Does the Mobile Phone Affect Social Development?
Evidence from Indonesian Villages*
Jahen F. Rezki†
January 10, 2023

Abstract
This paper analyses the impact of Information and Communication Technology (ICT) on social
development in Indonesian Villages. In this study, I use data from different waves of the Indonesian Village Potential Statistics (Potensi Desa) to determine whether mobile phone signal
strength affects social development indicators. The results indicate that villages with a strong
signal are statistically more likely to possess the proper infrastructure programs. Furthermore,mobile phones increase the availability of village libraries and access to credit for small andmedium enterprises (SMEs). Using the plausibly exogenous variation of lightning strike intensity
as the instrumental variable, this study suggests that higher mobile phone signal strength ispositively associated with the policies implemented by the village head and leads to better social
development. As the mechanisms, this study shows that mobile phones increase the likelihood of
having collective action and civic engagement, increasing villagers’ political participation and useof telecommunication services, thus leading to an increase in village government’s accountability.

Keywords: Mobile Phone, ICT, Village Government, Indonesia, Accountability, Policy-making
JEL Classification: D73, D78, H4, H7, L96, O1* I am deeply grateful to Giacomo De Luca and Andrew Pickering for their support. I would also like to thank Thomas
Cornelissen, Thilo Huning, Julien Labonne and Anirban Mitra, Michael Shallcross for their helpful comments andsupports. I am thankful to the attendees of CSAE Conference 2018, Nordic Conference on Development Economics 2018
and RES PhD Meeting 2018 for their feedback. I would also like to acknowledge BPS Statistics Indonesia, LPEM-FEB UI,The IPUMS-International Team and NASA Global Hydrology Resource Centre (GHRC) DAAC for providing the data forthis research. Thanks to Indonesia Endowment Fund for Education (LPDP-RI) for their financial support. This paper
supersedes an earlier working paper under the title "Call Your Leader: Does the Mobile Phone Affect Policymaking?".
All errors are my own.
† Department of Economics, Universitas Indonesia, Indonesia and LPEM-FEB UI, Indonesia. Email jahen.fr@ui.ac.id.

11

Introduction

The role of new media and the rapid growth of information and communication technology(ICT) have become increasingly significant over recent years. The development of ICT has been
documented by Aker and Mbiti (2010), who explain the growth of mobile phone adoption andits impacts on Africa’s economic development. The World Bank (2016) describes the extensive
growth of ICT throughout developing countries. Nonetheless, studies on the impact of ICT in
policy-making are limited. The majority of previous studies focus their research on the impact
of mass media (e.g. television, radio, newspaper) and information on voter turnout or politicalaccountability (Cook et al., 1983; Persson and Tabellini, 2000; Besley and Burgess, 2002; Strömberg,
2004b; Olken, 2009; Snyder Jr and Strömberg, 2010; Gentzkow et al., 2011; Enikolopov et al., 2011;
Aker et al., 2017).1 To my knowledge, studies on the impact of ICT, especially mobile phones, on
policy-making are scarce.
Despite many studies have observed the positive impact of ICT on several outcome indicators,
other studies have suggested the adverse effect of ICT, for instance on conflict. A seminal study
by Yanagizawa-Drott (2014) estimated the impact of communication through radio propaganda
on violence and mass killings in Rwanda. The study suggests that access to information boosts
the number of violence during the dark period of Rwanda conflict. A similar study in Indonesia
has shown a similar result, where areas with a better communication infrastructure tend to have ahigher probability of local conflict (Yudhistira et al., 2021). Moreover, Marler (2018) suggests thatmobile phones may also play a pivotal role in fuelling higher inequality, even though it may varyacross countries.
Indonesia is an ideal natural laboratory for this study because not only had the country recently
democratised and become more decentralised after being led by President Suharto for 32 years,
but also because ICTs (especially mobile phones) have been growing in use at an increasingrate (Rohman and Bohlin, 2014). In 2002, only 11.7 million people owned mobile phones, whilein 2016 almost 385.5 million people owned mobile phones. Moreover, Law No. 36/1999 on
telecommunication promoted the liberalisation of ICT sectors in Indonesia, which led to an increasein the number of telecommunication providers. Consequently, the cost to use and connect to amobile network decreased significantly (Lee and Findlay, 2005).
In terms of political context, the introduction of Law No. 22/1999 on regional administrations,
1 Several studies have investigated the role of mobile phones in developing countries, such as: Aker et al. (2017) on therole of mobile phones and newspapers in voting intention in Mozambique, Jensen (2007) on the effect of mobile phones
in the fisheries sector in South India, Aker (2010) on the impact of mobile phones on agriculture markets in Niger andBailard (2009) on the relationship between mobile phone diffusion and corruption in Namibia.

2followed by Law No. 6/2014 on village administrations, increased the power of local administrative,
as well as village governments. These laws were introduced to ensure village governance andto improve community participation at the village level. Indonesia has a substantial number ofvillages. In 2014, there were around 82,190 villages across the country. Moreover, there has been an
increase in local government responsibility, according to Martinez-Bravo (2014) and Martinez-Bravo(2017) who examined the role of local leaders in Indonesia after the country’s democratisation.
These studies suggest that local leaders play a significant role in implementing policy.
To discuss the role of mobile phone usage on social development2 , I use different waves ofdata from the village census (Podes), i.e. data from 2008, 2011 and 2014. The dataset used inthis study consists of 163,806 observations, which are collected from 54,602 villages across 33
provinces, 456 districts and 6,292 sub-districts. Podes provides extensive information about thevillage characteristics; it has data related to village-level administrations, village economic activities,
village geographical conditions, etc. The data also provides information about the ICT developmentin the village (e.g. signal strength, number of households with fixed-line subscription, number ofinternet cafes, number of public phones, etc.) Therefore, it becomes possible to investigate the roleof mobile phone usage in policy-making and collective action at the village level in Indonesia. I use
a binary variable to represent signal strength coverage at the village level as my main explanatory
variable. In this study, I focus on the impact of mobile phones via text messages and/or phone calls.
I employ a linear probability model and a probit model to estimate the impact of signal strength onpolicies.
A potential problem with using signal strength is the possibility that it is non-random, which
could bias the estimation results. Areas with better infrastructure and larger populations tend tohave better signal strength, because the telecommunication sector invests heavily in these areas.
This supported by Buys et al. (2009), who reveal that mobile phone signal depends on the presenceof a cell tower, size of the population, cost of maintaining the tower, cost of installing the tower, andthe national competition policy. Another potential problem is related to the measurement error of
the signal strength variable, which could affect the estimation results.
To address this concern, I employ an instrumental variable strategy by using the plausibly
exogenous variation of flash rate (or lightning strike) intensity at the village level. Greater instancesof lightning strike lead to slower connectivity. Given the fact that Indonesia is located in a tropicalarea, and that there is significant geographical variation within the country, I expect flash rate
2 In this study I use the term social development and policymaking interchangeably. Social development in this study
is specific to policies that will be beneficial for the wellbeing of the society, for example: building more infrastructure,providing trainings, granting financial access to do economic activities

3incidences to affect signal strength quality. Using data from the National Aeronautics and Space
Administration (NASA), I show that areas with higher lightning strike incidence have lower signal
strength.
This paper finds that higher signal strength is associated with an increased presence of village
rehabilitation programs. The results are robust for different estimation methods. I also find thatstronger signal strength will increase the incidence of village rehabilitation programs where thebeneficiaries are poor people. Moreover, the same patterns are observed for the availability ofvillage libraries and access to credit for small and medium enterprises (SMEs). I also find that higher
signal strength increases the likelihood of villages possessing libraries by 43 percentage points, andaccess to credit by 40 percentage points. I do not, however, find any effects on the provision oftraining to improve villagers’ skills.
As the types of villages in Indonesia differ greatly depending on location, urban (Kelurahan) or
rural (Desa), I investigated whether signal strength has heterogeneous effects. Signal strength isestimated to have a stronger influence in rural villages, especially with regard to village rehabilitation programs. However, I am unable to infer any meaningful results for villages in urban areas.
One potential explanation is that the different types of village governments are run differently:
desa in rural areas the village head is elected by the people and in urban areas, the village head is
appointed by the district and sub-district governments. Another possibility is that inhabitants ofurban areas have alternative sources for collecting information. Therefore, rural villages would
benefit more significantly from the introduction of ICTs.
I propose several mechanisms that explain why mobile phones affect policies. First, mobile
phone signal coverage affects villagers’ ability to initiate and organise civic engagement activities.
One channel to improve village conditions is through collective action and therefore access to better
mobile phone coverage will increase the likelihood of an improvement in villages’ infrastructures.
Moreover, better signal strength is associated with better government performance. I show thatareas with better signal strength will increase the likelihood of improving village governanceand will also reduce the incentives for corruption. The exchange of information and an increasein transparency increase village government accountability. Second, areas with better mobile
phone coverage increase the likelihood of individuals’ participation in village elections, which isconsidered a good indicator of better government performance and higher political engagement.
Well informed villagers will increase the pressure upon the village head to perform well. The
last mechanism is that better mobile phone signal strength will make villagers use mobile phones
more frequently and interact more with other villages and with their own village. I show that4

individuals who are living in areas with better signal strength will have higher telecommunication
consumption.
This study contributes to at least four strands of literature. First, it contributes to the importantrole of ICT and mobile phones in development economics, especially in the context of developing
countries (Jensen, 2007; Aker and Mbiti, 2010; Aker, 2010; Aker and Fafchamps, 2014; Aker et al.,2017) by showing that mobile phone use will increase the likelihood of having policies that will
improve villages’ conditions. The use of village level governments in a country like Indonesia may
provide a different perspective about the role of ICT on social development. Second, this study
relates to the effects of new media on policies and political accountability (Persson and Tabellini,
2000; Strömberg, 2001; Besley and Burgess, 2002; Strömberg, 2004a,b; Snyder Jr and Strömberg,
2010; Gentzkow, 2006; Gentzkow et al., 2011; Enikolopov et al., 2011; Reinikka and Svensson, 2011;
Andersen et al., 2011; Enikolopov and Petrova, 2015) by exploring the role of a non-traditional
media, such as mobile phones, which have different characteristics to other media in the case ofsome policies. The different characteristics of mobile phone will provide an alternative perspectiveon the role of traditional media on social development. Third, this study explores the role of media
and social and political mobilisation (Olken, 2009; Pierskalla and Hollenbach, 2013; Shapiro andWeidmann, 2015; Manacorda and Tesei, 2020) by highlighting the role of mobile phones in increasing
the likelihood of civic engagement and social activities. This study will extent the existing strand ofliterature that has shown the impact of mobile phones on social and political mobilisation whichstill rare in Indonesia. Finally, this study contributes to literature on the role of informed votersand government performance (Ferraz and Finan, 2008; Ferraz and Finnan, 2011; Pande, 2011) byshowing that villages with better signal strength coverage will have an increased likelihood ofparticipating in political activities such as voting to elect village heads. Studying this context inIndonesia is interesting because to my knowledge, study on the role of media on political activities
remains understudied.
Only a small handful of studies have investigated the role of mobile phones on policies. Previous
studies have only attempted to investigate the role of mobile phones and their impact on theinteraction between voters and their leaders. Grossman et al. (2014) and Grossman et al. (2017)
have investigated the role of ICT on the probability that citizens will contact their parliamentary
members in Uganda. Both studies have suggested that the introduction of ICT reduces the barrier
for citizens to interact with their representatives and increases their political participation. Moreover,
Grossman et al. (2014) has observed that marginalised people (e.g. women and poor people) will
receive more benefits as a result of easy access to telecommunication and will consequently be5more likely to contact their representatives. Furthermore, Grossman et al. (2017) have suggested
that implementing a mobilising strategy (e.g. sending texts to voters and encouraging them toparticipate in political engagement) increases the probability that voters will contact their electedleaders. I build upon these previous papers by showing that not are mobile phones important tocitizens’ empowerment, but also have a tangible effect on certain policies.
Within the context of Indonesia, studies that investigate the role of media in policy-making
are limited. One such study, which is closely related to the present study, is conducted by Olken(2009). This study has explored the impact of television and radio on social capital. Examining
data collected from village-level governments in Eastern and Central Java, this study has foundthat improved television and radio signal reception decreases the amount of social participation at
the village level, because people consequently spend more time listening to the radio or watching
television. Furthermore, the introduction of television and radio has no significant impact on thenumber of village meetings or infrastructure creation, which are the proxies for village governance.
However, my study shows that mobile phones have different characteristics to radio and television,
instead increasing the probability of civic engagement activity.
The distinct differences between a developing country and a developed country in terms of
ICT adoption and infrastructure, as well as quality of government, could be an interesting topic ofresearch. This study fills the gaps on the importance of mobile phone usage, as a means not onlyof increasing political participation, but also improving policies and political leaders’ decisions,
exploring the extent to which mobile phone usage affects policies, and in which places it significantly
contributes. This paper is able to isolate the endogeneity concern of mobile phone adoption byusing a relatively new instrument (e.g. lightning strikes) to examine the causal relationship between
mobile phones and policies (Andersen et al., 2011, 2012; Manacorda and Tesei, 2020). Finally, thisstudy could explain village leaders’ behaviour, which differs from that of other elected and/or
appointed policymakers.
The paper proceeds as follows: section 2 provides some institutional background covering both
the regulatory and political contexts, as well as the development of ICT in Indonesia; section 3
presents the conceptual framework. Section 4 describes the data specifications. The results are
explained in section 5, and section 6 discusses the potential mechanisms of mobile phone effects onpolicies. Section 7 concludes.

