rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 3.1**|**Import Substitution Policy Timeline**|
|---|---|
|**Date**|**Policy actions and Authorities’ declarations**|
|December<br>23, 2003|Resolution 220/2003-SICPME initiated the system on import licensing for<br>bicycles. This event was more or less the commencement of Argentina’s<br>new protectionism. The resolution also created the administrative<br>procedures for enforcing the restrictions.<br>_Source:_InfoLEG database website at<br>http://infoleg.mecon.gov.ar/infolegInternet/anexos/90000-<br>94999/91398/norma.htm.|
|August<br>11, 2005|Argentina limits entry points for certain goods. By virtue of General<br>Resolution 1924 of August 11, 2005, and with the purpose of<br>strengthening customs controls, the Federal Public Revenue<br>Administration limited the points of entry for certain categories of goods,<br>including textiles, shoes and toys.<br>On January 29, 2010, the General Customs Division passed new<br>legislation, adding several new tariff positions to the regime. (Only tariff<br>positions added in this new legislation have been included in this reported<br>measure.)|
||_Source:_Global Trade Alert 2010b.|
|August<br>17, 2007|President Cristina Fernández de Kirchner and Minister of Economy<br>Miguel Peirano announced measures to restrict imports, especially those<br>from East Asia. The restrictions covered textiles, footwear, toys, leather<br>goods, and computers. The measures applied included minimum values to<br>collect duties, specialized customs offices designation for certain<br>products, new technical standards for certain products, expansion of the<br>coverage of the import licensing system, and increased customs checks on<br>products originating in certain Asian countries.<br>Shortly thereafter, China banned soybean imports from Argentina.<br>_Sources:_ADN Mundo 2007; iProfessionalcom 2007|
|November<br>2, 2009|. .<br>Law 26,457, enacted on December 10, 2008, created an incentive scheme<br>for local investment in manufacturing motorcycles and motorcycle parts.<br>The scheme includes new restrictions on imports of assembled vehicles<br>and a tax deduction based on purchases of parts from local manufacturers.<br>Débora Giorgi, the minister of industry and tourism, while speaking<br>before the Association of Motorcycle Manufacturers, announced that to<br>promote import substitution the law would encourage domestic production<br>of motorcycles and motorcycle parts, the expectation being that within<br>five years imported parts would constitute no more than 30 percent of the<br>production value of a unit.<br>_Sources: Ámbito Financiero_at<br>http://190.224.163.233/ambitoweb/diario/2009/1126/texto/Not_20091126<br>495325.rtf; Río Negro 2012.|
|November<br>3, 2009|_<br>Law 26,393, enacted on June 25, 2008, created a system of bonuses<br>payable to assemblers of vehicles, internal combustion engines,<br>gearboxes, and axles that purchase locally made parts.<br>_Source:_Honorable Chamber of Deputies website at|

13
-8 -

A third measure of creditworthiness based on even moxe information can also be obtained by using censored regression models. Again suppose that (2.1) measures the propensity of a country to experience a debt-repayments problem with Yt measuring the excess demand gap for external funds. Suppose further that a strictly positiv **e** xcess demand gap will manifest itself in the country's letting repayments obligations gointo arrears, whereas no arrears will be observed for a country that is experiencing no repayments problem.5/ In this vein, if yit denotes observed arrears, we have the censored regression model (2.5) Yt =ytt if y%. > 0 = 0 otherwise.

Since in this model the full extent of the crisis is observed in the form of the level of arrears when these are strictly pcsitive, more information is utilized here than in the threshold crossing model (2.2). As a result, the creditworthiness indicator based on (2.5) will be more accurate than the analogous indicator based on (2.3). Ve will impose normality assumptions onthe censored regression model, and hence we will be estimating a _Tobit_ model.

Versions of all three types of models are estimated for a panel set of data of 109 developing countries observed through the years 1970-1986. The preferred specifications are then used to construct creditworthiness indicators. The final step is to investigate whether such creditworthiness ir 'icators exhibit any links to secondary market discounts as discussed in Section 6below. f/ 5 Some qualifications to this modelling are set out in Haj ivassiliou (1987).

6 Note that the more structural credit-rationing econometric model developed in Hajivassiliou (1987) will not be employed in this paper, because our main objective will be to examine anv predictive links between creditworthiness indicators and secondary market discounts, for which the "reduced form" approach taken here will suffice. Identifying the specific parameters of demand and supply functions is not of primary interest in this study.
investors from coming into the country at all. Indeed, the literature on technology transfer suggests that a one-size fits all policy is not desirable. As a country’s own innovative capacity grows, so should the strength of its intellectual property protection (e.g. Masksu 2002).

