rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Gatenaa i 73%<br>Honduras 67%<br>Colombia NE 52%<br>Per 45%<br>El Salvador i 42%<br>Brazil 37%<br>Ecuador 35%<br>Mexico 27%<br>Paraguay ME 24%<br>Brazil- cities MN 26%<br>DominicanRep, MN 27%<br>Bolivia (Plurinational State of) HN 24%<br>Panama MM 18%<br>Argentina ME 17%<br>Uruguay MM 10%<br>Chile MI 4%<br>0% 20% 40% 60% 80%<br><!-- End of picture text --> <!-- Start of picture text --> Guatenala Es 64%<br>Honduras i 62%<br>Colonbia TT 43%<br>Peru 40%<br>El Salvador 3.4%<br>Brazi] DE 28%<br>Ecuador 25%<br>Mexico ME 19%<br>Paraguay ME 19%<br>Brazil-cities MEM 19%<br>DominicanRep. ME 18%<br>Bolivia (Plurinational State of) HEN 18%<br>Panana ME 9%<br>Argentina Hl 7%<br>Uruguay Ml 5%<br>Chile H 2%<br>0% 10% 20% 30% 40% 50% 60% 70%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>10%<br>is°° alt | ii| _. Hl| | ol| al a| | all<br>2 & z & § 8 & 38 - & S<br>=a eS ‘3 a iG)<br>&° a I<br>s3<br>2 a<br>ee)<br>fea}<br>TM Poverty - Estimated rate TM Vulnerability rate<br><!-- End of picture text -->
# Figure 4. Sensitivity of inflation expectations to inflation shock

## A. Sensitivity of inflation expectations to inflation shocks

B. Sensitivity of inflation expectations to inflation shocks, all countries <!-- Start of picture text --> Percentage points Percentage points<br>0.8 1990-2018 1990-2004 2005-18 1.0<br>0.6<br>0.4<br>0.5<br>0.2<br>0.0<br>-0.2 0.0<br>All Advanced EMDEs<br>economies<br>C. Sensitivity of inflation expectations to D. Sensitivity of inflation expectations to<br>inflation shocks, advanced economies inflation shocks, EMDEs<br>Percentage points Percentage points<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies.

A.-D. Inflation expectations are five-year-ahead expectations of annual inflation. Inflation shocks are defined as the difference between realized inflation and short-term inflation expectations in the previous period.

Sensitivity is estimated using a panel regression of the change in five-year-ahead inflation expectations on inflation shocks, as described in Appendix 3. Bars denote medians and vertical lines denote 90 percent confidence intervals. Based on a sample of 24 advanced economies and 23 EMDEs during 1990H2-2018H1.

B.-D. Time-varying sensitivity is estimated by regressing the change in five-year-ahead inflation expectations on inflation shocks, as described in Appendix 3. Solid lines denote the median of estimates and the dotted lines indicate the median of 68 percent confidence intervals.

- B. Based on a sample of 24 advanced economies and 23 EMDEs during 2000H1-2018H1.

- C. Based on a sample of 24 advanced economies during 1995H1-2018H1.

- D. Based on a sample of 23 EMDEs during 2000H1-2018H1.

21
_Tuble 10. Coefficients (Standard Errors) and Variance Explainedfor Combinations of Student-level and School-type Variables Regressed on English Achievement_

