metadata: authors: 0: author: Masaki,Takaaki
1: author: Granguillhome Ochoa,Rogelio
2: author: Rodriguez Castelan,Carlos
count: Senegal
docna: 0: docna: Broadband Internet and Household Welfare in Senegal
docty: Policy Research Working Paper
subtopic: Information Technology,Telecommunications Infrastructure,Inequality,Energy Policies & Economics
teratopic: Information and Communication Technologies,Poverty Reduction,Energy
abstracts: cdata!: Senegal has experienced a rapid
            expansion in fixed and mobile broadband internet
            infrastructure over the past decade. This paper examines the
            relationship between access to broadband internet and
            household welfare between 2011 and 2018 by integrating the
            latest two rounds of household budget surveys with data on
            the location of fiber-optic transmission nodes and coverage
            maps of 3G mobile technology. The results show that 3G
            coverage is associated with a 14 percent increase in total
            consumption and a 10 percent decline in extreme poverty.
            These results are robust to controlling for household
            demographics and other spatial characteristics, such as
            region fixed effects, road density, nighttime lights, and
            elevation above sea level, as well as for access to
            complementary digital infrastructure, such as 2G coverage or
            fixed broadband internet. The findings are also robust to an
            instrumental variable approach that relies on distance to 3G
            coverage in neighboring areas. These effects are larger
            among households in urban areas and households headed by men
            or younger cohorts. Although they are in the same direction,
            the welfare effects of proximity to fixed broadband
            infrastructure are not statistically significant.
geo_reg_key: 517191,1927320
repnme: repnme: Broadband Internet and Household Welfare in Senegal
display_title: Broadband Internet and Household
            Welfare in Senegal
listing_relative_url: /research/2020/09/32386896/broadband-internet-household-welfare-senegal
topicv3: Poverty m1326687 695,Power and Electricity Sector m1327094 515,Broadband Networks m1327576 291,Mobile Phone m1505675 3092,Broadband m1627085 4739
publishtoextweb_dt: 2020-09-08T00:00:00Z
historic_topic: Information and Communication Technologies,Poverty Reduction,Energy
origu: EFI-AFR2-POV-Poverty and Equity (EAWPV)
no_of_pages: 24
author_keys: 0: author_key: 1620251
1: author_key: 1649264
2: author_key: 1180219
model_extractions: Policy Research Working Paper 9386 Broadband Internet and Household Welfare in Senegal _Takaaki Masaki Rogelio Granguillhome Ochoa Carlos Rodríguez-Castelán_ Poverty and Equity Global Practice September 2020
Policy Research Working Paper 9386

# **Abstract**

Senegal has experienced a rapid expansion in fixed and mobile broadband internet infrastructure over the past decade. This paper examines the relationship between access to broadband internet and household welfare between 2011 and 2018 by integrating the latest two rounds of household budget surveys with data on the location of fiber-optic transmission nodes and coverage maps of 3G mobile technology. The results show that 3G coverage is associated with a 14 percent increase in total consumption and a 10 percent decline in extreme poverty. These results are robust to controlling for household demographics and other spatial characteristics, such as region fixed effects, road density, nighttime lights, and elevation above sea level, as well as for access to complementary digital infrastructure, such as 2G coverage or fixed broadband internet. The findings are also robust to an instrumental variable approach that relies on distance to 3G coverage in neighboring areas. These effects are larger among households in urban areas and households headed by men or younger cohorts. Although they are in the same direction, the welfare effects of proximity to fixed broadband infrastructure are not statistically significant.

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 tmasaki@worldbank.org and crodriguezc@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
# **Broadband Internet and Household Welfare in Senegal**<sup>**1**</sup>

Takaaki Masaki<sup>2</sup> Rogelio Granguillhome Ochoa<sup>3</sup> Carlos Rodríguez-Castelán<sup>4</sup>

**JEL codes:** F63, I31, L86, O12 **Keywords:** Poverty, Household consumption, Mobile broadband, Africa, Senegal

> 1 The authors wish to thank Andrew Dabalen, Mark Dutz, Samantha Lach, Johan Mistiaen, Tobias Pfutze, Robert Zimmerman and peer reviewers of the report Digital Sources of Growth in Senegal for their comments. Thea authors would also like to thank Brian Blankespoor, Arthur Denis Pascal Foch, and Djibril Ndoye for their support accessing data. . The findings, interpretations, and conclusions expressed in this paper 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.

