rafmacalaba/data-use-mentions-extended huggingface dataset:
ment information is available in CRS. Moreover, because _DAC_ 2 _a_<sup>_agg_</sup> _RY_<sup>is complete, it corrects for the incomplete</sup> nature of the CRS data in a simple manner.

This method assumes that the sectoral distribution of incomplete CRS commitments is a good guide to the actual distribution of total disbursements across sectors. This assumption may not hold if, for instance, a donor’s propensity to report disaggregated aid to the CRS database varies by sector, or if donors that report a good deal of their aid to CRS have different sectoral preferences than donors that largely fail to report disaggregated aid. As a result, equation (5) may yield highly imperfect measures of sectoral disbursements, especially if CRS coverage is low, such that the sectoral distribution of CRS commitments that is used to allocate aggregate DAC2a disbursements across sectors is based on only a small subset of the total aid committed to a recipient.

To address these problems, I first restrict the analysis to the 1990-2004 period, for which CRS disbursement information is available. More importantly, I construct more complete data on earmarked education and health aid disbursements by accounting for additional information available in DAC Table 2a and DAC Table 5. Because the method is described in detail in the supplemental appendix, available at http://wber.oxfordjournals. org/, I provide only a brief summary here.

I begin with aggregate and sectoral gross CRS disbursements in a recipient-donor-year (RDY) format, labeled _CRSRDY_<sup>_agg_and</sup><sup>_CRS_</sup> _RDY_<sup>_s_(for</sup><sup>_s_=1</sup><sup>_, . . . , S_),respectively.For each RDY observation,the amount of</sup> aid that is absent from CRS is calculated as the difference between DAC2a and CRS disbursements: The aim is to allocate this total residual ( _RESRDY_<sup>_agg_)
estimated overall economic gain of about US$ 2.84 billion, roughly corresponding to 5% of GDP (United Nations Environment Programme, no date).

# 3. Infrastructure provision is not enough: Quality and reliability matter

The evidence reviewed in the previous section focuses on infrastructure provision and investments, and thus explores the role of infrastructure availability. However, the _quality and reliability_ of infrastructure also plays a key role in all prominent assessments of competitiveness. The Global Competitiveness Report by the World Economic Forum (WEF, 2018), the Doing Business reports and the Enterprise Surveys (ES) by the World Bank (2018a, 2018b), and the Business Environment and Enterprise Performance Survey by the European Bank for Reconstruction and Development and the World Bank (EBRD, 2018) – they all place a strong emphasis on the quality of public infrastructure. In addition to considering a wide range of factors, such as technical capacity, administrative obstacles, regulation, governance, and transparency, these assessments score the reliability and service quality of key infrastructure such as transport, electricity, water, and telecommunications.

## **Higher infrastructure quality is associated with higher productivity**

In a comprehensive review of the literature, Dethier, Hirn, and Straub (2011) examine the impact of business climate on productivity and growth in developing countries. They show that there are several key variables – including infrastructure quality – that have a significant impact on firm performance.

For 26 countries across Africa, Escribano, Guasch, and Pena (2010) assess the impact of infrastructure quality on total factor productivity of manufacturing firms between 1999 and 2005. They focus on the quality of various infrastructure services, including customs clearance, energy, water, sanitation, transportation, telecommunications, and information and communications technology. Overall, they confirm that productivity is, among other factors, determined by infrastructure quality. However, they also show that different infrastructure types matter more in some countries than in others. Low‐quality electricity, measured by several power supply and
4 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity However some authors argue that achievements such as improved highways, public transit services (Glaeser, 2014; Chatman and Noland, 2014) and advances in communication technologies (Partridge et al, 2009) have helped to reduce the productivity costs of sprawling.

When thinking of urban form, urban economists commonly use population density and city size as proxies. However, the main limitation of this kind of variables is that they assume a uniform distribution of people across space. Such an assumption ignores the fact that the urban form is the result of the interaction of individuals, firms, and government decisions on where to live, where to locate, and where to place infrastructure. Such interactions are often reflected in the layout of the city, e.g. the street layout, and the land use patterns across the urban space. In other words, urban form and land use patterns cannot be properly identified from density (Melo, et al., 2017). Nowadays, the evolution of areas such as Geographic Information Science and Computational Geometry, which has been reflected in friendlier and inexpensive analysis tools, together with the increase of availability of georeferenced data, has allowed economists to look beyond density and size when thinking of the effect of urban form on city productivity.<sup>1</sup> An example of this trend can be found in Harari (2016), who takes a slightly different approach and estimates the impact of urban form on city productivity. In her work, Harari uses DMSP/OLS Night-time Lights to delineate the urban extents of a set with over 450 Indian cities and uses those extent boundaries to calculate geometry-based shape metrics with an open source library written in Python 2.5 for ESRI’s ArcGIS, The Shape Metrics Tool, described in Angel et al. (2010). Contrary to other studies, she finds that more compact cities (
causal impact of the policy. Second is scale. Because of its comprehensiveness (including infrastructure investment and fiscal incentives) and large size (USD 34 billion), the New Industrial Policy is a good example of a “big push” development strategy in developing countries that is large enough to overcome the threshold effects of local economic development and generate observable impacts. Third is the heterogeneity across sectors. Some sectors received substantially more fiscal incentives than others. This sectoral difference in treatment allows us to assess the direct impact of the fiscal incentives as well as their indirect effects through spillovers between industries.

