Mechanization and Women's Work in Ghana Ghana Strategy Support Program Terms of Reference: Mechanization and Womens Work in Ghana PI: Valerie Mueller (ASU, IFPRI) Objective: We want to look at how the adoption of three-wheeler motorized trucks (TWMT) influences women. Women are likely affected by the adoption of TWMTs in three formative ways. First, women may benefit in the form of time savings since they primarily carry crop loads on their head from the plot to the home and sometimes also to the market. Second, women are likely to have additional time to allocate to cultivating more land, to start a business, for household chores, and to spend time weeding which enhances yields. Third, their ability to carry heavier loads to the market could increase sales, but its not clear if there is a gender-specific benefit from this. Tasks for the research assistant: We need to establish what women do in any given day to understand how they might be personally affected by time savings of the technology. The research analyst will evaluate gender-differentiated outcomes to see how time use patterns are changing from the time use module with the adoption of the technology: 1) hours spent on personal care (sleeping and resting, eating and drinking, personal care; exercising); 2) hours spent in school; 3) hours spent working (codes E, F, G, H, I, J, K, L); 4) hours spent commuting (code M); 5) hours spent on domestic work (codes N, O, P, Q); 6) hours spent taking care of children exclusively (code R); 7) hours spent taking care of adults (code S); 8) hours spent on social activities (codes T, U, V, W) The analyst will develop a plot level by season dataset for cultivated plots over the period of the study including the following variables: Plot level characteristics: longitude, latitude, soil quality, slope, road conditions to and from plot, road index, distance from plot to house, use of TWMT from module E, binary variable for whether the plot is owned exclusively by a woman, a binary variable for whether the plot is jointly owned by any members of the household, a binary variable for whether the plot is owned exclusively by a male of the household, a binary variable for whether a male make input decisions on this plot exclusively, a binary variable for whether a female makes input decisions this plot exclusively, a binary variable for whether the input decisions on this plot are jointly made by multiple family members, a binary variable for whether a female makes output decisions on this plot exclusively, a binary variable for whether a male makes output decisions on this plot exclusively, a binary variable for whether the output decisions on this plot are jointly made by multiple family members, binary variable for season (dry, wet), number of months in the season that road from plot to your house becomes difficult to use. Plot level outcomes: hectares cultivated, imputed level of production if we can get price information for these crops from a secondary source (quantity harvested times price for the crop summed over all crops produced on the plot), total quantity sold for main crops in these communities (primary and secondary market), binary variable for crops from this plot sold in a secondary market, time women in the household devote to preparing the plot, time women devote to weeding/fertilizing/non-harvesting activities on the plot, time women devote to harvesting on the plot, time men in the household devote to preparing the plot, time men in the household devote to weeding/fertilizing/non-harvesting activities on the plot, time men in the household devote to harvesting on the plot, time children devote to preparing the plot, time children devote to weeding/fertilizing/non-harvesting activities on the plot, time children devote to harvesting on the plot, total time takes to travel to plot from house by walking. Household variables: number of working age women (18-65), number of working age men (18-65), number of children (5-17), total household land owned, value of asset wealth, value of farm equipment, binary variable for whether the household is female headed, heads completed education, heads age, ethnicity of head, religion of head, main language spoken by household; indicator for district. Dataset should be in longform: HHID Plot ID SEASON OTHER VARIABLES 1 1 1 1 1 1 2 3 The research analysis will help evaluate the causal effect of using a TWMT on the plot level outcomes of interest with the guidance of the PI. Plot level GPS coordinates will need to be cleaned and matched to the names of the roster in the process. Timeline: August through September 2017: Preliminary results to present at the Ghana Policy Forum in Accra December 2017: Final analysis and dataset for review at the PIM Gender conference for feedback prior to finalizing the working paper.
|Effective start/end date||8/28/17 → 12/31/17|
- US Agency for International Development (USAID): $72,642.00
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