rafmacalaba/datause-encoder-data huggingface dataset:
knowledge declined. However, the analysis does not find that student test scores declined in a statistically significant way in response to the fee removal. Also using SACMEQ, Atuhurra (2016) finds that FPE in Kenya was associated with large achievement declines (in public schools) and argues that these are linked to lower teacher effort and disengagement of communities. Bold and others (2011) and Bold, Kimenyi, and Sandefur (2013) find that the decline in average quality in public Kenyan schools is mostly the result of selection—with higher socio-economic-status, and potentially higher-achieving, students switching to private schools after FPE—and not a decline in value-added.<sup>5</sup> It is unclear whether this pattern of switching to private schools occurred similarly in other countries where the number of private schools is often much lower than in Kenya.

While not linking changes to FPE _per se_ , Taylor and Spaull (2015) analyze the changes in “access to learning” between 2000 and 2007 in 10 African countries using SACMEQ data on test scores and household surveys (e.g. Demographic and Health Surveys—DHS) on enrollment. The study defines “access to literacy” as the product of the grade 6 completion rate and the proportion of grade 6 students who reach a basic level of literacy, with a corresponding measure of “access to numeracy.” The analysis finds that access to learning increased over this period in all the countries studied—despite the fact that the proportion of students who reached the basic literacy/numeracy threshold actually fell in 3 of the countries.

Le Nestour, Moscovitz, and Sandefur (2022) use data from the DHS and Multiple Indicator Cluster Surveys (MICS) to estimate literacy rates for birth cohorts ranging from the 1950s to the 1990s. The analysis shows that while average literacy has increased in all regions, including in Sub-Saharan Africa, “education quality” (defined as the expected literacy acquired after 5 years of primary schooling in a country at a particular time) has not—and, in particular, has declined in Sub-Saharan Africa. Using an interrupted-time-series approach, they show that education quality generally declined after FPE reforms were introduced (or more precisely, they show that average declines in school quality accelerated after the reforms).

Notwithstanding the results from Kenya discussed above, the lack of preparation and the need to rapidly scale up teacher recruitment has often been blamed for the potentially negative

> 5 This switching, rather than value-added, explanation is consistent with Lucas and Mbiti (2012a).  Also see Zuilkowski and others (2018) for a discussion on these switching patterns in Nairobi.

5
# **S2 RSDP Road Dataset**

Table S1 shows the assumed travel speed information by road type and condition that the Ethiopia Road Authority used in constructing the RSDP panel dataset. The dataset provided by ERA contains the speed of the road in each year (where a speed of 0km/h is assigned to roads that had not been constructed yet). As the table shows, an increase in speed limit indicates that a road was improved except for town roads. Consequently, as our analysis relies on increased speeds to indicate improvement, our analysis focuses on roads connecting towns rather than within town roads.

|Pavement type and condition|Speed before reha-<br>bilitation<br>or<br>con-<br>struction|Speed after rehabil-<br>itation or construc-<br>tion|
|---|---|---|
|Asphalt road (expressway)||120km/h|
|Asphalt roads (Highway - as-<br>phalt concrete or surface treat-<br>ment 50km/h|70km/h||
|Federal gravel Road (high class<br>gravel roads)|35km/h|50km/h|
|Regional gravel Road (intermedi-<br>ate class gravel roads)|25km/h|45km/h|
|URRAP roads - lower district<br>level<br>roads<br>(low<br>class<br>gravel<br>roads)|20km/h|35km/h|
|Earth surfaced roads (very low<br>class public roads)|20km/h|30km/h|
|Federal gravel or regional rural<br>roads to asphalt roads|25km/h to 35km/h|70km/h|
|Town roads (asphalt)|30km/h|30km/h|
|Town roads (cobbled)|20km/h|20km/h|
|Town roads (gravel)|15km/h|15km/h|
|Town roads (earth)|10km/h|10km/h|

**Table S1:** Road types, conditions, and speeds. Information provided by the Ethiopian Roads Authority

S3
# **Leveraging land to enable urban transformation: Lessons from Global Experience**<sup>**1**</sup>

Nancy Lozano-Gracia<sup>2</sup> , Cheryl Young<sup>3</sup> , Somik V. Lall<sup>4</sup> , and Tara Vishwanath<sup>5</sup> Key words: Urbanization, land transaction, land valuation; JEL classification: R14, R52; World Bank Sector: URBAN This paper was originally written as a background paper for the India Urbanization Review, which provides a policy framework to help Mayors and other policymakers anticipate and implement strategies for sustainable development. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 1 The authors are grateful to Gregory K. Ingram and Yu-Hung Hong for fruitful discussions. Yu-Hung Hong provided very useful comments to early versions of this document.

