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# Development Delays, Illegal Immigration and the Informal Sector in French Guiana

Moustapha Aladji, Mickael Cita, Gaelle Lebeau

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Moustapha Aladji, Mickael Cita, Gaelle Lebeau. Development Delays, Illegal Immigration and the Informal Sector in French Guiana. Annals of Reviews and Research, In press, 5 (7). hal-04100100

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Research Article
Volume 7 Issue 5 - September 2022
DOI: 10.19080/ARR.2022.07.555725

Ann Rev Resear
Copyright © All rights are reserved by Moustapha Aladji

# Development Delays, Illegal Immigration and the Informal Sector in French Guiana

<figure><img src="image_4.png" /></figure>

Moustapha Aladji<fnref n="1" />*, Mickael Cita<fnref n="2" /> and Gaelle Lebeau³

**Submission:** September 01, 2022; **Published:** September 15, 2022

*Corresponding author: Moustapha Aladji, PhD student in Economics university of Guiana-MINEA Research Unit, Guiana

## **Sumary**

The arrival of a population on a territory brings a potential source of additional work that overall allows the host country to increase its growth as shown by Chojnicki and Ragot (2012) in their study. However, the France has the particularity of being present on different continents and thus holding territories with great disparity. Through this article, we want to focus on immigration in French Guiana, looking for explanatory factors for the reasons for immigration to this territory. At the same time, we are asked about the contributions of this immigration to know if it represents an asset or a constraint for the development of the territory. The answer is obviously not binary, which gives more interest and complexity to the question. Thus, we were able to carry out a first theoretical analysis in order to grasp the most relevant elements to be studied in order to set up the empirical analysis. Then, we chose a Probit model that we then confronted with the Hosmer-Lemeshow test. The theoretical analysis allowed us to highlight various elements of informal integration (housing, working conditions) that also promote a form of precariousness. Empirical analysis, she rather highlighted the correlated factors that support this immigration rate. Finally, we observed the case of the Hmong population settled in French Guiana for several decades, which represent a relatively successful example of immigration with a positive economic impact.

**Keywords:** Development; Informal work; Precariousness; Underground economy; Unemployment

**Abbreviations:** OFPRA: Office for the Protection of Refugees and Stateless Persons; IM: Immigration; ST: Status; RE: Income; AGE: Age, LF: Family link; DIP: Diploma; CD: Choice of destination; ACT: Activity; UNHCR: United Nations High Commissioner for Refugees

## The Context of Guyanese Development and Immigration

French Guiana, the outermost territory of Europe, has had a significant migratory impact since the abolition of slavery in 1848. With 32% of its population born abroad, this impact continued even after the departmentalization. The diversity of the population of this territory is as a result of migratory waves and successive arrivals in the early 1960s. It then diversified with the arrival of native immigrants from Brazil, Haiti, Guyana, Suriname, and Saint Lucia.

## Rapid population growth

Population growth in French Guiana is largely attributed to its natural balance, but immigration has also contributed to this. The foreign population represents 32% of the Guyanese population, the average annual growth rate on this territory is 2.6% (INSEE, Population Census 2018). Since the mid-70s, the Guyanese population has been growing at a very rapid rate as shown in the figure below Figure 1. In 40 years, its population has multiplied

by 4.7 and estimated at 294,146 inhabitants on January 1, 2021. The demography of French Guiana is one of the most dynamic in France. In 2011, the geographer Frédéric Piantoni described immigration by its role in population growth and by the socio-economic strategies it deploys, emblematic of the relationship between globalized processes and local strategies. In addition, population growth is combined with a very dependent economy on metropolitan transfers and a binding legislative framework, producing social inequalities.

Recall that it is partly thanks to planned immigration policies from the seventeenth century, combined with deportation of slaves-the “coolie” workers, that the population was formed at first. After the departmentalization, it was necessary to sustain an economic valorization program (the CSG, the infrastructures), which favored a wave of immigration from South America but also from the Caribbean. Today, these are unplanned immigration channels, which result from socio-political crises and civil wars (Haiti and Suriname). According to INSEE data in 2021, immigrants

<footnote n="1">¹PhD student in Economics university of Guiana-MINEA Research Unit, Guiana</footnote>

<footnote n="2">²&³PhD student in Management Sciences university of the West Indies CREDDI LEAD Research Unit, Guiana</footnote>
represented about 30% of the population of French Guiana against 27.9% in 2013. However, immigration to French Guiana is probably underestimated due to the large number of people in

an irregular situation, having neither legal nor false documents to reside in French territory. This leads to uncertainties about the size of the population itself and its distribution.

<figure><img src="image_5.png" /><figcaption>**Figure 1:** Natural balance as main engine of growth of the Guyanese population. (Annual change rate, change rate due to natural balance, change rate due to apparent balance of input-output) Source: Insee, RP2018 principal exploitation, geography on 01/01/2021.</figcaption></figure>

Indeed, even today, French Guiana still faces border permeability. According to the border police of Saint Laurent, this explains why about 40% of people subject to prefectural expulsion order are already known to the police. An interview with a smuggler who has been carrying out this activity for about ten years, shows how lucrative this activity is (he could earn up to 3,000 euros per day by making 3 to 4 round trips) but he also exposes the porosity of the current borders.

As a result, there are many strategies for obtaining papers, such as the case of the "fake Chinese dads" of autumn 2013. They are structured around networks of various important varieties ranging from "individual businesses" to "mafias". Thus, women would propose white marriages or recognitions of paternity (when the child was born in French Guiana). The cost of obtaining a visa then varied from 3,000 to 5,000 euros, the commission can amount to 1,000 euros when there is an intermediary. However, these networks are very little dismantled compared to the number of people who benefit from their service. A study by D. Gadelha, (2013) published by the University of Para confirms the illegal immigration of Brazilians and strategies for obtaining papers in French Guiana. The network of immigration routes seems to adopt a community trait, each nationality already present on the territory of Guiana is responsible for organizing the passage of persons belonging to its country of origin, using well-established networks. Faced with the porosity of borders, part of public opinion is currently calling for the closure of Guiana's borders, in

response to illegal immigration from Brazil and Suriname or other countries on the Caribbean plateaus.

## Analysis of the development of irregular migration

The economic situation and labor market of this territory are driven by its population growth. According to INSEE, of all French departments, French Guiana has experienced a fairly significant economic dynamic in recent years, and regularly displays the highest growth rate of 3% on average and 4.1% in 2019. Guiana is presented as a real open-air construction site, where major works are multiplying infrastructure, housing, schools. The building and public works sector is particularly dynamic, and a source of many business and investment opportunities. Guiana shows an advanced technological development in the space sector and in that of renewable energies. However, this dynamic does not compensate for the economic delay that the territory is experiencing compared to other overseas territories such as Guadeloupe, Martinique. However, Guiana is the richest country in continental South America with a GDP per capita of 15,163 euros at the beginning of 2020, or 16,983 dollars, ahead of Uruguay $16,190, Chile $14,896 and Argentina $9,912 (Insee: CEROM, 2014).

