rafmacalaba/datause-encoder-data huggingface dataset:
# 1 INTRODUCTION

The United Nations High Commissioner for Refugees estimates that more than 45 million people around the world are internally displaced, comprising roughly half a percent of the global population<sup>1</sup> (UNHCR, 2020). For many, displacement is a “life long sentence”, as IDPs can remain in a state of protracted crisis for years and even decades (OCHA, 2017). The magnitude and duration of displacement raises questions about how the experience of displacement affects victims, particularly in the long-run.

Surprisingly,whilethereisnowalargeliteratureontheconsequencesofwartimevictimization for a broad range of social and political outcomes (Bauer et al., 2016), academic research has relatively less to say about displacement, especially in the long-term. This is in spite of the fact that displacement is a unique form of victimization: in being forced to relocate, victims are separated from their communities and social networks; they face violence and social exclusion in new host communities(Rozo andVargas, 2021); andthey areoftenseparatedfromproperty,assets,andsavingsthattheystruggletorecover(ThapaandHauff, 2005; IbáñezandMoya, 2010). These characteristics also mean that displacement effects are especially likely to persist and evolve over time. Understanding how displacement affects victims – both in the short and long-term – is thus important not just for the academic value but also to inform the design of policy aimed at addressing one of the most pressing global humanitarian challenges.

Inthispaperwebringnewevidencetobearontheeffectsofinternaldisplacementthrough anoriginalsurveyof1,500ruralhouseholdsspreadacrossfiftymunicipalitiesinColombia. Colombia is a country where almost one in ten citizens have been internally displaced by armed conflict (Internal Displacement Monitoring Centre, 2021) and where the success of a recent, fragile peace agreement may in part rest on the extent to which the plight of IDPs can be redressed.<sup>2</sup> Wecombinetheuniquenatureofthesample–householdsthatarelargelyhomogeneousalong key confounders, such as poverty, rurality, and conflict exposure, yet vary substantially in their incidenceofdisplacement–witharichsetofcovariatesandcausalinferenceapproachestoprovideplausibleestimatesoftheeffectsofdisplacementonavariedsetofvictimoutcomes<sup>3</sup> . Then,

> 1Internally displaced people (IDPs) remain within the boundaries of their country of origin. By contrast, refugees by definition cross international borders.

2WewouldliketonotethatColombiahasalsohadsignificantnumberofrefugeessince2015,particularlyfrom Venezuela. In this paper, we focus only on Colombian IDPs.

3ThesamplewasnotdesignedtoberepresentativeofallIDPsinColombia,alimitationwediscussmorefullyin the appendix and research design section. That said, we would argue there is still great value in the sample, given thatdatacollectiononIDPsisrelativelyrare,particularlyintheremoteareaswecover. Wewouldalsoargue,along the lines discussed in Aronow and Samii (2016), that a nationally-representative sample analyzed in a regression framework would not avoid the generalizability concerns associated with our sample.

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remains strongly linked to their economic growth. Hence, to achieve decarbonization in the transport sector without prejudice to economic growth, there is a dire need to break this link.

The long-term decoupling results show considerable differences in the decoupling status among countries by income levels. While many advanced economies have managed to achieve absolute or relative decoupling, in many developing countries per capita transport emissions are increasing at a much faster rate than per capita GDP growth. Increases in transport CO2 emissions were particularly pronounced in the two largest middle-income countries: China and India. Both are still contributing less to global transport CO2 emissions than their population shares, unlike the United States or the EU countries, but they are rapidly catching up and are to a large extent driving current increases in global transport CO2 emissions.

Next, to explore the underlying drivers of CO2 emissions, we conduct both index decomposition and econometric analyses. For the decomposition analysis we specify two identities. The first decomposition breaks down emissions into three factors: emission intensity measured by transportation emissions per unit of GDP, economic growth (in terms of GDP per capita), and population growth. In many developed countries, declining emission intensity contributes to the decoupling of transportation emissions from economic growth. In most developing countries, although emission intensity also fell, population and GDP growth outstripped these efficiency-related transport emission savings.

