# Towards Secure IoT Communication with Smart Contracts in a Blockchain
Infrastructure

Jawad Ali  Malaysian Insitute of
Information Technology,
Universiti Kuala Lumpur,
Malaysia
<EMAIL_ADDRESS>Toqeer Ali  Faculty of Computer
& Information System
Islamic University of Madinah
<EMAIL_ADDRESS>Shahrulniza Musa  Malaysian Insitute of
Information Technology,
Universiti Kuala Lumpur,
Kuala Lumpur
Malaysia
<EMAIL_ADDRESS>Ali Zahrani  Faculty of Computer
& Information System
Islamic University of Madinah
<EMAIL_ADDRESS>
###### Abstract

The Internet of Things (IoT) is undergoing rapid growth in the IT industry,
but, it continues to be associated with several security and privacy concerns
as a result of its massive scale, decentralised topology, and resource-
constrained devices. Blockchain (BC), a distributed ledger technology used in
cryptocurrency has attracted significant attention in the realm of IoT
security and privacy. However, adopting BC to IoT is not straightforward in
most cases, due to overheads and delays caused by BC operations. In this
paper, we apply a BC technology known as Hyperledgder Fabric, to an IoT
network. This technology introduces an execute-order technique for
transactions that separates the transaction execution from consensus,
resulting in increased efficiency. We demonstrate that our proposed IoT-BC
architecture is sufficiently secure with regard to fundamental security goals
i.e., confidentiality, integrity, and availability. Finally, the simulation
results are highlighted that shows the performance overheads associated with
our approach are as minimal as those associated with the Hyperledger Fabric
framework and negligible in terms of security and privacy.

###### Index Terms:

IoT, Blockchain Authorization, Hyperledger Fabric, BC, Blockchain Integrity.

##  I Introduction

The Internet of Things is undergoing exponential growth as everything is
increasingly connected via the internet. According to the Gartner research
report that predicts the future of IoT, there were almost 7 billion devices
connected via smart technology in 2017, and this is set to approach 20 billion
by 2020 [1]. Some areas that apply this technology in daily life include
automatic vehicles, home appliances, smart grid stations, health care
applications, the retail sector, industrial supply chains and logistics
management, security and surveillance, transportation and general industrial
control systems [2] [3]. These all comprise a smart infrastructure supported
by heterogeneous entities including web servers, end users, smartphones and
cloud resources, that function with and without human interaction, selecting
and providing information to the end users.

Millions of sensors and actuators are used in smart-environments to control
and monitor lighting and heating systems, elevators, and cameras. Apart from
this, Industrial control systems composed of numerous sub-systems, such as
Supervisory control and data acquisition (SCADA), distributed control systems
(DCS), other smaller systems, including programmable logic controllers (PLCs),
remote terminal units (RTUs), and others involved in running an industrial
operation [4]. These systems are composed of various interconnected sensing
devices, and their heterogeneous nature and swiftly developing technology
results in security and privacy risks that pose a major challenge to the IoT
community.

With the widespread adoption and advancement of this technology, IoT devices
face numerous security problems in terms of hardware, software and network
communications. Increased communication between IoT devices involves large
amounts of critical data and privacy-sensitive information that are
increasingly vulnerable: a recent DDoS attack known as _mirai_ [5] affected
millions of IoT devices. Several approaches have been proposed to optimise
security and privacy [6][7] [8], but due to the IoT’s rapid expansion at a
massive scale, no consensus has been reached regarding the optimal solution,
and several questions remain.

In IoT systems, the majority of communication between devices is facilitated
through client/server architecture or centralised architecture, through which
authentication and identification, and other security measures, are processed
by a central authority. The first major issue in these centralised systems,
where everyone is reliant on a central authority, is the possibility of a
single point of failure. Second, all connected devices must communicate via
the internet regardless of the distance between them, thus leading to command
processing overhead. Additionally, the current approach to IoT security
involves high maintenance costs in terms of central cloud servers and other
network equipment. In short, to migrate the current IoT centralised
architecture to a decentralised approach some fundamental capabilities are
required:

  * •

peer-to-peer communication,

  * •

distributed file sharing

  * •

autonomous device communication, and

  * •

efficiency and security.

The above requirements are conveniently available in a newly introduced
Blockchain (BC) technology known as Hyperledger Fabric [9]. BC is a shared and
replicated ledger that offers immutability, consensus, and finality. Although
there are several other BC technologies, BC such as that adopted by Bitcoin,
but Fabric reduces computation cycles and provides scalability, identity
management, and privacy, which is of the utmost importance in the IoT
paradigm. The cryptographic algorithms used in BC ensure superior security and
privacy for device data and IoT is increasingly adopting BC technology. As far
as BC’s applicability to IoT is concerned, it records the transaction
histories of smart devices in an immutable manner, thus eliminating the need
for a central authority. For messaging exchange between IoT-devices a smart
contract is used for successful agreement between all parties. The most
promising feature of BC, with regard to its application in IoT, is the
possibility of maintaining a transaction record across all devices, thus and
thus make a distributed and trustless ledger.

Generally, all currently adopted approaches are highly centralised and thus
unable to manage the IoT environment at the current scale. BC technology has
the potential to overcome the scalability and security issues associated with
IoT through decentralisation. This paper’s contribution is twofold: To
implement Hyperledger Fabric in a smart-IoT environment to assess the validity
of the communicating devices whether normal or malicious i.e., to assure users
of the integrity of the data from a particular device. Another important issue
is that the IoT network is growing very rapidly as predicted, and cannot, as
such, be efficiently and securely managed by the current centralised
mechanisms. In this study, we implemented IoT-based architecture in tandem
with BC (Hyperledger Fabric). We tested our scheme in a smart home-based
scenario, however, it will be applicable in other contexts, including smart
cities and smart industries.

The remainder of this paper is organized as follows: Section II presents some
background information concerning BC, smart contracts and Hyperledger Fabric.
In section III, we detail the need for security in IoT with a review of the
literature. Section IV presents our proposed architecture in detail. Section V
includes the experiment results and some discussion of the findings. Finally,
in section VI we conclude and propose potential future directions.

##  II Background

###  II-A Block-Chain

Blockchain (BC) is initially adopted by a very well-known crypto-currencies
called bitcoin [10]. BC record the transactions across many computers and
store in a decentralised way and thus form a immutable digital distributed
ledger. This ledger is distributed among all the nodes in the network and
every one have the copy of all the transactions record. Blockchain is composed
of two kinds of record: blocks & transactions. Every transaction is recorded
in the form of blocks and these blocks are then organized into a linear
sequences and form a merkle tree. Every block in BC contains a hash of the
previous block. New transactions are initially process by _miners_ which are
further append to end of the chain and cannot be modified or remove by anyone,
once accepted. Initially the BC is started with the first block called genesis
or initial block. After then each transaction is validated stored in the form
of block. Every block contains the hash to the preceding block and changes to
the previous block would produced different hash-code and thus visible to all
member in BC immediately. Therefore, the block-chains are considered to be
tamper-proof ledger. Figure 1 shows a sample block chain.

Figure 1: A sample Blockchain

####  II-A1 BlockChain and IOT

Authors in [11] put efforts on the case of expanding IoT-devices to a
decentralised way in order to be sustainable. From manufacturers perspective,
current centralised mechanisms needed a high maintenance cost in case of
distribution of software updates to a billion number of nodes in IOT. On the
consumer’s end, trust on devices is merely a big challenge where someone needs
a transparent security. In short, these issues cannot be tackle without a
trust-less and peer-to-peer architecture that can distribute data in a secure
and transparent way. In [12], authors argue that in current arising security
problems with IOT, BlockChain provides a better an elegant solution.

###  II-B Smart Contracts

Smart Contracts [13] also called crypto-contract are a computer program that
are used to transfer/monitor assets or digital currencies among parties under
certain rules. It does not only determine the conditions and penalties but can
also enforce those policies/agreements. These smart contracts are stored on
block-chains and BC is an ideal technology to store these contracts because of
its immutability and security. Whenever a transaction is suppose to happen,
smart-contract determine where the transaction should be transfer/returned or
from where the transaction was actually originated.

