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Impact of Migration and Remittances on Economic Growth in Nigeria
Impact of Migration and Remittances on Economic Growth in Nigeria
Imouokhome Peter Afen-Okhai
University of Lagos, Nigeria
DOI: https://dx.doi.org/10.47772/IJRISS.2023.7841
Received: 26 July 2023; Accepted: 08 August 2023; Published: 06 September 2023
ABSTRACT
Remittance flows have the potential to greatly improve the livelihoods of receiving households by smoothing their consumption and enabling investments back home. They can facilitate economic stability, improve creditworthiness, and attract investments to promote economic growth and reduce poverty rates for recipient nations. From World Bank Statistics, Nigeria was 8th on the list of top remittances from low and middle-income countries. The study has undertaken the investigation of a relationship between economic growth and remittance. The economic growth variable used in this study is proxy by GDP while explanatory variables are remittance, trade openness, foreign direct investment, government expenditure, capital formation. The study also investigated the causality among the variables used in the study. Using The ARDL estimation technique owing to the stationarity of all the variables at both level, first differencing and void of second difference series, the result of this analysis confirmed the presence of long run convergence among the variables under consideration. The short run result was presented was evident that remittance has a positive and statistically impact on economic growth under the period of study. However the result shows that there is presence of a long run convergence among the selected variables and the respective dependent variables. This suggests that there is a presence of long run relationship among the variables in consideration. Having confirmed the presence of long run convergence among the variables under consideration, the short run result was presented above which is evident that remittance have a positive and statistically impact on economic growth under the period of study. The long run estimates reveal that government spending has a positive and significant impact on economic growth. Moreover, it was deduced from the regression result conducted that trade openness and foreign direct investment has a positive impact on economic growth under the period of study. There was found a uni direction between the two variables. It is revealed that remittances does not cause changes in economic growth under the period of study. This was further supported by the regression result of positive relationship between government spending and economic growth under the period of study. It was therefore recommended that the Nigerian government should budget and expend more resources on productive sector of the economy especially on infrastructure which would attract the right Foreign Direct Investment (FDI) into the country and boost more growth. Also, the Nigerian government should set up a body to review the spending and allocation of remittances into the country and an independent commission be set up by the federal government to be saddled with the responsibility of coordinating and harnessing remittance flows into the country.
Key Words: Migration, Remittance, Growth Nigeria, Growth, Movement
INTRODUCTION
- Background to the Study
Migration is defined as the movement of people from one location to another in a country or from one country to another for the purpose of establishing a new residence (IOM, 2011; ACP Observatory on Migration, 2011). According to (Iheanacho & Ughaerumba, 2015) migration can be traced to the existence of the first set of humans on earth. Migration has taken various patterns in the course of slave trade, colonization, urbanization, industrialization and globalization. Movement of persons (migrants) from one place to another has been a trend adopted by various individuals. Although the definition of migration varies from different perspectives, there is a consensus that it involves the movement of people across a recognized political boundary to establish permanent or semi-permanent residence. The period of residence also varies, but most experts believe that six months of residence in a new location is enough to categorize one as a migrant.
The number of migrants from sub-Saharan Africa, mainly from Nigeria, has increased over time. In addition to being a significant potential economic force through remittances, Nigerians living abroad play a significant role in the economy as skilled repatriates (Elebiju & Fatokunbo, 2020).
Remittances on the other hand are defined by the United Nations Development Program (UNDP) as discrete transfers from migrant workers (workers who have lived abroad for at least a year) to a recipient in that recipient’s home country or place of origin (UNDP, 2020). Remittances may be kept and invested in cases when they are not needed for urgent consumption demands, which eventually benefits the economies of the worker’s home country.
Article 41 of the Nigerian Constitution recognises freedom of movement as a fundamental right. Thus, except in instances where an individual has or is reasonably suspected to have committed a criminal offence and where a court with the appropriate jurisdiction orders that they should be prevented from leaving the country, all Nigerian citizens are free to emigrate from the country..
Some studies indicate positive impacts of remittances, including on the economy (Iheke, 2012), on unemployment (Okeke, 2021), and on household welfare (Ajaero et al., 2018), amongst other outcomes. Other studies highlight barriers to positive impacts on development, for instance, political instability, ineffectiveness of the financial sector, bureaucracy, and corruption (Oluwafemi and Ayandibu, 2014).
Notably, a large proportion of migrants relocate to high-income countries. While it is expected that the policymakers and other stakeholders will rise to the occasion to formulate and execute policies that will improve the economy and cut down the rate of migration out of the country, the reverse is the case. The net migration rate in Nigeria has maintained a negative figure over the past 30 years implying that the emigration rate is more than the immigration rate. More worrisome is the fact that the negative value has continued to increase over the years. This reveals the desperation in Nigerians to leave their country. A major argument for migration is remittances from abroad. (World Bank, 2016) further revealed that Nigeria is the top remittance-receiving country in Africa and fifth in the world.
In 2022, international remittances to low- and middle-income countries (LMICs) amounted to US$647 billion. Such average monthly transfers of US$200-US$300 sent by migrant workers support many basic households and prove transformational for both households and local communities, enabling many families to achieve their ‘own’ Sustainable Development Goals (SDGs).
Furthermore, migrant remittances represent the most important contribution of the diaspora to the development of the countries of departure (Romano and Traverso 2020; Zimmermann 2017)
Referring to the traditional sources of finance, the migrant remittances represent about 7.79% of GDP in 2016, while official development assistance and foreign direct investment were estimated only at US$768 million and US$822 million, respectively, for all ECOWAS countries (WDI 2020).
Remittance flows have the potential to greatly improve the livelihoods of receiving households by smoothing their consumption and enabling investments back home. They can facilitate economic stability, improve creditworthiness, and attract investments to promote economic growth and reduce poverty rates for recipient nations.
Remittances are becoming increasingly important sources of income and, potentially, investment capital for households, as well as a stable source of external finance for governments in these countries. This inflow is the second largest source of foreign capital after exports and is quite large compared to foreign aid and FDI. (Samuel.O.G , 2023)
Remittances can also aid the enhancement of financial sector growth on the notion that some of these remittances are converted and deposited with banks thus making the funds available for lending to the private sector and this, in turn; facilitate economic growth (Bashir, 2020).
In Sub-Saharan Africa, the remittance inflows constitute 2.5% of GDP in 2020, amounting to $37 billion in the year under review. Remittance inflows to Sub-Saharan Africa soared from 14.1 percent to $49 billion in 2021 following an 8.1 percent decline in the prior year. Around 60 percent of total inflows originate from advanced economies such as France, Italy, the United Kingdom, and the United States. In West Africa, the country that received the largest remittance inflows was Nigeria amounting to $17.21 billion in 2020, depicting a 6.6% decline before the COVID-19 pandemic. According to the World Bank, Nigeria accounts for 50% of remittances to sub-Saharan Africa, with an increase in its remittances to $17.6 billion, which increases SSA remittance inflows to $45 billion by 2021.
Researchers have found both positive and negative impacts of remittances on economic growth (Ari, 2020; Buhari, Muhils and Osman, 2018; Chowdhury, 2015).
There is no convergence to the impact of remittances on economic growth given the various findings that have emanated. This is because contributory and causal factors have ranged from Demography, geography and timezone. (Ari, 2020) and (Olayungbo and Quadri, 2019) stated that. “the impact of remittances depends on a country’s socioeconomic conditions.”
Earlier debates on the migration-growth nexus argued that remittances are used mainly for subsistence consumption and other non-productive spending. In contrast, (Quartey et al, Citation 2018) confirmed that remittances are invested in productive investments, such as purchasing land, establishing small enterprises, and farm investments. This finding indicates that the inflow of remittance can positively impact investment via savings.
According to the IMF, Remittances are becoming increasingly important sources of income and, potentially, investment capital for households, as well as a stable source of external finance for governments in these countries. This inflow is the second largest source of foreign capital after exports and is quite large compared to foreign aid and FDI.”
Dimensioning the gains of migration, the World Bank says that, “Global welfare gains from increased cross-border labor mobility could be several times larger than those from full trade liberalization. Migrants and their families tend to gain the most in terms of increases in income and better access to education and health services. However, these gains are hindered by discrimination and difficult working conditions that immigrants from low and middle-income countries face in host countries.”
Migrant workers make an invaluable contribution to SDGs through remittances and investments. In particular, they contribute to ending poverty and hunger; promoting good health, quality education, clean water and sanitation, decent work and economic growth; and reducing inequalities.
Strategic partnerships and progress on remittances go hand in hand. Partnerships among public and private sector stakeholders have paved the way for lowering the cost of remittance transfers and provided
financial services for migrants and their families.
Digital remittances have the power to help transform rural economies while also reducing costs for remitters and enabling beneficiaries in rural areas to track and access funds quickly without having to travel long distances.”
The (World Bank migration and remittances brief, 2023) says that “In the post-COVID period, remittances have become even more important as a source of external financing. They have proved to be resilient. In 2022, remittance flows to low- and middle-income countries increased by 8 percent, to reach $647 billion, registering higher growth than our expectations six months ago. This increase is remarkable, given that it followed a 10.6 percent growth rate in 2021 and the economic environment seemed difficult due to slowing economies around the world, inflation, and the war in Ukraine.
In 2023, however, the growth of remittances is expected to moderate to 1.4 percent, to a level of $656 billion due to slowing economic growth in major source countries. Slower growth in remittances is expected in all regions, notably in Europe and Central Asia (1 percent) and South Asia (0.3 percent). In Europe and Central Asia, the growth in remittances is slowing down because of a high base effect, lingering weakness in flows to Ukraine and Russia, and the weakening of the ruble against the US dollar.
From the World Bank Statistics, Nigeria was 8th on the list of top remittances from low and middle-income countries. The top five recipient countries for remittances in 2022 were India, which received a total of $111 billion in the year, followed by Mexico with inflows of $61 billion, then China ($51 billion), the Philippines ($38 billion), and Pakistan ($30 billion) (figure 1.2a).
In Africa, remittance flows to Sub-Saharan Africa grew by 6.1 percent in 2022, to $52.9 billion. Regional growth in remittances in 2022 was largely driven by strong remittance growth in Ghana (11.9 percent), Kenya (8.5 percent), Tanzania (25 percent), Uganda (17.3 percent), and Rwanda (21.2 percent). Remittances to Nigeria, accounting for about 38 percent of total remittance inflows to the region, increased by 3.3 percent to $20.1 billion.
Preliminary studies indicate that remittances contribute to the economy of nations worldwide, especially low and middle-income countries (LMICs). They have been shown to help alleviate poverty, improve nutrition, and even increase school enrollment rates in these nations. Research has also found that these inflows of income can help recipient households become resilient, especially in the face of disasters.
At the same time, it’s worth noting that these transfers aren’t a silver bullet for recipient nations. In fact, some research shows that overreliance on remittances can cause a vicious cycle that doesn’t translate to consistent economic growth over time. (Richie, 2023)
- Statement of the Problem
Nigeria is the sixth-biggest beneficiary of diaspora remittances among low- and middle-income countries (LMICs) and the largest recipient in sub-Saharan Africa (SSA). This implies that there are more Nigerians who are resident outside the country compared to other African countries. This is an indication of the underdeveloped state of the economy, the prevalent lack of opportunities, and underemployment (Adeagbo & Ayansola, 2014). This is a situation known as brain drain, involving the exodus of skilled/trained/professional manpower in search of greener pastures. Could there be any appreciable gain from this phenomenon called brain drain? This can be asserted by examining the impact of remittance inflows on the Nigerian economy. Despite huge remittances received by the country, the problems of poverty, unemployment, and inequality still persist, and indication that Nigeria may not have efficiently utilized the gain from brain drain in terms of remittances (Adeagbo & Ayansola, 2014) hence, the need to examine the impacts of remittance inflows on economic growth in Nigeria.
It is also possible that the increases in remittances is an illusion resulting from changes in measurements and may not reflect the real financial inflow. Even if the increases are accurately measured cross country regression would not be able to detect the true effects of remittances on economic growth, hence a country specific study is appropriate (Clemens & McKenzie, 2014).
The impact could be negative or positive; and varied from country to country. The direct and indirect impact of remittances on economic growth needs to be properly flogged. Although, the direct and indirect impacts have been investigated to a reasonable extent; but, remittances have their component parts too. They need to be disaggregated into their component parts in order to know the component that contributes effectively to economic growth. Whereas the overall performance of remittances and economic growth is crucial, there are lots of consequences that affect the sending countries, such as brain-drain, income inequality, low returns on human capital accumulation and development due to inflation, poor labour productivity etc
For instance, The World Bank says that the cost of transferring US$200 across international boundaries to LMICs is still high —averaging 6.2% in the fourth quarter of 2022.
