rafmacalaba/data-use-mentions-extended huggingface dataset:
indicator related to general health, whether any household member reports an illness or injury.

4. **Satisfaction** is measured at the household-level. The family includes three outcomes measured on a four-point Likert scale from “Very Unsatisfied” to “Very Satistfied”: the respondent’s satisfaction with their family’s health; satisfaction with public health workers (i.e., clinic staff); and, among households with at least one member utilizing a western-style clinic in the last year (approximately half), satisfaction with the care they received.<sup>15</sup> Among these households with members utilizing the clinic in the last year, we ask whether they would return to the clinic for a future medical need. The last two satisfaction outcomes are asked across the different types of health episodes, so we average responses across individuals in a household when multiple episodes are reported.

5. **Clinic quality** includes three clinic-level outcomes. First, we construct an index of clinic service provision that aggregates measures related to organization (e.g., medicines sorted by expiration date and stored in a safe location), the types and frequency of services offered (e.g., family planning), number of staff on duty, and hours clinics are open. Second, we measure the proportion of staff who are aware of the 2010 policy that removed user fees for maternal and under-5 services. Finally, we measure employee satisfaction. The services offered and employee satisfaction are reported in the clinic survey; other measures are based on enumerators’ observations.

6. **Health service delivery** is measured among individuals who experience a health episode in the month before the endline survey (for childbirth episodes recall is over the past six months). The family includes outcomes derived from the household survey, including staff absenteeism and wait times, problems with clinic facilities or staff, satisfaction with services, staff attitude, drug availability, and fees paid. Per our analysis plan
# **5. Potential Mechanisms and Heterogeneity in Impacts**

The observed changes in aggregate demand for various services due to the pandemic reflect a combination of demand and supply shocks. As we highlighted earlier, there are three major channels through which the coronavirus pandemic can affect demand for services. First, as the pandemic began to take hold in much of the world, governments have introduced multiple measures, including travel restrictions, social distancing, and lockdowns, to reduce the spread of the virus. These measures directly affect the supply of some services, while inducing demand for substitutes such as remote working. These business closures limit the availability of some services, such as international flights, which effectively put a total hold on air travel and tourism.

Second, the pandemic could affect consumer demand through income and wealth channels as workers get laid off or experience cuts in wages and salaries due to business closures and economic slowdowns. Third, as the pandemic takes hold, consumers will reduce economic activities and alter their demand for certain services (such as hotels, restaurants, local malls, and shopping centers, and travel and leisure) for fear of contracting the virus. Besides such fearinduced withdrawal of demand, people may also reduce the consumption of some services deemed non-essential due to economic anxiety and elevated uncertainties about the future under the shadow of the pandemic. Such anxieties and negative economic sentiments are known to reduce consumer confidence and aggregate spending. In this regard, early studies from Europe and the United States show a significant decline in consumer spending across various sectors. Using daily transaction-level data from Denmark, for instance, Andersen et al. (2020) find a 25 percent decline in overall household spending following lockdown.

To identify whether supply-side or demand-side shocks drive our results, we interact with the number of weekly confirmed cases with the implementation of social distancing and lockdown measures. More specifically, we interact with social distancing and lockdown implementation indicators with an indicator variable for whether a country has registered the first 100 cases. However, we
India. The stock of urban-rural migrants represented less than 1.4 percent of the total population in the majority of Indian districts in 1991, with a mean equal to 1.7 percent (based on the Indian districts database at the University of Maryland; see below). The interdistrict migration represents a substantial share of the total migration, particularly rural-urban migration. In 1991, it accounted for less than 34 percent of the total migration for the majority of Indian districts (with a mean equal to 37 percent). However, the empirical analysis below rejects the relevance of this type of migration in determining rural poverty.

18 In fact, the National Sample Survey contains socio-demographic information on intradistrict ruralurban migrants, but we cannot use this information for two reasons. First, the number is not large enough to be representative at the district level. Second, the survey only asks about migration in the last 365 days, whereas we would need information on migrants in the last five years. Although we cannot use these data in the regressions, we nevertheless refer below to the national-level expenditure comparison between rural-urban migrants and stayers (Singh 2009).

19 This pattern receives strong support in the empirical analysis that follows, which suggests that rural poverty is negatively associated with the rural young adult population and with the share of literates in this group.

20 This assumption is supported by the results of regressing the 1981–91 change in the urban population in the 15–34 year age group ( ∆ _P_ 15 _U_ − 34 ) on the change in the rural population in the same age group ( ∆ _P_ 15 _R_ − 34 controlling for changes in the district’s total population and the total population in 1981). The

> coefficient of ∆ _P_ 15 _R_ − 34 is not statistically different from −1, indicating that changes in the rural population are reflected in mirrored changes in the urban population through either rural-urban migration or a rural-to-urban change in the status of villages (results available from the authors upon request).

