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Go Back       Himalayan Journal of Agriculture | Volume :3 Issue:2 | March 20, 2022
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DOI : 10.47310/Hja.2022.v03i02.001       Download PDF       HTML       XML



Effects of Financial Capital on Welfare Status of Cassava Processors in Oyo and Ogun States, Nigeria


Matthew Olufemi Adio*1 and Luke Oyesola Olarinde2


1Department of Agricultural Economics and Extension, Federal University, PMB 373, Oye-Ekiti, 371104, Ekiti State, Nigeria

2Department of Agricultural Economics, Ladoke Akintola University of Technology, PMB 4000, Ogbomoso 210214, Oyo State, Nigeria

*Corresponding Author

Matthew Olufemi Adio


Article History

Received: 28.02.2022

Accepted: 06.03.2022

Published: 20.03.2022


Abstract: Financial capital (formal and informal) has been identified as the major input for the development of agricultural sector as its traditional role in covering financial gap for farmers and processors for increased productivity which provides the foundation for this study. This paper assesses the welfare effects of financial capital, using a proportionate sample of 540 small scale cassava processors collected in Oyo and Ogun States, Nigeria. We found significant differences in the socioeconomic variables of the two processing groups of financial capital users, the more experienced and educated the processors were, the more likely they used formal financial. In addition, the choice of formal financial capital tends to significantly improve the processors’ welfare by 33.4% when compared to informal financial capital users. Formal financial capital use significantly increased welfare of the processors, but overall, financial capital is effective at improving these outcomes for more educated and experienced processing households. The results generally point to addressing the policy indicative variables (factors) influencing the use of any of formal and informal financial capitals. This is to promote and ensure the awareness and appropriate use of financial capital.


Keywords: Average Treatment Effect, Cassava processors, Financial capital, Endogenous Switching regression, Welfare, Nigeria

INTRODUCTION

Nigeria with an increasing population of over 180 million people, is unarguably endowed with abundant human and natural resources as well as favourable weather conditions (Bassey and Agnes 2016). As an agrarian nation, most promotion and processing activities are carried out in the rural areas thus the rural dwellers represents over 70 percent of active farmers in Nigeria. These farmers and processors are marginal farmers as their farming and processing activities are undertaken at the small scale level, yet more than 75 percent of the total food requirement of the nation is produced by these marginal farmers and processors who are consistently faced with serious constraints of inadequate capital, (Central Bank of Nigeria [CBN], 2010). Globally, financial capital (formal and informal) has been identified as the major input for the development of agricultural sector as its traditional role in covering financial gap for farmers and processors for increased productivity which provides the foundation for this study.


Moreover, financial capital/ loanable funds plays a fundamental role in determining access to the needed inputs that facilitate farming and other extensive agricultural practices including processing activities which ultimately transforms into increased output. The increased agricultural output on the other hand, establishes a forward linkage (multiplier effect) in terms of development to other sectors as well as higher income and better quality of life for the rural poor. In addition, financial capital for rural smallholders and processors, particularly in agriculture is assuming increasing importance in many parts of the world in response to the needs of less privileged entrepreneurs with limited capital base in the sector (Lawal, et al., 2019).


Different kinds of financial capital that could cover financial gap for farmers and processors for increased productivity and produce livelihood outcomes include among others, formal financial capital (commercial banks, microfinance banks, cooperatives and agricultural banks) and informal financial capital (ajo/esusu/adashe as the case may be), money from friends and relations, remittances (from children and relations), and others which may include sales of jewelry, livestock- cattle, sheep, goats, poultry etc. Financial capital examined for this study, consists of money and other forms of funding (formal) that farmers and/or processors use in investing in their farming and processing businesses. It comprises financial stocks and all types of income (informal) e.g – savings, salary, pensions, dividends and remittances) and other economic assets, including basic infrastructure and production equipment and ownership of livestock which are essential for the pursuit of any livelihood strategy.


