Journal of Agronomy and Research

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ISSN: 2639-3166
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    The Impact of Migration and Remittances on Employment in Agriculture in the Gambia

    Ebrima K. Ceesay 1  

    1The University of the Gambia, Banjul, the Gambia

    Abstract

    For economic growth and development in any WE African country the GDP progress is depending on the key push-pull factors as migration, personal remittances received, bilateral aids and, absolutely, employment in agriculture which is about 1/3 of the population and not a predominant and protected minority as happens in the industrialized EU and North America. In order to represent the framework of the reciprocal dependencies the present study used the statistics of Gambia from WDI covering the periods from 1960 to 2017 by applying linear regression models. The results confirmed that migration and remittances have significant positive impact on employment in agriculture because new investment in agriculture created new skilled and unskilled employment.  The results also found out that employment in agriculture has negative and significant impacts on foreign aids: 10% increase in migration, increases foreign aid by 50.3%. Increasing 10% of remittance, increase economic growth by 0.14% but 10% increases in employment in agriculture, decrease economic growth by 0.04%. To face globalization the economy of the Gambia should use the foreign aid to improve agriculture production and productivity thereby increase economic growth through human capital theory of migration, skilled migration, export and food security, the study recommends.

    Author Contributions
    Received 13 Apr 2020; Accepted 03 Dec 2020; Published 05 Dec 2020;

    Academic Editor: Giorgio Masoero, Accademia di Agricoltura di Torino, Italy.

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright ©  2020 Ebrima K. Ceesay,

    License
    Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Citation:

    Ebrima K. Ceesay (2020) The Impact of Migration and Remittances on Employment in Agriculture in the Gambia. Journal of Agronomy and Research - 3(2):1-16.

    Download as RIS, BibTeX, Text (Include abstract )

    DOI 10.14302/issn.2639-3166.jar-20-3318

    Introduction

    The population of the Gambia is almost 2 million inhabitants, and one of the smallest and smiling coast countries in West Africa. Thus, it is smallest, but illegal and legal ways of migration shows a major part in the society of Gambia.

    The contribution of agriculture is not that big compare to other sectors at 1/3 of the GDP of Gambia. This may be due to the fact that agriculture share of GDP lead to lower rainfall, high temperature. In the Gambia, less than half of the arable land is cultivated in the Gambia. The Gambia produces mainly peanuts, rice, millet, sorghum, corn, sesame, cassava, palm kernel, cattle, sheep and goats1.

    In Gambia, only less than 20% of the proposed budget for 2020 was allocated to agriculture. This cannot do anything for adaptation and mitigation strategies for the existing and future agricultural development.

    The development intervention (proxy employment in agriculture) is developed by IOM of Gambia in order to facilitate, protect, reintegrate and assist the migrants that were returned. The returnee benefits lots of facilities such as skills transfer, soft skills given. Personal remittance received have positive and significant impacts on economics in Gambia by using Vector error correction model both in the short run dynamics and in the long run 1,2. As it can be seen in both the Figure 1 (Figure 2 etc….)9 and the Table 1. In 2006 more migration occurred in the Gambia and the remittance as percentage of GDP was at 9.74%. From 2007 to 2008 constant growth of remittance occurred in Gambia. From 2008 to 2009 remittance inflows begin to have an increasing or upward trend for the Gambia. Further, export does not causes growth in Gambia2.

    Figure 1. Remittances’ inflow to GDP
    Figure 1.

    Figure 2. The mean point of the regression line
    Figure 2.

    Table 1. Percentage of Remittance Inflows to GDP for Gambia
    DATE Percentage of Gambians' remittance of GDP
    1/1/2006 9.74
    1/1/2007 6.97
    1/1/2008 6.97
    1/1/2009 8.86