In the second decomposition analysis, we further break down emission intensity (measured as CO2 emissions per unit of GDP) into carbon intensity of fuel consumption, energy intensity of transportation, and transport demand. We focus on the inland transportation sector, including rail and road sectors for which disaggregated data are available on travel volume measured by kilometers traveled by passengers and freight. Inland transportation is the largest source of global transport emissions, <mark>accounting for 84 percent of total emissions in 2018. The results again show that efficiency improvements hav</mark> e played an important role in flattening the emissions curve. Most countries have managed to reduce the energy intensity of inland transport. By contrast, there has so far been minimal progress in reducing the carbon intensity of energy consumption, and that concentrated in a handful of countries. Gasoline and diesel are still the dominant fuels for transportation; renewable fuels and electricity accounted for about 10 percent of the energy mix as of 2018.

To further explore the economic drivers of transport carbon emissions, we conduct country-level regression analysis to understand the associations between CO2 emissions and GDP, urbanization, 3
# **Appendix Table 7.1: Impacts on Web Traffic and AngelList**

||Appears<br>on<br>AngelList|Has<br>Alexa<br>Rank|Alexa<br>Global<br>Ranking|Has<br>Similar<br>Rank|Similar<br>Web<br>Ranking|
|---|---|---|---|---|---|
|**Panel A: Impact at Six Mo**|**nths**|||||
|Assigned to Treatment|‐0.022|‐0.042|‐304.7|0.013|447.5|
||(0.035)|(0.043)|(1412)|(0.047)|(2068)|
|Sample Size|346|346|188|346|160|
|Control Mean|0.308|0.535|11161|0.442|12364|
|Control S.D.|0.463|0.500|8383|0.498|9434|
|**Panel B: Impact at Eighte**|**en months**|||||
|Assigned to Treatment|‐0.034|0.032|120.6|‐0.008|1952.4|
||(0.041)|(0.048)|(1438)|(0.048)|(1945)|
|Sample Size|346|346|132|346|156|
|Control Mean|0.372|0.343|7407|0.442|12614|
|Control S.D.|0.485|0.476|5431|0.498|9949|

Notes: Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.

All regressions include controls for baseline level of outcome, and for strata used in randomization.

Alexa Global ranking and Similar Web ranking are expressed in 1000s, and are conditional on having a ranking at all.

# **Appendix 8: Follow‐up Survey Response Rates and Balance on Responders**

Appendix Table 8.1 reports the completion rates by treatment status for three definitions of completion. Initially we began with a longer follow‐up survey, which in addition to asking about our key outcomes, also asked a series of process questions about the Pioneers of the Balkans program and their reasons for participating or not participating. In order to encourage responses from more reluctant firms, we removed these questions to shorten the questionnaire for a second interviewing phase, with the short survey containing all the key outcomes in our pre‐analysis plan. Finally, for firms that we could not interview after multiple attempts, we attempted to collect basic information in a few minutes from them, asking for their current operating status, their number of employees, whether they had entered into negotiations with an outside investor to make an investment in their firm since August 2015, and how much new investment they had received since August 2015. In the second follow‐up, this basic information was restricted to whether the firm was still operating, and whether it has received external financing, and also used web searches and secondary contacts.

We see that the treatment group was more likely to respond to the full survey than the control group in the first follow‐up survey (p=0.066), but there is no significant difference in response rates for having at least the short survey, or at least basic information, and no significant treatment differences for the second follow‐up.

**<mark>Appendix Table 8.1: Follow‐up Survey Completion Rates</mark>**

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# APPENDIX: SOURCES FOR FISCAL DATA

No single uniform source was available for the fiscal data used in this paper. Country data was generally culled from various sources. Sources of flow fiscal data are listed below for individual countries:

1. Argentina

Overall public sector deficit and primary deficit are from C. Rodriguez (1991).

