# A Large-scale Examination of ”Socioeconomic” Fairness in Mobile Networks

Souneil Park  Telefonica ResearchBarcelonaSpain<EMAIL_ADDRESS>,
Pavol Mulinka  Centre Tecnològic de Telecomunicacions de
CatalunyaBarcelonaSpain<EMAIL_ADDRESS>and  Diego Perino  Telefonica
ResearchBarcelonaSpain<EMAIL_ADDRESS>
###### Abstract.

Internet access is a special resource of which needs has become universal
across the public whereas the service is operated in the private sector.
Mobile Network Operators (MNOs) put efforts for management, planning, and
optimization; however, they do not link such activities to _socioeconomic
fairness_. In this paper, we make a first step towards understanding the
relation between socioeconomic status of customers and network performance,
and investigate potential discrimination in network deployment and management.
The scope of our study spans various aspects, including urban geography,
network resource deployment, data consumption, and device distribution. A
novel methodology that enables a geo-socioeconomic perspective to mobile
network is developed for the study. The results are based on an actual
infrastructure in multiple cities, covering millions of users densely covering
the socioeconomic scale. We report a thorough examination of the fairness
status, its relationship with various structural factors, and potential class
specific solutions.

##  1\. Introduction

The debate on net neutrality (Ruiz and Lohr, [n.d.]), often framed as
“internet as a utility”, highlights the conflicting public/private aspects of
the Internet. Regardless of the position towards the issue, there is a common
agreement that quality Internet access is crucial and its necessity is
universal across all people. However, ensuring such a universal quality access
is a challenging goal that requires constant investments, e.g.,
infrastructural planning, deployment, operation and maintenance. As most
Internet service provides are private entities operating in a competitive
market, their objective might not be perfectly aligned with the needs of the
general public. Thus, questions could be raised about whether there is any
unfairness and discrimination in the service quality.

In this paper, we put forward the topic of _socioeconomic fairness_ , and
report our examination on a major UK Mobile Network Operator (MNO). 111As a
precise definition or a measure of fairness does not exist, we use this term
in a broader sense. We believe socioeconomic fairness will be a compelling
issue in mobile network operation as mobile Internet access become a primary
means of information access for the general public. Socioeconomic fairness is
also closely related to the recent digital inclusion initiatives of many
government bodies (e.g.,(Union, [n.d.]; USAID, [n.d.])), which emphasize
quality Internet access to all people. Many MNOs also recognize the initiative
and are making efforts accordingly (e.g., (Telefónica, [n.d.]; Orange, [n.d.];
Verizon, [n.d.]), for example, by extending coverage. While the initiative
highlights the importance of eliminating marginalization and exclusion,
detailed efforts so far have not deeply explored the socioeconomic dimension.
For example, the current practices of MNOs are agnostic from the socioeconomic
status of the customers. The management and optimization practices are based
on Key Performance Indicators (KPIs), such as coverage monitoring, and
voice/data service metrics, of each radio sector222A radio sector is the
minimum unit of network deployment and to which the end users’ devices connect
for voice and data services. Furthermore, this KPI driven network management
may create socioeconomic discrimination as a side effect. For instance, if
users or areas of a certain socioeconomic class generates more traffic, MNOs
could in turn deploy more resources and this could potentially further
incentivise users to generate more traffic.

Exploring socioeconomic fairness involves multiple challenges, not only
requiring a large-scale population but also detailed measurements at the level
of individuals (e.g., byte consumption of every TCP flow of a user). In
addition, the scope of an analysis needs to cover various aspects, including
network resource deployment, data consumption behaviors, and urban geography.

To the best of our knowledge, our work is the first to explore socioeconomic
fairness in mobile networks. Our study thoroughly examines the fairness status
based on millions of users densely covering the socioeconomic scale in
multiple cities and their data spanning over multiple years. We take benefit
of the MNO’s actual network infrastructure, and the UK census which measures
the socioeconomic status at a very fine geospatial granularity. A novel
methodology that combines these two datasets is developed. The methodology
enables a geo-socioeconomic perspective to mobile networks, which can be
replicated and facilitate follow up works.

In addition to understanding the current fairness status, we explore the
underlying factors and provide various lessons about the relationships, e.g.,
with population density, sector deployment density, data consumption patterns,
and device distribution. We further make efforts to generalize the findings to
other MNO’s through an open dataset. Based upon the lessons, possible
approaches to better fairness are also discussed.

