rafmacalaba/datause-encoder-data huggingface dataset:
Table A.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|_a_<sup>_n_</sup>|Share of Agricultural Land in CES function||0.55|
|_ρn_|CES Parameter for Agricultural Land and<br>Fertilizers||0.123|
|_θ_<sup>_c_</sup><br>0|Crop Technology Index in 2004|tons per Ha|4.89|
|_θ_<sup>_c_</sup><br>_T_|Crop Technology Index in 2100|tons per Ha|8.8|
|_κ_<sup>_c_</sup>|Convergence Rate of Crop Technology Index||0.025|
|_c_<sup>_o,c_</sup>|Crop Breeding Cost|share of _X_<sup>_O_</sup><br>_t_|0.0069|
|_ψa_|Merchantable Timber Yield Parameter 1||6.58|
|_ψb_|Merchantable Timber Yield Parameter 2||76.5|
|_v_|Minimum Age for Merchantable Timber|Years|11|
|_κ_<sup>_w_</sup><br>_v_|Timber Yield Gains of Vintage v|Share of Yield 0|0.011|
|_c_<sup>_p_</sup>|Forest Planting Cost|share of _X_<sup>_O_</sup><br>_t_|0.00005|
|_c_<sup>_o,w_</sup>|Forest Harvesting Cost|share of _X_<sup>_O_</sup><br>_t_|0.0012|
|_ξ_<sup>_H_</sup><br>1|Forest Conversion Adjustment Cost Parameter||43|
|_ξ_<sup>_H_</sup><br>2|Forest Conversion Adjustment Cost Parameter||0.0005|
|Final Goods|and Services|||
|_θ_<sup>_f_</sup><br>0|Food Processing Technology Index in 2004||1.5|
|_κ_<sup>_f_</sup>|Food Processing Technology Index Growth<br>Rate||0.0225|
|_c_<sup>_o,f_</sup>|Food Processing Cost|share of _X_<sup>_O_</sup><br>_t_|0.0043|
|_θ_<sup>_e_</sup><br>0|Energy Eciency Index in 2004||1.195|
|_κ_<sup>_e_</sup>|Energy Eciency Index Growth Rate||0.0225|
|_ρe_|CES Parameter for Drop-in Fuels and 1G<br>Biofuels||0.5|
|_α_<sup>_e_</sup>|Share of Biofuels in CES Function||0.09|
|_θ_<sup>_s_</sup><br>0|Timber Processing Technology Index in 2004||1.52|
|_κ_<sup>_s_</sup>|Timber Processing Technology Growth Rate||0.0225|
|_c_<sup>_o,s_</sup>|Timber Processing Cost|share of _X_<sup>_O_</sup><br>_t_|0.0626|
|_θ_<sup>_r_</sup>|Ecosystem Services Technology Index||0.64|
|_α_<sup>_C,r_</sup>|Share of Agricultural Land in CES Function||0.07|
|_α_<sup>_F,r_</sup>|Share of Managed Forest Lands in CES<br>Function||0.34|
|_ρr_|CES Parameter for Ecosystem Services||0.123|
|_θ_<sup>_R_</sup>|Eectiveness Index of Protected Lands||10|
|_c_<sup>_o,r_</sup>|Cost of Ecosystem Services|share of _X_<sup>_O_</sup><br>_t_|0.0020|
|_θ_<sup>_o_</sup><br>0|Total factor Productivity Index in 2004||1.854|
|_κ_<sup>_o_</sup>|Total Factor Index Growth Rate||0.0225|

7
5 Table 1 summarizes the results for diesel by income. High-income countries are further split by membership in the OECD. The results for individual countries and the summary statistics for gasoline and LPG are provided in appendix A. In nominal terms, high-income OECD countries had the lowest increases and low-income countries had the highest increases for all of the three oil products. In real terms, the highest price increases were found in high-income OECD countries for each of the three oil products. Despite the highest price increases in relative terms, none of these countries has reintroduced price controls. Lower-middle-income countries had the lowest price increases in real terms compared to other income groups, followed by lowincome and upper-middle-income countries, which had approximately the same average as well as median price increases.

