Memoire Online - Analyzing how to shift Informal Unit of Production (IUP) to formality:the case of Cameroon

Analyzing how to shift Informal Unit of Production (IUP) to formality:
the case of Cameroon

Abstract

For a country like Cameroon witnessing an informal employment rate of more than 90%, the shift towards formality requires an effective public assistance. This paper attempts to prescribe a way by which public funds could be less riskily and more efficiently allocated to IUPs according to their dynamism measured here by their profitability. The methodology used in this paper is the construction of a frame for assessing IUPs effectiveness with some of their positive core characteristics like education attainment of the owner and the sales, and a negative one, the costs. The main variables have been selected through a principal components analysis. A logistic regression has been used to estimate parameters necessary to complete the scoring equation aimed at identifying less risky and more efficient IUPs. As a perspective, it comes out that IUPs might be very motivated if a policy aimed at effectively allocating them public funds according to their dynamism is put in place. They could find more rationales to gather themselves for more effectiveness and greater probability to benefit from these funds, expand and then get access to bank credits.

1. Introduction

In Cameroon, the unemployment rate in 2005 was 4.4% according to the International Labor Organization definition. Besides how impressive this figure may appear, it hides a tremendous amount of precariousness and underemployment. Actually, underemployment touches more than three quarters of the potential working force in Cameroon. Underemployment and precariousness comprise both the unemployed and the employed population earning less than the legal minimum wage or working less than 35 hours a week. This underemployment is essentially nurtured by the informal sector that employs more than 90% of the actual working force. In fact, the underemployment rate was 70.6% in the non-farming sector and 86.8% in the informal farming sector. It is important to notice that this situation has been favored by the economic crisis of the 80s. Moreover, many evidences now show that one of the huge unanticipated drawbacks of the Structural Adjustment Policy focusing on the reduction of public spending has deeply affected the labor force in the sense that it has led to the reduction of the Government' s staff, so far the main formal employer. The private sector was at that time too under-expanded to absorb the bounce of job seekers. The country has therefore witnessed the development of an uncontrollable urban informal sector and the rise of precarious jobs. It is more than a necessity nowadays to try to cancel all this precariousness and informality that not only entertains harsh labor conditions and poverty but also perpetuate disconnection between public policies and people in real need. A way of doing that is to accompany dynamic informal units susceptible to earn the status of small formal production units to do so faster and comfortably. Cameroonian authorities, in the global framework of fighting against poverty have put in place in 2002 a structure, «Projet Integre d'Appui aux Acteurs du Secteur Informel (PIAASI)» aiming at ensuring the promotion of the informal economy. This action, managed by the Ministry in charge of labor and professional training is translated into the financing and training of actors of the sector in concern. Thanks to resources from the Heavily Indebted Poor Countries Initiative, the PIAASI was launched in 2005. It was essentially focusing on very small projects for self employment and

microenterprises like artisanal activities, very small scale esthetics, catering, shoes repairs, farming, etc. The authors of this paper want to show that such an action must be extended to the IUPs the most dynamic in order to take them to the scale of small formal enterprises. With This purpose, the present paper will realize a partition of IUPs in Cameroon using the survey 1-2-3, phase 2 of 2005 to distinguish stagnant units from dynamic units that could potentially evolve to become formal enterprises. We will therefore design a set of characteristics that could allow a public policy to efficiently finance such IUPs. We will use discriminatory procedures from SPAD software and logistic analysis from STATA software to make available the more relevant variables that differentiate stagnant from dynamic IUPs. Moreover, we intend to provide a set of parameters and characteristics gathered in a scoring equation that could allow any anonymous IUP to be appropriately ranked in each group provided some of its characteristics.

2. Methodological issues

Methodological issues in this paper stand on the concept of informality and the identification of dynamism amongst the IPUs through discriminatory and multivariate analyses.

The debate on a universal definition of informality is still pending. The term «informal» was used for the first time by Hart in 1971. It has been reemployed by the ILO in its report on Kenya in 1972. This evocation has underlined seven criterions to identify IUPs: exclusive use of local resources, family ownership of the unit, reduced scale of the activity, use of techniques that are essentially man power demanding, skills of the manpower are acquired out of formal training institutions and highly competitive markets without regulation. These characteristics were too numerous for a single unit to meet them all. Further criterions were therefore restricted to the scale and the lawfulness of the unit. The criterion of scale is the most easy to mobilize because it requires just a unit to have less that a threshold of employees (usually, 10). The scale criterion is not appropriate for international comparisons though, and doesn't take into account the smallness of enterprises like attorney offices, notaries, accountants that are modern and most of the time very profitable. To avoid that insufficiency, the criterion of legality has been settled. According to this criterion, an IUP is the one that does not respect the law, the pending question here still being; which laws among the numerous existing are required? This led to the ILO combining the criterions of smallness in terms of employment and non registration of the unit or of the regular workers. The survey 1- 2-3 that we will use in this paper has considered informal, any activity without a tax payer identification number and/or not handling written accounts according to the scheme required by the law.