##  2\. Related Work

The broader literature on inequality has observed structural unfairness in the
access to social capital or scarce resources, and its negative consequences to
the society. Studies have revealed unfairness in various areas: access to
health care, quality jobs, housing, education, public safety, and wider social
networks (Charles, 2003; Krivo and Peterson, 1996; Musterd, 2005; Reardon and
Bischoff, 2011; Smith et al., 2007; Cutler and Glaeser, 1997), generally
disfavouring the poor and minorities and escalating their social isolation. In
parallel, diverse policy efforts and social programs are being made to level
the playing-field (for example, through direct aids (The Scout Association,
2020) and infrastructural investment (OSCI, 2011)).

As mobile Internet access has become a vital resource for a large population,
we believe it is imperative to examine potential unfairness or discrimination.
While many works are made on large scale mobile network performance analysis
(e.g.,, user mobility (Pyrgelis et al., 2018; Hoteit et al., 2014), network
KPIs and planning (Serrà et al., 2017; Nika et al., 2014), network performance
metrics (Falaki et al., 2010; Tangari et al., 2019), and user Quality-of-
Experience (Rajiullah et al., 2019; Chen et al., 2014)), the body of
literature does not employ the socioeconomic perspective. The literature on
digital divide puts much emphasis on the topic. However, the studies primarily
take the users’ perspective (Pearce and Rice, 2013), and assess the inequality
in terms of technology access, skills, and benefits from the technology. The
literature has explored digital divide depending on country, race, gender, and
various sociodemographic factors (Jackson et al., 2008; Clayton and Macdonald,
2013; Chircu and Mahajan, 2009).

In contrast, we view the problem from the service providers’ perspective. In
fact, there has been a number of works from the mobile computing community
that share a similar perspective of serving a wider public. Perino et al.
(Perino et al., 2020) reports the effort of bringing affordable Internet
access to under-served, remote communities. A recent work by Sen et al. looks
into Free Basics, an initiative of Facebook to provide zero-rated web services
in developing countries, and study the performance impairments (Sen et al.,
2017). Elmokashfi et al.(Elmokashfi et al., 2017) specifically focus on
availability of mobile broadband, and examine the status across Norway.

There is a related branch of works that show correlations between
socioeconomic level of customers and traditional phone usages (i.e., calls and
SMSs). For example, (Soto et al., 2011; Frias-martinez et al., 2012) show that
socioeconomic status can be inferred with good precision using a large number
of Call Detail Records (CDR) features for a 500,000 habitants city. Similarly,
(Smith et al., 2013; Mao et al., 2013) argue that, by targeting large regions
of developing countries rather than individuals, good classification precision
can be achieved with only a few properties (e.g., volume of outgoing calls).
While we explore a different dimension from these works, our work also focuses
on the data traffic, which dominates today’s mobile network usage (Ericsson
Mobility Visualizer, [n.d.]) instead of CDRs.

##  3\. Data

We use two main data sources: (i) passive data usage traces of the MNO; (ii)
Index of Multiple Deprevation (IMD), a socioeconomic segmentation of the
geographic spaces of the UK.

Our study covers three cities in the UK: London, Birmingham and Liverpool. The
selection of the cities was made by taking the top 3 cities by population to
achieve scale of the analysis. As our investigation involves city-specific
aspects (e.g., sector deployment and urban geography), it is inherently
challenging to extend it to many cities. For the main findings, we present the
results for the three cities and examine their consistency. However, we often
focus our presentation on London as the city hosts a more diverse population
of different classes than the other two.

(a) London (b) Birmingham (c) Liverpool

Figure 1. Number of LSOAs per decile. (x-axis: IMD decile, y-axis:
proportion).

Our study is conducted over a three year time-span and the data was extracted
from various periods. The main analysis of socioeconomic fairness uses the
data from three periods: Oct.-Nov. of 2018, Feb.-Mar. of 2019 and Jan. of
2020. The results of all the periods are highly similar and the findings hold
consistently. We thus often focus on the results from a particular year in the
paper.

In addition, we examine the consistency of the findings in two more ways.
First, we check seasonal consistency by sampling a month of each season from
Spring, 2019 to Winter, 2020. Second, considering the deep impact of the
COVID-19 pandemic, we add the corresponding period, February to Mid April of
2020 and re-examine the main findings.

