Tajuddin Khan, Avinash Kishore, PK JoshiIFPRI, New Delhi, India

Accounting for Women in Adoption of New Technologies in Agriculture : A Case study

from Maharashtra, India

Direct Seeded Rice with Drum Seeder

Objectives 1. To understand the preference heterogeneity

between men and women for Direct Seeded Rice (DSR) Drum-seeder

2. To measure the willingness to pay for direct seeded rice (Drum Seeder) for both men and women

3. To find out factors that explain farmers technology adoption behaviour

Location of study

Note: In June 2014, the Thane district was bifurcated into two districts: Thane and Palghar.

Sampling and Data: Stratified random sampling

• 2 districts (Thane and Palghar) of Maharashtra

• 6 rice growing blocks from 2 district, four from Palghar (Palghar, Jawhar, Mokhada and Wada) and two from Thane (Shahapur and Morbad)

•Within each block, we randomly selected five villages (30 villages)

• Our sample consists of 666 respondents (329 men and 337 women) farmers from 400 households

1. 266 households (both men and women)2. 134 households (63 male and 71 female separately)

Selection of DSR-Drum Seeder Attributes and levels

Attributes Definition Levels

Seed rate (Kgs) Seed (kg) is required for one acre of land

5, 10, 15, 20

Labour saved (mandays)

Number of labour saving in one acre of land

8, 10, 12

Yield Increment (Kgs)

Yield increment is considered in DSR 50, 100, 150, 200

Weedicide cost (INR)

Weedicide cost is must in DSR is considered

400, 600, 800, 1000

DSR Price (INR) Price of Direct seeded rice (drum seeder)

4000, 5000, 6000, 7000

Example of choice set presented to survey respondents: BLOCK 1: SET 3 [Each respondent saw 9 such sets]

Accounting for Women in Choice of Technologies• Agricultural decisions are not undertaken by a unitary household (Duflo and Udry, 2004)

•Women farmers may have different preferences from the men in their families and adoption of a new technology may affect them differently• true particularly for rice cultivation since women contribute 60-80%

of labor in rice in Asia• Even greater share in transplanting labor

• Studies ignore women when sampling or compare FHHs & MHHs• Leaves out a huge number (92%) of women who live in male-headed

households

•We sample one woman and one man from each HH in our sample• 266 pairs of respondents were surveyed independently

Women and technology adoption in agriculture

•Women have slower observed rates of adoption of a wide range of technologies than men (Doss and Morris, 200)

• and lower willingness to pay for new products like the weather indexed insurance (Akter et al, 2016)

• Probably due to greater time and resource constraints• lower human capital endowment (education and exposure to the

outer world) and• poorer access to complementary inputs (Kamwamba-Mtethiwa, et.

al., 2012)

In our study, women were more interested in the new technology

Dependent variable: “Choose either of the two DSR with drum seeder combinations”

All households

Male respondent

-0.1950*

** 

Constant 0.9952***  

Card fixed effect Yes  

Household fixed effect Yes  

No. of observations 5867  

R2 0.4025  Note: Standard errors are in parenthesis and ***, ** and * denotes significance at 1, 5 and 10 percent level.

T-test results of difference between Men and Women MWTP for DSR-drum seeder attributes

Note: *** denotes significance at 1% level of significance

 Variable Men Women Diff.

T-test of sig. diff. in means

Seed rate (kg) -20.45 -16.48 -3.98 -1.27

Labor saved (person-days) 157.04 336.53

-179.4

9 26.39***

Yield increment (quintals) 1407.96 787.41

620.54 33.83***

Weedicide cost (INR) -0.16 -0.08 0.08 12.39***

Individual-level Marginal Willingness to pay (INR) for DSR attributes

Cont…

Individual-level Total Willingness to pay (INR) for DSR

Women’s Empowerment in Agriculture Index (WEAI): Part A

Note: Standard errors are in parenthesis and *** denotes significance at 1% level.Numbers in tables are based on Likert scale from 1 to 5, where 1 represents least say and 5 represents more say

Variable MenWome

nDifferen

ce t-testTo what extent do you feel you can make decisions if you want(ed.) to:Food crop farming 4.671 3.894 0.778 9.893***

(0.036)(0.069

)Agricultural production 4.434 3.634 0.801 9.381***

(0.040)(0.076

)

Inputs to buy 4.521 3.691 0.82910.224**

*

(0.039)(0.072

)Crops to grow 4.407 3.732 0.675 8.108***

(0.046)(0.069

)Crops to market 4.451 3.495 0.956 6.951***

(0.063)(0.142

)Own wage salary 4.378 3.904 0.475 5.848***

(0.044)(0.069

)

Women’s Empowerment in Agriculture Index (WEAI): Part B

Note: Standard errors are in parenthesis and *** denotes significance at 1% level.Numbers in tables are based on Likert scale from 1 to 5, where 1 represents least say and 5 represents more say

Variable MenWome

nDifferen

ce t-testWho, according to you, can decide whether to buy, sell, or rent/mortgage (self)?

Farm equipment (nonmechanized) 0.379 0.092 0.2879.262**

*

Farm equipment (mechanized) 0.0703 0.002 0.0674.713**

*

Do you feel comfortable speaking up in public? 0.406 0.274 0.132

3.545***

Did you work more than 10.5 hours in the previous 24 hours? 0.539 0.762 -0.222

-6.142**

*How would you rate your satisfaction with youravailable time for leisure activities? 4.5 3.494 1.006

5.705***

Conclusion• Men have a higher willingness to pay for attributes that increase income

(increase in yield) and or reduce cash costs (reduction in the seed-rate).

• Women value more for reduction in labor requirement (and possibly the accompanying drudgery) more than the men

• Women have a significantly lower say than the men in household decisions related to agriculture like choice of crops, inputs to buy and adoption and purchase of new technologies and equipment and in their families• Still it is not so that they have a zero say

• Extension should not leave women out and and should also highlight attributes of the technology that is of greater interest to them

• Capital subsidy is needed to promote its adoption by farmers in our study area

Thank You!