rafmacalaba/data-use-mentions-extended huggingface dataset:
. Subway installation has been motivated by the belief that it will shorten commuting times, reduce traffic congestion and vehicular emissions, and promote higher-density residential development near subway stations. Reduced vehicle emissions and energy efficiencies associated with higher-density development are frequently cited as carbon-saving advantages of mass transit systems.

From this perspective, subway cities should have had lower-carbon development paths, other things equal. We explore this proposition with our regression residuals, drawing on a recent global survey of subway systems by Turner and Gonzalez-Navarro (2018). Subways provide an excellent test of the non-Pigouvian supplement to carbon pricing for several reasons: They exemplify massive directed infrastructure investment; they are numerous but far from universal in most world regions; and their histories vary from over a century to less than a decade. If directed public investments can make a significant contribution to low-carbon development, the effect should register in a large sample that includes cities that have installed subway systems and cities that have not.

Our econometric results in Table 2 are very similar for all three urban radii, so we present the median case (40 km). We have residuals for 1,236 cities with populations greater than 500,000. The Turner/Gonzalez-Navarro survey identifies 132 of these as cities with subway systems. We divide the residuals into deciles, with the largest negative outliers in Decile 1. These are the cities whose measured CO2 concentrations are far lower than their predicted concentrations. In counterpoint, the cities in Decile 10 have measured concentrations far higher than their predictions. We count the number of cities with subways in each decile and present the results in Table 5. Figure 13 graphs the results with a regression line for easier interpretation. The results provide striking evidence of a negative relationship, with a decline of about 2 subway cities per decile. There are 23 subway cities
by setting rr and the long-run multiplier by setting T at an arbitrarily large number, which is taken to be (5 years). To calculate the fiscal multiplier using the coefficient estimates from the IPVAR, we first cumulate the discounted TT= 0 impulses of output and government consumption at different horizons and compute the ratio of TT= 20 the two impulses. That ratio is then multiplied by the average government consumption-to-GDP ratio in the sample to yield multipliers. Since the conditional multipliers are estimated from the panel of countries, they reflect an average estimate across those countries included in the panel. Thus, we use the average government consumption-to-GDP ratio in the sample to calculate the multipliers rather than country-specific government consumption-to-GDP ratios.

Equations (1) and (2) jointly denote the IPVAR system that is estimated with ordinary least squares (OLS) applied separately to each equation.<sup>6</sup> This yields model coefficients that depend on the fiscal position such that a given level of the fiscal position maps out to a set of model coefficients. For presenting the results, we evaluate model coefficients at specific values of the fiscal position which are mostly taken to be the 10<sup>th</sup> and 90<sup>th</sup> percentiles within the sample. Confidence bands are calculated by bootstrapping over 300 iterations. We report median estimates, along with the 16 - 84 percent confidence bands.

> 3 Including the fiscal position in the law of motion in Equation (2) is tantamount to having interaction terms with the fiscal position in the regressors of Equation (1). For this reason, we do not separately include the fiscal position as an endogenous variable in the IPVAR.

> 4 For instance, while Riera-Crichton, Vegh, and Vuletin (2015) condition multipliers on fiscal balances, Auerbach and Gorodnichenko (2013), Ilzetzki, Mendoza, and Vegh (2013), and Nickel and Tudyka (2014) condition on government debt.

> 5 In particular, we take the
see why parental death sometime in the past would matter to current adult consumption except via inheritance and (hence) wealth. Possibly the shock of parental death will have an impact, but then we control for a father‘s recent death (within the last two years) in all our regressions.

We can only convincingly treat one inheritance variable as endogenous, solving out other endogenous variables.<sup>19</sup> We do so for any inheritance taken together, and separately for land, excluding other forms of inheritance. We also drop any variables that could be endogenous by the same logic, notably own education. (Parents may decide that one son gets the schooling while the other gets the land.) We also drop fostering and age at marriage for the same reason.

> 18 For example, using data from the rural Philippines, Estudillo et al. (2001) show how sons are preferred for land inheritance, while daughters are preferred for investments in schooling.