2. Bolivia Overall public sector deficit is from Morales (1986). Primary deficit was calculated by deducting external interest payments as derived from World Bank (1991).

3. Brazil Overall public sector deficit is from Larrain and Selowsky (1991). Primary deficit was calculated by deducting external interest payments as derived from World Bank (1991), as well as domestic interest payments estimated by applying a constant interest rate of 5 percent to the stock data from Guidotti and Kumar (1991).

4. Chile Overall public sector deficit and primary deficit are from Marshall and Schmidt-Hebbel (1991).

5. Colombia Overall public sector deficit is from Easterly and SchmidtHebbel (1991). Primary deficit was calculated by deducting external interest payments as derived from World Bank (1991), as well as domestic interest payments estimated by applying a constant interest rate of 5 percent to the stock data from Guidotti and Kumar (1991).

6. Cote d'Ivoire

- Overall public sector deficit is from Easterly and SchmidtHebbel (1991). Primary deficit was calculated by deducting external interest payments as derived from World Bank (1991), as well as domestic interest payments estimated by applying a constant interest rate of 5 percent to the stock data from Guidotti and Kumar (1991).

7. Ecuador Overall public sector deficit is from Easterly and SchmidtHebbel (1991). Primary deficit was calculated by deducting external interest payments as derived from World Bank (1991), as well as domestic interest payments estimated by applying a 25
A6 Pakistan

**Table A6: Regional disparity** trends in Pakistan

|Year|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(Cyw|Relative<br>mean<br>Deviation<br>(Rw|Unweighte<br>gini index<br>(Gul|d Weighted<br>gini index<br>(Gw|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1990|1.512|0.215|0.186|0.125|0.116|0.081|0.014|
|1991|1.562|0.213|0.179|0.118|_<br>0.122|0.082|0.013|
|1992|1.577|0.214|0.177|0.115|0.125|0.083|0.013|
|1993|1.613|0.216|0.174|0.109|0.131|0.083|0.012|
|1994|1.525|0.191|0.153|0.096|0.116|0.073|0.010|
|1995|1.507|0.186|0.148|0.094|0.113|0.071|0.009|
|1996|1.511|0.188|0.153|0.096|0.112|0.073|0.010|
|1997|1.514|0.186|0.150|0.094|0.113|0.072|0.009|
|1998|1.516|0.183|0.141|00|09|0.069|0.008|

**Figure A6: Regional disparity trends in** Pakistan

<!-- Start of picture text --> 0.250<br>0.200 : : .:<br>0.150 , - P r ';<br>E<br>.. 0t . .. 0 tt : , ....... <...........-, *- 0'".i'' .s:t<br>0.050<br>0.000<br>1990 1991 1992 1993 1994 1995 1996 1997 1998<br>Year<br>-<br>Simple coefficient of variation (CVu) Weighted coefficient of variation (CVw) Relative mean Deviation (Rw)<br>4<br>Unweighted gini index (Gu) + Weighted gini index (Gw) Theil index (T)<br><!-- End of picture text --> 40
(and especially) the poor, and the existing system of income redistribution could be one of the important reasons behind this achievement.

**Figure 2. Growth incidence curves in Belarus, 2000–2015**

<!-- Start of picture text --> 10.4 16<br>15<br>10.3<br>14<br>10.2 13<br>12<br>10.1 11<br>10<br>10.0<br>9<br>9.9 8<br>7<br>9.8<br>6<br>9.7 5<br>4<br>9.6 dec01 dec02 dec03 dec04 dec05 dec06 dec07 dec08 dec09 dec10<br>dec01dec02dec03dec04dec05dec06dec07dec08dec09dec10<br>2000-2005 2005-2010<br>2000-2015 mean median 2010-2015 mean, 2000-2005<br>(a) Real disposable resources growth rates, (b) Real disposable resources growth rates,<br>15-year annual average, % 5-year annual averages, %<br><!-- End of picture text --> _Source_ : own estimates based on HBS data (disposable resources) and Belstat data (inflation).