> 2 Economist, Urban and Disaster Risk Management Department, World Bank, corresponding author (nlozano@worldbank.org)

> 3 Doctoral Candidate, University of California, Berkeley

> 4 Lead Economist, Urban and Disaster Risk Management Department, World Bank

> 5  Lead Economist, Economic Policy, World Bank
This paper contributes to the existing literature on this subject in three ways. First, using a newly collected dataset, it shows that investment promotion activities lead to higher FDI inflows to developing countries. Before this data set became available, it was not possible to analyze this question in a convincing manner in the context of developing countries. Second, the results suggest that the effectiveness of the investment promotion agency is affected by its legal status and the reporting structure. Besides having policy implications, obtaining intuitive results on these more nuanced questions gives us more confidence that the analysis captures the IPA effect rather than other factors. Third, our analysis provides evidence consistent with FDI incentives offered by other IPAs _within a geographic region_ diverting FDI inflows. There is no indication of such competition among countries within the same income group but located in different geographic regions.

Our analysis employs the data collected through a recent Census of Investment Promotion Agencies around the world. The Census contains information on investment promotion efforts in 109 countries, representing all income groups and geographic regions. About three quarter of responses pertain to developing countries. A unique feature of the Census is that it includes timevarying information on the existence of an IPA, its status and reporting structure, sector targeting and incentives offered to foreign investors.

Our identification strategy relies on the fact that the majority of IPAs target particular sectors in their efforts to attract FDI. Sector targeting is considered to be best practice by investment promotion professionals (Loewendahl, 2001, Proksch, 2004). It also allows us to identify the effect of investment promotion using the difference-in-differences approach. We compare FDI inflows into targeted sectors, before and after targeting, to FDI inflows into non-targeted sectors, during the same time period.<sup>3</sup> Our analysis is based on US FDI data, disaggregated by host country and sector and available for the period 1990-2004, provided by the US Bureau of Economic Analysis. We control for changes in host country business environment by including country-year fixed effects, for heterogeneity of sectors in different locations by including countrysector fixed effects and for shocks to supply of FDI in particular sectors by adding sector-time fixed effects.

> 3 Charlton and Davis (2006) use a similar identification strategy in their analysis of FDI inflows into OECD countries.

3
the borders between the CEFTA and the EU countries are even larger. We also observed significant heterogeneity in the border estimates across sectors. A byproduct of our analysis is that we were able to obtain ad-valorem equivalents of the trade barriers for services, which are not subject to tariffs. The GE experiments that we performed demonstrated that (i) integration with the EU will have large positive impact on the exports of the CEFTA countries, and (ii) a simple elimination of tariffs may heavily under-predict the impact of CEFTA integration with the EU.

We hope that the simplicity and flexibility of our methods, along with their compatibility with the standard quantitative trade models, will make them useful for benchmark policy analysis. In addition to offering a comprehensive account of the impact of non-tariff barriers to trade, which is especially relevant for services trade, we see an important application of our methods for quantifying the impact of regional integration and of the impact of “borders” within countries. While tariffs are not imposed for domestic trade, there is plenty of evidence that domestic trade is not frictionless and that proper quantification of domestic trade costs is important for quantifying the effects of both international and domestic policies.

25
The automated sequencing of instruction blocks also ensured that facilitators themselves did not know the treatment condition a group was assigned to, until the moment when they actually had to announce the selection method and the transparency/no-transparency to the group. This guarantees that the baseline decisions and the cheap talk/communication stage were unaffected by the facilitators’ knowledge of the treatment condition.

Outcome data were collected at various stages of the decision exercise. They included (1) study participants’ expectations about the amount embezzled by the decision maker, (2) the decision maker’s allocation decision, (3) the amounts spent on rewarding or punishing the decision maker, and (4) an endline question about the perceived fairness of the procedure by which the decision maker was selected.