While remittances are on track to overtake flows of foreign direct investment to developing countries, on the negative side, emigration of skilled workers can affect the delivery of health and education services in small economies.
Whatever the causes of migration (climate change, poverty, food insecurity, labor market failure, politics or conflicts, wage inequality, level of countries development, etc.), the contribution of migrant remittances to the economic development of countries of origin continues to be the subject of debates within political and various scholars (Benhamou and Cassin 2021; Abduvaliev and Bustillo 2020; Melvin 2019; Warner and Afifi 2014; Arestoff et al. 2012; Djajic 1986).
This is still a gap in the literature that is yet to be properly identified, most especially as it affects developing countries and Nigeria as a country. The direct impact of migration on economic growth is an area not very much studied in the literature. This present study is to investigate this gap for the Nigerian economy
Also, the huge remittance receipt have not translated into growth and development in the country. Therefore, it is an empirical question as to whether remittances from the diaspora can close this gap and promote economic growth.
- Objective of the study
Generally, this study set out to establish and analyze the empirical relationship between migration and remittances, and overall economic growth in Nigeria in the years 1985-2021. Specifically, the study is set to;
- To study the relative importance of the factors influencing the incidence of migration.
- To examine the relative importance of the factors influencing the size of remittances to Nigeria and its trend.
- To investigate the relationship amongst migration, remittances and economic growth in Nigeria.
- Research Questions
This thesis is set out to provide answers to the research questions below:
- What are the factors affecting the incidence of migration in Nigeria?
- What contributes to the size of remittances in Nigeria?
- Is there any relationship amongst migration, remittances and economic growth in Nigeria?
- Research hypothesis
This thesis tests the following null hypotheses
H Migration and remittances does not have a positive effect on Economic Growth in Nigeria
H There is no link between migration, remittances, and economic growth in Nigeria
- Justification for the Study
The findings of this study would be beneficial to migrants, social workers, families, and non-governmental organizations with an interest in migrant remittances. They will receive crucial information on how the funds supplied may be applied to more pressing needs and investments, which could produce more significant growth outcomes in the home and economic realms. The study’s findings would also provide helpful information for various government ministries and organizations tasked with overseeing the nation’s development requirements as well as its migration policies. This will assist in resolving the existing issue where data on foreign migration and the effects of remittances on migrant families cannot be easily recognized and measured in the nation.
The study will also be helpful for policymakers since it will provide them with knowledge on how to pick growth plans carefully. The data would particularly affect the creation and execution of programs and policies connected to remittances and their direct and indirect support of economic growth. Finally, the study would contribute to the body of knowledge already available on migration, remittances, and economic growth. It would also act as a starting point for latter researchers and students. This would contribute to the corpus of already available information and encourage additional investigation on the study’s topic.
- Scope of this study
The study focuses on the impact of migration and remittances on economic growth in Nigeria. For the purpose of this study, the years considered span a period of thirty-nine years from 1985 to 2021.The main justifications for this period to get accurate regression result on the effect of remittances on growth in Nigeria. A time series samples of thirty years is needed for accurate regression result.
- Organization of study
This thesis is divided into five chapters. The first chapter presents the introduction, statement of the problem, research objectives, and research questions, justification for the study and organization of study. Chapter Two presents the Conceptual Review, Theoretical Review and Empirical Review, as well as the implications of the review on the current study. Chapter three shows the research methodology, theoretical framework including the source of data, model specification, and estimation techniques. Chapter four shows the presentation of results, discussion of results as well as the comparison of results with previous findings. Chapter five contains the summary, conclusion, recommendations, and suggestions for future studies.
LITERATURE REVIEW
2.1 Conceptual Review
Remittance has been defined by many scholars from different disciplines and organizations. According to (Kihangire and Katarikawe, 2008), remittance is defined as money sent home by migrants working abroad to their home countries. Similarly, remittance has been defined as a portion of migrant workers’ earnings sent to their countries of origin and this could be in cash or gifts (Odozi et al. 2010; Chukwuone 2007; Quartey 2006). Moreover, (IMF, 1999) maintains that remittance is limited to money sent by migrant workers who have been staying in a foreign country for more than a year to his/her household in his/her country of origin and this does not include migrants that are self-employed.
Similarly, (Tewolde, 2005) argues that remittances are financial and non-financial materials that migrants receive while working overseas and sent back to their households in their countries of origin. (Ratha, 2003) also defines remittances as migrants’ funds’ transfers, which are resources that a migrant conveys into or takes out of a country. Consequently, International Organization for Migration (2006) largely defines remittances as the monetary flows connected to migration, that is, cash transfers by migrants or immigrants living abroad to a relation in their home countries. (International Labour Organization, 2000) also defines remittance as part of migrant workers’ income remitted back from their employment countries to their countries of origin.
Some of the recent literature on migration, remittance, and development often see migration and remittance as an alternative for economic growth in developing countries. These debates have created two major opposing schools of thought- migration optimists and pessimists. The first view is developmental optimism. This ideology was developed and popular in the 1950s and 1960s. Development optimists’ views are dated back to the period of massive labor migration from developing countries to developed ones. This period was termed the “dawning of a new era” (Papademetriou 1985). Governments of some developing countries encouraged emigration during this period because they believed in its contribution to the development of their countries (Penninx 1982; Beijer 1970; Kindleberger 1965). These theorists hold that migrants’ are agents of ‘change, innovators, and investors because their remittances and acquired wealth of knowledge and skills often aid development in their countries of origin (Odozi et al. 2010).
In contrast to the development optimists’ view, the pessimist views of the 1970s and 1980s, shaped by dependency theory, argue that remittances create dependency between the sending and receiving countries as well as senders and recipients (Binford 2003; Rubenstein 1992). In other words, structuralist/dependency theorists hold that migration is the cause of underdevelopment due to the massive movement of people (labour) out of their traditional communities.
Migration and remittances are believed to be the cause inequalities among households (Bin ford 2003; Rubenstein 1992; Reichart 1981; Lipton 1980; Rhooades 1979; Ameida 1973). For example, a poor society will reveal inequality among remittance receiving and non-receiving households. This is because some migrants abroad often send money home to equip their families while non-receiving households continue to wallow in poverty (Odozi et al. 2010; Dercon 2009).
2.2 Theoretical Review
Structuralist theorists argue that most remittances are spent on noticeable consumption and nonproductive projects (Lewis 1986; Entzinger 1985; Lipton 1980). Migration is also believed to have negative effects on sending countries/communities harmony and economies by uprooting its members (Haas, 2007). Similarly, it has been observed that migration and remittance often cause Dutch Disease and Ghost Town Effect (Carrasco and Rio 2007). An example that readily comes to mind is how the discovery of gold and diamond in South Africa led to the flight of men from home leaving most households to be headed by women (Adeagbo 2011). Remittance is considered to be a temporary source of income which could be detrimental to the households that receive it because it is artificial and uncertain. Migration from South to North is bad according to these theorists because it makes developing countries depend on high-income countries (Dercon 2009; Zoch 2007). The New Economics of Labour Migration (NELM- Pluralist Perspectives) emerged in the 1980s and 1990s as a response to developmental (migration optimists) and structuralist (migration pessimists) views of remittances and development. This approach seems to be more encompassing because it merged both migration optimists and pessimists views on development. Migration in this sense is professed as a household retort to income peril since migrants remittances serve as insurance for their families (Piot-Lepetit and Nzongang 2014; World Bank 2013; Odozi et al. 2010; Lucas and Stark 1985). It has been argued that households are able to expand resources such as labour in order to reduce income risks (World Bank 2013; Odozi et al. 2010; Stark and Levhari 1982).
Neoclassical Theory of Migration
The neoclassical theory was the first theoretical basis formulated to describe labor migration. Several researchers have contributed to creating the neoclassical theory of migration (Todaro, 1969; Harris & Todaro, 1970; Massey, 1993; Arango, 2000; Faist, 2004). Neoclassical theory observes migration as the end result of geographic differences between the supply and demand of labor. These differences exist globally. Neoclassical theory expressed that international migration arises due to variances in wage levels between countries and labor markets. According to this theory, labor migration would stop if wage discrepancies were eliminated. The principle suggests that wage variations between regions are the foremost cause of labor migration. Neoclassical theory proposes that global migration was tied to the demand and supply of labor in the world. Nations with labor shortages and excessive demand will have high wages that will attract immigrants from countries with excess workforce. The main premise of the neoclassical theory of migration is directed by the push factors that push the person to leave their place of origin and by the pull factors that lead them to move to destination country. Neoclassical theory concluded that the main causes of migration are different wages and access to work (Sjaastad, 1962; Todaro, 1976).
The neoclassical theory of migration was divided into two main classes, such as macroeconomic and microeconomic aspects. At the macro level, neoclassical economic theory states that the sole purpose of migration is the exceptional imbalance in the supply of labor, and the demand for labor, which leads to wage discrepancies in different countries. The macro level principle suggests that labor changes are due to differentials of wages, from low-wage regions to regions with excessive wages, and that capital will go in the opposite direction. Migration will progressively decrease the workforce at the destination of the sending end. Countries with low wages have a much wider range of people, and as a result, the large labor supply results in low wages. High-wage nations have surprisingly greater capital, which is often the reason why capital will shift to high-wage nations with low wages and manpower. When this movement occurs, wages go to a shared level. In the long term, based on neoclassical theory, migration flow will be minimized due to the fact that income convergences will decrease inducements to migrate (De Haas, 2008; Fagerheim, 2015). At the micro level, the neoclassical principle of migration considers migrants as a person with coherent actions, with the purpose of going deep into the thought of cost-benefit migration. When there is free choice and full access to information, they move to the areas where they can be most creative, that is, the region where they can earn the highest wages. This significant mobility is based on the precise abilities a man or woman possesses and, moreover, on the unique structure of the labor markets. The micro point of view of neoclassical explains migration through a cost-benefit exploration, as human beings desire to maximize their non-public income. People think about their net return on migration before making a choice. If the threat of getting a job and expected income in remote places extends beyond the rate of migration and the acceptance of opportunities, the individual may also find it extremely good to migrate. But because of the desire of individuals at the micro level, different individuals have clear expectations of migration (Massey, 1993; de Haas, 2008). According to the neoclassical view of migration, the workforce is moving from places with low global wages to countries with especially excessive wages due to wage variation between countries. Remittances provide a means of poverty reduction and development of the economy when immigrants send remittances to their homeland. On the other hand, this type of migration to distant places could damage the development and growth when the homeland loses relatively skilled workers, known as Brain Drain. Therefore, human capital losses can adversely affect the growth of the economy, as indicated in the principles of neoclassical (Fagerheim, 2015).
2.3 Empirical Review
Over the years, there have been rational arguments on the grounds of economic growth in developing economies and also why some nations have robust economic growth compared to others.
(Loto, and Abiola, 2016) investigated the contributions of foreign remittances on economic growth in Nigeria from 1980 to 2016, using the Vector error correction modelling (VECM) technique to analyze the long run and short run impact of disaggregated remittances that is Migrants’ Remittances and Workers’ Remittances to find out whether they will perform differently in relation to economic growth in Nigeria. The two components of remittances performed differently. While the Migrants remittance component exhibited a long run positive, statistically significant relationship with economic growth, the other component i.e Workers Remittance had a negative statistically significant impact in the long run, short run relationship was also established among the variables as the ECM term was negative and statistically significant. The results showed a unidirectional causality from GDP per capita to Migrants remittances while no causality was found between workers’ remittances and gross domestic product per capita.
(Fagerheim, 2015) investigated the impact of remittances on economic growth in the association of south East Asian nations (ASEAN) from 1980 to 2012 using ordinary least square regression (OLS) and instrumental variable two stage least square (IV 2SLS) method. In the presence of no endogeneity, the OLS result was upheld. The study revealed that remittances have mixed impacts on economic growth.
(Adeyi, 2015) examined remittances and economic growth in Nigeria and Sri Lanka from 1985 to 2014 using granger causality under the vector autoregressive (VAR) framework. The study found a uni-directional link in Nigeria from remittance inflows to economic growth while a bi-directional causality was found for Sri Lanka between remittances and economic growth.