At the level of regional integration, we observe great disparities with Metropolitan France where the GDP per capita is almost three times higher than that of French Guiana. These results are due to the economic structure of the department, which is mainly composed of sectors with low added value, low rate of industrial
development, and a greater share of informal activity, which is not very lucrative. "GDP is thus redressed by about 3% for fraud and tax evasion and about 1% for undeclared work." This adjustment is one-third higher than that of the metropolitan accounts. Informal activities, which are often low-productivity and poorly remunerated, therefore ultimately weigh little (about 4% of GDP) compared to public financial flows or expenditures related to the space sector.

## Economic data on the national integration of French Guiana: backwardness and inequality in development in comparison with the metropolis

Long anchored in an almost exclusive relationship with metropolitan France or the European Union, Guiana's economy is perpetually not permeable to its regional environment. Strong and persistent differences remain between the overseas territories and the Metropolis in the socio-economic field. To this effect, several parliamentary reports have been published in the 2010s and more recently on Victorien Lurel's report on real inequality overseas (March 2016). This report shows the gap between the standard of living of ultra-marines in comparison with France. The price difference with the metropolis is considerable and vary between 22% and 38% on food products.

In terms of education, especially school failure. The drop-out rate is twice as high in metropolitan France. According to a study carried out by the Ministry of National Education, during the citizens' defense days organized in 2016, the share of 18-year-olds with reading difficulties reached between 30 and 75% in

the French overseas departments (27.6% in Réunion, 30.4% in Martinique, 33% in Guadeloupe, 48.4% in French Guiana and 74.9% in Mayotte) against 10% in the France as a whole. Other inequalities remain in terms of investment spending on basic infrastructure (transport and sanitation infrastructure, electricity infrastructure, digital housing, etc.). Thus, in terms of sanitation, 56% of fresh water is of good quality in metropolitan France but only 12% in French Guiana, 23% in Guadeloupe and 42% in Martinique. 5% of Guyanese do not have access to drinking water.

**Population and immigration conditions:** According to data from the General Social Security Fund of Guiana's 2020 activity report, knowing that the territory is home to nearly 50,000 people in an irregular situation, mentioned 35,549 applications for AME of which only 19,717 were able to benefit from this. French Guiana has been subjected over the past twenty years to a flow of illegal immigration on both sides, with the arrival of asylum seekers from Latin America and the Middle East. In 2015, the figures were estimated at more than 20,000 asylum seekers.

Asylum is a right recognized by the French Constitution, which benefits victims of persecution or people who have good reason to fear persecution in their country of origin, on the basis of their opinions, religion or ethnic origin. To these reasons are added the victims of wars, as in Syria. Faced with this unprecedented migratory flow, in 2017, the State took measures by creating a branch of the French Office for the Protection of Refugees and Stateless Persons (OFPRA) in Cayenne, French Guiana. A law was passed in the assembly in 2018 on the procedure and deadline for processing asylum applications (Figure 2).

<figure><img src="image_6.png" /><figcaption>**Figure 2:** % distribution of the number of asylum applications by nationality in 2017.</figcaption></figure>

Sources: OFPRA report, 2017 (Dominican Republic, Syria, Other nationalities).

In French Guiana, there is no reception center for asylum seekers (CADA). Only the Red Cross offers an average of 600

accommodation places to asylum seekers for an average of 6,000 applications. Guiana was the sad precursor to the reform
of the asylum seekers' allowance (ADA) which is now endemic in metropolitan France. This allowance is paid on a card that only allows food purchases in stores. Asylum seekers had no access to housing due to deficits in state structures that could effectively accompany them, so the only solution was to pay for illegal housing in the slums to sleep, through their ADA. This is no longer possible today, aggravating the precariousness and distress of these people.

Computerization at certain stages means that some immigrants voluntarily choose to remain in an illegal situation and integrate informal activities, especially since circumvention practices remain numerous and accessible. For a majority of them, it is not so much the refusal to fit into the legal framework as the consequence of a practical impossibility and the fear of seeing the asylum application fail and having to return home.

## Spatial distribution of migrants and communities

The State holds more than 80% of the land, and urban areas are mostly occupied, so the cost of housing remains high, especially for people from countries with weak currencies, like what is found in the Americas and Caribbeans. Faced with this, the spontaneous settling of immigrant population in irregular situations appears in a large network of subletting of housing, sometimes themselves, illegal. These precarious dwellings are often unhealthy and have no access to water and electricity networks. They are often outside of maintenance and utilities. From their establishment to their maintenance, everything responds to a system of operation set up and managed by the populations themselves, whose pioneers were able to claim the territory. Indeed, it is preferable to belong to neighborhood associations to obtain land as well as to build on it: 89.6% of the owners (illegal immigrant) surveyed have only used the neighborhood association for the construction of their housing (self-financing, solidarity etc.), the economic contribution is still part of the informal system.

Finally, these neighborhoods are composed of 1000 to 3000 inhabitants, a critical size allowing them to request access to State services as well as a probable regularization of the populations settled after ten years of occupation F Piantoni [2]. This was the case for the inhabitants of the hill of Mont Ploërmel, demolitions were decided in 2019, followed by a regularization of the people occupying the side of the road belonging to the municipality, it has not yet been carried out for the part of Cayenne. Moreover, as Frédéric Piantoni [2] points out, the territory of French Guiana is bounded by physical margins, either by its natural characteristics, such as rivers, or by social borders creating ethnic "villages" in which certain nationalities predominate. These are, by way of example, the "urban squares [...] Chinese, Brazilian, Saramakas, Haitian neighborhoods of Bonhomme or Eau-Lisette. "There is therefore a strong cultural character in exchanges of all kinds - practiced on a given territory. It is mixed logics, the result of the contributions of the country of origin and the host territory that

prevail.” “Immigration neighborhoods are more than a default residence. They are the first anchors of the journey, airlocks and interfaces between the life before and the life projected; places of exchange, distress and dependence, where the precariousness of those who start a new life remains a reference to be overcome for oneself or for the descendants. (Op cit, F. Piantoni).

**Irregular immigration:** The economic impact observed on the ground through the activity: Regarding irregular immigration, it is obvious that we cannot rely on the data of organizations to talk about it, which is why we will approach it from information collected on the territory. Illegal immigrants are the most represented in the construction sector, where there is a major subcontracting policy. Large companies get contracts and call on smaller companies, or even craftsmen, to carry them out. However, they have too few employees to carry out the projects. The workforce in the construction industry is made up of Brazilians and Haitians. Three specific cases can be reported: sale of the craftsman's signature up to 10% of the amount of the contract (he has the work carried out), the craftsman provides the labor, or he effectively supervises the labor.