Our analysis consists of reduced-form evaluations of the Uttarakhand New Industrial Policy’s local impact on employment and firms in the non-agriculture sectors. We take advantage of recently available data— the 2013 round of the Economic Census of India, which captures almost all modern economic activities after the New Industrial Policy had been implemented for over a decade. We geo-reference the Economic Census to the digitized boundaries of towns and villages in India which have recently become available. This spatial granularity allows us to apply a rigorous boundary discontinuity design: taking towns and villages located in Uttarakhand within a short driving distance to the border between Uttarakhand and Uttar Pradesh as targeted places and taking similar bordering towns and villages located in Uttar Pradesh as controls. We explore a range of distance buffers from 40 to 100 kilometers on the either side of the border.

To assess validity of the quasi-natural experiment, we also geo-reference two previous rounds of the Economic Census, 1990 and 1998, to the same digitized boundaries. Although the georeferencing is not perfect due to the passage of time, we find no evidence that, in the period prior to the policy, the bordering places in Uttarakhand performed better or grew faster than the bordering places in Uttar 3
), Strojimport (Q=2.82); two engineering and design firmsMetrostov (Q=2.43), Plynostav Pardubice (Q=3 .37); two beer producers-Prazske Pyvovary Staroprame (Q=3. 13), Plzensky Prazdroj (Q=2.71); one construction firm-Vitkovicke Stavby (Q=2.49); one transport firmCechofracht (Q=2.68). Six of the bottom ten firms are water utilities-Vodovody a Kanalizaci Nachod (Q=0. 10), Zlin (Q=0.12), Prostejov (Q=0.14), Kraslice (Q=0.13), Slovacke (Q=0.13), and Nymburk (Q=0.12).

10. From a practical standpoint, the fixed effects model is costly in terms of degrees of freedom, and in a longitudinal dataset as ours, random effects have some intuitive appeal.
Ng, A. (2011). Machine Learning and AI via Brain simulations.

NPR. (2015). How Cellphone Use Can Help Determine A Person’s Creditworthiness. _Morning Edition_ . Retrieved from https://www.npr.org/2015/08/04/429219691/how-cellphone-usage-can-help-determine-a-person-s-credit-worthiness Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., ... Barabási, A.-L. (2007). Structure and tie strengths in mobile communication networks. _Proceedings of the National Academy of Sciences_ , _104_ (18), 7332–7336. https://doi.org/10.1073/pnas.0610245104 Palla, G., Barabási, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. _Nature_ , _446_ (7136), 664–667. https://doi.org/10.1038/nature05670 Pedro, J. S., Proserpio, D., & Oliver, N. (2015). MobiScore: Towards Universal Credit Scoring from Mobile Phone Data. In _User Modeling, Adaptation and Personalization_ (pp. 195–207). Springer, Cham. https://doi.org/10.1007/978-3-319-202679_16 Soto, V., Frias-Martinez, V., Virseda, J., & Frias-Martinez, E. (2011). Prediction of Socioeconomic Levels Using Cell Phone Records. In J. A. Konstan, R. Conejo, J. L. Marzo, & N. Oliver (Eds.), _User Modeling, Adaption and Personalization_ (pp. 377–388). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_35 Suri, T., Jack, W., & Stoker, T. M. (2012). Documenting the birth of a financial economy. _Proceedings of the National
The Impact of Ethiopia’s Road Investment Program on Economic Development and Land Use: Evidence from Satellite Data

# Supplementary Information

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. The views expressed in this paper are those of the authors and do not necessarily reflect those of the SNB. In addition, 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.
approach for producing interim model-based estimates because the alternative of using old census data carries risks especially if the distribution of census variables has changed over the intercensal period.

