source: Authors’ calculations based on ENUSAN (2019)
17
Figure 12. CUVI Component 5: Flood risk Figure 13. CUVI Component 6: Lack of road access
Bringing all components together leads to the Composite Urban Vulnerability Indicator (CUVI)
shown in Figure 14. For reference, Figure 15 shows the communes with their communal sections
in different colors for easier locating. 49
49
Note that we added the commune of Croix-des-Bouquets (green) in the north-east of the city although it is not
officially part of the arrondissement of Port-au-Prince because it falls within the criteria we use to define the PAPMA
area in Section 2.1.
18
Figure 14. The Composite Urban Vulnerability Indicator Figure 15. Overview of the communes and sections
within the metropolitan area
Source: Authors’ calculations Source: Authors’ calculations using CGNIS data
Vulnerability is the highest in the expected slum areas such as Martissant, Cite Soleil, west
Carrefour, along the Grise river in the western part of the commune of Croix-de-Bouquets and
along the Kenscoff route in the south. Less vulnerable areas in green include parts of the the
Petion-Ville and Pacot areas, as well as sparsely populated north of the city, and the east part of
Carrefour.
IV. Implications of the CUVI in terms of the vulnerability of the
population
The grid cells and associated communal sections areas classified according to their CUVI
vulnerability can be mapped to population levels to estimate the number of vulnerable
individuals in the PAPMA.
19
Before discussing the vulnerability levels, it is important to note that despite numerous
communal sections in PAPMA (29 with our definition), three quarters of the population lives in
only 9 of these sections communales (see Figure 16). More so about half the population of
PAPMA lives in 4 densely populated sections communales of Saint Martin in the commune of
Delmas, Turgeau and Martissant in Port au Prince, and Bellevue Chardonnière in Pétion-Ville.
Figure 16. Population distribution in PAPMA
15% 12% 11% 11% 7% 4% 4% 4% 5% 26%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1ère Saint martin (Delmas) 6ème Turgeau (PaP) 8ème Martissant (PaP)
7ème Bellevue Chardonnière (PV) 1ère Varreux (Cite Soleil) 3ème Etang du Jong (PV)
7ème Morne l'Hopital (PaP) 1ère Petit Bois (CdB) 11ème Rivière Froide (Carrefour)
Other communal sections
Source: Authors’ calculations
Note: CdB is Croix-des-Bouquets, PaP is Port-au-Prince, PV is Pétion-Ville
The vulnerable population is estimated by categorizing individuals as vulnerable if they reside in
the grid cells with the highest CUVI vulnerability status (the top quintile shown in dark red in
Figure 14), thus focusing on the 14 percent most vulnerable PAPMA residents, representing
390,931 residents. Figure 17 displays the proportion of each communal section categorized as
vulnerable, showing a range varying from 53.2 percent in Pétion-Ville to 0.1 percent in Cite Soleil.
These estimates indicate that focusing on geographical targeting alone would likely result in large
inclusion errors, which could potentially be avoided or reduced by complementing this analysis
with a focused household-level targeting and ground truthing.
20
Figure 17. Share of population categorized as most vulnerable in each communal section
1ère Montagne Noire (PV) 53.20%
4ème Bellevue la Montagne (PV)
3ème Etang du Jong (PV)
2ème Petit Bois (CdB)
8ème Martissant (PaP)
11ème Rivière Froide (Carrefour)
3ème Sourcailles (Kenscoff)
9ème Bizoton (Carrefour)
6ème Turgeau (PaP)
7ème Morne l'Hopital (PaP)
2ème Varreux (CdB)
7ème Bellevue Chardonnière (PV)
1ère Petit Bois (CdB)
3ème Bellevue (Tabarre)
10ème Thor (Carrefour)
1ère Saint Martin (Delmas)
1ère Varreux (Cite Soleil) 0.10%
0% 10% 20% 30% 40% 50% 60%
Source: Authors’ calculations
Note: CdB is Croix-des-Bouquets, PaP is Port-au-Prince, PV is Pétion-Ville
Figure 18 shows the spatial distribution of those vulnerable populations in PAPMA, thus
combining both the share of the population categorized as vulnerable and the overall share of
the PAPMA population living in each communal section. More than 90 percent of the vulnerable
population lives in 10 communal sections.
