Impact of local processing of agricultural raw materials on job creation in the West African monetary and economic union

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Impact of local processing of agricultural raw materials on job creation in the West African monetary and economic union

Paul-Alfred Kouakou Kouakou

Peleforo Gon Coulibaly University

Republic of Ivory Coast

Impact of local processing of agricultural raw materials on job creation in the West African monetary and economic union

Purpose. This work evaluates the relationship between the processing of agricultural raw materials and the level of employment in the West African Monetary and Economic Union (WAEMU).

Methodology / approach. The Dynamic Common Correlated Effects (DCCE) estimation proposed by Chudik and Pesaran (2015) is used. The data for this study come from the World Bank and the International Labour Office and cover the Consumer Price Index, human capital, trade openness, foreign direct investment, and agricultural manufacturing industry for the period 19902019.

Results. The results show that agricultural manufacturing, human capital and foreign direct investment have positive influences on job creation in WAEMU countries in the long term, even if this impact remains small. On the other hand, trade openness and inflation lead to a decrease in employment level. Indeed, the specialization in the export of unprocessed agricultural raw materials with low benefit, the massive import of consumer goods and the delay in terms of global competitiveness in most of these countries justify such results. Therefore, in order to boost job creation, it would be expedient to accelerate the process of developing local industries and promote the production of consumer goods.

Originality / scientific novelty. Previous studies on local processing of agricultural raw materials have mainly focused on the production process and related constraints. However, very little work has been done on their effects on economic growth and job creation. This study fills this gap. It extends the existing literature on the causal relationship between local processing of agricultural raw materials and job creation. Finally, the Dynamic Common Correlated Effects estimator is used to address this issue. agricultural investment export

Practical value / implications. The information generated will be useful to a number of organizations, including: research centers, universities, governments, governmental and non - governmental organizations, to better guide the development and implementation of policies and strategies for job creation and unemployment reduction. Finally, by knowing the existing relationship between local processing of agricultural raw materials and job creation, as well as the limitations of this raw material processing policy, the study provides the different ways to improve the capacity for job creation and unemployment reduction. Research on this issue is too important to inform policy makers on the structural transformation of their economies to achieve full economic growth and reduce the unemployment problem.

Key words: agriculture, industrialization, jobs, WAEMU.

Introduction and review of literature

For more than a decade, African countries in general and those of the West African Monetary and Economic Union (WAEMU The West African Monetary and Economic Union (WAEMU) is a West African organisation created on 10 January 1994 with the mission of achieving economic integration among its member states. It has eight (8) members: Cote d'Ivoire, Benin, Burkina Faso, Guinea-Bissau, Mali, Niger, Senegal and Togo.) in particular have been experiencing a progressively high growth rate, i.e. 5.8 % in 2019 compared to 3.9 % in 2008. However, despite their progress for economic and financial integration, and even with an increasingly high growth rate, these eight (8) countries are generally qualified as developing countries (WAEMU, 2019).

Indeed, these countries have specialized in the export of unprocessed agricultural raw materials since their independence, therefore behind in terms of industrialization, competitiveness and moving up the value chain. In terms of innovation and global competitiveness, the results are still unclear or even declining in most of these countries. Agriculture is still in the middle of the economies of these countries. On average, the agricultural sector accounts for 45 % of the Gross Domestic Product (GDP), 75 % of the value of exports, 15 % of government revenue and provides income for almost 80 % of the population.

However, the considerable weight of this sector in GDP growth does not necessarily lead to a massive increase in the number of jobs. Moreover, the jobs created in this sector are mostly informal and essentially subsistence activities. Overall, the level of anchoring of their economies in the agricultural sector is still high as well as unemployment, not forgetting the low rate of employment in manufacturing. Unemployment affects more than 70.2 % of young people, who represent almost 57.2 % of the population of the union. In addition, it should also be noted that the quality of jobs is extremely problematic in this area where more than 76 % of jobs are vulnerable (low income) in 2018 (WAEMU, 2019).

Since the technological revolution, manufacturing has been in the middle of these structural changes, steadily increasing output and employment and leading to unprecedented income growth and poverty reduction. While looking around the world at European countries, the United States, Japan, the Republic of Korea, China and the many other Asian tigers and dragons, we can see some evidence that these countries have begun their economic booms through the sectoral reallocation of resources to industries. This is the basis for structural change (AfDB, OECD and UNDP, 2016; Sen, 2019). However, countries considered to be least developed have failed to initiate such changes in the productive structures of their economies and remain with low or at best middle income levels (AfDB, OECD and UNDP, 2016). This is the case in WAEMU countries where the majority of jobs are in agriculture and are essentially subsistence activities.

Consequently, the current challenge for West African economies is to generate many decent jobs for its predominantly poor population. To do this, it is essential to turn to a structural transformation of its economy, i.e. to move from an economy with low value-added content (export of unprocessed agricultural products) to the production of goods with high value-added content (developing its agricultural manufacturing sector).