> 2 Economist, Poverty and Equity GP, World Bank (tmasaki@worldbank.org)

> 3 Consultant, Poverty and Equity GP, World Bank (rgranguillhomeoc@worldbank.org)

> 4 Lead Economist, Poverty and Equity GP, World Bank (crodriguezc@worldbank.org)
# **1. Introduction**

As the use of digital technologies expands in Sub-Saharan Africa, it is becoming more important to understand the effects of these technologies on welfare. The adoption of digital technologies—defined as “the Internet, mobile phones, and all the other tools to collect, store, analyze, and share information digitally” (World Bank 2016)—can potentially translate into economic opportunities among large numbers of people in the region. A small, but rapidly growing body of literature documents the positive impacts of Internet access on various outcomes of interest. Cross-national studies show, for instance, a positive relationship between economic development and the expansion of digital technologies in the form of increases in fixed broadband penetration rates (Czernich et al. 2011; García Zaballos and López-Rivas 2012; Qiang and Rossotto 2009). Studies also show that access to cellphone coverage improves employment, as well as welfare in terms of greater consumption and a reduction of poverty (Blumenstock et al. 2020; Klonner and Nolen 2010).

Most studies, however, focus almost exclusively on cellphone access, that is, second-generation (2G) technologies, and limited research on fixed broadband Internet.<sup>1</sup> Little is known about the effects of mobile broadband Internet—for instance, third-generation (3G) and fourth-generation (4G) technologies—on the welfare of individuals and households. This distinction is particularly important in the context of African countries, where a majority of people access the Internet through mobile phones rather than through fixed broadband Internet.<sup>2</sup> Recent evidence thus suggests that an expansion of fixed broadband Internet would enable more rapid job creation, more economic activity in Africa, and greater productivity and export growth among firms (Hjort and Poulsen 2019). New research also shows that the rollout of mobile broadband Internet has increased household consumption and reduced moderate and extreme poverty in Nigeria (Bahia et al. 2020).

This paper builds on the nascent literature on the welfare effects of mobile Internet in developing countries, and, in contrast to previous studies, it sheds light on the welfare impacts of both fixed and mobile broadband Internet coverage. Senegal has experienced a more rapid expansion in digital technologies over the past decade relative its peers, such as Côte d’Ivoire, Kenya, and Rwanda. A growing share of Senegalese have gained access to cellphone services and mobile and fixed broadband Internet. A recent report suggests that 98.7 percent of users access the Internet through mobile phones, emphasizing the importance of mobile broadband Internet in the country (ARTP 2019).

The study exploits the two most recent household budget surveys undertaken in Senegal: the 2011 Deuxième Enquête de Suivi de la Pauvreté au Sénégal (Second Poverty Monitoring Survey, ESPS-II) and the 2017–18 Enquête Légère Expérimentale sur la Pauvreté (2017–18 Light Experimental Poverty Assessment Survey, ELEPS). It integrates the data of these surveys with data on the expansion of mobile broadband coverage and fiber-optic network infrastructure to examine how the coverage of and proximity to digital infrastructure may influence household consumption and poverty status. It matches the locations of households—based on GPS coordinates of census enumeration areas (EAs, the primary sampling units)—with maps of terrestrial backbone networks and 2G and 3G coverage maps.<sup>3</sup> As discussed more extensively by Hjort and Poulsen (2019), the proximity of a given location

> 1 2G technologies enable voice, SMS, and limited Internet access, while third-generation (3G) technologies enable more rapid Internet browsing and data downloading.

> 2 The number of active mobile broadband subscriptions per 100 inhabitants in Africa in 2017 was 34.0, while the corresponding rate for fixed broadband subscriptions was 0.4 (ITU 2019).