###  II-C Hyper-Ledger Fabric

Hyper-ledger fabric [9] is recently proposed by IBM team and considers it as
an open-source blockchain platform and a truly scalable system for distributed
applications running on a large-scale network. The main reasons behind this
proposal is to cope with the limitations found in the other permissioned
BlockChain architectures. Unlike all the other BC technologies that rely on
order-execute paradigm while doing some kind of transactions, _fabric_
introduces execute-order paradigm where transactions executed at the first
stage before reaching to their final order. Some of limitations that fabric
overcome are as follows:

  * •

Performance overhead: The order-execute method is widely acceptable because of
its simplicity, but it execute transactions sequentially on all peers in the
network which may cause a performance overhead. A smart-contract with infinite
loop may launch a DoS attack, which can severely degrade the performance on
blockchain. This issue is resolve with fabric by execute the transaction first
and then order the transaction over the BC. One of important benefit of
execution at first is to be efficient and avoid DOS attack by denying the
unvalidated transactions before ordering.

  * •

Confidentiality: As discussed earlier that the design of other BC technologies
runs the smart contracts on all the peers. However, in many scenarios of
permissioned BCs, requires _confidentiality_ i.e. to restrict the ledger state
or transaction data. In Fabric the execution of smart-contract restricted to a
number of trusted peers (endorsers) that give assurance to the outcomes of
execution.

The transactions flow in Fabric comprises three different phases: Execution,
ordering and validation.

  * •

Execution Phase: In this first phase, the node in BC signs and send the
transaction-proposal for execution to endorser(s) (designated peers). The
information of endorsers are defined in the endorsement policy already. The
endorsers is suppose to simulate the transaction with respect to the
operations specified on the chaincode. After this simulation, each endorsers
generate two values i.e. _writeset_ value that contains the state-update
information and _readset_ defining the version of transaction-proposal
simulation. Finally, the endorser(s) signs and send the message now called
_endorsement(s)_ that composed of readset and writeset, to the client. The
client receive the message and wait until they satisfy the chain-code
endorsement policy. In particular, all the endorser(s) need to produce the
same execution result. After this, the client may proceed to make a
transaction and pass it to the ordering phase.

  * •

Ordering Phase: Upon adequate endorsement on transaction-proposal, client
assembles the _transaction_ and send it to the ordering service. This
transaction consists of all the parameters including chain-code information,
transaction metadata and set of endorsements. The ordering service organize
total order of transactions per channel (sub-network) that has been submitted.
Furthermore, the ordering service combine multiple transaction and arrange it
into _blocks_ and form a blocks that improves the overall throughput of
broadcast protocol. As it is known that there are a huge numbers of peers in
BC, only few of them are supposed to implement ordering service. Finally, the
ordering service also include the access control policies to check the clients
whether it can receive block or not.

  * •

Validation Phase: When the ordering service sends the block to the peer, it
will enter to validation phase for further checking that is done by three
sequential steps. (1) Endorsement policy evaluation occurs, if it is not
satisfied then the transaction disregarded and marked it as invalid. (2) To
check read-write conflict by comparing the key version to the current ledger
state if it is not matched then marked invalid and disregarded. (3) Finally,
the ledger update phase run and append the block to the block-chain.

##  III Security in IoT

In the past decade, Internet of Things provides an automation and smartness in
our daily lives. Due to the rapid advancement and deployment of IoT in the
current infrastructure leads to many security and privacy requirements.
S.Sicari et al. [14] outline the current problems in the existing IoT and
analyze to the most relevant solutions. The key security requirements are
confidentiality, authentication and access control. The author discussed
various scheme of confidentiality and authentication in context of Wireless
sensors networks (WSN), but because of heterogeneity and low power consumption
in IoT several questions arises that are:

  * •

Are the WSN easily adaptable to IoT with it heterogeneous nature of devices
and different applications.

  * •

Which communication layer is responsible for authentication process.

  * •

How to ensure the integrity and privacy of end-to-end devices in order to
prevent from malicious attacks.

Furthermore, access control that deals with user-access policies and usage
control in the wide area network such as RBAC, DAC, MAC and their extensions.
In context of IoT we needed to deal with streaming of data, Unlike traditional
DBMS, where we deal with the discrete form of data. The problem arises here is
the that the access control mechanism on data stream need more computation and
hence lead to performance issue. Several solutions related to access control
with data stream are discussed by the author and found the main challenges
lying in it. Some issues are:

  * •

How to ensure that the things could be recognized by the system where user is
not available.

  * •

How to manage with large volume of data stream in a standard recognized way.

###  III-A Privacy & Trust in IoT

Keeping in mind the versatility of IoT in various areas that are: Smart-Homes,
Smart-Cities, Smart-Health care, Traffic Control, smart parking system and so
on. For all these scenarios, everyone need their protections for his personal
information related to their movement from one place to another and
communication with different other peoples. Author in [15] investigated a
survey study on all approaches in context of privacy in IoT and find a wide
gap of research that need to be addressed before deployment in the current IoT
paradigm. The next important factor of security is satisfaction of trust
between parties while communication. This satisfaction is basically reflected
to the issues of identity management and access control mechanisms. In
addition, they put an overview of trust management in IoT and discuss some
past researches related to it. But all of them lacks a fully dynamic and
distributed approach that can be suitable for the upcoming scale of IoT.
Furthermore, most important missing item and a great challenge is a well-
definition of trust agreement which needs to be a common language.

###  III-B Literature study

David Airehrour [16] did a thorough study on the security and privacy while
routing in internet of things. On security in IOT they find out the privacy
preservation as a key issue because of heterogeneity in devices over the
network. Due to limited battery capacity in most of IOT devices, several
devices are not capable to end with the complete process of cryptography and
authentication and argued for a robust secure authentication system. In
general they classified the threats into two main groups:

  1. 1.

_General Security threats in IoT network_ : It includes the traditional
threats present in network such as DDOS, Man-in-the-middle attack etc.
However, considering the scalability and massiveness of IoT networks along
with their complexities & heterogeneity, these traditional system attacks
become more bigger challenge in IOT environment.

  2. 2.

_Threats specific to IoT security:_ These threats are related to the
interconnection between IOT devices. For example sensitive data & record
retrieving of a heart or brain patient, smart meter reading and forest-
reading. Reading of such sensitive data may be compromised while communicating
over the IoT network or may be results in malicious transmission to other
nodes for misuse.

Furthermore, this survey study highlighted the routing protocols i.e. (6LowPAN
and RPL) and their lack-ness in regards of security and privacy. Finally, the
author deliver some key considerations for the researchers in order to design
secure routing protocols for IOT. Some of recommendations are: secure routing
establishment, self-stabilization, location privacy and so on. In conclusion
this whole study has revealed that the current routing mechanisms and its
standards are insecure for IoT.

Yuichi Kawamoto et al. [17] puts his efforts on location based authentication
scheme in IoT. In this proposed architecture, they used ambient information
for uniqueness by collecting the data from IOT-nodes at certain place & time.
In his case the ambient information doesn’t bound to use the key-elements such
as SSID (service set identifier) and RSS (received signal strength) for the
freshness of data. Such kind of authentication methods are more useful in
scenarios like confidential information floating in military area or other
secret meetings which is limited to some users. The important factor in this
work is the unique information collected from different nodes that is further
used to authenticate the devices over network. Thus, it is needed to collect
real-time data as much as possible from many points in order to accomplish an
accuracy in authentication. The two metric for authentication are as follows:

  * •

_Freshness of Data_ : As it is known that the data and location of nodes is
continuously changes at interval of time, thus it is need to collect the real-
time-data in order to ensure the accuracy.

  * •

_Density of data collection_ : The amount of data collected from huge number
of point will lead to accuracy in the system. However, such gathering of
information from massive points could decrease the real-time performance of
system.

To uncover the optimal solutions for accuracy and efficiency, it is needed to
evaluate both these metrics for different occasions. The main limitations and
open research holes of this work are: (a) Real-time performance degradation in
case of data collection for huge number of nodes. (b) More characteristics
features added to ambient information could resolve the real-time collection
as well as accuracy.