Several studies in developing countries like Nigeria have considered a broad range of ways of accessing and factors that determine access to capital (credit) by farmers and processors and these vary from one geographical area to another. For instance, some authors, e.g. Dzadze et al., (2012), Ololade and Olagunju (2013), Etonihu et al., (2013); Baba et al., (2015) reported on credit (capital) independently for the formal and informal. No doubt, the above mentioned studies on both formal and informal capital sectors have their relevant importance. However, most of them have long been hypothesized that the formal is better than the informal without critical analysis of their respective relative importance. There is therefore the need to critically look into the issue of unavailable and insufficient capital with a view to proffering a substitute in which farmers and processors can have adequate access. Much more, there is dearth of literature on the effects of financial capital most especially on the welfare of smallholder cassava processors in South Western Nigeria, particularly in Oyo and Ogun States. This calls for action in order to bridge this gap if the problem of low capital among smallholder processors is to be addressed to the effect that their livelihood (welfare) will improve. This paper therefore analysed the effects of financial capital on welfare of cassava processors in Oyo and Ogun States, Nigeria.


Conceptual Review and Estimation Procedure

Household income is complex and difficult to measure in developing countries where a large part of the labour force is either self-employed or of the own- account worker type. Real per capita consumption expenditure has been used as an indicator of welfare in literatures some of which were Khonje et al., 2015; Mmbando et al., 2015 and Wondimagegn and Nyasha 2016. Therefore, in line with most poverty/welfare studies, some of which are Gaiha, et al., (2007), Grosh, et al., (2008); Adepoju and Yusuf (2013) per capita household consumption expenditure was used as a proxy for per capita household income because of the data available and that the respondents were unwilling to disclose their income. Per capita household expenditure were calculated as the sum of the per capita household cash expenditure on food and non-food items and the value of own produced consumption based on local market prices. Thus, a relative poverty line was constructed based on the Mean Per Capita Household Expenditure (MPCHHE) of the sampled respondents. Poverty/welfare categories were then established using the relative poverty line as in Gamba and Mgbenyi, (2004) and Gaiha, et al., (2007). Those who spent less than two-thirds of their MPCHHE were classified as poor (bad welfare) while non-poor (improved welfare) are those who spend two-thirds or more of their MPCHHE (Nigerian Bureau of Statistics, NBS, 2005).

(1)


(2)


Empirical Model: Endogenous Switching Regression

The endogenous switching regression approach was developed by Lee (2008) as a generalization of Heckman’s selection correction approach. It accounts for selection on unobservable by treating selectivity as an omitted variable problem (Heckman 1979). The propensity matching approach proposed by Rosenbaum and Rubin (1983) has been extensively used to examine the impacts of technology adoption on farm outcomes and household welfare, particularly when self-selection is an issue (Amare et al., 2012). However, a major objective of the propensity score estimation is to balance the observed distribution of covariates across the treatments (in this study, financial capital use situation as “formal/informal” users or not). Thus, the probit or logit estimates obtained in the estimation cannot be considered as determinants of Financial Capital (FC) use. Given our interest in examining the determinants of FC as well as its effects, we employ the endogenous switching regression model to account for selection bias in our estimation of the effects of FC use on welfare outcome of cassava processors. However, when the data available are only from a cross-sectional survey, as the one employed in this study and there is no information on the counterfactual situation of not using FC. An effective way of addressing the problem is to resort to an investigation of the direct effect of FC use by analyzing the differences in outcomes among processing households. Awudu and Wallace (2014) employed this approach to identify the factors that affected farmers’ decisions to adopt soil and water conservation technology in Africa and how this technology impacted farm yields and net returns. Thus, in the switching regression approach, the processors are partitioned according to financial capital use situation as users or not in order to capture the differential responses of the two groups.


The Endogenous Switching Regression (ESR) is a parametric approach that uses two different estimation equations for a given treatment (financial capital use situation and/or option and other alternatives), while accounting for selection process by including an inverse Mills ratio (Lokshin and Sajaia, 2004).