Among the possible variables like sales, numbers of employees, etc, profits have been chosen as the variable to discriminate between the less and the more effective. The less effective group will be constituted of IUPs that make monthly profits which are less than the nationwide median, the more effective being those with monthly profits which are more than the nationwide median. The profit is defined as the difference between sales and costs (mainly salaries and taxes). After we decided on the discrimination criterion, the concern was now to extract from the huge database the more relevant variables likely to explain the ranking in one group or another. The Principal components analysis (PCA) has been operated to realize the variable specification. The PCA like the factorial analysis are statistical tools that summarize the variability among a set of numerous variables. In fact, they seek to describe the variation of a given set of variables as linear combinations of the original variables in which each linear

combination is aimed at explaining a maximum of variation of original variables without being correlated to the other linear combinations. Most of the time, analysts just focus on the first two linear combinations that by definition explain most of the variability. It is therefore possible to scatter plot the IUPs according to the two axes obtained from the first two linear combinations and to represent the variables in the circle of correlation comprising the above mentioned axes.

The next step was to apply the multivariate discriminatory analysis techniques to differentiate the two groups of IUPs so that an anonymous IUP could be ranked in the appropriate group knowing only some core characteristics. For this purpose we both operated the so called credit scoring techniques and the logistic regression. The credit scoring is used in several areas like medicine, meteorology or finance, the latest using it to identify solvable clients. It consists of performing comparison tests using the Wilks' Lambda (£) as statistics' test on the core variables identified through the PCA process. Its applicability requires the observance of two hypotheses that are the equality of the covariance matrix of the two groups and the normality of the distribution of each population group. If £ tends towards 1 its influence on the differentiation is not relevant, in the contrary, the further it goes below 1, the more it influences the differentiation. Mindful that the Credit score technique requires the observance of these strong hypotheses, it is easier to cross over those requirements by applying a logistic

coefficients, i the index of the variable, X the variable, p the number of variables and P the probability of being ranked in the effective group. The above equality corresponds to the expression: P(Y=1/X=x) = 1/(1 _+e-(/31x1+...+ /3pxp)_.

The estimation of coefficients uses the maximum likelihood. The normality of the distributions of variables is required. We ranked an IUP in the effective group if its probability was more than 0.5. From the above process we could deduce the score of effectiveness defined as S(x) = /31x1+... + /3pxp and then rank the IUP according to their results in the scoring process.

We have deliberately chosen just to display the results obtained from the logistic regression because they have been found more relevant than the credit scoring method. In fact, the matrix of confusion of the logistic regression was stronger than the credit scoring one.

3. Data and variables specification

Data were obtained from the National Institute of Statistics of Cameroon² and were resulting from the 2005 survey 1-2-3 which had as objectives the follow-up of employment, informal sector and household consumption. We used the phase 2 that focuses on the informal sector by collecting data on the working conditions, manpower, contribution of the informal sector to the economy and issues and perspectives of that sector. The phase 2 is theoretically supposed to happen every four years. It will then be possible to update the parameters of the model we are proposing according to that periodicity.

As we mentioned above, the PCA was used for the variables specification. 4,815 IUPs were involved in the process. The first two axes were representing 58.5% of the total variability. When plotting the IUPs according to these two axes, the results suggested that they were relatively very close each other as shown on the graph 1 below. In fact almost all IUPs are surrounding the mean point.

Nevertheless, some points are found very far from the mean point suggesting that they are witnessing a relatively high level of activity with sales cros sing the threshold of the FCFA 1,000,000 (US$ 2,500). Theses points are represented by crosses proportional to the size of their activities. Variables were also represented in the correlation circle (see graph 2). This graph shows that almost the whole variables are very well represented, the arrows designating each being very close to the borders of the circle on the factorial plan. We can observe four groups of variables. The first is the set raw materials and intermediary consumption, the second, number of worked hours and the salaries, the third, variables indicating the gross benefice and the fourth, sales and costs.