> 3 Data on backbone networks have been taken from Africa Bandwidth Maps (database), Hamilton Research, Bath, UK,

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to a terrestrial backbone network affects the Internet speed available at the location. The provision of 3G or 4G coverage also enables access to high-speed Internet through mobile phones, while 2G coverage only provides for limited Internet browsing and applications.

The analysis concludes that mobile broadband coverage is associated with greater household consumption and with reduced poverty status. These relationships are robust after the study controlled for household demographics and other spatial characteristics, as well as for access to other, complementary digital infrastructure such as 2G coverage or fixed broadband Internet. The welfare effects, however, do not appear to be uniform across subgroups. The positive welfare effects of 3G coverage are more evident for households in urban areas or headed by men or younger cohorts. In addition, although there is a positive correlation between proximity to fixed broadband infrastructure and welfare, this relationship becomes insignificant after one controls for other geographical characteristics, such as region fixed effects, road density, nighttime lights, or elevation above sea level.

This paper makes two distinct contributions to the literature. First, it is among the first papers to measure the effects of mobile broadband and fixed broadband infrastructure on household welfare. Most studies focus on cellphone (2G) access, while much less is known about the welfare impact of fixed or mobile broadband Internet. The studies that do look at the Internet tend to focus on fixed broadband rather than mobile broadband Internet (Hjort and Poulsen 2019).<sup>4</sup> This distinction is important because mobile broadband Internet is the main technology used to access the Internet in Senegal. By considering both technologies, the study sheds light on the infrastructure gap and the complementarities that exist between the technologies.

Second, this appears to be the only paper that relies exclusively on the rich data available from household consumption surveys to measure the welfare implications of various types of digital technologies, that is, mobile phone access, fixed broadband Internet, and mobile broadband Internet.

The analysis applies a robust methodology that can be used to assess the impact of mobile and fixed broadband Internet across Sub-Saharan Africa as more data on coverage and connectivity become available. In addition, the results contribute an important perspective to the policy discussion on the degree to which digital infrastructure can serve as a welfare- and resilience-enhancing mechanism. By demonstrating that broadband coverage can improve consumer welfare and reduce poverty, the paper supplies evidence on the positive spillovers of connectivity. This empirical evidence can be useful to policy makers in addressing public investment gaps across various sectors of infrastructure.

The rest of the paper is structured as follows. Section 2 describes the data sources, while section 3 presents the estimation strategy. Section 4 discusses the results, and section 5 concludes.

> http://www.africabandwidthmaps.com/. Internet traffic in countries runs first through national backbone or fiber-optic networks, which are then connected to end users through last-mile infrastructure, such as fiber cables, copper cables, wireless transmission, or cellphone towers. 2G, 3G, and 4G mobile coverage maps have been obtained from three major telephone communication providers—Orange (or the local subsidiary Sonatel), Senegal Expresso, and Tigo—and Mobile Coverage Maps (database), Collins Bartholomew, HarperCollins Publishers, Glasgow, https://www.collinsbartholomew.com/mobile-coverage-maps/.

> 4 Bahia et al. (2020) are a notable exception. They explicitly test the impact of 3G and 4G technologies on consumption and poverty using data on Nigeria.

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# 2. **Data**

Senegal represents a valuable case study. The rise in the number of cellphone users and Internet subscribers in the country over the past decade has been significant compared with peer countries. Figure 1 illustrates the mobile cellular subscriptions and fixed broadband subscriptions in Senegal relative to peers—Côte d’Ivoire, Kenya, and Rwanda—as well as among the 5th and 95th percentiles overall in Sub-Saharan Africa.

The data on consumption and poverty are derived from official household budget surveys: the 2011 ESPS-II and the 2017–18 ELEPS. The 2011 ESPS was a nationally representative survey that was fielded between August and November 2011. It was conducted effectively with 17,891 of the 18,180 households sampled given that the response rate was 98.4 percent. About a third of these households were interviewed using the entire household questionnaire on expenditure. The 2017–18 ELEPS is comparable with the 2011 survey. The ESPS was administered among 1,065 of the 1,200 households sampled given that the response rate was 89 percent. The sample was spread across the country according to three geographic areas: Dakar, other urban centers, and rural areas.

