metadata: authors: 0: author: Munoz,Juan Eduardo
1: author: Gallegos Munoz,Jose Victor
2: author: Olivieri,Sergio Daniel
count: Ecuador,Venezuela, Republica Bolivariana de
docna: 0: docna: Big Data for Sampling Design : The
            Venezuelan Migration Crisis in Ecuador
docty: Policy Research Working Paper
subtopic: ICT Applications,Public Sector Administrative & Civil
            Service Reform,Economics and Finance of Public
            Institution Development,Democratic Government,State Owned Enterprise Reform,Public Sector Administrative and Civil
            Service Reform,De Facto Governments,Telecommunications Infrastructure,Inequality
teratopic: Public Sector Development,Poverty Reduction,Information and Communication Technologies,Governance
abstracts: cdata!: The worsening of Ecuador's
            socioeconomic conditions and the rapid inflow of Venezuelan
            migrants demand a rapid government response. Representative
            information on the migration and host communities is vital
            for evidence-based policy design. This study presents an
            innovative methodology based on the use of big data for
            sampling design of a representative survey of migrants and
            host communities' populations. This approach tackles
            the difficulties posed by the lack of information on the
            total number of Venezuelan migrants—regular and
            irregular—and their geographical location in the country.
            The total estimated population represents about 3 percent of
            the total Ecuadoran population. Venezuelans settled across
            urban areas, mainly in Quito, Guayaquil, and Manta
            (Portoviejo). The strategy implemented may be useful in
            designing similar exercises in countries with limited
            information (that is, lack of a recent census or migratory
            registry) and scarce resources for rapidly gathering
            socioeconomic data on migrants and host communities for
            policy design.
geo_reg_key: 119228,517191
repnme: repnme: Big Data for Sampling Design : The
            Venezuelan Migration Crisis in Ecuador
display_title: Big Data for Sampling Design : The
            Venezuelan Migration Crisis in Ecuador
listing_relative_url: /research/2020/07/32264822/big-data-sampling-design-venezuelan-migration-crisis-ecuador
topicv3: State Owned Enterprises m1326323 721,Poverty m1326687 695,Big Data m1327333 661,Mobile Phone m1505675 3092
publishtoextweb_dt: 2020-07-22T00:00:00Z
historic_topic: Public Sector Development,Poverty Reduction,Information and Communication Technologies,Governance
origu: EFI-LCR-POV-Poverty and Equity (ELCPV)
no_of_pages: 13
author_keys: 0: author_key: 428661
1: author_key: 1245110
2: author_key: 926510
model_extractions: Policy Research Working Paper 9329 Big Data for Sampling Design The Venezuelan Migration Crisis in Ecuador _Juan Muñoz José Muñoz Sergio Olivieri_ Poverty and Equity Global Practice July 2020
Policy Research Working Paper 9329

# **Abstract**

The worsening of Ecuador’s socioeconomic conditions and the rapid inflow of Venezuelan migrants demand a rapid government response. Representative information on the migration and host communities is vital for evidence-based policy design. This study presents an innovative methodology based on the use of big data for sampling design of a representative survey of migrants and host communities’ populations. This approach tackles the difficulties posed by the lack of information on the total number of Venezuelan migrants—regular and irregular—and their geographical location in the country. The total estimated population represents about 3 percent of the total Ecuadoran population. Venezuelans settled across urban areas, mainly in Quito, Guayaquil, and Manta (Portoviejo). The strategy implemented may be useful in designing similar exercises in countries with limited information (that is, lack of a recent census or migratory registry) and scarce resources for rapidly gathering socioeconomic data on migrants and host communities for policy design.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort 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. The authors may be contacted at solivieri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
# **Big Data for Sampling Design: The Venezuelan Migration Crisis in Ecuador**<sup>∗</sup>

Juan Muñoz<sup>1</sup> , José Muñoz<sup>2</sup> and Sergio Olivieri<sup>3</sup> JEL: C8, C83, F22 Keywords : Sampling design, stratification, Big Data, migration, Venezuela, weight calibration, maximum entropy.