However, “foreigners are paid two to three times less than an employee declared for a company”, which would be “one of the causes (if not the first) of the difficulties of medium-sized construction companies in French Guiana, a significant number of which have disappeared”. Labor inspection surveys show that only half of the employees in rice fields (Mana) and sawmills (Saint-Laurent du Maroni) are declared, and only half of the hours worked are paid. There are many examples illustrating the precarious working conditions of people in illegal situations, but also the income received is much lower (often less than half of a salary declared for an equivalent position).

Employers use the fear of deportation against these immigrants, when this could be for them a reason for regularization (by occupation of a job and therefore employment contract). The financial advantage is very significant for employers who do not comply with safety standards, pay net wages far below the market, no social contributions (3/4 of employees in the sector would not be declared). In view of the lack of quantitative information on the economic weight of persons in irregular situations, it seems necessary to consider a survey in order to be able to calculate it. We have illustrated these two figures below, the first of which highlights the components of illegal immigration and their position in their sphere of interaction (Figure 3).

Figure 4 below shows the different variables of the economic sphere. Each group of variables is separated by a dotted line, which means that from a qualitative point of view, the limits are porous, so that each term is a factor influencing the other. For example, the type of work chosen will depend on a cultural aspect, as will the products consume.
<figure><img src="image_7.png" /><figcaption>**Figure 3:** Components of illegal immigration and their position in their sphere of interaction.
(Immigration, Integration, Moyen=means, illegal immigration, status, origins).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 4:** Variables of the economic sphere.
(culture, economy, public service, security, education, health, justice, passeur=ferryman, squat=squatting, voiture=car, consummation=consumption, logement=accommodation, transport=transportation, travail=work, loisirs=hobbies).</figcaption></figure>

## Conditions of employment of immigrants

Among students, the more recent their arrival, the greater their proportion. Between 2010-2015, 10% of arrivals compared to 3% for those who arrived before 2000. Those who arrived before 2000 are too old for studies. For men and housewives, the same is true for the unemployed. Regardless of the period of arrival, the proportion is more or less constant. For immigrants who arrived between 2010 and 2015, the proportion of those not in the labor force is 37% compared to 17% for those who arrived

before 2000. There is a probability that these migrants will work in the informal sector. It is easy to imagine how it is possible to integrate two vulnerable categories at the same time. Far from wanting to list everything that may exist in French Guiana, it is worth asking whether international labor standards, where they are legislated, affect, here in Guiana, populations that suffer, through the accumulation of criteria harmful to employability in the formal sector of employment (Figure 5).
<figure><img src="image_9.png" /><figcaption>**Figure 5:** Activity of immigrants by period of arrival.
Source: INSEE, RP2018 main operation, geography as of 01/01/2021.
(actifs occupés= employed/workers, chômeurs= unemployed, retraités= retired, étudiants= students, femmes ou hommes au foyer= housewives or house-husbands, autres inactifs= other inactives).</figcaption></figure>

From the point of view of immigrants' access to employment, we compared the following periods: before 2000, between 2000 and 2010, and between 2010 and 2015. We see that there is less hiring than before 2000. Immigrants who arrived before 2000 in French Guiana more often had permanent contracts: 16% for those who arrived before 2000 compared to 5% between 2010

and 2015. The same is true for fixed-term contracts. This proves that it is increasingly necessary to have a professional qualification to integrate. There is also a high unemployment rate in Guiana (22%), which makes it even more difficult to access the labor market (Figure 6).

<figure><img src="image_10.png" /><figcaption>**Figure 6:** Employment conditions of immigrants.
Source: INSEE, RP2018 main operation, geography as of 01/01/2021.
(Contrat à durée limitée = limited term contract, contrat à durée indéterminée = permanent contract, non-salariés= non-salaried)</figcaption></figure>

The immigrant and non-immigrant labour force are disparate. The activity rate of the immigrant population represents 22% of the total labor force. Among the non-immigrant population, the level of education is decisive for access to employment. Conversely,

in the immigrant population, the lower the level of education, the higher the share of employed persons. As a result, it can be inferred that most immigrants are often in low-skilled jobs. The very issue of unemployment is subject to discussion, particularly
for immigrants awaiting a status favoring declared employment and those who opt to enter the labor market in parallel and remain there. This “unemployed” immigrant population, at work in the informal economy, is officially excluded from the French productive system, while being perfectly integrated into the local productive system, as if French law concerning immigration and labor were only a second layer of the Guyanese economy and barely even important. Thus, they can be at least socially included in the department. The IEDOM speaks of the role of social shock absorber that the underground economy plays, because it often avoids the worst situation for many Guyanese and foreigners arriving in Guiana for whom informal work is more a logical way of survival.

The immigrant population has few qualifications. Whether their arrival is recent or old, those without diplomas are always the most numerous to come and settle. Three out of five immigrants have no diploma. The immigrant population arrives in Guiana less and less educated because in the waves of recent immigration (2010-2015), the proportion of people without diplomas is higher than before 2000. The factors behind the low level of qualification of immigrants are the non-recognition of the diplomas of the country of origin of some immigrants, the lack of correlation between the professional experience of the latter acquired in the country of origin and the host country. In addition, we can take into account the fact that their requests do not meet the offers of the host country.

Also, it is important to take into account that underemployment and informal activity has a strong weight on the territory of Guiana, which allow immigrant populations and low-level training to access jobs and receive an income, even if the gaps remain significant with legal employment, especially because of the exploitation shown by employers towards these populations. These cases are particularly visible in construction and domestic or personal services.

## Economic Analysis of Development and Immigration: A Theoretical Framework

Development theories are based on principles that are indeed part of economic theory. The constant circulation of trade promotes accumulation, the latter being a factor of production and growth. Other principles have been added such as division of labor, which is induced by accumulation, mass production which is favored by division of labor; progress and innovation, free movement of capital and goods are also drivers of economic development and growth. These are all basic principles on which economic theories and development theories are based. Thus, for a long time, the principles of economics elaborated by the first theoreticians of classical economics, Adam Smith (1723-1790), David Ricardo (1772-1823) and Jean-Baptiste Say (1767-1832), served as the basis for theories of economic development and

development, because at the time, development would be equated with economic development.

