P R W P
9905
Surveying Informal Businesses
Methodology and Applications
Gemechu Aga
David Francis
Filip Jolevski
Jorge Rodriguez Meza
Joshua Seth Wimpey
Development Economics
Global Indicators Group
January 2022
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Produced by the Research Support Team
Abstract
e Policy Research Working Paper Series disseminates the ndings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the ndings out quickly, even if the presentations are less than fully polished. e papers carry the
names of the authors and should be cited accordingly. e ndings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. ey do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its aliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
P R W P 9905
Informal business activity is ubiquitous around the world,
but it is nearly always uncaptured by administrative data,
registries, or commercial sources. For this reason, there
are rarely adequate sampling frames available for survey
implementers wishing to measure the activity and charac-
teristics of the sector. is paper presents a methodology
to generate a representative sample of informal businesses
using an adaptive, geographically based method called
Adaptive Cluster Sampling. Developed for populations
that are clustered and/or rare, this method helps with e-
ciently sampling Primary Sampling Units—blocks—that
are fully enumerated, and from which Secondary Sampling
Units—businesses—can be randomly sampled to conduct
interviews. e paper shows how this methodology can
be applied to surveying informal businesses, often reduc-
ing both the average variance of population estimates and
eldwork eort. Practical considerations and guidance for
implementation and analysis are also provided.
is paper is a product of the Global Indicators Group, Development Economics. It is part of a larger eort by the World
Bank to provide open access to its research and make a contribution to development policy discussions around the world.
Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. e authors may be
contacted at [email protected].
Surveying Informal Businesses:
Methodology and Applications
1
Gemechu Aga, David Francis, Filip Jolevski, Jorge Rodriguez Meza, Joshua Seth Wimpey
2
JEL Codes: C83, O17, L22, R12
Keywords: Adaptive Cluster Sampling, Informality, Informal Businesses, Surveys
1
The authors would like to thank Norman Loayza for the guidance and constructive comments, and the participants
of the seminar held by the World Bank Development Economic Indicator Group, and the participants at the Sixth
International Conference on Establishment Statistics (ICES-VI). All remaining errors are those of the authors. The
findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not
necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors
of the World Bank or the governments they represent.
2
The authors are with the Enterprise Analysis Unit, World Bank, Washington DC., emails:
gayanaaga@worldbank.org; dfrancis@worldbank.org (corresponding author); fjolevski@worldbank.org
;
jrodriguezmeza@worldbank.org; and jwimpey@worldbank.org.
2
1. Introduction
In much of the world, a great deal of economic activity is informal (Schneider and Hassan,
2016; Loayza, 2016; Elgin et al., 2021). Estimates show that in the typical developing economy
about 70 percent of employment is in the informal sector, though this share of labor only makes
up about 30 percent of production (Loayza, 2018). This has led economists and policy makers to
have broader concerns that such labor is mis-allocated by being informal, dampening overall
productivity (Meghir et al., 2015; Ulyssea, 2018). While recent macro estimates point to a decline
of the informal sector as a share of GDP, this downward movement is minor, and informality by
and large remains persistently high, particularly in developing economies (Ohnsorge and Shu,
2021).
Informality as a whole has received a lot of attention, but recently a rising emphasis has
been placed on one portion of the sector: businesses that operate informally. These businesses are
the most analogous to their formal counterparts, yet they also are indicative of important distortions
in those economies. Businesses that could formalize and be competitive are excluded from an
economy’s tax base and may lack opportunities to scale the size of their business; those informal
businesses that could not survive after formalizing may employ labor that could be allocated more
efficiently elsewhere, and in ways that provide more support (through regular work, income, and
benefits) to those workers (Ulyssea, 2018). However, data on most such informal businesses is
lacking. The limited data that is available has been used to examine the constraints to business
formalization (De Mel et al., 2013; Campos et al., 2015; Benhassine et al., 2018), the
characteristics of the informal businesses (Amin and Islam, 2015; Islam, 2019), and the
interlinkages of the informal and formal sectors (Benjamin and Mbaye, 2012; Jolevski and Aga,
2019; Amin and Okun, 2020; Amin, 2021).
3
The size and persistence of the informal sector, and
the wealth of research studying informal business activity reflects the need for high-quality,
representative firm-level data collected in an efficient manner.
Businesses operating informally are frequently small and less productive, but widely
abundant (see La Porta and Shleifer, 2014), implying that the typical (modal) business often does
not appear in official registrar records, tax rolls, or most often, both. This very nature of informality
3
Several of these analyses rely on data reported by formal firms on informal firms’ activity.
3
implies that estimates of the population of informal businesses and their activity will be missing
or incomplete; and sampling frames for developing high-quality, representative survey data will
be unavailable. In turn, descriptions of the characteristics of many economies will be lacking
information on a sector that is a ubiquitous source of jobs and business activity.
This paper lays out a methodology for conducting an enumeration exercise that generates
a representative sample of informal businesses for a subsequent survey; it does so by utilizing an
established geographic sampling method, Adaptive Cluster Sampling (ACS). ACS assumes that
informal business activity is relatively clustered in certain areas and, in turn, it takes advantage of
the information from the discovery of some informal businesses to alter the probability of selection
of primary sampling units (PSUs) to capture concentrations of additional informal businesses more
efficiently. ACS accounts for this altered probability of PSU selection and is able to provide
estimates of the population totals of informal businesses, allowing any subsequent survey of those
businesses to be considered representative. In the process, ACS frequently allows for reductions
in fieldwork effort without an increase (and often a decrease) in the variance of these population
estimates, if they were done repeatedly—a point which is later illustrated in this paper through
simulation. The methodology is designed to be implemented in diverse situations, across different
countries, while maintaining comparability.
Informal businesses may operate completely outside all or some legal channels (without a
tax or business registration or license), or they may include businesses that operate informally by
non-compliant behavior, such as employing workers ‘off-the-books’ (Ulyssea, 2018).
Operationalizing data collection first requires a clear and functional definition of the underlying
population of interest. A useful and basic typology of informality is the following: informality
includes partial or full non-compliance with registration and licensing (legal informality), on
employment (labor informality), or tax non- or under-payment (fiscal informality) (World Bank,
2020). This paper focuses on businesses’ activities that are legally informal: that is, lacking one or
all of the registration or licensing requirements to operate. This definition must be anchored to the
local context of implementation but generally consists of those businesses that lack a business
license, do not exist on a business registry, and/or are not registered with the relevant tax authority.