∗This paper has benefited from comments by Ana Aguilera, Tara Vishwanath, Nandini Krishnan, Beatriz Godoy, Teresa Reinaga, Ana Rivadeneira, Tanja Goodwin, Carlos Vayas from Telefonica de Ecuador, Alexandra Escobar and Paul Guerrero from UNICEF - Ecuador. We are very grateful to Roberto Carrillo (ex-Director of NSO – INEC acronym in Spanish) and Christian Garces, Xavier Núñez and Francisco Céspedes from the sampling division of INEC for facilitating access to update information from the 2010 Census. Different versions of the paper benefitted with comments from participants to Conference on Inclusion of Refugees in National Surveys that Measure Poverty, October 2-3, 2019, Washington, DC; Research Conference on Forced Displacement, January 16-18, 2020, Copenhagen; and Urban Migration & Forced Displacement: Data Collection in Fragile States, February 27, 2020, Washington DC 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

> 1 Sistemas Integrales, Santiago, Chile.

> 2 Telefónica de Ecuador.

> 3 World Bank, Poverty and Equity Global Practice.
# **I. Introduction**

The República Bolivariana de Venezuela is experiencing a multifaceted humanitarian, economic, and social crisis, which has led to an exodus without precedent in the region. By September 2019, approximately 4.3 million people had left the República Bolivariana de Venezuela. This exodus also resembles the refugee crisis experiences in other conflict states, such as the Syrian Arab Republic, Afghanistan, Somalia, and Southern Sudan. Eighty percent of the Venezuelan emigrants migrated to Latin America, with the main recipient countries being Colombia, Peru, and Ecuador. At the beginning of the Venezuelan exodus, Ecuador was characterized as a country of transit to Peru or other Southern Cone countries, such as Chile and Argentina.

Since 2015, more than 1.2 million Venezuelans have passed through Ecuador in a " _humanitarian corridor_ ".<sup>4</sup> However, between 2015 and September 2019, almost 400,000 Venezuelans decided to settle in Ecuador .<sup>5</sup> The hardening of migration policies in other countries of the region, combined mainly with reasons of family reunification and the search for better economic opportunities, helped persuade many migrants to consider Ecuador as a destination country. The migration stock in Ecuador tripled between 2017 and 2018. By August 2019, it had almost doubled again when compared to the same period the previous year (Figure I-1).

<!-- Start of picture text --> FIGURE I-1: THEY ARE NO LONGER IN TRANSIT; MORE AND MORE ARE DECIDING TO STAY<br> 400,000 389,103<br> MONTHLY ARRIVALS<br> 350,000<br> ACCUMULATED MIGRATORY BALANCE (from JANUARY 2015)<br> 300,000 248,480<br> 250,000<br> 200,000<br>94,694<br> 150,000 33,942<br>10,058<br> 100,000<br> 50,000<br> -<br>2015 2016 2017 2018 2019<br>Source: Own estimates based data from the Ministry of the Government of Ecuador, August 2019.<br>62,037 70,950 73,284 76,469 89,015 82,117 93,303 91,356 70,842 97,688 67,434 81,142 72,556 87,828 86,924 90,828<br>30,397 26,988 26,839 30,345 39,178 47,071 48,758 30,073 29,631 52,166<br>6,205 5,659 7,456 9,540 8,361 7,278 6,710 7,869 9,991 10,862 13,726 12,767 8,760 8,012 7,938 6,186 5,982 6,102 7,161 8,890 9,652 11,344 12,082 10,801 14,716 13,071 13,130 12,400 14,647 16,869<br>JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL<br><!-- End of picture text --> The Venezuelan exodus coincided with the Ecuadoran economic crisis, but it was not the cause. Oil prices have been falling since mid-2014, and, as a result, the Ecuadoran government had to shrink spending, which was the main engine of growth. The economy stagnated in 2015 and contracted by 1.2 percent in 2016.<sup>6</sup> Due to fiscal pressures, the coverage of Ecuador's main conditional cash transfer program was also reduced. Working conditions deteriorated. Unemployment, underemployment, and informality rates increased to levels not seen in 10 years. Between 2013 and 2018, approximately 66,000 good jobs were lost. This

> 4 http://reporting.unhcr.org/node/2543.