Economic analysis of international migration is still a relatively undeveloped area of research. While issues raised by international trade and development-related capital flows have traditionally attracted the attention of economists, the amount of work done on migration flows is much more limited. In addition, the latter focused mainly on two issues: on the one hand, the constitution of explanatory theories of the structure of international migration. On the other hand, the definition and quantification of the externalities of migration flows for the labor markets of host countries and, to a lesser extent, countries of origin. In contrast, the formulation within the framework of the standard economic paradigm of a positive theory of immigration policy has been ignored until very recently. In 2000, E. Meyers thus differentiated six analytical approaches to this public policy (Marxist, realist, constructivist, national, domestic, and institutionalist approaches) borrowing their concepts and methods from several social sciences, including history, political science, and psychology, but without major influence of economics E. Meyers, (2000).

This observation of a standard economic theory with the carelessness to debate immigration policy is gradually being challenged by research developed from the late 1990s onwards. Pioneering work in this area is most certainly the 1996 article on the Political Economy of Immigration J. Benhabib, (1996). This first formalized version of a model for determining the immigration policy of a state is representative of the “usual” economic approach of this instrument: the admission of immigrants generates income transfers between national groups determining the structure of immigration preference cleavages.

The structuring of economic relations induced by immigration determines in turn the criteria for admission of foreigners enacted by the State. In other words, intertemporal and international variations in the degree of openness of national economies to immigration can be explained by the economic externalities of immigration, specific to each country. The demonstration of this relationship takes place in two stages. First, the standard theory explains the existence of social cleavages concerning the determination of immigration policy by the transfer of income between national groups generated by the admission of immigrants. The arrival of foreigners with factors of production modifies the relative factor endowment of the host economy, and consequently the income that nationals derive from their participation in productive activity. In theoretical models of standard economics, the impact of immigration on the labor market is analyzed as the effect of a “shock” on the volume of a “factor of production” labor, or even low-skilled labor.

Many social science researchers through the literature reviews on immigration, have tried through their studies, to
demonstrate that immigration has more or less an impact on the poorly educated natives, who have low incomes and who are often unemployed. Immigration is often seen as a threat to workers in the host country. Theoretically, we want immigrants to have skills that are different from those of workers already present so that they can fill the gaps in the labor market. In practice, immigrants often have characteristics that put them in competition with these workers. By increasing the supply of labor, they can reduce their wages and employment levels.

Many studies have attempted to empirically measure the effects of immigration on the labor market of the host country. To do this, they compared wages and employment in countries where there are many immigrants to wages and employment in regions or sectors where there are few immigrants. Based on the study by Gille Grenier [4], he pointed out in his study that it is difficult to make a comparison between regions, as regions differ from each other for many reasons other than the number of immigrants. He concludes that immigration increases wages and employment, and labor market conditions are better in Toronto because it is more economically prosperous. This is one of the reasons why immigrants choose this city. Other authors such as Altonji and Card (1991) studied the labor market in 120 major metropolitan areas of the United States with the 1970 and 1980 censuses. To better account for observable and unobservable differences between cities, they assess how changes in the economic situation of low-skilled workers between the two censuses are related to changes in immigration. Their method does not detect the negative effects of immigration on the employment and unemployment of low-skilled workers.

Transposing these facts into the case of this article, we will try to perform an empirical analysis using a probit model in order to understand the factors that encourage people to immigrate to Guiana. Glick (1947) and Rossi (1955) used an approach to study the explanatory factors of immigration. In their approach, they explain that an individual's life is marked by various events that punctuate it and determine the needs of the residential place and housing. These needs thus evolve over the course of life, and the transition from one episode to another can lead to dissatisfaction with the present place of residence, which would encourage the individual to migrate to meet his new needs.

## Study of the factors explaining immigration by a Probit model

Several empirical studies have been carried out on the probability of migration of individuals as well as their integration into the labor market in host countries. The Probit model is one of the models used in econometrics to analyze economic facts. Hatton & Williamson (2002), Pedersen & al. (2008), and Clark & al. (2004), Schiff (2007), de Melo (2007) have exposed cultural, geographical and economic factors as determinants of immigration

in their studies. They have developed a macroeconomic model that measures the impact of the policy of openness to immigration, which takes the following form: $MI = f(Xi; Pi)$ and leads to the theory that there is therefore a migration policy induced by Xit structural factors and an autonomous Pit migration policy. The distinction between the two allows us to measure the autonomous part of migration policy. We therefore consider that the variable P is not influenced by exogenous factors X, that is, it really measures the discretionary policy of governments; this condition is also necessary for the simple estimation of the parameters β.

We also have the article by Étienne Rusamira (2006). The objective of his article was to develop statistical and empirical models of interpolate and international migration in Canada. These models will have two essential aspects, one explanatory and the other predictive. These models, which integrate several variables (demographic, socio-economic and geographical) of migrants, should help to better explain why migrants choose one destination over another. He used the conditional Logit model also known as McFADDEN's Conditional Logit which was created in 1973 to determine the explanatory factors of migration. The model he used is based on a multiple process, i.e., several steps and sequential. Mathematically, this model is written as the following equation: '

$$Pr(y_i = j | x_i) = \exp(\beta x_{ij} + aW_i) | \sum \exp(\beta x_{ij} + aW_i) \text{ with } i \neq j$$

The conditional logit model has the following characteristics:

It verifies the property well known as the independence of irrelevant states (IIA), the ratio of probabilities associated with the choices between two modalities is independent of the other modalities. In other words, adding or removing a term does not change the relationship of the other terms. But the latter must be clearly distinct and weighted independently in the eyes of the decision-maker AGRESTI, (1990: 316). It makes it possible to integrate both the characteristics (or variables) of choice and those of individuals (AGRESTI, (1990: 317), GREENE (1993: 665). The multinomial logit model deals only with the characteristics of the latter (GREENE, 1993: 665).

**Descriptive analysis and estimation:** This part consists of detailing the immigrant population that we are studying from the samples that we will present. By definition, a sample is a set of individuals taken, following a well-defined process, in the larger set constituted by the target population. The number of individuals collected, often denoted n, is called the sample size. For the purposes of this study, our n that represents the sample size of our survey is 1000 households. The objective here is to see if the variables below depend on the immigrant variable. In the case where these variables depend on the immigrant variable, it will be concluded that the duration of an immigrant on the territory plays on the variable or variables that depend. Let's start with variable statistics (Table 1).
we have:

$$\phi(t) = \int_{-\infty}^{t} \frac{1}{\sqrt{2\pi}} \exp(-\frac{1}{2}x^2) dx$$

$$\phi(t) = \frac{1}{\sqrt{2\pi}} \exp(-\frac{1}{2}t^2)$$

The normal distribution is asymmetrical, we have:

$$\phi(-t) = 1 - \phi(t)$$

Its mean is equal to 0 and its variance 1.

We can write the probability pi in the form:

$$pi = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{a+\beta+\lambda zi} \exp(-\frac{1}{2}t^2) dt$$

### Generation of terms and conditions

Since our database is rather qualitative, we proceed to the following modality.

