rafmacalaba/data-use-mentions-extended huggingface dataset:
though we are comparing children fully exposed to children partially exposed (as opposed to children not exposed), our middle cohort of children was

> 25 This exposure is not on average. Every child in this cohort was exposed for four years, as virtually all are born after the program is in place, and they are all measured in 2005 when they are over 5 years old.

> 26 Another difference could come from sample composition effects. The Oportunidades data sampled households in 1997, while our survey represents the population of these localities in 2002.

31
# **Notes**

1 Baldwin (2011) proposes a political economy study which disentangles the theoretical mechanisms through which a unilateral liberalization affects developing countries.

> 2 Berthou and Fontagn ́e (2009) also explore how firms adjust their product mix and exported value as a consequence of a reduction of trade costs.

> 3 Our sample includes: China, India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand.

> 4 Several robustness tests demonstrate that our results are not driven by the countries included in the comparison group.

> 5 Multi-product firms’ models include Allanson and Montagna (2005), Baldwin and Gu (2009), Feenstra and Ma (2008), Eckel and Neary (2009), Nocke and Yeaple (2008), Bernard et al. (2009), Mayer et al. (2009), Arkolakis and Mundler (2008) and Dhingra (2009).

> 6 Her findings point out that Thai tariffs cut has a negative effect on process and product innovation among exporters, while it has a positive effect on product innovation of less export-oriented firms.

> 7 India, Indonesia, Japan, Korea, Philippines, Pakistan, Thailand and Singapore.

> 8 For industrial goods the average bound tariff level will go down to 8.9 percent with a range from 0 to 47 percent.

> 9 There is a lot of heterogeneity across other Asian destinations. For example, the average MFN tariff applied by Korea, Japan and Singapore is not changing over time, while Indonesia’s and Philippines’s average tariff falls by 3 p.p. and Pakistan and Thailand by around 1 p.p. and India’s MFN tariff falls more than 10 p.p.

> 10Other Asian destinations includes: India, Indonesia, Korea, Japan, Philippines, Singapore, Pakistan and Thailand.

> 11This figure is based on BACI dataset and French customs dataset (1999-2005). To make the figure clearer the outliers observations are excluded. Similar figures for
other necessities. In both Lebanon and KRI, a large majority of refugees report having experienced at least one of these shocks in the past year, and at rates higher than other groups (results not shown). This feature of the lives of forcibly displaced people highlights the importance of credit markets in aiding households to cope with large insecurity.

While subjective poverty rates are high for all refugees, a range of indicators also suggest improved financial well-being among refugee households in Kurdistan relative to Jordan, where similar data are available.<sup>14</sup> The vast majority of Syrian refugee households in Kurdistan and in the Jordan sample consider themselves to be poor and report that their earnings are inadequate to meet their basic needs. More than 60 percent report not having 100 Jordanian dinars (JDs) or 150,000 Iraqi dinars (ID) in savings (Figure 11). In fact, more than half of the refugees were in debt, owing someone outside the family at least that amount. Yet, refugees in KRI report relatively greater financial well-being than those in Jordan, including relatively lower rates of subjective poverty. In addition, refugees in KRI have better access to credit, with only one-third reporting they could not access a formal or informal loan of at least 150,000 ID from someone outside their family. In contrast, these rates are over 80 percent for a loan of 100 JDs in the Jordan sample.

> 14 These questions were not asked in the Lebanon survey.

9
range of individual characteristics (Blumenstock et al., 2015), and could conceivably be used to predict other outcomes of interest—such as lifetime customer value, or the social impact of a loan.

It has been widely acknowledged that mobile phones can enable low cost money transfers and savings in developing countries (Suri, Jack, & Stoker, 2012). Our results suggest that nuances captured in the use of mobile phones themselves can alleviate information asymmetries, and thus can form the basis of new forms of low cost lending. These tools together are enabling a new ecosystem of digital financial services.

# **7. REFERENCES**

Arráiz, I., Bruhn, M., & Stucchi, R. (2017). Psychometrics as a Tool to Improve Credit Information. _The World Bank Economic Review_ , _30_ (Supplement_1), S67–S76. https://doi.org/10.1093/wber/lhw016 Berger, A. N., & Udell, G. F. (2006). A more complete conceptual framework for SME finance. _Journal of Banking & Finance_ , _30_ (11), 2945–2966.

Björkegren, D. (2010). “Big data” for development. Proceedings of the CEPR/AMID Summer School. Retrieved from http://dan.bjorkegren.com/files/CEPR_Bjorkegren.pdf Björkegren, D., & Grissen, D. (2018). The Potential of Digital Credit to Bank the Poor. _American Economic Association Papers_

## _and Proceedings_ .

Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. _Science_ , _350_ (6264), 1073–1076.

