Optimizing Global Mobility policies using Causal Inference

by Prof. Gadi Ravid, Adv. Tsvi Kan-Tor and PhD Candidate Yoav Kan- Tor

GM newly applied policies can now be evaluated and optimized using cutting edge Causal Inference research methods

Introduction: What is Global Mobility and why should we study it?

The field of Global Mobility is concerned with the movement of people between two or more countries (ARC Relocation, 2022). It can examine the transition of refugees as a result of disasters, or the transition of workers as a result of economic activities (Nations, 2016). The field of Global Mobility is highly regulated by both the state and by corporations, and it is frequently impacted by more than one state regulation and by several corporate regulations. Each of these regulations has a specific purpose. Some examples of these purposes could be to address sociological issues like a country allowing entrance to elder care employees (Special Restrictions Applying to Foreign Caregivers Wishing to Change their Place of Employment www.piba.gov.il THIS INFORMATION SHEET EXPLAINS THE FOLLOWING SPECIAL RESTRICTIONS SET IN POPULATION AND IMMIGRATION AUTHORITY PROCEDURES AND REGULATIONS WHICH APPLY TO FOREIGN CAREGIVERS WHO WISH TO CHANGE THEIR PLACES OF EMPLOYMENT IN ISRAEL, n.d.), to increase economic activity through the relocation of global experts (Gamtkitsulashvili and Plekhanov, 2021), or to improve the sociological incorporation of refugees, as was seen during the Arab Spring Uprisings (ICC - International Chamber of Commerce, n.d.).

The need: The effects of legislation on Global Mobility.

Global Mobility impacts individuals, families, companies/corporations, societies, and the entire world’s economy (Nobre, n.d.). The success or failure of the transition is dependent on many factors. Well-designed legislation can guide the conduct of administrative and corporate organizations as to how to treat those involved in the Global Mobility process while simultaneously accomplishing the legislation’s overall sociological and economical goals. Currently, all countries have different types of laws addressing relocation and general immigration (green card lottery, professional relocation and refugee’s resettlement),

and each system is constructed to serve a different purpose. There are numerous debates over the implantation of these various programs, and the level to which they have met their goals.

Legislation is often designed to protect the relocated person's basic human rights throughout the entire immigration process, from their home country to their final destination. Currently, we are entering a period with a high percentage of dismissals and resignations due to the COVID pandemic (Cotofan et al., n.d.), Therefore, the field of Global Mobility is becoming even more important as a result of these widescale change to the global employment environment and an increasing number of climate and war refugees (a-connect, 2016). Asserting the legislative effects on the humanitarian and economic aspects of Global Mobility will remain imperative as Global Mobility legislation becomes increasingly complex.

 The problem: The challenge in estimating the effect of legislation on countries, markets or goals.

Major changes in the Global Mobility policies of any given country are generally made in order to achieve several different goals. Those goals are usually derived from a combination of various economic, social and immigration related factors. When looked at as a whole, these factors tend to reflect the public opinions of the aforementioned country at a given time.

Following the implementation of any new policy or legislation comes the need to evaluate the effectiveness of said policy. This requires evaluating to what extent the goals of the policy or legislation were achieved. This is difficult to accomplish, as there is no control group that can be used for comparative purposes. We can never know what could have happened if a different policy was adopted rather than the one that was implemented. This is where Causal Inference methodology becomes imperative.

By implementing Causal Inference methodology, researchers and policymakers can overcome the challenge of estimating the effectiveness of Global Mobility policy and legislative changes by isolating the effects of the specific changes made. By doing so, Causal Inference methodology will lead to finding the proper modifications that need to be made in order to achieve the desired results, without the use of a control group ((Pulido-Velazquez et al., 2011).

The technology: What is Causal Inference.