###  3.1. Network performance logs

We analyze data collected at a _middlebox_ used by the MNO to optimize traffic
and to log performance metrics about each user’s transactions. The dataset was
filtered to include only mobile phones, not M2M or IoT devices. A transaction
is an entry in the monitoring logs generated by the middlebox, and corresponds
to an individual flow handling encrypted or non-encrypted traffic generated by
an app of a mobile phone. Specifically, for every flow, the middlebox stores
the following layer four (L4) information: average Round Trip Time (RTT),
number of packet retransmission, and total bytes transferred. The monitoring
system then aggregates these metrics at the user-level (i.e.,, across all
transactions of a given user).

Note that latency and packet retransmission are among the key metrics used to
assess network quality. For instance, excessive RTT can hurt real time
applications as streaming or VoIP, and generally impacts flow throughput.
Also, a high number of packet re-transmissions are caused by network
congestion, poor connectivity with weak signal or signal distortion. The
middlebox is located at the PGw (packet network data gateway) /GGSN (GPRS
gateway service node) in the packet core, hence the metrics reflect the
performance within the MNO network (i.e., between the user device and the
egress point towards the Internet). In order to view these performance
measures through the lens of urban geography, we also analyze information
stored at the _Mobility Management Entity (MME)_ that keeps track of the radio
sector a mobile device is connected to and the coordinate of the sector. The
information is used to identify the residence of users at a fine-grained level
and their socioeconomic status.

We clarify that some results are normalized to avoid reporting raw values and
respect the MNO dataset confidentiality policy (the details of the
normalization method is explained with the results). In addition, the data
used in the study does not include any personally identifiable information
such as name or home address. We also emphasize that the performance measures
of the study are independent from the type of contracts/plans since the MNO we
examine does not perform any manipulation of the network performance (e.g.,
throttling) by them.

###  3.2. Socioeconomic indicator: IMD

IMD is a statistical measure maintained by the UK government. The measure
quantifies the relative deprivation for the areas in England at a very fine
spatial granularity. This index combines seven distinct dimensions of
deprivation: (i) Income deprivation, (ii) Employment deprivation, (iii) Health
deprivation and disability, (iv) Education, skills and training deprivation,
(v) Crime, (vi) Barriers to housing and services, and (vii) Living environment
deprivation. By combining the above seven dimensions, the overall IMD measure
takes a wider interpretation of deprivation and looks into how much a group of
people lacks of resources of various kinds, not just income. As such, the IMD
measure has been used in studies of various domains (e.g., (Rivas et al.,
2017; Mcgillion et al., 2017)).

The index is maintained for all areas of the spatial division of England,
called Lower-layer Super Output Areas (LSOAs). LSOAs are small areas divided
to have similar population size and social homogeneity. An LSOA has an average
of approximately 1,500 residents or 650 households. There are 32,844 LSOAs in
England, 4,835 in London, 639 in Birmingham, and 298 in Liverpool. IMD is
updated around every 5 years; the last three were published in 2010, 2015, and
2019.

Based on the deprivation score, a ranking is made with all LSOAs in England.
Beside the IMD scores, the government publishes also the _deciles_ , i.e., it
splits the 32,844 LSOAs into 10 equal-sized groups, from the most deprived
(index 1) to the least deprived (index 10). The LSOAs in decile 1 fall within
the most deprived 10% nationally and the LSOAs in decile 10 fall within the
least deprived 10%. The skyblue bars of Figure 1 shows the distribution of
LSOAs of the three cities over the IMD deciles. While London is not dominated
by a particular decile, the distribution of the other two cities is heavily
skewed to decile 1. We discuss the impact of this skewed distribution
throughout the paper.

##  4\. Socioeconomic Segmentation

As the MNO does not identify the socioeconomic status of its users, we develop
a method for a fine-grained socioeconomic segmentation. It first identifies
the home sector of users, and then maps the IMD decile score of LSOAs to the
sectors.

###  4.1. Home sector estimation

While there are many methods for identifying home of people from mobility
traces, they commonly aim at finding regularity during night time and/or
weekends ((Kung et al., 2014; Bojic et al., 2015)). Likewise, we develop a
method for home sector detection specific to our dataset with the same
intuition.