Figure 18. Distribution of most vulnerable population across PAPMA communal sections
29.2% 13.0% 11.6% 9.3% 8.0% 5.1% 4.9% 4.0% 3.6% 2.7%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
8ème Martissant (PaP) 3ème Etang du Jong (PV) 6ème Turgeau (PaP)
11ème Rivière Froide (Carrefour) 7ème Bellevue Chardonnière (PV) 9ème Bizoton (Carrefour)
1ère Montagne Noire (PV) 7ème Morne l'Hopital (PaP) 2ème Varreux (CdB)
1ère Petit Bois (CdB) 1ère Saint Martin (Delmas) 10ème Thor (Carrefour)
4ème Bellevue la Montagne (PV) 3ème Bellevue (Tabarre) 3ème Sourcailles (Kenscoff)
2ème Petit Bois (CdB) 1ère Varreux (Cite Soleil)
Source: Authors’ calculations
Note: CdB is Croix-des-Bouquets, PaP is Port-au-Prince, PV is Pétion-Ville
21
It is important to note here that while the CUVI provides some estimates of the size of the target
population several steps would be required in order to actually identify and enroll households in
such a program, and further targeting could improve accuracy with reduced inclusion errors. One
option is to proceed with massive registration on the ground, with the known caveats of high
costs, lengthy processes and dealing with the extremely complex security environment.
Another option that would allow for faster and more efficient enrollment would rely on a
collaboration with telecom operators. Using the information on the prioritization of certain areas
based on the CUVI, the telecom operators could provide an anonymized list of mobile phone
subscribers living in the area could form the basis of a potential beneficiary registry. This list could
be further refined by applying some filters based on call detail records (CDR) and/or satellite
imagery data to limit inclusion errors, for instance by excluding smartphones, or by analyzing the
CDR or roof materials to proxy poverty status. Identified beneficiaries can be reached out to
through bulk SMS, audio messaging or radio campaigns, encouraging them to consent and self-
register in the program (for instance through USSD). Telecom operators can then open mobile
money accounts or leverage those already associated with beneficiaries’ phone numbers to
initiate transfers. This methodology was successfully implemented in Kinshasa as part of the
COVID-19 social response. 50 Attention will need to be paid to offer an adequate communication
strategy to share all relevant information and ensure take up.
One caveat with this approach is the need for vulnerable households to own a cellphone to access
the benefit, and the need for the mobile payment ecosystem to be strong enough to support this
type of transfer. Recent estimates indicate a nationwide average of two third of the population
owning a cellphone in 2017 51, and according to the ENUSAN survey 84.2 percent of households
in PAPMA owned one in 2019. While cellphone ownership is lower in the areas with the highest
CUVI scores (81.8 percent) compared to the lowest (86.3 percent), it would still offer a remote
50
See https://www.brookings.edu/blog/future-development/2021/09/08/cash-and-the-city-digital-covid-19-social-
response-in-kinshasa/
51
Findex data, 2017, see https://globalfindex.worldbank.org/#data_sec_focus
22
assistance delivery channel for the majority of the population. Outreach and communication
efforts to encourage the purchase of SIM cards or cellphones (smartphones are not required)
could further help narrow the excluded population. An assessment should however be conducted
to ensure the infrastructure is in place for beneficiaries to either cash out their payment or use
the mobile money directly with vendors, and that regulations, including Know-Your-Customer
(KYC) are conducive to these processes (for instance if beneficiaries need formal identification to
open mobile wallets or cash them out).
V. Discussion
Limitations and caveats
Building on available information, the CUVI intends to (1) define a methodology reflecting the
specificity of the PAPMA while building on the existing methodology of SIMAST and (2) make the
most use out of various data sources to inform programming and targeting of assistance. There
are however important limitations and caveats to consider and that could be alleviated in further
iterations.
First, the exclusion of certain areas and therefore some segments of the population. Large parts
of Port-au-Prince are not covered by any of the surveys that were conducted since the last
consumption survey in 2012, making a detailed vulnerability analysis in some areas impossible.
The methodology allows for a coverage of 52 percent of the total PAPMA area, representing 76.2
percent of the population. With three quarters of the population covered we need to
acknowledge that our estimates might be biased. For example, the CUVI only covers the northern
coastal area of Martissant, a densely populated area that is notorious extremely vulnerable,
including due to very intense gang activity.