||||**_Coefficient_**_(St_|_andard_**_Errors)_**|||
|---|---|---|---|---|---|---|
|**_Variable_**|**_JE_**|**_2E_**|_3E_|_4E_|**_SE_**|_6E_|
|Constant|5.62|6.00|4.85|5.10|5.89|5.90|
||(.16)|(.21)|(.19)|(.19)|(.13)|(.12)|
|Logage|-5.02|-5.00|-|-5.14|-|_-5.05_|
||(.66)|( 66)||(.67)||(.67)|
|Repeater|-0.46|-0.46|-|-0.47|-|-0.44|
||(.06)|(.06)||(.06)||(.06)|
|**Farily**size|-0.04|-0.04||-0.04||4-004|
||(.01)|(.01)||(.01)||(.01)|
|Father prinmary|0.09|0.09||0°10||0.09|
||(.06)'|(.06)'||(.07)'||(.07)'|
|FatherJ.C.|0.15|0.15|-|0.16|-|0.13|
||(.06)|(.06)||(.06)||(.06)|
|FatherO|0.12|0.13|-|0.13||0.13|
||(.09)'|(.09)'||(.09)'||(.09)'|
|Father A|0.39|0.39|-|0.40|-|0.37|
||(.11)|(. 11)||(.12)||(.12)|
|Former B|-|-0.22<br>|||||
|||(.21)'|||||
|HighFee Paying|-|0.20|-|-|-|-|
|||(.25)'|||||
|LowFee Paying|-|-0.40|||||
|||(. 19)|||||
|DistrictCouncil|-|-0.77||-|||
|||(.19)|||||
|Femaleteacher|-|-|0.63|0.28|||
||||(.16)|(.08)|||
|Age of teacher|-|-|0.03|0.03|||
||||(.01)|(.01)|||
|Standard trained teacher|-|-|0.73|0.67|||
||||(.19)|(.18)|||
|Tcxts(class)||-|1.42|1.15|-|-|
||||(.33)|(.32)|||
|Pupil-teacher ratio|||-|-|0-<br>-. 06|_-0.05_|
||||||(.02)|(.02)|
|Texs|-|-|-|-|1.81|1.48|
||||||(.56)|_(.53)_|
|Trained teacher|-|-|-|-|0,03|0.02|
||||||(.005)|(.005)|
|Percent Variance explained|17|21|24|33|<br>32|37|
|(total)|||||||
|School|27|35|57|66|65|70|
|Class|8|10|0|5|2|11|
|Student<br>.|10|10|0|9|7|10|

_Notes: *_ Average for non-repeater, father no education at **grand** means as noted. **b** Measured from grand mean.

Significant, negative coefficients, stronger for the supervision of inexperienced than for experienced teachers result from these regressions. Although it doesn't stand to reason in the case of the supervision of experienced teachers, the negative coefficient for the supervision of inexperienced teachers may be touching on the fact that there are fewer trained teachers at, for example, district council schools, which, as we have already noted, report greater amounts of supervision at their schools. Intuitively, one would expect head teachers' supervision to have a positive effect; further investigation is clearly necessary to understand better what is producing not only a negative coefficient, but a negative connotation!

19
# 4 Empirical Findings and Analysis

Subsection 4.1 presents our partial estimates of the impact of DTAs on trade and FDI. Then, in Subsection 4.2, we translate the partial estimates into corresponding GE eects, and we analyze the total impact of DTAs on FDI within our framework.

## 4.1 Estimation Results

Our estimates of the eects of DTAs on trade are presented in Table 1. As discussed earlier, all estimates are obtained with the PPML estimator with three-way xed eects, including exporter-time, importer-time, and directional country-pair xed eects. In addition, all specications use time-varying border dummy variables to control for the presence of common globalization trends, and indicator variables for WTO membership ( _WTOij,t_ ), bilateral investment treaties ( _BITij,t_ ), and economic sanctions ( _SANCTij,t_ ). In order to highlight the importance of DTAs and their provisions, we develop the estimation analysis in three steps, depending on the denition of the indicator variables designed to capture the impact of trade agreements.

The estimates in column (1) include a single indicator variable, _PTAij,t_ , that reects the presence of a trade agreement of any type (e.g., deep or shallow) between _i_ and _j_ at time _t_ . Several ndings stand out from column (1). Most important for our purposes, we note that, while positive, the estimate on _PTAij,t_ is economically small and it is not statistically signicant. A possible explanation for this result is that we impose a common eect for all trade agreements in our sample, regardless of their type and depth. We demonstrate that this is indeed the case in column (2) of Table 1. Before that, however, we briey discuss the estimates of the other policy covariates in our specication.

