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**IOM (2019)** Profiles and reported vulnerabilities of migrants along the Eastern, Central and Western Mediterranean routes,

disponibile a: https://migration.iom.int/europe?type=arrivals

**Lushey C.J., Munro E.R.** (2014) Participatory peer research methodology: an effective method for obtaining young people’s

perspectives on transitions from care to adulthood?, in Qualitative Social Work, 14(4), pp. 522-537.

**Masini E.** (2000) Penser le Futur, Dunod, Paris.

**MLPS** (2019) Report mensili Minori Stranieri Non Accompagnati in Italia, disponibili a: https://www.lavoro.gov.it/temi-e
priorita/immigrazione/focus-on/minori-stranieri/Pagine/Dati-minori-stranieri-non-accompagnati.aspx

**MIUR, AGIA** (2017) Linee Guida per il diritto allo studio degli alunni e delle alunne fuori dalla famiglia di origine.

**Morgan D.L.,** **Kruger R.A.** (eds) (1998) The Focus Group Kit, SAGE, Thousand Oaks, CA.

**O’Leary Z.** (2004) Guide to Doing Research, SAGE, London.

**OXFAM** (2017) You aren’t human any more. Migrants expose the harrowing situation in Libya and the impact of European

policies, disponibile a https://www-cdn.oxfam.org/s3fs-public/file_attachments/mb-migrants-libya-europe-090817-en.pdf

**PAHO - Pan American Health Organization** (2005) Youth: Choices and Change. Promoting Healthy Behaviors in Adolescents,

disponibile a http://iris.paho.org/xmlui/handle/123456789/708

**Save the Children** (2019) Children Come First. Intervento in Frontiera. Dossier I Minori Migranti In Arrivo Via Mare. Dossier

I-VII, 2017-19.

**Sayad A.** (2002) La doppia assenza. Dalle illusioni dell’emigrato alle sofferenze dell’immigrato, Raffaello Cortina Editore,

Milano.

**Segatto B., Di Masi D., Surian A** . (2018) L’ingiusta distanza. I percorsi dei minori stranieri non accompagnati dall’accoglienza

alla cittadinanza, FrancoAngeli, Milano.

**SPRAR, ANCI, MdI** (2017) Atlante SPRAR In Migrazione. Accoglienza e Rifugiati: un’ordinaria emergenza, disponibile a:

https://www.sprar.it/wp-content/uploads/2018/11/Atlante-Sprar-2017_Light.pdf

**Terres des Hommes** (2017) Guida al Modello Faro: Salute Mentale e Supporto Psicosociale a minori migranti non accompagnati

e a famiglie con bambini in prima accoglienza, disponibile a https://terredeshommes.it/dnload/GuidaFARO-2017.pdf

**UNHCR** (2018) Desperate journey Refugees and migrants arriving in Europe and at Europe's borders, disponibile a: https://

data2.unhcr.org/en/documents/download/65373#_ga=2.230793311.1765494779.1537607900-1572128613.1533314743

**UNICEF** (2011) The State of The World’s Children 2011. Adolescence. An age of opportunity, disponibile a https://www.

unicef.org/sowc2011/pdfs/SOWC-2011-Main-Report_EN_02092011.pdf

**UNICEF** (2017a) The Adolescent Brain: A second window of opportunity. A Compendium, disponibile a https://www.unicef
irc.org/publications/pdf/adolescent_brain_a_second_window_of_opportunity_a_compendium.pdf

**UNICEF** (2017b) A deadly journey for children. The Central Mediterranean Migration Route, disponibile a https://www.unicef.

org/publications/files/EN_UNICEF_Central_Mediterranean_Migration.pdf

**UNICEF & CNR** (2017) Sperduti. Storie di minorenni arrivati soli in Italia, disponibile a https://www.unicef.it/Allegati/Bambini_

Sperduti.pdf

**UNICEF & IOM** (2017) Harrowing Journeys. Children and youth on the move across the Mediterranean Sea, at risk of

trafficking and exploitation, disponibile a https://www.unicef.org/publications/files/Harrowing_Journeys_Children_and_youth_ on_the_move_across_the_Mediterranean.pdf

**UNICEF & MDM** Médecins du Monde (2018) Enea. Manuale operative per la presa in carico psicosociale dei minori stranieri

non accompagnati, disponibile a https://issuu.com/medecinsdumonde/docs/enea-manuale_operativo

**UNICEF & REACH** (2017) Children on the move in Italy and Greece. Report June 2017, disponibile a https://www.unicef.org/

eca/media/921/file/REACH%20report%202017.pdf

**United Nations** (2018) Desperate and Dangerous: Report on the human rights situation of migrants and refugees in Libya,

disponibile a: https://unsmil.unmissions.org/sites/default/files/libya-migration-report-18dec2018.pdf

**U-Report on the Move** (2018) Conoscenza e servizi sulla violenza sessuale, disponibile a: https://onthemove.ureport.in/

poll/3293/

**Women’s Refugee Commission** (2019) More Than One Million Pains: Sexual Violence Against Men and Boys on the

Central Mediterranean Route to Italy, disponibile a https://www.womensrefugeecommission.org/images/zdocs/Libya-Italy Report-03-2019.pdf For all of the references, 4 July 2019 was the latest access.

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# **Glossary**

**Absconded** - the MLSP defines the term ‘absconded’ as an UASC who was registered as such upon arrival and insertion in the

reception system of a given country, but who subsequently left the reception facility in which he or she are hosted. UASC are reported as absconded, competent authorities inform Directorate General for Immigration and Integration Policies of the MLSP.

They remain absconded until they are found in Italy or reach the age of 18.

**Adolescent** - According to the United Nations, an adolescent is defined as any individual between the ages of 10 and 19. [64]

**Best interests of the child** - The best interests of the child is one of the fundamental values of the United Nations Convention

on the Rights of the Child (CRC). Article 3(1) of the CRC, states “In all actions concerning children, whether undertaken by public or private social welfare institutions, courts of law, administrative authorities or legislative bodies, the best interests of the child shall be a primary consideration”. [65]

**Child/children** - The United Nations Convention on the Rights of the Child (1989) defines a child as every human being below

the age of eighteen years unless under the law applicable to the child, majority is attained earlier (Article 1). [66]

**Former UASC** - For the purposes of this study, former UASC are individuals who, at the time of the interview, had already

reached the age of 18, but who came to Italy as children and were registered as UASC upon their arrival in Italy. This definition was developed for the specific purposes of the research.

**Gender-based violence** - Term that includes any harmful act perpetrated against the will of the person and based on socially

ascribed differences between men and women (such as gender). It includes acts inflicted physically, sexually, mental harm or suffering, threats of such acts, coercion and other forms of deprivation of liberty. These acts may occur in public or private.

The term ‘GBV’ is most commonly used to emphasize how the systemic disparity between males and females, which exists in every society in the world, acts as a unifying and fundamental feature of many forms of violence perpetrated against women and girls. [67]

**Sexual and gender-based violence** refers to any act perpetrated against the will of the person and based on gender norms and

an asymmetrical relationship of power. It includes threats of violence and coercion. It may be physical, emotional, psychological or sexual in nature and may take the form of a denial of resources or access to services. It is inflicted on women, girls, boys and men. [68]

**Humanitarian protection** - This form of protection was governed by the previous Article 5(6) of Legislative Decree no. 286/1998

to respond to protection needs based on serious reasons “particularly of a humanitarian nature or arising from constitutional or international obligations of Italy as a State” (www.camera.it/parlam/leggi/deleghe/98286dl.htm) and subsequently repealed by l.d. 113/2018.

**Participation** - The right to participate and to be heard is one of the fundamental principles affirmed by the United Nations

Convention on the Rights of the Child, Article 12, which states that the State Parties “shall assure to the child who is capable of forming his or her own views the right to freely express those views freely in all matters affecting the child, the views of the child being given due weight in accordance with the age and maturity of the child. For this purpose, the child shall in particular be provided the opportunity to be heard in any judicial or administrative proceedings affecting the direct, either directly or through a representative or an appropriate body, in a manner consistent with the procedural rules of national law”. [69]

**Prosieguo Amministrativo** - Term commonly used to refer to the possibility governed by Article 13 of Law 47/2017

under which when UASC, at the age of majority, despite having undertaken a pathway of social inclusion, needs continued protection until age 21. The Juvenile Court may order, even at the request of social services, with a motivated decree, that he/she is to be placed in the custody of the social services, however not later than the age of 21 (www.gazzettaufficiale.it/eli/ id/2017/04/21/17G00062/sg).

**Refugee status** - The term ‘refugee’ applies to any person who, as a result of events occurring before 1 January 1951 and

owing to well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion is outside his country of nationality and is unable or, owing to such fear, unwilling to avail himself of such protection; or who, having a nationality and being outside the country of his former habitual residence as a result of such 84


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events, is unable or, owing to such fear, is unwilling to return to it. (Article 1A of the Geneva Refugee Convention https://www.

unhcr.org/3b66c2aa10)

**Residence permit for family reasons** - Governed by Article 10 (1), letter b) of Law 47/2017 under which the Questore (Chief

of Police) issues a residence permit for family reasons for a child of 14 years of age who is entrusted, also pursuant to Article 9(4) of Law 184/1983 and subsequent amendments, or who is subject to the protection of an Italian citizen living with him/her, or for a child over 14 years of age who is entrusted, also pursuant to the same Article 9(4), of Law 184/1983 and subsequent amendments, or subject to the protection of a foreigner residing legally in the national territory or of an Italian citizen living with him/her.

**Residence permit for UASC** - Governed by Article 10(1), letter a) of Law 47/2017 under which the Questore issues a residence

permit for minor age in the case of UASC, found in the national territory and reported to the competent authorities. The residence permit for UASC is issued, at the request of the minor himself/herself, directly or through those who exercise parental responsibility, even before the appointment of a guardian pursuant to Article 346 of the Civil Code, and is valid until the age of majority. [70]

**Secondary movements** - The phenomenon of migrants, including refugees and asylum seekers, who for various reasons

move from their first country of arrival to seek protection or permanent resettlement elsewhere. This movement takes place without the prior consent of the national authorities, without an entry visa, without or with insufficient travel documents among those normally required, or with false documents. [71]

**Special cases** - Residence permits governed by the Testo Unico sull’ Immigrazione (Legislative Decree 286/1998) to meet the

temporary needs for the protection of foreigners in humanitarian need. Some residence permits for special cases – residence permits for medical treatment (Article 19, para.2, letter d-bis TUI), for disasters (Article 20) and for acts of particular civic value (Article 42) – were introduced by l.d.113/2018, while the other permits were already provided for by the previous regime, and the new legislation has only changed the name and/or completed the discipline. [72]

**Special protection** - The residence permit was introduced by Article 1(2) of l.d.113/2018 to protect foreigners who cannot be

rejected, expelled or extradited to a State if there are reasonable grounds for considering that that person risks being subjected to torture, also taking into account the existence in that State of systematic and serious violations of human rights under Article 19(1.1) of Legislative Decree 286/1998.