Table 1: Increase in World diesel Prices in Local Currency, 2003–12 (nominal) and 2011 (real)

|**Income:**|**All**|**Low**|**Lower**<br>**middle**|**Upper**<br>**middle**|**High**<br>All|Non-<br>OECD|OECD|
|---|---|---|---|---|---|---|---|
|**Percent increase in**<br>|**nominal pr**<br>|**ices in local c**<br>|**urrency, 2003–**<br>|**12**<br>||||
|Minimum|110|230|110|110|140|180|140|
|Maximum|1,440|1,250|690|1,440|520|280|520|
|Median|240|330|260|280|230|240|230|
|No. of countries|172|32|48|47|45|14|31|
|**Percent increase in**<br>|**realprices**<br>|**in local curre**<br>|**ncy, 2003–11**<br>|||||
|Minimum|–47|26|–47|19|66|89|66|
|Maximum|330|200|180|330|240|210|240|
|Median|130|130|110|130|150|140|150|
|No. of countries|160|31|44|43|42|11|31|
|**Ratio withprice inc**<br>|**rease in the**<br>|**United State**<br>|**s in real terms**<br>|**, 2003–11**<br>||||
|Minimum|0.2|0.5|0.2|0.4|0.6|0.7|0.6|
|Maximum|1.6|1.1|1.0|1.6|1.2|1.1|1.2|
|Median|0.8|0.9|0.8|0.8|0.9|0.9|0.9|

_Sources_ : Author’s calculations.

The average oil price having more than doubled in real terms since 2003 has posed considerable political challenges to governments that administer oil product prices. In the period leading up to mid-2008, in the face of rapidly rising world oil prices, a number of the governments that had been keeping domestic prices artificially low seriously explored options for price reforms. The fiscal pressure to press on with reforms subsided briefly after the global price collapse in late 2008, but those governments that had done little were caught by rising prices again soon thereafter. Where governments have passed on some or all of world price increases to domestic markets, soaring prices have led to calls for governments to take action, ranging from providing greater safety nets for the poor and reducing fuel taxes, to ordering oil companies to lower prices and granting outright price subsidies, even if temporary.

Many interlinked developments have affected costs, availability, and prices paid by endusers with respect to oil products in developing countries in the last several years:
How well do these models predict who returns and who seeks out restitution? We report receiver operating (ROC) curves for out-of-sample (test set) predictions in Figure A.6. The area under curve metric is .68 for the decision to return and .7 for restitution-seeking, which represents an improvement of 36% and 40% over guessing at random, respectively. In overall terms, these improvements are modest, which suggests other, unknown factors may be more predictive of these outcomes.

# 6 POLICY AND PROGRAM IMPLICATIONS

Displacement is a deeply traumatizing experience that affects millions around the world. The prospectofresettling,restituting,andrebuildingcommunitiesdestroyedbydisplacementalso represents a daunting challenge for societies in conflict and global humanitarian efforts. Understanding how displacement shapes victim’s trajectories is thus crucial for crafting informed policy-makingandadvocacy. Inthisproject, weadvanceourunderstandingoftheeffectsofdisplacement using original data from rural households with a high incidence of displacement in Colombia. The results have numerous implications for both the academic literature and policymakers.

First, the results provide strong evidence that there can be substantial heterogeneity of experience within the broad umbrella term of “wartime victimization”. Even after adjusting for other forms of wartime victimization, the experience of displacement appears to both undermine and mobilize victims in distinct ways that may set them apart from other conflict victims. Disaggregating wartime victimization may thus be one way of working towards resolving what areattimescontradictoryfindingsintheliteratureontheeffectsofvictimization. Withrespect to policy, this potentially means designing interventions that take into account the disproportionate economic burden faced by IDPs (Arias et al., 2014), or that leverages IDPs apparently higher capacity for collective action in ‘bottom-up’ peacebuilding efforts (Autesserre, 2021).