First, we note that the estimate of the impact of WTO is positive, large, and statistically signicant. This result is at odds with some of the existing literature, e.g., Rose (2004) and Esteve-Pérez et al. (2020) who nd that WTO membership did not promote international 20
33 Because deregulation of interstate pipeline transportation has not given all end uisers equal access to transportation and the wholesale gas market, the structure of deliveries differs among types of end users. Electric utilities and industrial customers, which gained the most under open access and unbundling, purchased on average more than 70 percent of their 1995 natural gas deliveries in the wholesale market and bought transportation services from pipeline or local distribution companies. In addition, industrial customers received 9.5 percent of their deliveries directly from interstate pipeline companies in 1995 (U.S. Department of Energy 1996). Electric utilities and industrial customers both took about 60 percent of their deliveries under firm transportation contracts, and 40 percent under interruptible contracts (table 6). Firm transportation contracts offer the security of highly reliable delivery of base load natural gas, while interruptible contracts offer the flexibility to acquire additional gas deliveries in the event of a sudden increase in demand. Industrial consumers and electric utilities purchase firm and interruptible transportation services in different amounts to build a contract portfolio that gives them the minimum acceptable level of supply reliability at the minimum cost.

**Table 6 Firm and Interruptible Deliveries of Natural Gas to U.S. Consumers, 1995** (millions of cubic feet)

|||Fir|m|Interr|uptible||
|---|---|---|---|---|---|---|
|Consumer category|Volume|P|ercentage of total|Volume<br>P|ercentage of tota|l<br>Total|
|Residential|4,846,360<br>(99.2)||34|3,958<br>(0.08)|0|4,850,318|
|Commercial|2,650,412<br>(87.4)||18|380,665<br>(12.6)|8|3,031,077|
|Industrial|5,140,048<br>(59.9)||36|3,439,537<br>(40.1)|69|8,579,585|
|Electric Utilities|1,758,945<br>(60.5)||12|1,147,860<br>(39.5)|23|2,906,805|
|Total|14,398,214<br>(74.3)||100|4,972,245<br>(25.7)|100|19,370,459|

Note: Figures in parentheses show the percentage breakdown between firm and interruptible deliveries for that category of consumers.

_Source:_ U.S. Department of Energy 1995b.

By contrast to electric utilities and industrial customers, commercial and residential customers have only limited access to the transportation and upstream gas markets. Commercial end users took on average 23 percent of their gas deliveries as a transportation service in 1995 and purchased the rest primarily from local distribution companies (table 7). They took 87 percent of deliveries under firm transportation contracts. Residential customers took almost all their gas deliveries from local distribution companies as firm sales services.
<!-- Start of picture text --> Share of HHs in Rural Areas Share of Female Headed HHs<br>(Difference from F2F Mean) (Difference from F2F Mean)<br>0.00 Ethiopia Malawi Nigeria Uganda 0.03 Ethiopia Malawi Nigeria Uganda<br>oe 0.01<br>—— [Ty<br>-0.01<br>-0.10 -0.02<br>-0.03<br>0.15 0.04<br>-0.05<br>-0.20 -0.06oa7<br>-0.08<br>-0.25 -0.09<br>mWith contact info mContacted m Interviewed With contactinfo MTMContacted &® Interviewed<br>Share of HH heads working in external employment Share of HHs engaged in crop farming<br>(Difference from F2F Mean) (Deviation from F2F mean)<br>0.12 Ethiopia Malawi Nigeria Uganda<br>0.00<br>-0.02<br>0.080.10 -0.04 ibe<br>-0.06<br>0.06<br>-0.08<br>0.04<br>-0.10<br>0.02 -0.12<br>0.14<br>0.00<br>Ethiopia Malawi Nigeria Uganda 0.16<br>mWith contactinfo mContacted m Interviewed With contactinfo mContacted sm Interviewed<br><!-- End of picture text -->
# **6 References**

Aberman, N., & Roopnaraine, T. (2020). To sell or consume? Gendered household decisionmaking on crop production, consumption, and sale in Malawi. _Food Security_ . https://doi.org/10.1007/s12571-020-01021-2 Akber, N. (2020). Does input subsidy discourage public investment in Indian agriculture? Evidence from ARDL applications. _Journal of Public Affairs_ , _22_ (2). https://doi.org/10.1002/pa.2337

- Akber, N., Paltasingh, K. R., & Mishra, A. K. (2022). How can public policy encourage private investments in Indian agriculture? Input subsidies vs. public investment. _Food Policy_ , _107_ (November 2021), 102210. https://doi.org/10.1016/j.foodpol.2021.102210

