A BIPOLARIDADE DO PROCESSO, A IGUALDADE PROCESSUAL E OS

thus platforms (Aker et al. 2016). Therefore, and as expected, in both cases the coefficient is significantly positive and very high, yet different between women and men. The results in Table 7.6 and the analysis of descriptive statistics (Section 7.2.1) demonstrate that rural women use the internet to a lesser extent than do rural men when a computer is available at home. This difference could stem from various factors, for instance unequal access to education, leading to illiteracy (and IT-illiteracy), as emphasised by Hilbert (2011) and also supported by results generated from the census data. Alozie & Akpan-Obong (2017) posit that these differences could be related to the fact that women are confined to the role of spectators and occasional consumers of ICTs. They are of the opinion that to bridge a digital gender divide in the use of technology, rural women in developing countries need to be targeted, so that they can become comfortable using and designing computer technology, and working in virtual spaces. This will be discussed further in Chapter 8.

Men and women who report to have a mobile phone at home are more likely to use the internet (p<0.01), by 2.5% for men and 1% for women. It could be that the impact of computers on internet use is greater than that of the telephone. An assumption could be that the purchase of the computer is intended to connect to the internet and use its services, which may not necessarily the intended purpose of a telephone. It could also be that the mobile phone is limited when it comes to using the internet and consequently ICT platforms.

In sum, the data show that rural women and men in Kenya currently attending school have a higher probability of using the internet compared to the other groups of rural women and men.

Lower coefficients for women show that they have a decreased likelihood of using the internet compared to men in the same category. Likewise, the presence of ICTs, and especially computers, increases the likelihood of females and males using the internet. The computer variable coefficients are high for both rural women and rural men. By looking at the coefficients for this ICT device, however, women appear to be more disadvantaged compared to men.

Having a computer at home represents an investment. There is a vast scientific literature (Hafkin & Huyer 2008; Antonio & Tuffley 2014; Alozie & Akpan-Obong 2017; Hilbert 2011;

Mumporeze & Prieler 2017), stressing that women do not have the same access to ICT devices as a result of discriminating intra-household gendered structures (for instance, that it is the men who are the final decision makers in regards to investments). Evidence from the census data presented in Section 7.2.1 reveals that the levels of internet use increase for individuals accessing a computer in rural Kenya. They also show, however, that rural women are at a disadvantage irrespective of economic activities, educational status or intra-household status. I will now explore variables that could be correlated with women farmers’ use of the internet in rural Kenya.

7.3.2. Women farmers’ internet use

Table 7.7 reports the marginal effects of internet use among women who report working on their own family agricultural holding in rural Kenya (above or equal to 18 years of age). The table shows the correlation between socio-economic variables and the reported internet use of Kenyan women farmers¹³³. The R² of 10.25% show that women farmers’ use of the internet

133 The motives behind this sub-sample of the Kenyan population are multiple. As previously emphasised, this research analyses the inclusion of gender relations on ICT platforms, via public policy intervention. It is therefore of relevance to analyse this sub-sample. First, individuals above or equal to 18 years of age were selected. The main reason behind this filtering was to keep the population above the legal working age in Kenya (because a farmer / agricultural worker is an economic activity). Second, since this dissertation bases its case specifically on gender relations of a sub-sample, namely female and male farmers, the sample is restricted to this part of the

may be related to a set of qualitative variables not captured through structural variables. The endogenous and dichotomous variable, ‘Internet use’, is a merged variable between different reported levels of frequency in internet use (cf. previous section for further explanation).

Table 7.7: Variables that are interrelated with internet use for women farmers above or equal to 18 years of age, rural Kenya. Marginal effects reported (dy/dx).

Variables Rural women farmers (dy/dx) Std. err.

Currently attending school (ref.: no education) 0.0135*** (0.00054)

Previously attended school (ref.: no education) 0.0013*** (0.0002)

Age -0.00003*** (0.0000006)

Divorced (ref.: married monogamous) -0.002* (0.00088)

Married polygamous (ref.: married monogamous) -0.0005** (0.00021)

Never married (ref.: married monogamous) 0.003*** (0.0005)

Separated (ref.: married monogamous) -0.003*** (0.00008)

Widowed (ref.: married monogamous) -0.0023*** (0.0003)

Relationship status (0=household head; 1=spouse) -0.0012*** (0.0002)

Computer at home (0=no; 1=yes) 0.05*** (0.00034)

Mobile phone at home (0=no; 1=yes) 0.0065*** (0.00017)

Small scale farmer (0=no; 1=yes) -0.0041*** (0.00023)

Informal sector (0=no; 1=yes) 0.0002 (0.00023)

Dependent variable (dichotomous variable): Internet use (0=never use internet; 1=use internet)

Number of observations women = 2,549,340 Pseudo R² women (McFadden) = 0.1025

***p < 0.01 **p < 0.05 *p < 0.1 Controlling for 44 counties in Kenya

Source: PHC special data processing.