Causal Inference is the practice of asserting the actual effect of a given variable on the overall result within a larger system. Causal relationships are different from associational relationships. Consider the following simplistic example: when researching sunburns, one might collect information regarding the rate of sunburns, and the sales of both sunscreen lotion and ice cream. We will discover that there is a relationship between the rate of sunburns and the sale of ice cream, as well as one between the rate of sunburns and the sale of sunscreen lotion.

This information may be sufficient if our goals are based on the fact that both ice cream sales and sunburn cream sales are both directly correlated with the rate of sunburns. Nevertheless, if we wish to actually prevent sunburns, we must determine their cause. A decrease or increase in the price of ice cream will not affect the rate of sunburns; as there is no causal relationship between the two, only an associational relationship. However, increasing the access to sunscreen lotion will result in fewer sunburns because the two factors are causally related. While the difference in the relationships may be quite obvious in this case, Global Mobility legislative and policy changes tend to be much less simple.

For asserting causality, the most commonly used method would be to conduct a randomized control trial (RCT), which is widely used to assess the effect of drugs in the clinical field. However, large scale economic and sociological issues can be difficult to investigate using RCT. From a practicality standpoint, it is impossible to determine the impact that a change in the minimum wage will have on employment by using randomized control trials. There are simply too many variables involved to control, and we cannot create a control group by randomly assign a minimum wage per person.

Examples: Where and how Causal Inference was used

The Nobel Prize in Economic Sciences laureates of 2021 received the award for their contributions to the field of economics, which involved solving the previously discussed challenge of measuring the effect of a given variable without the use of a control group. In their research, they showed that the labor market’s impact on minimum wages, immigration, and education can be estimated by using natural experiments, including the comparison of conditions that pave the way for Causal Inference (NobelPrize.org, n.d.).

Causal Inference can be used in the field of empirical law review. For example, in recent years, states throughout the US have enacted laws that prohibit discrimination against same sex couples in the adoption process. The purpose of the legislation is to allow same-sex couples to participate in the adoption process and to benefit children by increasing the number of adults eligible to adopt them. Religious adoption agencies that oppose same-sex adoption responded by threatening to stop adoption activities as a result of this legislation, as they claim the laws violate their religious beliefs. This resulted in a decrease in the total number of adoption agencies and the number of religious adoptive parents. The empirical question that we must answer is what effect the legislation has on the adoption industry and the chances of children to get adopted. Different antidiscrimination laws were passed in different states a long a span of more than twenty years. This heterogeneity allows for an observational study, meaning that empirical research could be conducted (Barak Corren, Kan-Tor and Tebbe, 2022). The research was based on data from the Adoption and Foster Care Analysis and Reporting System (AFCARS), which has case-level information regarding the progress in each adoption case coupled with the legislative status of each case. By using advanced machine learning and Causal Inference, it was shown that in general, antidiscrimination legislation did not increase overall adoptions. Despite this, there were specific subgroups like older children that benefited more from the legislation.

The commonality of the research is that it is based on observational retrospective data that is collected in a data registry. This method replaces the costly and complicated research interventions with Causal Inference methods.

Candidates: Where Causal Inference research could be used and what we could learn from it.

The Nobel Prize in Economics indicates that Causal Inference research methods can be applied to a number of fields, including the field of Global Mobility. There are a variety of Global Mobility research questions that can be answered using this research method. We have identified three current and relevant research topics where Causal Inference methods could be applied to illustrate this concept:

Migration of Arab Spring refugees to Europe 

The Arab Spring phenomenon, a sequence of pro-democracy protests and uprisings that occurred in the Middle East and North Africa, began in 2010 when protests in Tunisia and Egypt led to the fall of their regimes. This led to similar uprising attempts in Yemen, Bahrain, Libya, Syria, Algeria, Jordan, Morocco, Oman and others (Arab | Description, History, & Facts | Britannica, 2019). The failure of these rebellions against their regimes resulted in violent pushback from the heads of their respective governments. This widespread violence led to a massive wave of refugees attempting to enter Europe (Fargues, 2017). In light of these events, European countries found themselves needing to establish and revise their immigration policies to deal with all of these migrants, refugees and asylum seekers. This need intensified when an additional migration crisis occurred in 2015, causing a new wave of migrants to attempt to enter Europe (Wagner, 2015). In order to break down the resulting policy changes, we can use a chain of domestic events in European countries between 2012-2018 to analyze the type of policies that were adopted.

