The evidence in the preceding sections points to a reduction in transport costs through increased market access or decreased travel times—the time to the nearest markets and the larger relevant markets in the Northern Zone was significantly lower. Increases in market access can increase the productivity of farmers through access to better inputs and/or technologies. These increases in access can also increase market participation, as easier access to markets might incentivize households to sell some or more of their production.
In this section, we will explore the impacts of the NTH on agriculture-related outcomes. Namely changes in the probability of producing fruits and vegetables and cash crops, participation in agriculture market sales and changes in the agriculture productivity and commercialization of basic grains.
Table 30 shows the impact on the probability of cultivating cash crops (defined as coffee, sugar cane, and coconut) and Table 31 for the probability of producing fruits and vegetables. We choose these indicators to explicitly isolate an effect among crops that are more likely affected by an increase in market access. We find no consistent pattern of significant effects across methodologies on the probability of growing cash crops or growing fruits and vegetables.
In Table 32 we estimate the impact on market participation or commercialization. We use an indicator for having sold any of the household production in a nearby market. While we find that there was an overall increase in the probability of participating in commercialization in the full sample (10 percentage points more likely to sell after the road improvement), we only find differential impacts in group 1 and 2, where the households in treatment segment were less likely than their counterparts in adjacent road segments;
indicating that in this section of the NTH the effect on market participation was smaller. Indeed, these groups are on the west of the NTH where there is less commercial activity and the segment connecting to Guatemala was never constructed. The IV estimates from the continuous treatment design in panel B are similar to those in of the DID in panel A. The impact estimates are 10 percentage points in group one and 12 percentage points in group 2, given the estimated relationship between the time to the nearest market and the probability of participating in commercialization (0.25 and 0.03) and a reduction of 4.7 minutes on average among these groups. On the intensive margin of the value of agricultural sales, we do not find significant impacts as evidence in Table 33
64
TABLE 30 AGRICULTURAL PRODUCTION IMPACTS: PROBABILITY OF GROWING CASH CROPS Household produces cash crops (coffee, sugar cane, coconut).
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
Panel A: Segment Difference in Difference
Treatment Assignment#Post Road Improvement 0.0086 0.0062 0.00061 -0.0019 0.00072 0.0055
[0.0092] [0.0068] [0.0018] [0.0018] [0.0046] [0.0055]
Post Road Improvement (0/1) -0.018 -0.015 -0.0013 -0.0031 -0.012 -0.013
[0.0097]* [0.0067]** [0.00077] [0.0012]** [0.0055]** [0.0055]**
Construction Administrative Data
Improved Road Sub-Segment (0/1) 0.011 0.0063 -0.00063 -0.0024 0.00072 0.0012
[0.0071] [0.0051] [0.00068] [0.0012]* [0.0046] [0.0023]
% of Sub-Segment Improved 0.011 0.003 0.00071 -0.005 0.0016 -0.00079
[0.0093] [0.0057] [0.0010] [0.0022]** [0.0048] [0.0028]
Panel B: Continuous Time to Market Treatment
Time to nearest market -0.00026 0.000014 -0.000028 0.000043 0.00029 0.00013
[0.00064] [0.00031] [0.000060] [0.000048] [0.00019] [0.000064]**
Instrumental Variables
(a) Time to Market= ϕ(Treatment Assignment#Post) -0.0018 -0.0016 -0.0018 0.000052 -0.00004 -0.00048 [0.0019] [0.0018] [0.0090] [0.000050] [0.00026] [0.00048]
(b) Time to Market= ϕ(% of Sub-Segment Improved) -0.0021 -0.00065 -0.00021 0.00022 -0.000089 0.000043 [0.0018] [0.0012] [0.00032] [0.00011]** [0.00027] [0.00015]
Standard errors in brackets clustered at the segment level. * p<0.10, ** p<0.05, *** p<0.01
Notes: All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH. Column (6) shows impact across the whole NTH. Panel A shows the estimations using the difference in difference between segments methodology, where adjacent segments are compare before and after the treatment segment is improved. The bottom part panel A uses the administrative data on the construction progress of sub-segments. Panel B shows the estimations using the continuous time to market methodology, where all households experience improvements in travel times due to the improvement in the roads in the accessibility model. The bottom of panel B shows the instrumental variable estimation where the time to market is instrumented with the timeline of construction. K-P rk Wald F is the F statistic for weak identification test(Cragg-Donald or Kleibergen-Paap) of the excluded instruments