We go through the MME data of all the days throughout a month for each device,
and identify the ones who connects to the same sector at night time (midnight
- 8AM) for a substantial number of days. To ensure that the device is staying
in the same area during the night time window, we employ the radius of
gyration to estimate the spatial deviation of a device and filter out the days
when the estimated radius is larger than 2km. Finally, the method identifies
the home sector of a device as the one which was connected at least two weeks
during a month, and has the maximum connection duration. The devices that do
not have a home sector that satisfies the criteria are excluded from the
study. This method identifies the home for a large set of users: 2M for
London, 0.24M for Birmingham, and 0.13M for Liverpool ($\approx$10-20% of the
population of each city)
333https://www.statista.com/statistics/294645/population-of-selected-cities-
in-united-kingdom-uk/.

The purple bars of Figure 1 shows the distribution of the identified residents
of the three cities. We believe the general similarity of the distributions to
that of the LSOAs (i.e., Purple vs. Skyblue bars) supports the reliability of
our sample and the home detection method. We also acknowledge the limitation
of our data for Birmingham and Liverpool, whose population is heavily skewed
to decile 1. As there are only a few LSOAs of higher deciles (e.g., above 7 in
Birmingham), the user samples are very small for some deciles. As for decile 8
of Birmingham and decile 9 of Liverpool particularly, our dataset does not
have a user sample, thus, we do not have results for them.

Figure 2. Boundaries of LSOAs (red) and Polygons (blue).

###  4.2. Mapping IMD decile scores to sectors

While the IMD measure is available by LSOAs, the mobility data only allows
identifying the home of users by sectors. Thus, the IMD decile score needs to
be estimated for the sectors in order to complete the socioeconomic
segmentation. The challenge here is the mismatch between the boundaries of
LSOAs and the coverage of the sectors.

Depending on the deployment of the sectors, their coverage could be smaller
than the boundary of one LSOA or it could span over multiple LSOAs. Therefore,
we create a mapping between the sectors and the LSOAs by estimating the
sectors’ coverage and computing the overlap between the coverage and the LSOA
boundaries. A Voronoi tesselation was used to estimate the coverage of
antennas, which has previously produced reasonable results (Lin et al., 2013;
Park et al., 2018). Subsequently, it was observed that the Voronoi polygons
frequently have a finer granularity than the LSOAs (The average size of the
polygons is 0.33km2 and that of LSOAs is 0.37km2). Figure 2 depicts the
boundaries of LSOAs and the Voronoi polygons for an example area in London.
This suggests that it is feasible to estimate the IMD decile score of sector
based on the LSOA it covers. In addition, both LSOAs and sectors are based on
population density, such that small LSOAs are often associated with small
Voronoi polygons.

(a) Ratio of data consumed via 4G

(b) Avg. packet retran. frequency (normalized)

(c) Avg. RTT (normalized)

Figure 3. Network quality across socioeconomic classes and years in London

(a) Ratio of data consumed via 4G

(b) Avg. packet retran. frequency (normalized)

(c) Avg. RTT (normalized)

Figure 4. Network quality across socioeconomic classes and cities

In some cases, a sector polygon overlaps with a single LSOA, making it simple
to link the two units. However, many polygons span multiple LSOAs (examples
can be found in Figure 2). In order to examine the impact of such cases on the
study, it was first verified whether the LSOAs that overlap with a sector
polygon are similar in terms of their IMD decile. For each of the sector
polygons, the standard deviation of the IMD decile for the overlapping LSOAs
was computed. It is observed that for the majority of the cases, LSOAs
overlapping with a polygon tend to have similar levels of deprivation: for 72%
of the polygons of London, the standard deviation of the IMD decile scores is
less than 1.5, 73% for Birmingham, and 71% for Liverpool. Thus the mapping is
completed by simply taking the IMD decile value of the LSOA which has the
largest overlap with a sector.

For the mapping of the IMD decile scores, we took the most recent IMD survey
at the moment of the analyses. The IMD survey of 2015 was used for the
analysis of the years 2018 and 2019. Although there is some time gap between
the IMD survey and the analysis, we believe the mapping is valid since the IMD
decile scores do not change much over time (e.g., the kendall rank correlation
of the IMD deciles is 0.89 between the census of 2010 and 2015, and 0.84
between 2007 and 2015). The analysis of the 2020 data took the 2019 IMD
survey, as it was updated in September 2019.