We will finally keep only three variables representing each group: the sales, the number of hours worked and the costs per month. To these variables we will add control variables like education level of the owner, his age and the age of the IUP.

4. Findings and Results

From the curves below, we can notice that the informal non-farming sector (60%) has become since 2003 the main occupation of the population, crossing over the farming informal sector (38%). The public and the private formal sectors are stagnating since the 90s.

	Employment %	Mean age	Females (%)	Male (%)	Years of di	Experience within h i
Urban
Public	10.5	39.7	31.8	81.1	12.3	9.6
Private formal	11.8	36.1	20.4	79.1	10.9	5.9
Informal non-farming	67.4	31.2	45.4	66.4	7.0	4.5
Informal farming	10.3	37.2	57.4	52.0	5.2	12.6
Overall	100	33.3	42.2	67.9	7.8	6.0
Rural
Public	2.6	39.4	25.8	79.7	11.3	7.5
Private formal	2.0	35.9	15.4	79.3	7.6	6.7
Informal non-farming	22.5	31.9	55.0	44.3	4.3	6.2
Informal farming	72.9	33.3	52.7	35.3	3.2	12.2
Overall	100	33.2	51.8	39.3	3.8	10.6
Cameroon
Public	4.9	39.6	29.5	80.6	11.9	8.8
Private formal	4.7	36.0	18.9	79.1	9.9	6.2
Informal non-farming	35.2	31.5	49.8	56.3	5.8	5.3
Informal farming	55.2	33.5	53.0	36.2	3.4	12.2
Overall	100	33.2	49.1	47.4	4.9	9.3

Table 1 shows that across the country, more than one worker out of two is a business owner
working as a self-employer or with very few employees. In the rural area, almost one third of

the working population is family helping without effective salaries. The working class which is the more representative in developed countries accounts only for 8% in the whole country and 20.3% in urban area. Not surprisingly, the formality of positions goes side by side with the number of years of schooling.

Visible underemployment is a situation characterizing workers unwillingly involved in less than 35 hours a week in their main activity for reasons linked to their employer or to a bad economic situation. It was touching 12.2% of the working class in Cameroon in 2005. It is twice higher in the non-farming working sector than in all other sectors. It increases according to the level of education. A high working time could be also translated by a form of underemployment occasioned by the lowness of the productivity. This form of underemployment is called invisible underemployment and is usually estimated by the level of income. In Cameroon, the invisible underemployment rate is defined as the percentage of the working class earning less than FCFA 23,500 (US$ 65) a month for 40 hours worked a week. This rate is estimated at 69.3% of the actual manpower. It is within the informal sector that underemployment is more crucial with more than six persons out of 10. The underemployment affects more the rural area than the urban area. In the contrary of visible underemployment, invisible underemployment decreases with the level of education. The sum of these two forms of underemployment and the unemployment yields the global underemployment that touches three quarters of the potential working class in Cameroon. This problem is the more critical of the Cameroon employment issues because many trained young people declare exercising informal jobs to survive while hoping for a more stable and better rated job corresponding to their qualification and/or their level of studies.

Table 2: Rates of underemployment according to institutional sector, the gender and the area

Institutional sectors		Visible			Invisible			Overall
	Urban	Rural	Cameroon	Urban	Rural	Cameroon	Urban	Rural	Cameroon

Public	7.3	10.1	8.4	3.1	17.4	8.6	10.3	26.8	16.7
Private formal	6.4	5.6	6.2	13.6	31.8	19.0	19.3	34.2	23.7
Informal non-farming	17.1	23.1	19.9	54.4	66.8	60.1	64.8	77.5	70.6
Informal farming	15.6	7.6	8.0	70.2	85.7	84.8	75.1	87.5	86.8
Gender
Male	12.1	11.8	11.9	37.8	71.5	60.7	50.9	77.2	68.3
Female	18.2	10.5	12.4	56.6	85.2	78.2	70.9	88.3	83.6
Overall	14.7	11.1	12.1	45.7	78.6	69.3	68.3	83.6	75.8

The effectiveness of an IUP corresponds to its ability to create jobs in order to raise its production and make profits. The level of gross profit has therefore been considered in this paper to categorize IUPs. Meanwhile, table 3 below shows other possible criterions that we could have taken. The nationwide gross profit median is FCFA 28,000 (US$70) per month; a very low profit which mainly characterizes IUPs in Cameroon. But this median profit is stretched from a minimum of FCFA 82,000 (US$165) losses to a maximum of FCFA

8,899,000 (US$18,000) earnings with a standard deviation of FCFA 199,429 (US$400). We can also notice that those with employees are the more performing. A qualitative criterion could be settled on that aspect if credits have to be attributed to IUPs.