Second, a number of assumptions are made when assigning ENUSAN surveyed household to the
grid. The original survey was intended to be representative of the seven communes of PAPMA,
sampling 15 household in 30 clusters for each. The precise GPS coordinates allowed us to assign
households to a grid and trading off precision and coverage, a minimum of 5 households was
23
chosen as a threshold for the first 4 components to be computed. The results should therefore
be taken with caution.
Finally, this model could be significantly improved with more recent or different data sources
given acute recent crises. In particular, up-to-date household data (such as SIMAST) or household
survey data (such as ENUSAN) representative for the PAPMA would reflect recent deteriorations
in the living conditions of PAPMA residents. There are a number of additional sources of data
that would be of value for this model, for example the inclusion of service availability and quality
information.
The dangers and costs associated with data collection on the grounds in PAPMA however call for
some innovative techniques to be employed. Phone surveys, such as high-frequency, light
surveys with a limited set of questions, could be used in gathering data. Another option is the
use of Call Detail Records (CDR) data to refine both the estimates of the population living in an
area (by analyzing the utilization of cellphone towers) and the estimates of vulnerability
(discussed below). Another example is the use of satellite imagery to identify housing
characteristics used to predict vulnerability, as has been done in Kenya. 52 Ground truth data can
also be used to train machine learning algorithm to estimate the wealth of areas based on
geographic characteristics, for instance that poorer areas tend to be characterized by certain
terrain, roof materials or road quality, as was done in Togo.53
Implications for social assistance
Despite the caveats, the CUVI allows at the very least for a prioritization of areas in which high
shares of the population are vulnerable. This is particularly useful to inform choices related to
social assistance. Local authorities should be involved in designing and implementing a safety net
program to ensure adequate ownership of the program and facilitation of its roll-out. In the case
of Port-au-Prince, the approach could thus focus on collaborating with local authorities at the
52
See Abelson, Brian, Kush R. Varshney, and Joy Sun (2014). "Targeting direct cash transfers to the extremely poor."
In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1563-
1572.
53
https://www.poverty-action.org/study/using-mobile-phone-and-satellite-data-target-emergency-cash-transfers-
togo
24
communal section level to leverage their knowledge of the constituents and relevant
stakeholders. With more than half a million predicted vulnerable individuals in PAPMA, efforts
could focus on the 10 communal sections in which more than 90 percent of these individuals
reside to estimate a budget needed in those areas.
There are two main objectives a cash transfer could have: improve the livelihoods of the most
vulnerable to alleviate chronic poverty and food insecurity, and to smooth consumption and
promote recovery in response to a shock household may face. As described above both
modalities are relevant in the context of Port-au-Prince but correspond to different timelines of
implementation: the first would focus on regular, smaller transfers, while the latter will usually
focus on very few, larger payments to help in the short term.
Based on the experience from previous programs in Haiti the benefit amount for a regular,
monthly cash transfer could correspond to 20% of the minimum food basket (estimated at 123
USD per month in urban areas for a family of five), 54 or 24.6 USD per household. The budget
associated with a transfer corresponding to 20% of the minimum food basket would reach
1,755,982 USD per month, or about 21.1 million USD per year, as detailed in Table 2. For an
emergency cash transfer the amount could correspond to 70% of the minimum food basket, 55 or
86.1 USD, distributed once, corresponding to a total budget of 6 million USD. It is important to
note that these budgets only reflect the sum of transfers and would need to include additional
administrative costs related to the implementation of such a program, of the order of 5 to 10%
of the transfers.
54
The minimum expenditure basket (MEB) for food is computed to reflect the needs for 2,100 kcal calories per day
per person for a family of 5 over one month. The MEB was defined by the Cash Working Group in 2019 and price
data collected in December 2021
55
Previous COVID-19 response emergency cash transfers implemented by WFP, including through the WB MDUR
project, represented 70% of the minimum food basket. The Cash Working Group recommended transfers
representing 75% of the minimum food basket following the earthquake in August 2021 in the South.