Breiman, L. (2001). Random Forests. _Machine Learning_ , _45_ (1), 5–32. https://doi.org/10.1023/A:1010933404324 Breiman, L., & Cutler, A. (
Estimated with weighted OLS, normalised survey stratification weights applied. Standard errors in parenthesis, clustered on region. Significance levels: *** 0.01, ** 0.05, * 0.10.

First, these estimates suggest a robust and significant negative impact of infrastructure disruptions on firm utilization rate. Power outages, water outages, and transportation issues all contribute to reducing utilization, and therefore productivity and revenues.

Second, estimates indicate that for water and transport, indirect effects may be as or even more important than direct impacts, while the indirect impact of power outages seems to be smaller. However, indirect effects are imperfectly measured using regional disruption levels, as point estimates for regionlevel variables are sensitive to different controls and show large standard errors.<sup>3</sup> Nonetheless, one implication is that estimates looking only at direct impacts of infrastructure disruptions may be missing a large part of the economic cost.

Third, the interaction term (between direct and indirect impacts) is positive for all types of infrastructure. This means that direct impacts are less severe if the firm’s backward and forward linkages are disturbed anyways – and conversely: supply chain mediated indirect effects cause less harm when the firm already suffers from direct disruptions.

The estimated model suggests that infrastructure disruptions have a significant economic cost in the form of reduced business utilization rates. In the case of electricity outages, measured by the annual selfreported total blackout duration, this effect is robust significant across model specifications. Following this estimate, 100 additional hours of blackout decrease utilization by 0.13 to 0.32 percentage points on average (Table 4 model III).4 Note that this estimate is close to the results in section 4.1. based on selfreported losses, which suggest that 100 hours of blackout would decrease the annual utilization rate by about 0.29 percentage points in the model without interaction (Table 4 model I). This makes us confident that our identification strategy in the previous section is valid and
about 50 years before slowing and settling around 87 percent. Output per worker converges to 92 percent of that in the developed economy (not shown) – closer, because it takes account of the fact that participation is lower in the developing economy, but still some distance away.

The NE panel assumes the schooling distribution converges to that in the developed economy. This raises income per head to 69 _._ 4 percent. Part of this is attributable to the _experience college premium_ . If we set _r_ ̄ _e_ equal to _<u>re</u>_<sup>inthedevelopingeconomy</sup> after transition (eliminating the premium), the impact of increasing education is lower, raising income to 65 _._ 2 percent of the developed economy. About a quarter of the jump from increasing schooling is due to the experience college premium.

This experiment assumed that the returns to schooling do not respond to increases in the quantity. We repeat the experiment of increasing the distribution of schooling to that in the developed economy, while also changing the schooling return parameters to
||||N|Y|N|||
|Prices at 3-digit level|||||||||N|N|Y|||
|Industry-Specific Time Trend|||||||||||||-0.013***<br>(0.003)|
|Number of observations|6,427|6,427|6,427|6,427|6,427|6,427|6,427|6,427|6,382|5,284|1,596|1,173|1,173|
|R-squared|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.87|.|.|

_Source_ : See Table A1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. All employment specifications include industry and year fixed effects. Low-income affiliate employment is defined according to the World Bank income categories. Regressions in columns 12 and and 13 are Arellano-Bond dynamic panel data regressions include current rather than lagged offshoring to low and high income countries.
# **7. Summary and Conclusions**

In this paper, we have used new databases for the Dar es Salaam metro region to investigate the spatial dynamics of traffic congestion, regional air pollution, and impacts on vulnerable residents. We use Google Traffic to develop high-resolution traffic measures, which provide the first temporally- and spatially-comprehensive view of congestion in the metro area. We assess the spatial dynamics of air pollution using high-resolution nitrogen dioxide (NO2) measures from the European Space Agency’s new Sentinel-5P satellite platform. Our information on the spatial distribution of vulnerable residents uses demographic and poverty maps assembled from numerous sources by the WorldPop project of the School of Geography and Environmental Sciences, University of Southampton. We combine these information sources in a spatial grid with 1-km cells for the metro region.

Our empirical modeling highlights the impact of weather factors on the spatial distribution and intensity of NO2 pollution from the emissions of motor vehicles in the region. We identify critical weather dimensions in two econometric analyses. The first uses daily NO2 measures from Sentinel-5P and weather data from World Weather Online to measure the effects of temperature, rainfall, humidity and wind speed on air pollution, as well as the effect of cloudiness on measurement by Sentinel-5P. We aggregate regression-based NO2 predictions by month and find a strong seasonal pattern of weather impacts on pollution. In the second econometric exercise, we estimate a model that relates spatial displacement of NO2 pollution from its traffic source locations to the locations observed by Sentinel-5P. Our analysis clearly identifies wind direction as the critical driving variable and, like the other weather factors, wind direction is strongly seasonal. Taken together, these weather variables account for a major portion of spatial pollution dynamics in the Dar es Salaam region. We confirm the strong seasonality of wind direction in a 10-year analysis for the region.

