The impact of “Nitaqat” on Indian high-skilled migration to Saudi Arabia

Abstract

“Nitaqat,” the labor market nationalization policy of Saudi Arabia, aims to increase the employment of Saudi nationals by restricting the entry of foreign workers in the country. Data from a sample survey of Indian professionals working in Riyadh, Kingdom of Saudi Arabia was analyzed using Multivariate Analysis of Variance (MANOVA) Wilks’ lambda and Tukey Honestly Significant Difference tests. The findings from the exploratory study suggest that the Indian professionals who were laid off due to the policy are statistically significant between the sectors but not between the “Nitaqat” zones and the size of enterprises. The policy impact on new recruitments of Indian professionals is statistically significant between Nitaqat zones but not between sectors and the size of enterprises. The findings indicate that the opportunities for Indian high-skilled migration to Saudi Arabia are constrained in the construction sector and in yellow zone companies, while employment prospects remain in the banking and finance, education and IT and telecom sectors and in blue zone firms.

Keywords

“Nitaqat,” Indian migrants high-skilled migrants Gulf countries Saudi Arabia

Introduction

Since the 1990s, the Gulf countries have been attempting to restrict the admission of less-skilled migrant workers and to attract the high-skilled, especially those with tertiary level of education, in a bid to diversify their economy from sole dependence on oil towards a knowledge-based one (Khadri, 2018). In addition, the ongoing technological changes and adoption of these technologies, such as big data analytics, machine learning, artificial intelligence and automation, exacerbate the situation for low-skilled migrants seeking work in the region (Hertog, 2019). The impending implications of COVID-19 are also on display in deepening restrictions on the recruitment of low-skilled migrant workers on the one hand, and increase in demand for automation, technological adoption and high-skilled migrant workers, on the other (Alsahi, 2020).

Due to these ongoing changes, the demand for high-skilled migrant workers is likely to increase. In 2016, out of the estimated 15 million migrant workers in the Gulf countries, about five million were highly skilled (Hvidt, 2016). Moreover, the Vision 2030 documents of many Gulf countries, such as, Bahrain, Qatar and Saudi Arabia, as well as Abu Dhabi’s Economic Vision 2030 and Dubai’s Industrial Strategy 2030, outline their aims and needs to emerge as knowledge economies by attracting talent and resources for higher education and research and development (Bizri, 2018).

To meet these objectives, some countries have even liberalized their labor laws to favor the entry of high-skilled migrants. For example, to attract the high-skilled, Qatar in 2015 and the United Arab Emirates (UAE) in 2016 abolished the requirement for migrants to spend a minimum of two years away from the country between two contracts (Hvidt, 2016). Bahrain has reduced such restriction to 52 weeks (Khadri, 2018). Further, Kuwait and UAE have made provisions allowing high-skilled migrants to switch jobs without the probationary ban or requiring a no-objection certificate from the previous employer (Zahra, 2015). The UAE also introduced a long-term 10-year visa to the migrants in 2018 and the possibility of attaining permanent residency in 2019, though only for a limited and select group of investors and professionals with long-standing service records (Hvidt, 2019). The Indian businessman in Dubai, Yousuf Ali, was among the 6,800 persons of the first batch of beneficiaries of this visa, known as the “Golden Card” (Zaatari, 2019).

The Saudi government has also recently undertaken several policy initiatives to attract high-skilled migrants (Peck, 2017). In 2004, the Saudi government amended the Naturalization Law to allow residents who have stayed for 10 years or more to apply for citizenship with priority to migrant professionals in specific scientific fields like medicine, engineering, law, accounting and marketing (De Bel-Air, 2014). In March 2021, the Kingdom of Saudi Arabia (KSA) announced labor reforms easing restrictions and allowing migrant workers with certain exceptions to change jobs without the employer’s consent. The reform aims to facilitate the unrestricted mobility of migrant workers in and out of the country and to attract highly skilled workers (Aljazeera, 2021). However, “Nitaqat” a labor market nationalization policy of the Saudi Arabian government, might have implications for high-skilled migrants, even if the policy does not have specific references to high-skilled workers.

Announced in 2011 and enforced in 2013, Nitaqat aims to promote native employment in the private sector by restricting the employment of migrant workers. As described below, it does not make any distinction or exception of application to high-skilled migrant workers. In order to implement Nitaqat, the Ministry of Labour and Social Development of the Kingdom of Saudi Arabia divided its entire economic operations of the labor market into 41 activities. Each of these 41 activities was further divided into five types of enterprises based on the number of employees: Giant (3,001 and more employees), large (501–3,000 employees), medium (51–500 employees), small (11–50 employees) and very small (1–10 employees). Thus, 205 categories of enterprises were formed by activity and size (MLSD, 2017).

The Saudi government assessed each of these enterprises based on their share of native employees in the organization and classifies them into four Nitaqat zones (or color bands). The enterprises with a higher share of native employment are called blue zone (or platinum zone) enterprises. In comparison, those with progressively lower share of native employees are termed as green, yellow and red zone enterprises respectively. The Nitaqat zones of the enterprises were determined in such a way that half of the enterprises with relatively better percentages of native employment in a particular activity and size fall in blue and green zones, while the remaining half with worse percentages of native employment fall in the yellow and red zones (Al-Salloum and Zarah, 2012). The enterprises marked in blue and green zones with comparatively higher percentages of native employees have incentives like securing and renewing work permits of their expatriate workers. The enterprises in the yellow zone with a relatively lower percentage of native employees are given more time to improve their percentage of native employees. At the same time, the policy deals stringently with the red zone enterprises by banning further renewal and issuance of work permits of their expatriate workers and expansion of subsidiaries and branches.