    Sources: Retrieved from FRED Economic data, World Bank October 2019

    For Berthélemy, et al.3 Using World Bank bilateral data to study the effect of total aid on migration-push factor and found out that 10 percent increase in aid, increase migration by 1.5 percent. More aid, more migration of the youth because of the diversification of the aid to unproductive policy intervention. Thus, in the micro studied done on social protection interventions by4, revealed no agreement in the literature with development interventions having related with bidirectional effects. In the review of the relevant literature was identified only a migration development intervention by the New Zealand Recognized Seasonal Employment Programme date, which have direct influences on firms or employers’ migrants and countries of origin 5. The evidence base research in connection to migration interventions was found to be reliably fragile in most literatures. The lack of flexibility study to know the demand of skills or unskilled migrates, the labor market evaluation, gender sensitive policy for migration. This is very important in-order to gives unnecessary skills and training for displaced workers in the communities in which migration is the highest impacts factor. Impacts of migration interventions and development, as the studied of meta-analysis of energetic labour market programmesrevealed that wage subsidies is directly positive impact on labour market outcomes for immigrants more than the training 6.

    Experimental Procedure

    In this study, the secondary data were collected from WDI. After the data was cleaned up and arranged in excel format. The ID was selected to fill the missing values by using interpolation. The results from the data were imported into Eview, R and Stata. As indicated in Figure 2 below, why we applied linear regression methods; Firstly, after plotting the variables the regression line passed through the mean point as above and therefore we can generalize this to any linear regression line. Secondly, after excluding one outlier in the model, the results are statistically significant with lowest p-values. The mean point is indicated in yellow circle

    Materials and Methods

    Simple linear Regression Model

    Linear regression is a technique to quantify the relationship between the dependent variable and only one independent variable

    y = β0 1x

    In this equation, y is the dependent variable, is the variable on the vertical axis of the graph or the explained variable, while x, represents the variable on the horizontal axis or the independent variable. The value β0 (which can be negative, positive or zero) is called the intercept, while the value β1 (which can be positive or negative) is called ‘slope’ or ‘coefficient of regression’ or rate of change. Both, β 1 and β0 can be calculated from the following equations:

    and

    Multiple Linear Regression Model

    This paper is based on multiple regression analysis in which two or more variables are modeling and analyzing. The multiple regression analysis is to describe the relationship between one dependent variables called response variable and several independent variables called exogenous variables7,8, identified that and at the same time the spreads in which some independent variables have on the dependent variable.

    The multiple regression models can be much more accurate than the mono-factorial regression model. In our study, the dependent variable for the multiple regression analysis is development intervention (employment in agriculture) and the independent variables are net migration, net official aid received, economic growth, and net official development assistance and official aid received. All of the variables were used for the analysis throughout the periods from 1969-2016. Data were collected from WDI and used to obtain the regression equation and calculate the standard error, the t-statistic, the p-value and the R-squared. All these variables measure the goodness of fit or accuracy of the estimates of the model, especially the R-squared, which is called coefficient of determination in which the proportion of how much the total variance is explained by the independent variables in the model. Other tests were also used like F- statistic, t-ratios and p-values to test the hypothesis and indicate the rejection region in the model with degrees of freedom.

    If y is a dependent variable and x1,…, xk are independent variables, then the multiple regression model provides a prediction or forecast of y given xi of the form



    Where the assumption on the error terms are exactly as in simple linear regression. In order to estimate the coefficients and se (standard error of the estimate), one follows a process very similar to that followed in the case of only one predictor value. The left hand size variable is the dependent variable and the right hand size variable is the independent variables. The paper used the multiple regression analysis to direct predict the values of development intervention (employment in agriculture) to migration in Gambia.

    Empirical Model

    Do aid/development interventions/personal remittance affect irregular migration specifically? This is linear regression between aid and net migration and between development intervention and net migration decision and between personal remittances received and net migration.

    Does net migration affect total bilateral aid received in Gambia?



    Does personal remittance received affect net migration?

    (2)

    Do personal remittances received affect economic growth?



    Does net migration affect economic growth?



    Does net migration impacts on development intervention, when we considered employment in agriculture as a proxy for development intervention?



    Does development affect aid?



    Does aid affect economic growth?



    Multiple linear regression models between development intervention (proxy agriculture employment), net migration, personal remittances, bilateral aids and economic growth are given below.

    Do the net migration, net official aid received, personal remittances received and economic growth affect development intervention (employment in agriculture)?

    Does aid affect personal remittances received, net migration, development intervention and economic growth?