> 19 We tried using death of father and death of mother as two IVs for two inheritance variables (land and other) but these did not have sufficient power for credible identification.

19
To update non-labor income, we use administrative data<sup>10</sup> on the observed expansion of the main national cash transfer programs (Takaful and Karama) to simulate the expansions in coverage and amounts observed between FY18 and FY20 in the household survey. This adjustment is very important, as the cash transfers were introduced to compensate for some of the large economic reforms that started in 2016. The allocation rule is based on characteristics measured in the HIECS, which helps with allocation of the cash transfers. In addition, international remittances are adjusted by their observed growth rate using World Development Indicators (WDI) estimates of remittance flows (2.9 percent increase in FY20 compared to FY18). Domestic remittances are adjusted using growth rates in labor incomes.<sup>11</sup> Finally, other sources of income are updated by inflation.

Poverty rates are calculated based on the international poverty lines using household income per capita. The total household income per capita consumption aggregate is adjusted using 2011 purchasing power parity (PPP). Income poverty is estimated based on the $3.65 per day poverty line in 2017 PPP. We prefer to analyze income poverty due to the complexities involved in modeling changes in consumption from changes in income due to COVID-19.

Table 2.a: Two-year transition matrix used to update HIECS (males)

|||Upd<br>|ated HIECS<br>|FY20(no CO<br>|VID)<br>|||
|---|---|---|---|---|---|---|---|
|HIECS 2018|Agriculture|Industry-<br>Informal|Industry-<br>Formal|Services-<br>Informal|Services-<br>Formal|Not<br>working|All|
|Agriculture|11.1|0.8|0.0|0.6|0.2
</mark> ertrand et al., 2021). Since only these two studies contain multiple treatments, we do not attempt to draw more general conclusions on complementary program design features in this paper.

# **4.2 Evaluation sample**

The median study sample size is 2,126. Individual-level randomized designs have a sample size ranging from 952 to 6,014. Cluster randomized designs have larger samples ranging from 1,017 to 19,442 with at least 30 clusters per treatment arm across studies. The median sample size per cluster is 31, comparable to the median sample size per cluster of 26 noted in Muralidharan and Niehaus (2017).<sup>15</sup>

> 11  In Ethiopia, clusters were randomized into treatment and control, and, within treatment clusters, the participants were selected by local _Ketena_ committees. 12  In Comoros the design varied the treatment intensity, 40 percent of eligible households received the <mark>intervention in treatment clusters and 20 percent of eligible households received the intervention</mark> in control clusters.

> 13  A few studies also offer public works opportunities to the control group in the post-evaluation period in a randomized phase-in design, namely, Egypt infrastructure, Sierra Leone, Ethiopia, and Djibouti. 14 In Comoros, a third level of randomization was carried out: in every treatment household, the gender of the participant was further randomly chosen within the household.

> 15 All individual level randomized experiments can detect at least a $1.50 improvement in consumption expenditure and earnings, assuming a standard deviation of $8 with 5 percent type I error rate and 80 percent power. Among the cluster RCTs, there is an improvement in consumption and earnings––the Egypt studies can detect at least a $1.40 improvement, Comoros $2.10, Ethiopia $1.70, Sierra Leone $1.00, and Tunisia $1.90. These are computed using the country specific cluster sizes noted in Table 2 and
choice between bonds and syndicated loans and another one for the choice between domestic and international debt. The regression equation is as follows: The dependent variables are, alternatively: (i) a debt choice indicator _Dit_ that equals one if firm _i_ issued a bond (zero if it issued a syndicated loan) in day _t_ ; and (ii) a debt choice indicator _Dit_ that equals one if firm _i_ issued a given instrument domestically (zero if it issued the same instrument in international markets) in day _t_ . The _Crisisct_ indicator variable captures the crisis periods: the 2008-2009 period for the GFC or the economy-crisis years as identified by Reinhart and Rogoff’s financial crises database for domestic banking crises. The regressions also include different fixed effects, _θ_ , that represent either economy-industry fixed effects _θcj_ (to estimate aggregate changes in debt issuances within industries and economies) or firm fixed effects _θi_ (to estimate changes within firms).<sup>12</sup> The use of firm fixed effects in some specifications addresses the concern about compositional changes in the set of firms raising new debt financing and is key for the identification of market specific credit supply contractions. In these regressions, the sign and magnitude of our coefficient of interest, _β_ , are derived from the firms actually switching debt issuances during crises. Firms with a single issuance do not add identification power to the estimation. Because the regressions also include firms always issuing the same type of debt (not switching markets), the coefficient _β_ is smaller than it would be if we used only switching firms. Thus, our reported results based on the full sample of firms should be viewed somehow as lower bound estimates. The regressions with economy-industry fixed effects estimate whether aggregate movements across debt markets during crises (capturing issuances both within and across firms) go in the same direction as those within firms.