To measure access to fixed broadband Internet services across Senegal, the analysis relied on information on the locations of fiber-optic transmission nodes that was obtained from the Africa Bandwidth Maps database.<sup>5</sup> The analysis considers how distance to operational fiber-optic nodes may influence welfare and poverty across the country. Map 1, panel a, shows the locations of fiber-optic transmission nodes in Senegal. These nodes correspond to add or drop points (entrance or exit) in the long-haul fiber networks. It is useful to think of long-haul fiber networks as motorways that have junctions (on and off ramps that is, add and drop points) that feed smaller class roads (access fiber, wireline, and wireless networks). In the motorway scenario, even if a household is located close to the motorway, it may be a long drive to the nearest junction. The same applies to fiber-optic networks, in which the speed of fixed broadband Internet is determined by proximity to the transmission nodes rather than the network lines connecting the nodes. The transmission nodes are categorized by operational status: operational, under construction, and planned or proposed.

Mobile coverage data are drawn from two sources: the Mobile Coverage Maps database and mobile operators in the country. Data on mobile phone coverage and mobile broadband Internet coverage compiled in the Mobile Coverage Maps database by Collins Bartholomew are based on voluntary submissions made directly by mobile operators for the purposes of constructing roaming coverage maps for end users. The temporal coverage of the data varies depending on 2G and 3G coverage, respectively, in 2011–17 and 2014–17.<sup>6</sup> To ensure that the findings are not an artifact of potential missing information in the Mobile Coverage Maps database, the analysis also utilizes 2G and 3G coverage information collected directly from the three major mobile operators in Senegal: Expresso, Orange (or its local subsidiary Sonatel), and Tigo. While the coverage maps obtained from Expresso and Tigo are limited to 2016, Orange’s coverage maps are available for 2016–18. Orange-Sonatel dominates the fixed and mobile markets, dwarfing

> 5 See Africa Bandwidth Maps (database), Hamilton Research, Bath, UK, http://www.africabandwidthmaps.com/.

> 6 The Mobile Coverage Maps database contains no information on 4G coverage. Moreover, the coverage maps depend on information shared voluntarily by mobile operators. So, their quality should be checked for completeness against other sources of data. For this, the analysis here relies on data collected directly from major mobile operators.

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the market shares of other major providers, such as Expresso (22 percent) and Tigo (24 percent) (BuddeComm. 2020). Orange is also the first provider of 4G technologies in Senegal (Oduor 2016).

The data newly collected from providers show a much wider coverage of 3G technologies in Senegal than otherwise suggested by the Mobile Coverage Maps database. For instance, the median distance between the centroids of EAs in the 2017–18 ELEPS and the closest coverage areas of 3G technologies was 1.8 kilometers based on the Mobile Coverage Maps database (see map 1, panel b). Yet, the distance was only 0.4 kilometers based on the data provided by Orange-Sonatel for the same year (see map 1, panel c). There is thus a substantial discrepancy in mobile coverage data across the various sources of information. The analysis therefore combines the data from all the various sources of information to ensure that the 3G coverage maps are as complete as possible.

The analysis also uses the GPS coordinates of the centroids of EAs to match the locations of the households in the survey data and the fiber-optic nodes and mobile coverage maps. It would have been ideal if the exact locations of households were merged with spatial data on digital technologies. Nonetheless, the GPS coordinates of the centroids of EAs are the best source of geographical referencing data available for identifying the location of households. The same approach is adopted by Hjort and Poulsen (2019), who demonstrate that the distance between the terrestrial backbone infrastructure and sampling cluster locations still provides a reasonable measure of the accessibility to the Internet.