2

Institutional Background

6

2.1

Village Regulation and Political Context

In 1999, after Suharto’s presidency, a major change occurred in the process of democratisation anddecentralisation in Indonesia. During the Suharto regime (New Order Era), all decisions were madeby the central government, and local governments were controlled and monitored by the central
government.3 Law No. 22/1999 on regional administrations was introduced to provide major
reforms in terms of transferring decision-making powers to the district and village governments(Antlöv, 2003; Bebbington et al., 2006).4
Many reforms have since been made to local governments and also village-level administrations.
As a result, villages are now more autonomous. Indeed, villagers may now elect their village head
and are allowed to run their village-owned enterprises. Before 1999, the only source of revenue forthe village was via transfers from the district or sub-district governments. Moreover, the village
now also has more autonomy to accept or reject programs initiated by the central government(Antlöv, 2003).
Another reform provided by Law No. 22/1999 is the accommodation of cultural diversity within
Indonesia’s society. Villages can be managed through original customs and local traditions. Villages
also have village councils (Badan Perwakilan Desa, BPD) that consist of 5 to 13 members and serve asa democratic village body. These village councils have the power to introduce legislation, accept thevillage budget, and also monitor the village government. In terms of checks and balances, village
leaders have to submit a report on an annual basis to the BPD regarding their activities. Therefore,
village heads are accountable for the villages’ inhabitants, which is quite different from the previous
law which stated that the village leaders would report their activities to the district or sub-district
governments (Antlöv, 2003).
Recently, the government of Indonesia introduced Law No.6/2014 on village administrations.
This is the extended version of the previous regulation related to village governments (Lewis, 2015;
Antlöv et al., 2016). The bill attempted to empower village governance, reduce inequality between
villages and ensure the effectiveness of the proposed development programs. The major reforms
in this law are related to central government transfers. The central government will allocate up
3 During President Sukarno’s regime, the Indonesian government recognised approximately 250 types of administrative
government and community. For example, in Java and Bali, there was Desa, Nagari in West Sumatra or Minangkabau,
Dusun and marga in South Sumatra (Antlöv, 2003). However, the variation of these types of government did not matchthe leadership style of his successor, President Suharto, who preferred to exercise strong control over local government.
Therefore, in 1979, the government introduced Law No 5/1979, which aimed to impose the same type of institutions
across the country. Village-level governments would be controlled and supervised by a higher authority, therefore thevillage head did not have any ability to implement their own agenda. All decisions were made following the advice ofthe district or sub-district governments.
4 The discussion in this subsection is based on Evers (2000), Antlöv (2003), Bebbington et al. (2006) and Antlöv et al.
(2016).

7

to 10% of the national budget for transfers to village governments. Moreover, to ensure good
governance of the village administrations, this law also increases the power of BPD and introduces
village assemblies, which are government bodies that approve or reject the programs proposedby the village heads (Antlöv et al., 2016). The establishment of a village assembly also encourages
community participation in the village. Therefore, decision-making at the village level heavily relies
on communication between the village communities and the village leaders.

2.2

Village Administrative Institutions

Indonesia has five levels of administrative structure: central government, provincial government,
district government, sub-district government and village-level government. In 2014, there were
34 provinces in Indonesia. Each province was divided into two districts: Kota, or urban district(98 in 2014) and Kabupaten or rural district (416 in 2014) (Ministry of Home Office, 2014). Each
district was then further divided into sub-districts known as Kecamatan, and finally, the sub-districts
were divided into villages, which are the lowest administrative level. Similarly, with districts, there
are two types of villages: Desa rural and Kelurahan urban. In 2014, there were 81,190 villages inIndonesia, of which 72,949 villages (around 89%) are Desa and 8,412 villages (around 10%) are
Kelurahan (Central Bureau of Statistics, 2014).
Desa and Kelurahan villages are different in character, especially in regard to government
structure. The village head is elected by villagers every six years with a term limit of three periods.
On the other hand, the kelurahan village leader is appointed by the head of the district government.
Furthermore, everyone can become a village leader in the Desa. The Kelurahan leader, however,must be a civil servant. Therefore, Desa and Kelurahan are expected to have heterogeneous effects inthis study, due to the differences in government structure.
In terms of public goods provisions, villages can provide the goods funded from their budget orfrom other sources of funding, such as upper-level government (National Development Planning
Process or P5D) and donors (e.g. PNPM-Mandiri managed by the World Bank).5 In 2014, meanvillage revenues were around Rp 88.8 million (US $5,920), and the total village revenues werearound Rp 350.57 million (US $ 23,371).6 Moreover, almost 48% of the infrastructure programs(e.g. roads, bridges, schools, sanitation, clean water, electricity, clinics, markets and irrigation)
implemented at the village level were funded by the village’s own budget (Central Bureau of
Statistics, 2014). One way for a village leader to increase spending on public goods is by reducing
spending on administration (e.g. salary, meeting expenses, office equipment, etc.) and increasing
5 See Evers (2000) and Martinez-Bravo (2017) for further information.
6 1 US $ ≈ Rp15000

8

spending on infrastructure (Martinez-Bravo, 2017).

2.3

Telecommunication Sectors in Indonesia

The development of ICT, especially in the telecommunication sectors in Indonesia, cannot be
separated from the Asian financial crisis of 1997-1998. One of the recovery packages proposed by
the International Monetary Fund (IMF) stated that the Government of Indonesia had to rescind themonopoly power over Indonesia’s state-owned telecommunication company, PT Telekomunikasi
Indonesia (Telkom), and remove restrictions on foreign investment.7 Furthermore, Indonesia
joined the World Trade Organisation’s Agreement, by which one of their commitments was to urge
the country’s members to liberalise the telecommunication sectors under the supervision of an
independent regulator. The agreement also endorsed the eradication of Telkom’s monopoly in thedomestic, long-distance, and international telephone markets (Lee and Findlay, 2005).8
In 1999, the government of Indonesia passed a new law related to the telecommunications sector(Law No.36/1999), which introduced two important regulations. First, the government would
introduce duopoly markets between Telkom and Indosat, and slowly introduce private telecommunications companies into the markets. Second, the restrictions for foreign companies to enter thetelecommunication market would be removed (Lee and Findlay, 2005). The (partial) liberalisationof the telecommunication sector resulted in competition between six different providers. In 2016,
the largest Indonesian telecommunication companies, based on their market share, were Telkomsel(Telkom subsidiary company) with 77.6% share, XL Axiata with 11.2% share, Indosat with 6.2%
share and Hutchison Tri and Smartfren with a combined 5% share (PT Telekomunikasi Selular, 2016).
However, no further major reforms have been implemented in Indonesia’s telecommunication
sector, and there is no significant link between the post-authoritarian period and reform in this
sector (Baulch, 2017).
Even though no further reform has been made in the telecommunication sector, it cannot bedenied that Law No.36/1999 has significantly transformed Indonesia’s ICTs sector. By increasing
the number of competitors and providers, the cost to use and to connect over mobile networks has
dropped significantly (Lee and Findlay, 2005). As a result, many new telecommunications devices
have emerged in the market, making mobile phones more affordable for lower and middle-classindividuals.
7 From 1964 to 1989, Indonesia had two state-owned telecommunication companies, PT Telekomunikasi Indonesia(Telkom) and PT Indonesian Satellite Corporation (Indosat). Telkom exclusively provided all domestic services, whileinternational services were monopolised by Indosat. In 1989, due to a lack of funding in the telecommunication sector, thegovernment introduced Law No. 3/1989, which encouraged private investment and established a partnership between
Telkom and Indosat for the first time.
8 See Rohman and Bohlin (2014) for further information on the growth of telecommunication sector in Indonesia

9Since 2010, almost all Indonesian people have had access to telecommunication, and especiallyto mobile phones. From Figure 1, it is possible to observe the development of ICT subscriptionsin Indonesia. The growth of mobile phone adoption in Indonesia started to surpass the growthof the fixed telephone line in 2002; In 2002, 11.7 million people owned a mobile phone, comparedwith the 7.7 million people who owned a fixed telephone line. By 2016, the number of people who
owned a mobile phone increased to 385.5 million, compared to only 10.3 million people who owned
a fixed telephone line. Thus, it is clear that mobile phone subscriptions have grown significantly
since 2000, while fixed-line subscription has been steadily declining since 2011. Based on theIntercensal Population Survey (SUPAS), conducted by the Indonesia Central Bureau of Statistics, in
2005, approximately 23% of the population owned a telephone or mobile phone. According to the2010 Population Census, approximately 76% of the population owned a mobile phone (Minnesota
Population Center, 2017).9
Figure 1: The Development of ICTs Subscription in Indonesia (in 10,000 People)

Source: International Telecommunication Union, 2017

ICT infrastructure has become a main priority for the government of Indonesia. However, ICT
infrastructure is developing slowly, due to a number of problems that the country faces, especiallya lack of access to extensive investment. To increase access to telecommunication for all parts ofIndonesia, in 2005 the government initiated The Indonesian Broadband Plan (IBP), called the Palapa
9 See also the expansion of mobile phone subscriptions at the village level between 2005 and 2010 in Figure A1 andFigure A2. The figures demonstrate that mobile phone subscriptions have grown significantly. However, in some parts ofIndonesia, especially in Eastern Indonesia, the subscription rates were consistently lower for both years. One possible
explanation is that the quality of the ICT infrastructure in these areas is relatively poor compared to the rest of the country(See Figure A3.)

10

Ring Project, which sought to increase connectivity between islands in Indonesia, both in westernand eastern parts, and especially in remote areas. The target of this project is that, by the end of
2019, all areas of Indonesia will be covered and connected by ICT infrastructure (Rohman, 2014).
The association between ICT and policy-making in Indonesia has grown progressively stronger.
In 2014, the Ministry of Villages, Disadvantaged Regions and Transmigration introduced callcentres that would provide an opportunity for villagers to report their villages’ needs or problems.
Moreover, one recent example is when the former Governor of Jakarta, Basuki Tjahaja Purnama(Ahok), made his mobile phone number public to allow citizens to call or text him if they have aproblem that requires government attention. Jakarta has also introduced Qlue, a mobile applicationthat can report and monitor problems faced by Jakarta citizens.10

3

Conceptual Framework

In this study, three mechanisms are used to explain how ICTs, specifically mobile phones, affectgovernment policies and/or development. This study focuses on the effect of mobile phones via
calls or text messages to the village leader regarding policies. The mechanisms in this study are
motivated by the previously outlined studies. Section 6 provides an extensive discussion on thetestable mechanisms of this study.
The first mechanism proposed in this study suggest that mobile phones will affect village leaders’policies through an increase in civic engagement, political participation (e.g. voter turnout) andreduce economic rent. Previous studies have shown that mobile phones may affect the probabilityof political and social participation (see, inter alia, Manacorda and Tesei (2020); Pierskalla andHollenbach (2013); Shapiro and Weidmann (2015)). The presence of collective action also has apositive effect on political accountability. For example, a study by Devarajan et al. (2011) suggested
that there is an improvement in leaders’ performance and policy-making in Sub-Saharan countries
due to the existence of collective action and civil society. The mechanism proposed here is thatvillages with higher signal strength will increase the likelihood of having civic engagement activity
which will reduce the incentives for the policy-makers to implement rent-seeking policies.
Moreover, due to the presence of a stronger mobile phone signal, it will reduce the incentives
to obtain economic rent (Strömberg, 2004b; Besley and Burgess, 2002). Villages with better signal
strength will encourage their leaders to perform well and become less corrupt. Here, I proposethat village leaders will introduce policies that are beneficial for the majority of society because theincrease of information among villagers will exert pressure upon these policymakers to increase
10 See the website at http://qlue.co.id/site/11their performance. An exchange of information and access to ICT services will increase transparency.
From the village leader’s perspective, better ICT infrastructure makes them more aware of problems
in their village. Because village leaders become better informed, this affects their policy decisions.
As previously mentioned, Strömberg (2004b) has found that during the new deal program in the US,
governors would increase their spending in areas with higher radio coverage. Besley and Burgess(2002) has found a similar condition in India.
This mechanism will differ from Olken (2009), which found that television and radio have anegative impact on social participation in Indonesian villages. Whereas listening to the radio orwatching television are not, in themselves, activities that encourage or enable simultaneous participation in other activities, ownership of a mobile phone presents the individual with the capacity
to engage in several activities at once. Another potential channel is civic engagement represents
a direct channel to improve living standards. In Indonesia for example, social participation iscalled gotong royong, which is a social activity where people work together to improve their villages’
conditions. Mobile phones will play an important role in facilitating the organisation of this social
activity. Similarly, another study by Olken (2007) observed that social participation have a little oreven insignificant impact on reducing corruption. Thus, having better monitoring procedure does
not really reduce economic rents.
The second mechanism posits the hypothesis that mobile phones and media are related to political participation, especially voter turnout (Olken, 2009; Pierskalla and Hollenbach, 2013; Shapiro
and Weidmann, 2015; Manacorda and Tesei, 2020). Higher political engagement is associated with
political accountability. In this study, I argue that one channel that could affect the improvementin village policy-making is a higher rate of political participation or voter turnout during village
elections. Voters will become better informed about their leader’s performance and are willingto participate and use their vote to punish or reward the incumbents. This also relates to theliterature on the degree to which better informed voters affect political accountability. As it has
been suggested by previous studies, technology is associated with a dissemination of information
which could affect the probability of civic participation in political activities.
The last mechanism explains the direct link between mobile phones and the village leaders’policies. As has been observed by Grossman et al. (2017) and Grossman et al. (2014), mobile phones
reduce the barriers to interaction with government representatives, and therefore increase voters’
engagement with them. Adopting the same mechanism, this study assumes that villagers use theirmobile phones to interact with their village leader. Therefore, it is easier for a villager to attract theirleader’s attention when they have better access to ICT, i.e. better ICT infrastructure, and a better

12signal.
Villagers who are living in areas with better signal strength can maximise the usability of theirmobile phone to improve their welfare. On the other hand, villagers who are living in areas withweak signal strength need to meet their leaders directly to inform them of their difficulties. This
increases the cost required to inform the village leader of their problems, and consequently decreases
the village leader’s access to information on the kind of policy the villagers need. Thus, the leader isless likely to implement effective policies, due to a lack of information about the villagers’ problems.
Moreover, better signal strength is followed by higher mobile phone subscription rates. Figure
A1 and Figure A2 illustrate that areas with a stronger signal (See Figure A3) have higher mobile
phone subscriptions compared to area with a weak signal. Therefore, villages with better signal
strength exhibit an increase in the number of messages sent from villagers to their leaders. This
results in an increase in villagers’ consumption of telecommunication products.
It is also expected that there will be heterogeneous effects of ICT between rural and urban
villages. Strömberg (2004b) has observed that higher radio penetration has a significant effect onrural areas in the US. Because Indonesia has two types of village government, I also expect thatvillages with elected leaders will be significantly impacted by the expansion of ICT. Martinez-Bravo(2014) has shown that villages with appointed leaders tend to exhibit clientelistic spending, which
affects voters’ welfare. Moreover, since village leaders are elected by the villagers (only in desa), ICT
can also affect their chances of being re-elected, insofar as villages with better ICT infrastructure
will be better informed about their leader’s performance. This mechanism is also motivated by
Strömberg (2004a), Strömberg (2004b) and Snyder Jr and Strömberg (2010), who have found thatthe amount of information voters receive affects their vote as well as voter turnout. This impact
will also be related to the second mechanism, which is the the impact of signal strength on politicalparticipation at village elections.