The ESR model of financial capital use intuitively involves separate estimations for formal financial capital and informal financial capital sub-samples due to the possible systematic differences in mode of access and financial capital use. Financial capital thus becomes the selection criterion governing the livelihood outcomes (welfare) of the processors. Depending on the assumption regarding the relationship between the residuals of the selection and the outcome equations, both exogenous and endogenous switching regressions can be developed (Foltz, 2004). Letting represent financial capital use dummy where , a financial capital use selection criterion function can be expressed as

(3)

(4)


where, is a vector of processors socioeconomic characteristics, as well as instruments deemed to influence financial capital use decision of cassava processors is the vector of parameters to be estimated, and is the error term. Also let represent the processors livelihood outcomes (welfare). Based on the financial capital use function of Equation (3), outcome are observed for two different regimes (formal financial capital and informal financial capital) (Gebregziabher, 2008).

if and only if (5)

Formal financial capital

if and only if (6)

Informal financial capital

Where, is a vector of exogenously determined variables of processors is the coefficient vector, and the residuals.


In addition to the aforementioned, endogenous switching regression model can be used to compare the expected livelihood outcomes (welfare) between the two processing groups of financial capital users. That is, between the processors that uses formal financial capital (a) with respect to the processors that use informal financial capital (b), and to investigate the expected livelihood outcomes (e. g welfare in the case of the present study) in the counterfactual hypothetical cases (c) that the users of formal financial capital did not use, and (d) that the processor that use informal financial capital did not use. The conditional expectations for our outcome variables in the four cases are defined as follows:

(7a)

(7b)

(7c)

(7d)


Cases (a) and (b) represent the actual expectations seen in the sample. Cases (c) and (d) characterize the counterfactual expected outcomes. In addition, following Heckman et al., (2001), we calculate the effect of the treatment ‘to use’ on the treated (TT) as the difference between (a) and (c)

(8)


Which represents the effect of financial capital on the welfare outcomes of the cassava processing households that actually use financial capital (formal). Similarly, we calculate the effect of the treatment of the untreated (TU) for the processing households that actually use informal financial capital as the difference between (d) and (b),

(9)


We can use the expected livelihood outcomes described in (7a)-(7d) to calculate the heterogeneity effects. For example, farm households that used formal financial capital may have better food secured and have improved welfare than processing households that used informal financial capital not only because of the fact that they decided to use formal financial capital but because of unobservable characteristics such as collaterals, enterprise size, etc. Adapting Carter and Milon (2005) to our case, we define (welfare) as “the effect of base heterogeneity” for the two groups of processing households that decided to use formal financial capital as the difference between (a) and (d),

(10)

Similarly for the group of farm households that decided to use informal financial capital, “the effect of base heterogeneity” is the difference between (c) and (b)

(11)


Finally, we investigate the “transitional heterogeneity” (TH), that is if the effect of using financial capital is larger or smaller for the cassava processing households that use formal financial capital or for the processing household that uses informal financial capital in the counterfactual case, that is the difference between equations (5) and (6) (i.e., (TT) and (TU)).


A simultaneous model of outcomes and treatment (Financial Capital)

Let and denote individual outcome with and without a given treatment, respectively, noting that for each individual we only observe the outcome in one state or the other. Furthermore, let the conditional expectations of these variables, given a vector of observable characteristics be given by and , where and are unknown parameters. We can then write

(12)

(13)


Where and are zero mean error terms, assumed to be independent of , Let if individual received a treatment, and otherwise, so that the observed outcome is given by


In many economic examples assignment to the subsample receiving the treatment is not random and potentially endogenous to the outcome variables in the primary equation. The objective is to estimate the effect of undertaking the treatment while accounting for its endogeneity. To complete the model assume that there is some possibility that non-outcome related consideration are relevant to the decision to undertake the treatment. Let denote the cost of acquiring the treatment where

(14)


Where is a vector of exogenous variables, independent of , is a unknown parameter vector, and is a zero mean error term. The variables in may overlap with those in . Assuming individuals wish to maximize wages, the decision to undergo the treatment can be written as

(15)


Where is a vector containing all elements found in and and is a vector of reduced form parameters. The errors , are assumed to be independent of the exogenous variables, with variances and covariances for noting that it is not uncommon to assume joint normality. The covariances for the pairs are denoted and respectively. The specification in (15) is based on the assumption that, when making the decision to undergo the treatment, individuals know their individual gains from it. Relaxing this and assuming, for example, that a priori the individual expected gain from treatment is , would not change the reduced form in (15), although would not include and . This has no implication for the statistical properties of the estimators we consider, as these will depend upon and but it does not have implications for the economic interpretation of the results.