> 5 Ministry of Government of Ecuador. Available at https://www.ministeriodegobierno.gob.ec/migracion/. 6 World Bank (2018).

2
significant reduction was partially offset by the increase in jobs offering fewer hours per week and lower wages, or a combination of both.

The worsening of socio-economic conditions demands a government's rapid response. To do so, representative information on migrants and host communities is vital for evidence-based policy design. However, two main challenges are associated with conducting a representative survey of the host and migrant populations. Even though Ecuador has a reliable and updated sampling frame for the resident population, the total number of Venezuelans and their geographical location in the country were not available at the time of the survey.<sup>7</sup> The official data provide only the net flows of migrants who entered and exited the country through an official gate. However, no official estimates exist for those migrants who did not register their entry into the country to add up to the total Venezuelan migrant population. In 2019, Ecuador was in a weak position relative to its neighboring countries in the availability of such data. Peru gathered the National Census in 2017, and Colombia had collected not only the 2018 National Census but also a national migratory registry.

Unlike other Latin American host countries, Venezuelan migrants were expected to be spatially clustered in Ecuador throughout the corridor. For instance, while in Peru, over 84 percent of migrants are in Lima, in Ecuador, it is estimated that only around 60 percent of Venezuelan migrants live in five main cities – Quito, Guayaquil, Cuenca, Manta and Santo Domingo.<sup>8</sup> This phenomenon is likely standard in this type of population's movements, a fact that has significant implications on both the survey's intended analyses and its sampling design.

From an analytical standpoint, assessing the impact of immigrants on the host population depends on their spatial proximity and their relative density. From the sampling viewpoint, the clustering of immigrants would help to implement a design that has proven to be successful in conducting similar surveys elsewhere. For instance, such as those addressed to immigrants from the rest of the continent in South Africa (Plaza, Navarrete and Ratha, 2011); Syrian refugees and their hosts in Lebanon, Jordan and Iraqi Kurdistan (World Bank, 2018, and Aguilera, Krishnan, Muñoz, Russo, Sharma and Vishwanath, 2020); and Myanmar Rohingya in Bangladesh (World Bank, 2020 b).

The paper is structured as follows: section II describes the innovative use of Big Data from Telefonica de Ecuador to come up with a strategy for generating representative samples of host and migrant households for the Human Mobility and Host Community Survey (EPEC acronym in Spanish). Section III presents the implementation strategy. Section IV concludes by drawing general lessons from our experience in sampling migrant populations.

> 7 The Human Mobility and Host Communities Survey in Ecuador (Encuesta de personas en Movilidad y Comunidades de Acogida – EPEC acronym in Spanish).

> 8 World Bank (2020 a).

3
# **II. Big Data at Work**

Ecuador's most recent population and national housing census was conducted in 2010 by the National Statistical Institute (INEC acronym in Spanish). It reported the population and number of households in each of Ecuador's 40,681 census sectors. The census cartography is entirely digitized, and polygon shapefiles are also available for each sector. It has been partially updated over the years, and it has been the primary source of information for sampling design of all official surveys like labor, health, and nutrition, and multipurpose surveys, among others. These surveys are representative of the Ecuadoran population residing in the country at different geographical levels. However, the census cartography is not updated enough to capture the recent influx of Venezuelan migrants. Thus, it does not allow to build a representative sample of the migrant and host populations.

Defining a sampling strategy to yield representative samples of host and displaced populations demanded a key innovation: the incorporation of Big Data for the sampling design. The flow of migrants affects mainly urban areas or main cities across the corridor from the northern border with Colombia to the southern border with Peru. The geographical aspect of the phenomenon encouraged the use of Big Data from telecommunication companies because of its accuracy in urban areas. Two leading cell phone providers were contacted in Ecuador to request their collaboration. However, only one of them, Telefonica de Ecuador, had the technical capacity to implement the sampling strategy proposed. Telefonica de Ecuador has vast experience in processing and providing the information requested for this sample.<sup>9</sup> Even though its market share is around 30 percent, which represents approximately 4.6 million clients in Ecuador, it is the only private company with a significant presence in the República Bolivariana de Venezuela's mobile phone market. Thus, it could be expected to attract most Venezuelan consumers seeking to obtain cell phone services in Ecuador.