4

Data and Specification

The data used in this study are from the Indonesian Village Potential Statistics (Potensi Desa/Podes).
These data are collected by the Indonesian Central Bureau of Statistics (Badan Pusat Statistik/BPS)
every three or four years. The sample of Podes in every census is +/- 65,000 villages across thecountry. Podes is a census that provides information about Indonesian villages’ characteristics, suchas government administrations, public goods provided in the village, socio-economic characteristics

13and other comprehensive information.11 Because each wave has a different focus, however, somevariables are not reported on in all waves of the village census.
In this study, I merge three different waves of the village census, collected from 2008, 2011 and2014. The main difficulty when constructing this dataset is that The Central Bureau of Statistics inIndonesia uses village identifiers across waves inconsistently. Therefore, some of the villages have a
different identifier in different waves. Moreover, the introduction of new villages during the study
period is not easily detected. Given the difficulty in merging the dataset with a large number ofobservations, I chose to use only the villages that are consistently included in all of the waves.12
The dataset used in this study consists of 54,602 villages, which cover 33 provinces, 457 districts
and 6,292 sub-districts. In total, I have 163,806 observations for this study.13 Table 1 provides thesummary statistics for the data in this study. As previously mentioned, there are different types ofgovernment division at the village level. In this study, I use a comprehensive sample of villages inIndonesia, and analyse the results by dividing them according to the different types of government,
specifically between Desa and Kelurahan.

4.1

The Dependent Variables

This study aims to examine whether access to mobile phones increases choice of policies implemented with several mechanism as it has been discussed in section 3. I divide this study into 4
main areas of analysis: (1) the impact of mobile phones on villages’ infrastructures; (2) the impact ofmobile phones on the improvement of villagers skills via training; (3) the impact of mobile phones
on education, especially access to village libraries; and (4) the impact of mobile phones on access tocredits.14
With regard to the first analysis, I used two dummy variables to capture whether access tomobile phones increases or improves villages infrastructures (e.g. housing, sanitation and cleanwater). The first dummy variable is village rehabilitation programs, which is a binary variable that
represents whether the village has a rehabilitation program. From Table 1, it can be observed thatthe mean for this dependent variable is 0.38, and the standard deviation is 0.49.
11 In every survey, the enumerator collects the answers from a person who works in the village administration, andverifies this information against the village’s administrative records. Some measures of public goods, such as the numberof schools, health facilities, mosques, and churches, can be easily verified. Therefore, this survey produces accurate
information about the village.
12 To get villages that are included in all waves, I generate unique Village ID based on province, district, sub-district,and village code. Thus, I will only keep villages that are consistently included in all waves and have similar village id.
Villages that are newly established between 2011 and 2014 will be excluded. Another cleaner way to merge PODES is bylooking at the Master-File-Desa (MFD) managed by Statistics Indonesia.
13 While the data does not cover all villages, it covers the majority of Indonesia’s population. Specifically, this study
covers 54,602 out of 65,000 villages in Indonesia or around 84% of the total number of villages.
14 Due to the fact that each wave has a different focus, some variables are not available in all years. Only policy andoutcome variables that are consistently asked in all Podes waves are used in this study.

14

Table 1: Summary Statistics
Variable

Obs

Mean

Std. Dev.

Min

Max

163,806

0.38

0.49

0

1

163,806

0.12

0.33

0

1

163,806

0.15

0.35

0

1

163,806

0.02

0.15

0

1

163,806

0.11

0.31

0

1

163,806

0.20

0.40

0

1

163,806

0.65

0.48

0

1

163,806

16.14

12.26

0

88.91

Female leader

159,492

0.05

0.21

0

1

Age

159,492

44.25

8.14

17

99

Years of education

159,492

11.77

3.22

0

22

Log population

160,953

7.35

1.16

1.10

12.21

Main source of income

163,806

0.88

0.32

0

1

Muslim majority

163,806

0.72

0.45

0

1

Christian majority

163,806

0.39

0.49

0

1

Multi ethnic

163,806

0.73

0.44

0

1

Number of mosques

163,806

7.82

11.83

0

198

Number of churches

163,806

0.88

1.96

0

81

Number of village clinics

163,806

0.29

0.48

0

11

Number of village maternity units

163,806

0.24

0.46

0

14

Number of households with fixed line phone (in 000s households)

163,806

0.07

0.52

0

99.99

Monthly rainfall precipitation (mm)163,806

119.5799.63

0964.91Main Variables
Village rehabilitation programs(1 = there is program on village rehabilitation; 0 = otherwise)
Village rehabilitation programs for poor people(1 = there is program on village rehabilitation for poor people; 0 = otherwise
Capacity building programs(1 = village has capacity building programs (e.g. training); 0 = otherwise)
Capacity building programs for poor people(1 = village has capacity building programs (e.g. training) for poor people; 0 = otherwise)
Village libraries(1 = if village has village libraries; 0 = otherwise)
Credits for small enterprises(1 = if village has provided credits for small enterprises; 0 = otherwise )Signal(1 = signal is very strong; 0 = otherwise)
Mean flash rate density (flash rate/km2 )Other Variables(1 = agriculture; 0 = others)

Additional Informations
Number of provinces

33

Number of districts

457

Number of sub-districts

6,292

Number of villages

54,602

15

The second dependent variable is village rehabilitation programs for poor people, which
is a dummy variable that represents whether the village has a rehabilitation program but thebeneficiaries of this program are exclusively the poor. This dependent variable aims to investigate
whether poor people will benefit more from the improvement of ICT sectors. It can be seen that
only 12% of the sample has such a program with the standard deviation around 0.33.
For the second analysis, a dummy variable on whether the village has a training program (e.g.
training for villagers to enhance their skills) becomes the dependent variable. Villages could also
have training programs that might improve villagers’ skills, which can be used when these people
want to apply for a job or to start their own business. The mean for this variable is around 0.15 andthe standard deviation is around 0.35. Similar, I introduce another variable (training programs forthe poor) which represents whether the village has such programs specifically for poor people. The
mean for this variable is 0.02 and the standard deviation is approximately 0.15.
For education sectors, access to village libraries will be the outcome. Village governments maybuild village libraries. The aims of these programs are to increase access to education, especiallyfor pre-preschoolers, and also access to books. Therefore, I introduce a dummy variable on thepresence of village libraries as the independent variable for the third analysis. The mean for village
libraries is around 0.11 and the standard deviation is 0.31.
Finally, as previous studies have shown that access to a mobile phone is associated with accessto credit, the last analysis investigates the impact of mobile phones on access to credit, especiallycredit for small enterprise. Credit for small enterprise is a program for people who are planning tostart a small scale business (e.g. restaurants, clothing retailers, groceries, among others). Indonesiansmall and medium enterprises cover almost 97% of domestic employment (OECD, 2018). Here, I use
a dummy to establish whether the village has implemented such a program for their villagers. The
mean for this program is 0.2 which is slightly higher than for food and agriculture credit programs.

4.2

Explanatory Variables

Publicly available data related to mobile phones in Indonesia is based upon mobile phone signal
strength at the village level, which can be collected from Podes and first became available in 2008.
15 In Podes, signal strength is divided into three criteria: (1) no signal, (2) weak signal and (3) strong

signal. In this study, the core explanatory variable is signal strength, which is a dummy variable
that equals one if the village’s mobile phone signal is strong. Therefore, the value of the signal will
15 Other data related to ICTs in Podes include the number of households with fixed line subscription, the existence of

public phones, and the number of internet cafes.

16

be zero if the village’s signal strength is weak or non-existent.16 This variable is different to thatused in Olken (2009), which uses the signal strength for television reception. Moreover, the area ofstudy used in Olken (2009) is only Java, whereas my sample extends to almost all the villages in the
country.
From Table 1, it is evident that the mean for Signal is 0.65 and the standard deviation is 0.48.
Thus, almost 65% of the sample villages in the study have a strong signal. Between 2008 and 2014,
signal strength improved throughout the villages; whereas the average signal strength was 0.61
in 2008, it rose to 0.68 in 2014. These numbers demonstrate that even though signal strength has
improved over this period, around 35% of the sample still have limited access to a strong signal.
Figure A3 depicts the conditions of mobile phone signal strength in Indonesia from 2008 to 2014.
Almost all areas in Java are covered by a strong signal, because the ICT infrastructure in Java issignificantly better than on the other islands. The majority of Sumatra also has relatively strong
signal. Eastern parts of Indonesia, especially Papua, have poor signal strength, however. This isbecause the infrastructure quality in the eastern part of Indonesia is generally worse than on theother islands. It is also clear that from 2008 to 2014, signal strength greatly improved, especially inJava and Sumatra.

4.3

Specification and Identification Strategy

In this study, two main econometric methods are used to examine the impact of mobile phone usage
on policies: (1) a linear probability model (LPM) and (2) a probit model.
The linear probability model is as follows:

Yv,t = βSignalv,t + γXv,t + θv + ϑt + ev,t(1)

where Yv,t is the binary dependent variable in the village v at time t. Signalv,d,t is a dummy variable
that has a value of 1 if the village v and time t have a strong mobile signal, and 0 otherwise; γXv,trepresent the vector of control variables; θv are the village fixed effects; and ϑt are the year fixed
effects. The main coefficient of interest is β, because it estimates the within-village difference in theprobability of the dependent variable being 1 (Pr (Yv,t = 1)), between villages with strong versus
weak and non-existent signals that exhibit similar characteristics.
The control variables used in this paper are the set of rich datasets related to village-level
characteristics, such as variables related to village governance, e.g. female leader (i.e. 1 if female, 0
16 This is the margin that seems to have the strongest effect for these three categories. In the robustness check, I also test
using two dummies (e.g. strong signal and no signal) (See Table 8) and categorical signal strength (See A2).

17otherwise), age, years of education; village-level demographic, e.g. log of population, main sourcesof income (i.e. 1 if agriculture, 0 otherwise) and social indicators, e.g. Muslim majority (i.e. 1
if Muslim majority, 0 otherwise), Christian majority (i.e. 1 if Christian majority, 0 = otherwise),
multi-ethnic (i.e. 1 if more than one ethnicity, 0 otherwise), number of mosques, number of churches,
number of village clinics, number of village maternity units, number of households with a fixedline phone. The list of control variables for the village-level characteristics is displayed in Table 1.
The second method is the probit model, estimated via the following equation:

Pr (Yv,t = 1) = F ( βSignalv,t + γXv,t + θv + ϑt + ev,t )(2)

where F (.) is the cumulative distribution function of the standard normal distribution.17
The results from the LPM and probit estimations might be biased, however, because the signal
strength variable might not be entirely random (Angrist and Krueger, 2001; Angrist and Pischke,2009; Wooldridge, 2010). As has been shown by previous studies in related literature, mobile phonesignal strength might be endogenous and non-random (Aker and Mbiti, 2010; Manacorda and Tesei,2020). Areas with larger populations or better infrastructures and income levels might have better
signal strength. However, the large number of high buildings in the surrounding area could also
negatively affect mobile phone signal. This condition could also make signal strength endogenous,
because the decision to install telecommunication infrastructures might be correlated with the error
term which could also affect outcome variables in this study. For example, Indonesia regularly faces
many natural disasters (e.g. earthquakes, volcanic eruption, etc.). An expectation of such eventswould reduce the incentives for telecommunication services to invest their infrastructures in areas
with higher probability of facing natural disasters, leading to worse signal strength, and associatedoutcomes. This concern would potentially underestimate (downward bias) the estimations from
OLS or probit methods.18 Finally, reverse causality may also bias our result because it is possiblethat areas with better social development conditions will also have a good presence of mobile
phones signal.
Our last potential problem that could bias the results is the measurement error of signal strength.
The data on signal strength is based on the answers provided by the village administrations and,thus, may not accurately reflect the true condition of mobile phone signal strength. There is a higher
17 Logit and Probit, however, exhibit several limitations when using fixed effects. See Heckman (1979) and Greene(2004) for further explanation.
18 Another concern that may affect the empirical results in this study is attrition bias due to a small part of villages fromPODES are excluded from our samples. Attrition bias may affect our estimation results due to sample selection bias. To
isolate this, a battery of covariate has been used in our model which has been explained in equation (1) such as village
level characteristics. Nonetheless, it seems that there might be other factors that will bias our estimates from unobserved
factors.

18

probability that the information for the signal strength might be over reported, which could again
bias the estimations (Hausman, 2001). I will use instrumental variable strategy to deal with theseconcerns.