The growing emphasis of the Saudi Arabian government since 2011 to address the rising native unemployment problem through Nitaqat and its implementation across skill segments (low and high) may have hindered the prospects of high-skilled migration to Saudi Arabia. If so, how can the Saudi Arabian government neutralize the adverse effects of Nitaqat on high-skilled migrant workers and achieve its broader vision of a knowledge-based economy going forward? On the other hand, as a key source country of both low-skilled and high-skilled migrant workers to Saudi Arabia, India is expected to be impacted by these labor market reforms. Moreover, the issue of Indian high-skilled migration to Saudi Arabia has never attracted the attention of researchers and policy makers, who otherwise have focused mostly on matters pertaining to low-skilled migration (Rajan and Zachariah, 2020; Calabrese, 2020).

Given this background, the paper examines the impact of Nitaqat on Indian high-skilled migrant workers in Saudi Arabia. In particular, it tries to understand the significance and variation of the impact of the policy on them by sector, Nitaqat zone and the size of enterprises. The findings of the paper are expected to help anticipate shifts in future migration patterns and formidable policy options for both India and Saudi Arabia for a win-win solution. The paper is organized into five sections consisting of changing trends of Indian labor migration to the Gulf and Saudi Arabia, an overview of the Saudi labor market and the role of migrants, a discussion on methodology, presentation of findings and discussion, and conclusion.

Indian labor migration to the Gulf and Saudi Arabia

Since the 1970s, labor migration from India to the Gulf countries has increased manifold. However, the rise in migrants’ flows was sporadic at times due to the changing economic conditions in the Gulf. For example, on average, the annual flow of the Indian Emigration Check Required (ECR)¹ category of migrant workers to the Gulf was counted at 234,064 from 1979 to 1982. After that, it declined to 155,401 from 1983 to 1990 due to the oil glut of the 1980s. The flows have picked up from 1992 to 2001 and reached nearly 360,000 per annum (Rajan and Kumar, 2010). Annually, the average number of migrant workers increased in the years 2004–2006 from 566,908 and peaked at 848,601 in 2008 (MOIA, 2011). Thereafter, the number of Indian migrants to the Gulf declined to 722,139 in 2012 (MOIA, 2012, 2015) and recovered to 817,000 migrants in 2013. However, since then, it started declining continuously and reached to just 334,000 in 2019 (MEA, 2020).

One of the reasons for the rapid increase in the number of Indian migrant workers to the Gulf in the first decade of the 21^st century was the proactive approach of the Government of India (GOI) in incentivizing and supporting international migration through its newly established institutions. The Ministry of Non-resident Indian Affairs, first established in 2004, and later, renamed as the Ministry of Overseas Indian Affairs (MOIA), had an objective to establish a robust institutional framework for international migration and diaspora governance. A decade later, in 2016, the MOIA has been merged with the Ministry of External Affairs (MEA) and operates as an Overseas Indian Affairs (OIA) division under it. The key institutions set up by the erstwhile MOIA, such as the India Centre for Migration in New Delhi, Overseas Indian Centers in Abu Dhabi and Washington, Migration Resource Centers at many migrant origin states in India and Overseas Workers Resource Centre (OWRC) in Dubai, have had a strategic role in pursuant of two most important objectives, namely, (1) smooth and orderly migration and (2) protection and welfare of its migrant workers abroad (MOIA, 2014; ICOE, 2012). These supportive measures in the initial years of institutional set-up have quickly incentivized and fuelled emigration, particularly from the southern states of India. For example, the number of migrants from Kerala increased from 63,512 in 2004 to 180,703 in 2008; from Tamil Nadu, the number increased from 108,964 in 2004 to 150,842 in 2007; and from Andhra Pradesh, the number increased from 48,498 in 2005 to 105,044 in 2007 (MOIA, 2009).

However, by the end of the first decade of the 21^st century, the rapid rise in the number of emigrants and the consequent rise in their complaints of exploitation and cheating by employers and recruiting agents prompted the Government of India (GOI) to harden its approach with regard to raising these issues in various negotiating forums with the Gulf countries (Singh and Rajan, 2016). In addition, rising employment opportunities within India owing to its faster economic growth rate during 2006–2011 contributed to a decline in labor migration to the Gulf region (ILO, 2011; Potnuru, 2022). The number of migrants from India to the Gulf declined, as noted above, from 818,315 in 2008 to 722,139 in 2012 (MOIA, 2012, 2015). During this brief period between 2008 and 2012, while the annual number of migrants from India to the gulf declined, the same from Pakistan increased from 227,075 in 2008 (Wikramasekera, 2011) to 609,478 in 2013 (GIZ and ILO, 2015) and from Sri Lanka, it increased from 236,574 in 2008 to 247,356 in 2012 (GIZ and ILO, 2015). After that, by the end of the second decade of the 21^st century, however, the migration trends from almost all the South Asian countries, including that from India to the Gulf declined considerably (MEA, 2020; BEOE, 2021; ILO, 2019).

Starting from 2000, the Gulf countries have gradually tightened the policy allowing entry of migrant workers through the introduction and rigorous implementation of their respective labor market nationalization measures. For example, since 2000, Oman instituted a more rigorous implementation of its Omanization policy introduced in 1994. In 2001–2008, Bahrain revived a quota system introduced earlier in 1995. Qatar expanded its Qatarization policy, first launched in 2000 in the oil and gas sector to other sectors with a majority share of migrant labor. The UAE had also introduced the Emiratization of its labor market, limiting its focus on the public sector (Khadri, 2018). Apart from these nationalization policies, low migrant wages, increasing cost of living in the Gulf (Malit and Naufal, 2016; Ratha and Ganesh, 2019) and rising opportunities in India led to a sharp decline in Indian low-skilled migration to the Gulf (Rajan and Zachariah, 2020). As noted above, Indian annual ECR migration to the Gulf declined significantly from 817,000 migrants in 2013 to just 334,000 in 2019 (MEA, 2020).