When the model collapses to that considered by Roy (1951) and individuals choose to undergo the treatment purely on the basis of a comparison of the potential outcomes. Generally, costs are allowed to be relevant and includes variables in addition to which capture differences in costs across individuals. As observable characteristics are likely to influence the treatment cost appears in , then it is likely that unobservable characteristics also influence the cost of acquiring the treatment independently of their influence on the outcomes in the two sectors (formal and informal). Accordingly, has a positive variance. This is potentially an important point. Assuming the treatment effects operates through the intercept only (Robinson 1989, Heckman and Robb 1985), the observed outcome can be written as

(16)


Where The treatment effects for individual is while is the average treatment effects (ATE) of an individual “randomly” assigned to the treatment. Note the terminology is slightly misleading as there are no individuals who are randomly assigned. However, the term is meant to capture the idea that the unobservables capturing the treatment decision which are correlated with the outcome have been controlled for. Heckman (1990) refers to this as the experimental treatment average. The ATE of those undergoing the treatment is given by

(17)


Which comprises the sum of the experimental average and the return to the unobservables as they are priced in the treated sector. This is a special case of what Imbens and Kalyanaraman (2009) focus on the estimation of the experimental treatment average, .


METHODOLOGY

The study was carried out in two States of South Western Nigeria. The States are Ogun and Oyo. The states are endowed with a favourable climate and good vegetation for all year round cultivation of various cash and food crops, livestock rearing as well as processing activities. The states are well drained with rivers flowing from the upland in the north-south direction. Majority of the people in the study areas are smallholders who are involved in farming, processing and trading. They grow arable crops, (maize, yam, cassava, millet, rice, plantain, cocoa tree, palm tree, cashew, etc.) fruit crops, and also engage in small scale processing activities (e.g cassava processing) and rearing of poultry, goat, cattle and fish farming. Cassava processing activities are widespread in the rural areas being the most common processed crop in Oyo and Ogun States and small scale cassava processing equipment are by far more widespread in the area than for any other agricultural produce. Processing cassava root tuber into dry form reduces its moisture content and converts it to a more durable and stable product with less volume which makes it more transportable. Processing is also necessary to eliminate or reduce the level of cyanide in cassava and improve the palatability of the products.


Image is Available in PDF Format

Oyo State


Image is Available in PDF Format


Ogun State

Figure 1: Cassava Processing Site in Oyo and Ogun States


Summary of Variables and Descriptive Statistics

This study made use of primary data that were obtained through a cross-sectional survey of cassava processing households. A multi-stage sampling technique was used in selecting the 540 respondents (cassava processors). Data on five hundred and thirty five (535) respondents were used in the analysis and the remaining five (5) copies of questionnaire were dropped due to inappropriate or incomplete responses.


RESULTS AND DISCUSSION

Table 1 presents the summary definitions and descriptive statistics (mean and standard deviations) for the processing households. It can be observed from the Table that the average age of the processors is 46 years. This implies that most of the cassava processors were young and in their prime age in terms of productivity and this agrees with the findings of Oluwasola, (2010). Hence, given the necessary resources, they have high potentials to attain a high level of processing productivity. The relatively young age of the respondents should, all things being equal have positive impacts on enterprise size, earnings, and the ability to take risks and adopt modern innovations within the context of a familiar and clearly understood technological terrain. Processors in the study areas have an average processing experience of 16 years. These years of experience irrespective of the financial capital used is sufficient for a thorough understanding of the technical procedures of doing the business profitably. This is also an indication that the cassava processors are well versed in their production and they are likely to adopt new technology if privileged. It can also be inferred that the experience coupled with acceptance and adoption of improved processing technology will probably have direct relationship with welfare. This view is corroborated by Oluwasola (2010) and Muhammad-Lawal, et al., (2013). Education is measured as the number of years of schooling completed. The result in the Table also indicated that the processors had at least primary education. These results suggest that level of education among respondents with informal financial capital users was very low compared to that of the formal financial capital users. This is contrary to Yidana, et al., (2013) in a study on the impact of cassava processing on the livelihoods of women processors in Central Gonja district of the northern region of Ghana where most of the processors were not educated. The result further indicated that the ownership structure of the cassava processing business in the study areas was mostly sole proprietorship. This will make quick decision possible. Few of the respondent indicated partnership (joint business ownership). These findings are consisted with the findings of a study on “Cassava Master Plan” (2006).