Broadly speaking, we can see several types of policies that were enacted; Germany has consistently supported the EU by promoting domestic policies that support migration. In 2014, Germany granted around two-thirds of the EU's asylum applications on the basis of protective status (Harris, 2015), and by the end of the year, Germany was considered one of the European countries hosting the most refugees (217,000) at the time (Refugees, n.d.). Alternatively, France has presented an inconsistent approach. On the one hand, in 2012, France blocked the operation of railways across Italy to prevent migrants and refugees from entering. Furthermore, French government policies presented in the white papers, which were issued in 2013 and 2017, utilized the concept of "defense and national security" in order to prevent an influx of migrants and refugees (Kazunari Sakai and Gilles Ferragu, 2020). On the other hand, in 2014, France accepted the same number of asylum applications as Germany (Harris, 2015). It was also considered to be one of the countries hosting the largest number of refugees at the time (252,000) (Refugees, n.d.). The Visegrád Group (Hungary; Slovakia; The Czech Republic; Poland), also known as the V4 countries, have pushed back against the EU's approach by denying a large number of asylum and migrant applications (the Guardian, 2015). These denials can be clearly detected around the second migration crisis in 2015 (the Guardian, 2015).

By examining these different policies, three main methods on how to deal with these issues appear. The first involves fostering policies that are consistent with the EU’s open-door approach. The second is an inconsistent approach, and the third involves policies that are vehemently opposed to the EU’s open-door approach. We can assume that each approach will have an influence as to different levels: the individual and their family, the specific countries enacting the policies, and the EU along with areas the EU has jurisdiction over, as the organization has taken on the task of regulating and distributing immigrants among all of its member states. Furthermore, each approach will have a different impact on a variety of social and economic elements affected by the issue of immigration. If we wish to break down the implications of each method, we can use Causal Inference methods and collected data to analyze their impact. In addition, Causal Inference methods allow us to examine sociological and economical changes affecting migration other than the policies themselves.

By using Causal Inference methods, we could assert the actual effect of each approach on the influx of immigrants migrating to each country before and after the change of policy took place. After the above-mentioned period of change, 993,1790 migrants have migrated to Germany, 410,845 to France and 269,206 to the Visegrad Group (Europa.eu, 2019). Before this period, 446,738 migrants had migrated to Germany, 313,463 to France and 225,629 to the Visegrad Group (Europa.eu, 2019). The considerable difference between the numbers in Germany compared to France and the Visegrad Group can be explained by the differences in policies. It can be assumed that one of the goals of each enacted approach involves promoting the integration of immigrants into the country’s local society, as programs like pre-screening potential immigrants give countries some control over who they are allowing to enter (Immigration, 2018). Causal Inference methods such as the ‘diff and diff’ method allow us to use indicative data collected in databases such as crime rates, tax data, and data as to the number of approved citizenship applications in each country to see the effect of a policy change by analyzing the data before and after the change. This allows us to assess the success of each approach as to this particular goal in the eyes of the country level for instance.

Thai migrant workers migrating to Israel

During the early 2000s, the Israel-Thailand immigration system had detected dangers to the safety and wellbeing of temporarily immigrating agricultural workers coming from Thailand to work in the Israeli agricultural industry. Most of these workers had reached Israel through either private agencies, or they were illegally trafficked into the country (2020 Snapshot: Migrant workers from Thailand in Israeli agriculture, n.d.). To address this, two treaties were signed to encourage the legal recruitment of Thai agricultural workers and to protect the incoming workers from abuse and/or illegal actions.