65 TABLE 31 AGRICULTURAL PRODUCTION IMPACTS: PROBABILITY OF PRODUCING FRUITS AND VEGETABLES
Household produces fruits and vegetables
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
Panel A: Segment Difference in Difference
Treatment Assignment#Post Road Improvement -0.00045 -0.0021 0.015 0.0072 -0.006 0.0024
[0.0090] [0.0088] [0.012] [0.0092] [0.0062] [0.0051]
Post Road Improvement (0/1) 0.038 0.041 -0.019 -0.016 -0.0026 0.052
[0.012]*** [0.011]*** [0.0081]** [0.0079]* [0.0047] [0.0066]***
Construction Administrative Data
Improved Road Sub-Segment (0/1) -0.00033 -0.0013 0.013 0.01 -0.006 -0.0032
[0.0097] [0.0077] [0.0092] [0.0083] [0.0062] [0.0042]
% of Sub-Segment Improved -0.0019 -0.0022 0.016 0.02 -0.0044 -0.01
[0.0086] [0.0084] [0.016] [0.015] [0.0063] [0.0052]**
Panel B: Continuous Time to Market Treatment
Time to nearest market 0.00026 0.00032 0.00095 -0.00013 0.000069 0.00021
[0.00082] [0.00071] [0.0011] [0.00022] [0.00016] [0.00015]
Instrumental Variables
(a) Time to Market= ϕ(Treatment Assignment#Post) 0.000093 0.00054 -0.045 -0.0002 0.00034 -0.00021
[0.0019] [0.0023] [0.19] [0.00025] [0.00035] [0.00044]
(b) Time to Market= ϕ(% of Sub-Segment Improved) 0.00036 0.00048 -0.0047 -0.00089 0.00025 0.00056 [0.0016] [0.0018] [0.0049] [0.00066] [0.00036] [0.00028]**
Standard errors in brackets clustered at the segment level. * p<0.10, ** p<0.05, *** p<0.01
Notes: All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH. Column (6) shows impact across the whole NTH.
Panel A shows the estimations using the difference in difference between segments methodology, where adjacent segments are compare before and after the treatment segment is improved. The bottom part panel A uses the administrative data on the construction progress of sub-segments. Panel B shows the estimations using the continuous time to market methodology, where all households experience improvements in travel times due to the improvement in the roads in the accessibility model. The bottom of panel B shows the instrumental variable estimation where the time to market is instrumented with the timeline of construction. K-P rk Wald F is the F statistic for weak identification test(Cragg-Donald or Kleibergen-Paap) of the excluded instruments
66
TABLE 32 AGRICULTURAL PRODUCTION IMPACTS: PROBABILITY OF SELLING AGRICULTURAL PRODUCTION Household sells part agriculture output
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
Panel A: Segment Difference in Difference
Treatment Assignment#Post Road Improvement -0.11 -0.11 0.011 -0.0048 -0.03 -0.027 [0.049]** [0.046]** [0.052] [0.034] [0.031] [0.025]
Panel B: Continuous Time to Market Treatment
Time to nearest market 0.0079 0.0056 0.0048 -0.000051 0.00067 -0.0000095
[0.0052] [0.0035] [0.0030] [0.00081] [0.00091] [0.00052]
Instrumental Variables
(a) Time to Market= ϕ(Treatment Assignment#Post) 0.025 0.03 0.053 0.00014 0.0015 0.0021 [0.011]** [0.013]** [0.40] [0.00099] [0.0016] [0.0020]
(b) Time to Market= ϕ(% of Sub-Segment Improved) 0.016 0.0097 -0.0017 -0.00039 0.002 0.00072 [0.0090]* [0.0077] [0.019] [0.0030] [0.0018] [0.00096]
Standard errors in brackets clustered at the segment level.
* p<0.10, ** p<0.05, *** p<0.01
Notes: All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH.
Column (6) shows impact across the whole NTH. Panel A shows the estimations using the difference in difference between segments methodology, where adjacent segments are compare before and after the treatment segment is improved. The bottom part panel A uses the administrative data on the construction progress of sub-segments. Panel B shows the estimations using the continuous time to market methodology, where all households experience improvements in travel times due to the improvement in the roads in the accessibility model. The bottom of panel B shows the instrumental variable estimation where the time to market is instrumented with the timeline of construction. The table shows the mean and std. deviation of the outcome variable for the comparison in each group before the road improvement and the baseline mean in the full sample column. Time to market reduction shows the difference before and after the improvement for the treatment group in each column and the difference for households in improved segments between the end line and the baseline. K-P rk Wald F is the F statistic for weak identification test(Cragg-Donald or Kleibergen-Paap) of the excluded instruments
67 TABLE 33 AGRICULTURAL PRODUCTION IMPACTS: TOTAL AGRICULTURE PRODUCTS SALES ($)
Total Amount sold (USD)
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
Panel A: Segment Difference in Difference
Treatment Assignment#Post Road Improvement -106.9 -82.3 13.4 7.66 59.9 -30.1 [80.9] [77.4] [58.9] [47.2] [67.3] [54.5]
Standard errors in brackets clustered at the segment level.