Telefonica storages around 15 million call detail records (CDR) and 180 million external detail records (xDR) on average per day.<sup>10</sup> For this project, Telefonica processed more than 4.7 billion mobile registries adding up CDR and xDRs for a period between June 2018 and March 2019.<sup>11</sup> Telefonica's database was analyzed to determine how many of the active mobile phones in its database were likely to belong to a Venezuelan who lived in each of INEC's census sectors. The process was conducted in three phases:

- First, each mobile phone in the database was tagged as being active;

- Second, each of the active phones was tagged as likely to belong to a Venezuelan;

> 9 The company has been working since 2017 with the Ministry of Tourism in identifying strategic travel destinations based on the analysis of Big Data (Telefonica de Ecuador, 2018). It has also been working with Quito’s municipality on developing an urban mobility study for the Metro (Telefonica de Ecuador, 2017).

> 10 There are two ways of collecting data: active or passive. The active way is when an event like calls, text, data, etc. is made by the owner of the cellphone and it can be charged to the owner. The coordinates of these events are registered and stored for future analysis. The passive way is when an event is captured by the device and this could not necessarily be charged to the owner. Telefonica de Ecuador (2019)

> 11 There is plenty of evidence in the literature on the use of CDR and XDRs for defining the place of residency of individuals based on the implementation of micro-simulation techniques at the global level and across different issues like employment shocks, infectious diseases, and others. Telefonica de Ecuador (2019).

4
- Third, each of the active Venezuelan phones was assigned to the primary sampling unit (PSU) where the owner was most likely to reside.

# II.1 Identifying active mobile phones

A phone was tagged as being active if, during the past 90 days, it generated at least 60 of the following events in 30 different days: being turned on or off, making or receiving voice calls, sending or receiving text messages, or accessing the Internet.

# II.2 Identifying Venezuelan mobile phones

Active mobile phones were tagged as Venezuelan if they were registered under the name of a Venezuelan national or if, during the past 30 days, they were used at least 30 times to make/receive a voice call to/from the República Bolivariana de Venezuela; send/receive a text message to/from the República Bolivariana de Venezuela; or access a website of interest to Venezuelans (e.g., visa applications, job opportunities, etc.).

# II.3 Georeferencing the phones

Cellphones are mobile by nature. For this exercise, a phone's residence was based on the events generated between 8 pm and 6 am – the evening hours, which can be reasonably assumed to be the time when the owner is at home.<sup>12</sup> Each of the events uses one (or occasionally more than one) of Telefonica's antennas, and each of the antennas is characterized by a single territorial point – the centroid of its coverage area. The antennas used by phone in the evening hours during a given number of days are first clustered into groups of neighboring antennas, and the cluster most frequently used during that period is selected as the cluster that defines the owner's residence. The phone's location is computed as the average of centroids of antennas in that cluster, weighted by the number of events used by each antenna. Finally, the phone is assigned to the PSU where that point belongs.

# II.4 Extrapolating to the overall population

The above process determines how many of Telefonica's phones reside in each PSU, and how many of them are likely to be Venezuelan. The figures were adjusted by canton-wise adjustment factors based on Telefonica's market shares (known from other sources), to estimate the total number of phones from all companies and the total number of Venezuelan phones from all companies in each sector. Another adjustment factor based on the fraction of the population that uses mobile phones was used to make the figures consistent with the total number of households reported by INEC in each sector at the time of the 2010 Census.

# II.5 Results

At the country level, the total number of Venezuelan migrants estimated in 2019 by Telefonica de Ecuador was 470,095. This population represented about 3 percent of the total population in Ecuador in 2019 and

> 12 The workplace was also determined by monitoring cellphones’ activity from 9 am to 5 pm. The idea was to see whether Venezuelans move far away from their homes to seek for a job or not.