The study therefore recommended the need to employ remittances for small and medium scale enterprise development coupled with the creation of enabling macroeconomic environment. (Adarkwa, 2015) examined the impact of remittances on economic growth among selected West African countries from 2000 to 2010 in a linear regression model. The study found that remittance inflow was positively related to economic growth for Nigeria and Senegal while a negative impact was observed for Cameroun and Cape Verde. The study concluded that remittance inflows must be invested in the productive sector before it can positively impact economic growth.
(Kunofiwa, 2015) investigated the causal relationship between personal remittances and economic growth in Israel from 1975 to 2011 in a tri-variate causality framework with banking sector development as the third variable. The study employed Johansen co-integration test and the vector error correction model. The results showed that a significant long run relationship exists from economic growth and banking sector development to remittances while the long run causality from personal remittances to economic growth and banking sector development was found to be insignificant. Also no short run causal relationship exists among the variables.
(Fayomi, Azuh and Ajayi, 2015) investigated the impact of remittances on the Nigeria’s economic growth with a case study of Nigerian Diasporas in Ghana using primary data obtained through a questionnaire designed for 326 respondents living in Ghana. The study employed non-parametric tests as well as linear regression for the analysis. Findings revealed that remittances from the Nigerian Diasporas living in Ghana had significant impact on economic growth.
The study therefore recommended the installation of adequate infrastructure that could attract more remittances for the country.(Okoduwa, Ewetan and Urhie, 2015) in an examination of remittance expenditure pattern and human development outcomes, using household survey data on migration and remittances in the sub Saharan Africa 2009/2010, found that negligible portions of the remittances were actually committed to investment purposes, hence, the insignificant impact on human development outcomes.
(Akinpelu. Ogunbi, Bada and Omojola, 2013) explored the effects of remittance inflows on economic growth in Nigeria from 1991 to 2011. The study found a unidirectional causality from GDP to remittance inflows.
(Iheke, 2012) examined the effect of remittances on the Nigerian economy from 1980 to 2008 using regression analysis. The study found a positive statistically significant relationship between remittances and economic growth for the periods covered.
(Adenike Adeseye, 2021) carried out an empirical study on the Effect of Migrants Remittance on Economy Growth in Nigeria. In her paper, “Remittance inflow was used as dependent variable and gross domestic products, inflation, imports and exports were independent variables. In this study, secondary data were utilized. The study employs annual data obtained from world development and international financial statistics which covers the period of 29 years (1990-2018). Quantitative data collected were evaluated through descriptive statistics; and the hypotheses formulated were tested with the use of multiple linear regressions which includes ANOVA, Correlation, and Coefficient. And this was done with the aid of SPSS version 21. From the findings of the study and the tested hypotheses, it was discovered that significant relationship exists between remittance and gross domestic product, exports and imports in Nigeria while inflation has no significant relationship with remittance.” (Joseph and Oswald, 2014) while examining the relationship between remittances and economic growth in Ghana. In this study, they use Granger’s causality test and cointegration under the auto-regression vector (VAR). The results revealed that remittances were significantly associated with economic growth in Ghana. They observed that remittances caused marginal economic growth, but economic growth did not result to remittances. They also recognized that remittances have been of great help in supporting the well-being of migrant families. (Danmola and Abba, 2013) examine remittances and economic growth in Nigeria. In the study, they adopted the error correction model. The result revealed that remittances were significantly linked with economic growth in Nigeria. They concluded that funds should be transferred through the official channels and used for investment purposes to stimulate the growth and development of the country
(Muhammad et al., 2019) studied the effect of migrant remittances on economic growth in Pakistan between 1976 and 2016 using the autoregressive distributed delay (ARDL). The ARDL method was used to analyze the effect of workers’ remittances on the Pakistani economy. The survey results revealed that foreign direct investment, remittance inflow, and gross domestic products have a significant effect on Pakistan’s long-term economic growth, while consumption and inflation have a negative effect on the economic growth of Pakistan in the long term. They recommended that policymakers should motivate migrants to transfer funds through appropriate networks and engage in profitable investments that will stimulate economic growth. (Adigun and (Ologunwa, 2017) examined the effect of remittances on economic growth in Nigeria during the period 1980-2015. The result reveals that remittance is correlated with economic growth as it helps individual finances consumption, spending, and investments. Their study suggested that receivers of remittances should spend more on investment than consumption to impact the home economy. (Sebil and Abdulazeez, 2018) investigated the impact of remittances on Nigeria’s economic growth during the period 1981-2011. The influx of remittances was used as an indicator of dependent variables, while trade openness, foreign aid, foreign direct investment, and were economic growth indicators. The outcome stressed that remittance absolutely affect Nigeria’s economic growth. They stressed that the government should engage more effective policies that improve the remittance transfer channel, aid flows, and foreign direct investment as a growth strategy.
(Adeseye, 2021) studied the effect of migrant remittances on economic growth in Nigeria. The study employs annual data obtained from world development and international financial statistics which cover the period of 29 years (1990-2018). Quantitative data collected were evaluated through descriptive statistics and the hypothesis was tested with the use of multiple linear regressions which include ANOVA, correlation, and coefficient. It was discovered that a significant relationship exists between remittance and gross domestic product, exports, and imports in Nigeria while inflation has no significant relationship with remittance. (Ahmed et al.,2011) studied the impact of remittances, exports, and money supply (a broad measure for financial development) on economic growth in the context of Pakistan using bounds testing approach, the result revealed that remittance inflows and the lag effect of real output(yt-1) are significant in the short run and long run. Remittances have a positive impact on the economic growth of Pakistan in both the long and short run.
The study of (Kanu and Ozurumba, 2013) provided empirical support on the subject of remittances and economic growth. Focusing on the sub-Saharan African countries with an emphasis on Nigeria, South Africa, and Ghana. Their result showed that migrant remittances have a positive impact on the economic growth of the aforementioned economies. Also considering the casual relationship between remittances and economic growth, remittances were found to granger cause economic growth in Ghana and South Africa, but the report shows that the impact were felt more in South Africa than Ghana. The opposite was the case for Nigeria where remittances were not found to granger cause GDP, rather economic growth was seen to granger causes remittances.
(Oluyemi, Oluwaseun, 2022) say that continuous migration of skilled labor out of Nigeria is especially worrisome. This is because unemployment, rising poverty, inequality among other socio-economic challenges characterize the Nigerian economy. In fact, Nigerians have been recognized as one of the most mobile populations in Africa with Nigerian citizens found in almost all continents (Adeagbo and Ayandibu, 2014).
(Daniel, 2023) says that, “The history of transferring money by foreign workers to their home is very significant and cannot be overlooked as these remittances have impact on economic growth.” “Remittances provide support for the welfare of the relatives left behind thus contributing to the eradication of poverty in the recipient country.”
“It is not surprising that human mobility becomes more and more important and inevitable, because it affects the socio-economic life of the sending and receiving nations.” (Adenike, 2021)
(Samuel. O, 2023) says that the “The inflow of capital through international remittances in emerging economies has received widespread attention from the media, governments, development agencies, and the private sector because of its rising volume and dynamic economic impact on remittance-receiving countries. remittances represent household income from foreign economies arising mainly from the temporary or permanent movement of people to those economies.
More positive pronouncements on the impact of remittances on households’ investments which have their roots in the 1950s and 1960s ideas about migration as a major engine of development (through the diffusion of ideas, technology, skills, and so on) have been more recently given by the New Economics of Labour Migration (NELM) approach in the 1980s and 1990s (Bracking, 2003; Carling, 2004; Stark and Bloom, 1985; Robinson, 2004; Taylor, 1999).
In turn, remittances loosen the constraints facing poor households (Taylor, 1999). Thus, they are seen as being beneficial at a range of scales from the household to the national level as they increase disposable incomes while also stimulating demand for local goods and services (Ratha, 2003; Skeldon, 2002). Furthermore, they can also lead to the production of local capital markets as well as productive infrastructure (Ballard, 2003). Even more recently, the transnational migration school had sought to bring these divergent perspectives together, building upon the notion of transnational communities (Levitt, 2001) and viewing remittances as one component of the economic and non-economic flows linking sending and receiving countries.
THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY
3.1 Theoretical Framework
The most famous theory of international migration – The neoclassical theory is to be adopted in this study. This theory argues that labor emigration results from labor market imbalances and differences between labor supply and demand (Lewis, 1954) The theory stresses that people migrate from low-wage regions to other regions with higher income, better infrastructure, and other socioeconomic benefits. People migrate for incentives which are mainly in the form of remittances and other income from abroad. This is a motivation for labor, especially in those in developing countries with zero marginal product of labor and excess population. These remittances are useful alternative sources of income for participating households, the inflow fosters productivity in the emigrants’ country of origin. The immediate household of the emigrants is the direct beneficiary of remittance at the micro-level and the economy at large also stands to benefit from investments made by the remittance-receiving households (Wickramasinghe & Wijitapure, 2016; Flahaux & De Haas, 2016; Prakash, 2009).
The neoclassical theory is adopted because it explains the trend of migration and remittances in in Nigeria over the years as with the reasons and motives for the movement of labour in the country.
3.2 Model Specification
The main aim of this study is to examine the impact of migration and remittances on Economic growth in Nigeria. The model to be adopted in this study was adapted from (Loto, Abiola, 2016) with some modifications and is specified of the functional form:
GDP = f(REM, FDI, KAP, TRO, GE)……………………………………………… (3.1)
… (3.2)
Where:
GDP= Gross Domestic Product
REM= Diaspora Remittance
FDI= Foreign Direct Investment
KAP= Capital Formation
TRO = Trade Openness
GE= Government expenditure
3.3 Method of Analysis
The Auto-regressive Distributed Lag (ARDL) method, proposed by (Pesaran, Shin, and Smith, 2001), was used to achieve the objectives of the study in determining the impact of diaspora remittance inflows on economic growth in Nigeria. It is autoregressive in the sense that the prediction is explained by its lag, as well as a distributive lag component in the form of sequence lag exogenous variables, according to (Giles, 2013), it has many merits, including being superior to traditional co-integration techniques when used with a small sample size, allowing short-run and long-run relationships to be tested jointly, providing fair estimates for long-run and valid t tests when some independent variables are endogenous, according to (Srinivasan & Kalaivani, 2012), the variables are tested without consideration of the order of difference.
The ARDL estimation technique was also adopted owing to the stationarity of all the variables at both level, first differencing and void of second difference series.
3.4 Source of Data
Data source involves a range of activities from the research person in libraries extracting information from volumes of materials available as regards the research work. Secondary data was used in this research work and information was obtained from Central Bank of Nigeria (CBN) statistical bulletin and all other various sources. In order to fulfill the objectives of this study, this study utilized annual time series data for the period 1985-2021 obtained from Central Bank of Nigeria (CBN) statistical bulletin.
PRESENTATION AND ANALYSIS OF RESULT
4.1 Presentation of Results
This section presents the findings of this study alongside the respective interpretations ranging from the basic preliminary tests that covers the descriptive statistics, and stationary tests to more robust analysis of autoregressive distributed lag distribution model bound testing and likewise, the granger test of causality.
Table 4.1 Descriptive Statistics
LRGDP | LREM | LTRAO | LFDI | LGE | LKAP | |
Mean | 10.46565 | 21.01712 | 2.570485 | 0.271921 | 6.002046 | 29.73829 |
Median | 10.40441 | 21.05388 | 2.578400 | 0.371783 | 6.825633 | 29.73118 |
Maximum | 11.18987 | 23.91420 | 3.127254 | 1.756279 | 8.557643 | 30.06865 |
Minimum | 9.740823 | 14.70115 | 1.192453 | -1.633819 | 1.753486 | 29.36601 |
Std. Dev. | 0.516289 | 3.047395 | 0.443934 | 0.736922 | 2.328116 | 0.184440 |
Skewness | 0.139250 | -0.772282 | -1.423547 | -0.195396 | -0.494514 | -0.119423 |
Kurtosis | 1.439445 | 2.323877 | 5.079918 | 2.959687 | 1.743740 | 2.227129 |
Jarque-Bera | 3.874043 | 4.382681 | 19.16599 | 0.237946 | 3.941060 | 1.008830 |
Probability | 0.144133 | 0.111767 | 0.000069 | 0.887832 | 0.139383 | 0.603859 |
Sum | 387.2291 | 777.6335 | 95.10796 | 10.06106 | 222.0757 | 1100.317 |
Sum Sq. Dev. | 9.595945 | 334.3181 | 7.094797 | 19.54995 | 195.1245 | 1.224652 |
Observations | 37 | 37 | 37 | 37 | 37 | 37 |
Source: Author, 2023
Table 1 above summarizes the basic statistical features of the data under consideration including the mean, the minimum and maximum values, standard deviation, skewness, kurtosis and the Jarque – Bera test for the data. These descriptive statistics provide a historical background for the behavior of our data. It gives explanation into the nature of the data of the empirical analysis of selected variables used for the analysis. There seems to be evidence of significant variations as shown by the difference between the minimum and maximum values for the variables under consideration. The mean and median value of all the selected variables falls between the minimum and maximum values and they are of positive values. The maximum and minimum values indicate the highest points and lowest points of the variables captured in this study. The standard deviation measures the dispersion of our variables from their mean values. Further, the variables are all negatively skewed except RGDP while the peakedness of the series are composed of both platykurtic and leptokurtic series.