25
Table 2. Estimated annual budget for a transfer to the vulnerable population in the 6 prioritized communal
sections
A B C D
Vulnerable Vulnerable Transfer costs for Transfer costs for
Population Households one year of monthly one-time cash
(A/5) cash transfers transfer
representing 20% of representing 70% of
minimum food the minimum food
basket transfer basket (B*86.1)
(B*24.6*12)
8ème Martissant (PaP) 114,159 22,832 $6,739,947 $1,965,818
3ème Etang du Jong (PV) 50,769 10,154 $2,997,402 $874,242
6ème Turgeau (PaP) 45,287 9,057 $2,673,744 $779,842
11ème Rivière Froide (Carrefour) 36,374 7,275 $2,147,521 $626,360
7ème Bellevue Chardonnière (PV) 31,235 6,247 $1,844,114 $537,867
9ème Bizoton (Carrefour) 19,988 3,998 $1,180,092 $344,193
1ère Montagne Noire (PV) 18,962 3,792 $1,119,516 $326,526
7ème Morne l'Hopital (PaP) 15,599 3,120 $920,965 $268,615
2ème Varreux (CdB) 13,942 2,788 $823,136 $240,081
1ère Petit Bois (CdB) 10,592 2,118 $625,352 $182,394
Total 356,907 71,381 $ 21,071,789 $6,145,939
Source: Authors’ calculations
Note: CdB is Croix-des-Bouquets, PaP is Port-au-Prince, PV is Pétion-Ville
VI. Conclusion
Over the last decade, despite large amounts of aid, the vulnerability of Haiti’s population has
been worsened by a series of devastating disasters, an increase in violence, and political crises.
The COVID-19 pandemic has compounded these crises, making it imperative to be able to provide
support to extremely vulnerable populations across the country, including the Port-au-Prince
Metropolitan Area. With increasing urbanization of the population and deteriorating living
standards in this area the need is pressing to define options to target, enroll and provide
assistance to urban beneficiaries of social assistance.
26
In the absence of social registry data for PAPMA, we estimate the population size and build a
Composite Urban Vulnerability Index (CUVI) based on various sources of reliable information to
estimate the size of the most vulnerable population in PAPMA. Leveraging experiences in other
countries, including DRC and Togo, we identify options to prioritize the most vulnerable areas,
for instance through piloting remote end-to-end delivery of transfers, starting with geographic
(hotspot analysis) targeting, followed by further refining of the beneficiary lists based on CDR or
satellite data, enrollment through automated processes via SMS, and, finally, payment through
mobile wallets. These options would be relevant both for regular cash transfer programs and also
for ad-hoc shock response efforts. Recent history demonstrates how pressing and necessary this
issue is in PAPMA.
Beyond PAPMA this analysis could also be expanded nationwide, whether using survey data, CDR
or satellite imagery. Mapping vulnerability will allow for an identification of particularly
vulnerable areas, which should be prioritized for SIMAST expansions as well as potential safety
net expansions.
While this analysis sheds light on a large segment of the Haitian population, the work underlines
the need for more information to be gathered to accurately capture the vulnerability of the
population. We identify various options to include other data sources, including the use of
satellite imagery or phone surveys, as well as the possibility to take other factors into account to
improve the CUVI in future iterations. One key aspect of vulnerability in PAPMA that could for
example not be accounted for in the current framework is the issue of violence, which may affect
households through a variety of channels, including difficulties in accessing goods and services.
27
References
Abelson, Brian, Kush R. Varshney, and Joy Sun (2014). "Targeting direct cash transfers to the
extremely poor." In Proceedings of the 20th ACM SIGKDD international conference on
Knowledge discovery and data mining, pp. 1563-1572
Bhardwaj, G., Esch, T., Lall, S. V., Marconcini, M., Soppelsa, M. E., & Wahba, S. (2020). Cities,
crowding, and the coronavirus: Predicting contagion risk hotspots. World Bank, Washington,
DC.
Brelsford, C., Martin, T., & Bettencourt, L. M. (2019). Optimal reblocking as a practical tool for
neighborhood development. Environment and Planning B: Urban Analytics and City Science,
46(2), 303-321.
Brelsford, C., Martin, T., Hand, J., & Bettencourt, L. M. (2018). Toward cities without slums:
Topology and the spatial evolution of neighborhoods. Science advances, 4(8), eaar4644.
Cavallo, E.A., Powell, A., and Becerra, O. (2010). Estimating the Direct Economic Damage of the
Earthquake in Haiti. IADB.