> 12 Standard errors are clustered at the economy-industry-quarter level in all regressions reported in this paper.

15
Panel B suggests that, irrespectively of whether they are formal or customary, trust in land institutions in Africa is low: Almost two-thirds (61%) of respondents in the Afrobarometer survey think that rich people will be able to fraudulently register land they do not own. Such opportunities are perceived to be strongly biased in favor of the wealthy. The same is true for formally documented ownership rights: out of 11 African countries for which survey data are available, only two provide documented land rights to more than 2% of their population and in most cases, these are biased in favor of affluent males. Limited trust in and performance of formal institutions contrasts with appreciation for traditional leaders.

If accompanied by proper regulation, digital technology, while not a magic solution,<sup>10</sup> offers opportunities to enhance the quality and interoperability of data managed by registries, reduce the cost of operating them, and enhance their potential contribution to improving land use to increase equity, transparency, accountability, and resilience. Three areas are particularly relevant: First, use of digital records and procedures can reduce cost of service delivery, increase transparency and accountability of institutions and security of rights. It does so by adopting fully digital workflows that rely on routine use of unique land parcel identifiers; authentication of right holders via digital IDs and electronic signatures, digital payments, and allow establishing audit trails. This can reduce the potential for fraud or record tampering omnipresent in paper-based systems if linked to a discontinuation of paper records and increased quality and usefulness of land records via digital interoperability and mandatory cross-checks (e.g., with court records or tax clearance certificates) before transactions are registered and automatic updating of related records (e.g., on building permits). Moreover, automatic reporting of assessed land values and registered land prices and associated data (e.g., transaction volumes) at granular level will reduce transaction cost in land markets and facilitate credit supply by providing an objective basis for valuing banks’ collateral
<!-- Start of picture text --> 1B: Sub-national sample<br>Weak Link to Contemporaneous Conflict Strong Link to Cumulative Conflict<br>2 (correlation=0.088) s (correlation=0.283)<br>~ Qo ° ~ Bo “°<br>g%o CBMGES g oo 0 eeBio @8. oe, %0°, a<br>BBe eh0 5, 9 oTe po! S958 :<br>“ro 08 aesFHP me BRaro8 eq, 28~ & &3 ee«0 PS Boo gagoO” & °<br>2 ite Hoo” e 2 i 0° 0 a8 °<br>=e3 SaBEBE QP of =e3 Pagoi?28 oe B58 2,<br>é& | EERO Eo, 8. é CREELSe el<br>ag] g ee 5 Rg SBR 8°<br>° Racacinoe beat aed- o Santee Coaee SsBo sac te<br>Fatalities per capital (In) Cumulative fatalities per capital (In)<br>© Poverty rate at1.9USD(PPP) ———— Fitted values<br><!-- End of picture text -->