21 In
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<!-- Start of picture text --> Low INPRES district High INPRES district<br>80<br>75<br>70<br>65<br>Downward Upward Downward Upward<br>mobility mobility mobility mobility<br>-10 -5 0 5 -10 -5 0 5<br>Median 2013 district HDI - Birth district HDI<br>Birth district HDI<br><!-- End of picture text -->

# **Figure 6: Migration of mothers across districts**

Notes: Each dot is a birth district in our analysis. The INPRES intensity of each birth district is classified as either low intensity (left) or high intensity (right). The y-axis position of each birth district is determined by the district’s Human Development Index (HDI). Higher values of HDI indicate better conditions. The x-axis position of each birth district is the median difference between the HDI of the district where the mother is living in 2013 and the HDI of the district of birth. X-axis greater than 0 indicates upward mobility: the HDI of the district in adulthood is better than the HDI of the district of birth. X-axis less than 0 indicates downward mobility: the HDI of the district in adulthood is worst than the HDI of the district of birth. X-axis of 0 indicates no change in mobility. HDI is measured in 2013 using the Indonesia Database For Policy And Economic Research (INDO-DAPOER).
reflected by the recent Barloworld survey of 2010. At least, more than half of those in the automotive industry proclaim that logistics capacity of SA’s ports has improved in the last two years and they also think that the loading and docking capacity is now competitive and comparable to international practice. The Industry also gave customs a vote of confidence by confirming that compliance processes are being dealt with more efficiently than in the past and that corruption has decreased, and physical security of goods in the port has improved (Barloworld Logistics, 2010).

# **4. Why Was It So Important to Reduce Cargo Dwell Time?**

Aggressive policies against long dwell times are often pointed at by shippers as unfair and disproportionate. The complexity is to set tariffs and measures that are, on the one hand, strict enough

> 22 Some of the items stipulated in the agreements include Estimated Time of Arrival (ETA), Container lists or EDI capability.

16
58)+142 _._ 2 (3) In the absence of data from regulatory-grade monitors, satellite data may provide an alternative for calibrating the data from low-cost monitors. Therefore, we also calibrate the Purple Air data with the satellite data using a similar calibration procedure.Using the MERRA-2 satellite data for the calibration, there is only a single value for Dakar. Therefore, we use a Dakar-wide average of the Purple Air monitors to generate the satellite calibration model: Satellite Calibration PM2 _._ 5 = (Unadjusted PM2 _._ 5 _×_ 1 _._ 66) _−_ (Humidity _×_ 1 _._ 3) _−_ (Temperature _×_ 4 _._ 84)+249 _._ 6

> 13Additionally, we test including the number of particles larger than 2.5 micrometers as a predictor instead of the PM2.5 value, because the number of larger particles has a very high correlation with the regulatory-grade monitor values.

> 14In a similar manner, we generate a daily local calibration using the same data aggregated daily (Table A2).

12
There is a particularly rich literature on microeconomic transport-to-development linkages, particularly for rural roads and for Sub-Saharan Africa. This brings significant evidence that rural roads reduce poverty and contribute to household welfare by raising income and consumption, expanding (self-) employment opportunities, and improving food security. Rural localities benefiting from improved market access also see an increase in agricultural production, more widespread uptake of modern agricultural techniques, and reduced dispersion of prices. Reducing the isolation of small towns similarly leads to expanded output, increased specialization, and higher exports. Turning to national highway networks, the findings are that these contribute substantially to boost manufacturing output and associated exports, among other things by facilitating greater industrial specialization and enhancing competition. There is evidence that over time this may lead to the convergence of lagging regions, but at the same time tending to concentrate population and economic activity in regional primate cities.

In urban areas, public transportation systems have the dual effect of facilitating the decentralization of cities by improving the accessibility of lower cost outlying residential land, while at the same time facilitating the commuting of workers to access a larger range of employment opportunities in urban centers, thereby reducing informality.

Turning to other transport infrastructures, railways have been found to have a long-term impact on countries’ development patterns, with their tendency to lock-in spatial patterns of economic activity. The arrival of railroads in rural areas during the colonial period has been shown to have enhanced agricultural output and trade as well as improving food security. At the same time, inter-city railways have played a role in concentrating urban populations. Though not having comprehensive evidence in the dataset as in the other sub-sectors of transport infrastructure, the findings point out that port infrastructure also plays a vital role in international trade, significantly affecting a country’s international competitiveness, and – when operating efficiently – able to compensate substantially for the effect of geographical distance.

The exponential expansion of the academic literature on the development impacts of infrastructure has been fueled by the availability of much richer sources of microeconomic data on firms
- **Imbens, Guido W., and Donald B. Rubin.** 2015. _Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction._ Cambridge University Press.