Table 1: Summary of variable (variable names, definitions) and descriptive statistics

Variables

Definition of variables and measurement

Expected sign

Mean

Std. Dev. (SD)

Dependent/Endogenous (Outcome) variables


Welfare



Treatment (Endogenous/exogenous) variables





1. Formal FC use

1 if farmer used Formal FC, 0 otherwise

+

0.23

0.42

2. informal FC use

1 if farmer used Formal FC, 0 otherwise

+

0.25

0.43

Independent variables





Age

Age of the respondents (years)

+/-

46.00

8.00

Gender


+/-

0.084

0.278

MS (Single)

Marital status (Single)

+/-

0.0261

0.160

MS (Married)

Marital status (Married)

+/-

0.718

0.451

MS (Widowed)

Marital status (Widowed)

+/-

0.183

0.387

MS(Divorced)

Marital status (Divorced)

+/-

0.073

0.260

HHSIZE

Total number of household size

+/-

6.89

2.14


No formal education

+/-

0.232

0.422


Primary school (proportion)

+

0.364

0.482


Secondary school (proportion)

+

0.361

0.481


Tertiary (proportion)

+

0.043

0.203

EDU

Years of schooling

+

7.52

5.35

ENTSIZ

Enterprise size (1 small, 2 medium) (Proportion of labour used)

+

1.36

0.50

EXP

Processing experience (years)

+

16.00

8.99

OWNSTR

Ownership structure (1 sole, 2 partnership)

+/-

1.20

0.40

INITCAP

Initial capital (Naira)

+

23167.66

45258.37

FAMLAB

Total number of family labour (man/day) proportion of manday

+

4.06

2.48

HIREDLAB

Total number of hired labour (man/day) proportion of manday

+/-

3.99

2.36

EXTVIST

Extension visit (average visiting period

+/-

2.77

1.28


Distribution of Welfare Status and Descriptive statistics

Table 2 presents the t-test comparison of means of selected variables by financial capital user group for the processing households. The significant differences between the two groups of users were in particular, observed with respect to age, education, initial capital, family labour and outcome variables (welfare). Welfare status of formal financial capital users is 0.0574 percent better than that of informal financial capital users indicating significant differences in their welfare.


The average age for informal financial capital users is 6.342 years higher than that of formal financial capital users indicating significant differences in ages. This difference suggests that younger processors are better in the application of formal financial capital to their business than their older counterparts who preferred the use of informal financial capital. In addition, formal financial capital user had approximately one year of formal education more than an informal financial user counterpart. The implication of this is that the processors that used formal financial capital are more educated that their informal financial capital users. The difference in years of formal education between these two groups of financial capital users is statistically different from zero at the 5% level. The results further showed that in terms of initial capital, cassava processors using a formal financial capital had a significantly higher initial capital of about N1, 596.65 higher than the informal financial capital users. This implies that the formal financial capital users have more savings. The difference in initial capital between these two groups of financial capital users is statistically different from zero at 10% level. In addition, the results showed that in terms of labour, an informal financial capital user used a significantly higher family labour than a formal financial capital user. Results indicate that an informal financial capital user had approximately one more family labour than formal financial user counterpart. The difference in family labour between these two groups of financial capital users is statistically different from zero at the 10%. The implication of the above results reveals that there were significant differences in the socioeconomic variables and livelihood outcomes between the two processing groups of financial capital users.