Saudi Arabia received the lion’s share of the labor migration of Indian workers to the Gulf. For example, 275,172 workers migrated to KSA, constituting 42.9 percent of the total 641,355 Indian migrants to the Gulf in 2010. In 2012, it increased to 357,503 (47.8 percent). Since then, however, both the numbers and proportion of migrant workers to KSA have declined continuously. In 2013, Saudi Arabia received 354,169 Indian migrant workers, 43.3 percent of the total emigrants (816,665) to the Gulf (MOIA, 2014). It continuously declined in the following years and reached 143,000 emigrants by 2019 (MEA, 2020). One of the significant reasons for the decline of Indian emigrants to Saudi Arabia was the Nitaqat policy announced in 2011. The deportation of migrant workers from Saudi Arabia increased from 594,000 in 2012 to 665,000 in 2013 (De Bel-Air, 2018). Nearly 92,000 Indians with illegal status registered with the Indian Embassy for the emergency certificate to travel back to India in 2013. Of them, 69,800 received the certificate and returned to India, leaving the rest at deportation risk and facing a ban on their lateral return to Saudi Arabia (Ramavarman, 2013).

Apart from unskilled and semi-skilled labor migration, there are many Indian high-skilled workers migrating to the Gulf Cooperation Council (GCC) countries including Saudi Arabia. The emigration of Indian high-skilled workers to the Gulf is not much discussed and researched because of the lack of data and comparatively lower numbers compared to the unskilled migrant workers. The skilled migrants holding non-ECR passports are not required to seek an emigration clearance and are outside the ambit of the emigration data collected by the Government of India. Moreover, the large number and high-risk nature of the problems of unskilled migrant workers in the Gulf have overtaken and dominated the emigration policy discourse in the country. The Government of India had also purposefully kept the high-skilled migrant workers in the Gulf out of the purview of its regulatory and assistance mechanisms, assuming that the high-skilled migrant workers did not need protection during their emigration, employment and stay in the destination (Kumar and Rajan, 2014). There is also no systematic monitoring and estimation of Indian high-skilled migrant workers living in the Gulf. However, success stories of Indian doctors, engineers, academics, technicians and entrepreneurs working in the Gulf, as reported in the media, indicate their significant presence in the region. For example, Parween (2013) observed that many highly skilled Indians who had migrated for employment to the Gulf in the 2000s have eventually set up their own businesses there. He added that their financial success and the increasing number of Indian skilled migrants in the region led to the expansion of Indian schools and posh residential areas in the Gulf countries.

The Saudi labor market and migrant workers

Over many decades, since 1970, the increasing dependence of the Gulf on expatriate workers led to the growing population of migrants in each GCC country. Between 1975 and 2015, the share of migrants in the total population of the Gulf countries increased from 52 percent to 69 percent in Kuwait, from 21 percent to 52 percent in Bahrain, from 17 percent to 44 percent in Oman, from 59 percent to 86 percent in Qatar, and from 70 percent to 88 percent in the UAE. Compared to the others, KSA has a lower percentage and lower rate of growth of migrants during the same time in the region. The migrant population in Saudi Arabia has increased from 25 percent in 1975 to only 33 percent in 2015 (Kapiszewski, 2006; Dinoop and Ambady, 2020) and to 38 percent in 2019 (UNDESA, 2019).

Over the years, the share of employment of Saudi natives declined. At the end of the First Saudi Development Plan in 1974, natives represented 80.4 percent of the total labor force and migrant workers at 19.6 percent. However, as the workforce expanded, the share of native workers decreased to 57 percent by 1979 and further to 34 percent by 1989. After that, the native percentage stabilized at around 35 percent of the total workforce until 2008 (Al-Shammari, 2009) and declined to 25 percent by 2020 (GOSI, 2021). This decline in the share of native workers to the country’s total workforce has led to increased unemployment among Saudi Arabian citizens. It was only the public sector that had significantly contributed to the employment of nationals. About 74 percent of native employment in Saudi Arabia in 2012 was in the public sector compared with only 13 percent in the private sector. The remaining 87 percent of the private sector employment in the country was occupied by migrant workers (Koyame-Marsh, 2016).

The Saudi government’s earlier labor market nationalization policy - “Saudization” was introduced in 1995 and followed up with suitable amendments to help reduce the share of migrant workers in the kingdom’s total employment and increase that of the natives. The percentage of foreign workers in Saudi Arabia declined from 65 percent in the early 1990s to 50.6 percent in 2008 (Baldwin-Edwards, 2011). However, it increased again to 58 percent in 2020 (GCC-STAT, 2021). Recently due to the increasing number of young Saudis entering the labor market, the public sector could not absorb them fully. The private sector continued to be unattractive to the natives due to its low remuneration and working conditions. As a result, the unemployment rate among Saudis increased from 10 percent in 2008 (Baldwin-Edwards, 2011) to 12 percent in 2011 (HKS, 2015) and to 15 percent in 2020 (GOSI, 2021).