##  5\. Overview of socioeconomic fairness

To get an overview, we first segment the population of each city into 10
groups by IMD deciles and compare various quality measures described below.

Technology access: As for the technology access, we break down mobile data
consumption via technology, 4G LTE vs. others prior to 4G. 5G is not included
in the measurement since it was not widely deployed during the study period,
however, we discuss the implications to 5G deployment in the discussion
section.

For every user, we compute the ratio of data consumption made via 4G to that
made via the others. Figure 3(a) shows that, regardless of the socioeconomic
status, most of the consumption is made via 4G. The median of the ratio is
above 90% for all classes, consistently across all the three years and cities.
The variance within the deciles is also small, indicating that the 4G
deployment covers most of the users and area. The figure also shows that the
ratio further increases in 2020; the medians are slightly higher compared to
the previous years for all the deciles, and the variance is also smaller.

Network quality: The quality is examined as a next step, focusing on 4G only
as almost all consumption is made via 4G network. As discussed in Sec. 3, we
employ two key metrics: average packet re-transmission frequency and average
RTT. Fig 3(b) compares the daily average packet re-transmission frequency
across all deciles. For every user, this metric is computed by taking the
ratio of packet re-transmitted over total number of packets of a flow, and
then averaging them across all flows of a day. Fig. 3(c) compares the daily
average latency (i.e., average RTT experienced by the user, averaged across
all flows of a day). Results are normalized over the highest 90-percentile to
preserve MNO confidentiality. The results were simply re-scaled in order to
preserve the trend in the data. The normalization was done separately for each
year since our focus is inter-decile comparison within each year. Direct
comparison between the years is not possible accordingly.

Figure 3 shows the results for London throughout the studied periods. The
results do not reveal a clear evidence of unfairness, i.e., at the level of
IMD decile, the performance is not particularly better or worse among the 10
groups. Fig 3(b) and Fig 3(c) show that the medians are very close and the
central body of the distributions largely overlap among all the deciles. This
is also consistent across the three years.

We examine the two measures across the deciles in more detail by zooming into
the data of one year, i.e., Feb.-Mar. of 2019. Two methods are used. First, we
examine if a statistical difference is found among the deciles using ANOVA.
While One-way ANOVA detects a significant difference, the significance is
likely due to the scale of the samples, which is on the order of million. For
example, one-way ANOVA confirms significant difference of re-transmission
frequency among the deciles (F(9, 1024561) = 163.9, p $<$ .01); however, the
effect size (eta-squared) of this ANOVA model is .0016, which is far below the
value (.01) that is considered as a general rule of thumb of a small effect
size (Miles and Shevlin, 2001). The result is similar for RTT as well, where
the effect size is 0.0012.

Second, we also explore possible complicated associations that might not have
been detected by ANOVA using a number of popular machine learning methods
(i.e., logistic regression, Gradient Boosted Trees, and SVM). The task is to
identify the IMD decile of a user given the summary statistics of the user’s
network performance measures. While we omit the details, all three methods
produce a result similar to a random classification, indicating that
associations between network performance and IMD decile is hardly found.

In summary, the analysis made at the level of IMD deciles overall demonstrates
negative results. However, visible trends appear in the upper tail of the
distributions. Fig 3(b) shows that the retransmission frequency gradually
reduces along the IMD deciles whereas Fig 3(c) shows that the latency is
slightly higher in some higher deciles. We investigate these upper tail trends
in the next section.

Network quality of other cities: The analysis on the other two cities confirms
general consistency of the findings (recall that the data set does not have
user samples for decile 8 of Birmingham and 9 of Liverpool, so the results are
missing for them). Data consumption is mostly made via 4G for all deciles
(Fig. 4(a)), and the distributions of the packet retransmission frequency and
RTT largely overlaps among all the deciles (Fig. 4(b) and Fig. 4(c)). One-way
ANOVA detects a significant difference among the deciles, but again only with
weak or negligible effect sizes (eta-squared below .01). Interestingly,
similarity to London is also found around the upper tail to some extent
although the trend is a bit more noisy possibly due to the limited samples in
the higher deciles.

Network quality across seasons & COVID-19: The plots are omitted to avoid
redundancy as the same trends are observed. One-way ANOVA detects a
significant difference among the deciles, but again with negligible effect
sizes (eta-squared below .01). During the pandemic period (Apr. 2020), the
difference among the deciles became further negligible (eta-squared far below
.01).

