the determinants of the aspirations of the youths in agriculture
The first and the core issue is on the determinants of the aspirations of the youths in agriculture by gender: The feedback reads: “I do not understand, you just say it’s negatively correlated, that is the more assets, the less likely they want to farm. So, the poor are more likely to remain in farming, no?”
Here is my thought on that: Based on the knowledge I had of multinomial regression interpretation before now, I had issues also interpreting that particular outcome initially just as you rightly pointed out as I thought the result does not follow logical reasoning when we draw inference/implications like: …” the more assets (or let us say, the higher the asset), the less likely the youth will envisage farming”. What I found is that interpreting in this manner as it is for household asset might be slightly misleading. This prompted me to take another detailed look at other scholars interpretation of the multinomial regression, I observed that drawing implications like “…the more the asset, the less likely…” is only plausible when we have explanatory variable that has been categorized into for example high or low wherein we make low asset as the reference group. When this is not the case (i.e. when we slot in the variable into the multinomial logistic regression without categories), care must be taken in interpreting as the higher/the more…, especially when the antecedents of the respondents for example shows extremely low level of that variable.
For example, in one of the results, we had a positive correlation between desired placed of residence (in this instance: rural areas) and the aspirations of youth in agriculture. Now, in drawing implications, we cannot say that: the more the young people that desire to stay in rural areas, the more likely such adolescent will envision full-time farming. Rather, in this scenario, the correct form will be: The outcome implies that young people who desire to reside in rural areas in the future are more likely to envision a full-time livelihood in agriculture. From this, we can easily draw policy implication: “Government must ensure that rural areas are provided with basic social amenities and infrastructure required to make life conducive for the rural dwellers.”
If we juxtapose that with the household asset explanatory variable: We simply interpret as: “the probability of young people envisioning a career in agriculture is negatively affected by household asset or simply put as: household asset adversely affects the likelihood of youth aspiring for a career in farming. Reason: The negative correlation between household assets and the aspirations of young people in agriculture reflects the realities of the rural household asset situation, where the majority (94.3%) of the farming household had limited assets. My thoughts now: This to me makes a whole lot of sense because it will take a miracle for my parents for example to convince me into their profession regardless of the level of their influence on me when all I see in the household is lack and abject poverty. Maybe some form of education that offers more exposure can help my decision to remain in the same profession as my parents. The common nexus is that knowledge can help make young people more receptive to innovative and productive measures of that profession (e.g. farming). Policy Implication: Government and relevant stakeholders like COMACO must come up with improved strategies of enhancing rural agricultural productivity and marketing of farm produce to exit poverty. Attached is a short multinomial logistic regression study interpretation to buttress my point.
The second issue is on the literature review section with the headline: Intra-household and social network influence on youth aspirations. There, Dr Juliet provided this feedback: “I think it would be better to separate these two sections. Focus on literatures on gendered division of labour in agriculture for the intra-household section- and you can also refer to the difference between unitary vs an intra-household model”. My question and understanding of the last part of the sentence…”difference between unitary vs an intra-household model”. By that, do you mean the model whereby response from a member of the household interviewed is used to make a general inference for every member of the household? Whereas in our study, responses were obtained from fathers, mothers, sons and daughters from each household.
Dr Juliet also raised something similar in the result section with the headline: Intra-household and social network group influence on youth involvement in agriculture, where she advised that: “We should be cautious not to lump these two categories together. One suggestion is to call this section intra-household and inter-household/community influence and to use this distinction in the same way in the introduction. My response: I completely agree and appreciate that contribution which I will incorporate going forward but my concern is with the first line of statement: …cautious not to lump these two categories together. Question: May I know if you’re suggesting that I break that result presentation segment into two: the first being to discuss “intra-household difference (in other words, responses from father, mother, brother, sister and perhaps relatives in this context), and then inter-household/community influence which includes friends, media, church, youth organisation, NGO’s, and ministry of agriculture. If that is the case, I think separating the influences in the result on that basis might be a little bit difficult in that the 50-bean distribution method that provided the outcome of the various influence levels were shared by lumping them together when the respondents were answering the question. But I still completely agree on the need to make that distinction in the introduction section. I don’t know if you agree with me.
Also, Dr Thomas raised another valid point with other findings with the headline: The distribution of the reasons for not attending school in the present year by gender and asked: “Is this the average answer per individual? You have 11.5% of girls having children, but now more than 20% reported childbirth as one of the reasons for not attending school. How can this be? He also the issue of not indicating the sample size.
My Take on the issue:
It’s a very valid observation. But it’s a multiple response situation which I should have indicated beneath the chart. Usually, it does not add up to 100%. However, when I cross-checked the data set on SPSS, I discovered that one (1) of the data was incorrectly entered for childbirth as a reason for not attending school. Hence, the new value is now 18.4% as against the previous value of more than 20% mentioned earlier. So, the result is still more than the 11.5% of girls having children point you raised. Also, the sample size (N) for this same case (Reasons for not attending school) is 38 out of the 174 respondents. Out of the 38, it’s 19 for boys and 19 for girls. Some other charts don’t have the same sample size by gender. Moreover, I should have indicated the sample size (Number of cases by gender) for the distribution of the reasons why adolescent boys and girls are not presently attending school for better understanding.
Conclusively, with respect to my description of the ideal farm for adolescent boys and girls, Dr Juliet made a valuable point which reads: “These results mirror to a large extent the gendered differences in assets endowments and roles/responsibilities” Kindly let me know if my response below covers the point you raised:
The driving force behind female youth preference for animal draught power might not be unconnected with the ease of accessing the local technology in addition to the overwhelming desire to reduce the twain burden of manual farm operations and household chores. However, the narrative is quite different for mechanization as many of the female youth considers it as out of reach. Most of the adolescent girls lack access to productive resources such as livestock and arable land compared to teenage boys. For example, in the result of the distribution of productive resources discussed earlier, male youth own and managed more arable land than female youth. The boys also owned more livestock like chicken and goat compared to the girls. In extreme cases, only the boys owned high-value herds such as oxen and pig, most of which are products of inheritance and gifts at the expense of the girls.