The Nitaqat – reformed labor market nationalization policy with much more stringent measures announced in 2011, had a positive impact on Saudi employment. A report indicated that the total number of Saudis employed in the private sector increased from 1.13 million in 2012 to 1.47 million in 2013 and 1.52 million in 2014 (Koyame-Marsh, 2016). Further, it increased to about 1.9 million by 2017 (GLMM, 2021). The share of Saudis employed in the private sector also increased from 10 percent in 2012 to 13 percent in 2014. However, the Saudis’ overall employment situation has not improved considerably as intended by the policy because of two reasons. First, the Saudis’ employment growth rate in the private sector was far less than the total employment growth rate in the sector, and second, public sector employment in the country has not increased significantly. While the private sector employment has increased by an average of 11 percent per year from 2005 to 2017, public sector employment growth rate averaged at just three percent per year in the same period. Consequently, with rising native youth population in the country, Saudi’s unemployment rate increased to 15 percent in 2020 (GOSI, 2021).

Therefore, the government of Saudi Arabia determined to increase the share of Saudis in the private sector employment by strict enforcement of the Nitaqat. The 2030 Vision of the Saudi Arabian government sets ambitious targets to improve employment among its natives. These targets include: (1) raising Saudis' share of employment in the private sector to 50 percent, (2) reducing Saudi unemployment to nine percent, (3) increasing the participation of women in the labor force to 30 percent from 23 percent in 2018, (4) providing gainful employment to 950,000 students enrolled in technical and vocational education and (5) reducing the share of Saudis working in the public sector to 20 percent from the levels of 45 percent in 2017 (HKS, 2019).

However, there is ambiguity in understanding whether these reform measures would increase or decrease the demand for high-skilled migrant workers in Saudi Arabia. For instance, though its 2030 Vision also sets targets to enhance the skills of the natives,² the Saudi government would need a more extended period to match the industry’s demand for high-skilled workers through domestic supply. In the interim, however, Saudi Arabia may have to depend on high-skilled migrants to meet the needs of industry and training for higher education of its nationals. If so, India may benefit from this opportunity given its large base of higher education and the already demonstrated potential of its knowledge workers in the Gulf countries, the United States of America (USA) and other countries belonging to the Organisation for Economic Co-operation and Development (OECD) (Khadria, 2012, 2016). However, the growing emphasis of the Nitaqat policy to replace migrant workers with natives across skill segments may adversely impact the migration opportunities of Indian high-skilled migrant workers to Saudi Arabia. Therefore, to understand its potential impact on Indian high-skilled migration to Saudi Arabia and offer suitable policy recommendations, the paper investigates how the Nitaqat policy impacted Indian high-skilled migrants’ jobs in the past.

Data and methodology

The paper aims to understand the variation in the impact of Nitaqat on the Indian high-skilled migrant jobs in Saudi Arabia by Nitaqat zones, sectors and size of enterprises. We performed Multivariate Analysis of Variance (MANOVA) Wilks’ lambda and Tukey’s Post Hoc Tests using data from a survey of 59 Indian professionals working in Riyadh in 2017.

Since it was very difficult to obtain information on recruitment and layoff of migrant staff directly from the company, Indian migrant professionals who had placed in the managerial positions in various companies/institutions and witnessed the recruitment and layoff of high-skilled migrant staff (graduates and above) in their respective companies were identified with the help of Indian migrant networks and associations in Riyadh. The interview guide or questionnaire consisted of questions relating to general and personal information of the migrant professional, specific company information regarding the Nitaqat zone, size and sector of the enterprise and the approximate number (as witnessed by the respondent) of job recruitments and layoffs of high-skilled migrant workers of all nationalities and Indian high-skilled migrant workers in the company. The interview guide was shared with Indian migrant professionals who gathered information about company recruitment and dismissal practices. In the next phase, people in managerial or supervisory roles who were willing to be interviewed were selected for the sample study. The sample consisted of one respondent each from 59 enterprises employing Indian migrant professionals in Riyadh.

Personal interviews of the selected respondents were conducted in the Indian professional migrant communities and gatherings in Riyadh in January to March 2017. Riyadh was chosen as a research site because as the capital city and financial center of Saudi Arabia, it hosts a large number of enterprises employing migrant professionals. The impact of Nitaqat was assessed in terms of “job losses” and “new recruitments” of all high-skilled migrants of all nationalities and Indian high-skilled migrants in the company since the implementation of Nitaqat in 2013 until the survey undertaken in January to March 2017. The MANOVA tests were undertaken separately for “job losses” and “new recruitments” using high-skilled migrants of all nationalities and Indian high-skilled migrants as two dependent variables, and Nitaqat zones, sectors and size of the enterprises as three factors or independent variables. Thus, there are three rounds of MANOVA tests performed for “job losses,” each for the Nitaqat zones, sectors and sizes of the enterprises, respectively. Another three rounds of tests are performed for “job recruitments” using the same factors (Nitaqat zones, sectors and sizes of enterprises).

The findings are expected to help anticipate future migration scenarios in sectors and category-specific enterprises, and suggest policy for India and Saudi Arabia to maximize mutual gains from the high-skilled international migration.