Qinlong et al. [18] addressing the issues of security and privacy in Fog
computing. Basically, fog computing access the benefits of both the cloud and
IoT paradigms. Similarly the security concerns (confidentiality & access
control) with this kind of computing is also similar to Cloud and IoT. This
proposed work is based on ABE-attribute based encryption and ABS (attribute
based signature) which are the types of cryptographic techniques. ABE process
features an access control policies over vast majority of attributes for user
to perform decryption that can provide confidentiality and access control of
data.

Current ABE-based solutions cannot have the capacity to authenticate custom
(some) users to update the encrypted data. Thus, to update the cipher-data one
should prove his validity by public key management to cloud service provider
(CSP). However, it would create much burden on CSP in order to maintain a key-
list for identification. To cope with this issue, ABS (attribute based
signature) is used to provide help to CSP in regards of user validity. For the
above reasons, the authors uses the combination of ABE & ABS for fine-grained
access control in Fog computing paradigm. In the first step, user data is
encrypted with access and update policies and then fused the data to cloud
servers via fog nodes. Afterwards, those users whose attributes satisfy the
required access policies are capable to decrypt the encrypted data. For this
purpose, CSP is designated to check the signature in order to ensure the data
integrity by verifying the user policies. The contribution of this research in
terms of security analysis are to ensure Data confidentiality, to provide
authentication, fine-grained access control mechanism and collusion
resistance.

Michael et al. [19] proposed a platform for transactive IoT blockchain
applications with repeatable testing (PlaTIBART) that combines actor patterns
with custom DSL (domain specific language) and test network management tool.
DSL defines the roles that different clients in our network have, based on the
actor pattern. The advantage of DSL model is to implement a correct-by-
construction design means, that allows the verification stage on the model, to
check the internal consistency before any deployment is attempted e.g to
prevent inconsistencies: two clients requesting the same port on the same
host.

PLaTIBART uses Ethereum as its BC implementation, i.e DSL has Ethereum-
specific required setting such as ChainID and GasLimit etc. Future
implementation will be refactor these requirements on other BC platforms, i.e.
Hyperledger, formal Verification of internal-consistency of a configuration
file and a means of defining incremental adjustments to a test network by DSL.

IoT in context of security and privacy is a very challenging because of its
low constraints and resources capabilities of the heterogeneity among them.
Moreover, these devices retrieve, store and share huge amounts of data from
our personal (smart-home) to industry level, and thus attracting the community
to a significant privacy concern. In [20] the authors declares different zones
for privacy in order to classify diverse kinds of data. Each of the zone is
associated with context based policy checking technique, that is checked by
Home-Security-Hub before accepting to join or re-join data from device.
However, they do not consider the case of bypassing the hub and access the
device directly.

A research efforts proposed in [21] that relies of safe answers or
aggregations of data in which the user can send only a small data as much
possible to the third party or service provider. For ensuring privacy their
technique add noise to the data in the smart-home environment, but this noisy
data could lead to inaccurate and harmful services.

A study in [22] have been done regarding that the house-hold devices are more
prone to attacks by users devices such as smartphones. As per perception that
the router or gateway should offer security perimeter that can prevent from
internet attacks. Authors shows a demonstration on attacks that penetrate the
smart home network by a smart-phone applications. Such kind of attacks could
be able to modifies firewall and allow the external user to directly attack
the smart-device. Thus, it is concluded that routers and firewall at the home-
end are consider as poor protection against internet attacks and figure out
the need of extra security features on IoT devices.

Currently the CSIRO team proposed a new approach of integrating Blockchain
with IoT [23]. In his initial efforts they use smart-home technology in order
to realize that how blockchain can be deployed to IoT. The blockchain is
specifically use to provide an access control mechanisms of smart-devices
transactions located at smart-home. This research provides some extra security
features by introducing BC technology in IoT, however it lacks the concept of
consensus algorithm that every mainstream BC technology must have. Moreover,
this technique cannot provide a generalize form of Block-Chain solution to IoT
use cases.

In the light of above discussion, there is no technique that leverage the
standard Block Chain implementation for scale-IOT environment. We argue that
this research is the first step towards a generalize BC solution for the
emerging IoT area. For better understanding we consider smart-home at this
time as a use-case. In the next section, we will discuss our proposed IoT-
Fabric architecture.

##  IV Proposed Solution

In the current literature of IoT security and privacy several kinds of
security and privacy mechanisms have been proposed. All of these mechanisms
rely on centralised approach so-called client/server paradigm. Using this
approach, all the devices in IoT network are identified and authenticated
through a central point or server. But this centralised way supports today
small-scale IoT network and will not capable to respond in the growing IoT
system in the near future. Due to this rapid growth in IoT, existing solutions
are very expensive in terms of high maintenance cost and infrastructure
related to central cloud servers and other networking equipments.

Decentralised mechanisms in IoT infrastructure would remove several issues
discussed above i.e. allowing peer-to-peer communications between IoT nodes
will significantly reduce the cost of installation and maintenance. It will
also distribute the storage and computation load over the entire IoT network
that can achieve efficiency and prevention from a single point of failure. We
realize and implement Hyper-ledger Fabric: A Block-Chain technology in our
proposed solution. The benefit of adopting BC in our research is three-fold:
Decentralised or distributed, Permission-based and all-secure. In this paper,
we use smart-phone case study in order to exemplify our proposed architecture.
However it is pluggable and well-suited in other IoT case scenarios.

###  IV-A Hyper-Ledger Fabric based IoT architecture

As discussed above that we consider a smart-home based scenario for better
understanding. In a typical smart-home, user is connected to certain number of
IoT devices i.e. IP camera, thermo-state, smart-phone, smart-bulb and several
other sensors. The architecture shown in Figure 2 includes the components
namely the smart-home, Hyper-ledger fabric interface and Block-chain Peers /
Orderers. Smart-homes has equipped with a number of different smart-IoT
devices. Every device can share, store and update their transaction data. The
Fabric interface is used to provide distributed ledger where each transaction
from IoT devices is store in the sequence and thus output in Blocks. Smart-
contracts defines the policies where every transaction is checked against a
set of pre-defined policies. Finally, the consensus algorithm make an arc over
the entire transactional flow, that can provide the generation of agreements
on the order and to verify the correctness of blocks.

####  IV-A1 Initialization

Initialization of the network is the first step that needs before the
transactions are made. All the smart-devices in smart-home need to store the
device information in smart-contract. Information in smart-contract might
consist of every device information and its endorsement policies according to
our defined policy structure in application interface.

####  IV-A2 Handling Transaction in Smart-home

In a smart-IoT environment, communication between devices is either directly
or with other external resources i.e. cloud. Each smart-device requests some
other device that can serve some services e.g. if a smart-bulb needs data from
the motion sensor to start automatically in case if someone get to entrance of
home. For such direct communication between devices a shared-key is used, in
order to achieve user control over every transactions inside smart-home.
Smart-contract has list of all devices in a particular environment that can
share data by using secure shared key.

####  IV-A3 Transactional Flow in Fabric

A _Hyperledger-fabric_ consists of nodes or peers that constitute the network
of Blockchain. As fabric architecture is permissioned, so every node or device
participate using their identity provided by membership service provider
(MSP). The flow of transactions in fabric are complete in three different
phases i.e. execution, ordering and validation. In execution phase, a client
(smart-home device) send a signed transaction proposal to one or more
endorsers in the network. The endorsers (cf. Fig 2)are specific peers that are
defined in our smart-contract / chain-code via endorsement policy. After
execution and get enough endorsements on proposal, client then assembles the
transaction and submit to the ordering phase where all the transactions are
properly placed in order. Moreover, the ordering phase also turned multiple
transactions into blocks and also ensure that blocks on one channel are
correctly ordered. Finally, the validation phase checks the validity of
transaction sequentially as discussed in background section.

In our proposed architecture illustrated in Figure 2, we deploy smart-home
based scenario on the Fabric architecture. The benefit of adopting Fabric is
that, it totally separate consensus procedure from execution and validation
which we need for efficient and scalable IoT environment. The transaction
proposal begins from the smart-device to fabric interface where it first
passed through our defined endorsement policy in smart-contract develop in Go
language. The endorser peers recognizes and give decision on the transaction
from a particular device and proceed for making transactions i.e store, access
and monitor data. In the next step, as we discussed earlier that the ordering
phase automatically broadcasts the endorsements in order to establish
consensus and is responsible to make all the transactions in an orderly
fashion and finally formed the shapes of Blocks. Finally validation phase runs
and checks certain evaluation to prove that the transaction is valid or
invalid.