    Does net migration affect personal remittances received, aids, development intervention and economic growth?

    NMit



    Do personal remittances received affect net migration, aids, development intervention and economic growth?





    Where:

    EADI: Development Intervention (Proxy employment in agriculture)

    NM: Net migration

    EG: GDP growth

    Taid: total aid which is Net bilateral aid flows from DAC donors, United States+ Net bilateral aid flows from DAC donors, European Union institutions+ Net official development assistance and official aid received.

    PR: Personal remittance received

    Data and Descriptive Statistics

    A brief descriptive of the data, the name of the variables, data sources and comment used in this study are presented in the Table 2 and to cleaned the data for missing data, where interpolation was used to fill the missing values, because its advantages over the other methods is that, Linear interpolation is quick and easy to use, and may be adequate for well-resolved data comparing to Polynomial interpolation, Cubin Spline Interpolation. The periods 2018-2022 was used for forecasting purpose only. This means that the aim was to know the forecasted economic growth, net migration, personal remittance, employment in agriculture and bilateral aid received in Gambia for the upcoming years.

    Table 2. Sources of data.
    Name of Variable Source Comment
    GDP Current(US$) WDI Current GDP
    Net Migration WDI Net migration
    Personal remittance received WDI Personal remittance received($)
    Employment in agriculture WDI Employment in agriculture
    Bilateral aid WDI Bilateral aid received

    Results

    For all the following tests, 0.05 level of significance was used. The decision rule is: If the value of the probability is higher than the 0.05 level, then we accept the null hypothesis H0 .If the value of the probability is smaller than the 0.05 level, and then we reject the null hypothesis H0. To test the correlation, the test of hypothesis is as follows: H0 : X and Y are not correlated and Ha : X and Y are correlated.

    In the Table 3, the linear correlation coefficient of 0.636 has a p-value of 0.0000 indicating that Development Intervention when we take employment in agriculture as a proxy and economic growth of Gambia are positively correlated. Thus, the linear correlation coefficient of 0.057 has a p-value of 0.668 meaning that economic growth and net migration are not correlated in Gambia, because the p-value = 0.668>0.05. For the linear correlation coefficient of the relationship between net migration and personal remittance received are positively correlated. Net migration increases, personal remittances received from outside rises at approximately 58% the study confirmed. Current Total aid from donors outside has positively correlated with net migration, personal remittances received, economic growth and even development intervention when we considered employment in agriculture as proxy, because their p-values are less than 0.05.

    Table 3. Linear Correlation Coefficient test of data in Table 8.
    Covariance Analysis: Ordinary          
    Sample: 1960 2017          
    Included observations: 58
    Correlation          
    Probability          
      EADI EG NM PR TAID
    EADI 1.0000        
    EG -0.636 1.0000      
      0.0000        
    NM 0.141 0.0574 1.0000    
      0.2904 0.6685      
    PR -0.726 0.299 -0.580 1.000  
      0.0000 0.0224 0.000    
    TAID -0.718 0.320 -0.260 0.657 1.000
      0.0000 0.0141 0.042 0.0000  

    Sources: Author’s own computation by retrieved data from World Bank Using Eview 10

    From Table 4. Below, the results found out that personal remittances received from outside has significant positive impacts on economic growth in Gambia. 10% increase in personal remittances from abroad, raises the economic growth in Gambia by approximately 0.11%.

    Table 4. Estimation of Economic Growth model.
    Dependent Variable: EG        
    Method: Least Squares        
    Sample (adjusted): 1960 - 2017 
    Included observations : 58 after adjustments     
    Variable Coefficient Std. Error t-Statistic Prob.
    C -5.837 2.718 -2.147 0.03**
    PR 1.099 0.467 2.349 0.02**

    Sources: Author’s own computation by retrieved data from World Bank Using Eview.

    From Table 5, in Gambia, migration has positive, but fairly small impact on economic growth. The results of the study confirmed that migration does have impacts on economic growth. If migration increases by 0%, economic growth in Gambia will reduce by 1.8%. This reflects that migration has impact on economic growth of Gambia. If no migration, Youth migration both skilled migrates and unskilled migrates send remittances back home and that remittances is used to do social function, households consumption expenditure-children school fees, health care for children, food, clothing and some used to build houses etc.