- Baird, S., McIntosh, C., & Özler, B. (2019). When the money runs out: Do cash transfers have sustained effects on human capital accumulation? _Journal of Development Economics_ , _140_ (March), 169–185. https://doi.org/10.1016/j.jdeveco.2019.04.004

- Bastagli, F., Hagen-Zanker, J., Harman, L., Barca, V., Sturge, G., & Schmidt, T. (2019). The Impact of Cash Transfers: A review of the evidence from low- and middle-income countries. _Journal of Social Policy_ , _48_ (3), 569–594.

https://doi.org/10.1017/S0047279418000715

- Brugh, K., Angeles, G., Mvula, P., Tsoka, M., & Handa, S. (2017). Impacts of the Malawi social cash transfer program on household food and nutrition security. _Food Policy_ , (November 2016). <u>https://doi.org/10.1016/j.foodpol.2017.11.002</u>

- Aberman, N., & Roopnaraine, T. (2020). To sell or consume? Gendered household decisionmaking on crop production, consumption, and sale in Malawi. _Food Security_ . https://doi.org/10.1007/s12571-020-01021-2

- Aderinto, N. (2023). Tropical Cyclone Freddy exposes major health risks in the hardest-hit

- Southern African countries: lessons for climate change adaptation. _International Journal of Surgery:Global Health_ , (March), 1–3.

- Akber, N. (2020). Does input subsidy discourage public investment in Indian agriculture? Evidence from ARDL applications. _Journal of Public Affairs_ , _22_ (2). https://doi.org/10.1002/pa.2337

22
# **II.3 Education**

Why is education a suitable variable by which to measure IGM? Education as a measure of socioeconomic status relative to income in the context of developing countries has at least three advantages: (1) it contains less measurement error, reducing potential attenuation bias (see Solon, 1992); (2) it is fixed early in the life cycle, which avoids the life-cycle bias found in studies that use income (see Haider & Solon, 2006), in addition, education is closely linked to income and it is important by itself in terms of human development; and (3) it can be completely attributed to a specific individual, while income sometimes is hard to assign within a household (e.g., a household with multiple persons and home production, which may be specially relevant in the case of rural populations in poor countries).

There are two questions about educational attainment in the data set. The first one reports the total years of schooling completed by each individual (formal schooling regardless of the track or kind of study), and the second one is re-coded by IPUMS to capture educational attainment in terms of the level of schooling completed<sup>7</sup> and contains four categories: (1) less than primary completed, (2) primary completed, (3) secondary completed, and (4) university completed. In the main analysis of the paper, I use the latter variable, which has a lower number of missing values and it is available for more countries than the former.<sup>8</sup> This variable applies, to the extent possible, the United Nations standard of six years of primary schooling, three years of lower secondary schooling, and three years of higher secondary schooling.

In the sample, a majority of individuals report levels of education that correspond to less than completed secondary and near 50% less than primary (see Figure 3a), which supports the focus on primary completion that I will detail later as most of the action happens at lower levels of completion.<sup>9</sup> In addition, although the level of education in Latin America

> 7It does not necessarily reflect any particular country’s definition of the various levels of schooling in terms of terminology or number of years of schooling.

> 8Years of schooling is not available for Brazil 2010, Cuba 2002, Saint Lucia 1991, Suriname 2012, Trinidad and Tobago 1970, and Uruguay 2011.

> 9An additional reason to focus on primary completion is that these estimates are directly comparable to

10
the past week was work as self‐employed working alone or with unpaid family members in non‐agricultural activities and iv) In the agricultural sector if they were working in agricultural activities. The Mexican classification of informality is based on social security, while the in Indonesia it has to do more with the type of employment. These distinctions were made based on data availability and the definitions used by the governments of these countries<sup>7</sup> .

Most of the Information comes from household surveys. For Mexico the main source of data is the Encuesta Nacional de Empleo –ENE‐; which provides information on employment status, duration of unemployment, occupational category, sector of employment, education, and earnings; for all working age individuals. The main source of data for Indonesia is the Indonesian Family Life Survey IFLS. However, we also use the SAKERNAS, household survey to estimate the unemployment rate<sup>8</sup> .