In addition to the operational aspect of this definition, there are important policy
implications for surveying unregistered businesses. First, such businesses are prevalent and may
4
draw resources from and compete with the formalized business sector (this has been categorized
as the ‘parasitic view’)
4
; by contrast, a competing view is that widespread informality represents
a ‘segmentation’ between a formal labor market, the business environment they experience, and
their informal analogs (Perry et al 2007; Maloney 2007; Kanbur, 2017; Ulyssea, 2020).
Understanding the estimates and nature of informal sector businesses informs policy, particularly
considering the absence of consistently effective policies to address informality, e.g., the limited
success of formalization efforts (see Bruhn and McKenzie, 2014; Floridi et al 2020).
Existing methodologies, such as household-based sampling, are not designed to measure
informal sector businesses directly. Such methods often do not capture informal sector businesses
at their point of business or operation; when they do, businesses are reached via the places of
employment of household members and, consequently, they are unable to estimate business
density in a geographical area. Likewise, informal sector businesses are, in all but a few cases,
excluded from economic censuses, which tend to be infrequent and expensive. Informal sector
businesses, virtually by definition, are also not reached by official surveys proceeding from
administrative sampling frames. By contrast, the proposed ACS method reaches units at their point
of operation and is designed to measure informal sector businesses per se; this paper builds upon
existing ACS literature and shows that this method not only provides a precise, unbiased
population estimate but can also provide practitioners with large fieldwork efficiency gains when
compared to other methods.
However, the ACS method does bring trade-offs and challenges. As noted above, to
operationalize such a survey, a clearly delimited population of inference is needed. In this case,
this consists of un-registered business activity, within a delineated geographic area (e.g., a set
urban area). This implies that informality in non-covered locations, rural or excluded cities, will
not fall within the purview of these surveys. The approach also implies that portions of labor and
fiscal informality do not fall within the coverage of the survey, an argument to include potentially
sensitive topics on the informal activities of formally registered firms in traditional firm-level
surveys.
5
Practitioners should note that such a methodology, unlike household- or labor-survey-
linked methods, is not designed to measure household activity; as such, additional care must be
4
See La Porta and Shleifer (2014) for a discussion.
5
The authors, for example, are also experimenting on different ways to ask for these sensitive topics in the Enterprise
Surveys of the World Bank.
5
taken to avoid undercounting household-based informal sector businesses. Attention must also be
paid to fluctuations of informal sector movement over different areas or times of the day or week.
6
2. Collecting Data on Informal Businesses: Existing Methods and Trade-offs
Two main data collection methods are typically used for surveying informal businesses: i)
household surveys, the prototypical examples being labor force surveys (LFS) or living standards
surveys
7
; and ii) establishment surveys or censuses (ILO, 2013). For surveys it is useful to consider
each method as a multi-stage sampling procedure, where with some probability of selection, units
are selected in a first stage, with the selected units denoted as PSUs, followed by secondary
sampling units (SSUs), and so on.
8
Define all individual PSUs , where is the universe of
PSUs. Likewise, SSUs are defined . Sampling units at each stage are well-defined, so and
may exclude certain elements.
9
Each allows for the possibility of stratification.
The household survey approach is implemented through standard household surveys, such
as Labor Force Surveys, and can capture informal (i.e., “off-the-books”) employment as well as
employment in the informal sector (i.e., employment in legally informal activities). PSUs are
enumeration areas defined to capture households. As a result, often excludes explicitly non-
residential areas, such as markets, industrial centers, or commercial zones. Call this set of PSUs
.
10
By design, these surveys are representative of
—that is the geographical areas
delimited to capture households. Measures of informal businesses only require a few additional
questions in these surveys, exploiting economies of scale in a cost-effective way.
However, such approaches are not necessarily well suited for representative estimates of
informal businesses. The number of businesses and counts of individuals are not always mapped
one-to-one; an informal business can be jointly operated across individuals, and one individual
may operate several businesses. More fundamentally,
rarely accounts for the concentration
6
Mobility and timing issues are not unique to ACS but plague any attempt to sample informal businesses.
7
The most widely known of these is the World Bank’s Living Standards Measurement Survey (LSMS), which is run
globally, in partnership with National Statistics Offices.
8
Note that this embeds a single-stage sampling approach, where only PSUs are selected.
9
An example of this is where excludes certain inaccessible areas (like a government or military installations).
10
Note the use of subscripts ‘LFS’ and ‘ES’ to denote, by shorthand, the Labor Force Survey and Establishment
Survey methods. This notation is dropped from later discussions of the proposed method (Adaptive Cluster Sampling)
for simplicity.
6
of informal businesses in certain areas. It is not designed to capture these informal businesses in
their place of operation. As such, policy-relevant information such as the characteristics of these
businesses, information on sales, expenditures, investment, competition, and the interaction with
the business environment is de-linked from where these activities take place (with exceptions in
household-based businesses). So-called 1-2 methods, where a second module following the
household module is administered to an informal business/activity, implement a business-level
survey module at the place of business, avoiding this last problem. However, such surveys are still
limited to the representativeness of
.
11
A second approach is the establishment survey or census, which may include informal
businesses. In the case of an establishment (or economic) census, all units are selected with
certainty, removing a multi-stage framework. By contrast, an establishment survey requires a
suitable sampling frame, where each unit in that frame exists within the universe of applicable
businesses; call this
. In certain cases, they may come from an economic census, but they are
infrequent and tend to exclude legally informal businesses.
12
Accurate projections of informal
business activity also require credible estimates of
,
, which, virtually by definition, are
missing from censuses and administrative counts. In the absence of such adequate frames,
implementers usually construct a frame through a two-stage process, where an area (PSU,
)
is first enumerated to generate a frame, and then a selection of informal businesses are selected in
a second stage.
13
However, constructing such a frame is often intensely costly in terms of time and
effort, particularly if implementers lack a priori information on the location of informal businesses.
This paper proposes a specific solution to address the implied time, effort, and cost
developing such geographically based estimates and sampling frames: namely, we propose that
survey implementers use a sampling method designed to adapt and more intensively sample areas
where informal businesses are discovered. To do this, we use a sampling method known as
Adaptive Cluster Sampling (ACS) (see Thompson, 1990).
14
ACS is a method of essentially guided
sampling, where the discovery of informal businesses ( ) in a PSU ( ) triggers more
11
Note that in these cases informal businesses are a tertiary sampling unit (TSU), where even if those businesses are
selected with certainty, unit non-response will result in three-stage weighting.