    Table 5. Estimation of Economic Growth model, variables C, NM.
    Dependent Variable: EG        
    Method: Least Squares        
    Sample (adjusted): 1960 2017        
    Included observation: 58 after adjustments
    Variable Coefficient Std. Error t-Statistic Prob.
    C -1.832 2.235 -0.819 0.4158
    NM 5.05E-05 0.000117 0.43053 0.6685

    Sources: Author’s own computation by retrieved data from World Bank Using Eview

    Though, foreign aids increase the economic growth in most of the poor countries if aids are the only sources of funding. If total bilateral aids received are utilized in good way, it can be an important sources of income to reduces poverty and improve economic development of Gambia.

    As the results generated in Table 6 showed that the statistical analysis of migration has non-significant effect on employment in agriculture. As peoples migrates, those left behind there will be scarcity of employment in agriculture and in- turn will negatively impacts on food security and economic growth. Not only that, but those left behind are mostly women, children, elderly and disable peoples and in that their contribution to agriculture, food security and nutrition will be minimal. In the likelihoods they can be affected by poverty and hunger, malnutrition and diseases, lack of education especially children etc. That is one sided of the story. Another sides of migrates remittances also help those left behind to have quality education, good health, food security, new agriculture technology etc. In the most of the literatures that links migration and employment in agriculture found out that food security and migration can be direct, due to food insecurity and fluctuations of income.

    Table 6. Estimation of Employment in Agriculture model, variable EADI.
    Dependent Variable: EADI        
    Method: Least Squares        
    Sample (adjusted): 1960 2017        
    Included observations: 58 after adjustments
    Variable Coefficient Std. Error t-Statistic Prob.
    C 31.255 0.168 185.883 0.0000
    NM 9.4E-06 8.8E-06 1.067 0.2904

    Sources: Author’s own computation by retrieved data from World Bank Using Eview

    The results from Table 7 explained that migration and economic growth have non- significant impacts on total aids in Gambia, while remittance and employment in agriculture showed are significant impacts on the total aids in Gambia. 10% increase in migration, increases total aids by 50.3%.

    Table 7. Estimation of Total Bilateral aid received model, variable taid.
    Dependent Variable: taid        
    Method: Least Squares        
    Sample (adjusted): 1960 2017        
    Included observations: 58 after adjustments 
    Variable Coeff. Std. Error t-Statist Prob.
    NM 503.446 443.0 1.136 0.2608
    EADI 770450. 372676 2.067 0.04**
    EG 411198. 439147.1 0.936 0.3533
    PR 1033062 1890886 5.463 0.0000

    Sources: Author’s own computation by retrieved data from World Bank Using Eview

    The study confirmed that the total aids, employment in agriculture and economic growth has significant impacts on remittance. From the existing literatures, migration and remittances have both direct and indirect effects on the welfare of the population in the migrant sending countries.

    There is empirical evidence that remittances contribute to economic growth, through their positive impact on consumption, savings, and investment in macroeconomic. Remittances can also have negative impact on growth in recipient countries by reducing incentives to work, and therefore reducing labor supply. From Table 7, in Gambia remittance, migration and total aids have significant impacts on economic growth. An increase of 10% of remittance, increase economic growth by 0.14%. Only employment in agriculture has negative impacts on economic growth.10% increases in employment in agriculture, decrease economic growth by 0.04%. The economy of Gambia should deal more on export to improve agricultural development. For foreign aid can have both negatively and positively contribution to economic growth depending on the utilization of the funds.

    Discussion

    One of the studies that looks at the impact of migration on economic growth for 22 OECD countries between 1986 and 2006 proved an optimistic, but small impact of the human capital brought by migrants on economic growth. The involvement of immigrants to human capital accumulation tends to counteract the impact of population increase on capital per worker, but the net effect is fairly small. An increase of 50% in net migration of the foreign-born makes less than one tenth of a percentage point difference in productivity growth 8.

    Thus, remittances improve the likelihoods of reduction in poverty, increases education enrolment, improve investment, decreases food in-security, improve health. The results confirmed that remittances benefits both individuals, entire countries and the continents’ like in sub-Saharan Africa, Asia etc.