# **Data Analysis and Research Transparency**

The experimental design, along with a detailed explanation of the research design choices and the trade-offs involved were documented in a pre-analysis plan.<sup>3</sup> The pre-analysis plan organized the research questions hierarchically into primary outcomes and analyses related to the underlying causal mechanisms. The analysis plan also included the program code for a first pass of data analysis. This paper closely follows the pre-analysis plan, summarizing and interpreting the insights gained from these pre-specified analyses and scrutinizing the results through additional tests.

In this paper, both unweighted and population-weighted analyses of the experimental data are presented. Due to the sampling procedure, individuals from regions with fewer municipalities, municipalities with fewer villages, and villages with fewer voting-age residents were relatively more likely to be included. Therefore, analyses at the level of individual group members were weighted by the inverse of each study participants’ probability of being included into the sample.

Population inferences at the level of decision makers are more difficult to accomplish, given that the election outcomes may depend on the group composition, which in turn depended on individual

> 3The pre-analysis plan was finalized prior to accessing any data from the experiment and posted to the EGAP design registry. An anonymized version is available at `http://bit.ly/2mRxl3F` .

17
||(0.262)|(0.00573)|(0.00375)|
|---|---|---|---|
|Cyclical component of GDP growth|-0.0513***|-0.0637*|-0.0370***|
||(0.0104)|(0.0343)|(0.00779)|
|Real interest rate (%)|0.00272|-0.0142|-0.000229|
||(0.00482)|(0.0149)|(0.00393)|
|Net inflow of FDI (% GDP)|0.0296*|0.0460|0.0203|
||(0.0174)|(0.0391)|(0.0136)|
|Government debt (% GDP)|0.00239|0.0159**|0.00684***|
||(0.00215)|(0.00798)|(0.00201)|
|Domestic credit (% GDP)|-0.0231***|0.00947*|-0.00584*|
||(0.00525)|(0.00536)|(0.00303)|
|Urban population growth|0.188***|-0.791***|0.109**|
||(0.0582)|(0.214)|(0.0481)|
|Agriculture value added|-0.0540***|0.0802**|-0.0247***|
||(0.00935)|(0.0334)|(0.00866)|
|Dummy for legal strength|0.347|3.172**|0.549*|
||(0.262)|(1.378)|(0.291)|
|Countries leading in housing investment|1.007|-1.004|3.304***|
||(0.887)|(1.084)|(0.952)|
|Countries contemporaneous in housing investment|-1.426||-0.0767|
||(0.990)||(0.869)|
|Countries where no clear relation|4.053***||2.919***|
||(1.193)||(1.003)|
|Constant|6.783***|5.706***|5.276***|
||(0.764)|(1.126)|(0.876)|
|Observations|359|115|474|
|Number of countrycode1|13|4|17|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The evidence here corroborates findings of earlier studies for OECD countries showing that high international capital mobility helped step up investment in housing (Feldstein and Horioka (1980); Sachs (1981)). However, there is no existing evidence for developing countries, and this analysis makes a new contribution. The evidence clearly suggests that in the absence of internationally mobile capital and the depletion of domestic savings lies at the core of housing investment in low and middle-income countries. SSA follows the same pattern found in average for developing countries.

25
conditions (e.g. sexual abuse, violence prostitution); and (c) the reasons girls and boys work in the street, which also may be influenced by the different ways boys and girls are socialized and the expectations placed on them (e.g. boys are expected to earn income whereas girls are expected to nurture and care for others). Lastly, little or no information exists on how various intervention strategies differentially affect boys and girls, for example:

- (a) Underlying the correctional approach is the perception of street children as delinquents. Expressions of delinquency and its consequences are often gender defined, however, the gender differentiated effects of juvenile justice and youth corrections systems is not known.

- (b) The rehabilitation approach focuses on providing skills to children so that they can be reintegrated into "mainstream" society, but often the skills provided are based on gender stereotypes.

- (c) The outreach approach, which focuses on the empowermnent of individuals through learning, needs to recognize that girls and boys face different types of discrimination, barriers and opportunities.