Considers a smart-home based transaction (Figure 2) where a device send
transaction data for storage on Blockchain e.g. _store_ transaction. The
device first needs to prove his identity by matching the information defined
in smart-contract. By receiving the device request or proposal the smart-
contract identify the device and proceed for further procedure. After the
device authenticated, it may send the data i.e. temperature data, along with
the previous parameters (ID) for placement the transactions in order and store
them to hash chain-sequence of block. The other possible types of transactions
are _monitor_ and _access_ transactions. Such transactions are commonly
initiated by user or simply smart-home owner in this case where he is far and
need to access / monitor some data.

Furthermore, _channel_ as depicted in 2 is used to connect two different zones
i.e more than one smart-home. However, at this time we are focusing on single
home and multiple case scenario are out of scope in this research. In
addition, we integrate our proposed architecture with an open-source
distributed ledger known as IOTA [24], that is specifically designed to power
the upcoming future of IoT. It is known that per-missioned BC’s like the one
we used as a private BC for IoT network is although efficient but in a limited
nodes. As the nodes increases in the IoT network the permissioned model of
BC’s degrades performance and hardly to scale-up in environment. As it is
known that _fabric_ is pluggable architecture for in regards of consensus
algorithms. For this purpose, the proposed architecture in this research is
easy to integrate with DAG (Directed Acyclic Graph) for acquiring both side
benefits i.e. Fabric for Efficiency & DAG for scalability. DAG is recently
adopted in IOTA a public BC having no mining fee, designed for IoT network.
For more details regarding IOTA & DAG we refer the reader to [25].

Figure 2: Proposed IoT Block Chain Architecture

##  V Results and Discussion

In this section, we detail our analysis on security, privacy and performance
overhead in the smart-home use case. Before going to results and analysis
section we discuss some reasons behind choosing Fabric in our proposed
architecture. Hyper-ledger Fabric proposed by IBM [9] has gain a lot of
attention from market. Some of reasons behind choosing Fabric for our proposed
IoT architecture are as follows:

  1. 1.

Generalized BC: As discuss earlier, an industry big players are supporting
Fabric and make several solutions for it. Keeping this in mind, we argue that
our proposed solution will be easily adaptable by other use cases in IoT and
becomes a standardized Block-Chain technology for IoT-applications.

  2. 2.

Execute-order: Previous BC technologies relies on order-execute architecture
where mining of transactions degrade performance. Fabric introduces execute-
order where consensus phase is separated from execution, which ultimately
improve performance. For IoT, performance is a key challenge and Fabric
mitigate this issue comparing to other BCs.

###  V-A Security Analysis

The three main requirement for robust security design in every case are
mainly: Confidentiality, integrity and Availability or simply (CIA) [26].
Confidentially stated that only authorized entity must be granted access.
Integrity makes sure that the data is not changed or modified at the receiving
end and Availability means that the service or data to the user is available
when someone has needed. All of these requirements are addressed in our
proposed architecture. Table I analyze that how our proposed solution could
achieved the mainstream security requirements.

TABLE I: Security Evaluation Security Requirements | Solution Provided
---|---
Confidentiality | Matching ID in Smart Contract
Integrity | Hashing Mechanism
Authorization | Endorsement Policy checking

Further, we need to analyze the famous attack known as DDOS (Distributed
Denial of Service Attack) where an attacker uses several malicious or previous
infected IoT-devices and attack some particular target device. A number of
attacks have been discussed in [26] that can exploit the IoT-network in the
form of DDOS attack. Our architecture is somehow provides a hierarchical
prevention from such kinds of attacks. At the first level, attacker cannot
gain access to these smart devices because these devices are generally not
accessible physically. For instance, if attacker got access and supposed to
infect the device, the second level of our security defense is that, each
outgoing transaction has to be identified by our endorsement policy. Thus any
transaction or traffic that constitute the DDOS attack would be rejected and
cannot exit from the smart-home.

###  V-B Performance Evaluation

IoT with Block-Chain integration acquire extra computational overhead on the
smart-devices in terms of packet communication, time overhead and Energy
consumption. However, these low overhead somehow have no bigger impact and
significantly increase security and privacy. For communication between devices
IPV6 over (LoWPAN) is used in our smart-home devices because it is well-suited
for low-resource devices in IoT. Secondly, Fabric is a complex architecture
and the performance is depends on various parameters such as choice of
distributed applications, transaction size, ordering services, consensus
algorithm and network topology etc. Therefore, an in-depth performance
analysis is not possible in this phase of research. This research is focusing
on a standardize Block-Chain application for the growing need of IoT.

However, the evaluation done in Fabric architecture [9] rely on crypto-
currencies and is not directly applicable to our solution. To provide an
evaluation for our use-case we simulate two type of transactions in a smart-
home setting i.e. store and access. A store transaction means that a smart-
device would like to store a transaction data on Fabric i.e. temperature
sensor data, and access transaction is used to invoke some data. In our
simulation we analyze two types of experiments that are:

####  V-B1 Block Size Analysis

Recall that transactions are combined and shaped into form of blocks. So the
size of block is key factor that impact the overall throughput and latency. We
ran simulation of block size varying 0.2MB to 4.5MB. Figure 4 show throughput
measured at peers and Figure 4 illustrate end-to-end latency impacted by block
sizes. We observe in our case that the block size of 2 to 2.5MB is significant
in terms of throughput. However, the latency get worse as expected with
increase in block size, but in this particular case we assume that roughly
about 450 to 500 (ms) is acceptable.

Figure 3: Impact of Block Size on Throughput

Figure 4: Impact of Block Size on end-to-end Latency

####  V-B2 Transaction Size

We also investigate the number of transactions per block. Specifically, 1MB
block contains 230 store and 350 access transactions. The sizes calculated are
3.00KB for store and 4.20KB for access transactions. In general, the
transaction sizes in Fabric are larger because they also carry the certificate
information for approval.

####  V-B3 Transaction Payload Size Analysis

This experiment measures the latencies at different transactions payload
sizes. Transaction payload is actually the size of data that is going to
passed in a smart contract upon invoking. Table II shows the comparative
transaction payload analysis of Proposed IoT-BC and QUORAM-BC [27]. QUORAM is
also a permissioned public BC and based on Ethereum. As it is known that
Ethereum [28] uses mining for consensus which is slow as compared to Fabric,
and thus, not suitable for IOT environment. In QUORAM, they selected 32KB size
for transaction payload, therefore we also considered the same size for
comparison. The results show that the latencies increases with the increment
of 10KB in payload size. The total increase in transaction latency in QUORAM
25.23%, while in our technique the value is approximately 22.45%.

TABLE II: Latency with variable Transaction sizes |  | Latencies (s)
---
Transaction Payload
Payload Size (KB) | Proposed BC | Quoram BC [27]
1 | 0.225 | 0.325
10 | 0.280 | 0.383
20 | 0.320 | 0.384
30 | 0.330 | 0.407

##  VI Conclusion and Future Work

Security and privacy in IoT is extremely important these days and gain a
considerable attention from research and industry. Current security models for
IoT are not suitable anymore due to rapid-scale, high maintenance cost of
equipments, performance overhead and energy consumption. To cope with these
problems, we proposed a new approach of implementing IoT Application on
Fabric-BC. Hyper-ledger Fabric introduces a novel framework in BC that
separate the execution phase from consensus and implement policy-based
endorsements. Our representative case-study through out research was smart-
home. We presented solution for mainstream security requirements. We also
discussed performance overhead of some transactions and found no extra
overhead by our application interface developed on top of Fabric for IoT.
Furthermore, a comparison has been done with QUORAM-BC which shows that our
architecture is more efficient, specifically for IoT networks. Future work
will include applications of our framework to other IoT domains, in-depth
overhead analysis and integration with IOTA implementation.