Even though the indicators regarding the causal relationship of agricultural sector and economic growth in the WAEMU countries seem satisfactory and sufficient, it is difficult to assess causal effect of agricultural manufacturing sector on employment and the nature of their relationship. In this regard, the subsequent research questions were raised:

- What is the nature of the relationship between agricultural manufacturing and the level of employment?

- How does agricultural industry influence job creation?

- What is the nature of causal relationship among agricultural manufacturing and other factors (life expectancy, school enrolment, trade openness, inflation and Foreign Direct Investment) affecting unemployment?

- How do the other factors (life expectancy, school enrolment, trade openness, inflation and Foreign Direct Investment) and agricultural manufacturing influence the level of employment?

Addressing the answers of the above research questions, this section discusses some findings of previous empirical studies regarding economic activity and labour market performance to bring some evidence from the different regions of the world.

Okun (1962) developed an analysis linking growth and employment. Indeed, using quarterly data on the US economy between 1947 and 1960, the author demonstrated an inverse relationship between growth and unemployment of the order of 1:3. In other words, when the Gross Domestic Product increases by 1 %, ceteris paribus, unemployment decreases by 0.3 %. Thus, for a stable level of the active population, an increase in GDP leads to an increase in the level of employment.

Lavopa & Szirmai (2012) support this assertion by stating that increased manufacturing output generates new jobs for the simple reason that industry interacts with all other sectors of activity through input-output linkages.

Djahini (2019) explains that the manufacturing sector can act in two ways on the labour market. Indeed, it can either act in a direct way such as the important absorption of labour, or in an indirect way such as the creation of new jobs due to the interaction that exists between the manufacturing sectors and all the other sectors of the economy and this confirms the analysis by Soubbotina (2004). This author concludes that in order to benefit from decent jobs, sub -Saharan African countries need to turn to the manufacturing sector, as the development of this sector can contribute to reducing youth and female unemployment.

Khan (2011) examines the relationships between output growth, employment and poverty, linking them at the macro and micro levels. He argues that high economic growth can create employment opportunities at high productivity rates. The poor can thus increase their productivity and income, either in their existing occupations or by moving to new ones. This process can enhance productivity in various sectors and occupations, change the pattern of employment towards higher productivity occupations, and increase in income and wages. This could support greater essential household spending on children's education, thereby intensifying future productivity and supporting higher growth in the future.

Lewis (1954) analysed the relationship between industrialisation and employment using a two-sector model: one traditional (agricultural) and one modern (industrial). The main idea of his model is that the persistence of capital accumulation in the industrial sector should gradually absorb the surplus of workers in the agricultural sector. Hence, the development of the industrial sector is seen as a source of employment opportunities for the laid-off workers in the agricultural sector.

The analysis by Haris & Todaro (1970) supports that by Lewis (1954) and sheds important light on the analysis of the relationship between industrialisation and employment. He argues that labour-intensive rather than capital-intensive industries should be developed as they are likely to solve the unemployment problem.

Lewis (1954) analysed the relationship between industrialisation and employment using a two-sector model: one traditional (agricultural) and one modern (industrial). The main idea of his model is that the persistence of capital accumulation in the industrial sector should gradually absorb the surplus of workers in the agricultural sector. Hence, the development of the industrial sector is seen as a source of employment opportunities for the surplus workers in the agricultural sector.

Kapsos (2006) carried out an analysis of employment-economic growth elasticities in different regions of the world over the period 1991 -2003 using the calculation of arc elasticity. The results obtained in sub-Saharan Africa establish this elasticity at 0.73 over the period 1991-1995, 0.82 between 1995 and 0.53 between 1999 and 2003. The analysis by sector showed that the employment elasticity is more important in services and industry.

Ndinga et al. (2018) conducted a study on sub-Saharan African countries by calculating the employment-growth elasticity. From the results, they note that growth in the manufacturing sector is accompanied by job creation and productivity growth.

In contrast, Kumar & Pattanaik (2017) find a weak relationship between employment and output in the industrial sector for most states in India during the period 1981-2014. This would imply that with 1 % change in industrial sector output, employment will grow at a lower rate. They confirmed a negative employment growth rate despite reasonable output growth.

According to Yaiche (2019), the level of manufacturing development, which is measured by manufacturing value added as a percentage of GDP, has a positive and statistically significant effect on the level of GDP per capita. Accordingly, a 1 % increase in manufacturing value added increases GDP by 0.025 %. Clearly, manufacturing contributes positively to economic growth in these countries, but it is clearly small and therefore contributes only modestly to economic growth and h ence to job creation in Africa. This was shown in the results of Ayira (2019) when he investigated the macroeconomic and institutional determinants of unemployment in the countries of the West African Economic and Monetary Union.