4.1.1 Unit Root Test
It is believed that many economic variables are volatile in nature and possess unit root properties which if used for estimation may likely lead to spurious results. Hence, the need to ascertain the stationarity of the variable to ensure reliable estimation. This study employed the Augmented dickie fuller (ADF) and to unravel the stationarity status of the variables.
The Augmented Dickey-Fuller (ADF) test is used in this study to determine if proxies of the selected variables are stationary or not. The variable is considered stationary if the absolute value that is obtained from the ADF test is greater than the absolute MacKinnon values. However, the variables are considered non-stationary if the absolute value of the ADF test is less than the MacKinnon values in absolute terms. The null hypothesis for this test is that both variables possess unit root. On the other hand, the alternative hypothesis connotes the absence of unit root for both variables.
Table 4.2 Augmented Dickey Fuller Unit Root Test
ADF Unit Root Test | ||||||
Variables | Levels | 1st Difference | Order | |||
RGDP | -2.0482 | -3.8819** | I1 | |||
REM | -0.1994 | -3.4571* | I1 | |||
GE | 0.2978 | -5.5445*** | I1 | |||
TRAO | -3.3714* | -7.4398*** | I0 | |||
FDI | -3.4881* | -7.7524*** | I0 | |||
KAP | -2.7289 | -10.1657*** | I1 | |||
Critical Values | ADF z(t) | |||||
-4.356 | -3.595 | -3.233 | ||||
1% | 5% | 10% |
Author’s computation, 2023
The ARDL method of estimation relies on the time series properties of the data. This is to ensure the integration order of stationary is free from I(2) series so as to avoid spurious result which is unreliable and inconsistent. From the table above, it is clear that our variables of interest stationary order combined both the levels I (0) and 1st difference I (1) order for the ADF test. The abstruseness in the integration order of the series gives support to the use of ARDL bound test above other alternate co-integrating techniques. Hence this study adopts the Autoregressive distributed lag (ARDL) estimation method. The result of the unit root signifies a stationary at levels for TRAO and FDI while all other variables became stationary after first differencing at 5% significance level.
Table 4.3 Lag Length Criteria
Lag | LogL | LR | FPE | AIC | SC | HQ |
0 | -97.27561 | NA | 1.47e-05 | 5.901464 | 6.168095 | 5.993505 |
1 | 98.05109 | 312.5227* | 1.69e-09* | -3.202920 | -1.336502* | -2.558632* |
2 | 134.2008 | 45.44539 | 2.03e-09 | -3.211476* | 0.254728 | -2.014943 |
Author’s computation, 2023
This study performs prior analysis of the lag length selection using the lag length criteria of the unrestricted VAR model. The consistency of the sequential modified LR test statistic, Final prediction error, and the Hannan-Quinn information criterion suggests this study adopts a lag of 1 for its estimation.
Table 4.4 ARDL Bound test
Test Statistic | Value | k |
F-statistic | 7.107055 | 5 |
Critical Value Bounds | ||
Significance | I0 Bound | I1 Bound |
10% | 2.26 | 3.35 |
5% | 2.62 | 3.79 |
2.5% | 2.96 | 4.18 |
1% | 3.41 | 4.68 |
This study performs the ARDL bound testing to investigate if there is any long run convergence among the variables being investigated. This result is presented in table 4.4 above. It shows the long run result of the ARDL bound test statistics. Bound test cointegration method was estimated for each of the model. The result shows that there is presence of a long run convergence among the selected variables and the respective dependent variables. The bound testing the f-statistics is greater than I0 and I1 at other critical bounds. This suggests that there is a presence of long run relationship among the variables in consideration.
4.1.2 Regression Result
Table 4.5
Short Run Coefficients | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LREM) | 0.023948 | 0.005655 | -4.234491 | 0.0002 |
D(LGE) | 0.055872 | 0.008685 | 6.432967 | 0.0000 |
D(LFDI) | 0.002051 | 0.006242 | 0.328625 | 0.7450 |
D(LTRAO) | 0.004830 | 0.015054 | 0.320852 | 0.7508 |
D(LKAP) | 0.017992 | 0.046978 | 0.382977 | 0.7047 |
CointEq(-1) | -0.073418 | 0.030444 | -2.411607 | 0.0230 |
R-squared | 0.998543 | Mean dependent var | 10.48578 | |
Adjusted R-squared | 0.998111 | S.D. dependent var | 0.508667 | |
S.E. of regression | 0.022110 | Akaike info criterion | -4.573252 | |
Sum squared resid | 0.013199 | Schwarz criterion | -4.177372 | |
Log likelihood | 91.31853 | Hannan-Quinn criter. | -4.435079 | |
F-statistic | 2312.239 | Durbin-Watson stat | 2.532409 | |
Prob(F-statistic) | 0.000000 |
Long Run Coefficients | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LREM | 0.326184 | 0.150211 | -2.171503 | 0.0388 |
LGE | 0.761012 | 0.254502 | 2.990197 | 0.0059 |
LFDI | 0.156113 | 0.137997 | 1.131280 | 0.2679 |
LTRAO | 0.065789 | 0.221705 | 0.296741 | 0.7689 |
LKAP | -1.479962 | 1.291193 | -1.146197 | 0.2618 |
C | 57.022388 | 38.848810 | 1.467803 | 0.1537 |
Granger Causality Test
Table 4.6
Null Hypothesis: | Obs | F-Statistic | Prob. |
LREM does not Granger Cause LRGDP | 35 | 0.71410 | 0.4978 |
LRGDP does not Granger Cause LREM | 3.65566 | 0.0379 | |
LTRAO does not Granger Cause LRGDP | 35 | 1.55361 | 0.2280 |
LRGDP does not Granger Cause LTRAO | 0.16742 | 0.8466 | |
LFDI does not Granger Cause LRGDP | 35 | 1.57685 | 0.2233 |
LRGDP does not Granger Cause LFDI | 1.40996 | 0.2599 | |
LGE does not Granger Cause LRGDP | 35 | 3.69465 | 0.0368 |
LRGDP does not Granger Cause LGE | 3.34546 | 0.0488 | |
LKAP does not Granger Cause LRGDP | 35 | 1.53816 | 0.2312 |
LRGDP does not Granger Cause LKAP | 5.98256 | 0.0065 | |
LTRAO does not Granger Cause LREM | 35 | 3.31907 | 0.0499 |
LREM does not Granger Cause LTRAO | 0.62869 | 0.5402 | |
LFDI does not Granger Cause LREM | 35 | 1.14978 | 0.3303 |
LREM does not Granger Cause LFDI | 0.94256 | 0.4009 |
Post estimation
Table 4.7
Breusch-Godfrey Serial Correlation LM Test: | |||
F-statistic | 1.482754 | Prob. F(2,25) | 0.2463 |
Obs*R-squared | 3.817498 | Prob. Chi-Square(2) | 0.1483 |
The result of the serial correlation test between the variables using the Breusch-Godfrey Lm test is in the Table above. The absence of serial correlation is confirmed since the chi-square probability value of 0.14 is greater than the 5% significance level
Table 4.8
Heteroskedasticity Test: Breusch-Pagan-Godfrey | |||
F-statistic | 2.423805 | Prob. F(14,20) | 0.2346 |
Obs*R-squared | 22.02100 | Prob. Chi-Square(14) | 0.0782 |
Scaled explained SS | 5.892376 | Prob. Chi-Square(14) | 0.9691 |
The absence of heteroscedasticity is one of the basic assumptions of OLS. The result of the heteroscedasticity is presented in Table 4.6 above. After estimation, result shows that the Probability or p-value of the Obs* Rsquared is 0.07 which is greater than 5% significance level. In effect, this is the absence of heteroscedasticity.
Normality Test
4.2 Discussion of Results
This study was undertaken to assess the impact of migration and remittance on economic growth in Nigeria. The economic growth variable used in study is proxy by GDP while explanatory variables are remittance, trade openness, foreign direct investment, government expenditure, capital formation. Similarly, this study also investigated the causality among the variables used in the study. The data used for this study was obtained from the CBN Statistical Bulletin (2022) and the World Development Index (WDI). Finally, the period under review is from 1985 to 2021.
The ARDL estimation technique is adopted owing to the stationarity of all the variables at both level, first differencing and void of second difference series. The result of this analysis is presented in table 4.2 above. Having confirmed the presence of long run convergence among the variables under consideration from table 4.4, Thus short run result was presented above which is evident that remittance have a positive and statistically impact on economic growth under the period of study. This finding is in support apriori expectation of positive relationship between remittance and economic growth. It means that remittance received from outside the country has been channel properly to productive venture and industrial sector which has production, consumption and investment.
From the table 4.5 above, it can be deduced from the table that government spending on both current and capital has a positive impact on economic growth both in the short run and long run. The long run estimates reveal that government spending has a positive and significant impact on economic growth. Statisitically, a unit change in government spending will bring about 55% increases in economic growth in the short run and 76% increase in the long run.
Moreover, it can be deduced from the regression table that trade openness and foreign direct investment has a positive impact on economic growth under the period of study. Statistically, a unit in change in trade openness and will bring about 4% increases in short run and 76% increase in long run on economic growth. While a unit change in foreign direct investment will bring about 2% increase in the short run and 15% increase in long run on economic growth. In addition, capital formation has positive on economic growth in the short run while negative impact on economic growth in the long run has revealed by the regression results.
The causality test was conducted in this study to show the nature of relationship among the variables used in the study. From the table 4.6 GDP causes changes in remittance meaning that there is a uni direction between the two variables. It is revealed that remittances does not cause changes in economic growth under the period of study. It is further support by the regression result of negative relationship between remittance and economic growth. Similarly, there is bi-direction between GDP and GE. GE causes changes in GDP while GDP also causes changes in GE. This is further supported by the regression result of positive relationship between government spending and economic growth under the period of study.
The post-estimation of the study which test serial correlation and hetetroscadacity among the variables. It can be deduced from the table 4.7 and 4.8 that there is no autocorrelation and hetetroscadacity among the variables under investigation.
The f-statictic shows the overall effect of the variables on dependent variable which significant all confident level. The R-square is also high which is 99% meaning any variation economic growth are explain by the explanatory variables while the remaining 1% are explain by other factors that are not capture by the model.
4.3 Comparison of Result with Previous Study
The thesis set out to investigate the impact of migration and remittance on economic growth in Nigeria. The thesis has found out that remittance has a positive and significant impact on economic growth in Nigeria under the period of study. This implies that rise in these variables will stimulate better performance of the dependent variables while a fall worsen their performance. The results is said to be conformity with the findings of the previous studies like the work of Muhammad et al., (2019), Ologunwa, (2017), Adeseye, (2021), Sebil and Abdulazeez, (2018).
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary
The study has undertaken the investigation of a relationship between economic growth and remittance. Using The ARDL estimation technique owing to the stationarity of all the variables at both level, first differencing and void of second difference series, the result of this analysis is presented confirmed the presence of long run convergence among the variables under consideration. The short run result was presented above which is evident that remittance has a positive and statistically impact on economic growth under the period of study. However the result shows that there is presence of a long run convergence among the selected variables and the respective dependent variables. The bound testing the f-statistics is greater than I0 and I1 at other critical bounds. This suggests that there is a presence of long run relationship among the variables in consideration.
Thus short run result was presented above which is evident that remittance have a positive and statistically impact on economic growth under the period of study. It was found that GDP causes changes in remittance meaning that there is a uni direction between the two variables. It is revealed that remittances does not cause changes in economic growth under the period of study. It is further supported by the regression result of negative relationship between remittance and economic growth.