12
India’s Economic Census and Rwanda’s Establishment Census are two notable exceptions that include informal
businesses.
13
An example of this approach is the survey of informal businesses in townships in South Africa conducted by
Sustainable Livelihood Foundation (http://livelihoods.org.za/
).
14
The use of ACS to measure informal business activity has been piloted by the World Bank’s Enterprise Analysis
Unit (DECEA) for several years, following an initial pilot of the method in Harare, Zimbabwe (2016/17).
7
intensive sampling in adjacent PSUs (Salehi and Seber, 1997). PSUs are selected randomly first,
with or without stratification, and—based on a trigger threshold—adjacent PSUs are then
enumerated. As such, the process is no less naïve in the initial selection of PSUs than other
sampling approaches, but ACS exploits the information revealed in the process to efficiently target
the population of interest when that population is clustered and/or rare (Thompson and Seber,
1996). The sampling process to survey informal businesses follows a two-stage method. In the
first stage, ACS provides a probabilistic sample of PSUs which are fully enumerated to list all
informal businesses. In the second stage, businesses are randomly selected from the list for an in-
depth interview, discussed more in detail in Section 5.
3. Technical Foundations of Adaptive Cluster Sampling
15
ACS begins with the definition of PSUs, . For the sake of generalization, let be a
well-defined geographic area, divided into a grid of equal-sized squares. For this reason, PSUs will
also be described in this application of ACS as block areas, (henceforth BAs).
16
The first step
draws an initial sample of BAs of size ; all informal businesses are fully enumerated in these
BAs. Implementers set some condition , which is an expansion threshold: if a BA contains
sufficient units of the target population (
), additional BAs adjacent to all squares where
the count of units that meets or exceeds that threshold are sampled. Implementers can set specific
rules for this expansion, with the most straightforward being all eight adjacent neighbors. If these
newly sampled BAs meet condition , they trigger additional sampling of adjacent squares, and
so on.
Define a neighborhood as a collection of BAs that includes both an initially selected BA,
and in the case that it satisfies , an expansion into additional, adjacent BAs. In this case, we use
the pre-set rule of expanding into all eight, adjacent BAs; additional neighborhood expansions are
shown in Appendix Figure A.1. A cluster is the collection of all connected neighborhoods that are
enumerated because of the initial selection of BA . Figure 1 below shows clusters highlighted in
15
Note that the emphasis in this section is in the first stage of sampling informal businesses where BAs are selected
using ACS and then fully enumerated. The second stage is just a simple random selection from the resulting
enumeration list, which can be implemented via any number of random selection algorithms.
16
This term is also to differentiate from city blocks or public squares, with a ready comparison of ‘enumeration
area.’
8
green, where the initially selected BA is identified by a circle. The number in each square denotes
within that BA. The figure shows that changing the condition can change the extent of a
cluster.
17
Figure 1: Examples of Clusters under Different Expansion Thresholds ()
17
While these examples show a single square included in the initial sample, when multiple squares are included in the
original sample, it is possible for clusters to overlap partially or be fully duplicative.
9
Within a cluster there is a subcollection of BAs, termed a network, with the property that
selection of any square within the network would lead to inclusion in the sample of every other
square in the network. By definition, then, a network consists of adjacent BAs that each meet the
condition . The examples in Figure 2 below highlight the networks in orange with the equal-sized
or broader cluster highlighted in green. Note that in examples 1 and 6, the cluster and network are
identical. An edge unit is one not satisfying , but in an enumerated neighborhood with at least
one BA meeting . Green squares in Figure 1.b are all edge units. Note that in the case of example
1, where no expansion is triggered, the initial BA is a network size=1 with no edge units.
10
Figure 2: Examples of Networks under Different Expansion Thresholds ()
The traditional Horvitz-Thompson (1952) (HT) estimator of the population’s total and
mean of the number of BAs are unbiased with ACS, when the HT estimator is modified to account
for the specific BA selection probabilities that arise from adaptive expansion (Thompson, 1990).
18
18
Multiple unbiased population estimators are available using ACS. These include the HT estimator as well as the
Hurwitz-Hansen (HH) estimator, which is also modified for ACS sample inclusion probabilities. Rao-Blackwell
modifications of these estimators have been suggested by Thompson and Seber (1996), Salehi (1999) and Dryver and
Thompson (2005). Simulation and practical application work done by others has generally shown that the modified
11
In ACS a BA can be selected either initially or as part of an adaptive expansion, where selection
through expansion is a function of units () in all adjacent BAs.
19
This presents a challenge: we
have information on all BAs in a neighborhood, but we do not have information on the count of
units () in adjacent, unenumerated BAs (i.e., the white squares in Figure 1). As a result, one cannot
know if a BA could have been selected through an alternative expansion pattern, via unenumerated
BAs. This is an important difference with simple random sampling (SRS), where selection
probabilities are known. With ACS, it is not possible to compute the probability of inclusion in the
sample for all sampled BAs as not enough information is discovered for all sampled BAs to
produce these calculations. However, the subset of BAs that make up the networks within a sample
can have their inclusion probabilities computed. In other words, inclusion probabilities can be
computed for the discovered cluster, minus any edge units.
A specific selection probability is required for the ACS HT estimator. Define the
probability that a given BA, , is included in the sample as
, where initial BAs are selected using
SRS without replacement. Formally
is expressed as:
= 1
(. 1)
where is the total number of BAs in the study area;
is the number of initially selected BAs
before any expansion takes place;
is the number of squares in the network to which belongs;
and
denotes the total number of squares found in networks to which square i is an edge unit.
This probability can be used to write an HT estimator of the number of BAs for ACS as:
µ
=
(. 2)
Where
is an aggregated indicator of
values in network , where . For example, for
a population estimate,
is the sum of all informal businesses (
) in BA , that form part of
network (), that is:
=
.
20
= 1 with probability
if the sample intersects
HT estimator for ACS is more efficient than the HH estimators even when Rao-Blackwell adjustments are made (Tout
2009, Christman 1996, 1997, Thompson 1990, Salehi 2003). For that reason, the HT estimator is presented here.
19
Technically of course, it is all adjacent squares that conform to the chosen expansion pattern defined for the
neighborhood. See Appendix A.1 for some examples.
20
Note for completeness
is the total number of BAs in network .
12
network and
= 0 otherwise. Following Thompson (1990), the population and unbiased,
estimated variance of µ
are shown in Appendix A.2.