The first treaty was signed by the two countries in 2010 (Embassies.gov.il, 2022). As a result of the continued infringement of human rights that resulted in 83% of the workers being paid below the legal minimum wage (2020 Snapshot: Migrant workers from Thailand in Israeli agriculture, n.d.), among other things, led to the signing of a second treaty in 2020 (Embassies.gov.il, 2022). The 2020 treaty focused on increasing the quota of agricultural workers that may be recruited per year to 25,000 (Ministry of Labour, n.d.). In addition, the treaty allowed the government to take over handling the selection of applicants, the training of applicants, and in dealing with complaints from agriculture (Labour abuse fears rise for Thai migrant workers in Israel under new deal, 2020). Prior to the signing of the 2020 treaty, this was handled by the International Organization of Migration (IOM), a division of the UN.

We can break these events up into three time periods, to show that each treaty impacted various sociological and economic indicators in different ways. In order to decide on the best overall situation, we cannot focus solely on the indicator examined, but also on the level in question. By utilizing Causal Inference methods and collected data in addition to isolating sociological and economic changes that occurred during each time period, we can research the effect of specific characteristics of each period, like the existence of a treaty or U.N involvement, to analyze how these characteristics impacted the wellbeing of temporarily immigrating workers and their human rights. By collecting indicative data retrieved from self-report surveys concerning whether these workers received the minimum wage and the appropriate legal living conditions, and comparing the results of each period, we could assess which term was more beneficial for the wellbeing of the workers. Alternatively, we could use collected data retrieved from governmental databases to compare the number of work visa applications submitted in Israel to the number of applications submitted in another country similar to Israel in the context of agriculture and the process of migration to it during each period. By doing so, we would be able to refer to the similar country as a control group and could research which time period Thai workers preferred to work in the similar country rather than migrating to Israel. This would enable us to highlight which period in Israel was the least beneficial for the worker’s wellbeing.

An interesting question in this context regards the social gain of each period at different levels. As to the individual foreign worker, it is clear that the period that is most beneficial for his wellbeing has the most social gain. Nevertheless, we may conclude otherwise as to the country level. The period that is most beneficial for the workers' well-being may not be the most beneficial to all society. For example, we may discover that the period when the quota of workers exceeded may have had the most gain as to the wellbeing of workers although during that time crime rate has spiked. 

The research methods mentioned can also help us assess which period makes the most sense financially on each level. Using Causal Inference methods and economic data like GDP, we can effectively compare the three periods in each individual country, which would allow us to assess which status will be most beneficial for the countries. This will provide us with insight into a variety of questions such as the effect of enacting a treaty or whether it is financially beneficial to regulate the recruitment of foreign workers within the framework of a treaty. An additional question may regard the effect of enlarging the quota of workers, as Causal Inference analysis could help one decide if the financial gain is higher than the accompanying costs. We are also able to use these research methodologies and collective data as a way to analyze disposable. Disposable income data could be retrieved from a Thai governmental database and could be compared to the average disposable income in each period in order to assess which term was most beneficial for the individual worker. This comparison will also shed light on interesting matters like the effect of enacting a treaty at this level, as the accompanying costs of promoting a particular legislative move could potentially have been shifted the financial burden towards the individual worker via taxation.

Foreign experts in the cannabis field

In the United States, medical marijuana is now legal in 36 states and Washington DC (DISA Global Solutions, 2019). Despite this, marijuana is illegal on a federal level  (Williams, n.d.). The worldwide marijuana market was worth $37.4 billion as of 2021, and experts predict it could grow to $102 billion by 2026 (Prohibition Partners, 2021). The United States federal approach makes it difficult for those in the marijuana industry to receive basic banking services (Koski, n.d.), prevents legal marijuana companies from exporting their product to other countries or states, and prevents the import of global experts within the cannabis field (Hawryluk, n.d.; United States Drug Enforcement Administration, n.d.). The general expectation is that in the near future, the federal regulations regarding the illegality of marijuana will likely be diminished or even abolished (Dorbian, n.d.).