5.2 Conclusion
The reliable secondary sources of data interrogated in this study provide the basis for conclusion that Nigeria is one of the highest receivers of remittances across the world. Also, remittances constitute a veritable source of income for multitude of families and communities of the international migrants. Remittance flows to Nigeria has had a positive impact on the
social and economic growth of the country in the short run while economic growth impacts remittances severely. Thus, economic policies should be implemented to spur growth with proper coordination and effective utilization of remittances in the productive sector of the economy, a pre-requisite for further growth in the Nigerian economy. It is only when the economy grows that the effect of the large remittances received in the country will be felt. Also investigations will need to be carried out by anti-graft agencies on the use of remittances received from abroad and possible diversion and hoarding of currency especially in view of the exchange rate devaluation and the rising inflationary pressure.
5.3 Recommendation
The following recommendations are therefore proposed following the findings from this study.
- Given a statistically and positively correlated impact of government spending on economic growth, and the impact of trade openness and foreign direct investment on economic growth in the long run, the Nigerian government should budget and expend more resources on productive sector of the economy especially on infrastructure which would attract the right Foreign Direct Investment (FDI) into the country and boost more growth.
- The Nigerian government should set up a body to review the spending and allocation of remittances into the country especially in view of current Forex policies inconsistencies.
- An independent commission be set up by the federal government to be saddled with the responsibility of coordinating and harnessing remittance flows into the country. If established, the body will compliment and consolidate the efforts of the CBN in ensuring adequate flow and judicious utilization of remittances in the country.
- The Economic and Financial Crimes Commission (EFCC) should beam their search light on the corrupt practices inherent in the financial institutions through which formal remittances flow. This will go a long way in harnessing these resources to productive areas of the economy and build confidence among the international migrants in sending remittance via formal channels.
JULY, 2023
CERTIFICATION
This is to certify that this research work titled ‘Impact of Migration and Remittances on Economic Growth in Nigeria’ was carried out by Imouokhome, Peter Afen-Okhai, Matric number 189081031, of the Department of Economics, Faculty of Social Sciences, University of Lagos, under the supervision of Professor Risikat Dauda.
……………………… ………………………
Imouokhome A. Peter Date
(Researcher)
……………………… ………………………
Professor Risikat O.S Dauda Date
(Supervisor)
……………………… ………………………
Prof. W. A. Isola Date
(Head of Department Economics)
DEDICATION
God – my help in ages past, my hope today and forever.
ACKNOWLEDGMENTS
I am immensely grateful to my project supervisor – Professor Risikat Dauda for her motherly corrections and guidance during the course of supervision of this project. Her insights and contributions helped in instilling a self-discipline, hard work and belief that culminated in a successful project. I also thank the department of Economics at the University of Lagos for the opportunity to study under her tutelage.
Thank you.
REFERENCES:
- Aboulezz N. (2015) “Remittances and Economic Growth Nexus: Empirical Evidence from Kenya” International Journal of Academic Research in Business and Social Sciences. Vol. 5 (12) 285 – 296.
- Adarkwa M. A. (2015) “Impact of remittances on Economic Growth: Evidence from selected west African Countries” AHMR. Vol. 11 (2). 1-16.
- Adeagbo O. and Ayansola O. A. (2014) “Impact of Remittances on Development in Nigeria: Challenges and Prospects” Journal of Sociology Soc Anth. Vol. 5(3), 311-31
- Adeagbo O. and Ayansola O. A. (2014) “Impact of Remittances on Development in Nigeria: Challenges and Prospects” Journal of Sociology Soc Anth. Vol. 5(3), 311-318.
- Adegbite, O. O., & Machethe, C. L. (2020). Bridging the financial inclusion gender gap in smallholder agriculture in Nigeria: An untapped potential for sustainable development. World Development, 127, 104755. https://doi.org/10.1016/j.worlddev.2019.104755 [Crossref], [Web of Science ®], [Google Scholar]
- Adenike. A (2021). The Effect of Migrants Remittance on Economy Growth in Nigeria: An Empirical Study National School of Political and Administrative Studies SNSPA, Bucharest, Romania. DOI: 10.4236/ojps.2021.111007
- Adeseye, A. (2021) The Effect of Migrants Remittance on Economy Growth in Nigeria: An Empirical Study. Open Journal of Political Science, 11, 99-122. doi: 10.4236/ojps.2021.111007.
- Adeyi E. O. (2015) “Remittances and Economic Growth: An Empirical Evidence from Nigeria and Sri Lanka” Basic Research Journal of Education Research and Review. Vol. 4 (5) 91-97.
- Ahmad A. A. (2015): “Workers Remittances and Economic Growth: Evidence from Jordan” European Scientific Journal. Vol. 11(25), 40-54.
- Ahmed et al. (2011) “Introduction: Uprootings/regroundings: Questions of home and
- Ajaero et al.,(2018),“Rural-urban differences in the prevalence and predictors of depression among adolescents in South Africa.”https://www.researchgate.net/publication/329556042 Rural urban differences in the prevalence and predictors of depression among adolescents in South Africa
- Akinpelu Y. A., Ogunbi O. J., Bada O. T., and Omojola O. S. (2013) “Effects of Remittance Inflows on Economic Growth in Nigeria” Journal of Development Studies. Vol. 3 (3), 113-123.
- Apanisile, O. T. (2021). Remittances, financial development and the effectiveness of monetary policy transmission mechanism in Nigeria: A DSGE approach (1986–2018). Indian Economic Review, 56(1), 91–112. https://doi.org/10.1007/s41775-021-00110-z [Crossref], [Google Scholar]
- Ari, O.U.(2020). The impact of remittances on economic growth in developing countries: empirical evidence from Turkey. 8thInternational conference on culture and civilization 54-62
- Awaworyi Churchill, S., Nuhu, A. S., & Smyth, R. (2020). Financial inclusion and poverty: Micro-level evidence from Nigeria. Moving from the Millennium to the Sustainable Development Goals: Lessons and Recommendations, 11–36. https://doi.org/10.1007/978-981-15-1556-9_2 [Crossref], [Google Scholar]
- Barajas, A. Chami, R, Fullenkamp, C. Gapen, M. and Montiel, P. (2009): Do workers’ remittances promote economic growth? IMF working paper WP (09)/153
- Barro, R. (1991): “Economic Growth in Cross-Section of Countries”, Quarterly Journal of Economics, 106 (May) 407 – 443.
- Bashir, S. (2020). Exploring the linkages between remittances and economic growth determinants in low, lower middle, and upper middle-income countries: A comparative study. The Economic and Finance Letters,7(2), 268-275.
- Beijer, G. et al (1970).International and national migratory movements, in: International Migration, 8, 3, 93-109
- Benhamou and Cassin 2021; Abduvaliev and Bustillo 2020; Melvin 2019; Warner and Afifi 2014; Arest off et al. 2012; Djajic 1986). “The impact of remittances on savings, capital and economic growth in small emerging countries.” https://www.researchgate.net/publication/33938015 The impact of remittances on savings capital and economic growth in small emerging countries
- Bichaka, F., Fayassa and Christian, N. (2008): The Impact of Remittances on Economic Growth and Development in Africa. Department of Economic and Finance Working Papers Series, February (2008).
- Binford (2003) “Migrant Remittances and (Under) Development in Mexico.” Critique of Anthropology 23(3):305-336 DOI:10.1177/0308275X030233004
- Binford 2003; Rubenstein 1992. “Migrant Remittances and (Under)Development in Mexico September 2003” Critique of Anthropology 23(3):305-336 DOI:10.1177/0308275X030233004
- Buhari, Muhils and Osman, 2018; Chowdhury, (2015) ‘Buhari, D., Muhils, C. and Osman, D. (2018). The nexus between income inequality, international remittances and economic growth in Turkey. CEA Journal of Economics 5-16.
- Carling J. (2008) “The Determinants of Migrants remittances” Oxford Review of Economic Policy. Vol. 24 (3), 582-599.
- Carling, J. (2004). Emigration, return and development in Cape Verde: The impact of closing Borders. Population, Space and Place, 10, 113–132. https://doi.org/10.1002/psp.322
- Catalina, Susan (2022) The widespread impacts of remittance flows; https://wol.iza.org/articles/good-and-bad-in-remittance-flows/long
- CBN (2017) “Statistical Bulletin” Central Bank of Nigeria
- Chenery, H.B. and Strout, A. (1966) “Foreign Assistance and Economic Development” American Economic Review, 56(September): 679 – 733.
- Chukwuone 2007; Quartey 2006). Moreover, (IMF, 1999) maintains that remittance is limited to money sent by migrant workers who
- Chukwuone, N. (2007). Analysis of impact of remittance on poverty and inequality in Nigeria. Sixth PEP Research Network General Meeting. From <www.pep- net.org.> (Retrieved on 5 April 2014).
- Clemens M. A. and McKenzie D. (2014) “Why Remittances Appear to affect Growth” Center for Global Development. Working Paper Series 366, Pp.1-49.
- Danmola and Abba, (2013) “Impact of Foreign Direct Investment On Solid minerals Industry In Nigeria.”
- de Haas, H. A theory of migration: the aspirations-capabilities framework. CMS 9, 8 (2021). https://doi.org/10.1186/s40878-020-00210-4
- Denison, E.F. (1967): Why growth rates differ. Post-War Experience for nine Western Countries. Washington DC. Easterly W. and Levin, R. (2001): The Elusive Quest for Growth. The MIT Press, Cambridge MA.
- Dercon S 2009. Agriculture, Growth and Rural Poverty Reduction in Africa: Fallacies, Contexts and Prior-ities for Research. AERC Working Paper September 2014Journal of Sociology and Social Anthropology 5(3):311-318 DOI:10.1080/09766634.2014.11885635
- Dercon S 2009. Agriculture, Growth and Rural Poverty Reduction in Africa: Fallacies, Contexts and Prior-ities for Research. AERC Working Paper
- Douglas S. Massey, Joaquin Arango, Graeme Hugo, Ali Kouaouci, Adela Pellegrino and J. Edward Taylor “Theories of International Migration: A Review and Appraisal.”
- Dula, C. S., & Ballard, M. E. (2003). Development and evaluation of a measure of dangerous, aggressive, negative emotional, and risky driving. Journal of Applied Social Psychology, 33(2), 263–282. https://doi.org/10.1111/j.1559-1816.2003.tb01896.x
- Elebiju, A., & Fatokunbo, G. (2020). Nigeria remittances: Legal, regulatory and commercial issues in diaspora transactions. Mondaq. https://www.mondaq.com/nigeria/income-tax/929158/remittances-legal-regulatoryand-commercial-issues-in-diaspora-transaction
- Fagerheim M. G. (2015) “Impact of Remittances on Economic Growth in ASEAN” Thesis for Master of Philosophy in Environmental and Development Economics, OSLO University, May 2015. Remittances and the Growth of the Nigerian Economy
- Fayomi O., Azuh D. and Ajayi L. (2015) “The Impacts of Remittances on Nigeria’s Economic Growth: A Study of Nigerian Diasporas in Ghana” Journal of South African Business Research. Vol. (2015) Article ID. :598378, 1-12.
- Fields, G.S. (1980): Poverty, Inequality and Development, The MIT Press, Cambridge University Press, England.
- Fowowe, B. (2020). The effects of financial inclusion on agricultural productivity in Nigeria. Journal of Economics and Development, 22(1), 61–79. https://doi.org/10.1108/JED-11-2019-0059 [Crossref], [Google Scholar]
- Giles, T, Gilbert, S & McNeill, E (2013), ‘Do students want and need written feedback on summative assignments? Engaging students with the feedback process – a topic review activity,’ ergo: The Journal of the Education Research Group of Adelaide, vol. 3, no. 1, 15-21
- Giuliano, P. And Ruiz – Arranz, M. (2006) “Remittances, Financial Development and Growth,” IMF Working Papers, WP 05/234.
- Gupta, S. Pattillo, C. and Wagh, S. (2007): “Making remittances work for Africa”, Finance and Development 44, No. 2 (June, 2007): 1 – 9.
- Haas, (2007). “Turning the Tide? Why Development Will Not Stop Migration.” https://doi.org/10.1111/j.1467-7660.2007.00435.xCitations: 161
- Harris, I.R.M., Todaro (1970) “Migration, Employment and Development: A Two-Sector Analysis, American Economic Review, 60(March): 126 – 142
- IFAD (2021) IFAD Assessment Report https://www.ifad.org/ifad-impact-assessment-report-2021/
- IFAD, (2023) 13 reasons why remittances are important https://www.ifad.org/en/web/latest/-/13-reasons-why-remittances-are-important
- Iheanacho, N. N & Ughaerumba, C.(2015). “Post Migration poverty structures and Pentecostal churches social services in Nigeria.” American Research institute for policy development. (4)25 Retrieved from http://dx.doi.org/10.15640/rah.v4n2a7 DOI:10.15640/rah.van2a7 (Elebiju & Fatokunbo, 2020).