The presence of a recent census in Burkina Faso provided a valuable opportunity to evaluate the results of different models against ‘gold standard’ census-based estimates. In sampled areas, the estimates produced by the unit context model track the census-based estimates well and have lower MSEs than direct estimates. Across all areas, the correlation between the geospatial-based estimates and the census-based estimates is high, but this correlation was much higher in sampled areas than non-sampled areas. Models specified at the household level generated estimates that were moderately more accurate than those specified at the grid cell level, because the greater variation in per capita consumption allowed for the automated selection of a richer model. Both sets of estimates had lower MSEs than estimates under a model specified at the area level, which we think is due to the use of more granular auxiliary data.

Overall, the estimates for the countries without census data show large improvements in MSE reduction compared to direct estimates. In particular, the median cve in sampled areas is reduced between approximately 59% and 68%. The five countries that are the focus of this paper are neighbors and share many economic and social characteristics. Furthermore, all of them implemented highly harmonized surveys concurrently, and the set of geospatial variables available for model selection is identical. However, one cannot be certain that the results for Burkina Faso generalize to the other four West African countries as there are important differences to take into account. Burkina Faso is facing a significant internally displaced people crisis, affecting about 10% of the population, but hosts far fewer refugees than Niger or Chad. Burkina Faso and Guinea lack the large areas of mainly uninhabited desert that characterize Mali, Niger, and Chad. Nonetheless, the relatively low correlation between the geospatial estimates and the census estimates in non-sampled areas observed in Burkina Faso raises the prospect that the estimates for these areas could significantly change when upcoming
coarse (0/1)|564|0.138|982|0.153|-0.014|

**Notes:** This table compares characteristics of control and treatment plots using information collected before the treatment. The last column provides mean differences across treatment and control group characteristics.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

Table 2 reports the distribution of plot sizes and measurement error in the post-planting and post-harvest interviews. We present these discrepancies across quartiles of "true" plot size (based on GPS measurement) and report differences in terms of biases relative to the true size. That is, for each quartile and survey round we compute, (i) R R R R =<sup><u>(XX�S</u></sup> for the<sup><u>P−XX�T)∗100</u></sup> post-planting, pre-treatment round and (ii) R R R R =<sup><u>(XX�S</u></sup> bbR bb: P for the post-harvest, XX<sup>�</sup> TT<sup><u>P−XX�T)∗100</u></sup> post-treatment round.<sup>10</sup> Although the overall relative bias (at the sample mean) in self-reported bbR bb: P XX<sup>�</sup> TT plot size in the post-planting survey is just 18 percent, this disguises a strong, systematic pattern across the plot size distribution. Farmers overestimate the size of the smallest quintile of plots by

> 10 S and S stand for average self-reported plot size in the post-planting and post-harvest interviews, respectively. stands for average true plot
Table 4: Recovery Growth After Conflict: Ethnic Group Level

||(1)<br>ln(light per capita)|(2)<br>ln(light per capita)|(3)<br>ln(light per capita)|
|---|---|---|---|
|Confict Incidence|-0.311***|-0.312***|-0.312***|
||(0.054)|(0.054)|(0.054)|
|First 5 Years|-0.202***<br>(0.064)|||
|Remaining 5 Years|-0.130**<br>(0.060)|||
|First 10 Years||-0.200***|-0.136**|
|||(0.051)|(0.061)|
|Remaining 10 Years||-0.052|-0.054|
|||(0.087)|(0.087)|
|Fragile Peace|||-0.190**<br>(0.095)|
|Country/Year FE|YES|YES|YES|
|Group FE|NO|NO|NO|
|Observations|8,517|8,517|8,517|
|R-squared|0.847|0.847|0.847|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Unit of observation are ethnic groups. The dependent variable is the natural logarithm of night light per capita. Night light data are gathered form the National Oceanic and Atmospheric Administration (NOAA). “Conflict incidence” is a dummy defined through more
# **4 Results**

We provide three sets of results. In the first set of results, we analyze the labor response driven by exposure to herder-related violence. We show these results by season (i.e., in the post-planting and post-harvest seasons), estimate differential effects based on previous exposure to herder-related violence, and report results both for the full sample and for sub-samples of men and women. In the second set of results, we further explore the “shadow of violence” mechanism by estimating effects specifically in the survey waves collected after the 2018 spike in violence and within Nigerian states that implemented open grazing bans. Finally, in the third set of results, we examine household consumption patterns, agricultural marketing choices, and non-farm enterprise sales using a cross-sectional subset of our panel data to understand the possible welfare implications of the measured labor responses after exposure to herder-related violence.