    In their parts, Galiani, et al.9 confirmed that 1% percent increase in the aid to gross net income ratio increases annual real per capita GDP growth by 0.031%. A large number of studies to evaluate the impact of bilateral aid in promoting economic growth and development of recipient countries have been carried out. The results of these studies are differed, depending on the methods, country(ies) and even the time periods. For example, numerous studies 10-13, deliver evidence that bilateral aid have optimistic impact on growth. Adam 14,, and Carden 15find evidence for unimportant and even negative role of aid on economic growth. Several studies 16-19 provided an evidence that positive role of aid on economic growth can be realized only when certain conditions such as good macroeconomic condition, political stability and less corruption exist.

    A cross-country study of 71 developing countries found that a 10% increase in per capita, official international remittances will lead to 3.5 percent decline in the share of people living in poverty 20. Evidence from Latin America, Africa, South Asia, and other regions suggests that remittances reduce the depth and severity of poverty, as well as indirectly stimulate economic activity 21.

    Migration is a choice that influences the welfare of the household, the home community, and in the end the whole economy in various ways 22. The welfare implications of migration on the origin country are most often, though not always, sizable and positive. The economic impact of migration has been intensively studied but still often driven by ill-informed insights, which, in turn, can lead to public resentment towards migration. These negative opinions risk efforts to adapt migration policies to the new economic and demographic challenges facing many countries 23. Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9. Table 8.

    Table 8. Variables and the observation used in this study.
    Country Name Time taid EADIi PRI EGI NMI
    Gambia, The 1960 540000 33.979 -0.46316 -67.033 2033.6
    Gambia, The 1961 1430000 33.85 -0.42626 -57.4569 3657.8
    Gambia, The 1962 2550000 33.721 -0.38937 -47.8807 5282
    Gambia, The 1963 5120000 33.592 -0.35248 -38.3046 6906.2
    Gambia, The 1964 2550000 33.463 -0.31559 -28.7284 8530.4
    Gambia, The 1965 4370000 33.334 -0.2787 -19.1523 10154.6
    Gambia, The 1966 3850000 33.205 -0.24181 -9.57614 11778.8
    Gambia, The 1967 3270000 33.076 -0.20492 0 13403
    Gambia, The 1968 6260000 32.947 -0.16803 9.576144 13603
    Gambia, The 1969 4670000 32.818 -0.13113 2.435523 13803
    Gambia, The 1970 1310000 32.689 -0.09424 6.153847 14003
    Gambia, The 1971 4530000 32.56 -0.05735 -0.06588 14203
    Gambia, The 1972 5880000 32.431 -0.02046 0.241705 14403
    Gambia, The 1973 7440000 32.302 0.016432 9.250329 14438.4
    Gambia, The 1974 12200000 32.173 0.053323 5.878794 14473.8
    Gambia, The 1975 8850000 32.044 0.090214 12.39343 14509.2
    Gambia, The 1976 12330000 31.915 0.127106 7.351226 14544.6
    Gambia, The 1977 22480000 31.786 0.163997 3.439576 14580
    Gambia, The 1978 38280000 31.657 0.200889 6.316446 14640.6
    Gambia, The 1979 42380000 31.528 0.23778 -1.32818 14701.2
    Gambia, The 1980 66060000 31.399 0.162154 6.27008 14761.8
    Gambia, The 1981 86120000 31.27 0.132274 3.321894 14822.4
    Gambia, The 1982 58770000 31.141 0.08693 -0.76458 14883
    Gambia, The 1983 48390000 31.012 0.328483 10.88323 24025.8
    Gambia, The 1984 65790000 30.883 0.867147 3.535257 33168.6
    Gambia, The 1985 60220000 30.754 1.40581 -0.81226 42311.4
    Gambia, The 1986 1.21E+08 30.625 1.944473 4.091071 51454.2
    Gambia, The 1987 1.21E+08 30.496 2.483137 2.454333 60597
    Gambia, The 1988 1.03E+08 30.367 3.0218 4.476827 49001.6
    Gambia, The 1989 1.16E+08 30.238 3.560464 5.895722 37406.2
    Gambia, The 1990 1.15E+08 30.109 4.099127 3.558879 25810.8
    Gambia, The 1991 1.15E+08 29.98 4.637791 3.107039 14215.4
    Gambia, The 1992 1.34E+08 29.851 5.176454 3.378689 2620
    Gambia, The 1993 1.06E+08 29.847 5.715117 3.012101 1182
    Gambia, The 1994 87510000 30.405 6.253781 0.154346 -256
    Gambia, The 1995 54770000 30.689 6.792444 0.881848 -1694
    Gambia, The 1996 42520000 30.329 7.331108 2.223546 -3132
    Gambia, The 1997 45510000 30.458 7.869771 4.899999 -4570
    Gambia, The 1998 51580000 30.218 8.408434 3.499999 -3352.6
    Gambia, The 1999 39500000 30.484 8.947098 6.399999 -2135.2
    Gambia, The 2000 60440000 30.301 9.485761 5.5 -917.8
    Gambia, The 2001 58320000 30.387 10.02443 5.8 299.6
    Gambia, The 2002 73390000 30.988 10.56309 -3.25 1517
    Gambia, The 2003 70740000 30.929 11.10175 6.87 -1873.6
    Gambia, The 2004 68060000 30.905 6.315495 7.05 -5264.2
    Gambia, The 2005 64710000 31.451 5.770552 -2.35173 -8654.8
    Gambia, The 2006 81310000 30.959 6.049967 -0.55558 -12045.4
    Gambia, The 2007 1.08E+08 30.718 4.349639 3.04325 -15436
    Gambia, The 2008 1.17E+08 31.083 4.149896 6.255906 -15436
    Gambia, The 2009 1.45E+08 30.883 5.502995 6.665724 -15436
    Gambia, The 2010 1.50E+08 30.903 7.496898 5.908336 -15436
    Gambia, The 2011 1.76E+08 30.784 6.480762 -8.13044 -15436
    Gambia, The 2012 1.62E+08 30.539 7.515829 5.241569 -15436
    Gambia, The 2013 1.32E+08 30.23 7.984044 2.872769 -15436
    Gambia, The 2014 1.17E+08 30.086 11.20583 -1.40738 -15436
    Gambia, The 2015 1.19E+08 29.943 9.865488 4.058074 -15436
    Gambia, The 2016 1.01E+08 29.997 14.13313 1.94336 -15436
    Gambia, The 2017 3.38E+08 29.94 15.162 4.822611 -15436