We ask to immigrate:

- If the person is an immigrant = 1

- Otherwise = 0

<table><caption>**Table 4:** Table with a description of the variables.</caption><thead><tr><th>Effectifs des enquêtés</th><th>Codification of variables</th><th>Variables</th></tr></thead><tbody><tr><td>Observations</td><td>OBS</td><td></td></tr><tr><td>Immigré</td><td>IM</td><td>Dependent variable</td></tr><tr><td>Statut social</td><td>SS</td><td>Explanatory variable</td></tr><tr><td>Sexe</td><td>SEXE</td><td>Explanatory variable</td></tr><tr><td>Revenu</td><td>RE</td><td>Explanatory variable</td></tr><tr><td>Lien familial</td><td>LF</td><td>Explanatory variable</td></tr><tr><td>Hébergement</td><td>HEB</td><td>Explanatory variable</td></tr><tr><td>Diplôme</td><td>DIP</td><td>Explanatory variable</td></tr><tr><td>Choix de destination</td><td>CD</td><td>Explanatory variable</td></tr><tr><td>Âge</td><td>ÂGE</td><td>Explanatory variable</td></tr><tr><td>Activité</td><td>ACT</td><td>Explanatory variable</td></tr></tbody></table>

Source: author.

<table><caption>**Table 5:** Chi-square statistic table.</caption><caption>Tabulation Summary</caption><thead><tr><th>Variable</th><th>Catégories</th><th></th><th></th></tr></thead><tbody><tr><td>IMMIGRATION</td><td>2</td><td></td><td></td></tr><tr><td>SEXE</td><td>2</td><td></td><td></td></tr><tr><td>Product of Catégories</td><td>4</td><td></td><td></td></tr><tr><td></td><td></td><td>SEXE</td><td></td></tr><tr><td>Count</td><td>0</td><td>1</td><td>Total</td></tr><tr><td></td><td>0</td><td>103</td><td>220</td></tr><tr><td>IMMIGRATION</td><td>1</td><td>419</td><td>780</td></tr><tr><td></td><td>Total</td><td>522</td><td>1000</td></tr></tbody></table>

Source: Survey data.

The variables are:

Immigration (IM) (1 immigrant, 0 for non-immigrant), Status (ST) (1 for people who immigrated for the social status of Guiana, 0 for other reasons), Sex (SEX) (1 for Female, 0 for Male) Income (RE) (1 for people who have an income above €500, 0 for people who have an income below €500), Family link (LF) (1 for people who have a family link in French Guiana, 0 for people who do not). Accommodation (HEB) (1 for people accommodated, 0 for people who live in makeshift houses), Diploma (DIP) (1 for qualified people: qualifications by diplomas or skills, 0 for non-qualified people), Choice of destination (CD) (1 for people who have chosen Guyana as their final destination, 0 for people who are in transit) Age (AGE) and Activity (ACT) (1 for formal activity, 0 for informal activity).

**Presentation of data and choice of variables:** The data used come from 1000 households, over a period of 1 year, spread over three municipalities that we call living area. The use of qualitative variables as explanatory variables in a regression model is not problematic (Table 4 & Table 5).
Before starting the estimation of our probit model, we calculated the proportion of immigrants with the proportion of women in our sample. This calculation was passed by a statistical test of Chi-square. According to the results, the proportion of immigrants is =780/1000 = 0.78 and that of women in the sample is 478/1000 = 0.478. We checked by a chi-square independence test according to the following assumptions:

H0: Characters are independent

H1: Characters are dependent

## Estimation of the Probit model

In this section, the challenge of this demonstration is to present the synthesis of the results of the estimation of the Probit model. The results are obtained thanks to the EViews software. The basic model consisted of the twelve variables. After the first estimate, we got three statistically significant variables. The tables below give us the most information on the results. Recall that the Probit model is a type of econometric model with binary choice. That is, a choice between two options. It is characterized by the fact that it is based on a standard normal cumulative distribution. A standard normal cumulative distribution related to a random

variable is a function that signals the possibility that said variable has a value less than or equal to a certain number, which functions as a threshold.

First, we have an equation that explains the dependent variable Y, and this, as a function of one or more independent variables (X). In this empirical study, our Y, dependent variable is immigration. In this model using our survey data, we will try to see through the panel of our explanatory variables, which of them explain immigration to Guiana. Our dependent variable as we have specified above is immigration i.e., Y = immigration and the explanatory variables are: Social status of French Guiana, Sex, age, Graduate, Family link, Accommodation, Choice of destination, Activity, income, expenditure.

**Model results:** We estimate the probit model using the EViews software. In our results, the constant coefficient is the value of the inverse distribution function when no constraints are imposed, and the natural response rate is 0. The natural response rate is the probability that a unit will fail without being exposed to any stress. This statistic is used in situations with high migration or failure rates. If the natural response rate is greater than 0, the stress is not the cause of all failures in the analysis (Table 6).

<table><caption>**Table 6:** Estimate Results.</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>z-Statistic</th><th>Prob.</th></tr></thead><tbody><tr><td>C</td><td>-0.986499</td><td>0.360830</td><td>-2.733970</td><td>0.0063</td></tr><tr><td>REVENUE</td><td>1.132863</td><td>0.156546</td><td>7.236621</td><td>0.0000</td></tr><tr><td>SEX</td><td>-1.047751</td><td>0.129578</td><td>-8.085875</td><td>0.0000</td></tr><tr><td>AGE</td><td>0.155110</td><td>0.012989</td><td>11.94151</td><td>0.0000</td></tr><tr><td>DIPLA_ME</td><td>0.461386</td><td>0.131455</td><td>3.509842</td><td>0.0004</td></tr><tr><td>ACCOMMODATION</td><td>-1.549294</td><td>0.226622</td><td>-6.836479</td><td>0.0000</td></tr><tr><td>CD</td><td>-0.524488</td><td>0.178958</td><td>-2.930787</td><td>0.0034</td></tr><tr><td>STATUS</td><td>-1.462214</td><td>0.158511</td><td>-9.224703</td><td>0.0000</td></tr><tr><td>McFadden R-squared</td><td>0.560861</td><td>Mean dependent var</td><td></td><td>0.780000</td></tr><tr><td>S.D. dependent var</td><td>0.414454</td><td>S.E. of regression</td><td></td><td>0.269900</td></tr><tr><td>Akaike info criterion</td><td>0.478772</td><td>Sum squared resid</td><td></td><td>72.26347</td></tr><tr><td>Schwarz criterion</td><td>0.518034</td><td>Log likelihood</td><td></td><td>-231.3858</td></tr><tr><td>Hannan-Quinn criter.</td><td>0.493694</td><td>Deviance</td><td></td><td>462.7716</td></tr><tr><td>Restr. deviance</td><td>1053.816</td><td>Restr. log likelihood</td><td></td><td>-526.9080</td></tr><tr><td>LR statistic</td><td>591.0443</td><td>Avg. log likelihood</td><td></td><td>-0.231386</td></tr><tr><td>Prob(LR statistic)</td><td>0.000000</td><td></td><td></td><td></td></tr><tr><td>Obs with Dep=0</td><td>220</td><td></td><td></td><td></td></tr><tr><td>Obs with Dep=1</td><td>780</td><td>Total obs</td><td></td><td>1000</td></tr></tbody></table>

Source: Immigrant survey data (2020).