Results and discussion

General characteristics of the sample population

Table 1 presents general characteristics of the sample population. Of the total sample population of 59 respondents, 50 (85 percent) were males and nine (15 percent) were females. There were 11 respondents (18.6 percent) who each migrated from Kerala and Maharashtra, 10 (17 percent) from Andhra Pradesh and nine (15 percent) from Karnataka. The rest of the 18 respondents of the sample were from Tamil Nadu (5), Uttar Pradesh (3), Madhya Pradesh (2), Uttarakhand (2) and other states (6). Among the sample, 29 (49 percent) have a Master’s degree; 21 (36 percent), a Bachelor’s degree; six (10 percent), a PhD; and three (five percent) a diploma, as the highest educational qualification. The company of engagement of the 16 respondents (27 percent) was associated with construction, manufacturing and real estate businesses; 10 (17 percent) with IT and telecom; nine (15 percent) with banking and finance; eight (14 percent) with education; seven (12 percent) with wholesale and retail; three (five percent) with healthcare; two (three percent) with hotels and restaurants; and four respondents (seven percent) with other businesses. Sixteen respondents (27 percent) were engaged as IT specialists and managers, followed by 14 (24 percent) as general managers, 12 (20 percent) as accountants and financial managers, eight (14 percent) each as sales and operations managers and academic professionals, and one (two percent) as medical practitioner. The highest number of 32 respondents (54 percent) were in the age bracket of 36–45 years, followed by 14 (24 percent) aged 46–55 years, 11 (19 percent) aged 26–35 years, and two (three percent) aged 56 years and above.

Table 1.

General characteristics of the sample population.

Characteristics	Number	Percentage (%)
Gender
Male	50	85
Female	9	15
Origin (Indian state)
Kerala	10	17
Maharashtra	10	17
Andhra Pradesh	9	15
Karnataka	9	15
Tamil Nadu	5	8
Uttar Pradesh	3	5
Madhya Pradesh	2	3
Uttarakhand	2	3
Others	6	10
Age
26–35 years	11	19
36–45 years	32	54
46–55 years	14	24
56 years and above	2	3
Educational qualification
PhD	6	10
Master’s degree	29	49
Bachelor’s degree	21	36
Diploma	3	5
Sector of engagement
Construction, manufacturing and real estate	16	27
IT and telecom	10	17
Banking and finance	9	15
Education	8	14
Wholesale and retail	7	12
Healthcare	3	5
Hotels and restaurants	2	3
Others	4	7
Professional occupation
IT specialists and managers	16	27
General managers	14	24
Accountants and financial managers	12	20
Sales and operation managers	8	14
Academics	8	14
Medical practitioners	1	2
Work experience in riyadh
0–2 years	6	10
3–5 years	13	22
6–10 years	17	29
11–15 years	10	17
16–20 years	11	18
21–25 years	2	3
Total number of sample	59	100

Most of the respondents have had a fairly long period of work experience in Saudi Arabia. Of the total sample, 17 respondents (29 percent) have work experience of six to 10 years, followed by 13 respondents (22 percent) with three to five years of work experience in Saudi Arabia. Those with a longer period of work experience of 11–15 years, 16–20 years and 21–25 years constituted 17 percent, 18 percent and three percent, respectively. The remaining six (10 percent) respondents have had a short period of work experience of fewer than two years in Saudi Arabia. Due to incomplete information, one questionnaire out of the 59 samples is not included in the MANOVA analysis.

Impact of Nitaqat on job losses of high-skilled migrants

The one-way MANOVA (Wilks lambda and Tukey Honestly Significant Difference or HSD) test results on job losses of high-skilled migrants of all nationalities and Indian high-skilled migrants are shown in Tables 2 to 5. Table 2 presents the descriptive statistics of the sample for jobs laid off for high-skilled migrants of all nationalities and Indian high-skilled migrants between 2013 and 2017. The results in Table 3 show that the difference in high-skilled migrants’ jobs laid off by Nitaqat zone (blue, green, yellow and red) of the companies are not statistically significant, F (6, 106) = 0.811, p = 0.564; Wilks’ lambda = 0.914, partial n² = 0.04. Similarly, the difference in high-skilled migrants’ jobs laid off by company size (small, medium, large and giant) are also not statistically significant, F (6, 106) = 0.702, p = 0.648; Wilks’ lambda = 0.925, partial n² = 0.04 (Table 3).

Table 2.

Descriptive statistics of jobs laid off of all nationalities’ high-skilled migrants and Indian high-skilled migrants by Nitaqat zone, sector and company size.

Dependent variable/factors	Jobs laid off of all nationalities’ high-skilled migrants			Job laid off of Indian high-skilled migrants
Dependent variable/factors	Nitaqat zone	Mean	Std. deviation	N	Mean	Std. deviation	N
Blue	15.2	47.4	10	8.1	25.3	10
Green	27.5	41.7	33	12.7	20.7	33
Red	35.3	20.5	6	12.2	5.8	6
Yellow	75.3	160.7	9	20.3	31.5	9
Total	33.6	73.1	58	13.0	22.3	58
Sector	Mean	Std. deviation	N	Mean	Std. deviation	N
Banking	11.9	25.6	9	0.2	0.4	9
Construction	82.4	122.9	16	33.2	28.7	16
Education	17.4	26.7	7	4.3	6.1	7
Healthcare	6.7	11.5	3	1.7	2.9	3
Hotels	3.5	4.9	2	1.5	2.1	2
IT and telecom	6.0	11.4	10	3.2	6.3	10
Others	13.3	18.5	4	1.7	2.4	4
Wholesale and retail	37.7	38.5	7	20.7	26.5	7
Total	33.6	73.1	58	13.0	22.3	58
Company size	Mean	Std. deviation	N	Mean	Std. deviation	N
Giant	58.4	124.3	16	22.2	31.0	16
Large	29.6	50.0	17	9.7	18.9	17
Medium	22.0	30.1	21	10.0	17.5	21
Small	12.7	12.5	4	5.7	6.6	4
Total	33.6	73.1	58	13.0	22.3	58

Table 3.

Multivariate tests (Wilks' lambda).