- Iheke O. R. (2012) “The Effect of Remittances on the Nigerian Economy” International Journal of Development and Sustainability. Vol. 1 (2), 614-621.
- IMF (International Monetary Fund). (2023a) . Senegal: Sixth Review under the Policy Coordination Instrument and Third Reviews under the Stand-By Arrangement and the Arrangement under the Stand-By Credit Facility. IMF Country Report No 23/30. Washington, DC: IMF. https://www.imf.org/en/Publications/CR/Issues/2023/01/18/Senegal-Sixth-Review-Under-thePolicy-Coordination-Instrument-and-Third-Reviews-Under-the-528312.
- IMF. 2023b. World Economic Outlook 2023. Washington, DC: IMF.
- IMF. 2023c. “The Regional Economic Outlook: Sub-Saharan Africa – The Big Funding Squeeze.” April 2023.https://www.imf.org/en/Publications/REO/SSA/Issues/2023/04/14/regional-economic outlook-forsub-saharan-africa-april-2023
- Kanchan D. and Bimal S. (2014) “Relationship between Remittances and Economic Growth in Bangladesh: An Econometric Study. Bangladesh Development Research working Paper Series 1-14.
- Kihangire D, Katarikawe M (2008). “The Impact of Re-mittances on Macroeconomic Stability and Finan-cial Sector Deepening: Opportunities and Challenges for Uganda.” Working Papers From <http://www.bou.or.ug/bouwebsite> (Retrieved on 5 April 2014 (Odozi et al. 2010;
- Kunofiwa T. (2015) “Personal Remittances, Banking Sector Development and Economic Growth in Israel: A tri-variate Causality Test” Journal of Corporate Ownership and Control. Vol. 13(1), 806-819.
- Levitt, P. (2001) “The Transnational Villagers. University of California Press, Berkeley.”
- Lewis, S.A.W. (1954) “Economic development with unlimited supplies of labour”, Manchester School of Economics and Social Studies, 22 (May): 139 – 191.
- Lin, S. and Simmons, W.O. (2015): Do remittances promote economic growth in the Caribbean community and common market? Journal of Economic and Business 77, 42 – 59.
- Loto, Abiola (2016) “Remittances and the Growth of the Nigeria Economy.” https://dx.doi.org/10.4314/ejeb.v6i2
- Lucas R. E. and Stark O. (1985) “motivation to Remit: Evidence from Botswana” Journal of Political Economy. Vol. 93, 901-918.
- Lucas, R.E. (1988): “On Mechanics of Economics Growth”. Journal of Monetary Economics 22(July): 3 – 42.
- Maimbo S. M. and Ratha D. (2005) “Remittances-Development Impact and Future Prospects” Washington DC. The World Bank.
- Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. E. (1993). Theories of International Migration: A Review and Appraisal. Population and Development Review, 19, 431-466.
- Moukpe, Essossinam (2021) Migrant Remittances and Economic Growth in ECOWAS Countries: Does Digitalization Matter?; The European Journal of Development Research volume 34, pages2517–2542 (2022); https://link.springer.com/article/10.1057/s41287-021-00461-6
- Muhammad et al., (2019) “A Review on Emerging Pathogenesis Of Covid-19 And Points Of Concern For Research.”
- Myrdal, G. (1968): Asian Drama: An Inquiry into the Poverty of Nations (New York Twentieth Century Fund)
- Nwokoye, E. S., Igbanugo, C. I., & Dimnwobi, S. K. (2020). International migrant remittances and labour force participation in Nigeria. African Development Review, 32(2), 125–137. https://doi.org/10.1111/1467-8268.12421 [Crossref], [Web of Science ®], [Google Scholar]
- Nyamongo, E.M. Misatib, R.N., Kipyegonb, L., Ndiragin, I. (2012): Remittances, financial development and Economic Growth in Africa. Journal of Economic and Business 64 240 – 260.
- Odozi et al. (2010). “Household-level determinants of employment and earnings in rural Nigeria Household-level determinants of employment and earnings in rural Nigeria.”
- Okeke (2021) “Agile Methodology in System Development source” https://www.researchgate.net/figure/Agile-Methodology-in-System-Development-source-Okeke2021-retrieved-from_fig1_354310848
- Okodua H., Ewetan O. O. and Urhie E. (2015) “Remittance Expenditure Patterns and Human Development Outcomes in Nigeria” Journal of Developing Countries Studies. VOl. 5(2), 70-81.
- Okodua H., Ewetan O. O. and Urhie E. (2015) “Remittance Expenditure Patterns and Human Development Outcomes in Nigeria” Journal of Developing Countries Studies. VOl. 5(2), 70-81.
- Olayungbo and Quadri, (2019) “Remittances, financial development and economic growth in sub-Saharan African countries: evidence from a PMG-ARDL approach.” https://jfin-swufe.springeropen.com/articles/10.1186/s40854-019-0122-8
- Oluwafemi and Ayandibu, (2014). “Impact of Remittances on Development in Nigeria: Challenges and Prospects.” https://www.researchgate.net/publication/266372107 Impact of Remittances on Development in Nigeria Challenges and Prospects
- Oluyemi, Oluwaseyi (2021) Human capital flight and output growth nexus: evidence from Nigeria; https://www.emerald.com/insight/content/doi/10.1108/REPS-07-2020-0088/full/html
- P., Srinivasan & M., Kalaivani, 2012. “Exchange Rate Volatility and Export Growth in India: An Empirical Investigation,” MPRA Paper 43828, University Library of Munich, Germany.
- Papademetriou. D G. (1985) “Illusions and reality in international migration: migration and development in post World War II Greece.” PMID: 12159640 DOI: 10.1111/j.1468 2435.1985.tb00316.x
- Pennix, R. (1982). A critical Review of Theory and Practice: the case of Turkey.In: International Migration Review, 16, 4, 781-818
- Pesaran, M.H., Shin, Y. and Smith, R. (2001) Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16, 289-326. https://doi.org/10.1002/jae.616
- Piot-Lepetit and Nzongang 2014; World Bank 2013; Odozi et al. (2010); Lucas and Stark 1985). “Financial sustainability and poverty outreach within a network of village banks in Cameroon: A multi-DEA approach.”
- Price Waterhouse Cooper. (2020). PwC puts the annual financing gap for Nigerian SMEs at N617.3bn. Business Day, July 2020.
- Quartey P (2006). The impact of migrant remittances on household welfare in Ghana. Institute of statistical scientific and economic research, University of Ghana. AERC Working Paper: 1-38.
- Quartey, P., Ackah, C., & Lambon-Quayefio, M. P. (2018). Inter-linkages between remittance and savings in Ghana. International Journal of Social Economics, 46(1), 152–166. https://doi.org/10.1108/IJSE-12-2017-0618 [Crossref], [Web of Science ®], [Google Scholar]
- Ramirez, M.D. (2013): Do financial and institutional variables enhance the impact of remittances on economic growth in Latin America and the Caribbean? A Panel Cointegration Analysis. International Advances in Economic Research 19, 273 – 288.
- Ratha, 2003; Skeldon, (2002). “Analysis of Impact of Remittance on Poverty and Inequality in Nigeria.” https://www.pep-net.org/sites/pepnet.org/files/ typo3doc/pdf/files_events/ chukwone.pdf
- Ratha, D. (2003) Worker’s Remittances: An important and Stable Source of External Development Finance. In Global Development Finance: Striving for Stability in Development Finance, 157-175. Washington, DC: World Bank.
- Ratha, D. (2005) Workers’ Remittances: An Important and Stable Source of External Development Finance. In Maimbo, S. M and Ratha D. (ed) Remittances: Development Impact and Future Prospects. Washington DC: the World Bank
- Richie lionell (2023) Animated Chart: Remittance Flows and GDP Impact By Country; https://www.visualcapitalist.com/cp/remittance-flows-gdp-impact-by-country/
- Romano and Traverso (2020); Zimmerqmann 2017 “Disentangling the Impact of International Migration on Food and Nutrition Security of Left-Behind Households: Evidence from Bangladesh”
- Romer, P. (1986): “Increasing Returns and Long-Run Growth” Journal of Political Economy, (October), 1002 – 1037.
- Samuel Osei-Gyebia et al, (2023) The effect of remittance inflow on savings in Nigeria: The role of financial inclusion. https://doi.org/10.1080/23311886.2023.2220599World vhttps://www.worldbank.org/en/topic/migration/overview
- Samuel. O.G (2023) The effect of remittance inflow on savings in Nigeria: The role of financial inclusion https://doi.org/10.1080/23311886.2023.2220599
- Schultz, T.W. (1979): “The Economics of Being Poor”. Journal of Political Economy (August): 639 – 650.
- Sebil O Oshota & Abdulazeez A Badejo, 2015. “Impact of Remittances on Economic Growth in Nigeria: Further Evidence,” Economics Bulletin, AccessEcon, vol. 35(1), pages 247-258.
- Sjaastad, L.A. (1962) “The Costs and Returns of Human Migration.” Journal of Political Economy, 70, 80-93.
- Skeldon, R. (2002). Migration and Poverty. Asia-Pacific Population Journal, 17(4): 67-82
- Solow, R. (1956): A contribution to the theory of Economic” Quarterly Journal of Economics, 70(February) 65 – 94.
- Stahl, C.W. and Arnold, F. (1986) “Overseas workers’ remittances in Asian Development” International Migration Review 20(4): 899 –925.
- Stark, O., & Bloom, D. E. (1985). The new economics of labor migration. The American Economic Review, 75(2), 173–178. https://doi.org/10.2307/1805591
- Stark, O., & Taylor, J. E. (1991). Migration incentives, migration types: The role of relative deprivation. The Economic Journal, 101(408), 1163–1178.
- Tewolde, B. (2005). Remittances as a tool for development and reconstruction in Eritrea: An economic analysis. Journal of Middle Eastern Geopolitics, 1(2), 21-32.
- Tewolde, B. (2006). Migration in Eritrea: A brief account. Journal of Middle Eastern Geopolitics,
- Tewolde, H., A. Adeli, K. R. Sistani, D. E. Rowe, and J. R. Johnson. (2010). Equivalency of broiler litter to ammonium nitrate as a cotton fertilizer in an upland soil. Agronomy Journal102:251–257.
- Tewolde, H., K. R. Sistani, and D. E. Rowe. (2005). Broiler litter as a micronutrient source for cotton: Concentration in plant parts.Journal of Environmental Quality34:1697–1706 theories, Social Affairs.”: A Journal for the Social Sciences, 1(5), 13-32
- Tobechukwu Nneli et al (2022) Migration-relevant policies in Nigeria; https://www.mignex.org/sites/default/files/2023-02/d053f-mbp-migration-related-policies-in-nigeria-v2_0.pdf
- United Nations (2023) Digital remittances towards financial inclusion and cost reduction. https://www.un.org/en/observances/remittances-day
- United Nations Development Programme. (2020). Remittances (diaspora financing). Retrieved November 4, 2020, from https://www.sdfinance.undp.org/content/sdfinance/en/home/ solutions/remittances.html
- Wayne, T., Soetan, T., Bajepade, G., & Mogaji, E. (2020). Technologies for financial inclusion in Nigeria. Research Agenda Working Papers, 2020(4), 40–56. https://doi.org/10.13140/RG.2.2.35554.89287 [Google Scholar]
- WDI (2017) “World Development Indicator” World Bank
- Wickramasing He, A & Wijitapure, W (2016, Fall). “International migration and migration
- World Bank Group (2023) Remittances Remain Resilient but Are Slowing; Migration and Development Brief 38; https://www.knomad.org/sites/default/files/publication-doc/migration_and_development_brief_38_june_2023_0.pdf
- Yilmaz, B. (2015): Impact of Remittances on the Economic Growth in the Transitional Economies of the European Union. Economic Insight – Trends and Challenges
- Zoch B 2007. Developmental Potential of Migrant Remittances Focus on Development Policy. Poli-cy Papers. From <http://www.kfw-entwicklungsbank.de.> (Retrieved on 5 April 2014).