# **3. Estimation strategy**

To estimate the impact of fixed and mobile broadband Internet on household outcomes, the following equation is calculated: i 1D i i i , (1) where i is the outcome of interest, that is, the consumption or poverty status of household _i_ in region _r_ at year _t_ . In addition to total consumption, food and nonfood consumption are also separately ii considered. These consumption variables are all log-transformed because of the large skewness and yy kurtosis values. The poverty status of households is determined by the international extreme poverty line of $1.90 per day or low-middle income international poverty line of $3.20 per day.<sup>7</sup> To account for any potential bias arising from the sampling, we apply probability weights for our main results.<sup>8</sup> The use of sampling weights renders our estimates nationally representative. D i denotes access to fixed and mobile broadband Internet proxied by connectivity to the fiberoptic network and 3G coverage, respectively. Connectivity to fixed broadband Internet is ii−1 operationalized through a binary variable that takes a value of 1 if the household lives in an EA the centroid location of which is less than 1 kilometer from the closest functional fiber-optic node for fixed broadband Internet.<sup>9</sup> For mobile broadband Internet, the treatment is coded 1 if a given

> 7 <u>https://blogs.worldbank.org/developmenttalk/richer-array-international-poverty-lines.</u>

> 8 It is worth noting that our main analytical conclusions remain consistent regardless of whether sampling weights are applied in our regression. See the appendix for details.

> 9 The findings do not change if the distance threshold of 1 kilometer is shifted to other distances (see appendix A).

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household lives in an EA in which the centroid is covered by 3G technologies.<sup>10</sup> The treatment variables are lagged one year because it is most likely that there is some temporal lag between the arrival of digital infrastructure (e.g., fiber-optic nodes or 3G) and the manifestation (if any) of the welfare effect.<sup>11</sup> Because the first round of the 2011 survey does not reveal information on 3G mobile coverage, the welfare effect of 3G coverage can be tested only using the 2017–18 ELEPS. The analysis of the 4G network is not considered extensively in the main analysis of this paper because Senegal had not yet deployed a full 4G network in 2018, the year the latest household survey was collected. Indeed, 4G was only introduced in Senegal in 2016, and 4G coverage remains limited to a few urban centers of the country.<sup>12</sup> The set of control variables i includes household size, the marital status and sex of household heads, access to electricity, literacy of household heads, housing conditions (as measured in a ii composite index of dwelling characteristicsXX<sup>13</sup> ), and a group of EA-level variables, including nighttime lights, road density, and elevation above sea level.<sup>14</sup> In addition, and are region fixed effects and year fixed effects. Controlling for other types of infrastructure, such as road connectivity or electricity, ii ii is important given that, in most African countries, “a part of the backbone network runs parallel to θθ ττ other infrastructure such as roads or electricity cables” (Hjort and Poulsen 2019, 1053). Descriptive statistics on key outcome variables, treatment, and other covariates and controls are reported in table 1. The main object of interest is coefficient 1, which captures the effect of access to digital infrastructure on welfare and poverty. To account for the existence of heteroskedasticity and serial correlation, standard errors are clustered according to the relevant EAs. ββ To identify 1, the analysis assumes no endogeneity or E[ i |D i , i ] = 0. This assumption is violated if there are any unobservable, omitted variables that confound the relationship ii ii−1 ii ii ii between the outcome of interest and the treatment. It is plausible, for instance, that places covered by ββ εε XX , θθ , ττ 3G also tend to have local business environments that are more friendly to firm entry and job creation, which could, in turn, result in improvements in welfare. In this case, it would be incorrect to attribute improvements in welfare to 3G coverage alone.

> 10 The Mobile Coverage Maps database 3G coverage maps are provided as polygon shapefiles, while some of the 3G coverage maps shared by operators are available only as point shapefiles. In the case of the point shapefiles, households were coded as covered if they were in EAs in which the centroids were less than 1 kilometer from the closest coverage points.

11 As a robustness check, the contemporaneous effects of distribution terminals on consumption and poverty are also tested. The conclusions do not change whether the treatment is lagged or not (see appendix A, table A.2).

> 12 See Pop et al. (2018) for more detail . Although 4G is beyond the scope of this paper, 4G coverage maps received from Orange-Sonatel (for December 2017 and December 2018) are used to test the impact of 4G technologies on consumption and poverty. The results are reported in appendix A, Table A.7. The finding is that 4G coverage is positively correlated with consumption and negatively with poverty, but this relationship is not robust to the inclusion of demographic and geographical controls.