Center for International Earth Science Information Network - CIESIN - Columbia University.
2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11.
Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).
https://doi.org/10.7927/H49C6VHW.
Florczyk, A. J., Melchiorri, M., Corbane, C., Schiavina, M., Maffenini, M., Pesaresi, M., ... &
Zanchetta, L. (2019). Description of the GHS urban centre database 2015.
Gentilini, Ugo; Almenfi, Mohamed; Orton, Ian; Dale, Pamela. 2020. Social Protection and Jobs
Responses to COVID-19: A Real-Time Review of Country Measures. World Bank, Washington,
DC.
Glaeser, E. (2011). Triumph of the city: How urban spaces make us human. Pan Macmillan.
Lombart, M., Pierrat, K., & Redon, M. (2014). Port-au-Prince: un «projectorat» haïtien ou
l’urbanisme de projets humanitaires en question. Cahiers des Amériques latines, 2014(75), 97-
124.
Lozano-Gracia, Nancy; Garcia Lozano, Marisa (2017). Haitian Cities: Actions for Today with an
Eye on Tomorrow. World Bank, Washington, DC.
Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: an armed conflict
location and event dataset: special data feature. Journal of peace research, 47(5), 651-660.
Data retrieved in 2021.
28
Roberts, M., Blankespoor, B., Deuskar, C., & Stewart, B. (2017). Urbanization and development:
Is Latin America and the Caribbean different from the rest of the world? World Bank Policy
Research Working Paper, World Bank, Washington, DC.
Stevens, F. R., Gaughan, A. E., Linard, C. & Tatem, A. J. Disaggregating Census Data for
Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE
10, e0107042 (2015).
Tatem, A. J. (2017). WorldPop, open data for spatial demography. Scientific data, 4(1), 1-4.
29
Appendix
Appendix A. Population estimates for Port-au-Prince
This table shows the various population estimates of Port-au-Prince or Haiti as a whole.
Table 1. Port-au-Prince population estimates
Source: Authors’ compilation.
Appendix B. Distribution of the survey-based components: grid vs household
The figures below plot the kernel density function of the survey-based components of the CUVI
at both the grid level (left) and the household level (right) and compare them to a normal
distribution. Both distributions closely follow those of a normal distribution. There are no clear
signs of bunching.
Figure 1. CUVI distribution (grid level) Figure 2. CUVI distribution (household level)
30
Source: Authors’ calculations
Appendix C. CUVI Component I: HDVI equivalent indicator
The standard HDVI includes 20 indicators from 7 dimensions, which are demographic
vulnerability, health, education, labor conditions, food security, resources at home, and access
to dwelling services. The HDVI seeks to not only identify deprived households, but also the depth
of deprivation.
While SIMAST data is not available in Port-au-Prince, we use the ENUSAN data to calculate the
equivalent indicators – some of which are slightly adapted, as shown in the following table.
Table 2. Overview of indicators included in the adjusted HDVI
Source: Authors’ compilation
31
The adjusted HDVI built for PAPMA covers all seven dimensions and 17 out of 20 indicators. Not
all HDVI components, however, show relevant variation in the metropolitan area of Port-au-
Prince. But the first six indicators explain most differences across households in the capital. Those
cover demographic vulnerability, overcrowding, inactivity and unemployment, as well as
indicators on deprived lighting and water access.
Source: Authors’ calculations
Note that the vulnerability indicators here are the final, unweighted indicators, and therefore
do not correspond directly to the original composition explained below. The six indicators that
we choose to incorporate in the CUVI are defined as follows:
Figure 3: Demographic vulnerability
Demographic vulnerability: As per
the HDVI, we created five mutually
exclusive dichotomous variables
according different household types
classified as vulnerable: single-headed
with children, with children, single
headed with children and at least one
32
elderly over 65, with children and at Source: Authors’ calculations based on ENUSAN
least one elderly over 65. 56 (2019)
Figure 4. Overcrowding
Overcrowding: The total number of
household members divided by the
total number of rooms available in
the household.
Source: Authors’ calculations based on ENUSAN (2019)
Figure 5. Inactivity
Inactivity: The indicator consists of
the total number of household
members who fall within the inactive
category. Those include members
that consider themselves as inactive,
are students, retired, pensioners,
rentiers, working in household only,
disabled or other.