Developing country policies can help to promote spillovers. First, policies that improve the absorptive capacity of a country increase the potential of benefiting from knowledge spillovers. Using patent applications as a measure of technology transfer, Hascic and Johnstone (2011) find that absorptive capacity increases wind energy patent applications filed in developing countries by developed country inventors. Indeed, in their study, absorptive capacity proves to be more important than traditional technology transfer policies such as CDM.<sup>23</sup> By providing access to technology, trade policy can also help to promote spillovers from technology transfer. World Bank (2008a) includes a study of the effect of tariff and non-tariff trade barriers on trade flows of four clean energy technologies: clean coal, wind energy, solar photovoltaic systems, and energy-efficient lighting. Examining imports to the top 18 developing countries ranked by greenhouse gas emissions, they find that eliminating tariff and non-tariff barriers would increase trade volumes by 4.6 percent for clean coal to 63.6 percent for energyefficient lighting.

Enhancing absorptive capacity or improving access to trade promote spillovers in a way that offer little cost to innovators deciding whether or not to transfer a technology. In contrast, efforts to require technology transfer require a careful balancing act, so as to not discourage multinationals from choosing to participate at all. Wang (2010) illustrates this balancing act in a study on China’s policy towards CDM projects. While the Chinese government often acts as a broker to bring parties together when technology transfer is desired, its policies often hamper technology transfer from CDM. Most importantly, China has local content requirements. For

> 23 World Bank (2008b) provides a more general discussion of the role of absorptive capacity in technology transfer.

36
<!-- Start of picture text --> Q India: Marriage age<br>?<br>a<br>Bw<br>& t<br>2<br>2<br>~ . .<br>an Higher education<br>2<br>&<br>5<br>4<br>Q Urban<br>iT}<br>SO | |<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> nN Bangladesh: Marriage age<br>e<br>e<br>&<br>FI<br>& 2<br>®<br>5<br>3<br>ra<br>on Higher education<br>Q<br>£<br>5<br>o<br>o£94 Urban<br>wu ee<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> N Nepal: Marriage age<br>o®<br>fei]<br>v<br>EW<br>ES<br>6<br>3 Higher9 education<br>a<br>5Q<br>&Urban<br>By<br>3<br>i<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> nN Pakistan: Marriage age<br>©<br>v<br>—<br>gg<br>3 Higher education<br>Bal<br>g<br>a<br>£<br>:<br>4 N Urban<br>ioO<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
# 1 **Introduction**

Investment in physical transport infrastructure is costly. But such investment can bring economic transformation by integrating isolated regions into trade networks and intensifying trade flows within the existing network. These promises of economic integration motivated developing countries to invest generously in physical transport infrastructure during the last couple of decades. While construction of transport infrastructure is generally welfare improving,<sup>2</sup> much of the evaluation of its impact skirted the issue of how such investments may affect the price of transport services. Recent literature on supply chains recognizes that endogenous price responses are critically important in determining how the effect of a shock is transmitted spatially along the supply chain and is distributed unevenly across areas and people (Atkin and Donaldson, 2014; Allen, Atkin, Cantillo and Hernandez, 2020). Much of the recent evidence on the endogeneity of transport services comes from container and dry bulk shipping in international trade (Brancaccio, Kalouptsidi and Papageorgiou, 2020; Wong, 2018; Asturias, 2020). Except for the ongoing work by Allen, Atkin, Cantillo, and Hernandez (2020) on trucking in Colombia, little is known about how road transport costs vary with route characteristics and how sensitive the intensive and extensive margins of inter-regional trade are to transport costs in developing countries.<sup>3</sup> Similarly, evidence on the behavior of transport costs during the COVID-19 pandemic and its implications for regional trade networks is also rare in developing countries. This paper addresses these questions utilizing a unique data set consisting of nearly half a million _actual_ shipment data from a trucking platform in India.

The trucking sector is the predominant carrier of freight transported domestically in both developed and developing countries. It accounts for 63 percent of internal freight in India and 58 percent in the United States, and 70 percent in Africa region.<sup>4</sup> In the case of international trade where ocean shipping

> 2 For a survey of the evidence, please see Donaldson (2015), Redding and Turner (2015), and Berg et al (2017).