Historically, redistribution played an important role in Belarus. The share of general government expenditures in GDP stayed at the level of European welfare states (47.2% on average between 2000 and 2010). Even after impressive fiscal consolidation, when the average government expenditures dropped by 6.7% of GDP (see Figure 3), that share remained higher than in the upper-middle-income countries from Central and Eastern Europe and CIS. The fiscal consolidation of the last five years resulted only in a very moderate increase of the Gini index: from 0.266 between 2000 and 2010 to 0.281 between 2011 and 2015.

**Figure 3. Redistribution and inequality in Belarus, 2000–2015**

<!-- Start of picture text --> 51 0.290<br>49 0.285<br>47 0.280<br>45 0.275<br>43 0.270<br>41 0.265<br>39 0.260<br>37 0.255<br>35 0.250<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>General government expenditure, % of GDP Average, 2000-2010 Average, 2011-2015 Gini (RHS)<br><!-- End of picture text --> _Source_ : GFS, Belstat.

# **2.2. Revenues and expenditures of the general government**

# _Government revenues_

The need for fiscal consolidation was driven by a drastic reduction of general government revenues during the currency crisis of 2011 that had not been restored completely. As fiscal policy was quite conservative (the budget had a surplus of 0.6% of GDP on average between 2000 and 2010 and 1.4% of GDP between 2011 and 2015, see Figure 4a), general government expenditures followed revenues. That “conservative” policy was imposed by the size of operations “below the line”: deep interventions of the state into the economy required regular recapitalization of the largest state-owned banks and other types of support to the state-owned companies. For instance, 6
If we look more closely at how workers integrate into the labor force, several studies suggest that the informal sector is a port of entry. Ziss and Dick (2003) discuss the importance of informal jobs for youth worldwide, especially poor youth, concluding that young people often enter the informal sector to acquire job skills and then move into the formal sector or continue in informality. Bosch and Maloney (2010) find similar results for 15-24 year old youth in Latin America. Wahba (2000) observes that the average duration of informal jobs in Egypt is shorter than for formal, and concludes that informal work may be used as a stepping-stone to formal employment. In this line, Hemmer and Mannel (1989) noticed that in many countries, informal small firms train more workers than the formal education system and the job-training schemes combined. As Cunningham et al (2008) posits, experience in the informal sector could be a substitute for technical schooling where “graduation” implies getting a formal job.

Young people, governments, and non-governmental organizations often view entrepreneurship as a viable path into the labor force in spite of contrary evidence from the analytical literature.<sup>5</sup> Evans and Jovanovic (1989) find that people with greater assets are more likely to switch to self-employment from other employment. Since youth have not had time to accumulate capital, they would therefore be less likely to become selfemployed. Along the same line, Blachflower and Oswald (1998) report empirical results consistent with the hypothesis that entrepreneurs face capital constraints and reject other potential explanations as psychological characteristics. However, Dunn and Holtz-Eakin (2000) find that parental self-employment has a large and significant effect on the propensity to become self-employed, while financial assets exert a modest effect on the transition into self-employment.

Once youth enter the labor force, they do not necessarily give up their educational careers. Education increases the chances of getting better jobs and higher wages and young workers have lower “switching cost” of returning to school or college than prime age adults. Literature shows evidence of this pattern. Using a panel from 1989 to 1994 of urban adolescents in the city of Baltimore, US, Entwisle et al (2000) find that even though almost all students had begun work by age 17, only 23 percent worked every school year from age

> 5 For example, see OECD (2001) or World Economic Forum (2009)

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decision-making power in the household, reduce their chances of experiencing domestic violence, and improve health care practices among pregnant women (Goldin and Katz 2002; Jensen and Thornton 2003; Field and Ambrus 2008). At the macro level, improved cognition may lead to more growth (Hanushek and Woessmann 2009), while lower fertility rates may also contribute to economic growth through increased female labor supply (Bloom et al. 2009) and by allowing greater investments in the health and education of children.