## **4.1 Labor Response to Herder-Related Violence**

We first estimate how exposure to herder-related violence influences labor allocation decisions of individuals. As previously mentioned, we analyze three main types of work documented by in the GHS: self-employed, informal work (that is, own-account work or work for a non-farm enterprise run by someone in the household), agricultural work, and work for someone outside the household, including paid, wage work. We consider both outcomes on the extensive (whether they worked in each category at all in the last week) and intensive margin (how many hours they worked) for each type of work. One specific caveat applies: due to the fact that the majority of HRV incidents immediately precede and occur in the planting period, and because the measures of hours worked were only reported from 2015 onward, we do not observe a satisfactorily high number of observations for specification (2) for the intensive margin in the post-harvest period. As a result, we only report intensive-margin estimates for the post-planting period. The total number of hours worked is also presented as a key outcome variable
. Blanchard and Acalin (2016), for example, argue that FDI is now as volatile as portfolio capital flows.

In this paper we revisit these questions, focusing on emerging markets. We ask: how do the magnitude and volatility of various capital flows compare? How have they evolved over time? What are the observable empirical correlates of different flows?

We analyze trends in capital flows since the 1990s, including in the post-global-financialcrisis era. While a majority of previous studies have utilized annual data largely for reasons of availability and convenience, we work here with quarterly data. This allows us to analyze capital flows at business cycle frequencies and around country-specific sudden stops and global stops, events that are hard to pinpoint using annual data.

In terms of inflows into emerging markets, our results suggest that the patterns identified in earlier work persist despite recent structural and regulatory changes, and that much of the conventional wisdom survives. FDI inflows remain more stable than non-FDI inflows: FDI inflows have lower volatility; are more persistent; and decline by smaller amounts in both country-specific sudden stop and global stop episodes. Within non-FDI inflows, portfolio debt and bank2
the effect of non-price determinants of competitivity. A body of research shows that depreciating or undervaluing the real exchange rate is an effective way of promoting manufacturing exports (Rodrik, 2018; Freund and Pierola, 2012; Bernard and Jensen, 2004). According to the authors, exchange rate depreciation is an indirect subsidy. Reducing distortions faced by manufacturing firms, the depreciation gives manufacturing firms a competitive advantage. However, it is not clear if this competitive advantage increases exports more than domestic sales. The real exchange rate is from Penn World Table 10.1.

# **5.** **<u>Firm-level covariates</u>**

Covariates are variables used in both the first and second stages of entropy balancing. They are the determinants of the treatment’s occurrence (access to electricity). Company-level covariates are therefore company's characteristics that can explain a specific company's access to electricity.

- _Firms’ size:_ It is reasonable to say that the ability of firms to respond to challenges in access to electricity is strongly correlated with their size. Thus, the difference in terms of size can explain the fact that some companies have access to electricity whereas others may not. Since firms' size can explain the occurrence of electricity access, it is among the covariates in the first stage of entropy balancing, and a control variable in the second stage. It is important to note that the size in covariate and control variable only in the overall sample. In the WBES, small-sized companies are those employing no more than 19 people, medium-sized companies are defined as those employing between 20 and 99 people, while large-sized companies are classified as having more than 100 employees.

7
second category of reweighting techniques, which we refer to as non-PSW approaches, matches indicators between a phone or web survey and a reference survey at a more aggregate level than at the household/individual level. This group of approaches includes raking, post-stratification, and maxentropy. The non-PSW approaches select a set of indicators and adjust sampling weights so that weighted averages of the selected indicators are closely/exactly matched between a phone/web survey and a reference survey.

PSW and non-PSW adjustments are often conducted together. We consider two advantages of the combined applications of PSW and non-PSW approaches. First, for PSW to eliminate sampling bias, a set of assumptions, like strong ignorability, need to be satisfied, which cannot be easily tested, for which non-PSW serves as a complement for weighting purposes. Second, non-PSW approaches match the means of key indicators between a phone/web survey and a reference survey, but there is no guarantee that the distributions of these indicators are also matched. PSW approaches, by contrast, match the distribution of propensity scores. Since both approaches have their own strengths, conducting both PSW and non-PSW adjustments is reasonable.

This paper investigates whether reweighting can correct the bias of poverty projections produced by the SWIFT methodology. The performance of reweighting techniques differs by data and target indicators that were matched, and there is agreement in the literature that reweighting techniques reduce the biases in target statistics yet do not eliminate them (Lee (2006) and Dreze and Somanchi (2023)). Dreze and Somanchi (2023) created biased samples by dropping poorer households from a household survey and tested whether a non-PSW reweighting technique (maximum entropy reweighting, or maxentropy) can reduce biases in poverty rates and mean household expenditures. Although the biases in poverty rate estimates and means of household expenditures declined, substantial proportions remained.

However, existing literature lacks an assessment of how well reweighting techniques can reduce the biases of poverty projections produced by SWIFT or any other poverty projection method. Using phone or web surveys to estimate poverty necessitates the