|(4)<br>(5)<br>Profts<br>Profts (no subsidy)<br>(Mex$/ha)<br>(Mex$/ha)<br>609.48<br>763.60<br>(529.58)<br>(531.50)<br>223.80<br>36.00<br>(611.89)<br>(612.56)<br>-291.38<br>1539.96**<br>(567.34)<br>(568.21)<br>-57.59<br>-43.55<br>(536.10)<br>(542.22)<br>678<br>678<br>0.21<br>0.23<br>2639.20<br>2718.87<br>4024.33<br>4024.42<br>0.36<br>0.00<br>0.54<br>0.00<br>0.61<br>0.00<br>e run the following regression: _Yit_ = _β_0 +_βEExtensioni_ +<br>riod. We include randomization strata fxed effects and com-<br>group, the omitted category in our regression. We also report<br>zation survey conducted in June 2016. In column 1, we use as<br>ntains the value of farmers’ maize production (per hectare) in<br>mer in the 2015 season by the price the maize could be sold in<br>farmers when computing the value of the maize production.<br>stment in soil preparation activities, fertilizers (chemical and<br>at was the case), as well as the subsidy for fertilizer packages,<br>5, the dependent variable is the cost of production, not includ-<br>farmer sold maize, we use median prices at the municipality<br>re measured at the municipality level on the RHS of columns|
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|(3)<br>Costs<br>(Mex$/ha)<br>46.55<br>(273.37)<br>194.98<br>(229.50)<br>623.25**<br>(263.63)<br>353.14<br>(250.81)<br>678<br>0.22<br>5280.02<br>2351.52<br>0.00<br>0.01<br>0.00<br>ple of 678 farmers, w<br>st and_t_is the time pe<br>tcome for the control<br>from the Commerciali<br>ependent variable co<br>harvested by the far<br>ket as the price for all<br>include the total inve<br>aid by QFD (when th<br>ns 2 and 3. In column<br>. In localities where no<br>lue of one if prices we|
|(2)<br>Revenue<br>(Mex$/ha)<br>656.03<br>(543.49)<br>418.78<br>(599.52)<br>331.87<br>(564.96)<br>295.54<br>(543.03)<br>678<br>0.28<br>7919.22<br>4397.72<br>0.02<br>0.06<br>0.02<br>n. Using the full sam<br>the outcome of intere<br>rd deviation of the ou<br>design. We use data<br>on. In column 2, the d<br>total amount of maize<br>production in the mar<br>ch farmer. Total costs<br>d harvest machines p<br>ent variable in colum<br>e revenues and profts<br>dummy that takes va|
|(1)<br>Self-reported<br>yields (t/ha)<br>Extension (1=Yes)<br>0.21<br>(0.16)<br>Individual recommendation (1=Yes)<br>0.12<br>(0.18)<br>Grant (1=Yes)<br>0.07<br>(0.16)<br>Flexible (1=Yes)<br>0.08<br>(0.16)<br>Observations<br>678<br>R-squared<br>0.25<br>Mean dep. var. control<br>2.36<br>SD dep. var. control<br>1.33<br>T1: _βE_+_βI_+_βG_ =0<br>0.02<br>T2: _βE_+_βG_ =0<br>0.07<br>T3: _βE_+_βG_+_βF_ =0<br>0.02<br>Note: this table reports results on yields and profts earned by farmers in the 2015 seaso<br>_βI IndRecommi_ +_βGGranti_ +_βFFlexiblei_ +_αc_+_εit_ , where_i_corresponds to a farmer,_Y_ is<br>pute robust standard errors. At the bottom of the table, we report the mean and the standa<br>p-values of linear combinations of the estimates coeffcients that map into the original study<br>the dependent variable the maize yields (tons/ha) self-reported by farmers in the 2015 seas<br>the 2015 season. The value of the production (per hectare) is computed by multiplying the<br>the market. We take the median price faced by farmers who sold at least a fraction of their<br>In column 3, the dependent variable is the total cost of production cost self-reported by ea<br>organic), herbicides, pesticides, and labor. We also include the cost of renting the sowing an<br>also paid by QFD. In column 4, the dependent variable is the difference between the depend<br>ing the subsidies paid by QFD. We use median market prices at the locality level to calculat<br>level. To account for this imputation of prices (for 20 farmers in our sample), we include an<br>2-5. Standard errors in parentheses. *** p_<_0.01, ** p_<_0.05, * p_<_0.1.|

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