Starting with schooling outcomes, we find that while the intervention increased school enrollment in both treatment arms, the effect is 43% as large as the CCT arm.<sup>12</sup> Evidence from school ledgers for students enrolled in school also suggests that the fraction of days attended in the CCT arm is higher than the UCT arm. Using independently administered tests of cognitive ability, mathematics, and English reading comprehension, we find that while achievement is significantly improved for all three tests in the CCT arm compared with the control group, no such gains are detectable in the UCT arm. The difference in program impacts between the two treatment arms is significant at the 90% confidence level for English reading comprehension. In summary, the CCT arm had a significant edge in terms of schooling outcomes over the UCT arm: a large gain in enrollment and a modest yet significant advantage in learning.

When we turn to examine the incidences of pregnancy and marriage, however, unconditional transfers dominate. The incidences of pregnancy and marriage were reduced by 34% and 48% in the UCT arm, respectively, whereas no program impact on these outcomes was detected in the CCT arm.<sup>13</sup> The UCT advantage in marriage and fertility is particularly pronounced among those most likely to drop out of school at baseline, implying that a CCT offer is unsuccessful in deterring these

> 12 Our preferred measure of enrollment uses enrollment data for each term in 2008 and 2009, which are confirmed by the teachers during the school surveys. Self-reported measures of enrollment produce divergent results in terms of the relative effectiveness of the CCT and UCT arms. We discuss measurement issues in detail in Section 2 and present enrollment impacts using self-reported data as well as data collected from the schools in Section 4.

> 13 These results are almost identical if we examine teenage pregnancies and marriages by excluding from the analysis 13% of our study sample who were 18 years or older at baseline.

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the interviews were conducted with these 80 people, they were asked to provide further referrals, leading to another 129 people who were interviewed. Some of the participants who were obtained through the second round of referrals were uncomfortable with the questions on financial information and remittances, and refused to provide any contact addresses, which ruled them out from being invited to the training or participating in the four follow-up surveys.

In order to ensure that the surveys and the financial literacy training were conducted in the most effective and culturally appropriate way, we recruited individuals from these same migrant populations to lead the field work for each of the component studies. In fact each of these team leaders had a PhD, two in economics and one in psychology (but specializing in field studies of migrants), and so the level of training and skill for the providers of the financial literacy intervention is likely to be atypically high. Each of the team leaders recruited local assistants who were individuals drawn from the same population groups that were being studied. The questionnaires, powerpoint presentations and any written material handed out were available in English, Mandarin and Korean for the participants in the East Asian group, and in English for the Pacific and Sri Lankan groups (English is the language of schooling throughout the Pacific and the Sri Lankan group are highly educated even if English was not their first language).

# **2.2 Baseline Survey, Randomization, and Financial Literacy Levels**

Respondents were recruited in December 2010-January 2011 (Pacific Island sample), December 2010-February 2011 (East Asian sample) and January-March 2011 (Sri Lankan sample). The resulting sample sizes were 349 Pacific Islanders, 352 East Asians, and 209 Sri Lankans. A baseline questionnaire collected information on their use and awareness of different remittance methods, their financial literacy, with specific emphasis on knowledge relevant to remittances and use of financial instruments, and their background characteristics.

Within each of the three samples we formed eight strata, based on three baseline characteristics: i) reported frequency of remitting (remitting at least every three months or not); ii) knowledge of the saving in transactions costs from bundling two remittances of $100 into a single remittance of $200; and iii) knowledge of which credit card user would face the highest finance charges based on different repayment patterns. Individuals were then randomized by 8