rafmacalaba/datause-encoder-data huggingface dataset:
share of empty backhauls.

# **3.4 Stylized Model**

Given the evidence for these frictions, I present a model of trucking markets which organizes the main ideas in a simple setting. Using this framework, I walk through the intuition of how the three key frictions, geographic imbalances, search frictions, and regulatory barriers, generate empty trips. With this understanding, I discuss the implications for hypothetical as well as historical cases where empty trips are high.

This model is a simplification and extension of the models of Brancaccio et al. (2020a) and Demirel et al. (2010). Where those papers feature repeated decisions over time and multiple potential locations, I focus on a two-location, single-round-trip setting. This allows me to derive closed form comparative statics and highlight the intuition behind each friction. I also introduce the regulatory barriers, alongside the existing features of potential demand imbalances and search frictions in those paper, and show how the third force interacts with the others.

**Model Setup** Consider an economy with two locations, _A_ and _B_ , and two types of economic agents: shippers who demand shipping and carriers who provide shipping.

In each location _i ∈{A, B}_ , there is a population of _Di_ shippers, where _DB < DA_ . The fronthaul ( _A_ ) and backhaul ( _B_ ) shippers search for carriers in their respective markets. If they find a match, they negotiate with the matched carrier over rates. On agreement, the shipper pays the rate and receives value _vi_ for the shipment. Otherwise, the shipper takes an outside option and does not ship.

There is a population of _NA_ carriers who reside in _A_ ,<sup>9</sup> whose decision process is described in Figure 10. They first search for a fronthaul job from _A_ to _B_ . If they find a job, they negotiate with the matched fronthaul shipper via Nash bargaining, where the shipper has bargaining _γ_ . If they agree on a price, the carrier deliver the fronthaul shipment and travels to the destination at cost _cAB_ . Otherwise, the carrier takes an outside option with payoff 0. Given these outside options, the Nash bargain price _pA_ satisfies where _VA_ is the expected future profits of the carrier after they arrive in _B_ .

> 9Another source of empty trips is if carriers do not live at the origin of fronthaul shipments. This is the source of empty trips considered in Allen et al. (2020), where home locations matter because carriers have to travel to the origin of shipments and back from the destinations.

24
Table 1: Technologies Considered in This Study

||Residential|Commercial||I|ndustrial|||
|---|---|---|---|---|---|---|---|
||_Private_<br>_Dwellings_|_Services_|_Food_<br>_Products_|_Chemicals,_<br>_Rubber_<br>_& Plastics_|_Iron_<br>_& Steel_|_Non-_<br>_Metallic_<br>_Minerals_|_Other_<br>_Industry_|
|_Building Shell_||||||||
|Windows|G|G||||||
|Roof Insulation|G|G||||||
|Wall Insulation|G|G||||||
|Insulation (general)|A||_←−−−−−_|_−−−−−−−−−−−_|_−_A<sup>_∗_</sup>_−−−_|_−−−−−−−−−_|_−−−−−→_|
|_Lighting_|A,G|A,G||||||
|_Appliances_||||||||
|Washing Machines|G|||||||
|Refrigerators|A,G|||||||
|Televisions|A,G|||||||
|_Air Conditioning_|A|A||||||
|_Mechanical_|||G|G|G|G|G|
|_High Temp. Processes_|||G|G|G|G|G|

Discrete technology options and their encompassing sectors analyzed by Sikharulidze et al (2015) and Timilsina et al (2015) shown in Roman text, aggregation to technology classes and industry groups in the present study shown in italics. A = Armenia, G = Georgia.<sup>_∗_</sup> Different industries considered as a single aggregate.

in the areas of residential appliances and insulation, which indicate potential for long-run discounted abatement in the amount of 0.67 MTCO2 in Armenia and 6 MTCO2 in Georgia at or below zero net cost. The underlying scenarios of energy-efficiency penetration assume trajectories of capital-energy substitution over the 2015-2034 policy horizon that are shown in Panel B. Importantly, a substantial share of the assumed total abatement emanates from improvements in technology options whose efficiency improvements can only be acquired at a significant capital cost premium (particularly residential lighting, and commercial lighting in Georgia). The key issue here is that, even controlling for the cost of abatement and the “profitability” of the options under consideration, the penetration of energy efficiency and the overall quantity of abatement both turn on assumptions about the size of the market for technologies’ services. We shall see that this question of market potential figures prominently in the subsequent numerical analysis in Section 4. Panel C provides details of technologies’ costs of fuel and capital inputs in the 2015 base year, along with capital-energy elasticities of substitution implied by the penetration of energy efficient technology relative to the baseline scenario.<sup>8</sup> The magnitude of the latter reflect the steepness of the loci in panel B, and indicate the potential for larger energy efficency improvement at lower opportunity cost.