> 13 This wealth index is based on whether the household has the following: durable walls; durable roof; durable floors; access to toilet; access to water toilet; and electric or gas cooking fuel. Based on household responses to these questions, the index was calculated using a weighted average, with weights derived from principal component analysis. The index is used because including the different components as separate variables introduces multicollinearity in the regression. 14 Data on nighttime light, road density, and elevation are derived from NASA VIIRS (Visible Infrared Radiometer Suite), Meijer et al. (2018), and SRTM 90m DEM Digital Elevation Database, respectively. Positive expenditure on electricity bills is taken as a measure of access to electricity in both rounds of the survey because there is no other direct measure of household electricity access.

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D D D = ∑ / ∑ , (2) where is the distance between location _k_ and the closest 3G coverage areas, and is defined as the inverse of the distance between EA _j_ and location _k_ . Location _k_ corresponds to the centroid of kk jjkk 0.0833 × 0.0833 degree (II ∼10 × 10 km at equator) grid-cell _k_ = {1,...,n} defined on the national ww boundaries of Senegal. For this calculation, only those locations are considered that are within a 15 kilometer to 100 kilometer radius of enumeration _j_ .

The instruments are valid if they meet two requirements: excludability and relevance. First, the instruments are excludable if they are conditionally independent from the error term in equation 1. There is little reason to believe that factors directly influencing the welfare of households in EA _j_ also affect the distance to 3G coverage in neighboring areas outside EA _j_ . Second, instruments need to be relevant or closely correlated with the endogenous variable. Connective digital infrastructure, such as cellphone towers, tend to cluster geographically, driven by market demands that are also spatially concentrated. Thus, distance to 3G coverage in neighboring areas is likely to impact the probability that households are covered by 3G in EA _j_ , thus meeting the requirement of relevancy.<sup>15</sup>

# **4. Results**

The main results are reported in table 2, which illustrates that 3G coverage is associated with higher household consumption. A positive correlation is also found between consumption and connectivity to fiber-optic nodes (significant at the 0.10 level), but the estimated effect is not significant after one controls for demographic and geographical characteristics. The estimated welfare impact of 3G coverage is robust across various model specifications with and without the additional set of demographic and geographical control variables. The total consumption among households covered by 3G technology is about 14 percent greater than the total consumption of households not covered by 3G.<sup>16</sup> In particular, nonfood consumption is significantly positively correlated with 3G coverage. The average nonfood consumption of households with 3G coverage is about 26 percent greater than the average nonfood consumption of households without 3G coverage.

The results suggest that 3G coverage is also associated with lower extreme poverty (based on the international poverty line of $1.9 per day). The estimated effects of 3G coverage on poverty are negative and statistically significant, both with and without the additional set of demographic and

> 15 The intuition is similar to that of Acemoglu et al. (2019), who use waves of democratization in neighboring countries or countries in the same region as an instrument for democratization in a country surrounded by those countries.

> 16 Since our dependent variables are all log-transformed for consumption, we exponentiate the coefficients, subtract one from these numbers, and multiply by 100 to obtain a more precise, substantive interpretation of those coefficients. For instance, the coefficient for 3G coverage on total consumption (Column 2 in Table 2) is 0.134. This means for one unit increase in 3G coverage, total consumption is expected to increase by 14 percent, which is derived from exp(0.134)≈1.143 minus 1.

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geographical controls (Columns 7 and 8 in Table 2). Notably, households covered by 3G exhibit an extreme poverty rate lower by about 10 percent relative to households without 3G coverage.<sup>17</sup> While 3G coverage is also correlated negatively with moderate poverty (based on the international poverty line of $3.2 per day ), its effect is not robust to the inclusion of the additional set of controls (Columns 9 and 10 in Table 2).

