The next step is to verify the change in markets access during the study period for the continuous treatment approach. This step gauges the success of the project by examining the improvement in the direct outcome of the NTH improvement: reductions in access time to markets in the Northern Zone. We examine the changes in access to different markets in the area of influence of the NTH: Metapan, Chalatenango, Sesuntepeque, Ana Moros and San Salvador. While the effects of measures such as time to nearest market or access to a specific market are evident, it is necessary to empirically verify these changes, given that the IE design relies on these.
Essentially, these first regressions illustrate the intensity of treatment across different groups and across the complete road.
Table 22 through Table 24 show the results for impact on the traveling times for markets around the NTH and San Salvador17. As expected, we see reductions in travel time for all markets and the magnitude depends on the proximity of the group to each market. For example, the travel time to the market in Chalatenango was reduced by 32 to 52 minutes thanks to the improvement of the NTH for the full sample. Groups 1 and 2 include the households that are the nearest to Chalatenango. Thus, have lower reductions in travel times, between 7.08 and 14.8 minutes. Similar effects are present for the travel time to Metapan. The reduction in time to San Salvador is the most homogeneous across groups, since San Salvador is not connected by the NTH. Being the capital, it is the most important market and we included in the menu of markets available for households in
17 For brevity, we show the discuss the results for the biggest markets and include the estimation for the other markets in “Annex 2- Additional Results”
The “#” in the tables denotes the interaction between the indicator variables. For example, A#B is equal to one when both A and B are equal to one, if not it is zero.
53 the Northern Zone. Households in the northern zone had a reduction between 7.81 minutes 12.2 minutes in travel times to San Salvador because of the improvement in the NTH.
TABLE 22 IMPACT TO TRAVEL TIMES TO MARKETS IN MINUTES: CHALATENANGO
Time to Chalatenango Market (minutes)
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
First Stage and Segment Difference in Difference
Treatment#Post Road Improvement -14.2 -13.2 27.3 -43.1 -77.6 -32
[1.08]*** [1.11]*** [2.48]*** [2.83]*** [2.42]*** [1.20]***
Post Road Improvement 1.62 0.88 -32.6 -36.5 -2.7 23.1
[0.24]*** [0.22]*** [2.35]*** [2.28]*** [0.32]*** [1.23]***
Construction Administrative Data
Improved Sub-Segment -10.1 -7.08 -7.16 -40 -77.6 -42.2
[0.75]*** [0.59]*** [3.02]** [3.31]*** [2.42]*** [1.93]***
Post Road Improvement -4.64 -4.21 -15.2 -33.2 -2.7
[0.65]*** [0.54]*** [2.94]*** [3.00]*** [0.32]***
% of Sub-segment Improved -14.8 -9.37 -24.1 -57.4 -81.5 -52.4
[1.13]*** [0.89]*** [2.43]*** [3.74]*** [2.62]*** [2.02]***
Post Road Improvement 0.12 -1.87 0.4 -9.47 -0.23
[0.057]** [0.31]*** [0.87] [2.86]*** [0.18]
Mean of Comp. at Pre-Period 74.8 41.9 105.6 105.6 278 164.8
SD of Comp. at Pre-Period 39.7 27.9 19.7 19.7 30.6 88.1
Number of Clusters 113 142 96 138 135 410
Number of Households 1,219 1,496 1,006 1,652 1,415 4,667
Observations 5,549 6,795 4,560 7,639 6,490 21,406
Standard errors in brackets
Std. errors are clustered at the census segment level.
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.
* p<0.10, ** p<0.05, *** p<0.01
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TABLE 23 IMPACT TO TRAVEL TIMES TO MARKETS IN MINUTES: METAPAN Time to Metapan Market (minutes)
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full Sample
First Stage and Segment Difference in Difference
Treatment#Post Road Improvement -8.29 -1.17 23.2 -35.9 -84.7 -38.3
[1.02]*** [1.37] [3.09]*** [3.73]*** [2.42]*** [1.33]***
Post Road Improvement 0.45 -2.85 -55.7 -59.5 -2.95 28.5
[0.11]*** [0.51]*** [3.05]*** [2.96]*** [0.34]*** [1.39]***
Construction Administrative Data
Improved Sub-Segment -6.43 -12.5 -15.5 -39.9 -84.7 -48.6
[0.76]*** [0.98]*** [4.07]*** [5.03]*** [2.42]*** [2.29]***
Post Road Improvement -3.11 -1.61 -32.3 -51.7 -2.95
[0.36]*** [0.31]*** [4.52]*** [4.62]*** [0.34]***
% of Sub-segment Improved -9 -20.1 -50.7 -79 -89 -64.7
[1.03]*** [1.06]*** [2.34]*** [3.19]*** [2.63]*** [2.03]***
Post Road Improvement -0.27 3.81 0.3 -7.49 -0.24
[0.074]*** [0.36]*** [0.83] [2.32]*** [0.18]
Mean of Comp. at Pre-Period 94.5 124.3 220.9 220.9 375.9 239.5
SD of Comp. at Pre-Period 43.1 28.8 19.1 19.1 29.7 121.2
Number of Clusters 113 142 96 138 135 410
Number of Households 1,219 1,496 1,006 1,652 1,415 4,667
Observations 5,549 6,795 4,560 7,639 6,490 21,406
Standard errors in brackets
Std. errors are clustered at the census segment level.
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.