Network quality between weekdays and weekends: For all the above analyses, we
also separated weekends from weekdays and examined if notable difference of
network quality is found. The results are omitted since all the trends
described above were consistent between the two. However, we believe an
interesting future direction is to further divide the hours at a finer-grained
level and focus on peak hours.

##  6\. Socioeconomic Skews in Areas of Worse Performance

(a) Packet retransmission performance

(b) RTT performance

Figure 5. IMD decile segmentation of LSOAs of worse performance.

In this section, we move beyond understanding the average performance and
delve into the geographic areas of relatively worse performance, and examine
socioeconomic bias among them. For a finer-grained view than the IMD decile
segmentation, the population is further segmented by LSOAs. This finer-grained
division allows exploring diverse factors that are potentially associated to
the performance, including urban geography, sector deployment, and device type
distribution.

We aggregate the performance measures of individuals by LSOA and rank the
LSOAs in the following two steps. First, for each of the two measures, we
identify the set of users who fall below the third quartile (¿75%), whom we
refer to as the inferior experience group. Second, the LSOAs are ranked by the
ratio of residents who are included in the inferior experience group. We only
present the results for the most recent period (Jan. 2020) of London since the
findings hold consistently across the years and cities.

Figure 5 shows the IMD decile distribution for the LSOAs of worse performance,
i.e., the LSOAs with the highest (top 30%) inferior experience group ratio.
For comparison, the figure also shows the original IMD decile distribution of
all the LSOAs of each city. We make two findings: first, the existence of
socioeconomic skews in the areas of worse performance. For example, deprived
LSOAs show a greater share among the areas of worse packet retransmission
performance compared to their share in the general population. Second is the
opposite trend between the performance measures. In contrast to the packet
retransmission, less deprived LSOAs show a greater share among the areas of
worse RTT performance. Both findings consistently hold for all three cities.
The trends are also consistent with other LSOA percentile rank thresholds
(e.g., 20%, 10%) for the highest inferior experience group ratio.

The factors below are explored to understand the skews.

  * •

user & byte count: We count the number of residents and bytes consumed for
every LSOA in order to approximate the load generated from the area.

  * •

distance to sectors: This feature aims at comparing the density of the sectors
deployed across LSOAs. However, the comparison is challenging since accurately
estimating the coverage of the sectors and quantifying it by LSOA is
difficult. Instead, for every LSOA, we measure the distance to k nearest
sectors from its centroid and take the average.

  * •

device type distribution: We identify and aggregate the device types of
residents (i.e., manufacturer, brand, and model name) for every LSOA 444We use
a commercial database provided by GSMA to identify the device properties .

Weak association to user/byte count: A possible hypothesis for understanding
worse performance is the relationship with the load of the LSOAs, i.e., too
many users or much data consumption affects the performance measures. However,
we do not find support for the hypothesis. First, the correlation between the
number of residents and the ranking of LSOAs is very weak for both performance
measures (Table 1). Since correlation analyses are driven by general trends,
we also specifically examine if the LSOAs of worse performance have a greater
number of users or bytes than the others. A Mann-Whitney U test rejects the
hypothesis. In fact, the test for both measures confirms that the median of
the LSOAs of worse performance is lower than the median of other LSOAs.

User cnt. - Rank (retrans.) |  User cnt. - Rank (RTT) |  Byte cnt. - Rank (retrans.) |  Byte cnt. - Rank (RTT)
---|---|---|---
-0.128 | -0.028 | -0.024 | -0.152
Table 1. Correlation between user&byte and LSOA ranking.

Distance to sectors greater in LSOAs of worse RTT: One could speculate that
LSOAs with greater RTT may have sectors further away than others. However, a
correlation analysis does not show a general trend that matches the
speculation for both measures. We vary k from 1 to 5 and compute the average
distance to k-closest sectors for all LSOAs. When k is set to 5, a Spearman
correlation analysis shows a weak correlation, 0.088, between the average
distance and the RTT performance ranking of LSOAs. The coefficient is -0.210
for the average distance and the retransmission performance ranking. The
values are similar for other k values.