In the second part of the paper, we explore the distributional implications of our findings. Using our methodology for analysis of spatial pollution displacement, we quantify NO2 pollution exposure for residents in the displacement path. To
household-level data spanning 2006-2010 (that also forms a community-level panel) to examine how well households have been able to smooth consumption amid income fluctuations in the crisis, and also respond to a specific set of fiscal and credit initiatives supported by the government. Data for the Thai Socio-Economic Surveys (SES) are collected on an on-going basis; every month a sample is collected that is large enough to allow one to track the evolution of measures such as income or consumption spending with a considerable degree of precision at the national level.

To address simultaneity of changes in government spending and per capita expenditure, we estimate a dynamic panel regression with the community-level data, instrumenting the stimulus effect with second-order lagged outcome variables, and estimating the model via Generalized Method of Moments (GMM) (Arellano and Bond, 1991, Jalan and Ravallion 2002, 1998).

# **2. Thai Macroeconomic Experience in Recent Years**

In what follows we briefly summarize the recent macroeconomic experience of Thailand, in order to demonstrate that the country faced a significant economic shock in 2008/2009. As mentioned earlier, between the third and fourth quarters of 2008, Thai GDP fell by 4.2%, and it declined by a further 0.4% in the first quarter of 2009, as Figure 1 shows. The drop can be attributed to both external and local factors. Domestically, political instability, including an opposition blockade of the two main international airports and a change in government in late 2008, deterred tourists and investors. Globally, the world financial crisis contributed too, as Thai exports of high-tech manufactures fell sharply in the latter half of 2008 (see Figure 2), and consumers (including potential tourists) restrained their spending. Thailand was also buffeted by a rapid rise in the price of energy imports in 2008-09, but was a net beneficiary from the increase in the price of rice experienced in 2008 (and which was partly, but not completely, reversed in 2009), as Figure 2 also shows.

Some of
# A.2.   Telephone survey sample

A total of 717 households were interviewed in the telephone survey. These were households that had been part of the GHS panel and had been interviewed in wave 3, visit 2.

All of the households had been residing in the same LGA during the time of the GHS survey; thus, no migration occurred among the households selected for the phone survey. Migration in general was low during the other waves: out of 4,916 households in wave 1, visit 1, for the entire country, only 142 households had moved by the end of wave 2, visit 2 (that is, 2.9 percent). The majority of the households that had moved were from the South West zone (Lagos and surrounding states), which is outside of our geographical sample. Within the three zones studied, only 52 households moved between wave 1, visit 1, and wave 2, visit 2. Between wave 2, visit 2, and wave 3, visit 2, another 212 households moved; tracking surveys were administered to these households in lieu of the regular survey procedures (NBS 2014, 2016). Because our sample consists of households that were part of the panel, we do not have any movers in the sample. However, given the low rates of migration in our survey areas, we do not consider this to be a major limitation for the study.

The attrition rates between the GHS waves 1 and 2 and waves 2 and 3 were 5.7 percent and 8.4 percent, respectively. Some attrition between wave 2 and wave 3 resulted from not being able to reach some areas: a total of 14 enumeration areas could not be visited in the states of Borno and Yobe during wave 3, leading to the loss of 139 households from the sample. Furthermore, some households dropped from the sample because they refused to be interviewed, they were untraceable, or all members had died (NBS 2014, 2016). Since we are conducting analysis
US import bills of lading. These data scope out a large portion of a shipment’s journey; a typical observation includes where a shipment originated, ports of lading and unlading, vessel-specific IMO Number, where the shipment is ultimately sent within the US (shipment destination), and some product characteristics (e.g., shipment HS8 code, TEU volume, and weight). Unfortunately, Panjiva’s US Imports lack _direct_ measures of freight cost, and the value of the shipments are reported for a striking minority of observations.

Another useful data supplier is Clarksons Research—the world’s leading shipbroker. Its Shipping 19
2.70|0.62|0.38|

_Source:_ IBNET database, World Bank, and other resources.