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mode = title]Finding the instrumental variables of household registration: A discussion of the impact of China's household registration system on the citizenship of the migrant population

[1]
[1]The authors thank the Migrant Population Service Center, National Health Commission P. R. China for data support.

1]Jingwen Tan[orcid=0000-0002-3452-959X]
[2]
Conceptualization of this study, Methodology, Software
[1]organization=School of Economics, Henan University,
addressline=Jinming Avenue,

[1]

1]Shixi Kang[orcid=0000-0003-4847-5171]
[1]
Conceptualization of this study, Methodology, Software

[1]

[cor1]Corresponding author
[cor2]Principal Corresponding author

[fn1]These authors contributed equally to this work.

Migrant Social Integration instrumental variables China

Due to the specificity of China's dualistic household registration system and the differences in the rights and interests attached to it, household registration is prevalent as a control variable in the empirical evidence. In the context of family planning policies, this paper proposes to use family size and number of children as instrumental variables for household registration, and discusses qualitatively and statistically verifies their relevance and exogeneity, while empirically analysing the impact of the household registration system on the citizenship of the mobile population. After controlling for city, individual control variables and fixed effects, the following conclusions are drawn: family size and number of children pass the over-identification test when used as instrumental variables for household registration; non-agricultural households have about 20.2% lower settlement intentions and 7.28% lower employment levels in inflow cities than agricultural households; the mechanism of the effect of the nature of household registration on employment still holds for the non-mobile population group.

§ INTRODUCTION

China has experienced nearly three decades of rapid economic growth since reform and opening up in the 1980s resolved the institutional barriers to labour migration.2019 China's economic output exceeded 90 trillion yuan, an economic excellence closely associated with the massive movement of the rural population to the urban industrial sector[Li, 2019]. By 2020, the urbanisation rate of China's resident population exceeds 60%, with 13.52 million new jobs in cities[Li, 2020]. The massive influx of cheap labour provides a huge impetus for local economic growth and promotes the rapid development of regional manufacturing industries. However, in recent years a structural labour shortage in the region has become increasingly common and the surplus rural labour force is drying up[Fang, 2007, Garnaut et al., 2006].

An emerging view is that this phenomenon is simply a manifestation of a discriminatory and segmented labour market that has evolved as a result of restrictions on population mobility[Garnaut, 2010]. Prior to 2015, China had a dual household registration system across the country. The binary hukou system simply divided the population into agricultural and non-agricultural hukou, and the vast differences in entitlements attached to the two have had a profound impact on the regional allocation of labour endowments in China. The hukou system creates institutional barriers to the free movement of labour between provinces and cities and between urban and rural areas. For example, rural populations do not have access to many urban public services, including education and healthcare[Afridi et al., 2015], and a large number of rural children and elderly people are left behind in their hometowns and become surplus labour. For example, once they decide to move to an urban household registration, they have to give up ownership of their rural homes. With China's rapid urbanisation, the value of rural land on the urban fringe has increased significantly, and agricultural hukou, which is directly related to land rights, has acquired a higher monetary value, thus making rural migrants more inclined to retain their original hukou[Chen and Fan, 2016]. These barriers have led to an influx of labour into cities in the form of a 'floating population', which is hardly a long-term human resource.

Starting from the institutional context of China's dualistic household registration policy, this paper attempts to estimate the impact of the nature of household registration on the citizenship of the floating population. Considering the possibility of natural persons changing their household registration status for external reasons, this paper attempts to find instrumental variables of household registration to address the potential biased estimation problem. At the same time, the paper also explores the outcomes of the reform of the household registration system in terms of achieving the citizenship of the migrant population and facilitating the allocation of labour resource endowments.

§ REVIEW OF THE LITERATURE

Citizenship is generally defined as the process and phenomenon by which migrant workers are transformed into citizens by overcoming numerous barriers[Chen et al., 2013]. There is a consensus in the academic community regarding the incentive effect of the reform of the household registration system on the inflow of people to cities. For example, [Ito, 2008] finds through a general equilibrium model that the abolition of the household registration system will provide incentives for more rural people to flow to cities, thereby reducing the urban-rural gap. Such a reduction in the gap would also reduce potential social conflicts between the urban population and rural migrants, thus reducing the cost of maintaining social stability for the government[Liu et al., 2010]. In addition, reform of the hukou system will bring social benefits including capital accumulation and spillover effects[Glaeser and Gottlieb, 2008], and [Song, 2021] simulation of the abolition of the hukou system suggests that the redistribution of labour resources brought about by the disappearance of mobility barriers will lead to a significant increase in GDP and greater economic benefits due to the optimisation of both urban and rural labour markets. The results of the simulation show that the redistribution of labour resources brought about by the removal of the household registration system will lead to a significant increase in GDP, with greater economic benefits arising from the optimisation of both urban and rural labour markets.

Regarding the mechanism of the negative effect of the household registration system on the citizenship of the migrant population, academic research has focused on two main aspects, namely the willingness to stay and the employment situation. First, the household registration system deprives migrant workers of some of the public services to which they are entitled, thereby reducing this group's willingness to stay. For example, the lack of educational benefits for the children of rural migrants, who have difficulty attending local schools, changes the migration patterns of the mobile population at different ages and makes a significant proportion more inclined to stay in the countryside, thus becoming surplus labour[Golley and Meng, 2011]. For example, rural migrants who lack urban hukou have limited access to social insurance and health services, while inter-provincial migrants who lack hukou in the inflow area will be forced to return to the outflow area due to the lack of common access to a significant proportion of public services (e.g. health insurance)[Dreger et al., 2015]. The lack of public services will also bring about an increase in the cost of living for rural migrants. They often have difficulty accessing low-cost housing provided by the government and have to bear the additional housing burden of high prices. As a major factor affecting migrants' willingness to stay, the increased cost of living will undoubtedly increase the probability of moving out of the mobile population[Foote, 2016].

Second, the household registration system puts migrant workers in a vulnerable position in the job market. Newly arrived migrants are employed or self-employed, and the discrimination against rural migrants in the case of employment has been well researched, for example, their incomes are often much lower than those of similarly qualified residents with a local hukou[Lee, 2012, Ma, 2018]. In addition, the system can limit immigrants' self-employment; [Paulson and Townsend, 2004]find that individuals with family members working in financial institutions are more likely to start their own businesses, and that entrepreneurs are often forced to rely on their own wealth due to borrowing constraints in an imperfect financial mechanism. Migrants who lack a local household registration often find it difficult to become self-employed due to financing constraints and lack of interpersonal networks.

Most of the existing literature treats the household registration system as a general and holistic concept, ignoring the fact that it actually exists as a collection of multiple subdivisional forms such as local and non-local, agricultural and non-agricultural[Song, 2014]. Methodologically, there is little discussion of the problem of biased estimation due to changes in the household registration of the sample. The main reason for this problem is that there is no argument or research in the academic community on the existence of instrumental variables in household registration. Proposing and arguing for instrumental variables in household registration through qualitative and quantitative methods is the main contribution of this paper.

§ FINDING THE INSTRUMENTAL VARIABLES OF HOUSEHOLD REGISTRATION

* China's Household Registration System.

The household registration system is a household-based population management system implemented for citizens of the country settled in mainland China, which divides household attributes into agricultural and non-agricultural hukou based on geographical and family member relationships, indicating the legitimacy of natural persons living locally. This dualistic urban-rural household registration system inhibits the free movement of people between urban and rural areas and is discriminatory in terms of social welfare[Cai et al., 2001]. Although the hukou management system has been phasing out the division between agricultural and non-agricultural hukou since 2014, its effects are still widespread and far-reaching.

Due to the specificity of the dualistic household registration system and the differences in the rights and interests of agricultural and non-agricultural households, the use of household registration as a control variable for demographics is common in studies on labour economics in China. However, the status of a natural person's household registration is not permanent, and agricultural and non-agricultural hukou can shift from one to the other with the social behaviour of the natural person, such as working, pursuing education, etc[Yao and Lai, 2004, Li, 2003, Sun et al., 2011]. In empirical studies, when there is a possibility that the nature of the explanatory variable household registration changes with the status of the explanatory variable, potential endogeneity problems resulting from reciprocal causality may exist, causing disturbances to the unbiased assumptions of the estimates. The search for instrumental variables of household registration is therefore of considerable interest.