Multivariate tests factors (Wilks’ lambda)	Value	F	Hypothesis df	Error df	Sig	Partial eta squared
Nitaqat zone	0.9	0.8^b	6	106	0.564	0.044
Sector	0.6	2.4^b	14	98	0.007	0.254
Company size	0.9	0.7^b	6	106	0.648	0.038

Table 4.

Tests of between-subjects effects (Sector).

Source	Dependent variable	Type III sum of squares	Df	Mean square	F	Sig	Partial eta squared
Sector	All nationalities high-skilled migrants laid off	57,517.7	7	8216.8	1.7	0.140	0.189
Sector	Indian high-skilled migrants laid off	11,054.6	7	1579.2	4.6	0.001	0.391
Error	All nationalities high-skilled migrants laid off	247,411.7	50	4948.2
Error	Indian high-skilled migrants laid off	17,226.4	50	344.5

Table 5.

Multiple comparisons of jobs laid off of Indian high-skilled migrants between sectors (Tukey HSD).

(I) Sector	(J) Sector	Mean difference (I-J)	Std. error	Sig	95% confidence interval
(I) Sector	(J) Sector	Mean difference (I-J)	Std. error	Sig	Lower bound	Upper bound
Construction	Banking	33.0^*	7.7	0.002	8.5	57.4
	Education	28.9^*	8.4	0.024	2.3	55.5
	Healthcare	31.5	11.7	0.147	−5.4	68.5
	Hotels	31.7	13.9	0.327	−12.3	75.7
	IT and telecom	30.0^*	7.5	0.005	6.3	53.6
	Others	31.4	10.4	0.069	−1.4	64.3
	Wholesale and retail	12.5	8.4	0.813	−14.1	39.1
Banking	Education	−4.1	9.4	1	−33.7	25.5
	Healthcare	−1.4	12.4	1	−40.6	37.7
	Hotels	−1.3	14.5	1	−47.2	44.6
	IT and telecom	−3.0	8.5	1	−30.0	24.099
	Others	−1.5	11.2	1	−37.0	33.8
	Wholesale and retail	−20.5	9.4	0.375	−50.1	9.1
Education	Healthcare	2.6	12.8	1	−37.9	43.1
	Hotels	2.8	14.8	1	−44.3	49.9
	IT and telecom	1.1	9.1	1	−27.8	30.0
	Others	2.5	11.6	1	−34.3	39.3
	Wholesale and retail	−16.4	9.9	0.715	−47.8	15.0
Healthcare	Hotels	0.2	16.9	1	−53.4	54.6
	IT and telecom	−1.5	12.2	1	−40.2	37.1
	Others	−0.1	14.2	1	−44.9	44.8
	Wholesale and retail	−19.1	12.8	0.811	−59.6	21.5
Hotels	IT and telecom	−1.7	14.4	1	−47.2	43.8
	Others	−0.3	16.1	1	−51.1	50.6
	Wholesale	−19.2	14.9	0.898	−66.3	27.9
IT and telecom	Others	1.5	11.0	1	−33.3	36.2
IT and telecom	Wholesale and retail	−17.5	9.1	0.548	−46.44	11.4
Others	Wholesale and retail	−19.0	11.6	0.73	−55.8	17.8

However, the difference in high-skilled migrants’ jobs laid off between sectors (banking, construction, education, health, hotels, IT and wholesale and retail among others) of the companies are found statistically significant, F (14, 98) = 2.378, p = 0.007; Wilks’ lambda = 0.557, partial n² = 0.25 (Table 3). To further understand how the dependent variables (jobs laid off of high-skilled migrants of all nationalities and jobs laid off of Indian high-skilled migrants) differed from the independent variable (sectors), we looked at the tests of between-subjects effects. The results show that the sectoral difference is statistically significant on the Indian high-skilled migrant workers, F (7, 50) = 4.58; p = 0.001, partial n² = 0.39, but not on the high-skilled migrant workers of all nationalities, (F7, 50) = 1.66; p = 0.140, n² = 19 (Table 4). We have followed up these significant results with Tukey’s HSD multiple comparisons (Table 5). The results indicate that the mean scores of job losses of Indian high-skilled migrant workers are statistically significantly different between construction and banking (mean difference = 33; p = 0.002), construction and education (mean difference = 29; p = 0.024), and construction and IT and telecom (mean difference = 30; p = 0.005) sectors.

Impact of Nitaqat on the recruitment of high-skilled migrants

The MANOVA (Wilks Lamda and Tukey HSD) results on the job recruitments of high-skilled migrants of all nationalities and Indian high-skilled migrants are presented in Tables 6 to 9. Table 6 presents the descriptive statistics of the sample for job recruitments of high-skilled migrants of all nationalities and Indian high-skilled migrants between 2013 and 2017. The difference in recruitments of high-skilled migrants between companies of different sizes (small, medium, large and giant) is not statistically significant, F (6, 106) = 0.415, p = 0.867; Wilks’ lambda = 0.955, partial n² = 0.02. Similarly, the difference in recruitments of high-skilled migrants between different sectors (banking, construction, education, health, hotels, IT, wholesale and retail and others) is also not statistically significant, F (14, 98) = 0.627, p = 0.836; Wilks’ lambda = 0.842, partial n² = 0.08 (Table 7).

Table 6.

Descriptive statistics of recruitment of all nationalities’ high-skilled migrants and Indian high-skilled migrants by Nitaqat zone, sector and company size.