Source: Authors’ calculations based on ENUSAN (2019)
56
Note that those categories are not necessarily mutually exclusive. If more than one type applies for a
household, we choose the one with the more deprived score. Since the ENUSAN data did not include
details on intra-household relationships, it was assumed that the household is not single headed if both
women and men of the age 18-60 live in the household. Scores are as follows: single-headed with children
(2.8165), with children (3), single headed with children and at least one elderly over 65 (1.5703), with
children and at least one elderly over 65 (0.6126)
33
Deprived access to water: Composite Figure 6. Deprived access to water
measure of access to water for
drinking and other purposes. If the
household uses drinking water other
than water from a bottle, bag or
gallon, it gets a score of 1 (deprived).
If the household does not use water
provided by the national water and
sanitation company, or from a public Source: Authors’ calculations based on ENUSAN (2019)
fountain or from an artesian aquifer it
gets a score of 1.57
Figure 7. Unemployment
Unemployment: The indicator
consists of the total number of
household members who are
unemployed. Those include members
that consider themselves as not
working at all despite being of
working age, 18 or older but less than
65 years old. Source: Authors’ calculations based on ENUSAN (2019)
57 The weights for drinking and other purposes deprivation, respectively, are 0.8851 and 0.1149. The sum is therefor is multiplied
by 2 such that the maximum deprivation score can be 2.
34
Deprived access to lighting: Figure 8. Depriving access to lighting
Composite measure of lighting
deprivation based on the energy
source utilized for artificial lighting
and for cooking. We create two
variables: (1) is a dummy that takes
the value 1 if the household either
does not dispose of any artificial
lighting source, or uses candles, Source: Authors’ calculations based on ENUSAN (2019)
batteries, or kerosene (2) is a dummy
that is equal to 1 if the household
uses wood, straw or charcoal to cook
in PAPMA.58
Appendix D. CUVI Component II: Asset index
We add PCA asset index as components to the CUVI that we build using ENUSAN data. The index
includes information on the following asset ownership variables:
Table 4: Asset Deprivation Indicator
Indicator Specific variable included in PCA
Assets Solar panel, Generator, Mobile phone, Personal vehicle, TV, Computer/laptop,
Fridge/Freezer, Radio, Storage facility, Axe, Billhook.
Source: Authors’ compilation
Each variable of either PCA is constructed in binary form, where a value of 1 indicates that the
household does own at least one item of the asset and 0 that it does not. We then invert the final
PCA indicator such that a higher score means more less asset ownership rather than higher
ownership. We standardize the inverted indicator before adding it to the CUVI. We also run
58 Variable (1) is then weighted with a score of 0.8363 and variable (2) with 0.1636, and their sum is multiplied by 2 such that the
maximum deprivation score is equal to 2. The artificial lighting score therefore is weighted more heavily in the overall lighting
access variable.
35
robustness checks using DHS 2016 data and check for consistency with the ENUSAN 2019 data.
We also create PCA indices including access to services data together with the dwelling data.
Both robustness checks confirm the trends we see with the assets PCAs.
Source: Authors’ calculations
Appendix E. CUVI Component III: Dwelling index
We add another PCA index as the third component to the CUVI. This index measures the
household’s deprivation in terms of its dwelling’s characteristics and building materials. It
assesses if a household’s dwelling is vulnerable in the sense that its building materials for floors
and walls are weak.
Table 3. Dwelling deprivation indicator
Indicator Specific variable included in PCA
Dwelling Deprivation of floor material floor made with wood, earth or remains
Deprivation of walls material walls made of wood planks, earth, metal
sheets, cards/plastic or other primitive covers
Source: Authors’ compilation
Each variable of either PCA is constructed in binary form, where a value of 1 indicates that the
household does face a dwelling characteristic that is considered deprived. We then standardize
the indicator before adding it to the CUVI. We also run robustness checks using DHS 2016 data
and check for consistency. We also create PCA indices including access to services data together
with the dwelling data. Both robustness checks confirm the trends we see with the dwelling PCAs.