**Table** 12: **Types** of **Interventions Directed at Street Children**

|Approach||Characteristics|
|---|---|---|
|Correctional<br>|Social<br>|*Perceiveschildrenasdelinquents<br>|
|Approach|control|*Problemscanberesolvedthroughjuvenilejusticeandyouthcorrections<br>*Basedonassumptionsthattherootcauseofproblemispersonalpathology, and<br>therefore,interventionstrategyisclinicalatitsbestand punitiveatitsworst|
|Rehabilitative<br>Approach||*Viewschildrenasvictimsofchildabuseandneglect,extremepoverty,and<br>unstablehomelife<br>*Focusontransformingstreet childrenintoskilled,pro-socialschoolgraduates<br>throughworkandvalueseducation|
|Outreach<br>Approach||*Perceiveschildrenasagentsofchange (influencedbyFreire'sphilosophy that<br>individualschangetheworldwhilelearningaboutit)<br>* Focusonstructuralproblemsandindividualempowerment(useofstreet<br>educatorswhoprovideeducational, counseling,andadvocacyservicestochildren)|
|Preventive<br>Approach|Social<br>Change|*Perceivestheproblemofstreetchildrenasasymptomofstructuralproblems,<br>suchasmigrationtourbanareas,inabilityofcitiestoprovide adequateservices<br>(housing,employment,etc),breakdownoffamnilystructures<br>*Interventionsfocusoncommunity-based programs,environmental/structural<br>changes,suchasprovidingday-timeactivities,schooling,communitykitchens,<br>artisan cooperatives,etc.|

Source: Lusk, 1989 25
# TABLES

# Table 1: Estimates of transport costs, USD/TEU/kilometer

||ROAD||RAIL||SEA||
|---|---|---|---|---|---|---|
||Lower<br>bound|Upper<br>bound|Lower<br>bound|Upper<br>bound|Lower<br>bound|Upper<br>bound|
|EMERSON<br>(2009)|-|-|0.47|0.47|0.07|0.73|
|RODEMANN<br>(2014)|0.66|0.78|0.39|0.53|-|-|
|SEO (2017)|0.52|0.54|0.23|0.45|0.03|0.04|
|SLADKOWSKI<br>(2015)|0.6|1.78|0.23|0.56|0.04|0.7|
|SUN (2017)|0.84|0.92|0.14|0.25|-|-|
|MEDIAN|0.63|0.85|0.23|0.47||0.04|

## Table References

Emerson, Michael and Evgeny Vinokurov (2009). Optimisation of Central Asian and Eurasian Trans-Continental Land Transport Corridor. Emerson and Vinokurov not include distances, which were estimated by the authors of this paper.

Rodemann, Hendrik and Simon Templar (2014). The enablers and inhibitors of intermodal rail freight between Asia and Europe. Rodemann and Templar also include sea costs for each connection, but the sea path taken is not clear, so these costs are not considered.

Seo, Young Joon et al. (2017). Multimodal Transportation: The Case of Laptop from Chongqing in China to Rotterdam in Europe. Seo et al. cite a shipping cost from Chongqing to Shanghai via inland waterway. These are not considered.

Sladkowski, Aleksander and Maria Ciesla (2015). Influence of a Potential Railway Line Connecting the Caspian Sea with the Black Sea on the Development of Eurasian Trade. Some costs are converted from other currencies. Costs for containers less than 20 tons are not included. In cases where a range of values was cited for a single journey (e.g. "3,465 – 3,635 USD"), the mean was taken.

39
||||||(0.222)|_(0.452)_|(0.228)|_(0.422)_|
|---|---|---|---|---|---|---|---|---|
|STpostpri|||||-0.428*|_1.464***_|-0.238|_0.586*_|
||||||(0.171)|_(0.297)_|(0.188)|_(0.297)_|
|MUSprim<br>comp|||||0.074|_0.418_|-0.026|_0.174_|
||||||(0.097)|_(0.253)_|(0.117)|_(0.181)_|
|MUSpostpri|||||0.059|_1.185***_|0.450***|_0.423***_|
||||||(0.074)|_(0.187)_|(0.098)|_(0.124)_|
|_cons|-0.959***|_-0.490_|1.401***|_3.527***_|-0.992***|_-0.330_|1.455***|_3.660***_|
||(0.132)|_(0.334)_|(0.172)|_(0.185)_|(0.135)|_(0.337)_|(0.174)|_(0.189)_|

*** p <= 0.001 ** p <= 0.01 * p <= 0.05

Uneducated, upper caste, Hindu, unmarried, central region are the omitted categories. Standard deviations in parentheses.