* Identification Strategies: The Policy Context of China's Family Planning System.

Family planning is one of the basic national policies of China. Since 1980, the government has been promoting one child per couple nationwide, and in 1982, the Fifth Session of the National People's Congress designated family planning as a basic state policy. Family planning advocates one child per couple, hence the term "one-child policy", but the policy allows some families to have two children, such as rural couples with only one daughter or one parent who is an only child. Parents of over-born families are often subject to dismissal from public office or administrative sanctions, and the policy is more lenient in rural areas than in urban areas. These factors combine to create a tendency for rural families to have more children, i.e. holders of agricultural hukou have larger families and a larger number of children. This transmission mechanism is supported by a considerable amount of research and will not be repeated in this paper [Weiwang, 2010, Zhuang et al., 2014, Yao et al., 2010].

In 2015, China's Fifth Plenary Session of the 18th Central Committee announced the liberalisation of the second child, and the one-child policy that had been in place in mainland China for more than 30 years was officially consigned to history. Due to the lag in the impact of the family planning policy, which has lasted for more than three decades, and the specificity of intergenerational parenting behaviour, there are still significant differences in the household size and number of children between agricultural and non-agricultural households, with agricultural households being higher than non-agricultural households on these two indicators. As shown in Table 1, the differences in household size and number of children across the nature of hukou are intuitive in the grouped descriptive statistics of the 2018 China Migration Dynamics Test.

Subgroup descriptive statistics.

Household size     Number of children

Agricultural households         3.176737           1.313868

Non-agricultural households     2.906609           1.05539

Based on the above discussion, this study proposes that an attempt can be made to use household size or the number of children as instrumental variables for agricultural and non-agricultural hukou. The instrumental variables need to satisfy exogeneity and correlation, i.e. household size or number of children is highly correlated with household registration and unrelated to the explanatory variables. This will be discussed in detail in this paper.

* Statistical validation of instrumental variables.

How to argue statistically that household size can be used as an instrumental variable for household registration is the focus of this section of the paper. Previous studies on the validity of instrumental variables include [Angrist and Keueger, 1991] and [Acemoglu et al., 2001], who provide different arguments through qualitative discussions and quantitative analyses. [Angrist and Keueger, 1991] effectively argue that quarter of birth can be used as an instrumental variable for years of schooling by constructing different exclusionary instruments, but this method does not apply to the data in this article.  [Acemoglu et al., 2001] takes into account the path-dependent nature of institutions and their changes and uses colonial mortality as an instrumental variable for institutions [Acemoglu et al., 2001]. Family planning has long been a fundamental state policy in China, and its imprint as a historical institution is present in the country's demographic structure.

This paper begins with an analysis of the relevance of instrumental variables. The correlation between the instrumental variables and the explanatory variables can be verified by reporting the first-stage results of a two-stage least squares regression based on statistics. Table 2 reports the results of the interpretation of household size and number of children on the nature of household registration. Whether model (1) model (2) regresses the number of children and family size separately on the nature of household registration or model (3) regresses them jointly, both variables, family size and number of children, are significant at the 1% level. Interpreting the economic significance of the coefficients, in the results of the separate regressions, the probability of urban residence is reduced by 2.87% for each additional person in the mobile population's family size. The probability of urban hukou decreases by 4.75% for each increase in the number of children of the migrant population. Both family size and the number of children are put into the regression equation and the results are significant at the 1% level, despite the potential cointegration problem between the two variables. The coefficient on family size drops to 1.03% and the coefficient on the number of children drops to 3.88%. Table (4) in the subsequent section of this paper reports the results of the one-stage F-statistic, F=1365, which is significantly higher than the empirical value of 10. The household size and number of children of the migrant population are statistically correlated with the nature of their household registration.

Correlation analysis of instrumental variables.

(1)            (2)            (3)

OLS_1         OLS_2         OLS_3

family     -0.0287***     -              -0.0103***

(0.000790)     -              (0.00106)

child      -              -0.0475***     -0.0388***

-              (0.00102)      (0.00137)

_cons     0.269***       0.240***       0.261***

(0.00281)      (0.00178)      (0.00283)

N          152000         152000         152000

R2         0.008          0.013          0.013

The exogeneity of instrumental variables is equally important. If there are two or more instrumental variables for a given variable, the hypothesis of exogeneity of the instrumental variable can be tested by means of the p-statistic of the overidentification test. In the case of exactly identified cases, the specification of the conditions for the exogeneity of the instrumental variable can only be addressed by a qualitative discussion. This paper takes this reason into account by including both the number of children and household size variables together as instrumental variables for household registration. Admittedly, there is some correlation between the two variables of household size and number of children, and there may be problems of co-linearity in the regressions that affect the significance of the estimates. However, in the one-stage regressions, both were put into the regression equation for the nature of household registration at the same time and still reached the 1% level of significance. To address the potential over-identification and heteroskedasticity problems of multiple instrumental variables, this paper will add generalised moment estimation and iterative generalised moment estimation to the two-stage least squares regression, while reporting the results of limited information maximum likelihood estimation.

The exogeneity of the instrumental variables depends simultaneously on the explanatory variables. In this paper, with reference to previous studies, the selected citizenship indicator, i.e. the explanatory variable, is: "whether the migrant population is willing to settle in the inflow area". Using this as the explanatory variable, the results of the test for overidentification of the number of children and family size as instrumental variables of household registration are:

Score chi2(1) = 1.73991 (p = 0.1872)

Where the p-test rejects the original hypothesis that at least one of the two instrumental variables is endogenous, it passes the over-identification test and proves that the number of children and family size of the mobile population sample have no direct effect on their willingness to settle in the incoming city. The hypothesis of exogeneity is met when family size and the number of children of the mobile population are used as instrumental variables for household registration.

§ RESEARCH DESIGN

* Data introduction.

This paper selects data from the 2018 China Migrants Dynamic Survey (CMDS) organised by the National Health Care Commission for empirical analysis. The survey covered 31 provinces (municipalities and autonomous regions) in mainland China, and the sample was selected from the migrant population who had stayed in the local area for more than one month. The average age of the sample is in the 15-59 years old range. A stratified, multi-stage and large-scale PPS sampling method is used to investigate in detail the development, individual characteristics, social integration and employment of China's migrant population, providing data support for social science research in the direction of labour economics and demography. Data related to urban control variables are obtained from the statistical yearbooks of various cities.

At the same time, in order to discuss whether the impact of the household registration system on the employment situation of the Migrant population is limited by the status of the Migrant population, this paper uses the questionnaires of the mobile and household population from the 15-year China Migrants Dynamic Survey data, which are estimated by the Heckman two-step method.

* Model design.

The explained variables in this paper are dummy variables, and the binary models that can be used include Probit and Logit. According to [Nunn and Wantchekon, 2011], the regression process has fewer least squares estimation assumptions and is more robust to statistically significant results than methods such as likelihood estimation, and it is still recommended to use the LPM method when the explanatory variable is a dummy variable or an ordered variable.
The basic expressions of the IVLPM model are.

\begin{eqnarray}\label{101}
T_{i j}=\alpha+\beta h i_{i j}+X_{i j}^{\prime} \gamma+\varepsilon_{i j}, \varepsilon_{i j} \sim N\left(0, \quad \sigma^{2}\right)
\end{eqnarray}

\begin{eqnarray}\label{102}
h i_{i j}=k+\beta_{1} z_{i j}+\beta_{2} u_{i j}++X_{i j}^{\prime} \gamma+\varepsilon_{i j}, \varepsilon_{i j} \sim N\left(0, \quad \sigma^{2}\right)
\end{eqnarray}

In equation (1), $T_{i j}$is the willingness of the mobile population to settle; ${hi}$is the nature of the household registration of the mobile family, and $X_{i j}^{\prime} \gamma$ is an individual, urban control variable. $\varepsilon_{i j}$ is the random disturbance term.
In equation (2), z, u are the number of children and household size of the migrant population respectively. Based on the estimation of two-stage least squares, this paper reports the results of generalised moment estimation, iterative generalised moment estimation, and limited information great likelihood estimation simultaneously.