**Subsidiary protection** - One of the forms of international protection from which non-EU foreigners, including UASC, may

benefit, who has applied for international protection, governed by EU law (Directive 2011/95/EC – Qualification Directive – Recast). Persons eligible for subsidiary protection are all third-country nationals or stateless persons who do not qualify as a refugee but in respect of whom there are substantive reasons to believe that the person concerned, if returned to his or her country of origin, or in the case of a stateless person, to his or her country of former habitual residence, would face a real risk of suffering serious harm. For this purpose, ‘serious harm’ consists of: (i) the death penalty or execution; or (ii) torture or inhuman or degrading treatment or punishment of an applicant in the country of origin; or (iii) serious and individual threat to a civilian’s life or person by reason of indiscriminate violence in situations of international or internal armed conflict.

**Territorial Commission** - These are Commissions for the recognition of international protection, administrative authorities

tasked with examining applications for international protection submitted by foreigners. The composition, tasks and procedures of the Territorial Commissions are regulated by Legislative Decree 25/2008.

**Trafficking** - Article 3(a) of the United Nations Additional Protocol to Prevent, Suppress and Punish Trafficking in Persons,

Especially Women and Children supplementing the United Nations Convention against Transnational Organized Crime (2000) defines trafficking as “the recruitment, transportation, transfer, harbouring or receipt of persons by means of the threat or use of force or other forms of coercion, of abduction, of fraud of deception of the abuse of power or a position of vulnerability or of the giving or receiving payments or benefits to achieve the consent of a person having control over another person, for the purpose of exploitation.” Trafficking in persons may take place within the borders of a State or may be transnational.

**Unaccompanied and separated children (UASC)** - According to Article 2 of Law 47/2017, an UASC is a minor who does not

have Italian nationality or the nationality of a European Union Member State, who is for any reason present on the territory of the state or otherwise under Italian jurisdiction, who is without care and representation by his or her parents or other adults legally responsible for him or her according to Italian legislation. [73] 85


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**Young people/youth** - According to the United Nations, “young people” are individuals between the ages of ten and 24,

while “youth” are those between the ages of 15 and 24 (see A/36/215 and resolution 36/28, 1981). Depending on the context of reference, broader age ranges up to 30 years are taken into account. For example, to produce comparative statistics for all Member States, the European Commission uses an age range between 15 and 29 years (https://ec.europa.eu/eurostat/web/ youth). For the purpose of the report the term young people was used.

# **Annexes**

**Box 1 – Asylum, Migration and Integration Fund (AMIF) first-level reception facilities**

Established by the Ministry of the Interior by Ministerial Decree 1.09.2016 and financed by the Asylum, Migration and Integration Fund (AMIF) 2014–2020, the highly specialized governmental facilities – the FAMI centres in Italian – for the needs of immediate protection and assistance, provide adequate child-friendly reception services to UASC through socio educational activities and participation activities, information, protection and legal and socio-psychological advice. FAMI's design follows the reform of the reception system, which began in 2014 and was regulated and formalized by Legislative Decree 142/2015 and subsequent amendments, to respond to the constant increase in arrivals of UASC, and to better and more holistically respond to their needs, with a first piloting phase of the system.

FAMI's design is innovative in several respects: it includes important criteria, such as a multidisciplinary approach and coordinated and complementary support provided by specialized agents, and has introduced and experimented good practices, testing and consolidating innovations introduced by Law No. 47/2017, such as, for example, the social file. The first phase of the reception process, scheduled for a maximum of 30 days (as provided by the Legislative Decree 142/2015, amended by Law 47/2017), is carried out by multidisciplinary teams with the support of partner agencies (IOM, UNHCR, Save the Children, INMP, ANCI – Central Service), in the first-line reception of the UASC, ensuring the respect of his/her fundamental rights, with particular reference to the principle of the best interests of the child and through a holistic approach of ad hoc protection and coordination with local institutions and entities. Initially tasked to provide reception in 27 reception projects for a total of 1,350 locations across the country, to date there are eight operational projects located in Sicily and Molise for a total of 200 places.

Despite this recent reorganization following the decrease in arrivals, over the years the system has been able to guarantee protection and support of children in their choice of the most suitable legal status or inclusion process. This includes the application for international protection, family reunification or family fostering aimed at his/her protection and at achieving autonomy and self-awareness in line with his/her higher interest, needs and wishes. Actions include the involvement of the guardian, the application for a residence permit and support for age assessment, the referral of particularly vulnerable cases to specific institutions and local authorities and the multidisciplinary use of the social file. Several operational tools were developed and tested during project implementation, such as the Guidelines for first-line reception facilities containing Standard Operating Procedures for the assessment of the best interests of the child and the Standard Operating Procedures concerning information and legal support for the application for international protection of UASC hosted in first reception facilities. Particular attention was paid to the participatory activities for UASC, with for example, the participatory development of information tools for UASC, and to the sustainability of project actions through the creation of training for trainers within the system.

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**Box 2 – Legal framework on education and training**

The right to education, enshrined in Article 26 of the Universal Declaration of Human Rights of 1948 and reaffirmed by the Convention on the Rights of the Child of 1989 (Articles 18, 28 and 29), is harmonized in Article 38 of Legislative Decree 286/1998 (Testo unico sull’immigrazione), which states that “foreign children in the territory are subject to compulsory schooling; they are subject to all the provisions in force on the right to education, access to educational services, participation in the life of the school community”. In addition, the right to the completion of studies is reiterated for those who have reached 18 years of age by the following legislation: namely, Law 47/2017, the resulting Guidelines for the Right to Study of Pupils Outside their Home Families (MIUR-AGIA December 2017) [Ixxv] and the Memorandum of Understanding between the National Ombudsperson (AGIA) and the Ministry of Education, University and Research to promote and guarantee the rights of pupils (October 2018). [Ixxvi] In addition, Law 183/1984 establishes the right to education for children in foster care or who have been adopted.

Compulsory education of all children lasts ten years, from the age of six to 16. It also covers the first two years of upper secondary education. In this period of time, UASC must be admitted to the class that corresponds to their age, unless the teachers decide otherwise (Article 45 of the Regulation Implementing the Consolidated Text – Presidential Decree 394/1999). Nevertheless, the “Guidelines for the reception and integration of foreign students” (MIUR 2014) state that, in these cases, the child can be enrolled in the class that is immediately above or below, in order to limit as much as possible the age difference between pupils in the same class.

From 16 to 18 years of age, all children have the right and duty to education and training, which is fulfilled by obtaining a secondary school certificate or a professional qualification, or through apprenticeship. Children over the age of 16 who have not taken the middle school exam can prepare for it and take it by registering with the CPIA.

**Box 3 – Volunteer guardians (Law No. 47/2017)**

The guardian is the person who has the willingness and is able to legally represent a UASC. The guardian is a volunteer (Article 379 cc). Each volunteer guardian may be called upon to assist up to three unaccompanied UASC, unless there are special reasons (e.g. a group of siblings greater than three). As defined by the National Ombudsperson for Childhood and Adolescence, the guardian’s tasks are to:

**•**    - ensure that the individual under the age of 18 is guaranteed access to rights without any discrimination;

**•**    - promote the psychophysical well-being of individual under the age of 18;

**•**    - follow the education and integration pathways, verifying that his/her abilities, natural inclinations and
aspirations are taken into account;

**•**    - monitor reception conditions, security and protection;

**•**    - administer any assets of the person under the age of 18.

Pursuant to Article 11 of Law 47/2017, lists of volunteer guardians have been established. The procedure for the selection of guardians is defined within the guidelines issued by the National Authority for Childhood and Adolescence.
The individual interested in assuming the role of volunteer guardian can participate in the call for applications issued by the Local Authority for Children and Adolescents.
After verifying that the applicants meet all the requirements of the law, the competent office contacts them for an interview aimed at selecting the candidates who participate in the compulsory training course. During the training course, topics related to child development and immigration law are discussed in depth. The course addresses how to recognize the needs of a child and the behaviours to be adopted. At the end of the course, the selected and trained candidate is enrolled in the list of volunteer guardians at the Juvenile Court.

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**Box 4 – Possibility of family reunification for UASC**

The Ministry of Labour and Social Policies, in accordance with the DPCM 535/99, is proactive and carries out search activities in for the identification of family members of UASC present on the Italian territory. To this end, the Ministry has entered into an agreement with IOM for tracing family members in the countries of origin or in third countries where they reside. With specific reference to UASC asylum seekers who intend to reunite with a family member residing in a country of the European Union (EU), procedures can be activated under the Dublin Regulation III, as implemented by Legislative Decree 142/2015. To ensure that family reunification is line with the best interest of the child, the child is listened to, taking into account his or her age, degree of maturity and personal development, in order to verify the possibility of family reunification. All actions aimed at tracing family members wherever they reside are carried out in the best interests of the child and with the obligation of absolute confidentiality, so as to protect the safety of the UASC and his/her family members. IOM works closely with the social services of the municipalities where the UASC reside, the CSOs and organizations that manage the reception centres, and with the judges of the juvenile courts. This collaboration between separate bodies is further strengthened by partnerships established with institutions and other actors involved in the countries of origin or residence of the families of the UASC. 111 family surveys were completed and transmitted by IOM to the MLPS in 2018, and a further 45 in the first half of 2019. In total, 21 of these were carried out in EU countries and one in the United States. The main UASC nationalities for which they were conducted are Albania, Tunisia, Nigeria, Kosovo*, Morocco, Pakistan and the Gambia.

- References to Kosovo shall be understood to be in the context of United Nations Security Council resolution 1244 (1999).