* p<0.10, ** p<0.05, *** p<0.01
55 TABLE 24 IMPACT TO TRAVEL TIMES TO MARKETS IN MINUTES: SAN SALVADOR
Time to San Salvador Market (minutes)
Std. errors are clustered at the census segment level.
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.
* p<0.10, ** p<0.05, *** p<0.01
Increases in accessibility do not only decrease the time that it takes to access a given market. They also provide the possibility of changing markets given the time it takes to reach it. Next, we combine the effects across market choice and travel time and calculate impact of the road improvement on the time to the nearest market, allowing households to switch markets due to the improvements in segments of the highway each year. This is our continuous treatment variable in the markets access design and these results also serve as the first stage of the instrumental variable estimations of equations 4a and 4b.
Table 25 shows that across the sample the highway construction improved travel times to markets between 4 and 42 minutes; with treatment segments in groups 4 and 5 experiencing the largest decreases in travel time to markets. These two groups had larger average traveling times at baseline, as discussed before. On average, across the population in the Northern Zone, the traveling time to the nearest market decreased between sixteen and eighteen minutes on average. Note that the post improvement indicator in each group is negative, indicating that all households experience increases in market access as we discussed in the methodology section. We note that the estimate for group 3 is not significant when using the DID specification with the dichotomous variables but it significant with the progress DID variable (in addition, note that this group experience an improvement just not a significant one between treatment segment and comparison segment).
Group 3 compares segments three and four, for which there is significant overlap in the construction dates—
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the start dates only differ by one month and the end dates by three months. When using the percentage of the segment constructed (which captures differential delays in the construction phase), the results are consistent with the effects that we find in other groups. The significant impacts on travel in the table indicate that we the treatment assignment and progress variables are strong instruments for the reduction in travel times to the nearest markets.
TABLE 25 IMPACT OF TIME IN MINUTES TO NEAREST MARKET
(1) (2) (3) (4) (5) (6)
Group 1 Group 2 Group 3 Group 4 Group 5 Full
Sample
First Stage and Segment Difference in Difference
Treatment#Post Road Improvement -4.79 -3.83 -0.33 -36.1 -17.8 -11.5
[0.60]*** [0.66]*** [1.34] [1.66]*** [2.06]*** [0.72]***
Post Road Improvement 0.24 -0.24 -4.01 -6.16 -0.17 8.33
[0.057]*** [0.14]* [0.90]*** [0.91]*** [0.072]** [0.64]***
Construction Administrative Data
Improved Sub-Segment -3.76 -3.06 0.83 -23.8 -17.8 -15.9
[0.46]*** [0.36]*** [0.82] [1.66]*** [2.06]*** [1.07]***
Post Road Improvement -1.81 -1.57 -4.91 -10.7 -0.17
[0.22]*** [0.21]*** [1.21]*** [1.12]*** [0.072]**
% of Sub-segment Improved -5.25 -4.65 -3.42 -22.4 -17.6 -18.4
[0.61]*** [0.62]*** [0.80]*** [2.95]*** [2.12]*** [1.18]***
Post Road Improvement -0.16 -0.34 -0.97 -7.76 -0.34
[0.058]*** [0.23] [0.49]* [2.33]*** [0.16]**
Mean of Comp. at Pre-Period 52.9 41.9 21 21 54.7 64.5
SD of Comp. at Pre-Period 26.1 27.9 17 17 33.7 44.3
Number of Clusters 113 142 96 138 135 410
Number of Households 1,219 1,496 1,006 1,652 1,415 4,667
Observations 5,549 6,795 4,560 7,639 6,490 21,406
Standard errors in brackets
Std. errors are clustered at the census segment level.
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.
* p<0.10, ** p<0.05, *** p<0.01
57
R ESULTS
The results from both methodologies are presented in the Table 26 using the same notation as in the methodology section. Panel A, shows the estimates from the DID pipeline design format and the DID with construction progress. The bottom part panel A uses the administrative data on the construction progress of sub-segments using an indicator for the construction of the sub segment being finalized and the percentage of the sub segment that was finalized. 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. Panel B shows the results from the continuous market to access methodology, first the direct estimates for the time to nearest market and at the bottom the estimates from the two-stage least squares that use (a) the treatment assignment and timing, and (b) the construction progress variable as instruments for the time to the nearest market.
All equations include household and year fixed effects. Columns (1)-(5) shows the impact for each treatment Assignment group along the NTH. These five estimates are the main results for the segment DID design (𝜃𝜃𝑘𝑘’s).
Column (6) shows impact across the whole NTH, in the case of the DID design, it represents a weighted average of the impact estimates of each of the groups; the weights (𝑤𝑤𝑘𝑘) depend on the size and precision of the estimate in each group.
In panel B, the estimates in each column give us a way to compare the estimates from the continuous treatment design with the DID design. The full sample uses all observations to compute the estimates. This is the main result for the continuous time to market design (𝝆𝝆).
The result tables show 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. The rows labeled time to market reduction show average difference before and after the improvement for the treatment group in each group; and the difference for households in improved segments between the end line and the baseline for the full sample column. Rows labeled “K-P rk Wald F” are the F statistic for weak identification test (Cragg-Donald or Kleibergen-Paap) of the excluded instruments (the interactions of the treatment Assignment and post indicators in (a), and the percentage of the segment improved in (b)). We include the number of clusters, households, and time-household observations in each regression at the end of each table.
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TABLE 26 GUIDE FOR PRESENTATION OF RESULTS
(1) (2) (3) (4) (5) (6)
Std. errors are clustered at the census segment level.
* p<0.10, ** p<0.05, *** p<0.01