These changes are anticipated to have tremendous economic implications at different levels. When these changes do occur, we will be able to use Causal Inference tools to research the effect of opening the local cannabis market up to the federal and global cannabis markets. Specifically, we will be able to explore the contribution of importing global experts from the marijuana field on the local economy and on overall market growth.

The expected regulatory changes regarding marijuana legalization will have a significant impact on different levels, and we expect there to be interest as to which levels this change will be beneficial.  Using Causal Inference methods (for example diff and diff) and collected corporate data (such as stock market data) will enable us to compare the financial performance of US based companies to both their Canadian counterparts and to themselves, before and after the legislative change is made. Causal Inference will allow us to control the coinciding changes that affect the financial trends of the corporations that have little relation to the legislation.

This will give us valuable insights into multiple questions. The first is the effect of Global Mobility on the market, what involves analyzing the financial benefit of importing experts. The second regards investigating what the gain of suddenly being allowed to export experts into an advanced market is. We might discover that importing foreign experts benefits both corporations and society as a whole, yet we also might find that those profits do not completely coincide. For example, it might be that after a certain pay level, the societal benefits diminish while the corporate benefit increases.

All is well and peace has come to the land

In the article, we dealt with the highly regulated field of Global Mobility and discussed the phenomenon's effect and extent. We discussed the impact of regulation and the Global Mobility field on one another while addressing the inherent difficulty in measuring the influence of Global Mobility regulations on the phenomenon and other elements involved.

We presented Causal Inference, its nature, how to use it, and discussed its many benefits.

Against this background, along with the research questions presented demonstrating the combination of the Global Mobility field and the Causal Inference study, our recommendation is to encourage the continued integration discussed along with increasing the collection of reliable information as to the Global Mobility field in order to do so.

Recent technological developments in the study of Causal Inference alongside the proven success of using this research in the context of the Global Mobility field justifies now more than ever the integration of the two fields. Our recommendations will not only lead to new discoveries but will also take us one step closer towards creating agreed upon international definitions as to the Global Mobility field, which has been a major problem within this field.

The writers: professionals from both fields discussed, invite you to contact them for any inquiries, additional information or a desire for research collaborations at one of the addresses below:





Reference list


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About The Author

Prof. Gadi Ravid Holds a Ph.D in Adult Education and Management and serves as the Founder and VP of Academic Affairs at UIBE- Israel . He is a senior Lecturer of Business at Netanya Academic College and served as the Dean of two Schools. Prof. Ravid, a Global Mobility specialist, has held many senior positions in the HR field in addition to serving as the Academic Manager of the" Israeli Center for Management" and leading different forms of training for HR managers in the Israeli Industry. Last Year, Prof. Ravid was elected as the chairman of The Israeli Association of Organizational Consultants.

Adv. Tsvi Kan-Tor Managing partner of Kan Tor & Acco, an Israeli law firm specializing in Global Corporate Migration. Tsvika is the Co-author of 4 books on US Immigration Law. He is also a Member in the AILA, ERC and Israeli Bar association and serves as Chair of the visa committee - Israel America chamber of commerce. Tsvika has also served as a commentator, frequent speaker and lecturer being a global mobility and global corporate migration specialist.

PhD Candidate Yoav Kan- Tor Computer science PhD candidate specializing in data science, machine learning, and causal inference. Yoav has worked in interdisciplinary fields over the past 14 years, founded two companies, and his research led to several startups. He is also a member of the Data Science Center at the Hebrew University. Yoav has published numerous research papers and patents in the areas of computational medicine, medical devices, digital humanities, and empirical law.

The opinions expressed in this article do not necessarily reflect the opinion of ILW.COM.