After defining who will populate each sector we proceed to calibrate the model. Calibrating the model consists in finding the values of the parameters of the model that are consistent with the observed labor market outcomes. We need to calibrate 16 parameters. These are: two parameters that determine the distribution of skills among workers. We assume this distribution is a Beta type<sup>9</sup> . Income while unemployed; the bargaining power of workers ; the technological (or shift) parameter A1 in the formal sector production function; the technology parameter from agricultural production function, A0; the rate of arrival of shocks to the bad and formal δ and λ, respectively; the rate of arrival of informal sector employment opportunities _α_ ; the parameter determining the elasticity of labor in agricultural production γ; the migration cost M and the cost of posting a vacancy c; ξ and ρ, which correspond to the parameters of the matching function, m(θ)=ξ( _v/u_ )<sup>ρ</sup> , the interest rate r and income in the 7 Robustness checks were performed for other definitions with little change in the results.

8 Both surveys show opposite trend in unemployment rate. The rate in IFLS declines during the crisis, whereas in SAKERNAS it goes up. As discussed in Smith et.al. (2000), “this discrepancy may be due to the different construction of survey questionnaire on employment status. Both surveys have the similar lead‐in question. When respondents answered 'currently not working', SAKERNAS stop probing further, whereas IFLS asks two‐step follow‐up questions. First respondents are asked if they had worked for pay at least for one hour. If they answer 'No', then the second follow‐up question probes whether they had worked in a family‐owned business. “ It is not hard to imagine that IFLS is expected to have low unemployment rate. Unemployment trend in SAKERNAS goes well with our conventionally idea that unemployent goes up during recessions.

9 The beta distribution can take very different shapes depending on the values of the parameters, and has the advantage that is bounded both from bellow and above.
|P157034|2016|2020|12/22/2019|6 Active|Burkina Faso|
|---|---|---|---|---|---|
|P157035|2016|2017|9/30/2016|1 Closed|Afghanistan|
|P157035|2016|2017|4/6/2017|2 Closed|Afghanistan|
|P157035|2016|2018|6/21/2018|3 Closed|Afghanistan|
|P157036|2017|2017|6/27/2017|1 Active|Togo|
|P157036|2017|2018|1/5/2018|2 Active|Togo|
|P157036|2017|2018|6/29/2018|3 Active|Togo|
|P157036|2017|2019|1/16/2019|4 Active|Togo|
|P157036|2017|2020|10/30/2019|5 Active|Togo|
|P157036|2017|2020|1/24/2020|6 Active|Togo|
|P157036|2017|2021|7/23/2020|7 Active|Togo|
|P157038|2017|2017|6/27/2017|1 Active|Togo|
|P157038|2017|2018|12/28/2017|2 Active|Togo|
|P157038|2017|2018|6/24/2018|3 Active|Togo|
|P157038|2017|2019|1/4/2019|4 Active|Togo|
|P157038|2017|2020|10/28/2019|5 Active|Togo|
|P157038|2017|2020|12/30/2019|6 Active|Togo|
|P157038|2017|2020|6/30/2020|7 Active|Togo|
|P157043|2019|2019|11/2/2018|1 Active|Peru|
|P157043|2019|2019|4/3/2019|2 Active|Peru|
|P157043|2019|2020|10/8/2019|3 Active|Peru|
|P157043|2019|2020|12/19/2019|4 Active|Peru|
|P157043|2019|2020|6/27/2020|5 Active|Peru|
|P157054|2018|2018|6/21/2018|1 Active|India|
|P157054|2018|2019|10/8/2018|2 Active|India|
|P157054|2018|2019|4/3/2019|3 Active|India|
|P157054|2018|2020|10/16/2019|4 Active|India|
|P157054|2018|2020|3/25/2020|5 Active|India|
|P157055|2017|2017|6/30/2017|1 Active|Cote d'Ivoire|
|P157055|2017|2018|1/25/2018|2 Active|Cote d'Ivoire|
|P157055|2017|2019|10/17/2018|3 Active|Cote d'Ivoire|
|P157055|2017|2019|5/3/2019|4 Active|Cote d'Ivoire|
|P157055|2017|2020|12/13/2019|5 Active|Cote d'Ivoire|
|P157055|2017|2020|6/15/2020|6 Active|Cote d'Ivoire|
|P157079|2018|2018|2/8/2018|1 Active|Kyrgyz Republic|
|P157079|2018|2019|11/5/2018|2 Active|Kyrgyz Republic|
|P157079|2018|2019|5/12/2019|3 Active|Kyrgyz Republic|
|P157079|2018|2020|12/4/2019|4 Active|Kyrgyz Republic|
|P157079|2018|2020|6/15/2020|5 Active|Kyrgyz Republic|
|P157096|2017|2017|10/26/2016|1 Active|Sao Tome and Principe|
|P157096|2017|2017|4/19/2017|2 Active|Sao Tome and Principe|
|P157096|2017|2018|10/10/2017|3 Active|Sao Tome and Principe|
|P157096|2017|2018|4/25/2018|4 Active|Sao Tome and Principe|
|P157096|2017|2019|10/15/2018|5 Active|Sao Tome and Principe|
|P157096|2017|2019|5/2/2019|6 Active|Sao Tome and Principe|
|P157096|2017|2020|12/13/2019|7 Active|Sao Tome and Principe|
|P157096|2017|2020|6/12/2020|8 Active|Sao Tome and Principe|
||||**% decrease in**|**time‐trade**|**Lower Bound**|||
|---|---|---|---|---|---|---|---|
|**Baseline**|2.31|1.17|0.50|0.77|0.56|1.90|1.19|
|**Baseline with reduced**<br>**border delays **|3.96|3.60<br>|1.14<br>**% decrease in**|2.80<br>**time‐trade**|1.34<br>**Upper Bound**|6.83|2.99|
|**Baseline**|3.53|2.54|1.92|2.10|2.91|3.44|2.17|
|**Baseline with reduced**<br>**border delays **|5.89|6.04|2.94|6.53|3.94|9.77|4.17|
||||**% decrease i**|**n trade cost L**|**ower Bound**|||
|**Baseline**|1.96|1.01|0.47|0.67|0.50|1.66|1.13|
|**Baseline with reduced**|3.43|3.08|1.08|2.46|1.21|6.22|2.86|
|**border delays **||||||||
||||**% decrease i**|**n trade cost U**|**pper Bound**|||
|**Baseline**|2.95|2.18|1.77|1.84|2.48|2.96|2.05|
|**Baseline with reduced**<br>**border delays **|5.01|5.14|2.74|5.81|3.43|8.77|3.98|