4.4

Instrumental Variable

Although an instrumental variable strategy could be used to solve the problem with endogeneity,
it is quite difficult to find exogenous variables that would work in this study. As noted by Aker
and Mbiti (2010), the issue with ICT studies is that it can be difficult to find a credible instrumental
variable. In this study, what we need is a variable that captures and exploits the variation in
mobile phone signal strength coverage across villages and years. One possible variable is theplausibly exogenous factor of different lightning strike incidence (or flash rate, I will use these terms
interchangeably).
The mechanism on why lightinig strikes affect mobile phone signals is the following: lightning
strikes have strong relationship with the provision and quality of telecommunication infrastructure.
Andersen et al. (2012) and Manacorda and Tesei (2020) have observed that lightning strikes candamage telecommunication transmitters. This can result in less investment in telecommunication
towers because it will be very expensive to invest in areas with higher risks of lightning strikes.
Telecommunication providers will avoid putting physical infrastructures in such areas, thus reducing of mobile phone signal strength. Therefore, in order to be a valid instrument, higher flash
rate intensity will slow down the adoption of mobile phone devices and I expect that villages
with higher flash rate intensity will have lower mobile phone signal strength. The underlying
assumption here is that the variation of flash rate intensity only affects the dependent variables
through the growth of ICT coverage.
I use the mean of lightning strike intensity per km2 between 1998 and 2013 at the village level,
as the instrument (Andersen et al., 2011, 2012; Manacorda and Tesei, 2020).19 The data is available
across the world and, in this study, I was able to construct the flash incidence data at the Indonesianvillage level. Because the data provided by NASA is the average between 1998 and 2013 andtherefore time invariant, I used the interaction between flash rate intensity for the village v and timetrends to allow me to estimate the effects of the instrument on mobile phone signal strength in thepanel data setting. The use of the interaction term generates a continuous difference-in-differences
19 Data retrieved from LIS 0.1 Degree Very High Resolution Gridded Lightning Full Climatology (VHRFC) https://ghrc.nsstc.nasa.gov/hydro/details/lisvhrfc. This data is provided by the US National Aeronautics and Space
Administration (NASA). The data is the average flash rate between 1998 and 2013 and, therefore, is time invariant.
Andersen et al. (2012) and Manacorda and Tesei (2020) have determined that a consistent pattern exists for lightning
strikes across this period of time. See Cecil et al. (2014) for further information.

19

estimator that is able to identify the causal effect of time invariant variation from the flash rateintensity (Angrist and Imbens, 1995; Angrist, 1998). This interaction term will also capture the trendof increased mobile phone adoption across different villages. A similar approach has been used by
Manacorda and Tesei (2020), where the interaction between flash rate incidence and time trends
functions as an instrument for measuring mobile phone coverage in Africa.
Hence, the first stage of this estimation is:

Signalv,t = αv,t + Zv,t + γXv,t + θv + ϑt + µv,t(3)where Zv,t = Flash ratev ∗ time trend. I also include the set of control variables γXv,t , which is thesame as the covariate in equation 1, as well as village fixed effects (θv ) and year fixed effects (ϑt ).
The underlying identification behind this instrument relies on the assumption that the village
policies would not change over time unless due to the variation of the mobile phone signal, whichis due to the intensity of lightning strikes and interaction with a time trend. Therefore, when I
interpret the coefficient for Zv,t for the first stage results, higher incidence of lightning strikes is
expected to lead to a slower improvement of mobile phone signal.
A critical reader will then ask why this is a credible instrument and what are the problems
with this instrument? First, let me explain why this is a good instrument for measuring mobile
phone coverage, especially in the context of Indonesia. Indonesia has a higher flash rate incidence
due to its location and geographical characteristics. Cecil et al. (2014) have suggested that tropicaland sub-tropical regions tend to have a higher annual flash rate. Albrecht et al. (2011) have also
observed that high flash rates are linked to topographical features.
Figure 2 depicts the variation of lightning strikes in Indonesia between 1998 and 2013. Based on
the Figure, areas in Sumatra have higher incidences of lightning strikes. Most of this area is locatedat a higher altitude. Moreover, some parts of Java also exhibit higher lightning strike incidences.
One thing worth nothing is the high variation of lightning strikes across the country, which can beimportant to explain the variation in signal strength throughout Indonesia. The mean flash rate inIndonesia from 1998 to 2013 was 16.14 flash rate/km2 (See Table 1). This number is higher than the
global flash rate, which is around 2.9 flash rate/km2 , and the average flash rate in the tropics andsub-tropics (10 flash rate/km2 ).
Even though I am relying on the assumption that flash rate intensity is potentially exogenous
and will affect policy only through the slower improvement of mobile phone signals, there areseveral concerns that arise from using this variable, especially related to the exclusion restrictions.
First, as has been shown in Figure 2, lightning strike intensity is clustered and may be correlated20

Figure 2: Mean Annual Flash Rate Density between 1998 and 2013 (Flash Rate/km2 )Source: Author’s calculation from Cecil et al. (2014)

with geographical features (e.g. topography). Even though I estimate the model by including
several village fixed effects that could capture this, geographical features could affect decisions
over the placement of other infrastructures and therefore affect our outcomes. Second, areas withhigher flash rate incidences might also have a higher probability of correlation with other natural
disasters (e.g. storms or rains) which could affect the outcomes. This again leads to the violation of
our identification assumption for the exclusion restriction. Third, it is possible that there is a newtechnology which is unobservable and could minimise the impacts of lightning strikes on mobile
phone signals. All of these issues might raise some concerns with our identification strategy. Finally,
because the flash rate is adopted from the satellite image, measurement error may be a concern here.
Thus, violate the use of the instrument.
In order to at least mitigate some of these problems, I use a battery of control variables usedin the baseline specifications and also include village fixed effects which will capture the timeinvariant village characteristics (e.g. elevation, latitude, longitude). Moreover, to isolate the concern
over the correlation between topographical features and flash rate intensity, I use topographical(1=land, 2=valley, and 3=hill) by-year fixed effects. Because the flash rate might be correlated withstorms or rains, I also use the index for rain precipitation at the village levels. By applying all ofthese strategies, the underlying assumption of using flash rate intensity as the instrument variable
will be validated and the exclusion restriction will be plausible, conditional on the inclusion of afull set of covariates and several fixed effects to control some unobservable time invariant village
characteristics.

21

5

Results

In this section, I discuss the results from the LPM and Probit estimations of equation 1 and equation
2. I begin the analysis by discussing the association between signal strength and the policies madeby village leaders. I also discuss the results from 2SLS estimates of equation 3. Later, I will elaborate
the potential extension and robustness tests.

5.1

LPM and Logit Results

Village Rehabilitation Programs. Table 2 exhibits the estimation results for the village rehabilitation
programs. The dependent variable in this table is the dummy variable, which has a value of 1 if thevillage has a rehabilitation program, and 0 otherwise (columns 1 - 4) and the dummy variable, which
has a value of 1 if the village has a rehabilitation program specifically for poor people (columns 5 - 8).

Table 2: Baseline Results: Village Rehabilitation Programs
(1)

(2)

(3)

(4)

Village Rehabilitation Programs

(5)

(6)

(7)

(8)

Village Rehabilitation Programsfor Poor People

Signal strength

0.0052(0.0039)0.0054(0.0040)0.0054(0.0040)0.0016(0.0027)

0.016∗∗∗(0.0027)0.014∗∗∗(0.0028)0.013∗∗∗(0.0028)0.0090∗∗∗(0.0020)NR2
pseudo R2

1638060.468

1565060.479

1565060.480

1567451638060.387

1565060.403

1565060.403

156745

Estimation method
Village FE
Year FE
Urban by Year FE
Topography by Year FE
Controls

LPM
Yes
Yes
Yes
No
No

LPM
Yes
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes
Yes

Probit
No
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
No
No

LPM
Yes
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes
Yes

Probit
No
Yes
Yes
No
Yes

Mean dep. var.

0.38

0.38

0.38

0.38

0.12

0.12

0.12

0.12

0.098

0.050

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variables in this estimation are dummy
variable for village rehabilitation programs at the village level (columns 1 - 4) and dummy variable for village rehabilitation programs
where the beneficiaries are only poor people (columns 5 - 8). Time varying controls: log population, log (1+night light ), dummy forfemale leader, leader’s age, leader’s years of education, main source of income, dummy for Muslim majority, dummy for Christianmajority, dummy for multi ethnic, number of mosques, number of churches, number of village clinics, number of village maternity
units, number of households with fixed line phone, distance to district and distance to sub-district. Urban (1 = urban; 0 = otherwise),
topography (1 = land, 2 = valley, 3 = hill), . The years included in the regressions are 2008, 2011 and 2014. Columns (4) and (8) are themarginal effects for probit estimation methods. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Let me start by analysing the result for village rehabilitation. Column (1) provides the linear
probability model for this dependent variable. It suggests that by only controlling for village
and year fixed effects, signal strength does not have any affect on village rehabilitation programs.
Similarly, in column (2), even after including covariates we still do not observe any impact of22

signal strength on our outcomes. Similarly, in column (3) once I controlled for topography byyear fixed effects, which capture the different time trends for different district levels, the result
does not suggest higher signal strength is associated with having village rehabilitation programs.
Furthermore, in column (4) the probit model suggests no relationship between signal strength andvillage rehabilitation programs.
Moving to a similar program which is specifically implemented for poor people, column (5)suggests that a strong signal is associated with higher probability of having rehabilitation programsfor poor people. Even after controlling for time variant covariates (column 6) and district by-year
fixed effects (column 7), the point estimate suggests that a strong signal leads to a higher probabilityof the improvement of village conditions specifically for poor people, statistically significant at 1%.
The marginal effect from the probit model also suggests that a strong signal increases the outcome
by 0.9 percentage points and is statistically significant at 1%. However, because I do not control fortime invariant village characteristics in this model, we cannot isolate the within-village variationand may create omitted variable bias.
These results are consistent with my hypothesis that an increase in access to ICT will increase avillage leader’s incentive to implement policies that are beneficial to the people, even though only
to village rehabilitation programs for poor people.

Capacity Building Programs. The association between signal strength and training to increase
villagers’ skills is presented in Table 3. In column (1), regressing the capacity building dummy onsignal strength without including any covariates does not have any meaningful statistical evidence.
Controlling for some covariates in column (2) also suggests that signal strength does not have
any relationship with capacity building programs. Similarly, once controlled by district by-year
dummies (column 3), it shows that signal strength do not affect the probability of the presence of
training programs. The estimation result from the probit model suggests that higher signal strength
is associated with higher probability of the village having such a program. However, we cannot
isolate the possibility of omitted variable bias because it is estimated without controlling for village
time invariant characteristics.
Even though the evidence suggests that mobile phones have no association with capacity building programs, it is interesting to see whether the result would be different for the same program
where the beneficiaries are poor people. Columns (5) - (8) provide the estimation results for the newdependent variable. We can see that from the linear probability model, the results are statisticallyinsignificant for all specifications (columns 5 - 7). The marginal effect estimation result from the23

Table 3: Baseline Results: Capacity Building Programs
(1)

(2)

(3)

(4)

(5)

Capacity Building Programs

(6)

(7)

(8)

Capacity Building Programsfor Poor People

Signal strength

0.0015(0.0027)

0.0022(0.0028)

0.0024(0.0028)0.032∗∗∗(0.0022)0.00036(0.0012)0.000095(0.0012)0.000046(0.0012)0.0052∗∗∗(0.0011)NR2
pseudo R2

1638060.431

1565060.445

1565060.445

1567451638060.354

1565060.368

1565060.368

156745

Estimation method
Village FE
Year FE
Urban by Year FE
Topography by Year FE
Controls

LPM
Yes
Yes
Yes
No
No

LPM
Yes
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes
Yes

Probit
No
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
No
No

LPM
Yes
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes
Yes

Probit
No
Yes
Yes
No
Yes

Mean dep. var.

0.15

0.15

0.15

0.15

0.02

0.02

0.02

0.02

0.090

0.049

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variables in this estimation are dummy
variable for village capacity building programs at the village level (columns 1 - 4) and dummy variable for village capacity building
programs where the beneficiaries are only poor people (columns 5 - 8). Time varying controls: log population, log (1+night light ), dummy
for female leader, leader’s age, leader’s years of education, main source of income, dummy for Muslim majority, dummy for Christianmajority, dummy for multi ethnic, number of mosques, number of churches, number of village clinics, number of village maternity units,
number of households with fixed line phone, distance to district and distance to sub-district. The years included in the regressions are
2008, 2011 and 2014. Columns (4) and (8) are the marginal effects for probit estimation methods. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

probit model in column (8) suggests signal strength is positively correlated with capacity building
programs for poor people. Without controlling for unobservable time invariant village characteristics, we can interpret that higher mobile phone signal coverage is associated with an increase in thelikelihood of program implementation by 0.5 percentage points, statistically significant at 1%.

Village Libraries and Access to Credit. Table 4 provides the estimation results for the dummy
for village libraries and the implementation of providing credits for small enterprises. Columns(1) - (4) depict the results for village libraries. In column (1), the result of the linear probability
estimation method without controlling for any time variant covariates suggests that there is robust
evidence that signal strength increases the probability that a village will possess a library. The same
results can be observed in column (2) even after including control variables. In the most demanding
specification in column (3), signal strength increases the likelihood that a village will possess alibrary by 0.64 percentage points, statistically significant at 1%. The marginal effect from the probit
method also suggests that a stronger mobile phone signal is associated with an increase in thepresence of village libraries by 2.7 percentage points.
The results for credits for small enterprises are shown in columns (5) - (8). In general, different
specification methods suggest robust evidence that higher signal strength is associated with higher
probability of having access to credit. In column (5), regressing the dependent variable on the24Table 4: Baseline Results: Village Libraries and Access to Credit(1)
Signal strengthNR2
pseudo R2

0.0071∗∗∗(0.0022)163806
0.500

(2)

(3)

Village Libraries
0.0064∗∗∗ 0.0064∗∗∗(0.0023)(0.0023)156506156506
0.512
0.512

(4)

(5)

(6)

(7)

(8)

0.124

Credits for Small Enterprises
0.017∗∗∗ 0.015∗∗∗ 0.015∗∗∗ 0.071∗∗∗(0.0030) (0.0031) (0.0031) (0.0024)
163806156506156506156745
0.489
0.501
0.501
0.124

0.027∗∗∗(0.0022)156745

Estimation method
Village FE
Year FE
Urban by Year FE
Topography by Year FE
Controls

LPM
Yes
Yes
Yes
No
No

LPM
Yes
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes
Yes

Probit
No
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
No
No

LPM
Yes
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes
Yes

Probit
No
Yes
Yes
No
Yes

Mean dep. var.