**Box 5 – Access to work opportunities**

With regard to employment, the same rules apply to foreign UASC as to Italian minors. According to the Law on the Protection of Children from Labour [Law 977/67], combined with the School Reform Law [L. 52/2003 as amended by Law 296/2006], children may be allowed to work only after the age of 16 and upon completion of ten years compulsory schooling. Compulsory training, in contrast, is envisaged until the age of 16, and can be fulfilled in the school system, in the vocational training system or through apprenticeship. Therefore, a child may only enter into a contract other than an apprenticeship if this is compatible with school attendance or vocational training. According to the Reform of Article V of the Constitution [Constitutional Law 3/2001], labour law and policies are determined by the Regions following guidelines established at the national level. This gives rise to a complex and diversified framework at the local level. In most regions, in the absence of local legislation, national legislation determines which residence permits are valid for employment and access to the relevant services. Hence, all the residence permits for UASC allow them to work [Article 33, Testo unico sull’immigrazione d.lgs. 286/1999.], including permits for international protection (after 60 days from the request) [Article 22, d.lgs. 142/2015], except the residence permit for UASC [Article 10, L. 47/2017], with regards to which there is a normative gap on the issue. This gives rise to heterogeneous practices in different regional contexts.

Notwithstanding the definition of a regulatory framework at national level, rules regarding labour insertion, such as extracurricular internships, are also different between regions.

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**Box 6 – Gender-based Violence**

As widely documented, women, girls, men and boys face numerous risks related to sexual and gender-based violence (SGBV) in their country of origin, along migration routes and in Italy (United Nations, 2018; UNHCR, 2018; Oxfam, 2017; UNICEF, 2017b; IOM, 2017; Women's Refugee Commission, 2019). Studies and reports show that almost all women and female adolescents who travel along the central Mediterranean route have experienced sexual violence. Other forms of GBV also affect women and girls, such as forced marriage, domestic violence and female genital mutilation, as well as human trafficking. Moreover, a recent report by the Women's Refugee Commission (2019) points out that men and boys are also subjected to sexual violence during their journey to Italy, particularly in Libya as the main transit country to Italy. Refugees and migrants experience sexual violence at borders and checkpoints, during random arrests by armed groups and when kidnapped and imprisoned. Sexual violence, including sexual torture, against refugees and female and male migrants appears widespread in Libya. Moreover, fewer arrivals on the Italian coast mean longer periods spent in Libya and therefore an increase in the risks and consequences of violence. It is also clear that the risks do not end when migrants and refugees arrive in Italy. In particular, sexual harassment and sexual exploitation continue to be a problem. Although little information is available on domestic violence, it will inevitably continue to be a problem for women and children in Italy, due to, among other reasons, to the fact that asylum applications are submitted by the family, which may unwillingly cause women to remain in situations of violence.

The short- and long-term psychosocial and health impacts of SGBV are wide-ranging. They include depression, anxiety, post-traumatic stress disorder, unwanted pregnancies, sexually transmitted infections including HIV, and genital and rectum trauma. Despite all this, with appropriate support and assistance, the survivors can recover and overcome them. Recently, the Women’s Refugee Commission (WRC) found that there was a surprising difference between survivors living in good quality shelters, who therefore had access to care and support, and those outside the formal care system or who had more limited access to care. Survivors show a remarkable resilience and “had the ability to look ahead, while young unsupported men struggled and some of them had adopted negative coping mechanisms, including drug and alcohol abuse and petty crime” (WRC, 2019).

While a number of organizations and institutions provide comprehensive, people-centred services for male and female survivors, the need is far from being met. The Italian and anti-violence reception systems are characterized by limited decentralization and coordination; hence, survivors could fall through the cracks of the system. Despite the efforts of the Italian Government, the institutions and civil society, there are many shortcomings: an unaccountable and highly fragmented system, lacking any monitoring system, characterized by geographical disparities in the availability of assistance. While the protection system in Italy is promising, dealing with highly vulnerable UASC also entails addressing issues related to their specific conditions and challenging the knowledge, attitudes and beliefs of those who care for them.

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# **Endnotes**

1 United Nations Convention on the Rights of the Child, 1989.

2 Article 12 of the United Nations Convention on the Rights of the Child, 1989.

3 Available in Annex 2 of the full report in Italian.

4 Ibid 5 Ministry of Interior, Ministry of Labour and Social Policies, Ministry of Education, University and Research, Ministry of Justice, Central Service of SIPROIMI (Protection System for Beneficiaries of International Protection and for Unaccompanied Foreign Minors), AGIA (National Ombudsperson for Children and Adolescents).

6 Snowball sampling (also known as chain-referral sampling) is a non-probability (non-random) sampling method, used when sample characteristics are rare and difficult to find. This sampling method involves primary data sources nominating another potential primary data source to use in the research. In other words, the snowball sampling method is based on referrals from initial subjects in order to generate additional subjects. Therefore, when applying this sampling method, members of the sample group are recruited via chain referral.

7 This is approximately 10 per cent of the sample against around 7 per cent of the total number of UASC hosted in reception facilities.

8 For details see Annex 4 in the full report in Italian.

9 See Glossary.

10 See the website of the United Nations Secretary-General’s Office of the Envoy: www.un.org/youthenvoy/wpcontent/ uploads/2018/09/18-00080_UN-Youth-Strategy_Web.pdf 11 Italy ratified and implemented the Convention with Law 176/1991.

12 The Dublin system, currently governed by Regulation (EU) No. 604/2013 of the European Parliament and of the Council of 26 June 2013, establishes the criteria and mechanisms for determining the Member State responsible for examining an application for international protection submitted in one of the Member States by a third-country national or a stateless person (the Dublin III Regulation). With regard to family reunification within the Dublin system, see also Article 18(1) of Legislative Decree 142/2015.

13 According to Article 10 of Law 47/2017, the child must be informed of the procedure, which the Juvenile Court must authorize; this procedure ends with the adoption of an age determination measure, which is conveyed to the child and to the guardian. However, the interaction between this procedure and the procedure governed by Prime Ministerial Decree 234/2016 used for victims of trafficking in human beings (UASC) has not been clarified to date.

14 The model outlined both at the normative level and in practice can be classified as ‘mixed’, with both volunteer and institutional guardians.

15 Each guardian may assume the protection of up to three UASC (unless there are specific needs that require an increase of this number). For further information on the role and functions of the guardian, see the corresponding Information box in the Annex.

16 See Article 10 of Law 47/2017.

17 See Article 32(1a) of Legislative Decree 286/1998.

18 For example, residence permits for special cases introduced or modified by l.d.113/2018.

19 L.d.113/2018 provides for a transitional regime both for holders of residence permits for humanitarian reasons, who are deprived of the possibility of renewing such permits after the expiration date, but without prejudice to the power to convert them into residence permits for job seeking, or study or work purposes, and for those who have submitted an application for international protection before its entry into force, who may still be granted a two years residence permit for special cases under the transitional regime which is convertible into a residence permit for employment or self-employment after its expiration.

20 The Territorial Commission (Commissione Territoriale) is the entity responsible for the assessment of and recognition of international protection following an audition with the asylum seeker. It consists of: a president, who is a deputy Prefect; Ministry of Interior caseworkers; and a representative of UNHCR who sits in the decision-making panel.

21 AMIF reception facility: first-level reception facility funded by the EU Asylum Migration Integration Fund (AMIF) 22 Irrespective of legal status, at the age of 18, each child may be entrusted to the continued care and protection of social services until the age of 21 at the latest, including when the Juvenile Court considers that the person, despite having embarked on a process of social inclusion, needs this continued care and protection to ensure the full success of the pathway towards autonomy.

23 See Article 17 of Law 47/2017, which amended Article 13 of Law 228/2003.

24 The MLSP defines the term ‘absconded’ as minors who leave the reception facilities in which they are hosted.
Minors are defined as absconded until they are found in Italy or reach the age of 18.

25 At the time of completing the report, the 2018 data were not yet available.

26 Aspirations are defined as the individual’s desire to achieve certain goals, whereas expectations relate to the 90

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**At a crossroads. Unaccompanied and separated children in their transition to adulthood in Italy**

individual’s considerations about the possibility or probability of achieving them (Hansen and McIntire, 1989).

27 All the Congolese’s UASC interviewed come from the Democratic Republic of the Congo.

28 The regulation governing the various residence permits for special cases differs in terms of the possibility of conversion into another residence permit for work and/or study purposes. Hence, there is a distinction between forms of protection that allow, in principle, a longer-term integration (former residence permit for social protection pursuant to Article 18 of Legislative Decree no. 286/1998) and those that, on the contrary, meet a need for contingent protection and do not allow a stabilization of the regular presence on the territory (former residence permit for medical care pursuant to Article 19 of Legislative Decree no. 286/1998).

29 For further details, see section 3.2 30 The profile of the professional educator is defined in the Ministerial Decree of 8 October 1998, n. 520: “Regulation laying down rules for the identification of the role and the relative professional profile of the professional educator, pursuant to Article 6(3), of the legislative decree of 30 December 1992, no. 502.” With some regional differences, in order to work with minors, a professional educator with a university degree is often required, while generally, to work with adult migrants, a professional social worker is required. For this reason, these terms have been used in this report.

31 Personal Education Plan which defines the social inclusion pathway for UASC, and is prepared together with reception facility staff and the Social Worker municipal social services.

32 CPIA: Adult Learning centres or catch up schools. Over the last few years, with the influx of migrants and refugees, and especially of UASC, CPIAs provide Italian language courses and courses for the attainment of middle school compulsory schooling. Upper secondary level schooling is also offered.

33 The total number of UASC counted in the CPIAs was 3,347 (30 out of 39 CPIAs provided data for the purpose of the research). However, it should be noted that, at the time of the UASC course enrolment, their status of ‘unaccompanied’ or not was not recorded; the registration forms require the signature of a parent or legal representative, and this information is then reconstructed by the CPIAs on the basis of their direct knowledge of the children enrolled in their schools. For details, see Annex 1 in the full report in Italian.

34 See Cedefop, 2014.

35 In this regard, the UNICEF and REACH report (2017) provides timely data on the illiteracy rates of UASC in Sicily, where 48 per cent stated that they had completed lower secondary school; more than half that they could read (58 per cent) and write (54 per cent), and the remaining that they could read a little (29 per cent) and write a little (32 per cent) or were illiterate (13 per cent and 14 per cent).

36 According to the D.M. MIUR 139/2007, Article 3 (3), UASC can be enrolled in the CPIAs from the age of 16, but some local protocols and agreements allow for the enrolment of 15-year-olds provided that certain conditions are met (MIUR, Autorità Garante dell'Infanzia e l'Adolescenza, Guidelines for the right to study of students outside their families of origin, 2017).

37 See Intersos and UNICEF, Vademecum per l’inserimento lavorativo. Tutor e operatore. Version 2.0, Intersos, Unicef http://www.integrazionemigranti.gov.it/Attualita/Notizie/Documents/intersos_vademecum_tutor.pdf 38 For details on the target group and survey respondents, see Annex 2 of the full report.