As performance is affected by a variety of other factors than the distance to
sectors, the absence of a clear trend is not surprising. However, when the
question is framed specific to the LSOAs of worse RTT performance, we indeed
confirm that the average distance is greater than others (Mann-Whiteney U
value: 317897.0, p ¡ .01) with a substantial effect size (Common-language
effect size of 0.582555Common language effect size is defined as “the
probability that a score sampled at random from one distribution will be
greater than a score sampled from some other distribution.” (McGraw and Wong,
1992)). We also examine the hypothesis ‘LSOAs of frequent packet
retransmission would show greater average distance’ but do not find support.

Figure 6. Average Distance to Sectors by Population Density

Projecting the average distance with respect the dimensions of population
density and socioeconomic status provides insights for understanding the
socioeconomic skew in the areas of worse RTT. In Figure 6, every LSOA is
projected by their number of residents/$km^{2}$ according to the UK census and
the average distance to sectors. The data points are colored by their IMD
decile and sized by their RTT percentile rank (greater dots for worse RTT). We
make three readings from the plot: first, the relationship between RTT and the
distance to sectors, as bigger dots appear more frequently in the upper area
of the plot. Second, sectors are sparser in the LSOAs of lower population
density; the dots are centered in the left part as y-axis increases. Third,
LSOAs of lower population density and sparser sectors are usually less
deprived. Blue dots are increasingly frequent in the upper-left area.

In addition to the population density, we also find that the amount of data
consumption is associated to RTT. The number of bytes consumed is lower for
the LSOAs of worse RTT than the rest (Mann-Whiteney U value: 215066.0, p ¡
.01) with a substantial effect size (Common-language effect size of 0.394).
These observations altogether suggest that the socioeconomic skew against the
less deprived LSOAs is likely linked to the population density and lower data
demand rather that the socioeconomic status itself.

Device type difference and packet transmission performance: As we do not
observe a clear association between packet retransmission frequency and the
above infrastructural conditions, we extend the analysis to the devices of
users. It is likely that the types of mobile devices vary between LSOAs of
different socioeconomic conditions, and the devices could show different
networking performance.

In order to explore the association, we compute the correlation between the
ratio of residents with a particular device and the packet retransmission
performance ranking of LSOAs. The correlation is computed for the 20-most
popular devices in the city. As the distribution of devices follows a typical
long tail distribution, looking into the 20-most popular devices covers 64.5%
of the whole devices in London.

We find a significant (p ¡ .01), moderate degree of correlation for a number
of models; for example, the most popular device in London shows a Spearmann
correlation coefficient of -0.346, and the second most popular device shows
-0.348. The negative correlation between the share of a popular device and the
LSOA ranking suggests that minor models that are more prone to worse
performance could be more common in the LSOAs of greater packet retransmission
frequency. As we look into the top-20 models, the correlation is negative in
general: out of 15 models that show a significant correlation, 14 show a
negative correlation. Among the 14 models, five show a moderate degree of
negative correlation (¡-0.25). We do not disclose the name of the models since
the intention of the analysis is not to identify under-performing devices but
rather to verify the existence of an association.

We also run the same correlation analysis with respect to RTT; however, the
correlation is unclear. The direction diverges (out of 18 models that show a
significant coefficient, 9 are positive and 9 are negative), and the
correlation is weak (all coefficients lie between -0.2 and 0.2).

##  7\. Generalizability across Operators

Figure 7. Sector distribution overview of four MNOs (x-axis: average distance
to 5-closest sectors, y-axis: population density)

We make effort to ensure high validity of the findings, by covering a large
sample of users, multiple cities, and multiple years. We also explore the
possibility to extend the validity of the findings across other MNOs.

Without having a similar type of performance measures from other MNOs, it is
difficult to fully replicate the findings. However, we find the possibility of
comparing sector deployment of multiple operators through open databases
(e.g., OpenCellID). The comparison of sector deployment helps speculating on
the socioeconomic skew in the LSOAs of worse RTT performance, assuming that
the impact of the distance to sectors on RTT holds in general.

We extract the sector deployment of three other operators in London from
OpenCellID (https://opencellid.org/). Figure 7 plots the result of each
operator in the same way used in Figure 6 except sizing the dots by RTT
ranking as the performance measure is missing. For comparison, we add a plot
made with the data of the MNO studied in our work (upper-left). The same
trends are observed, implying the possibility of the same socioeconomic skew
against less deprived areas: sectors are sparser in the LSOAs of lower
population density that are often less deprived.