0.11

0.11

0.11

0.11

0.20

0.20

0.20

0.20

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variables in this estimation are dummy
variable for village libraries at the village level (columns 1 - 4) and dummy variable for credits for small enterprises where the beneficiaries
are only poor people (columns 5 - 8). Time varying controls: log population, log (1+night light ), dummy for female leader, leader’s age,leader’s years of education, main source of income, dummy for Muslim majority, dummy for Christian majority, dummy for multi ethnic,
number of mosques, number of churches, number of village clinics, number of village maternity units, number of households with fixedline phone, distance to district and distance to sub-district. The years included in the regressions are 2008, 2011 and 2014. Columns (4) and(8) are the marginal effects for probit estimation methods. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

signal strength without any covariates, the point estimate for signal strength is around 0.017 andstatistically significant at 1%. Even after including covariates in column (6) and also controlling for
district by-year trend in column (3), the point estimates are relatively similar and still statisticallysignificant at 1%. Finally, the estimation result from the probit model also suggests that signal
strength has a positive relationship with access to credit. In terms of the magnitude, from the mostdemanding specification in column (7), higher signal strength increases the likelihood of credit
programs for small enterprises by 1.5 percentage points.
In general, the results from the LPM and Probit exercises suggest that signal strengths haveeffects on some policies and are very important for poor people. However, as has been mentioned
before, we still have some endogeneity issues with the estimation results that probably could bias
the results. In the next section I will provide the results from the two stage least square (2SLS)
estimations to address these concerns.

5.2

Instrumental Variable

I performed a 2SLS estimation to address the endogeneity problem with previous estimations. As
mentioned previously, the flash rate intensity is used as an instrument for signal strength. Before
analysing the results from the 2SLS estimation, let us see whether the instrument has any relevanceto the signal strength. Table 5 depicts the first stage estimation results, where the dependent variable
is signal strength and the independent variable is the incidence of lightning strikes at the village
25levels interacting with a time trend. Column (1) is estimated without including any covariates andcolumn (2) is estimated by including several covariates, including precipitation. All covariates usedhere are similar to those used in the previous tables. I also include village and year fixed effects andalso topography by-year fixed effects.
I expect the association between the flash rate intensity × a time trend and the signal strength
will be negative. In other words, we can anticipate a slower growth of mobile coverage in areas
with higher flash rate intensity, which will be exploited by the flash rate intensity × a time trendinteraction. The first stage results indicate that the instrument reduces the signal strength and is
statistically significant at 1% for different specification methods. In terms of the magnitude, a one
standard deviation increase in the instrument is associated with a decrease in the signal strengthby 1.71 percentage points (29.578 × -0.00058 × 100). The instrument also passes the standard
test for validity, as the joint-F statistics of the Kleibergen-Paap test for weak identification for this
instrument range from 44.76 to 89.92 (Staiger and Stock, 1997).
The results for the reduced form in Table A1 also suggest that the instrumental variable inthis study have a strong reduced form relationship with the outcome variables, except capacity
building programs and also capacity building program for poor people. This is similar to what I
have observed in Table 3, where capacity building programs and capacity building programs forpoor people are not improved due to a strong presence of mobile phones signal.

Moving to the 2SLS estimation results, Table 6 contains all the results for the dependent variablesin this study. Village and year fixed effects are included in all columns, as well as topography
by-year fixed effects. I also employ the same covariates used in the LPM and Probit estimation
methods and the precipitation index. Again, as expected, the results from the first stage suggest thatthe instrument in this study has a robust and negative relationship with the signal strength. The
joint-F statistics of the Kleibergen-Paap test for weak identification do not show that this instrument
is weak. The F-test is also higher than the critical value from Stock-Yogo method.
For the interpretation of the 2SLS estimation methods, in column (1) for village rehabilitation
programs, we can see that the 2SLS method suggests that higher signal strength is associatedwith higher probability of the presence of village rehabilitation programs by 1.57 or 157 increasein percentage points and this is statistically significant at 1%. In column (2) we also see robust
statistical evidence that signal strength increases the probable incidence of village rehabilitation
programs by 1.26 or 126 increase in percentage points where the beneficiaries are poor people. Boththese results support the evidence obtain from the baseline specification in Table 2.

26

Table 5: Instrumental Variable Estimations: First Stage(1)
Signal Strength-0.00077∗∗∗(0.000082)
1638060.662(2)
Signal Strength-0.00057∗∗∗(0.000087)
156506
0.671

Village FE
Year FE
Topography by Year FE
Urban by Year FE
Controls

Yes
Yes
Yes
Yes
No

Yes
Yes
Yes
Yes
Yes

First StageF
Stock-Yogo Critical Value

87.81
16.38

43.27
16.38

Flash rate intensity × time trend
N
R2* Notes: The years included in the regressions are 2008, 2011 and 2014. Signal strength

is instrumented by mean annual flash rate density per km2 between 1998 and 2013 ina village interacting with a linear time trend. Time varying controls: log population,log (1+night light ), dummy for female leader, leader’s age, leader’s years of education,
main source of income, dummy for Muslim majority, dummy for Christian majority,
dummy for multi ethnic, number of mosques, number of churches, number of village
clinics, number of village maternity units, number of households with fixed line phone,
distance to district, distance to sub-district and rain precipitation. See Table 2 for further
information. Robust standard errors in parentheses and clustered at the village levels.
The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald test for
weak identification. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

For the capacity building programs, both columns (3) and (4) suggest that mobile phone signal
does not affect the likelihood of implementing the programs. This is also consistent with theevidence observed from the LPM method, where I do not find any statistical evidence to indicate
whether signal strength will affect the likelihood of having these programs. One possible explanation for these results is that village heads might prefer to improve the quality of infrastructure
rather than providing training because the beneficiaries of village improvement will be larger inscope than would be the case for training schemes targeted at specific individuals.
The evidence for village libraries in column (5) also suggests that signal strength is associatedwith a higher probable incidence village libraries and is significant at 1%. Stronger mobile phones
signal will increase the availability of village libraries by 51 percentage points. Finally, in column(6), the result from the 2SLS method suggests that strong mobile phone signal coverage is associatedwith an increase in the probability that policies will be implemented in relation to credit for small
enterprises, statistically significant at 5%. These results also suggest that villagers and village chiefs
consider an improvement in access to education and credit to be very important.

The results from the 2SLS estimation method indicate that signal strengths have a causal effecton some outcomes in this study. Yet we can see that the point estimates from the 2SLS estimation
methods are significantly higher than those obtain from the LPM and Probit methods. There are27

Table 6: Two Stage Least Square Estimations: All Dependent Variables(1)
Village Rehabilitation
Prog.
1.57∗∗∗(0.31)
156506(2)
Village Rehabilitation
Prog. for Poor People
1.26∗∗∗(0.24)
156506(3)
Capacity Building
Prog.
0.021
(0.16)
156506(4)
Capacity Building
Prog. for Poor People-0.11(0.071)156506(5)Village
Libraries
0.51∗∗∗(0.16)
156506(6)
Credit for
Small Enterprises
0.43∗∗(0.18)
156506

2SLS
Yes
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes
Yes

-0.00058***(0.000087)-0.00058***(0.000087)-0.00058***(0.000087)-0.00058***(0.000087)-0.00058***(0.000087)-0.00058***(0.000087)

F
Stock-Yogo Critical Value

43.27
16.38

43.27
16.38

43.27
16.38

43.27
16.38

43.27
16.38

43.27
16.38

Mean dep. var.

0.38

0.12

0.15

0.02

0.11

0.20

Signal strengthN
Estimation method
Village FE
Year FE
Urban by Year FE
Topography by Year FE
Controls
First stage
Flash rate intensity
× time trend* Notes: Robust standard errors in parentheses and clustered at the village levels. The years included in the regressions are 2008, 2011 and 2014. Signal strength is instrumented by mean annual

flash rate density per km2 between 1998 and 2013 at the village level interacting with a linear time trend. Time varying controls: log population, log (1+night light ), dummy for female leader,leader’s age, leader’s years of education, main source of income, dummy for Muslim majority, dummy for Christian majority, dummy for multi ethnic, number of mosques, number of churches,
number of village clinics, number of village maternity units, number of households with fixed line phone, distance to district, distance to sub-district and rain precipitation. See Table 2 for further
information. The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald test for weak identification. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

several explanations for these results. The first of these is the problem with the measurement
error from the signal strength, which biases the LPM and Probit estimates of the effect from thesignal strength toward zero (downward bias). Since, the 2SLS or IV estimate is unaffected by
the measurement error in the endogenous variable, the point estimates tend to be larger than the
LPM/Probit estimates (Angrist and Krueger, 2001; Hausman, 2001). Second, the IV estimates the
local average treatment effect (LATE) of the change in signal strength only for the sample in whichchoice of the treatment was affected by the instrument. Therefore, the point estimates for IV will besignificantly higher, because in this case it estimates the average difference in outcomes due to thevariation in the signal strength.
Finally, even though I have included several covariates to minimise the problem with the exclusion restrictions, it is possible that the instrument is still correlated with something unobservablein the error term. Nevertheless, the results from the diagnostic test and the first stage relationshipbetween the instrument and the explanatory variable conditional on a battery of control suggestthat the instrument passes the requirements to be a valid instrument. It also shows that we candisregard any concerns over the possibility of a weak instrumental variable in this study.

5.3

Extensions

Desa versus Kelurahan. In the previous section, I discussed the differences between Desa and
Kelurahan in terms of administrative characteristics. In this section, I further differentiate the sample
into two groups: Desa village and Kelurahan village. Table 7 presents the results of this analysis.

28

Table 7: Additional Results: Desa versus Kelurahan(1)

Signal strength
N

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)
Village
Libraries

(11)

(12)

Village Rehabilitation

Village Rehabilitation
for Poor People

Capacity Building

Capacity Buildingfor Poor People

Credit for
Small Enterprises

1.96∗∗∗(0.65)
139110-0.075(2.29)
16867

1.61∗∗∗(0.50)
139110

3.04(3.15)
16867-0.91∗∗∗(0.35)
1391108.96(8.24)
16867-0.20∗(0.11)
1391100.28(1.17)
16867

0.36(0.22)
1391105.34(5.30)
16867

0.59∗∗(0.28)
1391100.46(2.35)
16867

Estimation method
Sub-district FE
Year FE
Topography by Year FE
Controls
SampleFirst stage
Flash rate intensity
× time trend

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan-0.00027**(0.000079)-0.000097(0.000086)-0.00027**(0.000079)-0.000097(0.000086)-0.00027**(0.000079)-0.000097(0.000086)-0.00027**(0.000079)-0.000097(0.000086)-0.00027**(0.000079)-0.000097(0.000086)-0.00027**(0.000079)-0.000097(0.000086)

F
Stock-Yogo Critical Value

54.07
16.38

1.25
16.38

54.07
16.38

1.25
16.38

54.07
16.38

1.25
16.38

54.07
16.38

1.25
16.38

54.07
16.38

1.25
16.38

54.07
16.38

1.25
16.38

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variables in this estimation are dummy variable for village rehabilitation program at the village level (columns 1 - 2), dummy
variable for village rehabilitation programs where the beneficiaries are only poor people (columns 3 - 4) dummy variable for village capacity building program at the village level (columns 5 - 6); dummy variable for village
capacity building programs where the beneficiaries are only poor people (columns 7 - 8); dummy variable for village libraries (columns 9 - 10) and dummy variable for credit for small enterprises (columns 11 - 12). The F
statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald test for weak identification. See Table 2 and Table 6 for further information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

For each variable, odd numbered columns are the estimation results for desa and even numberedcolumns are the results for kelurahan. In this table, instead of using village fixed effects, I use
sub-district (kecamatan) fixed effects to capture the difference between desa and kelurahan within thesame sub-districts with similar characteristics. Moreover, I use the 2SLS estimation methods for the
analysis.
Before I analyse the second stage results, the standard diagnostic test for the instrumental
variable strategy suggests that there is a negative and statistically significant relationship between
the instrument and signal strength when I restrict the sample to desa only. However, I do not find
any first stage relationship when I only use the sample for kelurahan. Similarly, the F-statistics of theKlebergen-Paap test for desa is around 54.07 (Stock-Yogo critical value ≈ 16.38 but we have a weak
instrument issue for kelurahan (F-statistics = 1.25). This problem might occur because the sample forkelurahan is significantly lower compare to desa (12.12% relative to desa). Due to this, we only focus
our result for the desa only.
For village rehabilitation programs, column (1) in Table 7 suggests that signal strength in desa
villages is associated with higher likelihood of having such a program. In columns (3), again I findthat signal strength has a positive and statistically significant relationship with the implementationof village rehabilitation for poor people in desa.
The interesting finding here relates to desa villages, where both capacity building (column 5)and capacity building programs for poor people (column 7) have negative relationships with signal
strength, even though it is less significant for the latter. This finding for desa is quite different withwhat I find in the full sample. There is no obvious explanation for this finding, but it seems againthat village heads prefer policies that will benefit the whole society rather than just a small numberof people.