36
Figure 10: PCA based on dwelling deprivation
Source: Authors’ calculations
Appendix E. CUVI Component IV: Food consumption score data
The standard methodology for the FCS was applied for both the ENUSAN and SAMEPA based
indicators, with the standard weighting. Both datasets include the same questions but were
asked at different points in time, which allows for a snapshot of trends in food security. A
household’s food security is ranked as “poor” if the score is lower than 35, “acceptable” if lower
than 50, and “non-poor” if above 50. Although we plot both the ENUSAN-based and SAMEPA-
based data, we decide to include only the ENUSAN data, both for consistency and because it has
the much larger sample size than the SAMEPA. The ENUSAN-based FCS has a median of 48.1 and
mean of 49 with a standard deviation of 9.2.
Figure 11. FCS based on ENUSAN data (2019) Figure 12. FCS based on SAMEPA data (2020)
Source: Authors’ calculations based on ENUSAN (2019) and SAMEPA (2020)
37
Figure 13. Ranked ENUSAN-based Food Security Score per Figure 14. Ranked SAMEPA-based Food Security Score
555 sqm Area by Quintile (2019) per 555 sqm Area by Quintile (2020)
Source: Authors’ calculations based on ENUSAN (2019) and SAMEPA (2020)
Where there is data from both ENUSAN and SAMEPA, Figure 15 shows the mean percentage
change in the FCS from when variables were collected in the ENUSAN survey in 2019 to when
variables were collected in the SAMEPA survey in 2020. The median and average values of
percentage change are 3.7 percent and 5.2 percent, respectively. The data implies that most
households have a FCS that has stayed the same or gotten marginally better in the period
between 2019 and 2020.
Figure 15. Percentage Change in Food Security Score from 2019 ENUSAN to 2020 SAMEPA
Source: Authors’ calculations based on ENUSAN (2019) and SAMEPA (2020)
As for the PCA index, we invert the FCS data such that a higher score implies less food security,
i.e. more deprivation. We standardize scores before adding them to the CUVI.
38
Appendix F. Details on other data sources for PAPMA
Two other household surveys were conducted in PAPMA within the past five years, but both did
not provide relevant, additional data to ENUSAN.
The SAMEPA (Sécurité Alimentaire, les Moyens d'Existence et la Production Agricole or Food
Security, Livelihoods and Agricultural Production survey) from 2020 is a phone-based follow up
survey of the ENUSAN. It covers a total of about 3,000 households in PAPMA, of which 1,900
could be successfully matched within the relevant communes of the ENUSAN database. It
includes many of the same modules and additionally asks households a few questions about their
understanding of, and coping with, the COVID-19 pandemic.
The DHS (Demographic and Health Surveys) from 2016/2017 covers a total of 2,100 households
across the relevant communes. Its questionnaire is more focused on health outcomes. It also
includes details on the composition of each household, its education, its assets and access to
services. The DHS assigns each household surveyed to a GPS location at the center cluster of
households encompassing approximately 30 households within up to a 2 km radius each. The
DHS centroid locations are marked in blue in the below figure. The move of coordinates and
insufficient coverage made it less useful for this exercise.
Figure 16. DHS cluster-level coordinates
Note: GPS coordinates at the cluster level. For interviewed households to remain anonymous,
the cluster-level coordinates were randomly moved by up to 2km from the true location.