_Note:_ Since, there are no available data on OPEX cost-reflective tariffs for El Salvador and Jamaica, we use the value for the total cost-reflective tariff, multiplying it by 0.22, which is the ratio of the average OPEX cost-reflective tariff to the average total cost-reflective tariff from Andres et al. (2019). The far-right column shows the unit price conditional on paying for water services (i.e., self-reported expenditure on water greater than zero). The figure is calculated using sampling weights. CRT = cost-reflective tariff; CRTopex = OPEX cost-reflective tariff; IBT = increasing block tariff; m<sup>3</sup> = cubic meter; OPEX = operating expenditure; VAT = value added tax; VDT = volume-differentiated tariff. a These are providers that cover areas included in the household surveys and for which there are available data online. Most of the data come from IBNET.

b Data are from 2007 for Dire Dawa, and 2014 for Addis Ababa. The cost-reflective tariff includes both OPEX and CAPEX and is estimated using averages across 24 Sub-Saharan African countries in Andres et al. (2019), since the values otherwise presented for Ethiopia seem unrealistically high. c Data come from Abubakar (2016).

d Data come from the state-owned piped-water provider, Administración de Acueductos y Alcantarillados (ANDA). e Data are from the National Water Commission.

f Data from the Autoridad Nacional de Servicios Públicos are used for OPEX cost-reflective tariffs for the year 2015 in U.S. dollars and divided by 0.22, which is the relationship between the average of the OPEX cost-reflective tariffs across countries from Andres et al. (2019), and the average of total cost-reflective tariffs across countries from the same paper.

# **2.3 Definition of variables of interest**

In the following subsections we describe how we classify households by economic status (i
their step-by-step journey through Libya.

There are differences between the migration journey through Libya along the Western and the Eastern routes. Migration through the Western route almost always takes place in stages (Wittenberg, 2017). Migrants use smugglers to avoid the large number of checkpoints that have to be crossed, though there are few reports of detention and deportation along the routes heading north. Anecdotal evidence indicates that migrants from West African countries often stop in a locality until they save enough money (often by being employed informally) to be able to continue their journey (UNHCR, 2017b). The journey through the Eastern route is instead non-stop, characterized by strict cooperation between smugglers and human traffickers, and often involves cases of violence. In recent years, this journey has become very short, between ten days and three weeks (Micallef, 2017).

# **4 Data**

## **4.1 Migration data**

Data on migrants and their movements are retrieved from the IOM’s Libya Displacement Tracking Matrix (DTM). The IOM-DTM is the only source providing reliable data on migration flows in Libya. The DTM tracks the movements of migrants in Libya using the data collected at the IOM Flow Monitoring Points (FMPs) in the Libyan territory.<sup>13</sup> FMPs are located at important points of transit and permanence of migrants such as border crossings, bus stations, harbors, markets, public squares, areas where migrants look for work, etc. (IOM, 2016).

Migrants included in the DTM dataset live close by a FMP and are able to move across the country. These migrants are not victims of human trafficking or imprisoned in detention centers.<sup>14</sup> Most of them often remain for long periods in a given location, live in rented houses, and have a job (often informal) (IOM, 2018b, 2019).

In our empirical analysis, we use DTM data for the period June 2017 to February 2020
|x|x|
|Birth order x Birth year FE|||x|x|
|Birth order x State FE|||x|x|
|State x Birth year FE|||x|x|
|_Firstborn girl_ x State FE|||x|x|
|_Firstborn girl_ x Birth order FE|||x|x|
|Mother FE||||x|

NOTES: This table reports estimates of equation (1) in the text for neonatal mortality of second and higher order births. Neonatal mortality is mortality in the first month of life. Each column is a separate regression. The outcome is an indicator for death within one month of birth. We drop children that are less than 1 month old to allow each child in the sample “full exposure” to the risk of neonatal mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE in columns (1)-(3). The vector _Xijt_ comprises mother’s age at birth and, except in column (4), household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

55
children themselves and potentially more indicative of differential treatment by their parents (and other adults) and hence may indicate more clearly one particular source of gender disparity<sup>2</sup> .

This approach of using the rich data provided by the Demographic and Health Surveys (DHS) to examine gender disparity has been used in past studies (Arnold, 1992, Hill and Upchurch, 1995) which also examined gender differentials in outcomes using these data. This study updates theirs and includes the relatively unique feature of combining data at the country level with data from individual states of India and provinces of Pakistan. What's the value added of this innovation? In comparative data each country is typically treated as a single unit, irrespective of its size. India is enormous, with a population of over 900 million which is twice as large as all countries in Africa combined, or all countries in Latin America (South and Central America and the Caribbean) combined. The state of Uttar Pradesh has a population of over 140 million, which would make it the world's sixth largest country.

But size alone is not the right criterion for determining the value of cross-country (versus within-country) comparisons, as it depends on the empirical characteristics of the phenomena to be examined. Since countries, almost by definition, share a common currency and have free internal trade, one might expect economic phenomenon like inflation to be similar in all parts of the same country. In examining the relationship between money supply growth and inflation, little would be gained by considering Minneapolis and Miami separately,

> 2 VWhile some might argue that some gender disparities, like differential participation in the paid labor force or the gender division of labor, are the result of a joint, voluntary, and optimal decision-making on the part of a couple as a household unit, it would be very difficult to argue that female children voluntarily assume higher mortality risks.

3