> 3 Allen, Atkin, Cantillo, and Hernandez (2020) investigate the implications of imperfect competition in trucking for trade costs in remoter areas in Colombia. Their data consist of truck shipments at the aggregate origin-destination pair level, whereas our data consists of individuallevel transactions.

> 4 India has about 1.8 million kilometers (km) of roads. Rail freight constitutes around 27 percent of the total freight movement in India. It consists of a large infrastructure of more than 65000 km of rail network carrying more than 1400MMT of load annually. The source of data for the USA: https://www.bts.gov/newsroom/2017-north-american-freight-

2
Appendix: For Web Publication Only

|estimates<br>**s**<br>All others<br>(4)<br>0.043<br>(0.004)***<br>0.042<br>(0.004)***<br>0.046<br>(0.004)***<br>0.048<br>(0.004)***<br>0.038<br>(0.003)***<br>-0.002<br>(0.003)<br>0.035<br>(0.004)***<br>0.056<br>(0.004)***<br>0.073<br>(0.005)***<br>0.079<br>(0.004)***<br>Y<br>Y<br>0.533<br>0.499<br>0.43<br>7,458,651<br>leters versus non-completers<br>t) for the sample group indi-<br>red by class and month*year<br>vidual and month*year fxed|
|---|
|t efects of worker training: selected _β_ coefcient <br>**Estimation of employment across**<br>**course completers and non-completer**<br>**Employed [0/1]**<br>All registrants<br>MDIC registrants<br>Unemp. Benefc.<br>(1)<br>(2)<br>(3)<br>0.014<br>0.041<br>0.135<br>(0.006)**<br>(0.010)***<br>(0.014)***<br>0.008<br>0.04<br>0.119<br>(0.006)<br>(0.010)***<br>(0.009)***<br>0.002<br>0.039<br>0.105<br>(0.006)<br>(0.010)***<br>(0.010)***<br>0.001<br>0.028<br>0.105<br>(0.007)<br>(0.010)***<br>(0.011)***<br>0.03<br>0.012<br>0.094<br>(0.006)***<br>(0.009)<br>(0.018)***<br>-0.014<br>0.045<br>0.011<br>(0.006)**<br>(0.012)***<br>(0.011)<br>0.018<br>0.059<br>0.062<br>(0.006)***<br>(0.011)***<br>(0.011)***<br>0.037<br>0.068<br>0.083<br>(0.005)***<br>(0.012)***<br>(0.010)***<br>0.052<br>0.073<br>0.094<br>(0.005)***<br>(0.013)***<br>(0.010)***<br>0.059<br>0.074<br>0.098<br>(0.005)***<br>(0.013)***<br>(0.010)***<br>Y<br>Y<br>Y<br>Y<br>Y<br>Y<br>0.565<br>0.748<br>0.604<br>0.496<br>0.434<br>0.409<br>0.41<br>0.45<br>0.44<br>10,243,541<br>912,053<br>1,872,837<br>ng the diference in outcomes between course comp<br>urse completion (_β_ vector in equation 2 in the tex<br>consistent robust standard errors two-way cluste<br> an unreported constant term and vectors of indi<br>_._05, _∗∗∗p < ._01.|
|Appendix Table 4: Employmen<br>Outcome:<br>Sample<br>18 months prior to course start (t=-18)<br>12 months prior to course start (t=-12)<br>9 months prior to course start (t=-9)<br>6 months prior to course start (t=-6)<br>1 month prior to course start (t=-1)<br>At end of course (t=+6)<br>3 months after course (t=+9)<br>6 months after course (t=+12)<br>9 months after course (t=+15)<br>12 months after course (t=+18)<br>Individual fxed efects<br>Year * Month fxed efects<br>Mean of outcome<br>St. dev. of outcome<br>_R_2<br>_N_<br>Notes: Table presents selected coefcients measuri<br>at diferent time periods relative to the start of co<br>cated in the column headings. Heteroskedasticity-<br>reported in parentheses. All specifcations include <br>efects. Signifcance indicated by: _∗p < ._1, _∗∗p <_|