Dependent Variable : IMMIGRA_

Method: ML - Binary Probit (Newton-Raphson / Marquardt steps)

Date : 02/08/22 Time : 01:57

Sample: 1 1000

Included observations : 1000

Convergence achieved after 6 itérations

Coefficient covariance computed using the Huber-White method.
According to the results obtained, the variables Income (salary), Diploma (qualifications) and Age, choice of destination has a significant overall effect on the probability of migration (an individual's willingness to migrate) in French Guiana. The mean dependent variable (Mean dependent var) statistics indicate that the proportion of immigrants is 78%. This explains why we are facing a wave of economic migration according to the results of the survey and the proportion of migrants. Mac Fadden's R2 is equal to 0.560000. This value indicates that 56% of fluctuations in the probability of immigration are explained by the variables mentioned above. Through the results obtained, we verified the analysis of the marginal effects of all the variables, made an interpretation of the impact of each individual variable on the probability of immigrating. Thus, we observed in this estimate that out of 8 variables analyzed by the ordered choice model, the variables sex, income, social status, housing and age were significant and positive at the threshold of 1%, 5%. This confirmed our hypothesis that immigration is explained by the variables mentioned above.

These results are partly explained by the policy of family reunification and/or family migrants who often arrive at a younger

age and therefore have more opportunities for some to integrate into school. The second reason is that of economic migrants (with or without a job on arrival) and those whose language skills are relatively lower, especially those who come from these English-speaking countries. This econometric analysis made it possible to grasp the level of significance of the variables in the explanation. Thus, of the nine variables in the econometric model, three variables were found to be significant.

## Hosmer-Lemeshow test

This Hosmer-Lemeshow test was to check whether the fit of our model was good or not according to the following assumptions:

* H0: the fit of the Probit model is good

* H1: Probit model fit is bad

The very high value of the Hosmer-Lemeshow statistic (HL statistic) indicates a very significant difference between the observed and predicted values of the deciles, in this situation the H0 hypothesis is rejected

The results of this test attest that the probability is significant. The null hypothesis is not rejected (Table 7).

<table><caption>Table 7: Results of the Hosmer-Lemeshow test.</caption><thead><tr><th></th><th>Quantile of Risk Low</th><th>High</th><th>Actual</th><th>Dep=0 Expect</th><th>Actual</th><th>Dep=1 Expect</th><th>Total Obs</th><th>H-L Value</th></tr></thead><tbody><tr><td>1</td><td>0.0199</td><td>0.2320</td><td>98</td><td>91.1139</td><td>2</td><td>8.88612</td><td>100</td><td>5.85670</td></tr><tr><td>2</td><td>0.2320</td><td>0.3807</td><td>46</td><td>66.3366</td><td>54</td><td>33.6634</td><td>100</td><td>18.5201</td></tr><tr><td>3</td><td>0.3807</td><td>0.7512</td><td>58</td><td>43.5896</td><td>42</td><td>56.4104</td><td>100</td><td>8.44520</td></tr><tr><td>4</td><td>0.7512</td><td>0.9382</td><td>11</td><td>13.0462</td><td>89</td><td>86.9538</td><td>100</td><td>0.36907</td></tr><tr><td>5</td><td>0.9382</td><td>0.9689</td><td>3</td><td>3.89064</td><td>97</td><td>96.1094</td><td>100</td><td>0.21214</td></tr><tr><td>6</td><td>0.9689</td><td>0.9869</td><td>0</td><td>2.77344</td><td>100</td><td>97.2266</td><td>100</td><td>2.85256</td></tr><tr><td>7</td><td>0.9869</td><td>0.9975</td><td>4</td><td>0.84820</td><td>96</td><td>99.1518</td><td>100</td><td>11.8119</td></tr><tr><td>8</td><td>0.9975</td><td>0.9995</td><td>0</td><td>0.15437</td><td>100</td><td>99.8456</td><td>100</td><td>0.15461</td></tr><tr><td>9</td><td>0.9995</td><td>1.0000</td><td>0</td><td>0.01815</td><td>100</td><td>99.9818</td><td>100</td><td>0.01815</td></tr><tr><td>10</td><td>1.0000</td><td>1.0000</td><td>0</td><td>0.00017</td><td>100</td><td>99.9998</td><td>100</td><td>0.00017</td></tr><tr><td></td><td></td><td>Total</td><td>220</td><td>221.771</td><td>780</td><td>778.229</td><td>1000</td><td>48.2406</td></tr><tr><td>H-L Statistic</td><td></td><td></td><td>48,2406<br>344,165</td><td></td><td>Prob.Chi-Sq(8)</td><td></td><td>0.0000</td><td></td></tr><tr><td>Andrews Statistics</td><td></td><td></td><td>0</td><td></td><td>Prob.Chi-Sq(10)</td><td></td><td>0.0000</td><td></td></tr></tbody></table>

Source: Immigrant survey data (2020).

Goodness-of-Fit Evaluation for Binary Specification

Andrews and Hosmer-Lemeshow Tests

Equation: UNTITLED

Date: 02/08/22 Time: 03:13

Grouping based upon predicted risk (randomize ties)
The fit to the Logit model is good. We made a prediction to verify the results obtained below (Table 8).