29

Finally, I do not find any differences in terms of the provision of village libraries (columns 9 10) for either desa or kelurahan. Both columns show that the coefficients on signal strength for eachsample are not statistically significant. Finally, for access to credit in columns (11) and (12), the 2SLS
estimates that signal strength increases the probability of the implementation of the program indesa. This evidence suggests that mobile phone adoption will benefit rural areas, which is consistent
with previous studies on the relationship between access to technology and credit (Aker and Mbiti,2010).
In summary, there is clear evidence that the association between mobile phone signal and theoutcome variables is stronger in desa. For kelurahan, however, there is not enough evidence on therelationship between these variables. There are some explanations for these findings. First, thefact that in desa, due to its rural characteristics and lack of facilities, major improvements in somepolicies are essential; also, I do not have a sufficiently large sample size or statistical power for
kelurahan and therefore cannot draw any meaningful inference in relation to this sample.
Second, this result could be explained by the fact that village leader in desa has relatively more
power and, thus, ability to implement policies than the leader of kelurahan. Third, desa villages do
not have many alternative options through which to contact their leader. Once they have access tobetter mobile phone signal strength, it is easier for them to request assistance from their leaders.
Finally, small sample size for kelurahan may yield these findings.

Robustness Check. I performed a robustness check by introducing two dummy variables (strong
signal and no signal) as an alternative explanatory variable. This variable has a value of 1 if the signal
strength is strong for strong signal dummy and 0 if otherwise. Similarly, it has value of 1 if there
is no signal for no signal dummy and 0 if otherwise. This exercise could explain which variable
drives the outcomes more strongly. 20 Table 8 presents the results for these new variables. The LPM
model was used to test this query.

The result for village rehabilitation programs in column (1) suggests that both strong and no
signal strength affect the likelihood of having such a program. Strong signal has a positive andweakly significant relationship with the dependent variable and on the other hand, no signal
strength reduces the likelihood of a village having the program by 1.5 percentage points. In column(2), I find that the implementation of the program for poor people is negatively associated with
20 The use of strong signal and no signal to see the monotonicity of the main independent variable on our policy
variables. Nonetheless, the result for strong versus weak signal can be seen in Table A6. The result from Table A6 suggeststhat strong signal affects our policy variables more stronger compared to the weak mobile phones’ signal.

30

Table 8: Additional Results: Strong versus No Signal(1)
Village Rehabilitation
Strong signal strength

0.0075∗(0.0040)(2)
Village Rehabilitation
for Poor People
0.0031(0.0027)(3)
Capacity Building
0.0052∗(0.0029)(4)
Capacity Buildingfor Poor People
0.0000036(0.0013)(5)Village
Libraries
0.0079∗∗∗(0.0023)(6)
Credit for
Small Enterprises
0.0100∗∗∗(0.0032)

No signal strengthNR2-0.015∗∗(0.0067)1565020.524-0.016∗∗∗(0.0047)
1565020.482

0.0024(0.0040)
1565020.481

0.0013(0.0018)1565020.392

0.0022(0.0031)1565020.536-0.011∗∗∗(0.0040)
1565020.521

Estimation method
Village FE
Year FE
District by Year FE
Topography by Year FE
Controls

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

* Notes: Robust standard errors in parentheses and clustered at the village levels. The years included in the regressions are 2008, 2011 and 2014. Strong signal strength will be equal to
1 if signal strength is strong and 0 if otherwise. No signal strength will be equal to 1 if there is no signal and 0 if otherwise. Time varying covariates in this table are the same withcovariates used in Table 2. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

no signal strength. However, I do not find strong signal strength to increase the likelihood of
the implementation of the program. For capacity building programs in columns (3) and (4), theestimation methods suggest that a strong signal has a weak and positive relationship with theimplementation of the program. However, I do not find any relationship between these twodummies and capacity building programs for poor people. The result for village libraries in column(5) suggests that the likelihood of having the program is mostly driven by the presence of a strong
signal. If the village has strong signal, it increases the probability of the village having a library by
0.8 percentage points. Finally, in column (6), the result for access to credit suggests that both strong
and no signal affect the dependent variable. Villages with a strong signal have a 1 percentage point
higher probability of having access to credit, and on the other hand, villages with no signal will
have a 1.1 percentage point lower probability of having the program. In general, a strong signal has
a positive relationship with the outcomes and no signal will reduce the likelihood of having the
programs.
Because I construct the main explanatory variable from categorical data, I also test whether
using the categorical data provides a similar result. Table A2 depicts the result that arises fromusing categorical signal strength. This variable will have value 2 if the signal is strong, 1 if thesignal is weak and 0 if there is no signal strength. The first stage estimates a significant negative
relationship between the instrument and the new categorical signal strength. The joint-F statisticsof the Kleibergen-Paap test for weak identification is way above the minimum rule of thumb(F-statistics = 238).
The results suggest that categorical signal strength will increase the probability of having village
rehabilitation programs, and both results are statistically significant at 1% (columns 1 - 2). Similar
to the results obtained in Table 3, I do not find any evidence to demonstrate whether a categorical31

signal will affect the likelihood of a village having capacity building programs (columns 3 - 4).
I also find that higher categorical signal strength is associated with higher likelihood of villages
having libraries (column 5) and also access to credit (column 6). These results suggest that using
an alternative explanatory variable does not change the baseline results and still provides robust
causal evidence that mobile phone signal will affect some policies.
Finally, I also introduce a new dependent variable: a land conversion dummy. The variable
is a dummy to establish whether there is a land conversion policy from agricultural land to
non-agricultural land. The idea for this study comes from Besley et al. (2010), which posits thatin a competitive political institution, government will move from agricultural sectors to nonagricultural sectors. This idea is also related to the structural transformation theory, in which there
is a reallocation of economic activity from agricultural sectors to more advanced sectors. Table A3
depicts the estimation results for this analysis. It shows that in both LPM and 2SLS models, higher
signal strength is associated with a higher probability of having land conversion from agriculturalland to non-agricultural land, and is statistically significant at 1%.
Overall, the results of the outcomes are consistent with our hypothesis that there is a positiveeffect of mobile phone signal strength on outcomes. In the next section, I will explore some testablemechanisms that could explain why mobile phone signal could potentially affect some policies.

6

Mechanisms

In the previous section, I demonstrated that villages in Indonesia with strong mobile phone signal
coverage will also have a higher probability of the implementation of certain policies. In this section
I will try to disentangle the mechanisms which could explain why I observe this relationship. In
general, there are four potential channels that could explain the results in this study: (1) That
related to civic engagement or social participation. Mobile phone use will increase the likelihood of
mutual and reciprocal assistance to maintain public goods conditions (gotong royong); (2) Mobile
phone use also increases the political participation and makes villagers better informed, thus (3)Increasing accountability; and (4) Better mobile phone signal strength will increase the usage oftelecommunication services, especially for people in rural areas.

Civic Engagement. For this analysis, a binary variable on whether the village has civic engagement
activities (gotong royong) (e.g. mutual and reciprocal assistance (Bowen, 1986)) is the dependent
variable. Civic engagement or collective action (I will use the terms interchangeably) in Indonesia
are considered social activities that help others or that organise people to improve the conditions of32

their neighbourhood. Gotong royong has become a key element of Indonesian political and cultural
systems. Indeed, this activity is very important in villages and rural areas, especially for agriculturaland economic activities (Bowen, 1986). Therefore, higher civic engagement can be interpreted
in two ways: (1) the villages have good community activities and social relationships, or (2) thevillages lack necessary facilities and infrastructure or have just experienced a natural disaster, andtherefore the people are working together to improve their current condition.
Higher social participation or civic engagement could be interpreted as ways to improve villages’
conditions. To increase the probability of civic engagement, people need access to mobile phones.
In the Indonesian context, mobile phone use is one of the channels to increase social participation.
Unlike television, which reduces the probability of social activities (Olken, 2009), mobile phones
provide a channel that encourages social participation. This could have a similar impact to theusage of mobile phones to increase political mobilisation.
Table 9 depicts the estimation results for civic engagement, where the dependent variable will
have a value of 1 if a village exhibits civic engagement or gotong royong related activities, and 0
otherwise. Columns (1) to (2) present the results from the LPM method. Column (3) depicts themarginal effect for probit estimation, and column (4) shows the estimation results from the IV-2SLS
method. All columns use village and year fixed effects (except column 3). We can observe thatvillages with higher signal strength have a higher probability of participating in civic engagement
activities for all different specifications. The results are robust at the 1% for all columns. The
estimated coefficient is 1.93 for the 2SLS-IV estimation method or 4.48 (≈ 1.93/0.429) relative to a
standard deviation in civic engagement.

Overall, the results for civic engagement are quite different to those obtained in Olken (2009),
which found that media (such as television and radio) reduce participation in social organisationin Indonesia. Thus, the results of the present study demonstrate that mobile phones can be aneffective media tool to inform people about, or invite people to participate in, civic engagement
activities. People can contact one another more easily when they need help or when they want toorganise collective activities via mobile phones. This result is consistent with other studies on thepositive association between mobile phone adoption and collective action or political mobilisation(Manacorda and Tesei, 2020; Shapiro and Weidmann, 2015; Pierskalla and Hollenbach, 2013), where
mobile phone use has been found to act as a channel to improve policies through pressure or fromsocial participation activities.

33

Table 9: Mechanism: Civic Engagement
(1)

(2)

(3)

(4)

Civic Engagement
Signal strengthNR2
pseudo R2
Estimation Method
Village FE
Year FE
Topography by Year FE
Controls

0.0095∗∗∗(0.0031)1638060.571

0.0076∗∗(0.0033)
1565060.582

0.0070∗∗∗(0.0020)156745

1.93∗∗∗(0.32)
156506

0.313
LPM
Yes
Yes
No
Yes

LPM
Yes
Yes
Yes
Yes

Probit
No
Yes
No
Yes

Flash rate intensity
× time trend

2SLS
Yes
Yes
Yes
Yes
-0.00058***(0.000087)

F
Stock-Yogo Critical Value

44.76
16.38

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent
variable in this estimation is a dummy variable for civic engagement activities at the village
level. Column (3) is the marginal effect for probit regression. In column 4, signal strength isinstrumented by mean annual flash rate density per km2 between 1998 and 2013 at the village
level interacting with a linear time trend. The F statistic for the first stage is the F-statistic of
the Kleibergen-Paap rk Wald test for weak identification. See Table 2 and Table 6 for further
information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Villagers Political Participation. Another mechanism that could explain the impact of the introduction of mobile phones on policies is to see whether they increase villagers’ political participation(e.g. voter turnout). Higher political participation or turnout is considered a good indicator of
improved government performance. To analyse whether signal strength is associated with higher
political participation, I use data from the Indonesian Family Life Survey (IFLS) – a longitudinalsurvey in Indonesia that represents around 83% of the Indonesian population. I use household datafrom IFLS 4 and IFLS 5, which were conducted in 2007 and 2014, respectively. In this data, there
is no information about voter turnout during village elections, but the related information will bewhether the villagers voted during the recent village elections. The dependent variable for thisanalysis will be 1 if the villagers voted for the village head via an election and 0 if otherwise. For
this particular variable, I aggregated the data into the district levels and matched the data basedon the districts to which the village belongs. Table 10 depicts the information for this analysis. I
use district and year fixed effects because the variation that I have here for the dependent variable
is at the level of the district to which the villages belong. I also include control variables in all
specifications and topography by-year fixed effects in column (2).
From the table we can see that higher signal strength has a positive and robust effect on village
elections. The IV-2SLS estimates that higher signal strength is associated with an increase in voting
for the position of village head by 64 percentage points in column (1) where I do not include

34

Table 10: Mechanism: Village Election(1)(2)
Did you vote in the most recent village head election?
(1 = Yes ; 0 = otherwise)N

0.64∗∗∗(0.18)
58420

0.61∗∗∗(0.17)
58420

Estimation Method
District FE
Year FE
Topography by Year FE
Controls

2SLS
Yes
Yes
No
Yes

2SLS
Yes
Yes
Yes
Yes

-0.00029***(0.000076)-0.00029***(0.000076)

14.48
13.84

14.76
13.84

Signal strength

Flash rate intensity
× time trend
F
Stock-Yogo Critival Value* Notes: Robust standard errors in parentheses and clustered at the village levels. The sample in thistable is individual who are living in Desa. The dependent variable is the aggregate variable from whether
the respondent at village v in district d participated in the previous village election. The dependent
variable is from Indonesian Family Life Survey (IFLS) rounds 4 and 5 in 2007 and 2014, respectively.
Signal strength is instrumented by mean annual flash rate density per km2 between 1998 and 2013 at thevillage level interacting with a linear time trend. The F statistic for the first stage is the F-statistic of theKleibergen-Paap rk Wald test for weak identification. See Table 2 and table 6 for further information. ∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

35

topography by-year fixed effects. In column (2), including topography by-year fixed effects leads toa more precise point estimate, where the signal strength coefficient is around 0.61 with the standard
error around 0.17. The first stage relationship between the instrument and signal strength still gives
us a negative and statistically significant relationship. The F-statistics for this table are around 14.76,
thus removing any concern about the potential weakness of the instrument.
This finding supports the previous mechanism where higher mobile phone use was found toincrease village government accountability. The increasing level of political participation suggeststhat mobile phones influence voter turnout because they provide information for villagers. It is also
supports the idea that the ability of villagers to access better information about leader performance
may increase the incentives for the village head to perform well so as to avoid the villagers punishing them in the next set of elections.