It is plausible that the relationship between 3G coverage and welfare is being confounded by other factors. For instance, 3G coverage is likely correlated with the fiber network and with 2G cellular services, which are also likely to impact welfare directly. The estimated effects of 3G as reported in table 2 may thus be picking up the effects of fixed broadband Internet and mobile phone access together. Failure to control for these potential confounders could lead an overstatement of the relationship between 3G and welfare. As a robustness check, the same models as presented in table 2 are run, but with controls for connectivity to the fiber-optic network (or within 1 kilometer of the nearest fiber-optic transmission node) and controls for 2G coverage.<sup>18</sup> The main findings do not change (see appendix A, Table A.3). Furthermore, the main results are also robust to specifications including self-reported ownership of cellphone or Internet access as additional controls (see appendix A, Table A.4).<sup>19</sup> One may control for various demographic and geographical factors, but miss other, unobservable confounders that bias the estimates. To alleviate the issue of endogeneity, an instrumental variable approach is applied. The results of the two-stage least squares regressions are reported in table 3. The first-stage regression shows that the instrument is strongly and negatively correlated with 3G coverage (see appendix A, Table A.1). This makes sense given that the likelihood that households in a given EA are covered by 3G is expected to decline if the neighboring areas are distant from the closest 3G coverage areas. The two-stage least squares estimates show results that are similar to the results of the ordinary least squares regressions; the effects of 3G on total consumption are positive and statistically significant, and these effects are particularly pronounced in the case of nonfood consumption. As expected, the effects of 3G on poverty are also negative and statistically significant (though at the 0.10 level) in the two-stage least squares regressions.

The analysis also examined the results on various subsamples (Table 4). First, the sample was split into rural and urban areas. 3G coverage is significantly and positively correlated with consumption and negatively associated with poverty only for urban households (Table 4, panel a). Interestingly, this correlation is particularly pronounced for food consumption among urban households while 3G coverage is significantly and positively correlated with non-food consumption for rural households. The sample is also separated according to whether household consumption is below or above the median consumption level (in each survey). This exercise reveals that while the effect of 3G coverage is positive and significant for non-food consumption for poorer households, these effects lose

> 17 The point estimates do not change substantially if no weights are applied. According to our unweighted regressions, 3G coverage is associated with a 18 percent increase in total consumption and a 7 percent decrease in extreme poverty rate (see Table A.3 and A.4 in the appendix). To put this in context, similar work for Nigeria—integrating household panel data and historical coverage maps—finds an 11.1 percent increase in total consumption and a 7.9 percentage point decline in extreme poverty after three years of coverage (Bahia et al. 2020). Using data from 14 rural and geographically isolated areas in the Philippines between 2016 and 2018, Blumenstock et al. (2020) also find that the introduction of a new phone tower led to an increase in household income of 17 percent, and increased household expenditures by 10 percent.

> 18 Data on 2G coverage are also provided in the Mobile Coverage Maps database and by the mobile operators.

> 19 The self-reported ownership of cellphone or Internet access is measured by a positive expenditure on cellphone or Internet subscriptions.

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statistical significance for other measures of welfare, which is driven partly by a smaller sample size (Table 4, panel b).

The sample is likewise split by gender and age. The positive effects of 3G coverage on non-food consumption are particularly pronounced among man-headed households. Splitting the sample by age using age 50 as a threshold (roughly the median age of household heads) reveals that the positive welfare effects of 3G coverage are more evident among households headed by younger people.

A potential mechanism through which mobile broadband Internet translates into improvements in welfare may be the impact on labor outcomes. The expansion of digital infrastructure and access to the Internet may not only help the creation of jobs in the ICT sector, but also reduce transaction costs for people in finding jobs or productive inputs or improve labor productivity (World Bank 2016). The analysis has examined the relationship between 3G coverage and four different labor outcomes: (1) employment, (2) wage and salaried employment, (3) formal employment, and (4) earning per month or wage.<sup>20</sup> The effect of mobile broadband technologies on wage/salaried or formal employment is of particular interest because a shift away from informal, self-employment toward more productive wage/salaried or formal employment in private and public services is deemed a potential pathway to reducing poverty rates in Africa (World Bank 2016, 2019). To test this causal channel, equation 1 is estimated once again, using labor outcomes as the dependent variable.