According to Masters et al. (2018), Yeboah & Jayne (2018), despite policies for structural transformation of the economy, many Africans still work in low - productivity agriculture. They justify this by faster rural population growth and lower labour productivity. Economic transformation refers to two linked development processes: (1) sectoral change, through increases in labor productivity, especially in the sectors containing the majority of the labor force; and (2) structural change, the shift of workers and other resources from low-productivity sectors, such as subsistence agriculture, to high productivity sectors, such as industry and modern services. Always according to these same authors, economic transformation raises the general level of output per worker and hence is a fundamental driver of ri sing wages and incomes, improved living standards, and economic opportunities. Economic transformation creates sustained growth and is necessary to improve the material welfare of the population and increase resilience to shocks. These assertions are supported by Beegle & Christiaensen (2019), Benin (2019), Chadwick (2020), Calabrese & Tang (2023).

De Vries et al. (2021) made this observation when they found that high growth rates had little effect on job creation in African countries. According to them, despite the high rates of output growth, the level of employment was quite low. While many African economies have grown over the last few decades, their structure has not transformed. In contrast with other regions of the world, where the majority of people are employed in the secondary and tertiary sectors, a large share of Africa's labour force is engaged in agriculture and related activities, where average productivity is lower. Most of the workforce in African countries is employed in what is usually the least productive sector, agriculture (Martuscelli, 2020). Africa's economic growth is mainly based on low-value-added sectors, such as the oil and mining sector (a key source of income for most African states) and commodity exports (Yeboah & Jayne, 2018). These sectors are characterised by low job creation potential and modest wage levels. The growth of the last twenty years, instead of generating sufficient jobs, has led these countries to an increasing dependence on informal employment. In this regard, according to Yaiche (2019), 60 % of African workers are low-paid because they depend on the informal sector, which is synonymous with precariousness and exploitation. The structure of jobs and the predominance of agricultural jobs in the WAEMU are associated with high vulnerability of jobs on the continent. Indeed, the continent remains one of the regions where more than half of workers are selfemployed and generally work in sectors that are not very productive, according to Djahini (2019).

The literature reviewed above showed enormous empirical evidence of the effect of industrialisation on employment from different regions of the world. Unfortunately, very few research studies have been done in the WAEMU regarding agricultural manufacturing and employment. Those existing research works about agricultural sector in the WAEMU only cover the correlation between agricultural sector and productivity and state the trends and growth in commodity exports and GDP without considering the assessment of agricultural manufacturing impact on job creation and identifying the nature of the relationship. Moreover, those works suffer from improper methods and techniques, and analysis based on the old dataset. Thus, this significant research gap is addressed in our present study by considering the causal effect assessment of agricultural manufacturing on job creation and identifying the nature of the relationship between these two indicators. Furthermore, our analysis is based on the highest possible recent dataset and empirically valid new methods and techniques (DCCE).

Thus, we expect that the findings of this study based on empirically valid standard techniques with the new dataset from the WAEMU countries can incrementally contribute to the existing literature.

The purpose of the article. This study aims to assess the relationship between the processing of agricultural raw materials and the level of employment in WAEMU countries. In particular, it aims to determine the causal effect of the agricultural manufacturing on the job creation in WAEMU countries.

Methodology. Data on Employment (Empl) are taken from the International Labour Office, while those on Agricultural Manufacturing (Manu), Life Expectancy (Exp), School Enrolment (Edu), Trade openness (Tra), Consumer Price Index (CPI) and Foreign Direct Investment (FDI) are taken from the World Bank . These data are for eight (8) WAEMU countries: Benin, Burkina Faso, Cote d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo, during the period 1990-2019.

The Dynamic Common Correlated Effects (DCCE) estimator originally developed by Chudik & Pesaran (2015) was used in this study. This technique takes into account the Pooled Mean Group (PMG) technique and the mean recursive adjustment method is applied to correct for small sample bias. The basic idea of CCE estimation is to proxy the unobserved common factors using the cross-sectional averages of the observables in the regression. Comparatively, it has several advantages. For instance, it can be computed by least squares to aux iliary regression, and it does not require the knowledge of the number of unobserved factors.

Let's assume the following equation with heterogeneous coefficients (Pesaran, 2006):

In empirical applications, it was used, for example, in Eberhardt et al. (2013), Bond & Eberhardt (2013), McNabb & LeMay-Boucher (2014), and Gundlach & Paldam (2016).

However, the CCE estimator is consistent only in non-dynamic panels (Chudik & Pesaran 2015; Everaert & Groote, 2016).

In a dynamic panel such as:

The expectations regarding the effects of the explanatory variables on the dependent variable are shown in Table 1. A plus sign (+) would indicate a positive effect and a minus sign (-) would show a negative effect of the dependent variable on GDP.

Table 1

Used variables