Dependent variable/Factors	Recruitment of all nationalities’ high-skilled migrants			Recruitment of Indian high-skilled migrants
Nitaqat zone	Mean	Std. deviation	N	Mean	Std. deviation	N
Blue	1.3	0.5	10	1.3	0.5	10
Green	1.1	0.6	33	0.9	0.7	33
Red	1	0.6	6	1.0	0.6	6
Yellow	1	0.5	9	0.4	0.5	9
Total	1.1	0.5	58	0.9	0.6	58
Sector	Mean	Std. Deviation	N	Mean	Std. Deviation	N
Banking	0.9	0.3	9	0.7	0.5	9
Construction	1.0	0.6	16	0.8	0.6	16
Education	1.1	0.7	7	1.0	0.8	7
Health	1.3	0.6	3	1.3	0.6	3
Hotels	1.0	0.0	2	1.0	0.0	2
IT and telecom	1.3	0.7	10	1.1	0.9	10
Others	1.0	0.0	4	1.3	0.5	4
Wholesale and retail	1.14	0.4	7	1.0	0.6	7
Total	1.1	0.5	58	0.9	0.6	58
Company size	Mean	Std. Deviation	N	Mean	Std. Deviation	N
Giant	1.1	0.6	16	0.9	0.7	16
Large	1.2	0.5	17	1.1	0.7	17
Medium	1.0	0.5	21	0.8	0.6	21
Small	1.3	0.5	4	1.0	0.8	4
Total	1.1	0.5	58	0.9	0.6	58

Table 7.

Multivariate tests (Wilks’ lambda).

Factors	Value	F	Hypothesis df	Error df	Sig	Partial eta squared
Nitaqat zone	0.8	2.2^b	6	106	0.048	0.111
Sector	0.8	0.6^b	14	98	0.836	0.082
Company size	1.0	0.4^b	6	106	0.867	0.023

Table 8.

Tests of between-subjects effects (Nitaqat Zone).

Source	Dependent variable	Type III sum of squares	df	Mean square	F	Sig	Partial eta squared
Nitaqat zone	All nationalities’ high-skilled migrants’ recruitment	0.6	3	0.2	0.7	0.577	0.036
Nitaqat zone	Indian high-skilled migrants’ recruitment	3.5	3	1.2	3.1	0.033	0.149
Error	All nationalities high-skilled migrants’ recruitment	16.0	54	0.3
Error	Indian high-skilled migrants’ recruitment	20.2	54	0.4

Table 9.

Multiple comparisons of recruitment of Indian high-skilled migrants between Nitaqat zones (Tukey HSD).

Dependent variable	(I) Nitaqat zone	(J) Nitaqat zone	Mean difference (I-J)	Std. error	Sig	95% Confidence Interval
Dependent variable	(I) Nitaqat zone	(J) Nitaqat zone	Mean difference (I-J)	Std. error	Sig	Lower bound	Upper bound
Indian migrants	Blue	Green	0.4	0.2	0.369	−0.2	1.0
		Red	0.3	0.3	0.778	−0.5	1.1
		Yellow	0.9^*	0.3	0.018	0.1	1.6
	Green	Red	0.1	0.2	0.994	−0.6	0.7
	Green	Yellow	0.1	0.2	0.991	−0.5	0.6
	Red	Yellow	0.0	0.3	1	−0.8	0.8

The difference in recruitments of high-skilled migrants since 2013 until 2017 between the companies with different Nitaqat zones is, however, found statistically significant, F (6, 106) = 2.209, p = 0.048; Wilks’ lambda = 0.790, partial n² = 0.11 (Table 7). Further, the tests of between-subjects effects (Table 8) showing how the dependent variables (recruitment of high-skilled migrants of all nationalities and Indian high-skilled migrants) differed for the independent variable (Nitaqat zones) reveal that the difference in recruitments of high-skilled migrants between Nitaqat zones has a statistically significant effect on the Indian high-skilled migrant workers, F (3, 54) = 3.14; p = 0.033, partial n² = 0.149, but not on the high-skilled migrant workers of all nationalities, F (3, 54) = 0.665; p = 0.577, n² = 0.04.

Turning to Table 9, the mean scores show that the job recruitments of Indian high-skilled migrant workers were statistically significantly different between Nitaqat blue zone and yellow zone (mean difference = 0.86; p = 0.018) companies. This does not hold true between any other Nitaqat zone companies.

Thus, the above results indicate that the Nitaqat policy in Saudi Arabia is associated with significant job losses of Indian high-skilled migrant workers in the construction sector, while their jobs in the banking, education and IT and telecom sectors were somehow resilient. The construction sector was the third most important sector of employment in Saudi Arabia after public administration and defense and wholesale and retail sectors when the Nitaqat policy was announced. The construction sector was also the second most important sector of foreign employment, after the household sector, occupying about 91 percent of the total employment in the sector in 2011 (Baldwin-Edwards, 2011). Therefore, because of its sheer size and domination by the migrant workers, Nitaqat did significantly impact the sector. Saudi Arabia’s native employment in the construction sector increased by 34 percent compared to the 14 percent growth rate of foreign employment since the announcement of Nitaqat in 2011–2013 (HKS, 2015). Consequently, the share of foreign employment in the sector declined to 87 percent by 2017 (Hasanov et al., 2021). Moreover, it was also easier for the construction companies to adopt technology and automation replacing low-skilled migrant workers, apart from the aggressive recruitment of native workers to meet the Nitaqat range. Due to the overrepresentation of migrant workers (low-skilled and high-skilled) in the construction sector, job losses among the high-skilled migrant workers were also significant, as found in this paper.