4. ACS and Simple Random Sampling
ACS generally provides gains reducing what will be called here ‘fieldwork effort’, the
number of blocks to be enumerated, particularly, when the underlying population of interest is rare
and/or clustered (Borkowski and Turk, 2013).
21
ACS exploits data where concentrations of units
of interest are in adjacent BAs (clustering) and where networks of these concentrations occur
rarely: as such ACS can identify areas of notably high concentrations of these populations
(Thompson and Seber, 1994). The definitions of relative clustering and rarity are specific to
particular populations of interest; ACS has been shown to reduce fieldwork effort across a variety
of fields, including rare populations of interest for the natural sciences.
22
The application of ACS
to informal businesses presented in this paper is novel, however, and so the following sections
discuss practical details for implementers interested in this application.
ACS implementers face a variety of design decisions that include the choice of expansion
threshold , the delineation of the total geographic area , the size of BAs, the pre-set rule defining
neighborhoods, and if stratification will be utilized. What follows is a simulation of different
design choices and parameters to see how these choices perform against baselines based on SRS
of BAs. The simulations use a unique data set from Eswatini (at the time in 2014, Swaziland) that
provides a full census of all businesses operating in the country, including informal businesses.
Uniquely, the data set includes GPS coordinates, and so allows for a real-life estimate of the
21
Fieldwork effort has also been referred to as ‘sampling effort’.
22
ACS has been used in a variety of fields, including ecology, ethology, and environmental studies. More specifically,
ACS techniques have been used to estimate the density of freshwater mussels (Smith et al., 2003) sea urchins (Skibo
et al., 2008) black sea bass (Cullen et al. 2017), yellow perch (Yu et al., 2011) and thresher sharks landings along the
California coast (Hariharan et al., 2013); to monitor the populations of endangered species (Davis et al, 2011, Salehi
et al., 2015); to determine infestation extent and spread rates of forest insect pest (Coggins et al., 2010); to identify
disease levels in plants (Gattone et al., 2013) as well as their restoration levels (Bried, 2013), or simply the abundance
levels of specific plant species (Philippi, 2005, Bowering et al., 2017). The conclusions of most of the studies above
suggest that ACS tends to overperform other sampling techniques when populations are rare and clustered.
13
clustering of informal-sector businesses.
23
As noted, these comparisons are drawn relative to SRS
(which readers will note is equivalent to ACS with = ). All simulations set BA size to 150 by
150 meters
24
and use a neighborhood definition of eight adjacent BAs; they vary by changing
expansion thresholds () and the initial selection number of BAs,
. Later simulations build on
these by also varying geographic scope, density, and by adding stratification.
The simulations focus on the largest metropolitan area in Eswatini—Manzini and the peri-
urban neighboring city of Matsapha. Together, the area covers approximately 120 square
kilometers and has a daytime population of over 150,000. An overlay of BAs yields 5,616 BAs of
150 meters by 150 meters, as shown in Figure 3. These squares contain 4,471 establishments, the
true value of the population total. The population appears clustered and rare, with only about 13%
of squares containing any establishment at all. Simulations were run using Stata, and every
simulation contains 10,000 initial draws of BAs. For comparison, two sets of simulations were
run: one set for SRS and one for ACS. SRS simulations include samples (
) of 2%, 3%, 5%, 10%,
20% and 30% of the 5,616 BAs. ACS simulations include samples where the initial sample of
BAs, before any expansions, cover 2%, 3%, 5%, 10%, and 20% of the 5,616 squares and examine
the following possible values for = {1, 2, 5, 10}.
23
The simulation results presented here incorporate all 4,471 businesses, 1,901 formal and 2,570 informal businesses.
Simulations run using only the informal businesses show similar clustering and yield similar results and efficiency
gains.
24
The choice of a 150-meter-squared BA is as much practical as one driven by data: in an urban environment that can
include large clusters of informality, interviewers must be able to enumerate a given BA in a reasonable amount of
time. As described in later sections, the 150-meter-squared BA is an experience-based size that makes this fieldwork
feasible.
14
Figure 3: Population of Businesses in Eswatini, with Grid Overlay
Table 1 shows the results of these simulations. The top panel of the table presents ACS
results and comparable SRS simulation results are presented at the bottom (shaded portion).
Column (1) provides the four expansion thresholds () used to simulate results with six different
initial samples () presented as a percentage of the total, column (2) and in terms of raw numbers
of BAs, column (3). For ACS, the total BAs will be greater than , provided that at least one
expansion occurs; as a result, the exact number of total BAs for ACS cannot be known with
certainty. For SRS, all BAs sampled will always equal (as shown at the bottom section for SRS
results). For ACS the mean number of enumerated BAs, after 10,000 simulations, is presented in
column (4). Similarly, columns (5), (6), and (7) present the simulation means of the population
estimate —the total numbers of businesses, the mean of the standard error of this estimate, and the
mean bias of the estimate, that is the difference between population-estimate and the true value,
4,471.
25
The last three columns of the table show the relative effort, that is the total number of BAs
enumerated in ACS relative to set SRS baselines, namely samples of 10% (562 BAs), 20% (1,123
BAs), and 30% (1,685 BAs) of the total number of BAs, columns (8), (9) and (10). A relative effort
25
As explained in footnote 13, we are ignoring the randomization procedure that may be used to select businesses
within the enumerated blocks to simplify presentation as there is nothing innovative in this second stage.
15
below 100% indicates a fieldwork effort reduction of ACS relative to SRS; above 100% indicates
an increase in fieldwork effort.
Both ACS and SRS produce unbiased population estimates; only SRS, n=2% has a mean
bias with an absolute value greater than 1%. What is more, ACS estimates are consistently less
variant compared to SRS. It is useful to compare rows with similar values for ‘Total BAs’. The
last two rows of the table show that SRS samples of 20% and 30%, with a range of mean
enumerated BAs from 1,123 to 1,685, mean S.E. of 1,189 and 1,021, respectively. Illustrative of
the gains from ACS, all ACS rows with a mean “Total BA” count in that range, indicated by bold
italics, have demonstrably lower mean standard errors. Likewise, the relative effort gains of ACS
are apparent in the last three columns. As expansion thresholds increase, the measures of relative
effort decrease. The intuition being that with higher thresholds of , a triggered expansion is more
likely to efficiently discover clusters of informal business activity (i.e., there are fewer empty
squares enumerated).
16
Table 1: Simulated Results of ACS Parameters compared to SRS
Initial BAs (
)
Mean of 10,000 Simulations
Relative Effort ACS to
SRS
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
C % #
Total
BAs
Mean Pop.
Estimate
(
.