27
Demirgüç-Kunt and Detragiache (2002) are the first to use a cross-county database to study the link between deposit insurance and financial crises. Their model of banking crisis uses 1980-1997 data from 61 countries. After controlling for numerous other determinants, they find that weaknesses in deposit-insurance design increase the likelihood that a country will experience a banking crisis. More precisely, deposit insurance significantly reduces banking stability in countries whose contracting environment is poorly developed, but in stronger environments deposit-insurance schemes have little significant effect on stability. Sensitivity tests indicate that this finding is not driven by reverse causality.<sup>3</sup> Investigation of individual design features shows that deposit insurance proves troublesome in countries where coverage is extensive, where authorities amass a large fund of explicit reserves and earmark it for insolvency resolution, and where the scheme is managed by government officials rather than administered in the private sector.

An over-riding theme that emerges from research on financial crises concerns the adverse influence defects in bank transparency and in mechanisms for enforcing counterparty obligations exert on the quality of a country’s regulatory regime. A large and growing body of evidence supports the hypothesis that the more effectively the private and public contracting environment serves to control incentive conflict, the more readily prudential regulation and supervision can rein in the moral-hazard incentives that deposit insurance might otherwise generate.

> 3 The experience of countries that introduce deposit insurance as a result of a crisis does not contribute to these results; in fact, observations for each country’s crisis period are dropped from the sample. To doublecheck this issue, the authors also analyze a two-stage model that first estimates the probability of adopting explicit deposit insurance and then inserts this estimated variable into a second-stage crisis equation.  The first-stage results indicate that sample countries decide to adopt deposit insurance because other countries adopt it as it becomes perceived to be best practice.  In the second stage, the influence of deposit-insurance variables becomes even more significant, indicating that allowing for reverse causality does not alter the results.

8
sample frame. An alternative approach matches survey data reported by the household head to all phone numbers associated with the household. As shown in Table S7, the predictive accuracy of these models is slightly attenuated relative to the benchmark results (Table S3).

We also explore the extent to which CDR can be used to predict other measures of socioeconomic status. Our main analysis focuses on the household’s ultra-poor designation as the ground truth measure of poverty, since this label was both carefully curated and the actual criterion used to determine TUP eligibility. In Table S8, we report the accuracy with which CDR (obtained from the household head, who is typically male) can predict consumption and asset-based wealth (elicited from the primary woman of each household).<sup>11</sup> In general, these machine learning models trained to directly predict consumption or asset-based wealth do not perform well. This result contrasts with prior work documenting the predictive ability of CDR for measuring asset-based wealth (e.g. Blumenstock et al., 2015). We suspect a key – difference in our setting aside from the fact that we are matching CDR to socioeconomic – status at the _household_ rather than the _individual_ level is the homogeneity of the beneficiary population: whereas Blumenstock et al. (2015) uses machine learning to predict the wealth of a nationally-representative sample of Rwandan phone owners, our sample consists of 535 individuals from the poorest villages of a single province in Afghanistan, where even the relatively wealthy households are quite poor.

# **4 Discussion**

Our key finding is that, in a sample of 535 phone-owning households in poor villages in Afghanistan, machine learning methods leveraging phone data are nearly as accurate at identifying ultra-poor households as standard asset- and consumption-based methods. Further, we find that methods combining survey data with CDR perform better than methods using a single data source. However, as we demonstrate empirically, low rates of phone ownership — or the inability to access data from all operators — can undermine the value of CDR-based targeting. In our setting, the CDR-based approach still works well if households without phones are targeted before the CDR-based algorithm selects the poorest households with phones. However, this approach may not be appropriate in other contexts where phone ownership is less predictive of wealth, or where potential beneficiaries have the ability to strategically underreport phone ownership (Bj ̈orkegren et al., 2020).

As mobile phone penetration rates continue to rise in LMICs (GSMA, 2020), and as

> 11Due to the design of the TUP survey, which interviewed women in the household, we cannot avoid this mismatch between the survey respondent and the phone owner.

15