Second,thestudyunderscoresthatthereissubstantialvariationamongIDPsinwhatcomes after displacement. Explaining and addressing this variation is likely crucial for the long-term well-being of IDPs. With respect to return, ongoing security concerns originating from postconflictinstability(Premetal., 2020)anddifficultyrecoveringpropertyappearasmajorimpediments. Interventiondesignshouldthustakeintoaccountthefraughtsecuritysituationpresent in many post-conflict settings. In terms of restitution-seeking and the recovery of property, a key insight is that few victims formally pursue restitution, and that many in our sample claim to lack access to representation in what is an often labyrinthine legal process (Sanín, Huertas and Hernández, 2019). A key area for policymakers to intervene is thus in facilitating the resti22
**_VI.1_ European bank credit flows towards countries** **<u>outside the Euro Area were severely impacted...</u>** Total Credit Flows by European Banks _€ millions (2005Q1-2012Q4)_

**_VI.2_ ...but growth rates of outstanding foreign** **<u>claims to EMDEs have been least affected</u>** Growth of Cons. Foreign Claims of European Banks _%, YoY (2000Q1-2012Q3)_

<!-- Start of picture text --> 3,000 50%<br> 2,000 40%<br>30%<br> 1,000<br>20%<br> -<br>10%<br> (1,000)<br>0%<br> (2,000) -10%<br> (3,000) -20%<br> (4,000) -30%<br>Within Euro Area Outside Euro Area EMDEs Advanced Europe Other Advanced<br>Source: ECB Source: BIS<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2001Q1 2001Q4 2002Q3 2003Q2 2004Q1 2004Q4 2005Q3 2006Q2 2007Q1 2007Q4 2008Q3 2009Q2 2010Q1 2010Q4 2011Q3 2012Q2<br><!-- End of picture text -->

**As a sign of deleveraging pressures, European banks also retrenched much more than other international banks in EMDEs and in some cases these banks filled the gap that European banks left behind. This also provides scope for stronger banks in developing countries to step in. However, European banks remain dominant** (Exhibit VI.3). Specifically, while European banks represented 71 percent of total foreign claims at its 2007 peak, this fraction has fallen to 59 percent. European banks also account for a significant portion of total foreign claims to EMDEs although its share has declined from 73 percent at its 2007 peak to 62 percent.

**_VI.3_ European bank foreign lending operations are** **<u>becoming relatively less important</u>**

Foreign Claims of Euro Banks as Fraction of Total Claims _% (2000Q1-2012Q3)_ <!-- Start of picture text --> 75%<br>70%<br>65%<br>60%<br>55%<br>Foreign claims to EMDEs All foreign claims<br>Source: BIS<br>2000Q1 2000Q4 2001Q3 2002Q2 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1<br><!-- End of picture text --> 23
**Table 3:** Extortion Predicts Poor Outcomes: Economic and Safety