)
Mean
S.E.
Bias
SRS
10%
SRS
20%
SRS
30%
ACS
1
2
112
1,047
4,477
593
0.14%
186%
93%
62%
3
168
1,209
4,474
580
0.07%
215%
108%
72%
5
281
1,404
4,468
573
-0.07%
250%
125%
83%
10
562
1,758
4,472
572
0.02%
313%
156%
104%
20
1,123
2,314
4,473
574
0.04%
412%
206%
137%
30
1,685
2,804
4,475
576
0.09%
499%
250%
166%
2
112
580
4,471
648
-0.01%
103%
52%
34%
2
3
168
721
4,469
612
-0.05%
128%
64%
43%
5
281
917
4,471
586
0.01%
163%
82%
54%
10
562
1,300
4,466
577
-0.11%
232%
116%
77%
20
1,123
1,911
4,457
576
-0.31%
340%
170%
113%
30
1,685
2,443
4,458
577
-0.30%
435%
218%
145%
5
2
112
233
4,508
831
0.83%
42%
21%
14%
3
168
328
4,447
742
-0.54%
58%
29%
19%
5
281
490
4,472
661
0.03%
87%
44%
29%
10
562
844
4,461
611
-0.23%
150%
75%
50%
20
1,123
1,470
4,459
591
-0.26%
262%
131%
87%
30
1,685
2,045
4,454
586
-0.39%
364%
182%
121%
10
2
112
166
4,494
1,009
0.52%
30%
15%
10%
3
168
240
4,460
884
-0.24%
43%
21%
14%
5
281
375
4,491
753
0.44%
67%
33%
22%
10
562
683
4,475
654
0.08%
122%
61%
41%
20
1,123
1,265
4,473
611
0.04%
225%
113%
75%
30
1,685
1,832
4,472
597
0.01%
326%
163%
109%
SRS
2
112
4,420
2,486
-1.14%
3
168
4,455
2,238
-0.37%
5
281
4,432
1,901
-0.87%
10
562
4,475
1,530
0.09%
20
1,123
4,488
1,188
0.37%
30
1,685
4,492
1,004
0.46%
Note: Authors’ calculations based on 10,000 simulation runs for each row’s parameters. The true population value is U=4,471.
Bias (Col. 7) is
.
, where
.
is the average (mean) population estimate of the total number of informal businesses, averaged
over 10,000 simulations. The relative effort columns are based on the total enumerated BAs compared to the SRS samples of 10%,
20%, and 30% respectively. In the last three columns, figures in bold and blue indicate a lower relative fieldwork effort of ACS vs.
SRS.
Since the fieldwork effort of ACS cannot be known ahead of time, it is helpful to compare
different sampling methods with approximately the same values of (mean) effort. Consider
Figure 4. The figure shows the mean standard deviation of population estimates from 10,000
17
simulations on the y-axis; the x-axis shows the mean amount of the fieldwork effort, that is the
percentage of BAs that are enumerated. The sampling methods that are shown include SRS, and
ACS with expansion thresholds, = {1, 2, 5, 10}. We leave the bias of these estimates aside, as
Table 1 confirms that both ACS and SRS are unbiased. What is first clear is that, at a given level
of mean standard deviation (that is, holding the value of the y-axis constant), ACS results in lower
fieldwork effort by reducing the share of BAs that are enumerated. Intuitively, at lower expansion
thresholds, fieldwork effort shifts rightward: as more expansions are triggered, naturally fieldwork
effort increases. As increases, fieldwork effort reduces, at generally no cost in terms of the
variability of these estimates (recall that SRS is equivalent to an expansion threshold = ). An
illustration of how increasing affects the rareness and clustering of a population is provided in
Appendix A.3. Similarly, at roughly equivalent fieldwork efforts (on average)—that is, holding a
value of the x-axis constant—ACS generally provides lower mean standard deviations. There is a
noteworthy exception: at lower fieldwork efforts, the mean standard deviation for ACS can exceed
that of SRS. This is because at lower fieldwork efforts, not enough expansions are triggered,
resulting in more variable estimates of the total populations (this is further illustrated in the
leftward mode in the distribution shown in Appendix A.5.) Under these conditions, then, ACS
generally provides less variant population estimates on average, but does not strictly improve over
simple random sampling under all conditions.
18
Figure 4: Comparison of Select ACS and SRS Parameters over 10,000 Simulations
Note: Authors’ calculations based on 10,000 simulation runs. Fieldwork effort (x-axis) represents the mean share of block areas
enumerated over all runs; the standard deviation (SD, y-axis) is the standard deviation of all 10,000 of the population estimates
for each case.
ACS and Stratified Random Sampling
In many cases additional information may be available that allows for stratifying a
geographic area before sampling. With business activity (including informality), stratification
variables that may be useful include land-use categorization, population density, road density,
vegetative cover, nighttime lights, or any geographically mapped variable where one category is
significantly more likely to intersect with business activity. In what follows, we use a nighttime
lights satellite imagery to generate a stratification variable. Nighttime lights are readily available
to the public and have been used widely as predictors of concentrated economic activity
(Donaldson and Storeygard, 2016). Figure 5 shows the result of a binary stratification using
nighttime lights overlaying the establishment locations, with higher concentrations shaded.
Despite the relatively low resolution of the lights data at 500m resolution the strata are very
0
600
1200
1800
2400
3000
3600
4200
51%33%27%21%16%11%9%5%4%
Total Squares Enumerated (Fieldwork Effort)
SD of Population Estimates' Distributions by Method
ACS, C=10
ACS, C=5
ACS, C=2
ACS, C=1
SRS
19
effective at discerning economic activity with 98% of establishments laying within the highest
probability strata which also contains 43% of all squares.
Figure 5: Binary Stratification Using Nighttime Lights
With well-defined strata in hand, survey implementers will likely want to use that
information to disproportionately design their sample to capture more informal business activity
while limiting the demands on fieldwork effort. In this case, we expect implementers will want to
draw disproportionately higher samples in the high-density areas, which constitute a lower number
of overall BAs but with higher concentrations of businesses. Specifically, we consider a case where
using SRS, an implementer draws BAs in the high-density areas relative to the low-density areas
at a ratio of 8:1. For ACS, since the final distribution of the sampled BAs is not known beforehand,
for comparison purposes we need to choose a starting ratio that is disproportionate, but that will
become more so as there are presumably more expansions in the high-density areas.
26
For the ACS
simulations, we choose a ratio of high- to low-density BAs of 5:3.