<!-- Start of picture text --> 1A: Cross-country sample<br>Weak Link to Contemporaneous Conflict Strong Link to Cumulative Conflict<br>o= (correlation=0.183) o= (correlation=0.301)<br>~ ° . °<br>- a = °% 5<br>8B,© > oRBO 9%,ot g9 8 BB]= Sgoa099° ge ° °8<br>ba=2 - ° 0 hte?Sh oPF9 @of o @3 0 %oS° Sp9 - ehBROO° | os ©<br>£e48 > S fe 2 BS8 oP, 0<br>F |, Sods0 em ae Re> © 0, P|Bel (
been favorable to female schooling. First, I use INSEC data on the total number of school destructions per district to test whether such destructions had an effect on primary schooling completion (Columns 1 and 2). For both genders I find a statistically insignificant effect, which is likely due to the fact that a district-level analysis lacks power in identifying the effect of school destructions. School destructions were a rare and isolated aspect of the conflict,<sup>7</sup> which one would expect to have had a large effect on schooling at a very disaggregated level. But this effect is unlikely to be captured by an analysis carried out at the district level, unless school destructions are correlated with characteristics of the conflict relevant at the district level, such as the use of schools as army barracks (Hart, 2001; Caddell, 2007). Second, I use INSEC data on the total number of abductions by Maoists per district to test whether a larger number of abductions, often targeted at school children, had an adverse effect on schooling. Results in Columns (3) and (4) indicate that abductions had a negative effect on female primary schooling. An increase in the number of abductions (per 1000 inhabitants) by one standard deviation (16.82) decreases female primary schooling attainment by 3.7 percentage points. Put differently, the effect of a dramatic move from the 5<sup>th</sup> to the 75<sup>th</sup> percentiles of the district-level distribution of total abductions is a decrease of 1.6 percentage points in female primary completion. Third, I test whether primary schooling completion improved more in districts controlled by Maoists, and thus where the insurgents were better able to affect schooling provision according to their ideology (Columns 5 to 8). There is no clear-cut definition of control by the insurgents, with discrepancies between the classifications used by the People’s Army and the government (Hatlebakk, 2007), and so I use two
, ACLED provides a very rich set of data and we therefore show results for our within-country case study and the African continent using the ACLED data.

# Empirical Relationship between Conflict and Poverty

In what follows we will take an agnostic view regarding the channel through which the correlation of violence on poverty rates arises. Instead, we will rely on the related literature for identification and simply try to understand the involved magnitudes in macro relationship at the country and regional level. If conflict prevents children from building their human capital or hinders investments in the recovery period, then past conflict should be related to poverty rates today. The channels of human capital accumulation and expectations will then lead to a correlation between poverty rates and past conflict. Theoretically, we expect reverse causality from poverty to conflict to generate a contemporaneous relationship. If poverty triggers conflict, then increases in poverty should increase contemporaneous conflict risk.

We will assume that the relationship between violence and poverty works through the local level and re-code conflict counts into per capita counts, i.e. divide fatalities by the overall population of the country or region. This assumes that the effect of violence on the poverty rate is more severe if a conflict is affecting a larger share of the population. In a country like India a fatality count will mean that a much smaller share of the population is affected than in a small country like Burkina Faso.

> 3 The case of the Arab Republic of Egypt shows how difficult this is in practice. Fiscal reforms were accompanied by a World Bank project for social security. However, a government change from Morsi damaged the project because the new government tried to establish control and, according to the national data, poverty has increased. Ironically, sharing fiscal resources with the population is also a way of building fiscal control.

8
# **Figure 8. Exchange rates, yield differentials and foreign reserves**

# **A.  Bilateral exchange rate with U.S. dollar**

<!-- Start of picture text --> Percent Change since June 2014<br>20 Change since first NIRP announcement<br>10<br>0<br>-10<br>-20<br>Euro Area Sweden Denmark Switzerland Japan Hungary<br><!-- End of picture text -->

# **C. Real effective exchange rate**

<!-- Start of picture text --> Percent Change since June 2014<br>10 Change since first NIRP announcement<br>5<br>0<br>-5<br>-10<br>E. Yen and yield differential with U.S.<br>Exchange rate Percentage points<br>14 0<br>-0.2<br>12<br>-0.4<br>10 -0.6<br>-0.8<br>8 US$ per 1000 Yen<br>-1<br>Japan-US yield spread (RHS)<br>6 -1.2<br>2012 2013 2014 2015 2016<br>Euro Area Sweden Denmark Switzerland Japan Hungary<br><!-- End of picture text -->