57
20 TABLE IV Optimal Investment and Hedging for a Risk-averse Firm A. Elasticity of CapitaL Demand - 2; Risk Aversion - .1

||No Forward<br>lMarket|-|Fo|rward Narket||_ _|
|---|---|---|---|---|---|---|
|Correlation<br>between<br>depreciation<br>and<br>inflation<br>differentiaL<br>(Pqc)|FOIwithout<br>forward market<br>CKC)<br>.(KF)<br>.________|FDI with<br>forward<br>market<br>______ .______|Ouantity of<br>Currency the<br>firm is<br>interested<br>in selling<br>at the end<br>of the<br> **_**__<br> period(Z)|Revenue<br>generated<br>by the<br>project<br>CR)|ZJR|K/IKF|
|0.00|85.57|90.87|190.82|200.18|0.95|0.94|
|-0.20|87.94|91.20|157.16|200.55|0.78|0.96|
|-0.40|90.53|92.15|116.08|201.62|0.58|0.98|
|-0.60|93.37|93.88|78.80|203.47|0.39|0.99|
|-0.80|96.51|96.42|40.45|206.21.|0.20|1.00|

**B.** Elasticity of Capital Demard = 3.33; Risk Aversion = .1

|_____<br>_____|No Forward<br>market<br>|_<br>_<br>_<br>_<br>_<br>|F<br>_<br>_<br>_<br>_<br>_<br>_<br>_|orward Markat<br>_|_<br>_<br>_<br>_|_<br>_<br>_<br>_|
|---|---|---|---|---|---|---|
|PqE|TK|KF|z|R|Z/R|KI/KF|
|0.00|84.33|89.39|134.08|138.67|0.97|0.94|
|-0.20|86.84|89.76|107.66|139.06|0.77|0.97|
|-0.40|89.60|90.88|61.75|140.29|0.56|0.99|
|-0.60|92.67|92.83|55.66|142.39|.<br>0.39|0.99|
|-0.80|96.11|95.78|28.79|145.54|0.Z0|1.00|

C. Elasticity of Capital Demand = 2; Risk Aversion = .2

||lNo<br>Forward<br>Market<br>|E|||Forward Market<br>R<br>|<br>126<br>||<br>09||<br>KF|
|---|---|---|---|---|---|---|
|0.00|j<br>_76.1_|83.82|176.01|192.26|0.92|-0.91|
|**Table A2a: Baseline balanc**|**e for those r**|**esponding**|**to at least**|**one 7-yea**|**r follow-up **|**survey**|
|---|---|---|---|---|---|---|
||Overall<br>Mean|Overall<br>S.D.|Control<br>Mean|PI<br>Mean|Traditiona<br>Mean|l p-value<br>equality|
|_Baseline strata variables_|||||||
|Monthly profits|185|340|191|176|188|0.140|
|Commerce sector|0.47|0.50|0.47|0.46|0.47|0.167|
|Production sector|0.28|0.45|0.29|0.28|0.27|0.126|
|Female|0.52|0.50|0.54|0.51|0.51|0.720|
|_Other baseline variables_|||||||
|Age of Owner|40.8|10.8|41.3|40.1|40.9|0.448|
|Years schooling|8.5|4.4|8.6|8.7|8.3|0.153|
|Firm age|12.2|9.2|12.7|11.6|12.4|0.346|
|Monthly sales|1336|2603|1364|1320|1328|0.900|
|Weekly sales|409|829|408|425|394|0.587|
|Weekly profits|66|122|67|62|68|0.531|
|Capital stock|1569|4297|1673|1493|1549|0.834|
|Number of employees|2.9|4.1|2.9|2.9|2.9|0.744|
|Personal initiative index|4.23|0.47|4.24|4.23|4.21|0.718|
|Business practices|0.58|0.14|0.58|0.59|0.58|0.365|
|Sample Size<br>Notes: Baseline (2013) char<br>Monetary values are expre<br>denote firms randomly assi<br>traditional business trainin<br>means across the three gro|1250<br>acteristics f<br>ssed in term<br>gned to the<br>g groups res<br>ups.|or entrepre<br>s of Septe<br>control gr<br>pectively.|393<br>neurs inter<br>mber 2021<br>oup, person<br>P-value of e|435<br>viewed at<br>USD.**Cont**<br>al initiativ<br>quality tes|422<br>least once i<br>**rol, PI,**and<br>e training gr<br>ts for equal|n 2021.<br>**Traditional**<br>oup, and<br>ity of|