26
We note that the final sample ratio for C=1 is approximately 9:1, for C=2 it is approximately 6:1, and 3:1 for C=5.
20
Figure 6: Comparison of Select DS-ACS and DS-SRS Parameters over 10,000 Simulations
Note: Authors’ calculations based on 10,000 simulation runs. Fieldwork effort (x-axis) represents the mean share of block
areas enumerated over all runs; the standard deviation (SD, y-axis) is the standard deviation of all 10,000 of the population
estimates. DS: disproportionate, stratified indicates a comparatively high initial starting share in high-density nightlight
areas (Figure 4). For Simple Random Sampling (SRS) this ratio is 8:1. For ACS, this ratio is 5:3 for initial starting squares,
as the expansions in the high-density areas are expected to make the ratio more disproportionate.
Figure 6 replicates Figure 4 but with all simulated samples using the two-category
stratification. As without stratification, the ACS methods (now denoted with the prefix DS for
disproportionate, stratified) require far less fieldwork effort for a given standard deviation (that is,
holding the y-axis value constant). Very importantly, for a given level of sufficiently high
fieldwork effort, ACS methods all provide less variable population estimates. This is partially due
to the fact that, with well-defined strata, the underestimations that resulted before are minimized;
compare Appendix A.5 to A.6. Note that since expansions will necessarily require that more BAs
are enumerated, fieldwork effort (especially at lower expansion thresholds) will trend upward.
Lastly, readers should note that even ACS without stratification generally reduces the standard
deviation of population estimates and reduces fieldwork burden even over SRS with stratification.
We provide an illustration of this in Appendix A.7; Appendix A.8 also replicates these exercises
0
500
1000
1500
2000
51%33%27%21%16%11%9%5%4%
Total Squares Enumerated (Fieldwork Effort)
SD of Population Estimates' Distributions by Method
DS-ACS, C=10
DS-ACS, C=5
DS-ACS, C=2
DS-ACS, C=1
8:1 DS-SRS
21
with synthetic data that increases population density (given that Eswatini is a generally sparsely
populated country) and shows that ACS’s benefits largely hold throughout.
5. Implementing ACS in practice
Practically, implementers must first clearly define delineated geographical areas—that is
. While ACS can be applied in rural areas, this paper focuses on urban settings. When defining
this geographical area, three broad sources of information are considered, often in combination:
(1) administrative and natural boundaries; (2) supplementary information likely to be correlated
with informal business activity; and (3) local knowledge. Implementers should balance the
tradeoffs of relying on any single one or particular combination of these information sources.
Administrative boundaries (or delineated statistical areas like a census tract) can correspond to
official statistics, important for policy-relevant comparisons. However, administrative boundaries
may be insufficient. This will be the case when political boundaries are outdated, or when
informality occurs in the outskirts of administrative areas. Implementers will likely need to consult
alternative sources of information—such as land-use or, even, night light intensity—as well as
local knowledge.
The grid of block areas, BAs, can be easily generated using GIS
27
software, such as ArcGIS
or QGIS. BAs should be small enough that they can be enumerated in a reasonable timeframe, but
large enough to capture concentrations of informal businesses. Since 2016, the World Bank’s
Enterprise Analysis Unit has been piloting an ACS approach in a survey of informal businesses,
known as the Informal Sector Business Survey (ISBS). From several initial rounds of the
implementation of the ISBS, a practical suggestion is to begin with BAs sized at 150 by 150 meters.
However, implementers will want to consider their own restrictions, such as budget and staffing,
the relative density of informal businesses, and the appropriateness of other design decisions, such
as the choice of neighborhood definitions. All of these choices will be interlinked. Stratification
will require mutually exclusive categories and in the case of the ISBS, six stratification categories
have proven quite useful: residential, commercial/industrial, mixed residential and
commercial/industrial, market areas, open areas, and inaccessible.
27
Geographic Information Systems.
22
Informal businesses or establishments ( ), as defined earlier, are those that are legally
informal, that is they lack full or partial formal registration. The definition of legal informality
must be uniquely tailored to each country based on relevant legislation and regulation for formal
registration. In the case of the ISBS, an interview is administered to eligible informal business
activities randomly selected from the list of informal businesses discovered as part of the
enumeration exercise (). Combining ACS and a random selection of businesses within the
enumerated blocks is not only an appropriate method of providing unbiased population estimates,
with comparatively low variance and fieldwork efficiency, but also a means—as in the case of the
ISBS—of providing a framework for more detailed information, while preserving
representativeness.
Implementers will also want to establish monitoring and quality control procedures for both
data and fieldwork effort. Since the latter concern is the main focus of this paper, a few key details
merit attention. Specifically, the integration of GPS-linked software is often important to ensure
enumerators sufficiently apply the listing exercise to all potential units in selected BAs.
Sufficiently doing so requires full coverage of paths without enumeration outside of a delineated
BA; path tracking software can be used for this purpose.
28
Implementers may also want to integrate
fieldwork quality controls to minimize known potential pitfalls, such as undercounting of
household-based activities.
Implementing this methodology also requires close monitoring of fieldwork efforts, since
under ACS, the total number of BAs to be enumerated is not known a priori. Practically, it may
be recommended to enumerate the initial random sample of BAs to calibrate the value selected for
the expansion threshold (). Based on simulation exercises such as those presented in Section 4,
it is recommended to initially select a somewhat high value of , as a more conservative approach.
That section showed that along the two dimensions of fieldwork effort and variance of population
estimates, ACS is widely more efficient on both dimensions, holding the other constant: i.e., if one
holds the level of variance constant, ACS is almost always more efficient in terms of fieldwork
effort. However, practically, a survey implementer may want to have control over the expansions
that occur through ACS, as they trigger additional fieldwork—which can be costly. That is, an
implementer can start with a higher expansion threshold and then evaluate if the triggered
expansions are within the needs and limits of the survey.
28
For example, the ISBS is integrated with the open source OruxMaps.
23
While implementers will be interested in the population counts and characteristics available
from the enumeration exercise, they will also want to use the much more extensive information
collected through in-depth interviews with the randomly selected subset of informal businesses,
which captures more detailed information on informal businesses’ operations. In the case of the
ISBS, second-stage selection for an interview happens in real-time, during the enumeration of
BAs. This is made possible using readily available CAPI software.
29
As explained above, the first-stage enumeration of BAs () is unbiased even when unequal
probabilities of selection are considered. This requires the calculation of first-stage weights
implied by the inverse of . 1, that is
= 1/
(that is, the first-stage weight). A sub-script
for stratification is omitted, but readers should note that BA selection probabilities (
) are often
within strata, which can be multiple urban areas and/or categories within these urban areas.