5
<!-- Start of picture text --> at Pasto«<br>ot, =<br>*.. xduitok 2<br>Portoviejo— a 3<br>SzpsECUADOR -<br>, }.* Riobamba* .<br>Guayaquil is Me ‘<br>{ Cuenca ,<br>Machalas: P<br>sbxfM*:<br>.oer '<br>© OpenStreetMap contributors, © CARTO<br><!-- End of picture text --> <!-- Start of picture text --> \lovers[Ti twa<br><!-- End of picture text --> <!-- Start of picture text --> Pe<br>Pe<br>=== --- —_—_—<br><!-- End of picture text -->
# **III. Implementation**

The primary sampling strategy was very standard. A nominal sample of 2,800 households was to be selected in two stages, with 200 Census sectors -hereafter referred to primary sampling units, or PSUs- in the first stage and 14 households per PSU in the second stage. However, the design departed from the standard in the way it addressed stratification in both phases.

## III.1 First sampling stage

The 200 primary sampling units (PSUs) were stratified into three categories depending on the Venezuelan migrant density , defined as the ratio between the number of Venezuelan cellphones in the PSU (as per Telefonica's prediction) and the total population of the PSU (as per INEC's population census), and allocated into these strata as follows:

- _100 High-density PSUs_ : where the density was more than 0.15.;

- _80 Medium-density PSUs_ : where the density was between 0.05 and 0.15.; and

- _20 Low-density PSUs_ : where the density was less than 0.05.

Within each stratum, the sample was selected with probability proportional to the number of households reported by the 2010 Census.

## III.2 Second sampling stage

All households in each of the selected sectors were listed and stratified into three categories considering nationality and demographic composition. In each stratum, households were selected as follow:

- _Seven_ non-Venezuelan households (i.e. Ecuadoran, Peruvian, Colombian or any other nationality except Venezuelan).

- Seven Venezuelan households without children aged less than 5 years.<sup>13</sup>

- All Venezuelan households with children aged less than 5 years.

Within each stratum, the sample was selected by systematic equal-probability sampling.

The second stage posed some fieldwork management difficulties but is conceptually straightforward. A full listing operation of all households was conducted in the 200 PSUs selected in the first stage. The listing operation consisted of a complete enumeration of all physical structures in the area, with each physical structure being classified as a residential dwelling, commercial building, hospital, church, collective residence, in construction, etc. The listing operation collected information about the number of households occupying each residential dwelling and the number of Venezuelan members living in each household. For each Venezuelan household, the existence of children under five years of age was also gathered. Notice that given the high mobility of migrants, the listing operation was done twice: first a month before fieldwork and then immediately before fieldwork.

To ensure the quality and completeness of the listing operation, enumerators relied on the updated cartography from NSO and high-resolution paper maps identifying all buildings within each segment. The

> 13 In the PSUs with fewer than seven childless Venezuelan households, all of them were selected. Since this was the case in many PSUs, the sample size was reduced from 2,800 to 1,871 households.

7
procedure was implemented using smartphones and an application designed on the SurveyCTO Collect platform. Length, latitude, and altitude information was also collected for monitoring enumerators' work remotely. Enumerators then created a record for each residential unit and households following the protocol described in the 2019 Manual for Enumerators.<sup>14</sup>

# III.3 Selection probabilities and sampling weights

Given the sampling design discussed above, the probability phij of selecting a household in the second-stage stratum hij of sector hi of first-stage stratum h is given by:

## where

- kh is the number of sectors selected in the first-stage stratum h (20 in a stratum of low density, 80 in medium density and 100 in high density),

- nhi is the population in sector hi , as per the 2010 Housing and Population Census;

- Nh is the population in first-stage stratum h , as per the 2010 Housing and Population Census;

- mhij is the number of households selected in the second-stage stratum hij ; and Mhij is the total number of households in the second-stage stratum hij .

Notice, two fractions on the right-hand side of (1) represent the probability of selecting the sector in the first stage and the conditional probability of choosing the household in the second stage, respectively. The first probability is correct regardless of the accuracy of Telefonica's predicted number of Venezuelan phones in each sector.