    Figure 3. Plot of Net Migration
    Figure 3.

    Figure 4. Plot of personal remittances received
    Figure 4.

    Figure 5. Plot of Total Aid (taid)
    Figure 5.

    Figure 6. Plot of Employment In Agriculture
    Figure 6.

    Figure 7. Plot of Economic Growth
    Figure 7.

    Figure 8. CUSUM test
    Figure 8.

    Figure 9. Residual test
    Figure 9.

    Conclusion

    When a multiple regression analysis was used, the results confirmed that migration and remittances have significant impact on employment in agriculture in the Gambia. The impact is larger for remittances than migration itself. 10% increase in remittance, increased the employment in agriculture by approximately 0.29%, if other variables remained constant. Bilateral aid has fairly positive significant impacts on employment in agriculture, while economic growth has significant negative impact on employment in agriculture. This is confirmed in the study done by Clemens 24 disbelief about the ability of development aid to affect large variations in youth employment. There is empirical evidence that remittances contribute to economic growth, through their positive impact on consumption, savings, and investment in macroeconomic. Remittances can also have negative impact on growth in recipient countries by reducing incentives to work, and therefore reducing labor supply. From table 17, in Gambia; remittance, migration and total aids have positive significant impacts on economic growth. Increases 10% of remittance, increase economic growth by 0.14%. Only employment in agriculture has negative impacts on economic growth.10% increase in employment in agriculture, decreases economic growth by 0.04%.The economy of Gambia should deal more on better utilizations of aids so that to improve agricultural development.

    Acknowledgements

    Thanks to the anonymous referees and to the editor Giorgio Masoero for the valuable advice.

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