39 FRA and AGIA (2018). Guardianship for Unaccompanied Children in Italy, April, available at: https://fra.europa.eu/en/news/2018/innovative-italian-legislation-updates-approach-guardianship 40 FRA (2015).

41 In this regard, reference should be made to the Palermo Model, which was studied in depth in the case study in Sicily.

42 An important reference in this respect is the recommendations issued by the United Nations Committee on the Rights of the Child as part of its observations on the implementation of the United Nations Convention on the Rights of the Child.

43 After requesting the MLPS to decide on the conversion of the permit for minor age, for cases where it is possible.

44 The Circular of the Ministry of the Interior – Department for Civil Liberties and Immigration, of 3 January 2019, stresses that UASC who, as a result of the provisions introduced by Law no. 47/ 2017, are granted the opportunity to continue their pathway of reception and integration until the age of 21, remain within the SIPROIMI system.

45 The special protection is short, and the renewal is conditional on the persistence of legal claims to be established by the Commission. It can never be converted.

46 See Intersos and UNICEF, Vademecum per l’inserimento lavorativo. Tutor e operatore. Version 2.0, Intersos, Unicef http://www.integrazionemigranti.gov.it/Attualita/Notizie/Documents/intersos_vademecum_tutor.pdf. See also the Pathways for the social and job integration of UASC and young migrants on the website of the MLSP and the related integration policies in the related link (http://www.integrazionemigranti.gov.it/Progetti-e-azioni/Pagine/Percorsidi-integrazione-socio-lavorativa-per-minorinon-accompagnati-e-giovani-migranti-.aspx) as an example of possible pathways supported at the institutional level.

47 This is also the case for 9.8 per cent of the 2017 Protection System for Asylum Seekers and Refugees (SPRAR) projects (see the 2017 SPRAR Annual Report).

48 Specifically, Percorso I funded 960 social and work integration pathways for UASC’s transition to adulthood (from 91

UNICEF and REACH report

illiteracy rates of UASC in Sicily


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**Fondazione ISMU**

the age of 16) and young migrants (up to 22 years of age). Subsequently, Percorso II funded 850 work integration pathways followed by a third phase, which, with a new notice, provides for the financing of a further 170 pathways.
These pathways are supported by the Protezione Unità a Obiettivo Integrazione (PUOI, Protection Unit for Integration) project for 4,500 additional beneficiaries, currently in the start-up phase. See https://www.anpalservizi.it/home 49 With respect to the traineeships carried out under Percorso I, “the entire pathway and, in particular, the traineeship experience is generally considered positive by the recipients, since they allow an improvement in living conditions, not only in economic terms; they increase the technical-professional knowledge and skills” (Report of Evaluation of the Percorso Action I by MLSP-Anpal services, 2017, p. 29 et seq.).

50 For further information on these forms of semi-independence, see the Lombardy case study in chapter 7.

51 Family foster care is one of the forms of alternative care provided for by the United Nations Guidelines on Alternative Care for Children, but it is not limited to this. The Guidelines aim to improve the implementation of the Convention on the Rights of the Child on the protection and welfare of children without parental care, or at risk of losing it, defining the desirable guidelines for policy.

52 For further details, see: Youth, ExCom 66th meeting, 31 May 2016, www.refworld.org/pdfid/5800cdea4.pdf 53 The data that emerged from the research were not clear as to whether these procedures are mainly determined by the lack of requests to initiate these procedures at the same time, or rather, as a legal procedure of the individual police stations.

54 The difficulties faced in Sicily in particular have also been evidenced by recent studies (Save the Children, 2017– 2019), where it emerged that reception facilities sometimes struggle to cope with a lack of resources to ensure logistics in transporting UASC to schools or CPIAs. Another problem arises with the transfer from one facility to another (from the first to the second level of reception, upon turning 18) in which school enrolment is not always guaranteed or the attended hours recognized.

55 For an overview of the phenomenon, see WRC (2019).

56 ‘Agency’ refers to the ability of an agent (natural person or other subject) to act independently in the world and to make free choices (Giddens, 1992).

57 See the definition in the Glossary.

58 For further information on the issue of social housing, see: Ferri et al. 2017.

59 Bourdieu and Wacquant (1992) define social capital as “the sum of the resources, both real and virtual, that come from an individual or a group through a solid network of relationships, partly institutionalized, of mutual acquaintance and identification”. For further information on the concepts of formal and informal relations in the field of migration, see Ambrosini (2014: 171–174; idem 2011).

60 The central idea of this theatre methodology, conceived over 50 years ago in Brazil by the pedagogue Paulo Freire, is to use the language of drama to understand and transform the daily oppressive, realities, small and large, towards liberation. This theatre method has also been adopted by Matemù and other meeting centres in Rome, such as Laboratorio 53 and Asinitas.

61 The institutional working group was composed of: the Civil Court of Palermo, the Guardianship Judge, the Prosecutor's Office at the Juvenile Court of Palermo, the Police Headquarters of Palermo, the University of Palermo, the Provincial Health Authority of Palermo, and the Regional Education Authority for Sicily.

62 The cartella sociale, or social file, records information related to the reception and social inclusion path of UASC in Italy. It is included in the national database of the Ministry of Labour and Social Policies.

63 Council of Europe, Ministers’ Deputies, CM/Rec(2019)4, Recommendation CM/Rec(2019)4 of the Committee of Ministers to member States on supporting young refugees in transition to adulthood.

64 https://data.unicef.org/topic/adolescents/overview/0/ 65 https://www.unicef.org/sites/default/files/2019-04/UN-Convention-Rights-Child-text.pdf 66 Ibid 67 https://gbvguidelines.org/wp/wp-content/uploads/2015/09/2015-IASC-Gender-based-Violence-Guidelines_lo-res.pdf 68 www.unhcr.org/sexual-and-gender-based-violence.html<www.unhcr.org/sexual-and-gender-based-violence.html> 69 https://www.unicef.org/sites/default/files/2019-04/UN-Convention-Rights-Child-text.pdf 70 (www.gazzettaufficiale.it/eli/id/2017/04/21/17G00062/sg) 71 www.europarl.europa.eu/RegData/etudes/BRIE/2017/608728/EPRS_BRI(2017)608728_EN.pdf 72 (https://www.camera.it/parlam/leggi/deleghe/98286dl.htm) 73 (https://fra.europa.eu/sites/default/files/fra_uploads/fra-2018-it-guardianship-legal-update_en.pdf); www gazzettaufficiale.it/eli/id/2017/04/21/17G00062/sg)(www.gazzettaufficiale.it/eli/id/2017/04/21/17G00062/sg.

Ixxv https://www.garanteinfanzia.org/sites/default/files/linee_guida_per_il_diritto_allo_studio_delle_alunne_e_degli_ alunni_fuori_dalla_famiglia_di_origine.pdf Ixxvi See: www.miur.gov.it 92

cartella sociale


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**Fondazione ISMU is an independent research centre**

**promoting research and training activities on**

**migration, integration and diversity in multicultural**

**and multiethnic contemporary societies.**

**www.ismu.org**


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Sébastien Desbureaux, Jorge Escurra, Aude-Sophie Rodella, and Esha Zaveri

## Quantifying the Impact of Water Salinity on Global Agricultural Productivity


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this model generates more firepower for transformational solutions to help countries grow sustainably.

Please visit us at www.worldbank.org/water or follow us on Twitter at @WorldBankWater.

#### About GWSP

This publication received the support of the Global Water Security & Sanitation Partnership (GWSP). GWSP is a multidonor trust fund administered by the World Bank’s Water Global Practice and supported by Australia’s Department of Foreign Affairs and Trade, the Bill & Melinda Gates Foundation, the Netherlands’ Ministry of Foreign Affairs, Norway’s Ministry of Foreign Affairs, the Rockefeller Foundation, the Swedish International Development Cooperation Agency, Switzerland’s State Secretariat for Economic Affairs, the Swiss Agency for Development and Cooperation, U.K. Department for International Development, and the U.S. Agency for International Development.

Please visit us at www.worldbank.org/gwsp or follow us on Twitter #gwsp.


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# Salt of the Earth

## Quantifying the Impact of Water Salinity on Global Agricultural Productivity

Jason Russ, Richard Damania, Sébastien Desbureaux, Jorge Escurra, Aude-Sophie Rodella, and Esha Zaveri


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© 2019 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent.

The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

**Rights and Permissions**

The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given.

Please cite the work as follows: Russ, Jason, Richard Damania, Sébastien Desbureaux, Jorge Escurra, Aude-Sophie Rodella, and Esha Zaveri. 2019. “Salt of the Earth: Quantifying the Impact of Water Salinity on Global Agricultural Productivity.” World Bank, Washington, DC.

Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications,

[The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights](mailto:pubrights@worldbank.org)

[@worldbank.org.](mailto:pubrights@worldbank.org) _Cover photos_ _(left to right)_ : A'Melody Lee / World Bank, Wietze Brandsma from Pixabay, Bishnu Sarangi from Pixabay.

_Cover design:_ Jean Franz, Franz and Company, Inc.


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### Contents

Abstract 1 Introduction 1 Data 3 Empirical Strategy 7 Main Results 8 Robustness Tests 9 Simulating Global Impacts of Water Salinity On Agricultural Production 13 Conclusions 18 Notes 18 References 19

**Figures**

1. Mekong River Basin Commission Monitoring Station Locations 5

2. CWC Monitoring Stations and Irrigated Districts in India 7 3. Location of Gemstat Monitoring Stations 14 4. Yield Loss When Electrical Conductivity Exceeds 100mS/M 15 5. Flexible Estimation of Yield Losses Due To Electrical Conductivity 15 6. Global Predicted Levels of EC, 1992-2010 16 7. Estimated Mean Annual Kilocalorie Equivalent Loss Due To Saline Water, 2001-2013 17 8. Estimated Mean Losses in Agricultural Production Due To Saline Water, 2001-2013, by Country 17

**Tables**

1. Summary Statistics of Major Datasets 3

2. Water Quality Datasets 4 3. Impact of Electrical Conductivity on Net Primary Productivity, Mekong River Basin 10 4. Impact of Electrical Conductivity on Net Primary Productivity, India 11 5. Impact of Electrical Conductivity on Net Primary Productivity, Global 12 6 Impact of Electrical Conductivity on Net Primary Productivity, Alternative Thresholds, Mekong River Basin 13 7. Impact of Electrical Conductivity on Net Primary Productivity, Alternative Thresholds, India 14 Salt of the Earth iii

Gemstat Monitoring Stations

CWC Monitoring Stations

Water Quality Datasets

Water Quality Datasets


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**Abstract**

Salinity in surface waters is on the rise throughout much of the world. Many factors contribute to this change including increased water extraction, poor irrigation management, and sea-level rise. To date no study has attempted to quantify impacts on global food production. In this paper we develop a plausibly causal model to test the sensitivity of global and regional agricultural productivity to changes in water salinity. To do so, we utilize several local and global datasets on water quality and agricultural produc tivity and a model which isolates the impact of exogenous changes in water salinity on yields. We then train a machine learning model to predict salinity globally in order to simulate average global food losses from 2000-2013. These losses are found to be high, in the range of the equivalent of 124 trillion kilocalories, or enough to feed over 170 million people every day, each year. Global maps building on these results show that pockets of high losses occur on all continents, but can be expected to be partic ularly problematic in regions already experiencing malnutrition challenges.