The analysis shows that 3G coverage is positively correlated with wage/salaried employment, formal employment and earnings per month, although the effects on wage/salaried employment and earnings become insignificant with the additional set of controls, which implies that these relationships may be confounded by other demographic and geographical factors (Table 5). 3G coverage does not have a significant impact on overall employment. These findings are consistent with other studies (e.g., Bahia et al. 2020; Hjort and Poulsen 2019) showing similar results: that access to the Internet translates into increased employment in wage or higher-skilled jobs. Our finding draws attention to the potential role that digital technologies can play in improving labor outcomes – and particularly, employment in “better” jobs.

# **5. Conclusion**

Despite the potential for development, evidence on the effects of Internet access on household welfare remains scarce. Most studies on digital technologies focus on cellphone (2G) access, while the studies that look at broadband tend to focus on fixed rather than mobile broadband. Yet, it is through mobile broadband that most people in African countries use the Internet (ITU 2019).

This paper provides new evidence on the positive impacts of mobile broadband coverage on household welfare in Senegal. It uses rich data available from household consumption surveys to analyze the implications of different types of technologies for welfare in 2011–18. To accomplish this, the analysis integrates household locations with data from maps of the terrestrial backbone networks and 2G, 3G, and 4G coverage maps. This allows an examination of the impact of both fixed and

> 20 For this analysis, an employed individual is an individual of working age (15–64) who had worked at least one hour during the seven days previous to the interview. Wage and salaried employment includes those workers who work in a place that is not their own farm or in a business not run by their own household. Formal employment is defined as those workers whose employers or company holds a national identification number and accounting. Wage is derived based on the question in the 2017–18 ELEPS which asked how much a person earned in the last 12 months.

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mobile broadband infrastructure on household consumption and poverty. The results show that mobile broadband coverage is positively associated with the levels of household consumption in Senegal. The welfare effect of 3G coverage is evident in both urban and rural areas (though its magnitude appears to be larger in urban areas), and particularly among young man-headed households. However, the analysis does not find sufficient evidence to suggest that fixed broadband has an independent impact on consumption or poverty. The analysis also shows some evidence that one of the causal mechanisms through which mobile broadband coverage yields welfare gains is its impact on labor outcomes.

In Senegal, as in other African countries, mobile broadband is the dominant channel through which people access the Internet. A recent estimate suggests that 98.7 percent of Internet users in Senegal access the Internet through mobile phones (ARTP 2019). Reliable, timely evidence of the potential impact of mobile broadband on welfare is key to comprehensive policy discussions on digital technologies. The findings of this study, which shows that mobile broadband coverage can have positive effects on consumption and poverty reduction, provide important evidence for policy makers on the potential spillovers of connectivity.

In particular, this evidence can be useful in informing the cost-benefit analysis of policies aimed at achieving universal Internet access. Network coverage is limited in African countries, especially in rural areas, where 82 percent of the extreme poor live and earn their livelihoods primarily from subsistence farming. While fixed broadband access is important for businesses (Fernandes et al. 2017; Hjort and Poulsen 2019), the evidence presented in this paper showcases the concrete benefits of mobile broadband for household welfare, highlighting its potential as a pathway to achieve universal coverage. Overall, improving the availability of affordable digital infrastructure in rural areas is key to avoiding the risk of a widening digital divide between urban and rural areas.

A related point refers to the importance of a lack of competition among service providers, which has been found as one of the main determinants—in addition to accessibility costs and market size—of the large disparity in cellphone coverage systems in Sub-Saharan Africa (Buys et al. 2009). The importance of competitive environments in digital infrastructure is relevant in Senegal, where OrangeSonatel was the only 4G provider up to 2018–19. The need for competition to exploit fully the potential of digital technologies is highlighted by studies showing that increased competition in digital infrastructure, such as a higher number of mobile operators and the reduction in the market power of broadband monopolies, has measurable impacts on welfare by reducing prices and incentivizing new entrants (Decoster et al. 2019; Rodríguez-Castelán et al. 2019).

As this paper shows, mobile broadband can serve as a welfare-enhancing mechanism. Future work should focus on a better understanding of the uses of mobile Internet by households, such as in leisure versus productive activities (for example, job searches), as well as on the role of accessible content. Additionally, subsequent research may shed light on the implications of mobile broadband as private and public platforms, including mobile money and e-government applications, which, in the region, now tend to be based mostly on 2G technology.

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