Estimating and Decomposing Groundnut Gender Yield Gap: Evidence from Rural Farming Households in Northern Nigeria

Round 1

Reviewer 1 Report

Dear Authors,

please refer to the file attached

Best Regards

Comments for author File: Comments.pdf

Author Response

Please see the Word attachment

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear author, This manuscript was performed by statistical analysis models for finding the gender effects on yield of groundnuts, however, the survey tools or sheets need to indicate in supplementary part. In addition, the gender issues will be a matter of debate on a political issue. Thus, those be carefully and should be approached rationally, and it seems that the statements should not be spread out only with self-assertion. The findings and results were newly and novelty. Kind regards, Reviewer.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

The reviewed manuscript deals with a very important issue, which is the attempt to find a solution for poverty alleviation in Sub-Saharan Africa (SSA). Need to know that Sub-Saharan Africa has a disproportionately higher number of poor people in the Word that any other region. The available data show that as many as 13% of the total word population live in the SSA and it is estimated that 55% of people in this region live in poverty. The authors analyze a whole range of different reasons for the current situation and evaluate demographic data using classical methods of statistical analysis. It should be noted that the above methods are correctly selected and applied. Noteworthy is the number of parameters (about 40), which are subject to detailed statistical analysis. Using cross-sectional data collected from 1,311 households, authors estimate and analyze the groundnut gender yield using two complementary econometric models that allow building counterfactual outcomes to disentangle the impact of sex of the household head on the groundnut yield gap and analyzing specific policy options for closing the identified gap. Authors use the exogenous switching regression and Oaxaca – Blinder decomposition models to estimate and analyze the gender yield gap among smallholder groundnut farmers in Nigeria. The selection of the cited references is correct, the results of statistical analyzes, summary and conclusions do not raise any scientific or substantive objections. The tables are clearly and legible.

The work should be published in its current form.

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Authors,

Thank you for your revision of the article!

Comment 1:

Thank you for this! However, this has not been done consistently in the whole article. 

Comment 2:

I am not sure about the fact that it would not be possible to calculate the probability of the household head being male given observables, especially if you think household head gender is not random and you fully believe in your “treatment” variable. My intention/ wish was to have a matching mechanism between households. I am not sure how the present approach controls for unobservables and or for omitted variable bias.

Comment 3:

The authors added a sentence in reply to my comments. However, I wanted to make clear to the authors that they could potentially obtain similar results with a more complicated OLS estimating equation, which does not contain only simple linear terms but potentially interactions with the  gender of the household head variable and so on.

Comment 4:

Ok you basically just deleted what you wrote before.

Comment 5:

Ok, thank you.

Comment 6:

Ok, thank you.

Comment 7:

Ok.

Comment 8:

You intended the path dependency problem in the sense of the variable addition. For path dependency I was referring to the problem identified for example by Irving Fisher in creating price indexes. I guess you do not have that problem if you change all variables at the same time right? I.e. you do not want to identify each variable’s effect. Or do you?

Comment 9:

Exactly so we assume the farmers are right in their knowledge, ok. But is that even related to the treatment variable, i.e. is the fertility different between female and male headed households? I think it is shown to be different between soil fertility class 3 and 4.

 

Author Response

Please see the attachement

Author Response File: Author Response.docx

Round 3

Reviewer 1 Report

Dear Authors,

thank you for your work!

I think there are quite a few differences in the results between PSM and non PSM but I believe you have checked better and realised that main results are consistent among different model versions.

Also for the next set of results you say are available upon request, I guess we have to either ask you to put them online or believe you.

Thank you again for your work!

Best Regards