The consistency of the trends implies that the deployment practice could be
similar between them. It also motivates future research for extending the
validity of the findings we made with actual performance measures. The
findings of our work could hold if the distribution of socioeconomic status of
users, device types and data consumption behavior are not particularly
different depending on the operators.

##  8\. Lessons & Conclusion

In this section, we conclude the paper by summarizing the main findings and
discussing the implications.

Fair performance, on average: The first finding is the balance of the average
performance across the classes, which is relieving considering that our work
was motivated to examine possible unfairness. As discussed early on, this
finding is compelling as mobile network has become a crucial resource for the
general public, and understanding the fairness of it contributes to a broader
range of research areas including social inequality and digital divide. In
addition, the finding addresses a possible concern that deprived classes might
be suffering from an infrastructural unfairness, which has been observed in
many inequality studies. In fact, we see that the infrastructure management
practice of operators, which in principle deploys more sectors in the areas of
higher population density and data consumption, is leading to a denser
deployment in deprived areas.

Two-sided socioeconomic skew: However, our study does raise alarm for the
LSOAs experiencing relatively worse performance as we see a socioeconomic skew
among them. More importantly, we find that the socioeconomic skew is two-
sided: each of the two performance measures we employ reveal opposing trends.
Moreover, the two-sided socioeconomic skew is observed consistently across the
three cities, which implies the existence of structural factors behind it. In
a broader context, the finding provides implications to the research community
and practitioners, highlighting the importance of considering urban geography
in mobile environments and reflecting on subsequent socioeconomic impact. The
opposing trends also call for future research on multiple different approaches
towards addressing the skew.

Sparser sectors in less-deprived areas: One side of the socioeconomic skew was
associated to the sector deployment which could be seen as disfavouring the
less-deprived class: less-deprived LSOAs more frequently show a sparser sector
deployment and worse RTT performance. We further found relationships of sector
sparsity with population density and data consumption, which shed light on
possible solutions. For example, an MNO could pay special attention to LSOAs
of lower population density and consider additional deployment even if the
demand is relatively low. We believe the finding is timely as MNOs are
progressively deploying 5G network: despite the difference of the technology
from 4G, our work put forwards a new objective to consider in the deployment
process, and provides lessons through a retrospective view of the 4G
deployment about what geographical attributes could play a role and how.

Related to the lessons, the interplay between sector deployment and diverse
geo-socioeconomic factors is an interesting topic for future research. For
example, demographic and lifestyle factors could be playing a role behind the
data consumption behaviors. The portion of greater age population gradually
increases along the IMD decile: people over 65 are 9% in decile 1, and 19% in
decile 10; people over 45 are 29% in decile 1, and 47% in decile 10 (ONS,
2019). Existing surveys have observed different degrees of engagement with
technology, especially with smartphones, among different generations (Jiang,
2018). In addition, the relationship with different connectivity conditions
(e.g., quality of home broadband and availability of WiFi access points) could
be studied. A few prior articles already observed cases where people employ
different channels for data consumption due to economic reasons (Sherman et
al., 2015; Hamilton, [n.d.]). A deeper analysis of the traffic types (e.g.,
content types, apps, etc.) by areas is another direction of future work that
will further expand the understanding of the performance dynamics.

Effect of devices than the infrastructure: The analysis of the other
socioeconomic skew we see for the packet transmission performance provides
multiple interesting findings. First, we found an opposing trend where the
deprived LSOAs were more prone to worse performance. Second, the skew did not
show much association to the infrastructural conditions. Third, a moderate
degree of correlation was observed between the share of popular devices and
the performance. We believe our results provide empirical evidences that give
guidance to future studies: whether to focus on the infrastructure or end-user
devices, and particularly on which devices the focus should be. An immediate
future work could conduct a large-scale examination of end-devices and observe
the performance impact more directly, particularly by including the devices
that are more common in deprived LSOAs. Such a work would be important
especially as 5G phones are being increasingly available in the market and
adopted by people over time. The direction of research could also lead to new
device benchmarks tailored to networking performance and initiatives for
informing the public about the results, which can address the skew
potentially.

###### Acknowledgements.

This work has been partly supported by the CHIST-ERA-17-BDSI-003 FIREMAN
project funded by the Spanish National Foundation (under Grant
PCI2019-103780), and the European Union’s Horizon 2020 research and innovation
programme under grant agreement N° 101021808.

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