In contrast, due to the low share of migrant workers and the relatively high-skilled nature of jobs in the banking, telecom and education sectors, the jobs of Indian high-skilled migrant workers in these sectors were found resilient to Nitaqat. The need to meet the increasing demand for highly educated or trained natives and to prepare them for high-skilled technology-oriented jobs necessitate retaining university teachers in the education sector. The expansion of the education sector in Saudi Arabia, apart from creating a large number of administrative, managerial and accounting jobs for Saudis, can increase the demand for foreign higher education teachers. The expansion of higher education in Saudi Arabia can offer Indian higher education teachers with opportunities to migrate to Saudi Arabia.

The wholesale and retail sector, the second most important employment sector in Saudi Arabia, also have a higher representation of migrant workers. Almost 80 percent of employment in the wholesale and retail sector was occupied by migrant workers in 2011 (Baldwin-Edwards, 2011). It declined to 77 percent by 2017 (Hasanov et al., 2021). Although our MANOVA significance test failed for this sector, it accounted for the second highest average number of high-skilled migrant jobs lost to Nitaqat after the construction sector. Therefore, we anticipate that the wholesale and retail sector may not offer a significant number of jobs to high-skilled migrants in the future, as it is relatively easier to transfer some of the high-skilled migrants’ jobs in this sector to Saudi Arabian youth with higher education degrees. For example, retail jobs in major sales categories such as clothing, electronics, cosmetics, gold and jewelry, and phones and cars have already been reserved for Saudi Arabian men and women (Rahman, 2020).

These results also show that while the recruitment of high-skilled migrant workers declined after the implementation of Nitaqat most significantly in yellow zone companies, blue zone firms continued to recruit Indian high-skilled migrant workers almost on an equal basis as they did before the implementation of Nitaqat. The results did not indicate any particular direction of recruitment in the green and red zone companies. Thus, the blue zone companies, irrespective of the sector and size, show potential for future recruitment of high-skilled Indian migrant workers. Moreover, if the company is either from the banking, education or IT and telecom sector and characterized as a Nitaqat blue zone company, it will have the most potential for new recruitment and retention of jobs for Indian high-skilled migrant workers. Also, given the demonstrated potential of a large number of Indian doctors and nurses in the USA, the United Kingdom (UK) and other OECD countries (Potnuru, 2017; Potnuru and Khadria, 2019), the migration of Indian healthcare professionals to Saudi Arabia may also offer another opportune area for the future.

Conclusion

Against the backdrop of Saudi Arabia’s attempt to replace migrant workers with native workers, to increase the share of high-skilled migrant workers, and to transform the Saudi economy into a knowledge-based one, the paper examined the varying impact of Nitaqat on Indian high-skilled migrants’ job losses and new recruitments between 2013 and 2017. Using one-way MANOVA Wilks’ lambda test, the paper found that the difference in the impact of Nitaqat on Indian high-skilled migrants’ job losses based on sectors is statistically significant but not on Nitaqat zones and size of the enterprises. In contrast, the impact is statistically significant for new recruitments of Indian high-skilled migrants based on Nitaqat zones but not on sectors and size of the enterprises. The findings show that the high-skilled migrants’ job losses in the construction sector were significant, while the jobs in the banking and finance, education, IT and telecom sectors were somehow resilient. In addition, the new job recruitments declined significantly in yellow zone companies while blue zone companies continued to recruit Indian high-skilled migrant workers.

A policy option for both India and Saudi Arabia is to cooperate and facilitate the mobility of high-skilled workers in the fields of banking and finance, education, and IT and telecom sectors where the high demand can be met by Indian skills. The jobs in the construction and real estate sectors will increasingly be automated and taken up by the natives in the future. The jobs for the high-skilled declined in these two sectors after the implementation of Nitaqat and most significantly in yellow zone companies. The potential areas for the recruitment of Indian high-skilled migrant workers would be in banking and finance, education, IT and telecom sectors and in blue zone companies.

Due to the continued emphasis of the Saudi government to address the native unemployment problem through Nitaqat and its Vision 2030, Saudi Arabia will be less likely to welcome large numbers of unskilled migrant workers. Since the proportion of migrant workers in the construction sector in Saudi Arabia is already high, it is anticipated that the recruitment of high-skilled migrants, such as construction engineers, architects and technicians will be constrained in the future. As the findings of this study suggest, only a few sectors like the banking and finance, education and IT and telecom with high-end technology jobs will offer opportunities for the Indian high-skilled migrant workers. The Saudi government may make some exceptions to Nitaqat application in these sectors and skills where the demand will be high in its goal to become a knowledge-based economy.

Mega-events like the Dubai Expo 2021–2022 and the World Cup 2022 in Qatar may have increased the demand for migrant workers including low-skilled to the Gulf countries in the interim. For example, Sheikh Mohammed bin Rashid Al Maktoum, the Vice President and Prime Minister of UAE stated that UAE has created 248,000 jobs in 2020 amidst the pandemic (Rizvi, 2021). Qatar has added 43,000 new employees (all levels of skills) in the first quarter of 2020 compared to the previous quarter (fourth quarter of 2019), while the volume of employment in KSA, Oman, Bahrain and Kuwait declined during the same period. Migrant workers constituted 95 percent of total employment in Qatar in the fourth quarter of 2020 (GCC-STAT, 2021). Ultimately, India and other South Asian countries sending low-skilled migrants to the Gulf countries need to rethink their migration strategy and reposition themselves as the source of trained and high-skilled workforce. This would also significantly reduce protection and welfare issues facing low-skilled migrant workers in the region. In the future, a study with a larger sample size covering various cities in the Kingdom and in the region is warranted.

Footnotes

Acknowledgments

The infrastructural support provided by the FORE School of Management, New Delhi for undertaking this research is gratefully appreciated.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Basant Potnuru

Notes

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