30
Specifically, re-write . 1 by substituting =
, giving:
1
!
(
)
!
!
(
)
!
(. 3)
. 3 can be expressed in exponentiated natural logs (as shown in . 4) which can be computed
using programs such as Stata and R.
31
()
(
!
)
(
!
)
(
)
!
(
!
)
(
!
)
(
)
!
(. 4)
Implementers can also calculate a second-stage weight, as the inverse probability of
selection for an interview within an enumerated BA. Analysis of the data of interviewed
businesses, at the level of SSUs (), can then be done using weighting given by
()
(
)
.
29
The ISBS currently uses the World Bank’s Survey Solutions. For more details, see https://mysurvey.solutions.
30
Note that while stratification can help reduce fieldwork effort due to the grouping of similar BAs, it can present an
issue when there is the possibility of networks crossing strata due to expansions (as initial PSU selection probabilities
are a function of strata). The ISBS deals with this issue by not allowing expansions that cross strata. Also, note that
where stratification is defined as separate urban areas (and not further stratification within an urban area), the issue is
fully ignorable.
31
For example, the Stata function ln factorial(n) can be applied to . 4. Code for this calculation is available from
the authors upon request. Implementers should also note that it is important to set variable storage to ‘double’ to
maximize storage precision. Note that storage precision may be machine and processor dependent.
24
Statistical packages—notably Stata—are equipped to handle such multi-stage sampling and
weighting.
32
Since the probability of selection, (
)
, is set before enumeration,
implementers will most likely want to apply weight adjustments based on unit refusals and the
misclassification of units during fieldwork.
33
Implementers can adjust
(
)
based on the total
number of encountered informal businesses in a BA. The ISBS specifically applies three such
adjustments, based on different assumptions, all of which remove confirmed formal businesses: i)
counting all unit refusals as ineligible (strict assumption); ii) counting unit refusals with visible
signage or permits as ineligible (median assumption); and iii) counting all unit refusals as eligible
(weak assumption).
6. Discussion and Conclusions
While this paper lays out a proposed methodology for surveying informal businesses—
along with several practical guidelines and informative results—several challenges remain. The
methodology requires intensive enumeration, which must go hand-in-hand with monitoring. Some
of these challenges can be minimized by technology, through data and fieldwork quality control,
including path monitoring. There also remain risks to under-counting of informal businesses that
are not readily visible, including those in households or that intentionally remain less visible,
perhaps to avoid detection by authorities. This is borne out by the range of the share of businesses
operating in the household. Examples from iterations of the ISBS include 59 percent of informal
Somali businesses operate within the owners’ household compared to just 14 percent in Lao PDR.
Missing these types of businesses during enumeration is a potential form of design-based bias.
32
In Stata, this can be done by correctly specifying the svy: prefix via svyset. For example, the following code snippet
will declare survey data, with two-stage sampling, with stratification in the first stage (given by strata). The first stage
weight (wt_1) is at the level of an identified cluster (id_cluster), while the second stage is identified at the level of id_j
with a second-stage weight. For a discussion, see:
https://www.stata.com/support/faqs/statistics/stratified-multiple-
stage-designs/
svyset id_cluster [pw=wt_1], strata(strata) || id_j [pw=wt_2]
33
In the case of ISBS, (
)
is a function of a ’s position in an enumerated roster. Enumerators may encounter
either refusals or list an ineligible business (e.g., one that is registered), resulting in the need for a weight
adjustment.
25
Some of the selected BAs are market centers, often the largest concentrations of informal
business activity and those with higher selection probabilities, in turn. However, this raises
logistical challenges due to the sheer number of these businesses, making it daunting for a single
enumerator to cover the entire BA in one visit. This may require assigning more than one
enumerator per square. Secondly, markets may be open some days of the week and at certain times
of the day, which requires visiting the area at the right time. In open markets, operators may sit
next to each other, which may result in greater refusal. Finally, it is important to note that the
survey may require visiting areas with safety issues. In some cases, the area could be outright
dangerous for enumerators; such areas may need to be excluded from the survey with implications
in the extent of representativeness (via the definition of ).
One last, specific challenge is that policy makers and researchers may want to build panels
of data for informal businesses (or even of BAs/PSUs), as the dynamics in and out of informality
are an important area for analysis. Likewise, implementers may want to recontact businesses in
the course of implementing an ISBS-type survey. Revisiting businesses and building such panels
is notably difficult when informal businesses are mobile or have no fixed premises. Recontacting
such businesses may require the collection of contact information, such as mobile numbers,
requiring full compliance with any existing privacy protocols.
26
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Appendix
A.1. Examples of Neighborhood Definitions
The figure below demonstrates some possible definitions of neighborhood. For this paper, we use what is
known as a second-order neighborhood containing the original square and its 8 adjacent neighbors shown
in Example 1. All ‘B’s’ are said to be in the neighborhood of ‘A’. Example 2 is a first-order neighborhood
that is also commonly used in ACS. Example 3 is rarely used while Example 4 is a version of ‘strip-
sampling’ that is sometimes used for certain ecological phenomena.
31
A.2. Population and Estimated Variance, HT Estimator Using ACS
In terms of variance, it is valuable to consider notation in terms of separate networks. Denote two
separate networks, the -th and -th networks, respectively. Using SRS without replacement, the
probability,
, that an initial sample has an intersection of at least one BA is given by (Thompson, 1990):
= 1
+
(
)(
)
(. 1)
In turn, the variance of µ
is given by:
[
µ
]
=
(. 2)
Where is the total number of networks in the population. An unbiased estimator of this
variance is then expressed by:
[
µ
]
=
(. 3)
32
A.3. Illustrations of Clustering and Rareness Relative to Expansion Thresholds
The realized reduction in fieldwork effort follows from both the underlying characteristics
of a population of interest and the details implementers chose for the ACS design (see Borkowski
and Turk [2013] for a discussion). Consider, for example, a population that is visibly clustered,
but whose rareness depends on the expansion threshold parameter chosen (). Figure A1.a shows
a population that can be considered clustered but not rare under = 1. Figure 2.b shows the exact
same population, but the plane has been ‘flooded’ by raising the expansion threshold, to = 4,
such that the BAs containing fewer than 4 units cannot be ‘seen’. As such, the y-axis in 2.a shows
the raw count of the underlying population, while the axis in A1.b is reduced by 3 (the highest
count that does not trigger expansion). As a result, the ‘flooded’ population is now both clustered
and rare.