||(1)<br>Bad<br>Econ|(2)<br>Econ<br>Hardship|(3)<br>Debt<br>Barrier|(4)<br>Fear<br>Any|(5)<br>Fear<br>Count|(6)<br>Violence|
|---|---|---|---|---|---|---|
|Extortion|0.23<sup>_∗_</sup>|0.16<sup>_∗_</sup>|0.84<sup>_∗∗∗_</sup>|0.11<sup>_∗∗_</sup>|0.48<sup>_∗∗_</sup>|0.20<sup>_∗_</sup>|
||(0.11)|(0.08)|(0.23)|(0.04)|(0.17)|(0.09)|
|Detained at Border|-0.08|-0.15<sup>_∗_</sup>|0.64<sup>_∗∗_</sup>|0.07|0.07|-0.05|
||(0.10)|(0.07)|(0.23)|(0.04)|(0.17)|(0.07)|
|Round 3|-0.06|0.26<sup>_∗∗∗_</sup>||0.09<sup>_∗∗_</sup>|0.48<sup>_∗∗∗_</sup>|0.14<sup>_∗_</sup>|
||(0.07)|(0.06)||(0.03)|(0.11)|(0.06)|
|Female|0.02|0.16|0.31|0.13<sup>_∗_</sup>|0.33|0.08|
||(0.15)|(0.10)|(0.38)|(0.05)|(0.21)|(0.11)|
|Age|0.02<sup>_∗∗∗_</sup>|0.01<sup>+</sup>|-0.00|0.00<sup>+</sup>|0.03<sup>_∗_</sup>|0.01|
||(0.01)|(0.00)|(0.01)|(0.00)|(0.01)|(0.01)|
|Indigenous|-0.02|0.15<sup>_∗_</sup>|0.73<sup>_∗∗∗_</sup>|-0.04|-0.10|0.02|
||(0.09)|(0.07)|(0.20)|(0.04)|(0.15)|(0.07)|
|Years of Education|0.00|-0.00|-0.04<sup>+</sup>|-0.00|-0.01|0.00|
||(0.01)|(0.01)|(0.02)|(0.01)|(0.02)|(0.01)|
|Tattoos|-0.32<sup>_∗_</sup>|-0.12|-0.55|-0.02|-0.38|0.01|
||(0.15)|(0.11)|(0.37)|(0.08)|(0.29)|(0.14)|
|Children in U.S.|0.00|0.09|0.05|0.09<sup>+</sup>|0.29|0.11|
||(0.12)|(0.08)|(0.28)|(0.05)|(0.18)|(0.09)|
|Children in G.T.|-0.01|0.12|0.36|0.00|0.12|-0.07|
||(0.11)|(0.08)|(0.24)|(0.05)|(0.16)|(0.08)|
|Assets Left in U.S.|-0.01|-0.14|0.04|0.07|-0.17|0.21<sup>_∗_</sup>|
||(0.13)|(0.10)|(0.31)|(0.06)|(0.20)|(0.10)|
|ln(Time U.S.)|-0.06<sup>_∗_</sup>|-0.04<sup>_∗_</sup>|-0.25<sup>_∗∗∗_</sup>|-0.01|-0.11<sup>_∗_</sup>|-0.06<sup>_∗∗_</sup>|
||(0.03)|(0.02)|(0.05)|(0.01)|(0.05)|(0.02)|
|employed|-0.35<sup>_∗∗∗_</sup>|-0.19<sup>_∗∗_</sup>||-0.09<sup>_∗_</sup>|-0.38<sup>_∗∗_</sup>|-0.16<sup>_∗_</sup>|
||(0.09)|(0.07)||(0.04)|(0.13)|(0.07)|
|Constant|3.44<sup>_∗∗∗_</sup>|2.06<sup>_∗∗∗_</sup>|7.50<sup>_∗∗∗_</sup>|0.58<sup>_∗∗_</sup>|1.68<sup>_∗_</sup>|1.44<sup>_∗∗∗_</sup>|
||(0.38)|(0.28)|(0.85)|(0.19)|(0.68)|(0.32)|
|Observations|515|516|1047|516|516|512|

Standard errors in parentheses Robust standard errors clustered by respondent + _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Next, we examine whether economic hardship and fear of crime predict changes in civic action. Overall, we find some tentative evidence supporting a model of mobilization based on economic grievance. Table 4 shows that more negative assessments of one’s economic situation predict greater interest in civic action, significant at 95%. While coefficients for each type of political behavior are positive, the overall result in Column 1 regarding the 20
# **2. Data Description and Empirical Strategy**

We use data from nationally representative student assessments conducted for mathematics in 2019 and 2021. In 2019, a nationally representative sample of grade five students sat for the assessment. In 2021, a nationally representative sample of grade five students from the same schools sat for the 2021 assessment, specifically designed to ensure comparability with the 2019 assessment. Additionally, the 2021 assessment traced and assessed the students who had participated in the 2019 assessment. Of the 3,922 students who participated in the mathematics assessment in 2019, 3,411 participated in the 2021 assessment leading to an attrition rate of 13 percent. In 2021, the assessment was complemented with student and teacher questionnaires providing a rich data set to analyze variables affecting learning during COVID-19-induced school closures. (For more details on the data, see the Appendix.) Using the data from student assessments, student, and teacher questionnaires, we test five hypotheses based on a reading of previous literature on learning loss due to temporary school closures or during summer recess: _H1_ : Student learning declined due to COVID-19-induced school closures.