* p<0.10, ** p<0.05, *** p<0.01
Notes: All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH. Column (6) shows impact across the whole NTH. Panel A shows the estimations using the difference in difference between segments methodology, where adjacent segments are compare before and after the treatment segment is improved. The bottom part panel A uses the administrative data on the construction progress of sub-segments. Panel B shows the estimations using the continuous time to market methodology, where all households experience improvements in travel times due to the improvement in the roads in the accessibility model. The bottom of panel B shows the instrumental variable estimation where the time to market is instrumented with the timeline of construction. The table shows the mean and std. deviation of the outcome variable for the comparison in each group before the road improvement and the baseline mean in the full sample column. Time to market reduction shows the difference before and after the improvement for the treatment group in each column and the difference for households in improved segments between the end line and the baseline. K-P rk Wald F is the F statistic for weak identification test(Cragg-Donald or Kleibergen-Paap) of the excluded instruments
68
Next, we present results on the intensive margin of agricultural production: quantities of basic grains production and the total value of agricultural production and sales. Table 34 shows the impacts on agriculture production quantities for basic grains (corn, beans and sorghum). These are the staple crops in the Norther Zone and given the lack of significant changes in the probability of producing cash crops and fruits and vegetables, we explore in this section increases in productivity for staple crops. We do not find differences in agricultural productivity for basic grains19 due to the improvement of the NTH.
We find significant increases in the value of quantities designated for auto consumption in the previous year.
From the DID estimations, we find significant effects among group 4, with an increase of 40 to 53 USD in the amount of agriculture production use for consumption in the household or auto-consumption. In regressions (not-shown) we can trace the effect to increases in the stored quantities of corn. The estimates from full sample using the continuous approach shows a marginally significant effect of 8.4 USD on average across the NTH.
19 We estimated the effect for each staple crop separately and the conclusion estimates are qualitatively similar.
69 TABLE 34 AGRICULTURAL PRODUCTION IMPACTS: BASIC GRAINS PRODUCTION QUANTITIES (KGS)
Basic grains quantity produced(Kg)
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
Panel A: Segment Difference in Difference
Treatment Assignment#Post Road Improvement -107.1 -109.7 -110.7 -52.7 29.8 18.2 [116.0] [107.3] [91.5] [72.3] [51.8] [50.1]
(b) Time to Market= ϕ(% of Sub-Segment
Improved) 18.5 18.7 28.7 6.08 -2.5 1.33
Standard errors in brackets clustered at the segment level.
* p<0.10, ** p<0.05, *** p<0.01
Notes: All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH. Column (6) shows impact across the whole NTH. Panel A shows the estimations using the difference in difference between segments methodology, where adjacent segments are compare before and after the treatment segment is improved. The bottom part panel A uses the administrative data on the construction progress of sub-segments. Panel B shows the estimations using the continuous time to market methodology, where all households experience improvements in travel times due to the improvement in the roads in the accessibility model. The bottom of panel B shows the instrumental variable estimation where the time to market is instrumented with the timeline of construction. The table shows the mean and std. deviation of the outcome variable for the comparison in each group before the road improvement and the baseline mean in the full sample column.
Time to market reduction shows the difference before and after the improvement for the treatment group in each column and the difference for households in improved segments between the end line and the baseline. K-P rk Wald F is the F statistic for weak identification test(Cragg-Donald or Kleibergen-Paap) of the excluded instruments
70
TABLE 35 AGRICULTURAL PRODUCTION IMPACTS: AUTO-CONSUMPTION ($) Total Amount consumed (USD)
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
Panel A: Segment Difference in Difference
Treatment Assignment#Post Road Improvement 26.8 8.14 54.1 53 -0.41 16.4 [29.1] [26.5] [23.8]** [19.4]*** [15.0] [14.7]
(b) Time to Market= ϕ(% of Sub-Segment
Improved) -5.96 -6.83 8.85 1.84 0.14 -0.21
Standard errors in brackets clustered at the segment level.
* p<0.10, ** p<0.05, *** p<0.01
Notes: All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH. Column (6) shows impact across the whole NTH. Panel A shows the estimations using the difference in difference between segments methodology, where adjacent segments are compare before and after the treatment segment is improved. The bottom part panel A uses the administrative data on the construction progress of sub-segments. Panel B shows the estimations using the continuous time to market methodology, where all households experience improvements in travel times due to the improvement in the roads in the accessibility model. The bottom of panel B shows the instrumental variable estimation where the time to market is instrumented with the timeline of construction. The table shows the mean and std. deviation of the outcome variable for the comparison in each group before the road improvement and the baseline mean in the full sample column. Time to market reduction shows the difference before and after the improvement for the treatment group in each column and the difference for households in improved segments between the end line and the baseline. K-P rk Wald F is the F statistic for weak identification test(Cragg-Donald or Kleibergen-Paap) of the excluded instruments
71