<table><caption>**Table 8: Prediction results.**</caption><thead><tr><th rowspan="2"></th><th colspan="3">Estimated Equation</th><th colspan="3">Constant Probability</th></tr><tr><th>Dep=0</th><th>Dep=1</th><th>Total</th><th>Dep=0</th><th>Dep=1</th><th>Total</th></tr></thead><tbody><tr><td>P(Dep=1)&lt;=C</td><td>158</td><td>60</td><td>218</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P(Dep=1)&gt;C</td><td>62</td><td>720</td><td>782</td><td>220</td><td>780</td><td>1000</td></tr><tr><td>Total</td><td>220</td><td>780</td><td>1000</td><td>220</td><td>780</td><td>1000</td></tr><tr><td>Correct</td><td>158</td><td>720</td><td>878</td><td>0</td><td>780</td><td>780</td></tr><tr><td>% Correct</td><td>71.82</td><td>92.31</td><td>87.80</td><td>0.00</td><td>100.00</td><td>78.00</td></tr><tr><td>% Incorrect</td><td>28.18</td><td>7.69</td><td>12.20</td><td>100.00</td><td>0.00</td><td>22.00</td></tr><tr><td>Total Gain*</td><td>71.82</td><td>-7.69</td><td>9.80</td><td></td><td></td><td></td></tr><tr><td>Percent Gain**</td><td>71.82</td><td>NA</td><td>44.55</td><td></td><td></td><td></td></tr></tbody><thead><tr><th rowspan="2"></th><th colspan="3">Estimated Equation</th><th colspan="3">Constant Probability</th></tr><tr><th>Dep=0</th><th>Dep=1</th><th>Total</th><th>Dep=0</th><th>Dep=1</th><th>Total</th></tr></thead><tbody><tr><td>E(# of Dep=0)</td><td>148.20</td><td>73.57</td><td>221.77</td><td>48.40</td><td>171.60</td><td>220.00</td></tr><tr><td>E(# of Dep=1)</td><td>71.80</td><td>706.43</td><td>778.23</td><td>171.60</td><td>608.40</td><td>780.00</td></tr><tr><td>Total</td><td>220.00</td><td>780.00</td><td>1000.00</td><td>220.00</td><td>780.00</td><td>1000.00</td></tr><tr><td>Correct</td><td>148.20</td><td>706.43</td><td>854.63</td><td>48.40</td><td>608.40</td><td>656.80</td></tr><tr><td>% Correct</td><td>67.36</td><td>90.57</td><td>85.46</td><td>22.00</td><td>78.00</td><td>65.68</td></tr><tr><td>% Incorrect</td><td>32.64</td><td>9.43</td><td>14.54</td><td>78.00</td><td>22.00</td><td>34.32</td></tr><tr><td>Total Gain*</td><td>45.36</td><td>12.57</td><td>19.78</td><td></td><td></td><td></td></tr><tr><td>Percent Gain**</td><td>58.16</td><td>57.13</td><td>57.64</td><td></td><td></td><td></td></tr></tbody></table>

Source: Immigrant survey data (2020).

Expectation-Prediction Evaluation for Binary Specification

Equation: UNTITLED

Date: 02/08/22 Time: 03:25

Success cutoff: C = 0.5

After estimating the parameters of the Probit model, we arrived at the results below. To obtain the percentage of correct predictions, one must add the diagonal values, comparing with the number of total observations, 100 x 158+720/1000 = 87%. We noticed that after the estimate, the results of our two models are almost similar. There are no big differences in the results in terms of gap. At this threshold set at 50%, our surveys show that for every 100 observations, the analysis model correctly predicts 878 or 87.80%. As each observation is taken separately, Table (9) shows that the observations for which the dependent variable (Dp=0) and which have been correctly estimated are 158 or 71,82 % and those for which Dp=1 and correctly estimated are 720, or 92,31 %. This result proves that immigrants who possess the characteristics defined in our explanatory variables have a probability of 87.80% of immigrating to French Guiana.

To conclude, three of the variables mentioned in this part of the analysis predominate over the other variables, it is the social status of Guiana, the family link that migrants have on the territory, and the sex that correlates with the arrival of these migratory flows of recent years. However, according to our data,

the conditions for the integration of legal immigrants into the labor market in French Guiana have improved unambiguously over the past five years, due to the fact that there was a shortage of labour in French Guiana, unlike metropolitan France where the situation of immigrants in the labor market is deteriorating year in year out, especially when it comes to natives.

The results of these tests indicate the dependence of certain explanatory variables and the explained variable. In addition, the factors that explain immigration to Guiana can be explained by the social status of Guiana compared to these neighboring countries. Migrants, on the other hand, choose Guiana according to the permeability of its borders, the family link they have on the territory (a family member or friend lives in Guyana is the source of motivation). Age also plays a very important role in this immigration process; it has been found that the variables age and sex are determinants of immigration to Guiana. We found that there are more immigrant women than men, this is explained by the fact that some of these immigrant women join their spouse or partner already in the territory. This significant presence of women in immigrant Guiana is part of informal family reunification, i.e.
one in three immigrant women arrived in this territory to join her
spouse or family.

These women do not necessarily work once they arrive in
the territory and therefore came as wives. However, there was
a second reason for this female migration. Single women, either
women of certain elites, dissatisfied with their status as women
in their country of origin, or mothers, these women did not
necessarily emigrate with their spouse, or to find the latter, but
alone in order to seek a better life.

## Example of positive economic impact of immigration: the case of the Hmong of Guiana

As Frédéric Piantoni [2] said, “Immigration and development were the two axes on which the national ambition in 1946 in favour of overseas territories was based. The promise of legitimate development, and the incorporation of Guiana into the French state. These two parameters were systematically taken into account in all development plans until the decentralisation laws in 1982. The state turned to the second plan of 1952-1965 for a planned productive economy. The exploitation of agricultural, mining, forestry and fisheries resources is part of an industrial framework intended for export. This line has always been the reference in subsequent plans. The agricultural sector is a major example of a development logic combining immigration. The objective was not to revive traditional export crops such as sugar cane, annatto and cotton, but to look for new ways that are more adapted to the international market by focusing on two poles: livestock and agriculture on the alluvial plain (Terres Basses).

The main feature of these economic projects remains their
low sustainability over time and their inability to go beyond the
experimental stage in terms of economic production and planned
immigration of that time. Among the experiments, the green plan
presented in 1975, was undoubtedly the most ambitious and the
most revealing of their insufficiency in the means made available
to the Guyanese society during the expedition of Kourou two
centuries earlier. Guiana then appeared as a new frontier whose
immense forest resources and available areas could finally be
mobilized in service to France. At that time, the State envisaged
the creation of 10,000 jobs and 15 billion in investments over five
years. This considerable economic project was coupled with a ten-
year immigration project composed of 30,000 people from Europe,
metropolitan France and other French overseas departments.
The Bumidom (Bureau for the Development of Migration in the
Overseas Departments) was responsible for recruiting migrants
in metropolitan France and Europe.

In French Guiana, this migration project aroused a general
rejection of locally elected officials and the population because
of the upheaval of socio-political structures that it would likely
generate (the 1974 population census, is 57,348 inhabitants).
Faced with local resistance, the French government had to reduce
its ambitions to a few breeders and farmers hence the arrival of
the Hmong. As a result, the economic and human outcome were

lower than expected. At the end of the Lao civil war that led to
the advent of the communist regime in 1975, Erick Gauthier [5],
a significant number of Hmong refugees fled Laos for countries
such as the United States, France especially Guiana and Australia.
Refusing to be forcibly installed, some engaged against the
Laotian communists alongside the French, then the Americans,
in the last quarter of the twentieth century. Victims of genocide
by the Lao authorities, others on the contrary participated in the
communist movement, then found themselves in government
positions. The Hmong are descendants of village groups in Laos.
Fleeing the communist state, they were interned in 1975 in Thai
refugee camps. The recognition by the Office of the United Nations
High Commissioner for Refugees (UNHCR) of their status as
political refugees allows them to be welcomed in various Western
countries.