As for the rest of the population, currently attending school increases the probability of using the internet (1.35%, significantly positive), compared to women farmers who have no education. The fact of having attended school increases the probability of using the internet, however by very little (coefficient is less than 1%), compared to women farmers who have no education. As we may have expected, the use of internet is thus related to education. The

‘currently attending school’ variable is especially interesting in its interference with internet use, as presented in Table 7.6 and Section 7.2.2.

Age has a negative relationship with internet use, even though the coefficient is very small (less than 1%). When the individual’s age increases by one year, the probability of using the internet decreases, but only by 0.003%. These results corroborate with the descriptive statistics data analysis. It shows that the younger part of the female farming population tends to use the internet to a higher degree, although it seems to connected to educational status to a larger extent than age (cf. also Section 7.3.1, p. 20 on further explanations of the age variable). The census data show that the levels of internet use increase substantially for women farmers who are currently attending school. This also happens to be the younger part of the women farmer population.

population. Third, and as stated in Section 7.3.1, 86% of the farmers are based in rural Kenya. Therefore, I analyse the population based in rural Kenya. Also, the filter ‘women farmer above or equal to 18 years of age residing in rural areas of Kenya’ was purposely chosen to connect the statistical analyses to the qualitative surveys with the small-scale female farmers (n=26). Fourth, compared to Table 7.6, two out of the 17 ‘main employers’ reported by farm women in rural Kenya are reported in the dataset [small scale farmer, employed in the informal sector].

In the previous analysis, all 17 main employment types were kept to stabilise the dataset. These two employment types are the main ones reported by female farmers above or equal to 18 years of age in rural Kenya. These two variables were also kept to reduce the level of heterogeneity in the dataset.

The probability of using the internet decreases for women farmers married in a polygamous relationship, compared those who are monogamously married (negative, though very small coefficient, 0.2%). For women who reported not being married, the likelihood of using the internet increased by 0.03% compared to women married in a monogamous relationship.

Compared to monogamously married women, those who are separated or widowed have a decreased probability of using the internet. Both coefficients are however very small, i.e. 0.03%

and 0.02% respectively. Generally, the different marital statuses of women farmers decrease the likelihood of them using the internet, compared to monogamously married women (except for unmarried women). These results are consistent with the coefficients presented in Table 7.6.

The relationship between the exogenous variables per marital status and internet use is quite small. Marital status is not the main explanatory variable when it comes to understanding the internet use of women farmers.

Contrary to what the results show in Section 7.2.3.2, there is a decreased likelihood that female farmers who are spouses use the internet, compared to household heads. However, the coefficients are very low and therefore provide evidence of the fact that there is no causal link between any of the two relationship statuses, or between women and men farmers. The literature (Codjoe 2010; Agarwal 1997) suggests that coefficients could have been expected to be higher.

Having a computer at home (p<0.01) increases the probability of using the internet by 5%. The coefficient is high and so is the level of significance. It shows that a computer present in the home increases the likelihood that female farmers will use the internet. In Section 7.2, I assessed more in depth the patterns of internet use between women farmers and men farmers who say they have a computer at home. These results show that a higher proportion of women report using the internet when a computer is available at home. Yet, there is still a gap between genders. The data show that men farmers are at an advantage compared to women farmers. This corroborates the findings from the descriptive statistics and the econometric analysis.

In this regard, in the reports by the World Bank and the FAO (George et al. 2011; The Food and Agriculture Organisation 2014), computers are presented as ICT solutions that can enable farmers in developing countries to use technical content made available via the internet, for example on knowledge-based platforms. In the same reports, it is however stressed that women farmers computer access may not be equal to that of men farmers because of various socio-economic barriers (education, intra-household social status). Hafkin & Huyer (2008) put forward similar arguments. Such obstacles may consequently prevent women farmers from using services offered by knowledge-based platforms. Here, the analysis shows that computer access largely increases women farmers’ possibility to use platforms, but that education interferes as one fundamental variable.

Women who report to have a mobile phone at home have an increased likelihood of using the internet. The coefficient is very small (less than 1%). It was expected that this coefficient would be higher since, in a number of articles in the economic literature, authors make the assumption that mobile-based internet platforms have the highest potential in reaching farmers with services (Karippacheril et al. 2013; Courtois & Subervie 2015; Ogutu et al. 2014). As it appears here, computers have a stronger relation to the likelihood of using the internet compared to the mobile phone.

Women who report ‘self-employed small-scale farming’ as their main employment type have a negative likelihood of using the internet. The coefficient is nonetheless less than 1%. Working

in the informal sector does not have a significant relationship with internet use (the coefficient is also very small). Interpretations of such a result could mean that small-scale women farmers are to a larger extent excluded from entering into use with internet services compared to those who report working informally.

In sum, women farmers currently attending school have a higher probability of using internet services. Thus, similar to previous econometric analysis but for a large sample of the population, such results may imply that equal access to education is not enough to reduce a digital gender divide. As a result, such findings complement analyses from the descriptive statistics section (cf. Section 7.2). Moreover, the presence of a computer increases the likelihood for female farmers of using the internet. Section 7.2 provides strong evidence of this.

It should however be emphasised here that the reported internet use figures are generally lower for women farmers compared to men farmers, which brings us to the last section of this Chapter.