> 8Elasticities are estimated from OLS fits of (10) to annual projections of technology costs and emissions under baseline and counterfactual scenarios.

12
and each loan type. We do not observe interest rates at the loan level, but anecdotal evidence suggests that interest rates are fairly standardized within products for a given lender. To capture losses, we then subtract the amount in default 24 months after the initial loan application, which could be zero in case of no defaults. We picked 24 months to allow six months for the loans to be issued and 18 months for the termination of loans with the maximum duration.<sup>5</sup>

# **3 Results**

We now describe our identification strategy and results. As our partner bank applied a strict threshold rule based on the EFL score, across which both the likelihood of loan approval and loan terms vary discontinuously, we apply a regression discontinuity (RD) design. Specifically, letting be the borrower's EFL score, be the EFL cutoff, (1) be the outcome of interest for borrower ii conditional on being above the EFL cutoff and ii (0) ss ss̅ YY be the outcome of interest for borrower conditional on being below the EFL cutoff, we ii ii YY estimate by approximating lim ii (Hahn et al., 2001). We estimate the EE(YYii(1) −YYii(0)|ssii = ss̅) lim ssii→ss̅<sup>+ EE(YYii(1)|ssii) −lim</sup> ssii→ss̅<sup>−EE(YYii(0)|ssii)</sup> variety of bandwidths around and polynomial functions of various orders. ssii→ss̅<sup>+ EE(YYii(1)|ssii)and</sup> ss<sup>lim</sup> ii→ss̅<sup>−EE(YYii(0)|ssii)using a</sup> In our main results we estimate a linear model on either side of the threshold. ss̅ Normalizing the threshold , and letting represent the observed outcome variable for person , we estimate ii ss̅ = 0 yy 1 2 ii where is the indicator function and ii ii ii is a vector of control variables representing age, ii ii ii ii ii yy = αα+ β (ss ≥0) + γγ ββ(ss < 0)ss + γγ ββ(ss ≥0)ss + δδXX + εε gender, log business revenues, and Equifax score. Estimates from models with higher order ii ββ(⋅) XX polynomials are included in the appendix. The estimation is done in Stata using the command _rdrobust_ , described in Calonico et al. (2014a).

At the outset, in Table 1 we note that none of the demographic or business characteristics are discontinuous across the EFL threshold. Columns 3 through 5 present

> 5 If the financial institution stops reporting a loan amount for a given borrower before 24 months are over, we use the amount in default in the last month when the amount was observed (after the first six months after the loan application). If the amount in default exceeds the calculated size of the loan (which may be the case if it includes other loans from the same institution), we replace the amount in default with the size of the loan.

9
# 3.4.3 Common External Tariff and Cash for Cooperation

There is a long literature on various aspects of PTAs that shows that their effects often depend on the existence of a CET.<sup>28</sup> Given that between the Tokyo and Uruguay Rounds—the period we analyze in the empirical work—the EU expanded to include new members that share its CET, we analyze the of such accessions on MTL.

The use of a CET raises the practical issue of how the tariff revenue is to be distributed over the different countries. If all goods enter the EU via one port, does that country receive all the revenue? In order to address this issue, PTAs with a CET specify revenue transfer mechanisms. Therefore, one key difference relative to other PTAs is the existence of a mechanism for transfers, which Large can use to provide an incentive for Small to supply the regional good. In the context of the EU such transfers go well beyond tariff revenue distribution and therefore we now model the effect of a CET in the presence of transfers and show that such transfers will either partially or fully replace the manipulation of MFN tariffs to obtain cooperation from regional partners.

The possibility of transfers also raises the question of whether preferences in this model would be given in the fi rst place, since the motive for Large in using them is to obtain e<sup>S</sup> . Below we show the conditions under which Large will optimally use both transfers and preferences. This will show that the model can explain preferential treatment even if Large can also use cash to obtain Small’s cooperation.

If Small can deviate in the provision of the public good then its incentive compatibility constraint is still (12). If we allow a transfer T from Large to Small when cooperation starts then we have: where the left-hand-side represents Small’s gain from deviating and the right-hand-side the discounted value from cooperating. Using the de fi nition of G<sup>S</sup> in (7), we obtain the equilibrium level of e<sup>S</sup> for a

> 28 Cadot et al. (1999) argue that “deepening integration is likely to work toward reinforcing protectionist pressures against nonmembers” when there is a CET but not necessarily if the PTA has no CET in place. Bagwell and Staiger (1998) indicate that in the absence of a CET PTAs would undermine reciprocity and non-discrimination, the main pillars of the multilateral trading system. However, they also show that PTAs with a CET could still be efficient in terms of reciprocity as long as external tariffs are in line with the non-discrimination principle.