39
Social Protection & Jobs Discussion Paper Series Titles
2020-2022
No. Title
2203 Cash in the City: The Case of Port-au-Prince
by Olivia D’Aoust, Julius Gunneman, Karishma V. Patel and Caroline Tassot
March 2022
2202 Tracing Labor Market Outcomes of Technical and Vocational Training Graduates in Saudi Arabia:
A study on graduates from the Technical and Vocational Training Corporation (TVTC)
by Nayib Rivera, Mehtabul Azam and Mohamed Ihsan Ajwad
January 2022
2201 From Protracted Humanitarian Relief to State-led Social Safety Net System: Somalia Baxnaano Program
by Afrah Al-Ahmadi and Giuseppe Zampaglione
January 2022
2110 Early Lessons from Social Protection and Jobs Response to COVID-19 In Middle East and North Africa
(MENA) Countries
by Alex Kamurase and Emma Willenborg
December 2021
2109 Migration in Bulgaria: Current Challenges and Opportunities
by Daniel Garrote-Sanchez, Janis Kreuder, and Mauro Testaverde
December 2021
2108 Social Protection and Labor: A Key Enabler for Climate Change Adaptation and Mitigation
by Jamele Rigolini
December 2021
2107 Intent to Implementation: Tracking India’s Social Protection Response to COVID-19
by Shrayana Bhattacharya and Sutirtha Sinha Roy
June 2021
2106 Social Assistance Programs and Household Welfare in Eswatini
by Dhushyanth Raju and Stephen D. Younger
June 2021
2105 The Coal Transition: Mitigating Social and Labor Impacts
by Wendy Cunningham and Achim Schmillen
May 2021
2104 Social Protection at the Humanitarian-Development Nexus: Insights from Yemen
by Yashodhan Ghorpade and Ali Ammar
April 2021
2103 Review of the Evidence on Short-Term Education and Skills Training Programs for Out-of-School Youth
with a Focus on the Use of Incentives
by Marguerite Clarke, Meghna Sharma, and Pradyumna Bhattacharjee
January 2021
2102 Welfare, Shocks, and Government Spending on Social Protection Programs in Lesotho
by Joachim Boko, Dhushyanth Raju, and Stephen D. Younger
January 2021
2101 Cash in the City: Emerging Lessons from Implementing Cash Transfers in Urban Africa
by Ugo Gentilini, Saksham Khosla, and Mohamed Almenfi
January 2021
2011 Building the Foundation for Accountability in Ethiopia
by Laura Campbell, Fitsum Zewdu Mulugeta, Asmelash Haile Tsegay, and Brian Wampler
January 2020
2010 Safety nets, health crises and natural disasters: Lessons from Sierra Leone
by Judith Sandford, Sumati Rajput, Sarah Coll-Black, and Abu Kargbo
December 2020
2009 A Reforma do Bolsa Família: Avaliação das propostas de reforma debatidas em 2019
by Matteo Morgandi, Liliana D. Sousa, Alison Farias, e Fabio Cereda
November 2020
2008 The Role of Social Protection in Building, Protecting, and Deploying Human Capital in the East Asia and
Pacific Region
by Harry Edmund Moroz
October 2020
2007 Boosting the Benefits of Cash Transfer Programs During the Early Years: A Case Study Review of
Accompanying Measures
by Laura Rawlings, Julieta Trias, and Emma Willenborg
October 2020
2006 Expansion of Djibouti’s National Family Solidarity Program: Understanding Targeting Performance of
the Updated Proxy Means Test Formula
by Vibhuti Mendiratta, Amr Moubarak, Gabriel Lara Ibarra, John van Dyck, and Marco Santacroce
August 2020
2005 Assessing the Targeting System in Georgia: Proposed Reform Options
by Maddalena Honorati, Roberto Claudio Sormani, and Ludovico Carraro
July 2020
2004 Jobs at risk in Turkey: Identifying the impact of COVID-19
by Sirma Demir Şeker, Efşan Nas Özen, and Ayşenur Acar Erdoğan
July 2020
2003 Assessing the Vulnerability of Armenian Temporary Labor Migrants during the COVID-19 pandemic
by Maddalena Honorati, Soonhwa Yi, and Thelma Choi
July 2020
2002 Getting it Right: Strengthening Gender Outcomes in South Sudan
by Samantha de Silva, Abir Hasan, Aissatou Ouedraogo, and Eliana Rubiano-Matulevich
July 2020
2001 The Science of Adult Literacy
by Michael S. C. Thomas, Victoria C. P. Knowland, Cathy Rogers
January 2020
To view Social Protection & Jobs Discussion Papers published prior to 2020, please visit www.worldbank.org/sp.
ABSTRACT
Following the 2010 devastating earthquake and subsequent cholera epidemic, Port-au-Prince’s residents have
been increasingly affected by food insecurity, socio-economic unrest including periods of complete lock-down,
and gang violence. In light of the insecurity which limits the possibilities to collect the necessary information
to target the vulnerable residents of Port-au-Prince, this paper aims at providing meaningful evidence to
inform the remote targeting and delivery of a potential social assistance program. Putting together household
and geospatial data, we compute a composite vulnerability indicator for the metropolitan area, offering a
first snapshot of inequality and vulnerability within the city, and discuss the results’ implications for social
protection programming.
ABOUT THIS SERIES
Social Protection & Jobs Discussion Papers are published to communicate the results of The World Bank’s work
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