Table 3: Performances of IUPs per type of occupation and per type of area (monthly in ,000 FCFA)

		Sales	Production		Added value		Gross profit
	Mean	Median	Mean	Median	Mean	Median	Mean	Median
Type of occupation
Self employment	133.8	47	77	36	43.7	19	43	19
Non salary job	173	65	110.9	46	58.2	22	54.7	21
Salary job	704.3	300	529.9	200	328.3	122	254.3	85
Mixt	650.6	255	601.9	255	304.8	171	226.7	112
Overall	173.8	57	110.2	41	62.4	28	57	28
Urban area
Self employment	206.5	90	112.3	56	66.9	34	65.6	32
Non salary job	283.3	117	187.2	84	96.5	44	89.9	38
Salary job	897.1	301	625	280	360.7	172	274.1	112
Mixt	683.7	311	643.7	311	326.9	197	241.1	120
Overall	275.2	105	168.3	69	95.5	41	86.5	37
Rural area
Self employment	83.2	31	52.4	26	27.6	12	27.2	12
Non salary job	109.1	45	68.9	37	37.2	16	35.3	15
Salary job	463.5	200	411.2	150	287.8	100	229.5	61
Mixt	504.3	113	445.9	113	222.5	69	172.7	41
Overall	104.6	37	70.5	30	39.8	14	36.9	13

Table 4 displays some of the characteristics of the two groups generated. The more effective group is in almost all aspects averagely greater than the less effective except in terms of age of the owner and duration of exploitation where we could not find significant disparities.

The parameters of the logistic regression are shown on table 5. The second column provides the estimated coefficients; the fourth one displays the probability of rejecting the nullity of the coefficients; and the last one shows the marginal effects of every variable. Sales, hours spent at work, costs and to a lesser extent, education levels are all significant at a threshold of 5%. As expected, costs affect negatively the effectiveness of IUPs; this should be interpreted cautiously though, because great expenses sometimes mean higher production for higher sales and higher profits. This means also that the Government can effectively alleviate (0.24) the informal sector by reducing or canceling some of their taxes to favor their entrance into formality. The variable that influences the most is the amount of sales with an odd ratio of 0.25. The time spent at work is almost neutral in terms of impact on the effectiveness. As we can notice from the last column, marginal effects are very low for all the variables at stake.

Number of observations = 4809 ; Prob > chi2 = 0.000; Log likelihood = -5 17.70446; Pseudo R² = 0.8447

Table 6 below shows that 96.9% of the IUPs have been well ranked from the logistic regression while only 72.3% were so with the Wilks' test approach.

We can therefore easily compute for any anonymous IUP the score of effectiveness S;

S = 0.03Education + 0.25Sales - 0.24Costs, and rank it according to it final score in order to efficiently allocate them credit for their expansion to a formal activity.

5. Discussion

Cameroon is witnessing a historical increase in informality mostly touching the non-farming sector. Its contribution to the GDP has evolved from 22% in 1993 to almost 50% 12 years later. This sector provides 90.4% of jobs in the country. Most of these jobs are precarious and casual. One of the biggest issues that this sector faces is that, it doesn't have access to the credit market; this hampers its process of comfortably evolving to the formal sector. The PIAASI has an approach of promoting the informal sector by allocating credits to selfemployment projects. We analyzed through this paper how it would have been more efficient for the PIAASI to allocate funds to IUPs according to their dynamism in order to accompany them in the formal sector which is legally more advantageous (protection, credit access, accountability, sales...) and in the long run, economically safer than the informal sector. These advantages are mostly ignored by informal sector operators. Through the scoring of IUPs, the Government could allocate them promoting funds that would encourage small IUPs to gather and be more efficient in order to fulfill the requirements and benefit from that unprecedented opportunity. The sectors of the microfinance and banking could therefore treat more respectfully the IUPs which otherwise pay huge interests for very few loans due to the high level of risk that constitute their portfolio.

We thank the National Institute of Statistics (NIS) of Cameroon which has granted us the data
and supervised us in partnership with the «Institut Sous-regional de Statistiques et

d'Economie Appliquee» (ISSEA). We also thank Mr. GOUNE TEKOMBONG Russel Romeo for his critical reading of this paper.

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