Village Government Accountability. The next analysis is to see whether access to mobile phones
will make villagers better informed about their leaders’ performance and ultimately lead to better
village government accountability. I expect that the presence of better signal strength will affect bothvillagers and the village head. For the villagers, it makes them better informed about their leader’sperformance. Better access to mobile phones due to better signals will make it easier for villagers
to monitor their leaders. For village leaders, better signal strength will increase the pressure uponthem to perform well, because they know that their people will be aware if they perform poorly or
implement rent-seeking policies.
To address this question, I use data from the community-facility surveys from IFLS 4 and IFLS 5.
Specifically, I use the information about villagers’ perceptions of the relative quality of governanceand absence of corruption in village government. I aggregate the community-level data into the
sub-district levels so that I have the sub-districts-level average perception for these two variables.
For the variable concerned with villagers’ perceptions of the quality of governance, I use acategorical variable with the value 4 if the respondents fell that compared to the previous survey thevillage governance is much better and 1 if it is much worse. For villagers’ perception of corruption,
the variable will have value 3 if the incidence of corruption becomes higher and 1 if it becomes
lower. Table 11 illustrates the result of this analysis. In this table, I use 2SLS methods and control forvillage and year fixed effects as well as topography by-year fixed effects for all columns. Columns(1) and (2) provide the 2SLS estimates for perception of good governance and columns (3) and (4)depict the 2SLS estimates for perception of corruption.
First, we can infer that signal strength is associated with higher perception of good governance.

36

Table 11: Mechanism: Perception of Good Governance and Corruption
(1)

(2)

Perception of Good Governance
1 = much worst; 4 = much better

(3)

(4)

Perception of Corruption
1 = lower; 3 = higherN

2.55∗∗∗(0.61)
29058

3.77∗∗∗(1.36)
25850-1.88∗∗(0.92)
25104-6.52(4.50)
22206

Estimation Method
Village FE
Year FE
Topography by Year FE
Controls

2SLS
Yes
Yes
Yes
No

2SLS
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
No

2SLS
Yes
Yes
Yes
Yes

-0.00057***(0.00013)-0.00040***(0.00014)-0.00036***(0.00013)-0.00022(0.00014)

19.25
16.38

8.16
16.38

7.15
16.38

2.22
16.38

Signal strength

Flash rate intensity × time trend
F
Stock Yogo Critical Value* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variable in columns (1)and (2) is a categorical variable about villagers perception of community/village government good governance (1 = muchworst and 4 = much better). The question in IFLS would be: "According to your opinion, comparing to [year], how is
the governance in this village/township?". The dependent variable in columns (3) and (4) is a categorical variable about
villagers perception of community/village government corruption. The question in IFLS would be: "Since [year], how was
the incidence of corruption, collusion and nepotism in village government office changed?". The dependent variables arefrom Indonesian Family Life Survey (IFLS) rounds 4 and 5 in 2007 and 2014, respectively. Signal strength is instrumented bymean annual flash rate density per km2 between 1998 and 2013 at the sub-district levels interacting with a linear time trend.
The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald test for weak identification. See Table 2 andTable 6 for further information. The unit observation here is at the village levels. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

In column (1), the point estimate from regressing the dependent on signal strength without any
covariates is around 2.55 and statistically significant at 1%. The result from the first stage in the same
column also suggests that I still have a strong and negative association between signal strength andthe instrument and I can reject the possibility that the first stage is weak. In column (2), by adding
time varying covariates, the point estimate for signal strength becomes relatively higher and still
significant at 1%. However, the F-statistics for the instrument in column (2) are barely below theminimum rule of thumb. Nonetheless, I still have a negative and robust relationship between the
instrument and also signal strength. This is probably because I have a significantly lower numberof observations, which reduces the sample size or power. Even so, we can still infer that higher
mobile phone signal coverage will improve village government accountability.
Moving to villagers’ perception of corruption, I expect that higher signal strength will reducethe incentives to engage in corrupt activities. The IV-2SLS estimation methods suggest that signal
strength will indeed reduce the perception of corruption, and this is statistically significant at 5%in column (3). However the result becomes negative and statistically insignificant once I include
covariates (See column 4). Although the results for villager perception on corruption are not robust
for all specifications, we can still infer that access to mobile phones via better signal strength will
reduce the probability of corruption. The findings for these two variables could be interpreted to37

indicate that village governments will perform better when they are being monitored by villagers.
Again this finding supports the initial hypotheses that mobile phones will improve village government accountability through better governance while making village governments less corrupt.

Household Telecommunication Expenditure. The final testable mechanism will be the effect ofsignal strength on the usage of telecommunication services itself. Intuitively, higher signal strength
will increase the usage of mobile phones and lead to an increase in spending for telecommunication
consumption. However, there is no testable evidence to indicate whether higher usage of mobile
phones will also increase the interaction with village leaders. The only possible evidence for thisis anecdotal. For example, the previous governor of Jakarta provided his personal mobile phone
number so the public could call him when they had issues. He received around 5,000 texts every
week and addressed the issues by himself.21 Another example is during the Obamacare vote in the
US in which voters were encouraged to call their senator during the vote.22 From this anecdotal
evidence we can conjecture that access to mobile phones could easily become a channel to increaseinteraction with policy-makers, thus affecting policies.
Nonetheless, in this analysis I want to see whether people will use mobile phones when they
are living in areas with higher signal strength. I use the data from IFLS 4 and 5 to answer thisquestion, specifically using the household survey data. The dependent variable here is the log
average household telecommunication expenditure at the level of the district to which the village
belongs. Table 12 provides the IV-2SLS estimates for this analysis. All specifications are controlled
by district and year fixed effects and time varying village characteristics. Topography by-year fixed
effects are included in column (2).
The results from Table 12 indicate that households living in areas with higher signal strength
spend more on telecommunication services. The results are positive and statistically significant
at 1% for all different estimation methods. This result confirms that when villages have stronger
coverage, people are incentivised to use mobile phones more than those who live in areas with weak
coverage. The result from the first stage also suggests that we still have a negative and statisticallysignificant relationship between the instrument and signal strength. The F-statistics for the firststage are around 14.12, which suggests there is no weak instrumental variable issue.
I also want to see whether people who are living in rural areas benefited more due to better
signal strength. Previous studies have shown that poor people or people who are living in rural
21 See https://www.thejakartapost.com/news/2014/08/04/jakarta-welcomes-first-ethnic-chinese-governor.
html
22 See https://edition.cnn.com/2017/02/03/politics/congress-phone-calls/index.html

38

Table 12: Mechanism: Effects on Household Telecommunication Expenditure(1)
Log Communication
Expenditure(2)
Log Communication
ExpenditureN

11.5∗∗∗(3.11)
57853

11.2∗∗∗(3.01)
57853

Estimation Method
District FE
Year FE
Topography by Year FE
Controls

2SLS
Yes
Yes
No
Yes

2SLS
Yes
Yes
Yes
Yes

-0.00029***(0.000077)-0.00029***(0.000076)

14.12
13.84

14.25
13.84

Signal strength

Flash rate intensity
× time trend
F
Stock-Yogo Critical Value* Notes: Robust standard errors in parentheses and clustered at the village levels.

The
dependent variable is log average household expenditures for telecommunication at village
v in district d. The dependent variables are from Indonesian Family Life Survey (IFLS)
rounds 4 and 5 in 2007 and 2014, respectively. Signal strength is instrumented by meanannual flash rate density per km2 between 1998 and 2013 at the village level interacted by atime trend. The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald
test for weak identification. See Table 2 and Table 6 for further information. ∗ p < 0.10, ∗∗p < 0.05, ∗∗∗ p < 0.01

39

areas receive more benefit from the introduction of better ICT infrastructures. Table A4 provides theevidence for this analysis. Here, I divide the sample into rural villages (desa) in column (1) and urban
villages (kelurahan) in column (2). I use the IV-2SLS estimation method for the analysis and it showsthat households living in desa with higher signal strength will spend more on telecommunication.
On the other hand, the result for kelurahan is statistically insignificant. This result confirms theprevious studies, which have shown that the introduction of mobile phones will benefit people inrural areas more than people who are living in urban areas. But the insignificant result for kelurahan
could also derive from the small sample that we have for this type of village, which gives us a lessprecise point estimate.

6.1

Mediation Analysis

Following Dippel et al. (2017) and Dippel et al. (2020), I also extend the mechanism exercise bydecomposing the share of the treatment effect from the mediator variables. Table 13 provides theresults for the mediation analysis. Panel A provides the total effect, direct effect, and indirect effectfrom the mediator on the policy or social development indicators in this study. Moreover Panel B
depicts the effect from village elections. Panel C shows the mediator effect from good governance,
and finally Panel D illustrates the effect of communication as the mediator in this study. We findthat the first stage F-statistics and the second stage F-statistics for all mechanisms suggest that ourmechanisms used in this study are able to explain the direct and indirect effects from the signal
strength. As we can see the F-statistics value in all panels are substantially higher than 40.
In general, village rehabilitation for poor people, village libraries and credit for small enterprises
can be explained by all the mediator variables. Although for village libraries, the effect of communication is statistically insignificant. In terms of the size of the mediator effect on our outcomes,
we can see that the indirect effect plays a significant role on the dependent variables. For example,
civic engagement could explain almost 98.9% (≈ 1.281/1.286 ∗ 100; Column 1 panel A) and 99.6%(≈ 1.166/1.179 ∗ 100; Column 2 Panel A) of the impact on the village rehabilitation and village
rehabilitation for poor people. The indirect effects from civic engagement are also statisticallysignificant at the 1% level.
Village election turns to play a significant role in mediating the impact of signal on outcome
variables. The indirect effects, or the effect of village election as the mediator is approximatelyaround 81.7% for village rehabilitation program for the poor (in Column 2 Panel B), 91.4% for village
libraries (in Column 5 Panel B), and 85.5% for credit for small enterprises (in Column 6 Panel B). All
indirect effects are statistically significant at the 1%.

40

In panel C, good governance also influences the impact of signal strength on the outcome
indicators. It has a statistically significant indirect effects in columns (2), (5), and (6), with thepercentage of its effects are around 81%, 90%, and 87%, respectively. Finally in Panel D, spending
for telecommunication slightly explained the impact of mobile phones signal on several outcomes.
It only affects village rehabilitation for poor people and credit for small enterprises, although it isstatistically significant at the 5%.
To sum, all the mechanism proposed in this study plays an important role in explaining theimpact of signal on policies. Although the role of civic engagement and village elections are more
substantial compared to good governance and telecommunication expenditure.

41

Table 13: Mediation Analysis Following Dippel et al. (2017) and Dippel et al. (2020)(1)
Village
Rehab.

(2)
Village Rehab.
for Poor People(5)
VillageLib.

(6)
Credit for
Small Enterp.

1.179***(0.002)(3)(4)
Capacity Capacity Build.
Building for Poor People
Panel A
Civic Engagement-0.082-0.085(0.140)(0.060)Total Effect1.286***(0.0040)0.400***(0.142)0.601***(0.033)

Direct Effect

0.004(0.003)0.012***(0.002)0.014***(0.002)0.002***(0.0008)0.016***(0..001)

0.033***(0.002)

Indirect Effect1.281***(0.306)

1.166***(0.255)-0.097(0.140)-0.088(0.061)0.384**(0.148)0.568***(0.184)

1st Stage F-stat
2nd Stage F-stat
N

225
1891
156745225
1891
156745225
1891
156745225
1891
156745225
1891
156745225
1891
1567450.736***(0.198)0.693***(0.211)

Total Effect

0.339(0.209)1.156***(0.240)Panel B
Village Election
0.404**-0.136*(0.185)(0.075)

Direct Effect

0.007(0.005)0.005(0.004)0.008**(0.004)0.002*(0.001)0.011***(0.003)0.034***(0.004)

Indirect Effect

0.309(0.208)0.945***(0.236)0.410**(0.189)-0.123*(0.073)0.673***(0.206)

0.586***(0.213)

1st Stage F-stat
2nd Stage F-stat
N

225
73
58420

225
73
58420

225
73
58420

225
73
58420

225
73
58420

Total Effect

0.191(0.155)0.870***(0.157)

225
73
58420
Panel CGood Governance
0.204-0.061(0.141)(0.059)0.415***(0.135)0.510***(0.154)

Direct Effect

0.002(0.007)-0.002(0.006)0.0009(0.006)0.003(0.002)0.010*(0.005)0.025***(0.007)

Indirect Effect

0.224(0.156)0.703***(0.162)0.241(0.141)-0.056(0.057)0.375***(0.139)0.444***(0.158)

1st Stage F-stat
2nd Stage F-stat
N

225
125
35681

225
125
35681

225
125
35681

225
125
35681

225
125
35681

225
125
35681

0.149
(0.214)0.869***(0.284)

Panel D
Communication Expenditure
0.112-0.147(0.220)(0.093)

Total Effect

0.436(0.272)0.807***(0.240)

Direct Effect-0.006(0.016)0.032**(0.012)-0.012(0.013)-0.002(0.006)0.045***(0.009)0.063***(0.015)

Indirect Effect

0.439(0.291)0.753**(0.306)0.118(0.222)-0.145(0.100)0.099(0.214)0.808**(0.351)

1st Stage F-stat
2nd Stage F-stat
N

225
47
7793

225
47
7793

225
47
7793

225
47
7793

225
47
7793

225
47
7793

Year FE
Topography by Year FE
Controls

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

* Notes: Robust standard errors in parentheses and clustered at the village levels. Time varying covariates in this table are the same withcovariates used in Table 2. Village FE are included in Panel A and C. District FE are included in Panel B and D. ∗ p < 0.10, ∗∗ p < 0.05,∗∗∗ p < 0.0142