**Introduction**

Policy discussions surrounding agricultural water use tend to revolve around water scarcity and vari ability, and its impact on agricultural productivity. While these are indeed critical areas of focus, partic ularly in the context of climate change induced rainfall variability, challenges from water quality constitute an equally important threat that is currently underappreciated. Over extraction of surface and groundwater, sea-water intrusion, rainfall variability, and poor irrigation water management all contribute to increased salinization of water supplies and in many regions, with deleterious effects on crop production.

Although the harm that saline soil and water can have on agricultural productivity is well known, and indeed has a long history, to date there has been no attempt to quantify this impact globally. In this paper, we take advantage of several regional and global datasets on agricultural productivity, land use, and water quality and estimate the average impact of high levels of salinity in surface water on crop production. We start with regional analyses in the Mekong River Basin and river basins of India where high-quality data exists. Using hydrological models and data on surface water irrigation, we link agricul tural productivity to upstream water quality monitoring stations which indicate the levels of water salinity that is flowing onto downstream fields for irrigation. By controlling for confounding factors which also impact agricultural productivity (such as rainfall, temperature, and geographic factors), and carefully accounting for the direction of stream flow, we isolate the impact of plausibly exogenous changes in electrical conductivity (EC), the most commonly used measure of salinity in water, on downstream agricultural yields. We then expand the sample and conduct the same analysis using monitoring stations from the GEMStat database, which covers regions in 36 countries around the world.

These estimates are then used to simulate the average annual fall in yields due to saline waters.

Impacts on crop production are seen at relatively low levels of salinity and tend to rise at a near linear rate as water salinity rises. A satellite-based measure of agricultural productivity, combined with estimated elasticities of agricultural yields to saline surface water, and globally modeled data on water salinity reveal significant annual losses in food production. A simulation of global food losses due to Salt of the Earth 1

satellite-based measure of agricultural productivity

GEMStat database

globally modeled data on water salinity

regional and global datasets

local and global datasets

local and global datasets

regional and global datasets


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salinity is performed by training a predictive model of EC at a 0.5 degree between 1992 and 2013, and by combining these predictions with the elasticities we estimated. It is estimated that water salinity reduces global agricultural production by 124 trillion kilocalories per year, the equivalent annual food budget of 170 million people. These results suggest that saline surface and irrigation water are a global concern for food security that is underappreciated and deserves greater attention.

The relationship between saline water and agricultural production is well known and documented as far back as ancient Sumer (Thompson 2004). Salt can interfere with crop growth in a variety of ways; by displac ing other important nutrients like nitrogen and phosphorus, interfering with photosynthesis and chloro phyll production, withdrawing water from surrounding soil, and increasing plant energy requirements for extracting water from soil (Warrence, Bauder, and Pearson 2002). Crops are not universally or uniformly impacted by salinity. Plants which have higher Na [+] and Cl [− ] concentrations in their leaves tend to be more salt-tolerant as it reduces the amount of salt ions that enter plant cells through osmosis (Munns and Gilliham 2015). Recently it has been estimated that approximately 1,125 million hectares of land are salt-affected, of which approximately 76 million hectares have been salinized by human-induced activities (Sanower 2019).

To our knowledge, there has been no global study attempting to estimate the impact of salinity on crop production. The literature on this topic is composed primarily of regional estimates based on crop production models or crop specific analyses which examine sensitivity to salinity for specific crops.

Recent regional estimations include Dam et al. (2019) which finds that in central Vietnam, for each 1 percent increase in EC levels, yields of paddy rice decline by 0.24 percent. Clarke et al. (2015) estimates that in coastal Bangladesh, irrigating with saline surface and groundwater will reduce crop yields by at least 25 percent by the end of the century. Similarly, Dasgupta et al. (2014) estimates that rice yields will fall by 15.6 percent in by 2050 in 9 coastal districts in Bangladesh due to climate induced increases in salinity. Genua-Olmedo et al. (2016) finds that in the Ebro Delta in Northeast Spain, rice productivity will fall by up to 50 percent by 2100 due to the effects of sea-level rise on soil salinity.

A deep scientific literature exists on the sensitivity of specific crops to salinity. A thorough review of this literature was conducted by Tanji and Kielen (2002) where threshold and slope values for 81 crops in terms of EC are collected and listed ~~.~~ [1] Similarly, Machado and Serralheiro (2017) review the literature on salt tolerate of vegetables. In table 2 they compare soil salinity and irrigation water salinity tolerance thresholds (i.e. the EC at which yields begin to decline) for 19 common vegetables. They find that vege tables generally have a soil salinity tolerance below 250 mS/m, and that irrigation water tolerances are generally around 30-50 percent less than soil salinity tolerances. Several more recent studies examine specific crops. For example, Rameshwaran et al. (2016) find that sweet peppers become sensitive to water salinity at levels exceeding 143 mS/m and decline by a slope of 11 percent thereafter. Arslan et al.

(2015) found that yields of chickpeas, lentils, and faba beans fell by 50 percent when EC in irrigated water exceeded 420, 440, and 520 mS/m, respectively. This implies that based on the staple diet of coun tries, salinity may have an amplified impacts on countries relying on agricultural production that is more salinity sensitive. While crop based studies are informative they do not provide an indication of the global loss of food production due to salinity. To address this issue this paper provides estimates of the average impacts on yields across all crops using a variety of recently available data sources and finds a remarkably stable yield response function that holds across wide geographies.

2 Salt of the Earth


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**Data**

To estimate the impact of water salinity on global agricultural productivity, we use several georefer enced datasets related to water quality, agricultural yields, land cover, and weather. A description of these datasets can be found below and summary statistics are provided in Table 1.

Water Quality Data on electrical conductivity (EC) was used to measure salinity in water. EC measures the ability of an electrical charge to pass through water. Distilled, or deionized water is a very poor conductor of electric ity and as dissolved ions in water increase, it becomes a better conductor. In surface and groundwaters, these ions are generally dissolved salts which is why EC is a widely used indicator of water salinity

**TABLE 1. Summary Statistics of Major Datasets**

**Variable** **Obs** **Mean** **Std. Dev.** **Min** **Max**

**Mekong River Basin**

Δlog(NPP) 3227 −0.00256 0.146958 −0.52872 0.573803 Electricial Conductivity 3784 127.8139 442.9442 3.2 3598.75 Electrical Conductivity > 100 mS/m 3784 0.075317 0.263937 0 1 Precipitation (m/year) 3784 1.545724 0.423054 0.4649 3.0135 Precipitation^2 (m/year) 3784 2.56819 1.433598 0.216132 9.081182 Temperature (C) 3784 27.31999 1.070222 21.225 29.70833 Temperature^2 (C) 3784 747.5272 57.06304 450.5006 882.5851 Distance to station (km) 3784 40.70026 23.44734 1.501745 99.66087

**India**

Δlog(NPP) 23,786 0.022225 0.281151 −2.70913 3.197244 Electricial Conductivity 26,830 405.1288 311.6987 48.43333 2230 Electrical Conductivity > 100 mS/m 26,830 0.061014 0.23936 0 1 Precipitation (m/year) 26,830 1.156739 0.59924 0.1696 4.8393 Precipitation^2 (m/year) 26,830 1.697122 2.267632 0.028764 23.41882 Temperature (C) 26,830 26.36198 1.755244 15.13333 29.66667 Temperature^2 (C) 26,830 698.0349 90.26891 229.0178 880.1112 Distance to station (km) 26,830 54.64403 24.02906 0.783259 99.94202

**Global**

Δlog(NPP) 55,358 0.014986 0.276158 −3.8473 4.766428 Electricial Conductivity 68,984 596.4569 1666.453 0 41914.29 Electrical Conductivity > 100 mS/m 68,984 0.100792 0.301055 0 1 Precipitation (m/year) 68,984 1.199992 0.657942 0.0005 5.1165 Precipitation^2 (m/year) 68,984 1.872862 2.244765 0.00 26.17857 Temperature (C) 68,984 21.59153 6.87379 2.333333 31.1 Temperature^2 (C) 68,984 513.4423 267.854 5.444445 967.21 Distance to station (km) 68,984 50801.36 29429.34 0 99997.37 _Notes:_ Table summarizes the main datasets use in each of the three models employed in this paper.

Salt of the Earth 3

Water Quality Data on electrical conductivity

georefer enced datasets


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(Miller, Bradford, and Peters 1988). EC as a measure of salinity has the added benefit of being easily measured at monitoring stations or with handheld monitors, making it a very widely available water quality indicator.

Three distinct datasets were used to measure surface water quality. These datasets are from the Mekong River Basin Commission (MRC), the Central Water Commission (CWC) of India, and GEMStat of the United Nations Global Environmental Monitoring system for freshwater (UNGEMs). Table 2 summarizes these datasets. The MRC data spans four countries (Cambodia, Lao PDR, Thailand, and Vietnam) and covers the main tributaries of the Mekong River. Of the 121 stations with data on EC, 23 are in Cambodia and Lao PDR, 21 are in Thailand, and 54 are in Vietnam (figure 1).

In India, the Central Water Commission or CWC (under the umbrella of the Ministry of Water Resources) maintains a network of 375 river monitoring stations throughout India that cover almost all major river basins in the country. Water samples are taken at specific time intervals (monthly or quarterly), and then sent to regional laboratories for analysis.

Finally, the GEMStat data from UNGEMS is used for the global analysis. This data is collected by the United National Environmental Programme (UNEP) and is self-reported by participating countries. It contains over 3 million observations for 224 water quality parameters in 71 countries. EC is one of the most documented pollutant in GEMstats with 167,914 observations of EC in 1,719 stations and 71 coun tries. As with the Indian CWC data, frequency of observations varies significantly across countries and monitoring stations. Some monitoring stations have multiple observations per month while others have seasonal or annual observations (e.g., one station in Japan has 1,721 observations of EC only). For all datasets, observations at a single monitoring station are collapsed at the annual level. This leaves us with a panel dataset of year/station observations.