- **International Labour Organization.** 2020. “ILOSTAT database.” _`https: // ilostat. ilo. org/ data/`_ , Accessed: 2022-04-22.

- **Kling, Jeffrey R, Jeffrey B Liebman, and Lawrence F Katz.** 2007. “Experimental Analysis of Neighborhood Effects.” _Econometrica_ , 75(1): 83–119.

- **Lee, David S.** 2002. “Trimming for Bounds on Treatment Effects with Missing Outcomes.” National Bureau of Economic Research Working Paper 277.

- **Mart ́ınez A., Claudia, and Marcela Perticar ́a.** 2017. “Childcare effects on maternal employment: Evidence from Chile.” _Journal of Development Economics_ , 126: 127–137.

- **Martinez, Sebastian, Sophie Naudeau, and Vitor Pereira.** 2017. “Preschool and child development under extreme poverty: Evidence from a randomized experiment in rural Mozambique.” The World Bank Policy Research Working Paper Series 8290.

- **Mateo Diaz, Mercedes, and Lourdes Rodriguez-Chamussy.** 2016. _Cashing in on Education: Women, Childcare and Prosperity in Latin America and the Caribbean._ Latin American Development Forum.

- **Miller, William, Hubert Anton, and Andrea Townson.** 2008. “Measurement properties of the CESD scale among individuals with spinal cord injury.” _Spinal cord_ , 46: 287–92.

- **Minist`ere de l’Education Nationale, de l’Alphab ́etisation et de la Promotion des Langues nationales.** 2020. “Annuaire Statistique de l’Education Prescolaire 2019/2020.”

- **Minist`eres en charge de l’ ́education et de la Formation, UNICEF, UNESCO
# **Physical risks**

# _Loss of ecosystem services_

The banking sector is exposed indirectly to physical risks by providing credit to enterprises whose production processes depend on ecosystem services. To assess this exposure, we determined the amount of credit that Brazilian banks allocate to firms with business processes dependent upon ecosystem services. We relied on the ENCORE database, which provides a list of 86 different business processes that directly depend on 21 ecosystem services (see Table 2).<sup>10</sup> We followed the methodology laid out in DNB (2020) to link our data on banks’ credit portfolio by economic sectors (e.g., agriculture) with their associated production processes (e.g., rainfed arable crops).<sup>11</sup> Furthermore, to determine which ecosystem services might be more critical to production processes and which potential impacts might be of greatest concern for banks, we leveraged on ENCORE’s dependence materiality assessment. Using sector research and expert interviews, ENCORE’s materiality assessment translates into a rating ranging from very low to very high reflecting how a disruption in the ecosystem service provision could materially affect business performance and be reflected in financial losses (see Figure 2 for an illustrative example).<sup>12</sup> Based on the sectoral credit outstanding of Brazilian banks, the materiality of potential dependencies and impacts of ecosystem services provide a preliminary estimation of banks’ physical risk exposure to biodiversity loss. We show the total amount of credit that, as of March 2021, is exposed to each ecosystem service by dependency materiality rating. For example, consider a company dependent on two ecosystem services (e.g., flood protection and disease control). Each Brazilian real this firm borrowed would count as one when measuring the exposure to each service. This metric provides a full account of the financial system exposure to each individual ecosystem service, though business processes are often dependent on multiple ecosystem services hence the former should be analyzed individually and
# **3.2 District inequality estimation and trends**

To estimate district inequality using night-time lights data, we followed Mveyange (2015) to calculate inequality indices for 2,182 districts in 653 regions across 38 countries in Africa. We computed several measures of district inequality as outcome variables: Gini and the entropy measures (mean logarithmic deviation (MLD) and Theil)). These measures have several desirable properties, e.g., symmetry, mean independence, Pigou-Dalton transfer sensitivity, and population size independence (Haughton and Khandker 2009). However, our main results are based on Gini—a commonly and widely used measure of inequality.

To calculate Gini at the district level, we identified (based on geographical administrative units’ data from the Global Administrative Areas database<sup>19</sup> ) and cut countries’ districts into 0.01 square decimal degree grid cells (about 1.1 km<sup>2</sup> at the equator—an equivalent to the size of a town or village). We then exploited the spatial and temporal variation of light intensity across these grid cells to estimate average inequality at the district level across countries for all the years between 2001 and 2012. We calculate Gini<sup>20</sup> as follows: where i is grid cell rank order, n is total number of grid cells, yi is grid cell value light intensity per capita, and P is grid cell population count. For the robustness checks we use the entropy<sup>21</sup> measures of inequality and a measure of spatial inequality proposed by Bonet (2006).<sup>22</sup>

# **3.3 District inequality trends and summary statistics**

Figure 3 plots the average trends of district light intensity Gini between 2001 and 2012. The figure shows the variation across districts with no mining activities, with active mines, and with closed mines. The figure suggests that overall there was a modest
(including the UK),<sup>7</sup> (ii) high-income European Free Trade Association (EFTA) countries,<sup>8</sup> (iii) the new member states of the EU (referred to as NMS13)<sup>9</sup> , (iv) non-EU European countries<sup>10</sup> , (v) Latin America, (vi) Asia, (vii) the Middle East and North Africa, (viii) Sub-Saharan Africa, and (ix) other high-income OECD countries that are not in Europe (mainly, the US, Australia, Canada and New Zealand).