Arriving in French Guiana in 1977, they were settled there
with the idea of populating French Guiana and developing
agriculture. This project was part of the "Green Plan", launched by
the Secretary of State for the Overseas Departments and Overseas
Territories at the time Olivier Stirn in 1986. It started from an
overwhelming observation for this underpopulated territory.

At the time, Guiana had only 55,000 inhabitants for a territory
representing a fifth of the metropolis, with very few resources
exploited outside the gold panning of gold and a very small
economic activity dependent on the metropolis. The former
president of the Regional Council of Asian origin, Dr. Ho-A-Chuck,
pleaded in favor of their installation as well as that of the Catholic
organizations requested by Father Yves Bertrais, whom they
received permission to settle there, Pierre Dupont-Gonin [6].
They now represent nearly 5% of the Guyanese population and
represent nearly 80% of farmers. They have very varied practices;
they cultivate everything that can meet the local demand and
priority is given to polyculture. Regarding livestock, it covers vast
territories, the two largest breeders in France (in area and number
of cattle) are in French Guiana.

We can state clearly through facts that Hmong immigration
was an obvious success despite being challenged at the time.

## Conclusion

The issue of migration is a recurring topic that currently
arises in most developed and developing countries, particularly
on the challenges and impact of immigration on host countries.
Guiana being a territory belonging to a developed country, but
which is nevertheless an underdeveloped territory Rosele-
Chim [7], seemed to us an interesting territory to study on this
question. We assumed as the main issue that migration could play
an important role in the development process of host countries
through different channels. First of all, we asked about the sources
of migration and their impact. Guiana is a rich territory for South
America, so, many immigrants come to settle there for economic
or family reasons. In addition, we also recalled the permeability
of the borders that has made Guiana a land of migration with a
relationship between economic interests and easy access rather interesting for migrants. In addition, it is interesting to note that in its history, Guiana has always been a welcoming land sometimes hostile but paradoxically it is perhaps this hostility that could has played on this need to continually have newcomers (especially in the context of slavery). Currently, despite its economic difficulties, this department still remains a welcoming land for immigrants, who come mainly from neighboring countries (Brazil, Suriname) [8-28].

However, is this migratory windfall beneficial for growth or does it bring imbalance?

In response, we noted that today Guiana is characterized by great diversity and constant mobility of border communities. Its population is composed of several groups. Long considered as a land of immigration, today, Creoles, Bushinenges, Amerindians, Hmong, Africans, metropolitans and other communities of more recent immigration through the different waves of migration, coexist on the territory. However, this migration is not necessarily wanted, controlled or supervised. This leads to irregular immigration and harsh immigration conditions (unhealthy housing, illegal low-income jobs). We can also question the fact that these conditions do not maintain the spatial distribution of migrants that we found who were in cohabitation but with social borders Piantoni [3] linked to ethnicities. Yet, despite this, it seems to us that these international migrants are actively participating in the economic development of their host country.

We were able to see this by analyzing the activity. First of all, those who arrive early enough try to integrate through schooling or studies. We also note that the proportion of workers increases with the time of presence in the territory. Thus, we can assume that in the medium term, they manage to find a legal activity. However, the low rate of asylum applications and supporting infrastructure do not favour this transition from informal to formal. In addition, we also see that informal work has advantages for some. Indeed, if it is not rewarding or beneficial to the State, the absence of a tax and standards allows a certain flexibility.

Thus, certain fields of work take advantage of these opportunities to offer work or even exploit these populations. This certainly allows the immigrant to pay for his subsistence on the territory (feeding the economy) and possibly to return a part, but above all it will allow the employer to gain purchasing power knowing that he will spend less.

In order to deepen our point, we wanted to propose an analysis based on a questionnaire processed by a probit model. For this, we relied on literature at first. We have found that theories of development in relation to immigration are based on principles that are indeed part of economic theory.

Thus, standard theory explains the existence of social cleavages concerning the determination of immigration policy by the transfer of income between national groups generated by the

admission of immigrants. The arrival of foreigners with factors of production changes the relative factor endowment of the host economy and, consequently, the income that nationals derive from their participation in productive activity. However, the Council of Europe's report on migration published in 2019 contradicts all conventional wisdom that migrants threaten employment and social security systems in the countries where they settle. The report states that migrants contribute decisively to the economic and cultural wealth of the countries that receive them.

Economic and demographic studies show that a more ambitious immigration policy in terms of reception would have a positive impact on public finances. It is important to remember that immigrants by their very subsistence participate in the creation of the wealth of host countries. Their contribution to development is determined by many factors, including the nature of migration, where it is destined and how successful or unsuccessful countries are in taking advantage of the phenomenon and addressing its negative effects.

Thus, the will and capacity of Guiana to take advantage of the phenomenon will be the essential element for a migration with an optimal positive impact on its development. Indeed, migration and development are inseparable and interdependent processes that take place in a globalized context. Migration cannot replace development just as development does not necessarily depend on migration, but each process can profoundly influence the other (OECD, 2008). Thus, with all this information, we have set up a model to understand the sources of immigration to French Guiana and analyzed the economic impact of this migration. As a reminder, these results come from a survey of 1000 people, 78% of whom are direct immigrants and 22% of the descendants of immigrants born in the territory.

The results obtained from our empirical analysis state that, the variables Income (salary), social status of French Guiana, family link and Age have a significant overall effect on the probability of migration (an individual's willingness to migrate) in French Guiana. Indeed, 56% of immigrants arrived for one of these reasons. We see from this that the reasons for immigration to Guiana are either economic or social. These two points are important to ensure responsible development. The will to earn manifests, a willingness to participate in work and therefore in the creation of wealth of the country. Regarding social status and family ties, these will be indirect factors that will promote wealth creation. However, this willingness of migrants will be confronted with the reality of the territory's willingness to encourage aspirations of immigrants. On the contrary, we note that in Guiana, little infrastructure goes in this direction which therefore favors an informal economy that does not participate in the national taxation effort.

Thus, we can put forward as an argument according to the observations that immigration better accompanies the development process provided that it is well supervised and guided, it generates benefits for both the host countries and the
countries of origin. An example of successful immigration is the
case of the Hmong of Guiana who today through their agriculture
contributes to the development of this territory. The arrival of a
population on a territory brings a potential source of additional
work, which would theoretically allow the host country to increase
its growth Chojnicki and Ragot (2012).

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This work is licensed under Creative
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DOI:10.19080/ARR.2022.07.555725

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