- Zoch B 2007. Developmental Potential of Migrant Remittances Focus on Development Policy. Poli-cy Papers. From <http://www.kfw-entwicklungsbank.de.> (Retrieved on 5 April 2014)
APPENDIX
Appendix 1 -Unit root test (level)
Null Hypothesis: LRGDP has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 4 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -2.048255 | 0.5537 | ||
Test critical values: | 1% level | -4.273277 | ||
5% level | -3.557759 | |||
10% level | -3.212361 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LRGDP) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:13 | ||||
Sample (adjusted): 1990 2021 | ||||
Included observations: 32 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LRGDP(-1) | -0.145023 | 0.070803 | -2.048255 | 0.0512 |
D(LRGDP(-1)) | 0.363694 | 0.181386 | 2.005082 | 0.0559 |
D(LRGDP(-2)) | 0.460357 | 0.187851 | 2.450650 | 0.0216 |
D(LRGDP(-3)) | -0.145901 | 0.203296 | -0.717676 | 0.4796 |
D(LRGDP(-4)) | 0.163277 | 0.188032 | 0.868345 | 0.3935 |
C | 1.392387 | 0.669040 | 2.081170 | 0.0478 |
@TREND(“1985”) | 0.006844 | 0.003514 | 1.947753 | 0.0628 |
R-squared | 0.440050 | Mean dependent var | 0.041473 | |
Adjusted R-squared | 0.305663 | S.D. dependent var | 0.038480 | |
S.E. of regression | 0.032064 | Akaike info criterion | -3.851497 | |
Sum squared resid | 0.025703 | Schwarz criterion | -3.530867 | |
Log likelihood | 68.62395 | Hannan-Quinn criter. | -3.745217 | |
F-statistic | 3.274480 | Durbin-Watson stat | 1.736810 | |
Prob(F-statistic) | 0.016238 |
Null Hypothesis: LREM has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 9 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -0.199411 | 0.9896 | ||
Test critical values: | 1% level | -4.339330 | ||
5% level | -3.587527 | |||
10% level | -3.229230 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LREM) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:20 | ||||
Sample (adjusted): 1995 2021 | ||||
Included observations: 27 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LREM(-1) | -0.030445 | 0.152676 | -0.199411 | 0.8446 |
D(LREM(-1)) | 0.480857 | 0.256225 | 1.876698 | 0.0802 |
D(LREM(-2)) | -0.443409 | 0.169974 | -2.608680 | 0.0198 |
D(LREM(-3)) | 0.093860 | 0.171298 | 0.547936 | 0.5918 |
D(LREM(-4)) | -0.308571 | 0.148864 | -2.072839 | 0.0558 |
D(LREM(-5)) | -0.073042 | 0.143317 | -0.509652 | 0.6177 |
D(LREM(-6)) | 0.126713 | 0.127466 | 0.994090 | 0.3359 |
D(LREM(-7)) | -0.279873 | 0.120479 | -2.323000 | 0.0346 |
D(LREM(-8)) | 0.117728 | 0.111774 | 1.053264 | 0.3089 |
D(LREM(-9)) | -0.245508 | 0.110838 | -2.215011 | 0.0427 |
C | 1.514841 | 2.357301 | 0.642617 | 0.5302 |
@TREND(“1985”) | -0.021152 | 0.044172 | -0.478868 | 0.6389 |
R-squared | 0.701911 | Mean dependent var | 0.132134 | |
Adjusted R-squared | 0.483312 | S.D. dependent var | 0.485667 | |
S.E. of regression | 0.349102 | Akaike info criterion | 1.034200 | |
Sum squared resid | 1.828088 | Schwarz criterion | 1.610127 | |
Log likelihood | -1.961694 | Hannan-Quinn criter. | 1.205453 | |
F-statistic | 3.210951 | Durbin-Watson stat | 2.321686 | |
Prob(F-statistic) | 0.019135 |
Null Hypothesis: LGE has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | 0.297823 | 0.9979 | ||
Test critical values: | 1% level | -4.234972 | ||
5% level | -3.540328 | |||
10% level | -3.202445 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LGE) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:33 | ||||
Sample (adjusted): 1986 2021 | ||||
Included observations: 36 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LGE(-1) | 0.014066 | 0.047228 | 0.297823 | 0.7677 |
C | 0.308475 | 0.109774 | 2.810097 | 0.0083 |
@TREND(“1985”) | -0.010989 | 0.010401 | -1.056530 | 0.2984 |
R-squared | 0.216151 | Mean dependent var | 0.188611 | |
Adjusted R-squared | 0.168645 | S.D. dependent var | 0.182049 | |
S.E. of regression | 0.165990 | Akaike info criterion | -0.674126 | |
Sum squared resid | 0.909236 | Schwarz criterion | -0.542166 | |
Log likelihood | 15.13426 | Hannan-Quinn criter. | -0.628068 | |
F-statistic | 4.549970 | Durbin-Watson stat | 1.702187 | |
Prob(F-statistic) | 0.017982 |
Null Hypothesis: LTRAO has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -3.371431 | 0.0713 | ||
Test critical values: | 1% level | -4.234972 | ||
5% level | -3.540328 | |||
10% level | -3.202445 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LTRAO) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:36 | ||||
Sample (adjusted): 1986 2021 | ||||
Included observations: 36 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LTRAO(-1) | -0.365436 | 0.108392 | -3.371431 | 0.0019 |
C | 0.925991 | 0.246193 | 3.761236 | 0.0007 |
@TREND(“1985”) | 0.003040 | 0.004609 | 0.659686 | 0.5140 |
R-squared | 0.286303 | Mean dependent var | 0.045548 | |
Adjusted R-squared | 0.243048 | S.D. dependent var | 0.278382 | |
S.E. of regression | 0.242201 | Akaike info criterion | 0.081557 | |
Sum squared resid | 1.935823 | Schwarz criterion | 0.213517 | |
Log likelihood | 1.531966 | Hannan-Quinn criter. | 0.127615 | |
F-statistic | 6.619045 | Durbin-Watson stat | 2.347275 | |
Prob(F-statistic) | 0.003828 | |||
Null Hypothesis: LFDI has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -3.488148 | 0.0559 | ||
Test critical values: | 1% level | -4.234972 | ||
5% level | -3.540328 | |||
10% level | -3.202445 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LFDI) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:40 | ||||
Sample (adjusted): 1986 2021 | ||||
Included observations: 36 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LFDI(-1) | -0.556178 | 0.159448 | -3.488148 | 0.0014 |
C | 0.305554 | 0.242848 | 1.258212 | 0.2171 |
@TREND(“1985”) | -0.006655 | 0.011023 | -0.603682 | 0.5502 |
R-squared | 0.269642 | Mean dependent var | 0.046306 | |
Adjusted R-squared | 0.225377 | S.D. dependent var | 0.763475 | |
S.E. of regression | 0.671955 | Akaike info criterion | 2.122404 | |
Sum squared resid | 14.90026 | Schwarz criterion | 2.254364 | |
Log likelihood | -35.20327 | Hannan-Quinn criter. | 2.168461 | |
F-statistic | 6.091648 | Durbin-Watson stat | 1.968428 | |
Prob(F-statistic) | 0.005602 |
Null Hypothesis: LKAP has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 2 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -2.728973 | 0.2321 | ||
Test critical values: | 1% level | -4.252879 | ||
5% level | -3.548490 | |||
10% level | -3.207094 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LKAP) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:44 | ||||
Sample (adjusted): 1988 2021 | ||||
Included observations: 34 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LKAP(-1) | -0.760640 | 0.278727 | -2.728973 | 0.0107 |
D(LKAP(-1)) | -0.049457 | 0.209580 | -0.235981 | 0.8151 |
D(LKAP(-2)) | -0.383835 | 0.162622 | -2.360286 | 0.0252 |
C | 22.44973 | 8.206257 | 2.735684 | 0.0105 |
@TREND(“1985”) | 0.010514 | 0.004358 | 2.412695 | 0.0224 |
R-squared | 0.614234 | Mean dependent var | 0.018505 | |
Adjusted R-squared | 0.561025 | S.D. dependent var | 0.116734 | |
S.E. of regression | 0.077343 | Akaike info criterion | -2.146093 | |
Sum squared resid | 0.173474 | Schwarz criterion | -1.921628 | |
Log likelihood | 41.48358 | Hannan-Quinn criter. | -2.069544 | |
F-statistic | 11.54379 | Durbin-Watson stat | 1.957116 | |
Prob(F-statistic) | 0.000010 |
Appendix 2 -Unit root test (first difference)
Null Hypothesis: D(LRGDP) has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -3.881907 | 0.0236 | ||
Test critical values: | 1% level | -4.243644 | ||
5% level | -3.544284 | |||
10% level | -3.204699 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LRGDP,2) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:17 | ||||
Sample (adjusted): 1987 2021 | ||||
Included observations: 35 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LRGDP(-1)) | -0.622294 | 0.160306 | -3.881907 | 0.0005 |
C | 0.030190 | 0.014279 | 2.114270 | 0.0424 |
@TREND(“1985”) | -0.000215 | 0.000594 | -0.362028 | 0.7197 |
R-squared | 0.322395 | Mean dependent var | 0.000938 | |
Adjusted R-squared | 0.280044 | S.D. dependent var | 0.041792 | |
S.E. of regression | 0.035460 | Akaike info criterion | -3.758991 | |
Sum squared resid | 0.040238 | Schwarz criterion | -3.625675 | |
Log likelihood | 68.78233 | Hannan-Quinn criter. | -3.712970 | |
F-statistic | 7.612568 | Durbin-Watson stat | 2.251779 | |
Prob(F-statistic) | 0.001975 |
Null Hypothesis: D(LREM) has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 9 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -3.457158 | 0.0656 | ||
Test critical values: | 1% level | -4.356068 | ||
5% level | -3.595026 | |||
10% level | -3.233456 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LREM,2) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:24 | ||||
Sample (adjusted): 1996 2021 | ||||
Included observations: 26 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LREM(-1)) | -2.464579 | 0.712892 | -3.457158 | 0.0038 |
D(LREM(-1),2) | 1.714578 | 0.612271 | 2.800358 | 0.0142 |
D(LREM(-2),2) | 1.374382 | 0.594070 | 2.313501 | 0.0364 |
D(LREM(-3),2) | 1.281166 | 0.490212 | 2.613494 | 0.0204 |
D(LREM(-4),2) | 0.958178 | 0.440313 | 2.176129 | 0.0472 |
D(LREM(-5),2) | 0.733021 | 0.345011 | 2.124634 | 0.0519 |
D(LREM(-6),2) | 0.782018 | 0.280951 | 2.783470 | 0.0146 |
D(LREM(-7),2) | 0.460131 | 0.234099 | 1.965543 | 0.0695 |
D(LREM(-8),2) | 0.481311 | 0.150425 | 3.199664 | 0.0064 |
D(LREM(-9),2) | 0.220419 | 0.114372 | 1.927212 | 0.0745 |
C | 1.600755 | 0.553388 | 2.892646 | 0.0118 |
@TREND(“1985”) | -0.043451 | 0.016335 | -2.659974 | 0.0187 |
R-squared | 0.861249 | Mean dependent var | 0.035087 | |
Adjusted R-squared | 0.752231 | S.D. dependent var | 0.640952 | |
S.E. of regression | 0.319043 | Akaike info criterion | 0.857054 | |
Sum squared resid | 1.425036 | Schwarz criterion | 1.437714 | |
Log likelihood | 0.858293 | Hannan-Quinn criter. | 1.024263 | |
F-statistic | 7.900035 | Durbin-Watson stat | 2.082620 | |
Prob(F-statistic) | 0.000288 |
Null Hypothesis: D(LGE) has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -5.544565 | 0.0003 | ||