The level of citizenship of the migrant population is not only reflected by their willingness to settle in the inflow area, but also by their employment status in the inflow area, which objectively reflects their ability to integrate into the inflow city. Therefore, this paper further considers the employment status of the mobile population as a citizenship indicator. The employment status of the migrant population is correlated with its own demographic characteristics that reflect the quality of the labour force, such as education and gender, and direct estimation may give rise to endogeneity problems caused by sample selection bias, which is tested for robustness through propensity score matching.

The paper further speculates that the mechanism of action between the employment status of the sample and the nature of its household registration may be influenced by whether the sample is a local resident as a status. To test this conjecture, the paper uses the Heckman two-step method through the Household Registration Survey of the 2015 China Mobility Monitoring Data.

* Descriptive statistical analysis.

The explanatory variable in this paper is the willingness to settle of the migrant population, and we choose the CMDS questionnaire "Are you willing to settle in the local area?" The core explanatory variable in this paper is the nature of household registration, in which agricultural households are assigned a value of 0 and non-agricultural households a value of 1. Agricultural to resident households are assigned a value of 0 and non-agricultural to resident households a value of 1.

The control variables in this paper include two parts: urban control variables and individual control variables. In existing studies on the citizenship of the migrant population, factors such as years of education, family size, age and insurance participation are frequent demographic control variables. Variables such as the level of urban healthcare and disposable income per capita are frequently found as urban control variables. In this paper, gender, years of education, work status, range of mobility, logarithmic total household income, and logarithmic total household housing expenditure of the mobile population are selected as individual control variables. GDP per capita, industrial structure, number of primary school teachers per 10,000 population, and number of hospital beds per 10,000 population were used as urban control variables.
Table 3 reports the descriptive statistics for all variables in the study. Approximately 30% of all migrants have a desire to settle in the inflow area.

Variable descriptive statistics.

Variable Name     Explanation of variables                                                                                N          mean       sd                             min                            max

stay              c]@c@Willingness to settle
(1 willing; 0 unwilling)                      132993     0.307      0.461                          0                              1

hukou             c]@c@Nature of household
(1 non-agricultural; 2 agricultural)            132993     0.179      0.384                          0                              1

family            Family size (number of persons)                                                                               132993     3.128      1.207                          1                              12

child             Number of children                                                                                132993     1.268      0.897                          0                              9

gender            Gender (1 male; 0 female)                                                                                132993     0.513      0.500                          0                              1

edu               Number of years of education                                                                                132993     10.38      3.430                          0                              19

emploey           c]@c@Work status
(1 Working; 0 Not working)                              132993     0.842      0.365                          0                              1

scope             c]@c@Mobility
(0 inter-county; 1 inter-municipal; 2 inter-provincial)     132993     1.337      0.747                          0                              2

lnincome          Logarithmic total household income                                                                             132993     8.759      0.690                          0                              13.81

lnhoscost         Total logged housing expenditure                                                                               132993     4.893      3.100                          0                              10.82

pgdp              GDP per capita                                                                                132993     95,208     1l39,754     1l12,656     191,942

3gdp              Share of tertiary sector GDP                                                                                132993     58.26      10.77                          26.54                          80.98

pteacher          Number of primary school teachers per 10,000 population                                                        132993     27.08      19.40                          0                              121.2

pbed              Number of hospital beds per 10,000 population                                                                  132993     1,981      1,498                          0                              14,653

§ EMPIRICAL PROCESS AND DISCUSSION

* Household registration and willingness to settle among the migrant population.

Instrumental variable regression.

(4)                 (5)                 (6)                 (7)

TSLS                LIML                GMM                 IGMM

hukou                                     -0.202 ***          -0.203 ***          -0.198 ***          -0.199 ***

(0.0474)            (0.0475)            (0.0473)            (0.0473)

gender                                    -0.0306 ***         -0.0306 ***         -0.0306 ***         -0.0306 ***

(0.00253)           (0.00253)           (0.00253)           (0.00253)

edu                                       0.0363 ***          0.0363 ***          0.0361 ***          0.0361 ***

(0.00172)           (0.00173)           (0.00172)           (0.00172)

lnhoscost                                 -0.0227 ***         -0.0227 ***         -0.0227 ***         -0.0227 ***

(0.000597)          (0.000598)          (0.000597)          (0.000597)

lnincome                                  0.0622 ***          0.0622 ***          0.0620 ***          0.0620 ***

(0.00277)           (0.00278)           (0.00277)           (0.00277)

emploey                                   -0.168 ***          -0.168 ***          -0.168 ***          -0.168 ***

(0.00576)           (0.00576)           (0.00575)           (0.00575)

marry                                     0.170 ***           0.170 ***           0.170 ***           0.170 ***

(0.00327)           (0.00327)           (0.00326)           (0.00326)

pgdp                                      -0.00000123 ***     -0.00000123 ***     -0.00000123 ***     -0.00000123 ***

(4.25e-08)          (4.25e-08)          (4.24e-08)          (4.24e-08)

gdp                                       0.000673 ***        0.000674 ***        0.000664 ***        0.000664 ***

(0.000161)          (0.000161)          (0.000160)          (0.000160)

pteacher                                  0.000153 *          0.000153 *          0.000151 *          0.000151 *

(0.0000887)         (0.0000888)         (0.0000886)         (0.0000886)

pbed                                      -0.00000111         -0.00000111         -0.00000114         -0.00000114

(0.000000964)       (0.000000965)       (0.000000963)       (0.000000963)

_cons                                    -0.370 ***          -0.370 ***          -0.366 ***          -0.366 ***

(0.0300)            (0.0301)            (0.0299)            (0.0299)

N                                132993              132993              132993              132993

R2                                        0.061               0.061               0.062               0.062

One-stage regression results     4cF=1365, p=0.000

Over-identification test         4cScore chi2(1) = 1.73991 (p = 0.1872)

Table 4 presents the results of the instrumental variable regression models. Model (4) is the result of the IVLPM regression. Model (4) indicates that after controlling for individual as well as city-related variables, the regression coefficient of the nature of household registration on the willingness to settle of the mobile population is -0.202, which is significant at the 1% level. The intention to settle in the inflow area is on average 20.2% lower for mobile population groups with non-agricultural hukou compared to those with agricultural hukou.

According to model (5), the findings of the two-stage least squares regression and the limited information maximum likelihood estimation regression are similar, demonstrating that there is no weak instrumental variable problem in the empirical evidence. Models (6) and (7) using generalised moments estimation and iterative generalised moments estimation also find similar results to the two-stage least squares regression, with the heteroskedasticity problem of the potential disturbance term not significantly interfering with the empirical study and the estimation results being robust.

Judging from the economic significance of the model, the willingness to settle in the inflowing cities is significantly lower for mobile people holding non-agricultural hukou than for those holding agricultural hukou. The explanation that can be obtained is that the economic level of their hometowns is higher for non-agricultural hukou holders and lower for agricultural hukou holders, compared to the greater improvement in quality of life and stronger willingness to stay for agricultural hukou inflowing cities. It has been argued that the main motivation for migration is to obtain more economic rewards[Cheng and Yin, 2020]. Push-pull theory is a classic theory in the field of population mobility[Lee, 1966]. In the framework of this theory, there is an income gap between the urban and rural dual economy, and mobile people holding agricultural hukou are subject to greater push and pull from their hometown during their mobility, while non-agricultural mobile people are subject to less push and pull, and these differences are further reflected in their willingness to settle through their hukou.

An analysis of the impact of household registration on the citizenship of the migrant population from one indicator alone may not be sufficient to support the conclusion. This paper further analyses the effect of household registration on the employment status of the mobile population in an attempt to test the mechanism: again, the employment status of the mobile population with non-agricultural households is lower than the employment status of the mobile population with agricultural households.

* Household registration and the employment of the migrant population.

2.1 Stepwise regression results

Stepwise regression results.