7Conclusions

This study explores the village-level data from three waves of Indonesia’s village census to investigate the role of mobile phones in policy-making. I find that increased signal strength is associatedwith village rehabilitation programs, especially for poor people. I also find that a stronger mobile
phone signal will increase the likelihood of villages having libraries and access to credit for small
enterprises. The results are robust after implementing various estimation strategies.
To acknowledge the potential bias caused by several endogeneity problems, I performed instrumental variable estimations, using lightning strike intensity as the instrument for assessing
signal strength. The results from the 2SLS support the results from the LPM and probit estimation
methods, even though the 2SLS results yield a higher magnitude to the other methods. Nevertheless,the results from the 2SLS reveal that mobile phone signal strength has a causal relationship with avillage leader’s policies.
I also extended the analysis by splitting the sample into rural villages (desa) and urban villages(kelurahan). The results indicate that there is a heterogeneous effect between urban and rural villages.
The results for kelurahan are statistically insignificant. This is likely because there is a significant
difference in terms of the government characteristics between urban and rural villages. Whereas
village leaders in kelurahan are appointed by the district government, and most of them are civil
servants, village leaders in desa are elected through village elections and have greater power thanvillage leaders in kelurahan.
There are several channels that might explain why mobile phones will affect policies. First, I
argue that civic engagement and social participation will increase due to higher signal strength andultimately exert pressure for the leaders to perform well. Second, I show that villages with higher
signal strength will have higher political participation in village elections. Third, village heads invillages with stronger mobile phones signals will be more accountable and less corrupt. Finally,
higher signal strength will increase the usage of mobile phones for the purpose of interacting
directly with leaders.
These findings could be useful to enhance the role of mobile phones in policy-making throughout the world. Some policy recommendations for Indonesia would be to increase access to ICT
infrastructure in the country by expanding ICT availability, especially in remote areas. This can also
be implemented in other countries, as many areas throughout the world still lack investment in ICT
infrastructure. This study also contributes to our understanding of the role of mobile phones onleaders’ performance and policy choices.
Although this study argued that lightning strike poses as a good instrumental variable, but
43issues with the instrument validity need to be acknowledged and become the avenue for futurestudies. Alternative instrumental variable that may isolate the endogeneity problem from theindependent variable may help future studies in this area. Other than alternative instrumental
variables, this study suggests a number of potentially fruitful areas of analysis that could be pursued
in the future. First, using alternative ICT data could potentially enhance the robustness of the results;
for example, data on ICTs before the expansion of ICT infrastructure in Indonesia. Aggregating data
to the district level could also help explain the impact of mobile phones on district governments,
since these governments have a greater responsibility to provide service delivery. Additionally,
information related to the types of interaction between villagers and their leaders would help toidentify the types of communication that could lead to better policies and accountability. Finally,
due to the growth of social media in the past decade, it would be useful to analyse the impact ofdigital media (e.g. smart phones, tablets and government monitoring app) and social media (e.g.
Twitter and Facebook) on policies.

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49

Appendix A

Figures and Tables
Figure A1: Mobile Phone Subscriptions at District Level in 2010

Source: Author’s calculation from Population Census 2010

Figure A2: Mobile Phone Subscriptions at District Level in 2005

Source: Author’s calculation from SUPAS 2005

50

Figure A3: Mobile Phone Signal Strength at Village Level between 2008 and 2014
Panel A: Podes 2014

Panel B: Podes 2011

Panel C: Podes 2008

Source: Author’s calculation from Podes 2014, 2011 and 200851

Table A1: Reduced Form

Flash Rate Intensity
× time trend
N
R2
Estimation Method
Village FE
Year FE
Topography by Year FE
Controls(1)
Village Rehabilitation(2)
Village Rehabilitation
for Poor People(3)
Capacity Building(4)
Capacity Buildingfor Poor People(5)
Village
Libraries(6)
Credit for
Small Enterprises-0.00090***(0.00011)
1567450.138-0.00073***(0.000085)
1567450.042-0.0000062(0.000091)
1567450.013

0.000064(0.000040)
1567450.003-0.00029***(0.000079)
1567450.014-0.00025***(0.000095)
156745
0.006

OLS
Yes
Yes
Yes
Yes

OLS
Yes
Yes
Yes
Yes

OLS
Yes
Yes
Yes
Yes

OLS
Yes
Yes
Yes
Yes

OLS
Yes
Yes
Yes
Yes

OLS
Yes
Yes
Yes
Yes

* Notes: Robust standard errors in parentheses and clustered at the village levels. The years included in the regressions are 2008, 2011 and 2014. See Table 2 and Table 6 for further

information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table A2: Robustness: Alternative Explanatory Variable(1)
Village Rehabilitation(2)
Village Rehabilitation
for Poor People(3)
Capacity Building(4)
Capacity Buildingfor Poor People(5)
Village
Libraries(6)
Credit for
Small Enterprises

N

0.54∗∗∗(0.074)1565060.45∗∗∗(0.056)1565060.015(0.053)156506-0.034(0.023)1565060.15∗∗∗(0.047)1565060.13∗∗(0.055)156506

Estimation Method
Village FE
Year FE
Topography by Year FE
Controls

2SLS
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes

-0.0017***(0.00011)-0.0017***(0.00011)-0.0017***(0.00011)-0.0017***(0.00011)-0.0017***(0.00011)-0.0017***(0.00011)

238.27238.27238.27238.27238.27238.27

Categorical signal

Flash rate intensity
× time trendF* Notes: Robust standard errors in parentheses and clustered at the village levels. The years included in the regressions are 2008, 2011 and 2014. Categorical signal strength equals to 2

if signal strength is strong, 1 if signal strength is weak and 0 if there is no signal. Categorical signal strength is instrumented by mean annual flash rate density per km2 between 1998
and 2013 at the village level interacted by a time trend. The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald test for weak identification. See Table 2 andTable 6 for further information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

52

Table A3: Effects on Land Conversion(1)(2)
Land Conversion
from Agri. to Non-agri. LandN
R2

0.012∗∗∗(0.0034)
1565060.493

1.06∗∗∗(0.23)
156506

Estimation Method
Village FE
Year FE
Topography by Year FE
Controls

LPM
Yes
Yes
Yes
Yes

2SLS
Yes
Yes
Yes
Yes

Signal strength

Flash rate intensity × time trend-0.00058***(0.000087)

F

44.76

* Notes: Robust standard errors in parentheses and clustered at the village levels.

The dependent variable is dummy for land conversion from agricultural land to
non-agricultural land. Signal strength is instrumented by mean annual flash ratedensity per km2 between 1998 and 2013 at the village level interacted by a timetrend. The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk
Wald test for weak identification. See Table 2 and Table 6 for further information. ∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table A4: Effects on Household Telecommunication Expenditure: Desa versus Kelurahan(1)(2)
Log Communication ExpenditureN

8.71∗∗∗(2.74)
49681-444.5(3361.8)
8140

Estimation Method
District FE
Year FE
Topography by Year FE
Controls
Sample

2SLS
Yes
Yes
Yes
Yes
Desa

2SLS
Yes
Yes
Yes
Yes
Kelurahan-0.00030***(0.000091)

0.000013(0.000098)

10.63

0.017

Signal strength

Flash rate intensity × time trendF* Notes: Robust standard errors in parentheses and clustered at the village levels. The

dependent variable is log average household expenditures for telecommunication atvillage v in district d. The dependent variables are from Indonesian Family Life Survey(IFLS) rounds 4 and 5 in 2007 and 2014, respectively. Signal strength is instrumented
by mean annual flash rate density per km2 between 1998 and 2013 at the village levelinteracted by a time trend. The F statistic for the first stage is the F-statistic of theKleibergen-Paap rk Wald test for weak identification. See Table 2 and Table 6 for further
information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

53

Table A5: Additional Results: Random Number < 0.5 versus Random Number > 0.5(1)

Signal strengthN
Estimation method
Sub-district FE
Year FE
Topography by Year FE
Controls
SampleFirst stage
Flash rate intensity
× time trend
F

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)
Village
Libraries

(11)

(12)

Village Rehabilitation

Village Rehabilitation
for Poor People

Capacity Building

Capacity Buildingfor Poor People

Credit for
Small Enterprises

1.70∗∗∗(0.58)
78231

1.37∗∗(0.59)
78392

1.59∗∗∗(0.49)
78231

1.18∗∗(0.46)
78392-0.15(0.25)
78231

0.066(0.29)
78392-0.057(0.11)
78231-0.20(0.13)
78392

0.61∗∗(0.28)
78231

0.28(0.28)
78392

0.55∗(0.30)
78231

0.72∗(0.38)
78392

2SLS
Yes
Yes
Yes
Yes
Lower Half

2SLS
Yes
Yes
Yes
Yes
Upper Half

2SLS
Yes
Yes
Yes
Yes
Lower Half

2SLS
Yes
Yes
Yes
Yes
Upper Half

2SLS
Yes
Yes
Yes
Yes
Lower Half

2SLS
Yes
Yes
Yes
Yes
Upper Half

2SLS
Yes
Yes
Yes
Yes
Lower Half

2SLS
Yes
Yes
Yes
Yes
Upper Half

2SLS
Yes
Yes
Yes
Yes
Lower Half

2SLS
Yes
Yes
Yes
Yes
Upper Half

2SLS
Yes
Yes
Yes
Yes
Lower Half

2SLS
Yes
Yes
Yes
Yes
Upper Half

-0.000344***(0.000096)-0.000291***(0.000097)-0.000344***(0.000096)-0.000291***(0.000097)-0.000344***(0.000096)-0.000291***(0.000097)-0.000344***(0.000096)-0.000291***(0.000097)-0.000344***(0.000096)-0.000291***(0.000097)-0.000344***(0.000096)-0.000291***(0.000097)

12.8

8.94

12.8

8.94

12.8

8.94

12.8

8.94

12.8

8.94

12.8

8.94

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variables in this estimation are dummy variable for village rehabilitation program at the village level (columns 1 - 2), dummy variable for village

rehabilitation programs where the beneficiaries are only poor people (columns 3 - 4) dummy variable for village capacity building program at the village level (columns 5 - 6); dummy variable for village capacity building programs where thebeneficiaries are only poor people (columns 7 - 8); dummy variable for village libraries (columns 9 - 10) and dummy variable for credit for small enterprises (columns 11 - 12). The F statistic for the first stage is the F-statistic of the Kleibergen-Paap
rk Wald test for weak identification. See Table 2 and Table 6 for further information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table A6: Additional Results: Strong versus Weak Signal(1)
Village Rehabilitation
Strong signal strength

0.027∗∗∗(0.0070)(2)
Village Rehabilitation
for Poor People
0.039∗∗∗(0.0050)(3)
Capacity Building-0.0088∗∗(0.0043)(4)
Capacity Buildingfor Poor People-0.00069(0.0019)(5)
Village
Libraries
0.0023(0.0033)(6)
Credit for
Small Enterprises
0.031∗∗∗(0.0042)

Weak strengthNR20.025∗∗∗(0.0067)1565060.480

0.030∗∗∗(0.0048)
1565060.403-0.013∗∗∗(0.0039)1565060.445-0.00085(0.0017)1565060.368-0.0047(0.0030)1565060.512

0.018∗∗∗(0.0038)
1565060.501

Estimation method
Village FE
Year FE
District by Year FE
Topography by Year FE
Controls

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

LPM
Yes
Yes
Yes
Yes
Yes

* Notes: Robust standard errors in parentheses and clustered at the village levels. The years included in the regressions are 2008, 2011 and 2014. Strong signal strength will be equal to
1 if signal strength is strong and 0 if otherwise. No signal strength will be equal to 1 if there is no signal and 0 if otherwise. Time varying covariates in this table are the same withcovariates used in Table 2. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

54

Appendix B

Not for Publication
Table B1: Additional Results: Java versus Non-Java
(1)

(2)

Village Rehabilitation
Signal strengthN
Estimation method
Sub-district FE
Year FE
Topography by Year FE
Controls
SampleFirst stage
Flash rate intensity
× time trend
F

(3)

(4)

Village Rehabilitation
for Poor People

(5)

(6)

(7)

(8)

Capacity Building

Capacity Buildingfor Poor People

(9)

(10)
Village
Libraries

(11)

(12)

Credit for
Small Enterprises-0.069(0.71)
43233

31.7(112.9)1135121.36(0.86)
43233

6.67(23.8)
1135124.12∗(2.20)
43233

3.33(12.0)
1135120.43(0.35)
43233-0.93(3.43)
113512

1.69(1.05)
43233-0.93(4.05)
113512

2.76∗(1.54)
43233-0.50(3.13)
113512

2SLS
Yes
Yes
Yes
Yes
Java

2SLS
Yes
Yes
Yes
Yes
Non-Java

2SLS
Yes
Yes
Yes
Yes
Java

2SLS
Yes
Yes
Yes
Yes
Non-Java

2SLS
Yes
Yes
Yes
Yes
Java

2SLS
Yes
Yes
Yes
Yes
Non-Java

2SLS
Yes
Yes
Yes
Yes
Java

2SLS
Yes
Yes
Yes
Yes
Non-Java

2SLS
Yes
Yes
Yes
Yes
Java

2SLS
Yes
Yes
Yes
Yes
Non-Java

2SLS
Yes
Yes
Yes
Yes
Java

2SLS
Yes
Yes
Yes
Yes
Non-Java-0.000187*(0.000096)-0.000027(0.000095)-0.000187*(0.000096)-0.000027(0.000095)-0.000187*(0.000096)-0.000027(0.000095)-0.000187*(0.000096)-0.000027(0.000095)-0.000187*(0.000096)-0.000027(0.000095)-0.000187*(0.000096)-0.000027(0.000095)

3.81

0.08

3.81

0.08

3.81

0.08

3.81

0.08

3.81

0.08

3.81

0.08

* Notes: Robust standard errors in parentheses and clustered at the village levels. The dependent variables in this estimation are dummy variable for village rehabilitation program at the village level (columns 1 - 2),

dummy variable for village rehabilitation programs where the beneficiaries are only poor people (columns 3 - 4) dummy variable for village capacity building program at the village level (columns 5 - 6); dummy variable
for village capacity building programs where the beneficiaries are only poor people (columns 7 - 8); dummy variable for village libraries (columns 9 - 10) and dummy variable for credit for small enterprises (columns 11 12). The F statistic for the first stage is the F-statistic of the Kleibergen-Paap rk Wald test for weak identification. See Table 2 and Table 6 for further information. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.0155