The data from each household in hij should be affected by a sampling weight (or raising factor) whij , equal to the inverse of its selection probability (i.e. whij = 1 / phij ) to produce unbiased estimates from the sample.

# III.4 Non-response adjustment and calibration

To account for non-response during the listing operation, the total number of households in each secondstage stratum hij (the denominator of the second ratio in (1)) was estimated as: where Yhij is the total number of households listed in hij and Y'hij is the total number of households registered in hij by the 2010 Housing and Population Census.

The prior weights whij are constant for all members of all households in hij . These weights were calibrated considering the household characteristics hijk in hij . For the Ecuadoran population, the survey matches INEC's 2019 demographic projections in three geographic areas (i.e., Guayas, Pichincha, and rest) and five age categories by gender (i.e., 0-4, 5-9, 10-14, 15-64 and 65+ years old). For the Venezuelan population,

> 14 Centre for Evaluation and Development (2019).

8
the net migrant flows from the Ministry of Governance was matched. Weights are the same for all members of the household. We achieved this by looking for a set of weights { ωhijk } that differed as little as possible from the set { wihj } subject to several constraints, using the maximum entropy principle (Wittenberg, 2010).

∑hi hi∗ = 1 The term hi is the mean of the xc characteristic within household hijk . In other words, this minimization problem asserts that we should pick the distribution xxcc kk ρ<sup>*</sup> hijk that meets the moment constraints mm and the normalization restriction and deviates as little as possible from the prior distribution r<sup>*</sup> hijk , requiring the least additional information. The solution is given by: ∗ = hi ) (6) ee<sup>XXλλ�</sup> where is the Lagrange multiplier for the constrains ρρ kk E(X),Ω(λλ<sup>�</sup> and Ω is the normalizing factor to scale the sum of the adjusted weights to the target population N . The numeric calculations used Stata's maxentropy λλ̂ algorithm.

9
# **IV. Final remarks**

Weak economic growth and deterioration of social outcomes since the plunge in oil prices combined with a high influx of Venezuelans demanded a rapid response from Ecuador’s government. The survey (i.e., EPEC) was designed and implemented to produce comparable findings of the lives and livelihoods of host communities and Venezuelan migrants and refugees for policy design. Even though Ecuador has a reliable and updated sampling frame, the lack of information on how many Venezuelan migrants are settled in Ecuador and where they reside in the territory posed challenges for the design and implementation of the survey. This difficulty shows in many developing and developed countries that are affected by a rapid influx of people. The exclusion of these types of populations from national sampling frames provides a biased picture of the situation the country is experiencing (World Bank, 2018). Moreover, as the number of migrants and refugees continues to increase, it becomes more relevant to develop strategies to include them in frequent representative socio-economic surveys.

The contribution of this study relies on the innovative methodological approach of using Big Data for sampling design. The methodology brings a proper solution for approximating the total number of migrants -including regular and irregular migrants- and their location in a country. The strategy implemented in this survey can be useful in designing similar exercises in countries with limited information (i.e., lack of a recent Census or a migratory registry) and scarce resources to rapidly gather socio-economic data of migrants and host communities for policy design.<sup>15</sup> Even though access to Big Data is beneficial for sampling design, researchers and policy makers could face a limitation in the lack of knowledge and technical capacity from telecommunication companies to implement the method. Also, Big Data are particularly precise when both market share and coverage -i.e., urban areas- are high. Thus, information on geographic regions (e.g., rural areas) where coverage is low, or market shares are small might be taken with caution. Notice that this type of population is hugely mobile and difficult to locate if the information is not frequently updated. This poses significant trade-offs between having the most updated data and the cost of getting it. Lastly, although the confidentiality of users is not an issue in this type of exercise (the number of Venezuelan phones was requested as an aggregated PSU-level figure), our telecommunication partner still censored the precise number when it referred to fewer than five clients.

> 15 Notice that the Government of Ecuador approved Executive Decree 826 on July 25, 2019 for conducting a migratory registry to be finalized by end March 2020.

10