29
Table A.2: First Stage Regressions

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent variable|Log( quantity<br>sold in kg)|Market Access*<br>Quantitysold|Wealth*<br>Quantitysold|
|Total quantity sold (log)|0.79357|-1.03973|-0.06482|
||(14.55)***|(1.37)|(0.76)|
|Market Access* total quantity sold|-0.00038<br>(1.08)|0.83603<br>(63.53)***|-0.00061<br>(0.93)|
|Land* total quantity sold|-0.01117|-0.37602|0.81089|
||(2.01)*|(1.93)*|(46.69)***|
|Market Access*Land|0.00542|0.22452|0.00916|
||(3.00)***|(2.67)**|(1.50)|
|Household size(log)|0.28469|6.43147|0.26293|
||(1.65)|(2.25)**|(0.84)|
|share of adult female|0.17752|8.79632|-0.53469|
||(0.68)|(1.01)|(1.43)|
|Share of kids (5yr & Older)|-0.21700<br>(0.55)|8.24562<br>(0.93)|-0.71575<br>(0.87)|
|Share of old (>65 yr)|-0.15645|-7.88725|0.11643|
||(0.69)|(1.09)|(0.37)|
|Dummy if member of Scheduled|0.20481|9.82536|0.08623|
|Caste/Scheduled tribe|(1.42)|(1.97)*|(0.27)|
|Age of household head|-0.02840|-1.19531|-0.05302|
||(1.21)|(1.69)|(1.19)|
|Age of household head Squared|0.00034|0.01265|0.00060|
||(1.54)|(1.89)*|(1.49)|
|Education of Household Head|0.00258|0.22652|-0.00662|
||(0.12)|(0.41)|(0.14)|
|Number of adult male|-0.02978|-1.85371|0.00714|
||(0.82)|(1.77)*|(0.10)|
|Agricultural Wage (log)|0.19128|20.38366|-0.17682|
||(0.36)|(1.22)|(0.24)|
|Constant|2.73026|-5.96058|6.44325|
||(1.14)|(0.10)|(2.11)**|
|R-squared|0.69|0.98|0.98|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% All regressions include dummies for crop, month of sales and district.
# **5.3 Results by worker characteristics**

While the international movement of skilled and highly educated workers is subject to less binding administrative regulations (many _ad hoc_ visas are currently available for highly skilled workers that want to move internationally), primary educated workers and/or those employed in unskilled-intensive occupations suffer more stringent administrative constraints to international mobility. We therefore expect that visa provisions in RTAs stimulate relatively more unskilled workers. To test such an heterogeneous effect of visa provision on migrants with different skills we use the OECD DIOC dataset,<sup>28</sup> and test the effect of visa provisions on skilled _vs_ unskilled immigrants. Based on the occupation of workers, we consider high-skilled those immigrant workers in ISCO occupation 1 (Managers, senior officials and legislators), 2 (Professionals) and 3 (Technicians and associate professionals). Conversely, workers in other occupations are classified as low-skilled (i.e. ISCO occupations 4-9). Results are reported in Table 7. In line with the intuition, the presence of visa provision in RTAs increases the bilateral stock of low-skilled migrants by 17.9%, and the stock of unskilled immigrants by 9%.

As a robustness check of previous results we use a sector-based definition of workers’ skills. Namely, we classify as primary educated migrants those employed in low-skill intensive sectors such as: (i) agricultural, fishery and related laborers (ISCO code 92), (ii) laborers in mining, construction, manufacturing and transport (ISCO code 93), and (iii) stationary-plant and related operators. Results, reported in Table 8, show that migration related provisions in RTAs favor in particular migrant workers in primary low-skill intensive sectors (see columns 1-2 in Table 8).<sup>29</sup>

## – Table 8 about here –

All in all, the results in Tables 7 and 8 confirm the intuition that visa provisions in RTAs are beneficial for international migration subject to more binding administrative regulations (i.e. unskilled workers). To strengthen this intuition, in Table A4 we approximate the restrictiveness of administrative regulation on emigration with a dummy for the existence of exit permit requirement to emigrate from origin country _i_ , and interact it with our visa provision dummy. In line with our reasoning, visa provision is more effective in origin countries where an exit permit is needed to emigrate. In particular, for origins that require ana exit permit to emigrate, the presence of visa provision in RTA increases bilateral migration by 22%.

> 28 Available here: `https://www.oecd.org/els/mig/dioc.htm` . OECD DIOC data are available only for the period 2000-2015.

> 29It should be noted that point estimates in Tables 7 and 8 cannot be directly compared to baseline results because the OECD DIOC database covers only a sub-set of countries while our baseline regressions use a wider set of country-pairs.

13