_Note: “Reduced border delay” assumes a 50 percent reduction in import and export delays for BRI countries. Simple average by region across all destinations._ Table 14 presents the results with reduced border delays for BRI countries. In terms of delays, all regions experience a decrease in the average time to cross borders. South Asia experiences the largest reduction in the average time to cross borders, which decreases by 64 hours from 166, followed by Middle East and North Africa. The importance of improvement in custom policies is reflected in the lower and upper bound results for trade costs and time to trade. We find that the percentage decreases in time‐trade and trade costs are three times as large as those in the baseline scenarios. For instance, along BRI corridors the improvements in shipping times would range between 11 percent for the China‐Pakistan Economic Corridor (CPEC) and 25.5 percent of the China‐Central Asia‐West Asia Economic Corridor (CCWAEC) in the upper‐bound. Similarly, trade costs would fall by 10.2 percent for the China‐Pakistan Economic Corridor (CPEC) and by 21.6 percent for the China‐Central Asia‐West Asia Economic Corridor (CCWAEC) in the upper‐bound scenario. These results show that border delays represent a large component of the overall time it takes to trade and emphasize the importance of complementary policies for reaping the benefits of the BRI.

# **b. Corridor management**

The Belt and Road Initiative is structured around two main components, (i) the Silk Road Economic Belt (SREB), and (ii) the 21st Century Maritime Silk Road (MSR). The “Belt”, in turn, is organized along six economic corridors. As discussed in Kunaka (2018), along with the new and updated infrastructure, management of the transport network along these corridors may improve through a set of complementary policy actions (e.g. harmonization of technical standards, single clearance mechanism). In this section, our goal is to estimate the additional benefits in terms of shipment times and trade costs that can be achieved by improving the overall management of those economic corridors. To implement this in a practical way in our GIS analysis, we run an additional scenario in which the speed of 29