16
# Methods for Measuring the Effect of Adjustment Policies on Income Distribution

# by Anne Maasland

# Table of Contents

|1.|Introduction||1|
|---|---|---|---|
|2.|Suggested Methodologies:<br>A R|eview|2|
||2.1 Qualitative Approaches:<br>2.2 Qualitative Approaches:<br>2.3 Quantitative Approaches:<br>2.4 Quantitative Approaches:|General Equilibrium<br>Partial Equilibrium<br>General Equilibrium<br>Partial Equilibrium|4<br>8<br>12<br>22|
|3.|Conclusion||30|
|Table|1||33|
|Refere|nces||34|
We assessed the external validity of our experiment and the representativeness of our sample by comparing them with the sample of the 2019 Household and Multipurposes Survey (EHPM), a nationally representative survey in El Salvador that is conducted through face-to-face interviews. Of the approximately 75,000 individuals interviewed in the EHPM, 15,680 (21 percent) meet our eligibility criteria (See Table A2 in the Appendix).<sup>17</sup> When we restrict the EHPM sample to individuals who meet our criteria, we find no meaningful differences in age, gender, and age of the oldest child with our sample (See Table A3 in the Appendix).<sup>18</sup> We also verify that the randomization of individuals in the Phone/WhatsApp intervention was not correlated with the characteristics and treatment assignment of the impact evaluation studied in Amaral et al. (2021). Therefore, we show in Figure A2 that there is no correlation across gender, age or mobile phone usage.

# **3 Data and Summary Statistics**

## **3.1 Data Sources**

We use four sources of data to assess the relative convenience of the two survey modes: administrative records provided by the survey firm, responses to the enrollment survey, responses to the midline survey, and responses to the parenting intervention’s baseline and endline surveys.<sup>19</sup> _Administrative records provided by the survey firm._ This data includes information on whether the individuals completed or did not complete the midline survey, the reason for non-completion (for those who did not complete it), and the number of reminder calls made to each person.

_Enrollment survey._ The enrollment survey included questions on respondent’s gender and age, mobile phone ownership status, whether they lived with and cared for a child eight years old or younger, and the

> 17The eligibility criteria are described in footnote 12.

> 18We only find differences between the EHPM and the survey-mode experiment samples in educationallevel variables; the survey-mode experiment sample is more educated. This difference could also stem from the fact that only 60 percent of our sample completed the baseline survey. Therefore, educational attainment data is available only for a subsample of 358 (out of 599) individuals.

> 19In Figure 1 we present the different stages of data collection that we describe in this section.

10
# **2. Data**

To assess the capital raising activity of Arab countries in equity, corporate bond, and syndicated loan markets, we assemble a comprehensive data set on firms’ security issuances from 1991 through 2014. To benchmark the Arab region against other regions in the world, we include firms’ issuance activity from all over the world. Our data on firms’ capital raising activity come from the Thomson Reuters Security Data Corporation (SDC) Platinum database, which provides transaction-level information on new issuances of common and preferred equity, publicly and privately placed bonds, and syndicated loans with an original maturity of one year or more. Given that the SDC Platinum database does not collect data on debt issuances with maturities shorter than one year, the data set does not cover commercial paper. Because our analysis focuses on corporate financing, we exclude all public sector issuances, comprising securities issued by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. We also exclude mortgage-backed securities and other asset-backed securities. The data set includes 138,091 firms and 719,242 security issuances: 199,742 equity issuances, 294,159 bond issuances, and 225,341 syndicated loan issuances. The analysis focuses on 12 Arab nations, for which the data set includes 1,630 firms and 4,372 security issuances: 1,398 equity issuances, 650 bond issuances, and 2,324 syndicated loan issuances.

To classify equities and corporate bonds as domestic or international, we compare the market location in which the securities are issued to the issuing firm’s nationality. For offerings that take place simultaneously in more than one market, we consider tranches in each market as separate issuances. The data set includes 382,535 issuances in domestic markets and 105,916 issuances in international markets. For syndicated loans, the nationality of the banks that participate in the deal is used to distinguish between domestic or cross-border lending. Domestic loans are those in which only domestic banks participate in the syndication, whereas international loans entail the participation of at 6
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure D.8. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable<br>Tercile 3<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.1<br>.04<br>.02 .05<br>0<br> (95% CI) (95% CI)<br>β β 0<br>-.02<br>-.05<br>-.04<br>.2 .05<br>0<br> (95% CI) .1 (95% CI) -.05<br>β β -.1<br>0<br>-.15<br>-.1 -.2<br>.1<br>.05<br>0<br> (95% CI)<br>β -.05<br>-.1<br>-.15<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation for the tercile 3 of the PEP holders intensity. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the election of 2015, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