Figure A1: Examples of a Clustered but non-Rare Population
0
10
20
30
40
Clustered but not Rare (C=1)
3
13
23
33
Clustered and Rare when C=4
33
A.4 Summary of Eswatini Economic Census
34
A.5. A Direct Comparison of ACS and SRS, Fixing Specific Parameters
The figures below show comparisons between two sets of simulations, each corresponding
to a row in Table 1, with approximately the same (mean) amount of fieldwork effort (as shown in
panel b). These are ACS: C=1, n=2% (mean fieldwork of 1,035 BAs in table 1), and SRS: n=20%
(with associated fieldwork of 1,092 BAs). What is immediately clear, then, in panel a is that ACS
provides a mean unbiased population estimate with much lower mean standard error, underlining
the intuition that the adaptive discovery process allows for more statistically efficient estimates at
the same level of fieldwork effort. Note a small peak of negatively biased estimates (that is, to the
left of the distribution) that occurs in cases where a comparatively low initial selection of BAs fails
to produce sufficient expansions (or misses networks completely) to estimate the true population
mean. Panels c and d show what occurs if the ACS n is increased to 5%, compared to an SRS of
n=20%: both mean variance and mean fieldwork effort drop noticeably, with the disappearance in
the distribution of a small peak of downwardly biased estimates in panel a. The figures also give
an important nuance. While the last columns in Table 1 show the mean fieldwork effort in ACS
vs. SRS, panels b and d in Figure 4 show the distribution of these relative efforts. Under ACS with
n=2%, C=1 roughly half of the simulated fieldwork efforts are reductions relative to a (known)
effort using an SRS n=20%. When n=5%, C=2, virtually the entire mass of simulated fieldwork
effort is below the effort in SRS n=20%.
0 2.0e-04 4.0e-04 6.0e-048.0e-04 .001
Density
0 2000 4000 6000 8000 10000
mhat
ACS 2% C=1 SRS 20%
True Pop in Black
POP Estimates 5% and 95% distribution tail bars shown
0 .001 .002 .003 .004
Density
0
500
1000 1500
ACS 2% C=1 SRS 20%
Enumerated squares
35
A.6. A Direct Comparison of ACS and SRS, Fixing Specific Parameters with
Stratification
As it was the case of examining proportional SRS, introducing proportional stratification
to ACS (S-ACS) results in small but significant improvements in the distribution of population
estimates and expected fieldwork requirements. Expected fieldwork requirement distributions
appear somewhat sensitive to condition C when comparing naive ACS with S-ACS as shown
below. In addition to the improvements in fieldwork certainty (and sometimes reductions) shown
below, adding stratification to ACS also appears to reduce the left-tail error visible in some of the
figures shown above.
34
34
Left-tail error (here observations with pop estimates <3,000) is anywhere from 2% to 25% smaller depending on
the specific parameters.
0 5.0e-04 .001
.0015
Density
0 2000 4000
6000 8000
10000
mhat
ACS 5% C=2 SRS 20%
DS-SRS 5% & 40%
POP Estimates 5% and 95% distribution tail bars shown
0
.002 .004 .006
Density
0
500 1000
ACS 5% C=2 SRS 20%
DS-SRS 1:8 5%:40%
Enumerated squares
36
A.7. Comparison of ACS and SRS with Fixed Parameters, with and without
Stratification
0
600
1200
1800
2400
3000
3600
4200
51%33%27%21%16%11%9%5%4%
Total Squares Enumerated (Fieldwork Burden)
SD of Population Estimates' Distributions by Method
SRS
8:1 DS-SRS
ACS, C=5
DS-ACS,
C=5
37
A.8. ACS and SRS with Increased Density
As the population density of the area covered by our data set is relatively low compared to
larger cities in other countries, an artificial data set was simulated by duplicating and randomly
displacing the population of establishments in Matsapha and Manzini. This synthetic data set is
used to evaluate how the density of the underlying population might affect the efficiency of the
ACS estimators relative to SRS. The duplication and displacement is conducted through four
iterations, so the new simulated population is approximately four times as large. The geographic
area remained the same and the proportion of occupied squares rose from 13% to 42%. The
resultant data is shown in Figure A.8.1 and visual inspection confirms that clustering is retained
through this process.
Figure A.8.1: Synthetic Data
38
Figure A.8.2: ACS with Greater Density Population
0 1.0e-04 2.0e-04 3.0e-04 4.0e-04 5.0e-04
Density
10000 15000
20000 25000
mhat
ACS 5% C=5 SRS 30%
Population estimates
0 .001 .002 .003 .004 .005
Density
0 500 1000 1500
enumerated
Enumerated squares line at 30%
0 1.0e-04 2.0e-04 3.0e-04
Density
5000
10000 15000 20000
25000
mhat
ACS 5% C=10 SRS 30%
Population estimates
0 .002 .004
.006 .008
Density
0 200 400 600 800 1000
enumerated
Enumerated squares line at 30%
39
A.9. Useful Categories for Stratification
Strata
Stratification of Blocks
Definition
1
Residential
Any use of land that serves as a place
of residence. This includes: multi-
family homes (apartment buildings),
single family residences (houses,
including backyards), or mobile home
parks.
2
Commercial/Industrial
Urbanized areas that encompass retail
or service activities or land uses for
the production, storage, and
distribution of manufactured goods.
Commercial areas include: business
centers, retail centers (shopping malls)
or retail stores, service areas (banks,
restaurants, repair shops), office space,
space of public assembly (auditoriums,
convention halls, stadiums), and
institutional space (government
buildings, hospitals, schools).
Industrial areas include: industrial
parks, and localities in which
industrial production occurs (ex: steel
mills, paper mills, chemical plants, oil
refineries).
3
Mixed (Residential/Commercial/Industrial)
Includes areas in which there is a
combination of the two strata
discussed above
4
Market Centers
Any form of land use with a dense
concentration of sellers, such as
farmer’s markets, bazars, etc. This a
characterized by near certainty of
encountering informal businesses.
5
Open Area
Refers to accessible open space where
little commercial or industrial activity
is expected. Examples of this are:
parks, zoo, gardens, cemetery,
camping space, agricultural fields,
vacant spaces.
6
Inaccessible
Land in which there are no roads or
walkable paths. It includes: open water
(any water body such as lakes or
rivers), restricted access areas (such as
airport runways, military bases),
forests, and mountainous areas.