_H2_ : Learning loss is greater in schools with longer duration of COVID and non-COVID school closures.

_H3_ : Learning loss is greater among students with less-educated parents/guardians.

_H4_ : Learning loss is greater among students with lower access to and usage of digital devices.

_H5_ : Learning loss is greater among boys than girls.

To test the first hypothesis (H1), we employ two identification strategies to analyze learning trends during COVID-19. The first strategy compares average scores from a nationally representative sample for grade 5 in 2019 to the average scores from the same nationally representative sample for grade 5 in 2021. The sample of schools was developed in 2019 according to the list of all schools in the country with students in grade 5. The same schools were surveyed in 2021. The sample frame was not renewed due to no major changes in the sampling frame over the two years. This identification strategy allows us to compare the performance of grade five cohorts in 2019 and 2021 and assess the trends in system-level student outcomes. The caveat with this identification strategy is that it ignores the changes in student composition or other factors that change from one year to the next and thereby may affect cohort-level learning outcomes.

Our second identification strategy allows us to control for the student population and use data from students who appeared for mathematics assessment both in 2019 when they were in grade five and in 2021 when they were in grade seven. This allows us to control for time-invariant student-level characteristics and analyze the change in student-level learning outcomes. Though the attrition rate was high, we compare the learning outcome of students in 2019 who appeared for the 2021 assessment and those who did not and find that the difference between the student populations is 3
The magnitude of change in demand varies with government response to the pandemic. Countries with a higher number of cases and stricter social distancing and lockdown measures experience the largest drop in demand for services. Both supply disruptions due to business closures and demand-driven changes in economic activities are at play, with demand-side factors playing a greater role in most services. The varying impacts we find across sectors point to the need for targeted public policies that ease the economic burden of the pandemic on the sectors that are hit heavily. Finally, the paper shows the value of using near real-time "big data" to rapidly assess the impacts of the pandemic at a more granular level to inform immediate and medium-term policy responses.

Our paper is related to recent studies on the impacts of COVID-19 on consumer demand and spending. Andersen et al. (2020) and Baker et al. (2020) used high-frequency transaction-level data to study the impacts of the pandemic on household spending in Denmark and the United States (U.S.), respectively. Both studies report a sharp decline in spending in a range of consumption categories as COVID-19 spread. While these results are informative, it is difficult to replicate or extrapolate the findings to other countries or globally, mainly because such transaction-level data are difficult to find in several other countries.

Our paper also relates to a burgeoning body of macroeconomic studies that attempt to quantify the impacts of COVID-19 on national and global economies (Breisinger et al., 2020; Gourinchas, 2020; Eichenbaum et al., 2020; Guerrieri et al., 2020; Fornaro and Wolf, 2020). Many of these studies provide evidence on the macroeconomic implications of the pandemic, drawing largely on historical data, macroeconomic models, and simulations. A significant challenge in estimating the impact of the pandemic, as highlighted in these early studies, is a lack of reliable data on the spread of the pandemic and associated economic and health damages. These data are needed to design alternative containment policies at minimum economic cost (Stock, 2020). Moreover, high-frequency cross-country data on consumer spending are lacking to assess the global impacts of the pandemic. Our paper fills this gap by using near real-time Google search data that reasonably predict consumer demand.