# **B. Nominal effective exchange rate**

<!-- Start of picture text --> Percent Change since June 2014<br>15 Change since first NIRP announcement<br>10<br>5<br>0<br>-5<br>-10<br>D. Euro and yield differential with U.S.<br>Exchange rate Percentage points<br>1.4 0<br>1.3 -0.4<br>1.2 -0.8<br>
**Table 3.** Sudden and slow onset type distribution.

|||**Sudden**|**-onset**<br>|||**Slow-o**|**nset**<br>||
|---|---|---|---|---|---|---|---|---|
|**Individuals**|**Total**<br>|**PI**|**Risk in**|**duced**|**Total**<br>|**PI**|**Risk i**|**nduced**|
||**Vulnerabilit**||||**Vulnerabilit**||||
||**y**||**CRI**|**IRI**|**y**||**CRI**|**IRI**|
||**(1)**|**(2)**|**(4)**|**(5)**|**(1)**|**(2)**|**(4)**|**(5)**|
|Argentina|100%|50.2%|13.3%|36.5%|100%|49.9%|12.3%|37.8%|
|Bolivia(Plurinational State of)|100%|55.8%|7.0%|37.2%|100%|54.4%|7.5%|38.1%|
|Brazil|100%|57.1%|9.2%|33.8%|100%|
# **1. Introduction**

The Syrian crisis has caused one of the largest episodes of forced displacement since World War II and some of the densest refugee-hosting situations in modern history. The Syrian Arab Republic’s immediate neighbors host the bulk of Syrian refugees, with Turkey, Lebanon, and Jordan all ranking in the top five countries globally for the number of refugees hosted. In 2018, according to the United Nations High Commissioner for Refugees (UNHCR), Turkey hosted 3.5 million Syrian refugees, Lebanon 0.94 million, and Jordan 0.68 million.<sup>4</sup> This paper characterizes the displacement and welfare of refugees from Syria, who were living in Jordan (Amman governorate, Za’atari and Azraq camps, and areas surrounding these camps in Mafraq and Zarqa governorates), the Kurdistan Region of Iraq (KRI), and Lebanon. The analysis is based on refugee registration data from UNHCR, quantitative data from the 2015–16 _Syrian Refugees and Host Communities Surveys_ (SRHCS), and qualitative data from focus groups and in-depth interviews.<sup>5</sup> The SRHCS was the first attempt at a comparable and comparative analysis of forced displacement across countries with differing policy regimes governing refugees and internally displaced people. The survey’s goal was to document host countries’ main concerns about hosting refugees and the different strategies they adopted to address the influx of forcibly displaced people. The specific objectives of the multi-country analysis were:

- i. To assess the socio-economic and living conditions of a representative sample of the Syrian refugee and host community populations

- ii. To understand the implications for social and economic conditions in host communities

- iii. To identify strategies to support Syrian refugees and host communities in the immediate and longer term.

There were several challenges associated with conducting a representative survey of the host community population and the forcibly displaced, discussed in detail in Krishnan et al. (2018). In particular, there were no reliable sampling frames for
the baby.

Such findings represent critical qualitative information for project planners. With this kind of knowledge, it should be possible for MOH officials to design IEC strategies and messages to combat specific mistaken beliefs; to differentiate IEC strategies for a given ethnic group where their beliefs and behavior differ importantly from others; and hence increase the practice of preventive health care and the use of modern health services. Without such knowledge, it would be easy for the belief to be perpetuated that the main cause of Ethiopia's high levels of mortality and disease is the low coverage of rural health facilities.

Lessons for the future. In an informal evaluation of the assessment, the Bank staff involved drew a number of conclusions for the future:

# Specific to EthioRia

- The information was valuable, was obtained in a quick and costeffective way, and could not have been gained from a traditional KAP survey--because, given existing levels of ignorance about the determinants of behavior, no one would have known the relevant questions to ask in a questionnaire survey.

- A number of quantitative questions had been asked about numbers of children, etc., to which the answers were not statistically