Table 2: Descriptive statistics of Two Processing Groups of Financial Capital Users (Formal and Informal)

Variables (Total sample 535)

Formal financial capital users (n = 245)

Informal financial capital users(n = 290)

Difference

Welfare

0.5918(0.0314)

0.5344(0.0293)

0.0574*

Age (years)

46.061 (0.501)

52.403 (0.4778)

-0.6342**

Gender (proportion)

0.0828 (0.0179)

0.0857 (0.0162)

-0.00296

Single (proportion)

0.0714 (0.0688)

0.0844 (0.0121)

0.0130

Married (proportion)

0.0859 (0.022)

0.0794 (0.0220)

-0.00647

Widowed (proportion)

0.0918 (0.0292)

0.0824 (0.132)

-0.00946

Divorced (proportion)

0.0513 (0.0353)

0.0867 (0.0126)

0.0354

Household Size (numbers)

6.760 (0.127)

7 (0.132)

0.2401

No formal education (proportion)

0.105 (0.0275)

0.0779 (0.0132)

-0.270

Primary school (proportion)

0.0667 (0.0179)

0.0941 (0.0158)

0.0275

Secondary school (proportion)

0.0881 (0.0204)

0.0819 (0.0148)

-0.00621

Tertiary (proportion)

0.0870 (0.0588)

0.0840 (0.0126)

-0.00297

Education (years)

7.196 (0.339)

6.507 (0.279)

-0.689**

Enterprise Size (proportion of labour used)

1.331 (0.0301)

1.379 (0.0285)

0.0487

Processing Experience (years)

15.841 (0.557)

16.8 (0.540)

0.959

Ownership Structure (proportion)

1.188 (0.0250)

1.207 (0.0238)

0.0191

Initial Capital (N)

11122.86 (883.454)

9526.21 (443.603)

-1596.65*

Family Labour (proportion of man/day)

3.841 (0.155)

4.245 (0.148)

0.4040*

Hired Labour (proportion of man/day)

4.135 (0.150)

3.862 (0.139)

-0.272

Standard errors in parentheses.

**p<0.05; * p<0.1

Determinants of choice of Financial Capital usage on Livelihood Outcomes

The estimates of the determinants of choice financial capital use on welfare are presented on Tables 3 and 4. The full information maximum likelihood approach estimates both the choice of financial capital use and the livelihood outcomes (welfare) equations jointly. Thus, the selection equations, representing determinants of choice of financial capital use are given in the second column of each of the Tables 4-6, providing two different sets of results due to slightly different specifications. Given that the coefficients can be interpreted as normal Probit coefficients, the results of the selection equations are first interpreted followed by the interpretation of the coefficients of the variables of the two samples (formal and informal) representing the residuals derived from the first-stage regressions for the potential endogenous variables.


Determinants of Choice of Financial Capital on Welfare:

For the formal financial capital users, the variable representing age of the farmer is negative and significantly different from zero, suggesting that the older processors are, the less they are likely to use formal financial capital type. In addition, the variable representing education of the farmer is positive and significantly different from zero, suggesting that more-educated farmers/processors are the more likely they are to use formal financial capital type. The positive and significant coefficient of the variable suggests that good knowledge and firm understanding of the financial capital types may increase the benefits of usage on the welfare of the processors (Table 4). The variable for processing experience is positive and significantly different from zero, suggesting that processors that are more experienced are the more likely to use formal financial capital type, confirming the significance of processing experience for processors in the usage of financial capital type. For the informal financial capital users, the variable representing age of the farmer is positive and significantly different from zero, suggesting that older processors are more likely to use informal financial capital type. This result confirms the importance of experience accrued by the processors as a result of age, as a means of reducing uncertainty about the usage of informal financial capital type.


The correlation coefficients rho_1 and rho_2 are both positive, but significant only for the correlation between the welfare equation and the informal financial capital use equation. Since rho_2 is positive and significantly different from zero the model suggests that individuals who chose to use informal financial capital have better welfare in cassava processing than a random individual from the sample would have earned, and those using formal financial capital do no better or worse than a random individual. The likelihood ratio test for joint independence of the three equations is reported in the last line of the output. The variables sigma, /lns1, lns2, /r1, and /r2 are ancillary parameters used in the maximum likelihood procedure. Sigma1 and Sigma 2 are the square-roots of the variances of the residuals of the regression part of the model and lnsig is its log. r1 and r2 are the transformation of the correlation between the errors from the two equations. This finding is supported by the report from Ahmed and Mesfin (2017) that if non-members of agricultural cooperatives had been members, their consumption per adult equivalent would have been higher. This implies that agricultural cooperatives increased household welfare measured in terms of consumption expenditure. In support of this, the result of our study implies that the usage of financial capital (formal and or informal) leads to better welfare for cassava processing households and a better alternative to credit which is usually accompanied with a lots of bottleneck.