82
|**Table 3: Effects of Agen**|**t Rollout on M**|**obile Mone**<br>Used|**y Usage and R**<br> <br>Dep|**emittances**<br>endent vari<br>|**(Follow-up Su**<br>able:|**rvey)**<br>|IHS|
|---|---|---|---|---|---|---|---|
||Is aware of<br>mobile<br>money|<br>mobile<br>money in<br>last 3<br>months|Saved<br>money in<br>any type of<br>savings|Saved<br>money in<br>mobile<br>account|Received<br>money via<br>any channel|Received<br>money via<br>mobile<br>money|<br>transport<br>costs for<br>receiving<br>money|
|Panel A: ITT for all clusters|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Agent|0.010|0.002|-0.018|-0.007|0.006|0.007|-0.448*|
||(0.023)|(0.013)|(0.022)|(0.009)|(0.011)|(0.010)|(0.248)|
|R-squared|0.042|0.015|0.021|0.014|0.010|0.011|0.035|
|Control group mean|0.594|0.183|0.737|0.090|0.135|0.100|1.994|
|Panel B: ITT byAirtelpriority||||||||
|Agent*High priority|0.006|0.002|-0.015|-0.010|0.011|0.009|-0.760**|
||(0.033)|(0.017)|(0.027)|(0.011)|(0.015)|(0.013)|(0.337)|
|Agent*Low priority|0.015|0.002|-0.020|-0.004|0.000|0.006|-0.135|
||(0.033)|(0.019)|(0.034)|(0.013)|(0.017)|(0.014)|(0.368)|
|R-squared|0.042|0.015|0.021|0.014|0.010|0.011|0.037|
|F-test p-value (Agent*High = Agent*Low)|0.857|1.000|0.925|0.717|0.629|0.856|0.212|
|Control group mean - High priority|0.608|0.180|0.771|0.079|0.131|0.096|2.138|
|Control group mean - Low priority|0.580|0.186|0.702|0.101|0.140|0.103|1.855|
|Observations|8844|8844|8844|8844|8844|8844|1212|

Note: Panel A shows results from OLS regressions of the dependent variables on a dummy variable that is equal to 1 if the cluster was randomly selected for the agent rollout and equal to zero otherwise. Panel B shows results from similar regressions, where the agent rollout dummy is interacted with two dummy variables indicating Airtel priority rating. In clusters with higher priority, the professional services firm spent more time rolling out agents and a larger fraction of these clusters received an agent than clusters with low priority. The randomization was stratified by priority rating. All regressions include randomization strata dummies and household head age. Control group means are means of the dependent variables. Transport costs are for households who received money via any channel. Standard errors clustered at the cluster level in parenthesis. Statistical significance levels: * p<0.10, ** p<0.05, *** p<0.01. To deal with multiple hypothesis testing, we calculated sharpened q-values and indicate statistical significance levels: † q<0.10, †† q<0.05, ††† q<0.01.

23
(or at least a large share of it publicly available) at a point in time, allowing me to analyze mobility at a very disaggregated geographical level. However, the main disadvantage of this data set is that it does not link all the individuals with their parents because both (individuals and parents) need to be part of the same household. Below, I explain how this is addressed and I refer to recent evidence showing that the coresidence bias is likely to be very small for the indicators used in this paper.

# **II.1 Countries and smaller administrative units**

The 24 countries under study are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Trinidad and Tobago, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Saint Lucia, Suriname, and the Rep ́ublica Bolivariana de Venezuela (see Table A1 in the Appendix for the details about the fraction of the data available by census), and they represent 91 samples of these 24 countries drawn at various points from 1960 to 2012.

In terms of geography, IPUMS reports residence at the time of the interview for at most two levels of administrative units in which the households were enumerated. These variables contain the geographies for every country harmonized spatio-temporally to provide spatially consistent boundaries across samples in each country. This allows me to assign individuals to “coarse” (roughly similar to states in the U.S.) and “fine” administrative units (roughly similar to counties in the U.S.). The sample spans 400 provinces (admin-1 units) and 6,684 districts (admin-2 units). The baseline estimates will make use of the former to avoid issues derived from having a reduced number of observations per administrative unit but estimates using the latter are also reported in the Appendix.

Costa Rica 1963, Dominican Republic 1960 and 1970, Ecuador 1962, Honduras 1961 and Mexico 1960. I also omit the 1995, 2005 and 2015 inter decennial Census counts of Mexico.

6