Agricultural Productivity and Land Use To measure changes in agricultural productivity, we use a satellite- based estimate of net primary production (NPP). NPP is linearly related to the amount of solar energy that plants absorb over a growing season (Running _et al_ . 2004). There is substantial evidence that there is a strong positive correlation between satellite-derived estimates of NPP and crop yields (Lobell _et al_ . 2002; Lu and Zhuang, 2010; Tum and Gunther 2011), and for this reason it is a frequently used proxy of yields in the economics literature (Strobl and Strobl, 2011; Blanc and Strobl, 2013; Blanc and Strobl, 2014; Zaveri, Russ, and Damania 2019). [2]

**TABLE 2. Water Quality Datasets**

**Location**

**Mekong River Basin (Cambodia, Lao**

**PDR, Thailand, and Vietnam)** **India**

Source Mekong River Basin Commission Central Water Commission for India

**Global (36**

**countries)**

GEMStat Number of Monitoring Stations Included 121 425 1,124 Frequency of Observations Approximately monthly, with gaps Varies by station Varies by station Years of Observations 2000-2013 2000-2013 2000-2013 4 Salt of the Earth

datasets from the Mekong River Basin Commission (MRC)

GEMStat data from UNGEMS

GEMStat


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**FIGURE 1. Mekong River Basin Commission Monitoring Station Locations**

_Notes:_ Map shows location of water quality monitoring stations in Mekong River Basin Commission dataset.

Our time-varying NPP data comes from the moderate resolution imaging spectroradiometer (MODIS), whose data starts in 2000. We use the annual MOD17A3 measures from 2000-2013 generated by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana (Zhao _et al_ ., 2005) which corrects for cloud contamination prevalent in MODIS land products ~~.~~ [3] Our interest is in estimating NPP from cropland, as opposed to natural forests or vegetation. To do so, we make use of a new and unique land cover dataset developed by the European Space Agency’s (ESA) Salt of the Earth 5

moderate resolution imaging spectroradiometer (MODIS)

Mekong River Basin Commission dataset

MOD17A3 measures

land cover dataset

land cover dataset

land cover dataset


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Climate Change Initiative. This dataset provides information on 37 land cover classes based on the United Nations Land Cover Classification System at a 300m resolution. The data relies on state-of-art reprocessing of four different satellite missions (MERIS, SPOT-VGT, AVHRR, and PROBA-V) [4] ~~.~~ We use a cross-walking table from Poulter _et al_ . (2015) that characterizes plant functional types across the ESA classes to guide the grouping of classes into a single cropland category ~~.~~ [5] Our final data measures changes in NPP for each 0.1-degree gridcell (approximately 11x11 km at the equator) which contains a minimum level of cropland in the year 2000 (the first year of our dataset).

There are tradeoffs when choosing the minimum level of cropland for a gridcell to contain to be included the analysis. Too low of a threshold and the analysis will include gridcells which are dominated by nat ural vegetation or urban land, and thus of no interest to the study, potentially biasing the results. Too high of a threshold and the analysis becomes too restrictive, where only agriculture situated in large farming communities is included. For this reason, we test a range of thresholds, with the main analysis using a 30% cropland minimum threshold to be included in the analysis, and robustness checks using a 75% and 90% threshold.

Irrigation Data Gridcells included in this analysis are restricted to those where it is believed that agriculture is irri gated. This ensures that the EC measured in the surface water is like the EC in the water which is feeding the crops. In India, data on irrigation by district was obtained from the Ministry of Agriculture and the International Crops Research Institute for the Semi-Arid Tropics. Only districts for which greater than 50 percent of agricultural land is irrigated by surface irrigation is included in the analysis (see Figure YY).

For the Mekong River basin analysis, as well as the global analysis, data on irrigation was obtained from Food and Agriculture Organization’s Global Map of Irrigated Areas (GMIA) version 5.0 ~~.~~ [6] GMIA gives a global raster of area equipped for irrigation for the year 2005 at a resolution of 0.083 degrees.

This data was aggregated up to the 0.1-degree gridcell described in the previous section, and gridcells are only included if some land area is equipped for irrigation according to GMIA. GMIA does not distinguish between groundwater irrigation and surface water irrigation. For the Mekong analysis, this is not a concern as surface water irrigation comprises 91 percent of total irrigation in Thailand, 99 percent in Lao People’s Democratic Republic and Vietnam, and 100 percent in Cambodia (FAO 2016).

Thus, we can be reasonably confident that nearly all of the NPP included in the analysis is irrigated using surface waters.

For the wider, global study, there is unfortunately no way to distinguish between ground and surface irrigated crops. However, tt is well-known that ground and surface-water bodies interact with each other. Various studies in the hydrological literature have begun to show that groundwater can be a major and potentially long-term contributor to contamination of surface water ((e.g. Yu et al. 2018; Delsman et al., 2015; De Louw et al., 2010; Holman et al., 2008). The interactions between river water and ground water are determined by the relative difference between groundwater level and river stage (Mukherjee et al., 2018). In regions that are exposed to over-extraction of groundwater, the resulting lower dis charge can reduce groundwater’s contribution to the baseflow and can substantially decrease the stream 6 Salt of the Earth

United Nations Land Cover Classification System

data on irrigation by district
Irrigation Data Gridcells

Global Map of Irrigated Areas

data on irrigation by district

Irrigation Data Gridcells


Input:
**FIGURE 2. CWC Monitoring Stations and Irrigated Districts in India**

_Notes:_ Figure shows districts in blue where at least 50 percent of agriculture is irrigated using surface irrigation. Black dots show the location of the CWC monitoring stations.

flow of rivers (Mukherjee, 2018). This in turn can enhance salt concentrations in rivers because of lower dilution capacity. Excessive irrigation can also raise water tables from saline aquifers and cause seep age of saline groundwater into freshwater (Mateo-Sagasta et al., 2010).

Weather Data Our weather data comes from Matsuura and Willmott (2001). This gridded dataset contains monthly observations of precipitation and average temperature at the 0.5 degree gridcell level. We transform this data into average monthly temperature, and total precipitation (mm), per year, for each gridcell.

**Empirical Strategy**

To determine the impact of saline water on crop productivity, we estimate a crop production function.

Specifically, we estimate the following equation: D log( _NPPit_ ) = α + λ * _g_ ( _WQit_ ) + d * f( _climateit_ ) + s _i_ + ry + q _c_ - _Y_ + e _it_ (1) Salt of the Earth 7

Weather Data


Input:
where _NPPit_ is net primary productivity in gridcell i of year t; _g_ ( _WQit_ ) is a measure of water quality;  _f_ ( _climateit_ ) are measures of temperature and rainfall; s _i_ are gridcell fixed effects, which account for time invariant factors which impact agricultural productivity; ry are year fixed effects; and qc* _Y_ are region specific time trends, to account for local changes in economic or political structures which may impact agricultural production.

As discussed in the prior section, several restrictions are placed on the dataset to ensure accuracy.

First, only gridcells with at least 30 percent cropland according to the ESA dataset are included. In robustness checks, this threshold is changed to 75 percent and 90 percent. Agriculture in gridcells must also be irrigated, as described in section 2.3. Gridcells must be no more than 100 kilometers from their matched monitoring station, to ensure that water quality at the station accurately reflects irrigation water quality. This threshold is somewhat arbitrary and chosen to balance two competing factors: higher distance thresholds reduce the average relationship between measured upstream water quality and actual water quality in the gridcell, while lower distance thresholds reduce the number of observations in our study. In section 5 we present results where this threshold is varied for robustness.

In order to avoid bias caused by reverse causality, gridcells are carefully matched to monitoring sta tions which are strictly upstream from them. It is well known that if drainage is not carefully managed, irrigated agriculture can impact the salinity of discharged water. Thus, downstream water salinity can be impacted by upstream agricultural production. In addition, water extractions for agriculture or other uses can also impact downstream salinity levels. Thus, to ensure that the impact of water salinity on agricultural production is properly identified and that salinity levels are entirely exogenous, a careful matching of gridcells to upstream monitoring stations was done. In the Mekong and India analyses, this was done through the use of a hydrological connectivity approach, where the streamflow from each gridcell was traced upstream until a monitoring station was found. The hydrological connectivity approach is a process in which areas of land (such as districts or grid cells) are connected to water quality stations though the drainage network based on the direction of surface run off. To achieve this process, the following steps were taken: (i) delineation of the drainage network of the study area; (ii) connecting the areas of land (districts in India and grid cells in the Mekong) with the drainage network; (iii) identi fying the areas of land which are being impacted by the water quality stations following the drainage path; (iv) obtaining the distance between water quality station and each of the districts and grids. For tractability, this process was changed for the global analysis. Here, given the wide geographic range of monitoring stations across the world, tracing streamflows was deemed infeasible. Thus instead, grid cells were matched the nearest monitoring station that is at a higher elevation than the gridcell itself.

Since water can only flow from higher elevations to lower elevations, this still ensures water can only flow from the monitoring station into the gridcell, and not the other way around.

**Main Results**

Beginning with the Mekong analysis, results for estimating equation 1 are shown in 4a. A threshold of 100 millisiemens per meter (mS/m) is used to test for impacts. This threshold is where the Mekong River Commission and other sources estimate that one should start seeing impacts on irrigated crops (Kongmeng and Larsen 2014; Tanji and Kielen 2002). The figure plots of the coefficient of the binary variable indicating 8 Salt of the Earth

ESA dataset


Input:
observations where electrical conductivity in the upstream monitoring station exceeds 100 mS/m. Each plotted coefficient is from a different regression where the restriction on cropland within each gridcell is varied from 30 percent, 75 percent, and 90 percent. Results show that when EC exceeds 100 mS/m, there is a reduction in agricultural productivity of 5.7-8.2 percent. As the cropland requirements become more restrictive, the point estimate increases slightly, though it is not very sensitive. 4b shows the similar results for the India dataset. Like the Mekong region, productivity falls by 5.5-6.6 percent when EC exceeds this threshold. This threshold is exceeded by approximately 5 percent of observations in both India and the Mekong River basin. Results using the GEMStat data are shown in 4c. Globally, it is found that when EC exceeds 100 mS/m, yields decline by 11.0 to 13.5 percent.