Test critical values: | 1% level | -4.243644 | ||
5% level | -3.544284 | |||
10% level | -3.204699 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Dependent Variable: D(LGE,2) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:34 | ||||
Sample (adjusted): 1987 2021 | ||||
Included observations: 35 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LGE(-1)) | -0.902617 | 0.162793 | -5.544565 | 0.0000 |
C | 0.345311 | 0.079511 | 4.342912 | 0.0001 |
@TREND(“1985”) | -0.008924 | 0.002921 | -3.054524 | 0.0045 |
R-squared | 0.492759 | Mean dependent var | 0.002947 | |
Adjusted R-squared | 0.461056 | S.D. dependent var | 0.211520 | |
S.E. of regression | 0.155283 | Akaike info criterion | -0.805318 | |
Sum squared resid | 0.771610 | Schwarz criterion | -0.672003 | |
Log likelihood | 17.09307 | Hannan-Quinn criter. | -0.759298 | |
F-statistic | 15.54319 | Durbin-Watson stat | 1.973077 | |
Prob(F-statistic) | 0.000019 |
Null Hypothesis: D(LTRAO) has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -7.439815 | 0.0000 | ||
Test critical values: | 1% level | -4.243644 | ||
5% level | -3.544284 | |||
10% level | -3.204699 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LTRAO,2) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:38 | ||||
Sample (adjusted): 1987 2021 | ||||
Included observations: 35 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LTRAO(-1)) | -1.268288 | 0.170473 | -7.439815 | 0.0000 |
C | 0.181922 | 0.102357 | 1.777325 | 0.0850 |
@TREND(“1985”) | -0.006706 | 0.004699 | -1.427207 | 0.1632 |
R-squared | 0.633710 | Mean dependent var | -0.004016 | |
Adjusted R-squared | 0.610817 | S.D. dependent var | 0.440349 | |
S.E. of regression | 0.274710 | Akaike info criterion | 0.335614 | |
Sum squared resid | 2.414897 | Schwarz criterion | 0.468930 | |
Log likelihood | -2.873252 | Hannan-Quinn criter. | 0.381635 | |
F-statistic | 27.68127 | Durbin-Watson stat | 1.939645 | |
Prob(F-statistic) | 0.000000 |
Null Hypothesis: D(LFDI) has a unit root | ||||
Exogenous: Constant, Linear Trend | ||||
Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
t-Statistic | Prob.* | |||
Augmented Dickey-Fuller test statistic | -7.752496 | 0.0000 | ||
Test critical values: | 1% level | -4.243644 | ||
5% level | -3.544284 | |||
10% level | -3.204699 | |||
*MacKinnon (1996) one-sided p-values. | ||||
Augmented Dickey-Fuller Test Equation | ||||
Dependent Variable: D(LFDI,2) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 09:41 | ||||
Sample (adjusted): 1987 2021 | ||||
Included observations: 35 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LFDI(-1)) | -1.366796 | 0.176304 | -7.752496 | 0.0000 |
C | 0.147294 | 0.267617 | 0.550392 | 0.5859 |
@TREND(“1985”) | -0.004290 | 0.012448 | -0.344600 | 0.7326 |
R-squared | 0.653083 | Mean dependent var | 0.064627 | |
Adjusted R-squared | 0.631401 | S.D. dependent var | 1.219841 | |
S.E. of regression | 0.740594 | Akaike info criterion | 2.319088 | |
Sum squared resid | 17.55134 | Schwarz criterion | 2.452404 | |
Log likelihood | -37.58404 | Hannan-Quinn criter. | 2.365108 | |
F-statistic | 30.12061 | Durbin-Watson stat | 1.724867 | |
Prob(F-statistic) | 0.000000 |
Appendix 3- Lag Criteria
VAR Lag Order Selection Criteria | ||||||
Endogenous variables: LRGDP LREM LFDI LTRAO LKAP LGE | ||||||
Exogenous variables: C | ||||||
Date: 07/17/23 Time: 12:19 | ||||||
Sample: 1985 2021 | ||||||
Included observations: 35 | ||||||
Lag | LogL | LR | FPE | AIC | SC | HQ |
0 | -97.27561 | NA | 1.47e-05 | 5.901464 | 6.168095 | 5.993505 |
1 | 98.05109 | 312.5227* | 1.69e-09* | -3.202920 | -1.336502* | -2.558632* |
2 | 134.2008 | 45.44539 | 2.03e-09 | -3.211476* | 0.254728 | -2.014943 |
* indicates lag order selected by the criterion | ||||||
LR: sequential modified LR test statistic (each test at 5% level) | ||||||
FPE: Final prediction error | ||||||
AIC: Akaike information criterion | ||||||
SC: Schwarz information criterion | ||||||
HQ: Hannan-Quinn information criterion |
Appendix 4-Bound Test
ARDL Bounds Test | ||||
Date: 07/17/23 Time: 12:12 | ||||
Sample: 1986 2021 | ||||
Included observations: 36 | ||||
Null Hypothesis: No long-run relationships exist | ||||
Test Statistic | Value | k | ||
F-statistic | 7.107055 | 5 | ||
Critical Value Bounds | ||||
Significance | I0 Bound | I1 Bound | ||
10% | 2.26 | 3.35 | ||
5% | 2.62 | 3.79 | ||
2.5% | 2.96 | 4.18 | ||
1% | 3.41 | 4.68 | ||
Test Equation: | ||||
Dependent Variable: D(LRGDP) | ||||
Method: Least Squares | ||||
Date: 07/17/23 Time: 12:12 | ||||
Sample: 1986 2021 | ||||
Included observations: 36 | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LFDI) | -0.011448 | 0.007052 | -1.623373 | 0.1161 |
D(LKAP) | -0.010160 | 0.054374 | -0.186845 | 0.8532 |
C | 4.233966 | 2.065928 | 2.049426 | 0.0503 |
LREM(-1) | -0.019927 | 0.006948 | -2.867842 | 0.0079 |
LFDI(-1) | 0.005802 | 0.008531 | 0.680154 | 0.5022 |
LTRAO(-1) | -0.000739 | 0.016643 | -0.044421 | 0.9649 |
LKAP(-1) | -0.102789 | 0.078767 | -1.304964 | 0.2029 |
LGE(-1) | 0.055276 | 0.012053 | 4.586202 | 0.0001 |
LRGDP(-1) | -0.100238 | 0.040629 | -2.467168 | 0.0203 |
R-squared | 0.634934 | Mean dependent var | 0.040251 | |
Adjusted R-squared | 0.526766 | S.D. dependent var | 0.037410 | |
S.E. of regression | 0.025735 | Akaike info criterion | -4.269613 | |
Sum squared resid | 0.017882 | Schwarz criterion | -3.873733 | |
Log likelihood | 85.85303 | Hannan-Quinn criter. | -4.131440 | |
F-statistic | 5.869907 | Durbin-Watson stat | 2.740597 | |
Prob(F-statistic) | 0.000221 |
Appendix 5-Regression Result
ARDL Cointegrating And Long Run Form | ||||
Dependent Variable: LRGDP | ||||
Selected Model: ARDL(1, 0, 0, 1, 0, 1) | ||||
Date: 07/17/23 Time: 10:10 | ||||
Sample: 1985 2021 | ||||
Included observations: 36 | ||||
Cointegrating Form | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(LREM) | 0.023948 | 0.005655 | -4.234491 | 0.0002 |
D(LGE) | 0.055872 | 0.008685 | 6.432967 | 0.0000 |
D(LFDI) | 0.002051 | 0.006242 | 0.328625 | 0.7450 |
D(LTRAO) | 0.004830 | 0.015054 | 0.320852 | 0.7508 |
D(LKAP) | 0.017992 | 0.046978 | 0.382977 | 0.7047 |
CointEq(-1) | -0.073418 | 0.030444 | -2.411607 | 0.0230 |
Cointeq = LRGDP – (-0.3262*LREM + 0.7610*LGE + 0.1561*LFDI + 0.0658 | ||||
*LTRAO -1.4800*LKAP + 57.0224 ) | ||||
Long Run Coefficients | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LREM | 0.326184 | 0.150211 | -2.171503 | 0.0388 |
LGE | 0.761012 | 0.254502 | 2.990197 | 0.0059 |
LFDI | 0.156113 | 0.137997 | 1.131280 | 0.2679 |
LTRAO | 0.065789 | 0.221705 | 0.296741 | 0.7689 |
LKAP | -1.479962 | 1.291193 | -1.146197 | 0.2618 |
C | 57.022388 | 38.848810 | 1.467803 | 0.1537 |
Appendix 6-Autocorrelation Test
Breusch-Godfrey Serial Correlation LM Test: | ||||
F-statistic | 1.482754 | Prob. F(2,25) | 0.2463 | |
Obs*R-squared | 3.817498 | Prob. Chi-Square(2) | 0.1483 | |
Test Equation: | ||||
Dependent Variable: RESID | ||||
Method: ARDL | ||||
Date: 07/17/23 Time: 12:22 | ||||
Sample: 1986 2021 | ||||
Included observations: 36 | ||||
Presample missing value lagged residuals set to zero. | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
LRGDP(-1) | 0.013112 | 0.030896 | 0.424382 | 0.6749 |
LREM | 0.001106 | 0.005701 | 0.194019 | 0.8477 |
LFDI | 0.002139 | 0.006423 | 0.333012 | 0.7419 |
LFDI(-1) | 0.001162 | 0.006458 | 0.179928 | 0.8587 |
LTRAO | 0.001776 | 0.015054 | 0.117952 | 0.9070 |
LKAP | -0.000337 | 0.046197 | -0.007302 | 0.9942 |
LKAP(-1) | 0.001561 | 0.046376 | 0.033664 | 0.9734 |
LGE | -0.004298 | 0.008902 | -0.482837 | 0.6334 |
C | -0.175816 | 1.752371 | -0.100330 | 0.9209 |
RESID(-1) | -0.353948 | 0.209731 | -1.687629 | 0.1039 |
RESID(-2) | -0.199009 | 0.233224 | -0.853295 | 0.4016 |
R-squared | 0.106042 | Mean dependent var | -6.66E-16 | |
Adjusted R-squared | -0.251542 | S.D. dependent var | 0.019419 | |
S.E. of regression | 0.021725 | Akaike info criterion | -4.574237 | |
Sum squared resid | 0.011799 | Schwarz criterion | -4.090384 | |
Log likelihood | 93.33626 | Hannan-Quinn criter. | -4.405359 | |
F-statistic | 0.296551 | Durbin-Watson stat | 2.009464 | |
Prob(F-statistic) | 0.975452 |
Appendix 7-Causality Test
Pairwise Granger Causality Tests | |||
Date: 07/17/23 Time: 16:06 | |||
Sample: 1985 2021 | |||
Lags: 2 | |||
Null Hypothesis: | Obs | F-Statistic | Prob. |
LREM does not Granger Cause LRGDP | 35 | 0.71410 | 0.4978 |
LRGDP does not Granger Cause LREM | 3.65566 | 0.0379 | |
LTRAO does not Granger Cause LRGDP | 35 | 1.55361 | 0.2280 |
LRGDP does not Granger Cause LTRAO | 0.16742 | 0.8466 | |
LFDI does not Granger Cause LRGDP | 35 | 1.57685 | 0.2233 |
LRGDP does not Granger Cause LFDI | 1.40996 | 0.2599 | |
LGE does not Granger Cause LRGDP | 35 | 3.69465 | 0.0368 |
LRGDP does not Granger Cause LGE | 3.34546 | 0.0488 | |
LKAP does not Granger Cause LRGDP | 35 | 1.53816 | 0.2312 |
LRGDP does not Granger Cause LKAP | 5.98256 | 0.0065 | |
LTRAO does not Granger Cause LREM | 35 | 3.31907 | 0.0499 |
LREM does not Granger Cause LTRAO | 0.62869 | 0.5402 | |
LFDI does not Granger Cause LREM | 35 | 1.14978 | 0.3303 |
LREM does not Granger Cause LFDI | 0.94256 | 0.4009 | |
LGE does not Granger Cause LREM | 35 | 2.70581 | 0.0831 |
LREM does not Granger Cause LGE | 1.45487 | 0.2494 | |
LKAP does not Granger Cause LREM | 35 | 0.15907 | 0.8537 |
LREM does not Granger Cause LKAP | 4.47478 | 0.0199 | |
LFDI does not Granger Cause LTRAO | 35 | 1.72259 | 0.1958 |
LTRAO does not Granger Cause LFDI | 0.68805 | 0.5103 | |
LGE does not Granger Cause LTRAO | 35 | 0.48906 | 0.6180 |
LTRAO does not Granger Cause LGE | 0.90505 | 0.4153 | |
LKAP does not Granger Cause LTRAO | 35 | 0.49089 | 0.6169 |
LTRAO does not Granger Cause LKAP | 0.57046 | 0.5713 | |
LGE does not Granger Cause LFDI | 35 | 0.91162 | 0.4127 |
LFDI does not Granger Cause LGE | 2.04204 | 0.1474 | |
LKAP does not Granger Cause LFDI | 35 | 0.72375 | 0.4932 |
LFDI does not Granger Cause LKAP | 0.47403 | 0.6271 | |
LKAP does not Granger Cause LGE | 35 | 0.63985 | 0.5344 |
LGE does not Granger Cause LKAP | 5.01771 | 0.0132 |
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