Table 4: Determinants of Choice of Financial Capital on Welfare


Selection

Formal Financial Capital Use

Informal Financial Capital Use

Variable

Coeff.

Coeff.

Coeff.

Age

-0.00306

0.00185

0.00128*

Age square

0.00354***

-0.0000952*

-0.0000474

Education

0.00889

0.00396*

0.00444

Household size

0.0388

-0.00568

-0.0298

Processing experience

0.00163

0.001*

-0.00343

Initial capital

-8.44e06

5.85e-06

-2.09e-06

Extension visit

-0.250

0.0617

-0.250

Trainings attended

-0.1062

0.108

-0.1062

Constant

-0.00173

0.7769

0.861

Married

-0.562



Widowed

-0.655



Divorced

-1.346***



/Ins1

-0.691



/Ins2

-0.690



/r1

-0.2326



/r2

-0.350



Sigma_1

0.5012



Sigma_2

0.5016



rho_1

-0.2285



Rho_2

-0.3365



Log likelihood

-722.823



Prob > chi2

0.5705



***p<0.01; *p<0.1


Effect of Financial Capital Use on Welfare

The estimates for the Average Treatment Effects on the Treated (ATT), which shows the causal effects of financial capital use on food security and welfare using ESR are presented on Tables 5 - 9. The ATT estimates account for selection bias arising from the fact that formal and informal financial capital users may be systematically different.


Full sample estimates: The results of full sample estimates on Table 5 reveals that the formal financial capital use tends to significantly increase welfare of the processors. Specifically, formal financial capital use improved welfare by 33.36% when compared to the causal effects of informal financial capital use on processors. These findings suggest that promoting the use of formal financial capital in cassava processing enterprise can be beneficial to farmers’ welfare and food security. Awudu, and Wallace (2014) had earlier found similar result in their work on the adoption and impact of soil and water conservation technology.


Table 5: Effect of financial capital use (on Food security and welfare)


Mean Outcome




Formal

(n=245)

Informal

(n=290)

ATT

Difference

(%)






Welfare

0.3402

0.2551

0.0851

33.36*






ATT = Average Treatment Effects on the Treated; *p<0.1


Conclusion and Implications

This study examined the effects of financial capital on food security status and welfare of cassava processors in Oyo and Ogun States, Nigeria, using various econometric tools, techniques and indices (Per capita household expenditure, food security index and endogenous switching regression). The findings revealed that that there were significant differences in the socioeconomic variables and livelihood outcomes between the two processing groups of financial capital users. Also from the study, the more experienced processors were, the more likely they used formal financial capital type and that the more-educated farmers/processors are the more likely they used formal financial capital type. In addition, the choice of formal financial capital significantly improved processors’ food security and welfare by 4.37% and 33.4% respectively when compared to informal financial capital users. We found that financial capital is effective at improving the welfare and food security status for more educated and experienced processing households. This study had also contributed to the body of literature on the effects of financial capital most especially on the food security and welfare of smallholder cassava processors in South Western Nigeria, particularly in Oyo and Ogun States.


The following recommendations emanated from the study:

  1. Financial capital should not only be viewed as a good substitute to credit for the small scale cassava processors but also considered as an alternative means of improving the welfare and food security status of the processing households. Thus further promoting, deepening, and supporting FC as appropriate for cassava processors in lieu of credit is recommended to serve as a substitute to the issue of unavailable and insufficient capital.

  2. Our study revealed that education and experience affected the choice of formal financial capital use as compared to informal capital. This suggests that there should be enlightenment and advocacy for the processing household, since FC is thought to play a crucial role in making funds available thereby enabling farmers and processors to purchase key inputs

  3. Policies that will make use of the indicative variables (factors), promote and ensure the awareness and appropriate use of financial capital are encouraged.


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