(8)             (9)             (10)                (11)                (12)

OLS             OLS             OLS                 OLS                 OLS

hukou                                     -0.0563 ***     -0.0902 ***     -0.0555 ***         -0.0872 ***         -0.0728 ***

(0.00275)       (0.00281)       (0.00281)           (0.00287)           (0.00284)

gender                                    -               0.167 ***       -                   0.169 ***           0.167 ***

-               (0.00188)       -                   (0.00193)           (0.00190)

edu                                       -               0.00826 ***     -                   0.00845 ***         0.00846 ***

-               (0.000331)      -                   (0.000342)          (0.000344)

lnhoscost                                 -               0.00630 ***     -                   0.00621 ***         0.00524 ***

-               (0.000322)      -                   (0.000332)          (0.000333)

lnincome                                  -               0.0613 ***      -                   0.0613 ***          0.0509 ***

-               (0.00171)       -                   (0.00178)           (0.00178)

marry                                     -               -0.0812 ***     -                   -0.0781 ***         -0.0623 ***

-               (0.00238)       -                   (0.00249)           (0.00249)

pgdp                                      -               -               0.000000402 ***     0.000000138 ***     0.000000526

-               -               (3.18e-08)          (3.06e-08)          (0.000000702)

gdp                                       -               -               -0.000733 ***       -0.00116 ***        -0.000496

-               -               (0.000101)          (0.0000967)         (0.00133)

pteacher                                  -               -               0.0000427           -0.000161 **        0.0000966

-               -               (0.0000723)         (0.0000692)         (0.00193)

pbed                                      -               -               0.0000109 ***       0.00000701 ***      0.0000208

-               -               (0.000000763)       (0.000000727)       (0.0000245)

_cons                                    0.852 ***       0.184 ***       0.834 ***           0.224 ***           0.151

(0.00105)       (0.0139)        (0.00574)           (0.0149)            (0.175)

Individual control variables     -               $\surd$         -                   $\surd$             $\surd$

Urban control variables          -               -               $\surd$             $\surd$             $\surd$

urban fixed effect               -               -               -                   -                   $\surd$

N                                140240          140150          133080              132993              132993

R2                                        0.004           0.091           0.007               0.093               0.131

Table 5 presents the results of the stepwise regression model. Model (8) indicates that the regression coefficient of -0.0563 for household registration on the employment of the mobile population, conditional on the inclusion of other control variables, is significant at 1% level, indicating that the mobile population with non-agricultural household registration is at a lower level of employment relative to the mobile population with agricultural household registration. Model (11), which includes both urban and individual control variables, has a slightly higher regression coefficient (-0.0872) compared to the regression coefficient when no control variables are included. Model (12) adds urban fixed effects to the regression controlling for both urban and individual control variables. The regression coefficient is slightly lower (-0.0728) compared to the regression coefficient without controlling for fixed effects.

In previous studies, mobile people with non-agricultural hukou were more likely to enter higher-income jobs than those with agricultural hukou, but this mechanism was not significant among lower and middle-income groups[Li and Wen, 2018]. In terms of the employment structure of the labour force, most regions of the country offer relatively basic jobs. At the same time, the proportion of self-employment is higher among labour force groups holding agricultural hukou[Sun, 2017]. This may explain why mobile people holding non-agricultural hukou do not perform as well as those with agricultural hukou in terms of inflow to urban employment.

2.2 Propensity score matching

Propensity score matching.

2*Matching Status     2c1:1 Matching     2c1:2 match     2cradius matching     2cnuclear matching

ATT                  t                 ATT                 t               ATT                    t                  ATT                    t

pre-match                            -.0550***            -21.25            -.0550***           -21.25          -.0550***              -21.25             -.0550***              -21.25

post-match                           -.0678***            -16.79            -.0657***           -17.32          -.0694***              -16.99             -.0715***              -14.35

Propensity score matching can effectively alleviate the endogeneity problem caused by sample selection bias. As shown in Figures 1, 2, the standardised bias of each variable is substantially reduced after matching. and most of the observations were within the common range of values. This result indicates that nearest neighbour matching better balances the differences between the treatment and control groups in the sample, and the assumptions of balance, and common support are satisfied. This paper uses the matched sample for the analysis to estimate the impact of household registration on the employment of the mobile population.

Variable standardisation deviation.

Common range of values for propensity scores.

Table 6 shows the results of the PSM estimation, where the average treatment effect for all four matching methods is around -0.0678 for the matched sample of individual characteristics control variables, which is the same conclusion as the regression results above: non-agricultural households are less likely to obtain a job compared to agricultural households. Propensity score matching uses likelihood estimation methods by default and does not lend itself to coefficient size comparisons with benchmark regressions based on OLS estimation methods.

2.3 Discussion: migrant vs. domiciled population

Sample selection model.

(13)                         (14)                      (15)

Mobile population groups     Local household group     Heckman's two-stage return

hukou                                     -0.0825 ***                  -0.1387 ***               -0.0610 ***

(0.00306 )                   (0.01283)                 (0.01423 )

Individual control variables     $\surd$                     $\surd$                    $\surd$

Urban control variables          $\surd$                      $\surd$                   $\surd$

N                                159883                       12,000                    174,225

R2                                        0.097                        0.209                     -

LR test                          -                            -                         P=173.63

Lambda                           -                            -                         -0.1174671

Table 7 analyses whether the transmission mechanism of the effect of household registration on employment is affected by the sample's status as " migrant or household population" by using a sample of the household population from eight cities sampled in the 2015 CMDS and a sample of the migrant population from the national sample. Equation 13 shows the regression results for the mobile population sample, while Equation 14 shows the regression results for the domiciled population sample. It can be seen that the employment status of non-agricultural households is significantly lower than that of agricultural households for both the mobile and household populations.

The design of the selection equation is particularly important in the sample selection model. In this paper, logged housing expenditure, logged household income, and years of schooling are used as explanatory variables in the selection equation. In general, the household population has a higher probability of owning a home locally compared to the mobile population, and therefore has a smaller housing expenditure. Similarly, the household population tends to have a greater institutional advantage in terms of schooling and income compared to the mobile population. In the selection equation, all three selection variables are significant at the 1% level. Also, the likelihood ratio test (chi2 = 173.63) rejected the original hypothesis that the sample selection model should not be used.

Equations 13 and 14 estimate the correlation between household registration and employment status from both the migrant and household population perspectives respectively, and Equation 15 analyses the migrant and household populations as a whole through the sample selection model. According to the final results in Table 7, the coefficients after the sample selection model estimation are reduced and the corrected regression coefficient of household registration on employment of the mobile population is -0.0610. The mechanism of the effect of the nature of household registration on employment in not influenced by the status of the mobile or household population.

§ CONCLUSION

The use of household registration as a control variable for demographics is common in studies on labour economics in China due to the specificity of the dualistic household registration system and the differences in the rights and interests of agricultural and non-agricultural households. In the policy context of family planning, this paper proposes the use of household size and the number of children as instrumental variables for household registration, and discusses qualitatively and statistically validates their relevance and exogeneity. It is finally concluded that the family size and number of children of the mobile population are statistically correlated with the nature of their household registration, in line with the hypothesis of correlation of the instrumental variables. The two instrumental variables passed the over-identification test, proving that the number of children and family size of the migrant population sample have no direct effect on their willingness to settle in the city upon re-entry. The hypothesis of exogeneity is met when family size and the number of children of the migrant population are used as instrumental variables for household registration.

Starting from the institutional context of China's dualistic household registration policy, this paper empirically analyses the impact of the nature of household registration on the citizenship of the migrant population through a two-stage least squares approach. After controlling for urban and individual control variables as well as fixed effects, the following conclusions are drawn: non-agricultural hukou has about 20.2% lower willingness to settle and about 7.28% lower employment level in inflowing cities compared to the agricultural hukou group; the mechanism of the effect of the nature of hukou on employment in not influenced by the status of the mobile or hukou population. Whether analysed in terms of subjective willingness to settle or objective employment, agricultural hukou shows a higher level of citizenship compared to non-agricultural hukou.

Although the division between agricultural and non-agricultural hukou has been phased out of China's hukou management system since 2014, the vast differences in the rights and benefits attached to the two types of hukou and the decades-long duration of their implementation still have a profound impact on the regional allocation of labour endowments in China. The nature of household registration remains a very important control variable in the study of issues related to labour economics in China. The main contribution of this paper is to propose an instrumental variable for the household registration system and to argue for the feasibility of this instrumental variable.

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