Next, we estimate a more flexible model. 5 shows results from creating six bins of EC, each 40mS/m wide, with the 0-40mS/m bin as the comparison group. The line graph displays the change in yields (left axis) due to a corresponding EC value shown on the x-axis. Results show a nearly linear decline in yields as EC rises. Even when EC is in the lowest bin, at 40-80 mS/m, the decline in yields is statistically detect able, and significant in magnitude at between 5.2 and 7.4 percent, relative to the omitted bin. Further, the regression is not sensitive to changing the cropland land use restriction, as all point estimates are within a tight range and all 95 percent confidence intervals overlap. The bar graphs display the percent age of observations falling into each of the buckets and correspond to the right-axis. Across all 3 sam ples, non-impacted observations (0-40 mS/m) represent about 50-60 percent of the sample. The next bin (40-80 mS/m) is about 30 percent of the sample, and all subsequent bins are less than 10 percent.

**Robustness Tests**

In this section we present results from two additional specifications that have been tested to corrobo rate the main results presented in section 4. First, we show results where we vary the distance threshold to each upstream monitoring station. Then we show results where we control for other water quality parameters in the same regression.

Here we present results from all three regional analyses for thresholds of 50km and 150km, alongside the main results. Tables 3-4 present results for the Mekong River Basin, India, and global results, respec tively. In each table, results from 9 regressions are shown. The specification across all regressions are the same, with the only variation coming from the distance to monitoring station threshold and the share of cropland threshold. By changing the distance threshold, the station that each gridcell is matched to does not change, as gridcells are always matched to their nearest upstream station. Rather, what is changed is the sample of gridcells that are included in the regression, with more gridcells included as the threshold increases. Note that in each table, columns 2, 5, and 8 are the regressions depicted in 4 for cropland thresholds of 30, 75, and 90 percent, respectively. In all regions, the coefficient on EC is not sensitive to changes in the monitoring station distance threshold. Similarly, weather control variables as not sensitive as well, implying that changes to the sample to not have a significant effect on estimates.

We next test different thresholds of high EC tested for in the Mekong River Basin and India regions. In the main results, we use 100mS/m. The true sensitivity of agriculture to EC varies by crop. The original threshold was chosen as this is the recommendation made by the Mekong River Commission and other sources as to where one should start seeing impacts on irrigated crops (Kongmeng and Larsen 2014; Tanji Salt of the Earth 9

GEMStat data

India dataset


Input:
**TABLE 3. Impact of Electrical Conductivity on Net Primary Productivity, Mekong River Basin**

**Dep Var:** Δ **log(net primary**

**productivity)** **(1)** **(2)** **(3)** **(4)** **(5)** **(6)** **(7)** **(8)** **(9)**

Electrical Conductivity

- 100 mS/m

−0.0550 (0.0138)*** −0.0605 (0.0137)*** −0.204 (0.0226)*** 0.0501 (0.00647)*** 0.175 (0.0572)** −0.00260 (0.00106)* −0.0764 (0.0149)*** −0.182 (0.0352)*** 0.0427 (0.00964)*** 0.131 (0.116) −0.00187 (0.00213) −0.0793 (0.0146)*** −0.199 (0.0293)*** 0.0461 (0.00799)*** 0.109 (0.0966) −0.00144 (0.00177) −0.0834 (0.0140)*** −0.200 (0.0282)*** 0.0452 (0.00782)*** 0.0949 (0.0889) −0.00112 (0.00163) −0.0801 (0.0163)*** −0.196 (0.0469)*** 0.0433 (0.0132)** 0.117 (0.149) −0.00160 (0.00271) −0.0820 (0.0151)*** −0.234 (0.0404)*** 0.0513 (0.0112)*** 0.138 (0.121) −0.00199 (0.00220) −0.0851 (0.0143)*** −0.240 (0.0387)*** 0.0521 (0.0108)*** 0.166 (0.111) −0.00248 (0.00203) Precipitation (m/year) −0.207 (0.0282)*** Precipitation^2 (m/year) 0.0519 (0.00783)*** Temperature (C) 0.268 (0.0826)** Temperature^2 (C) −0.00437 (0.00153)** −0.0578 (0.0138)*** −0.202 (0.0236)*** 0.0508 (0.00667)*** 0.223 (0.0628)*** −0.00352 (0.00116)** Year Fixed Effects Y Y Y Y Y Y Y Y Y Gridcell Fixed Effects Y Y Y Y Y Y Y Y Y Geographic Time Trends Country Country Country Country Country Country Country Country Country Distance to nearest station 50km 100km 150km 50km 100km 150km 50km 100km 150km Cropland Threshold >30% >30% >30% >75% >75% >75% >90% >90% >90% N 2623 3784 4367 1746 2576 2986 1224 1821 2144 R-sq 0.647 0.674 0.689 0.610 0.645 0.657 0.588 0.620 0.630 _Notes:_ Table shows results from estimating equation 1 for the Mekong River Basin sample via ordinary least squares. Each column is from a separate regression. Specifications across regressions remain constant, with only the sample of gridcells included differing. Columns 1-3 include gridcells where land area is at least 30 percent cropland. Columns 4-6 restrict the sample to gridcells to those with at least 75 percent cropland, and columns 7-9 restrict to sample to gridcells with at least 90 percent cropland. Gridcells are also restricted based on the distance to the nearest upstream monitoring station. Columns 1, 4, and 7 include gridcells less than 50km from the nearest upstream station, columns 2, 5, and 8 include gridcells less than 100km from the nearest upstream station, and columns 3, 7, and 9 include gridcells less than 150km from the nearest upstream station. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.


Input:
**TABLE 4. Impact of Electrical Conductivity on Net Primary Productivity, India**

**Dep Var:** Δ **log(net primary**

**productivity)** **(1)** **(2)** **(3)** **(4)** **(5)** **(6)** **(7)** **(8)** **(9)**

Electrical Conductivity

- 100 mS/m

−0.0968 (0.0095)*** −0.0651 (0.0047)*** 0.346 (0.02)*** −0.0881 (0.0063)*** −0.327 (0.0529)*** 0.00403 (0.00102)*** −0.103 (0.0107)*** 0.372 (0.04)*** −0.104 (0.0147)*** −0.715 (0.110)*** 0.0113 (0.00209)*** −0.0564 (0.00634)*** 0.334 (0.03)*** −0.0911 (0.00962)*** −0.599 (0.0701)*** 0.00892 (0.00135)*** −0.0558 (0.00505)*** 0.351 (0.0248)*** −0.102 (0.00857)*** −0.506 (0.0632)*** 0.00711 (0.00122)*** −0.121 (0.0143)*** 0.402 (0.06)*** −0.111 (0.0203)*** −0.762 (0.130)*** 0.0125 (0.00245)*** −0.0554 (0.00777)*** 0.347 (0.03)*** −0.0924 (0.0123)*** −0.724 (0.0829)*** 0.0115 (0.00159)*** −0.0532 (0.0061)*** 0.353 (0.03)*** −0.102 (0.0113)*** −0.663 (0.0755)*** 0.0102 (0.00145)*** Precipitation (m/year) 0.310 (0.03)*** Precipitation^2 (m/year) −0.0706 (0.00853)*** Temperature (C) −0.494 (0.0927)*** Temperature^2 (C) 0.00740 (0.00176)*** −0.0656 (0.0060)*** 0.325 (0.02)*** −0.0776 (0.0068)*** −0.407 (0.0592)*** 0.00563 (0.00114)*** Year Fixed Effects Y Y Y Y Y Y Y Y Y Gridcell Fixed Effects Y Y Y Y Y Y Y Y Y Geographic Time Trends State State State State State State State State State Distance to nearest station 50km 100km 150km 50km 100km 150km 50km 100km 150km Cropland Threshold >30% >30% >30% >75% >75% >75% >90% >90% >90% N 11655 26830 35074 8720 20663 26865 6516 15698 20558 R-sq 0.473 0.476 0.474 0.469 0.478 0.474 0.463 0.467 0.471 _Notes:_ Table shows results from estimating equation 1 for the India sample via ordinary least squares. Each column is from a separate regression. Specifications across regressions remain constant, with only the sample of gridcells included differing. Columns 1-3 include gridcells where land area is at least 30 percent cropland. Columns 4-6 restrict the sample to gridcells to those with at least 75 percent cropland, and columns 7-9 restrict to sample to gridcells with at least 90 percent cropland. Gridcells are also restricted based on the distance to the nearest upstream monitoring station. Columns 1, 4, and 7 include gridcells less than 50km from the nearest upstream station, columns 2, 5, and 8 include gridcells less than 100km from the nearest upstream station, and columns 3, 7, and 9 include gridcells less than 150km from the nearest upstream station. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + _p_ <0.1, * _p_ <0.05, ** _p_ <0.01, *** p<0.001.


Input:
**TABLE 5: Impact of Electrical Conductivity on Net Primary Productivity, Global**

**Dep Var:** Δ **log(net primary**

**productivity)** **(1)** **(2)** **(3)** **(4)** **(5)** **(6)** **(7)** **(8)** **(9)**

Electrical Conductivity

- 100 mS/m

−0.0917*** (0.0156) −0.112*** (0.0131) 0.552*** (0.02) −0.114*** (0.00501) 0.141*** (0.00744) −0.00420*** (0.000199) −0.112*** (0.0131) 0.552*** (0.02) −0.114*** (0.00501) 0.141*** (0.00744) −0.00420*** (0.000199) −0.0814*** (0.0224) 0.834*** (0.06) −0.221*** (0.0211) 0.219*** (0.0187) −0.00570*** (0.000500) −0.114*** (0.0197) 0.777*** (0.04) −0.206*** (0.0147) 0.222*** (0.0128) −0.00609*** (0.000326) −0.114*** (0.0197) 0.777*** (0.04) −0.206*** (0.0147) 0.222*** (0.0128) −0.00609*** (0.000326) −0.116*** (0.0304)

1.14***

(0.09) −0.345*** (0.0329) 0.359*** (0.0413) −0.00866*** (0.000929) −0.135*** (0.0247) 0.992*** (0.06) −0.308*** (0.0200) 0.292*** (0.0245) −0.00758*** (0.000543) −0.135*** −0.0247 0.992*** (0.06) −0.308*** −0.02 0.292*** −0.0245 −0.00758*** −0.000543 Precipitation (m/year) 0.568*** (0.03) Precipitation^2 (m/year) −0.117*** (0.00714) Temperature (C) 0.121*** (0.0103) Temperature^2 (C) −0.00337*** (0.000276) Year Fixed Effects Y Y Y Y Y Y Y Y Y Gridcell Fixed Effects Y Y Y Y Y Y Y Y Y Geographic Time Trends State State State State State State State State State Distance to nearest station 50km 100km 150km 50km 100km 150km 50km 100km 150km Cropland Threshold >30% >30% >30% >75% >75% >75% >90% >90% >90% N 33236 68984 68984 16550 35070 35070 9110 20002 20002 R-sq 0.243 0.237 0.237 0.281 0.275 0.275 0.325 0.314 0.314 _Notes:_ Table shows results from estimating equation 1 for the global sample using the GEMStat database via ordinary least squares. Each column is from a separate regression. Specifications across regressions remain constant, with only the sample of gridcells included differing. Columns 1-3 include gridcells where land area is at least 30 percent cropland. Columns 4-6 restrict the sample to gridcells to those with at least 75 percent cropland, and columns 7-9 restrict to sample to gridcells with at least 90 percent cropland. Gridcells are also restricted based on the distance to the nearest upstream monitoring station. Columns 1, 4, and 7 include gridcells less than 50km from the nearest upstream station, columns 2, 5, and 8 include gridcells less than 100km from the nearest upstream station, and columns 3, 7, and 9 include gridcells less than 150km from the nearest upstream station. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

GEMStat database


Input:
and Kielen 2002). However, as shown in 5, impacts globally are found at lower values of EC. While the Mekong River Basin and India datasets do not offer enough variation to precisely estimate a semi- parametric model like that in 5, we do use alternative cut offs of 75 and 125 mS/m. Results are shown in Tables 6 and 7 for the Mekong River Basin and India, respectively. In both cases we do see that impacts are found at all thresholds. In both cases, results are monotonic and scale with the threshold that is used.