We have detailed information on various labor-market outcomes of the individuals in the dataset. In terms of labor market status, people are labeled as (i) out of the labor force, (ii) seeking employment, or (iii) currently employed. For the employed workers, there is detailed information on the characteristics of their jobs. These variables include the sector of activity at the one-digitdisaggregation level (using NACE classifications) and detailed occupational categories at the three-digit level (using ISCO-08 classifications). This information enables us to categorize jobs according to the degree of exposure to COVID-19.

The 2014 survey of the EU-LFS includes a special migration module that contains further information on immigrants. These variables include the reason for migration, the level of language fluency of the migrant, the main barriers to labor-market participation (legal documents, recognition of foreign qualifications, language fluency, ethnic or religious background), and the extent to which the migrant is overqualified for the job. These data allow us to better understand the skill profiles of migrants in comparison to those of native-born workers.

To assess the task content and different characteristics of occupations, we use two surveys administered by O*NET, the program sponsored by the US Department of Labor to improve the understanding of the nature of work. The O*NET database contains a vast array of standardized, occupation-specific
locations that experienced conflict, which are a more appropriate comparison group than all locations.

# **_3.5 Outcomes_**

The outcome of concern is female child marriage, a formal marriage or informal union before age 18. Since we are interested in how time-varying conflict may affect child marriage, we construct our analysis sample so that an observation is a child and her age in years (e.g. age 15, age 16). The age range for the DHS woman’s module restricts our analysis sample to women aged 15-49 at the time of the survey. Girls are theoretically at risk of child marriage and thus potentially have observations from age zero to age 17.<sup>11</sup> Based on the age at first marriage question, we know the age a girl aged 15 or over first married, if she has ever married. We use this retrospective question to construct our “failure” indicator (our outcome). It is one if the girl marries at that observation’s particular age and zero if, at that particular age, a girl is not yet married. Girl-age observations after she has married are excluded from our analysis sample (“failure” has already occurred), consistent with the concept of a hazard in survival analysis terms. So, for example, if a girl marries at age 15, she would have observations from ages zero to 15, including the indicator for getting married on her age 15 observation, and then she will no longer be in the sample as she is no longer “at risk” for the event of child marriage, since she has been married. The appendix and Table 12 provide an illustration and further discussion of this data structure.

# **_3.6 Key covariates: Conflict_**

Our focus covariate of concern is conflict. From the UCDP data (Pettersson & Öberg, 2020; Sundberg & Melander, 2013), we calculated total fatalities for each calendar year. We matched and merged this conflict data
Figure 4: The distribution of log total adult equivalent consumption across years <!-- Start of picture text --> 6 8 10 12 -2 0 2 4<br>Log total AE consumption Log total AE consumption (after removing zone specific time trends)<br>2011 2013 2015 2011 2013 2015<br>.8 .8<br>.6 .6<br>.4 .4<br>kernel density kernel density<br>.2 .2<br>0 0<br><!-- End of picture text --> Note: This figure shows the distribution of log total AE consumption. The left panel shows the distribution of nominal values of log total AE consumption for each rounds of survey. The right panel removes zone specific trends to account for inflation at local level.

markets in Addis Ababa or Dire Dawa. Despite the above liberalization steps, the government still has a heavy hand in the coffee trade. First, following the creation of Ethiopian Commodity Exchange (ECX) in 2008, private traders are required to sell coffee through ECX (Gabre-Madhin, 2012). Other forms of government intervention include allocating licenses. For instance, the government frequently revokes export licenses from traders who hoard excessive amount of coffee (over 500 metric tons) (Tefera and Tefera, 2013), and closely monitors the domestic market to ensure that export-quality coffee is not being sold in local markets. Despite these government interventions, domestic coffee prices are highly correlated with international prices as shown in figure 2.

# **4 Empirical Strategy**

## **4.1 The pass-through of coffee price shocks to consumption**

To estimate the pass-through of international coffee price changes to household consumption, I use ESS data. ESS data allows me to measure household-level exposure to coffee price shocks using the share of household farmland allocated to coffee. The estimation equation is as follows: where _i_ indexes households, _γi_ is household fixed effect, and _γzt_ is