The remainder of the paper is organized as follows. Section 2 provides a detailed description of the Google Trends data, which capture consumer demand in near real-time, and the search terms that we carefully selected for each service. Section 3 lays out our empirical specification and identification strategies, while Section 4 presents and discusses the results. We 5
of the three months. To calculate , we measure missed or partial coupon payments based on our newly collected bond-level dataset on default and restructuring outcomes (see Appendix B2). Appendix ii,jj,mm CC B2 also explains how we account for bond haircuts when calculating , that is, how we deal with exchanges of old into new bonds at a loss. In a nutshell, to compute returns in restructuring months, we ii,jj,mm NN combine the old defaulted bonds and the new instruments that creditors receive in the exchange, with the implicit assumption that creditors keep the newly restructured bond in the portfolio. We then account for the share of debt written off (face value debt reductions, if any) as well as potential cash payments that are made in the wake of a restructuring (including for the settlement of past arrears). In case the restructuring only involves a rescheduling of maturities but no face value debt reduction (sometimes called “debt reprofiling”), the write-off is zero, while the change in maturities should be reflected in the secondary market price of the new bond. This also implies that the exact NPV haircut estimation approach chosen (see Section 3) is not decisive for the computation of the returns series. In fact, the haircut estimate is a snapshot at one point in time, while returns are measured continuously on a monthly level. Ultimately, the key default-related variable that matters for the returns is missed coupons, since much of the returns come from interest payments and if these are not made, total returns drop. We do not account for transaction fees or taxes (See Appendices B2 and B5 for further details).

We use _real_ ex-post returns as the baseline measure, although we show results for nominal returns as well. By definition, all bonds in the sample are denominated in GBP or USD, so we need historical inflation data for the United Kingdom and the United States to compute real returns. For this purpose, we rely on the historical inflation indices provided by the Bank of England (for GBP bonds) and by the US Bureau of Labor Statistics (for USD bonds). For a USD bond, the monthly inflation rate is measured as U = U U U , so that the real ex-post return for this USD bond is given by ,mm = ππ ,mm U . A comparable process applies to UK bonds.,mm−1 ,mm−1<sup>35</sup> ii,jj,mm �CCNNCC −CCNNCC �/CCNNCC rr jj,ii,mm ,mm RR −ππ To arrive at yearly global portfolio returns,<sup>36</sup> we compute the monthly weighted global average across all bonds actively traded in month _t_ .

> 35 Appendix B5.5 shows that the results are similar when using inflation rates 12 months ahead.

> 36 Our results are similar when comparing bond prices at the beginning and end of a year (adding all within-year coupon payments) instead of annual averaging.

21
of the international comparability. It is further important to carefully consider the trade-off between easing understanding by the respondent and introducing enumerator effects, as questions requiring more explanations and enumerator initiative, such as questions involving visual aids, are harder to standardize. The challenges resulting from the need to translate concepts to languages that may not have the relevant equivalents, and the complexity this introduces, should not be underestimated and need to be understood better.<sup>35</sup>

# **4.3 Order of the sections in the survey**

Given the length of the survey, and indeed of many other surveys in developing countries, one can hypothesize that the duration of the survey and the order of questions play a role in explaining measurement error. We randomly assigned the order of the cognitive, noncognitive and technical sections in both the test and the retest and use this to assess the effect of the order of the sections. Table 10 shows that for the cognitive and noncognitive skills the order in which the section appeared in the test and retest does indeed significantly affect their test-retest correlations. But contrary to our prior, there is no clear evidence of survey fatigue, as there is no systematic degradation of the reliability when a section comes later in the survey.<sup>36</sup> Instead the test-retest correlation for noncognitive skills was highest, and indeed above the .7 threshold, when it comes last, and differences between different test-retest combinations are significant. In contrast, the test-retest correlation for technical skills is highest when it comes first. This matches well with our observations in the field that noncognitive questions, which are more abstract, tend to raise eyebrows when the survey starts with them, whereas discussion about farming practices allowed a smoother start of the survey. Overall these results suggest that careful attention to the order of different sections when designing a survey instrument can reduce measurement error, while survey duration and fatigue may not be that important. Good practice would be to start with questions on topics that the respondent finds more natural to talk about, and ask the more abstract noncognitive questions towards the end of the survey, so that the respondents are less on the defensive for this section, and any potential annoyance generated by such questions does not affect the other sections.

> 35 The noncognitive questions posed the largest challenges for translation during survey preparation, and understanding concepts such as “active imagination”, or “generating enthusiasm” were difficult even for the (university level trained) enumerator team.

> 36 Analysis of the random order of the questions within sections leads to a similar conclusion.

25