**Simulating Global Impacts of Water Salinity On Agricultural Production**

The results of this paper demonstrate just how sensitive global agricultural production is to salinity in water. Using this information, we perform a simulation to quantify the magnitude of agricultural losses that saline water causes. The GEMStat database, though having wide global coverage, does not cover all land where there is agricultural production. Notably, big gaps in the data are present in China and Sub Saharan Africa, two major agricultural areas where high salinity might be a problem (Figure 3).

To fill gaps in the GEMS data, we train a machine learning model to predict continuous values of EC globally at a 0.5-degree scale between 1992 and 2013. More specifically, we construct a dataset covering the drivers of salinity identified in the scientific literature and use a Random Forest algorithm which finds the best fit between observed and predicted values over other commonly used algorithms (linear regressions, support vector machines etc). To estimate the model we proceed as followed. First, we combined EC data

**TABLE 6: Impact of Electrical Conductivity on Net Primary Productivity, Alternative Thresholds, Mekong**
**River Basin**

**(1)** **(2)** **(3)**

**Dep Var:** D **log(net primary productivity)**

**EC > 75ms/m** **EC > 100ms/m** **EC > 125ms/m**

Electrical Conductivity exceeds threshold −0.0409 (0.0125)** Precipitation (m/year) −0.201 (0.0236)*** Precipitation^2 (m/year) 0.0507 (0.00668)*** Temperature (C) 0.224 (0.0628)*** Temperature^2 (C) −0.00353 (0.00116)** −0.0578 (0.0138)*** −0.202 (0.0236)*** 0.0508 (0.00667)*** 0.223 (0.0628)*** −0.00352 (0.00116)** −0.0578 (0.0138)*** −0.202 (0.0236)*** 0.0508 (0.00667)*** 0.223 (0.0628)*** −0.00352 (0.00116)** Year Fixed Effects Y Y Y Gridcell Fixed Effects Y Y Y Geographic Time Trends Country Country Country Distance to nearest station 50km 50km 50km Cropland Threshold >30% >30% >30% N 3784 3784 3784 R-sq 0.674 0.674 0.674 _Notes:_ Table shows results from estimating equation 1 for the Mekong River Basin sample via ordinary least squares. Each column is from a separate regression, and regressions vary by the threshold of EC used in the EC indicator variable. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

Salt of the Earth 13

GEMStat database

EC data


Input:
**TABLE 7. Impact of Electrical Conductivity on Net Primary Productivity, Alternative Thresholds, India**

**(1)** **(2)** **(3)**

**Dep Var:** D **log(net primary productivity)**

**EC > 75 mS/m** **EC > 100 mS/m** **EC > 125 mS/m**

Electrical Conductivity exceeds threshold −0.0315 (0.00477)*** Precipitation (m/year) 0.329 (0.0212)*** Precipitation^2 (m/year) −0.0779 (0.00682)*** Temperature (C) −0.419 (0.0590)*** Temperature^2 (C) 0.00587 (0.00113)*** −0.0656 (0.00600)*** 0.325 (0.0212)*** −0.0776 (0.00682)*** −0.407 (0.0592)*** 0.00563 (0.00114)*** −0.0752 (0.0102) *** 0.326 (0.0212)*** −0.0771 (0.00679)*** −0.392 (0.0585)*** 0.00540 (0.00112)*** Year Fixed Effects Y Y Y Gridcell Fixed Effects Y Y Y Geographic Time Trends State State State Distance to nearest station 100km 100km 100km Cropland Threshold >30% >30% >30% N 26830 26830 26830 R-sq 0.475 0.476 0.476 _Notes:_ Table shows results from estimating equation 1 for the India sample via ordinary least squares. Each column is from a separate regression, and regressions vary by the threshold of EC used in the EC indicator variable. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001.

**FIGURE 3. Location of Gemstat Monitoring Stations**

_Notes:_ Figure shows the location of GEMStat monitoring stations with data on EC from 2000-2013. The size and color of the dots denotes the number of years for which there is EC data at each station during this time period.

14 Salt of the Earth

GEMStat monitoring stations


Input:
**FIGURE 4. Yield Loss When Electrical Conductivity Exceeds 100mS/M**

a. Mekong river basin 0 –2 –4 –6 –8 –10 –12 –14 –16 –18 –20 30 percent 75 percent 90 percent Cropland threshold b. India 0 –2 –4 –6 –8 –10 –12 –14 –16 –18 –20 30 percent 75 percent 90 percent Cropland threshold c. Global 0 –2 –4 –6 –8 –10 –12 –14 –16 –18 –20 30 percent 75 percent 90 percent Cropland threshold _Notes:_ Figure shows results from estimating equation 1 in 3 different regions. Figure a (left) if in the Mekong River Basin, figure b (center) is in India, and figure c (right) is global using GEMStat data. In each region, coefficients from three different regressions are shown. The three regressions vary the restrictions on the share of cropland within a gridcell needed to be included in the regression. The left estimate includes all gridcells with at least 30 percent cropland in the year 2000. The mid and right estimate include gridcells with at least 75 percent and 90 percent cropland, respectively. The point estimate shown is the coefficient on a binary variable indicating observations where electrical conductivity in the upstream monitoring station exceeds 100mS/m. Error bars show the 95 percent confidence interval based on standard errors clustered at the gridcell level.

**FIGURE 5. Flexible Estimation of Yield Losses Due To Electrical Conductivity**

0 –5 –10 –15 –20 –25 –30 –35 –40 –45 –50 0-40 40-80 80-120 120-160 160-200 >200 EC (mS/m) 30 percent 75 percent 90 percent 100 90 80 70 60 50 40 30 20 10 0 _Notes:_ Figure shows results from estimating equation 1 using water quality data from GEMStat. The three regressions vary the restrictions on the share of cropland within a gridcell needed to be included in the regression. The blue line includes all gridcells with at least 30 percent cropland in the year 2000. The red and green lines include gridcells with at least 75 percent and 90 percent cropland, respectively. The point estimate shown is the coefficient on an indicator variable which indicates if EC is within the range given on the x-axis. Error bars show the 95 percent confidence interval based on standard errors clustered at the gridcell level.

Salt of the Earth 15

GEMStat data

water quality data from GEMStat

water quality data


Input:
**FIGURE 6. Global Predicted Levels of EC, 1992–2010**

_Notes:_ Figure shows average predicted EC from 1992-2010 for each gridcell from Désbureaux et al (2019).

from India, the Mekong Basin and GEMStat to have the broadest geographical coverage as possible.

Second, we randomly split our dataset between a training sample (80% of the observations) and a testing sample (20% of the observations). Third, we train the model on the testing sample and assess the predic tive power on the testing sample. Fourth, we choose the best model as the one that maximizes the correla tion coefficient between observed and predicted values in the testing sample. Finally, we use the best model to predict EC for every years between 1992 and 2013 globally at a 0.5 degree scale. More information on the covariates used and the estimation procedure can be found in Desbureaux et al (2019) which extended the approach to more parameters. The correlation coefficient between observed and predicted values of EC in the testing sample is of 88 percent. 6 shows the average predicted EC for each gridcell from 1992-2013. Where values are poorly predicted, the predicted EC tend to be lower than observed EC.

Consequently, the results can be interpreted as conservative predictions of the risk of high salinity.

Using the coefficients shown in 5 (based on 30 percent cropland), annual changes in NPP, and the EC dataset described above, we estimate annual losses in agricultural productivity due to EC for each 0.5x0.5-degree gridcell where cropland exceeds 30 percent of landcover, globally. We then convert the resulting loss of NPP to kilocalorie equivalents, following Strobl and Strobl (2011) and Imhoff et al.

(2014). Globally, average annual losses from 2001-2013 due to saline water total 124 trillion kilocalorie-equivalents. In total, assuming 2,000 calories/person/day, these calories represent enough food to cover the food budgets of 170 million people, annually.

The distribution of these losses is shown in Figure 7. With the exception of Sub-Saharan Africa, where most food production is rain fed, all continents have large hotspots. Very wet regions, such as the Amazon basin, Southeast Asia, and the southeastern United States also avoid losses from salinity, despite being important agricultural zones. Figure 8 aggregates losses by countries and shows further 16 Salt of the Earth

EC dataset

EC dataset

EC dataset


Input:
**FIGURE 7. Estimated Mean Annual Kilocalorie Equivalent Loss Due To Saline Water, 2001–2013**

_Notes:_ Figure shows estimated average annual kilocalorie equivalent losses across space for the years 2001-2013. Losses are estimated based on the coefficients in 5, predicted EC from Désbureaux et al (2019) and net primary productivity data from Zhao et al., 2005.

**FIGURE 8. Estimated Mean Losses in Agricultural Production Due To Saline Water,**
**2001–2013, by Country**

United States Argentina India Ukraine France Poland China Russia Canada Spain Germany Australia Romania Turkey Mexico Italy Hungary Morocco Pakistan Belarus 0 5,000 10,000 15,000 20,000 25,000 Calories lost per year, 2001–2013 (billions) _Notes:_ Figure aggregates estimated average annual losses based on the country which the centroid of each gridcell lies.

Salt of the Earth 17

net primary productivity data

net primary productivity data

net primary productivity data