rafmacalaba/data-use-mentions-extended huggingface dataset:
temporal variation and the 2016 drought. Dummy variable for standard precipitation index below −1 is a common measure of drought that does not work well in our case. The likely reason is that the precipitation distribution has a long right tail driving the standard deviation upward. Accumulated excess rainfall is correlated to the total number of days with excess rainfall and gives similar results. Finally, we already explained why the NDVI may be endogenous, but we run the corresponding specification for completeness. It captures both floods and droughts as well as anything else that can affect greenness.

As a robustness check, we relax some of the assumptions behind the simulations. We present national statistics when drawing only the weather shock; the weather and idiosyncratic shocks jointly (as in the main results); residuals inside groups of households with similar characteristics; and residuals. In general, drawing residuals leads to noisier measures as they might exceed the effects of the shocks. This discrepancy worsens when drawing residuals inside groups. Our main simulation is directly conducted on consumption per capita to keep all variations. In the annex, we present the ones based on the prediction of percentage change in consumption per capita. Results are similar when drawing shocks but vary when adding residuals’ draw since the residuals from the log regression behave better. We also present simulations based on IHS3 and IHS4 only. Then, we relax the assumption of independence between drought and rainfall. We make them perfectly correlated by drawing them simultaneously. Results are very similar. Finally, we also present matrices of the number of vulnerable versus poor households for each IHS year in the annex.

# **5 Conclusion and Policy Implications**

This study examines the impact of droughts and excess rainfall on the welfare of Malawian households using a nationally representative household survey and exogenously measured weather shock indicators. After taking into account household characteristics as well as idiosyncratic shocks and carefully dealing with transfers and aid in response to drought, we quantify vulnerability to poverty during 2004–2016. The results indicate that weather shocks have
season to analyze its effects on 2019-20 consumption.<sup>15</sup> There are several ways to define the functional form of the weather variable. Most studies either focus on levels or level changes in weather indicators. Another common approach is to use frequencies at which weather realizations fall into different categories (e.g., days with temperature between 0<sup>_◦_</sup> _−_ 10<sup>_◦_</sup> , etc.). This approach is advantageous as it avoids

> 13More information available at `https://www.longwarjournal.org/mapping-taliban-control-in-afghanistan`

> 14The Survey contains information on 1495 clusters in 275 districts of 34 provinces; roughly about 60 households were interviewed per district. The cluster is the lowest administrative unit, the primary sampling unit in IE-LFS 2019. And the district is the administrative unit above the cluster.

> 15To identify the growing months in the year, we rely on the agricultural module from the 201112 round of the Afghanistan IE-LFS (known as Afghanistan Livelihoods Conditions Survey, 2011-12) conducted at the cluster level. This survey collected detailed information about the most grown crops in the cluster and the annual cropping cycle for the crop. We first identify the most grown crop for each region, which helps determine the relevant growing months.

9
improve their conditions.

Thus, while this analysis presents a snapshot of the status of WASH in CCs at the upazila (sub‐district) level in Bangladesh, it also seeks to understand the extent of the problem in relation to the incidence of poverty and stunting. Specifically, it identifies regions with elevated levels of poverty, high stunting and low WASH in CCs, which are those where the UPs may not be able to improve outcomes on their own and prioritized interventions are called for.

The remaining sections of this brief describe the data used for this analysis and method used to create a WASH index, the status of WASH in upazilas and concludes with a discussion on the way forward.

# Data Sources

This analysis uses three main sources of data to combine upazila level HCF WASH data, poverty data and stunting data for children under five.

For the WASH indicators, data from a 2017 rapid assessment survey of WASH by the Community Based Health Care (CBHC), Directorate General of Health Services (DGHS) and the Ministry of Health and Family Welfare (MoH&FW) was used. The survey covers 63 zilas (districts) and 469 upazilas out of 492 upazilas. As of June 2016, DGHS registry<sup>1</sup> showed that 13, 394 CCs were in operation. The rapid assessment was designed for scale rather than depth. It only contains six questions on the type of water, sanitation and hand‐washing facility and the state of their functionality that the CCs self‐reported online.<sup>2</sup> The questions on functionality highlights the value‐added of this survey as this information is not usually captured in surveys. To verify the reliability of this data, CBHC used a validation survey (WaterAid Bangladesh, 2018)<sup>3</sup> , the results of which we compare herein.

> 1 http://facilityregistry.dghs.gov.bd/index.php

> 2 For details, see Annex‐Table 1
adoption: the Kleibergen-Paap statistic is 111.8.

# **5.2 Robustness**

In this section we report the results of several robustness tests to our baseline analysis. A potential concern about the baseline results is that the association between trade and robotization could reflect complementary investments in other forms of technological progress and/or changes in the intensity of material inputs or capital usage. To assess this possibility, in Table 2 we add to the baseline specifications presented in Table 1 controls for ICT capital, non-ICT capital and material inputs. We also control for bilateral import tariffs applied by developed countries. The inclusion of these controls does not appreciably alter the estimated magnitude of the association between robot adoption and trade flows.

Another potential concern is that the baseline estimates might be driven by a select few sectors. To assess whether this is the case, in Table 3 we exclude the automotive, rubber and plastics, and metal industries, the three sectors in which automation is most widespread. If anything, excluding these sectors magnifies the estimated elasticity of trade flows with respect to robot use. Moreover, results remain statistically significant.

Our baseline estimation period includes the great depression of 2008/2009. We therefore worry that our results might be influenced by cyclical variations associated with the crisis. To address this concern, Panel A in Table 4 restricts the sample to the pre-crisis (i.e. pre 2008) period, whereas panel B presents estimates for the post-crisis (i.e. post 2009) period. The results suggest that the estimated impact of robotization on trade was qualitatively similar in the pre- and postcrisis periods. In both cases, the estimates of the relationship between robot adoption and trade suggest that robotization catalyzes trade, but reduces net imports from developing countries.

As an additional robustness check, we use variations over longer time periods (i.e., over 5-year intervals) for identification, instead of the yearly variation we exploit in the baseline analysis. The results are presented in Table A3 in
) Indonesia USA<br>80 80 80<br>60 60 60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 0<br>age age 45 50 55 60 65 70 75 80<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>China (CHNS) Korea UK<br>80 80 80<br>60 60 60<br>40 40<br>40<br>20 20<br>20<br>0 0<br>45 50 55 60 65 70 75 80 45 50 55 60 65 70 75 80 0<br>age age 45 50 55 60 65 70 75 80<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>Lowess Plot bw=0.3<br>Working Hours<br> Working Hours<br>Working Hours<br> Working Hours<br><!-- End of picture text --> Notes: Average hours worked per week by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. The following data sources are used: China: 2009 CHNS and the 2008 CHARLS pilot; Indonesia: 2007 Indonesia Family Life Survey (IFLS); Korea: 2006 Korean Longitudinal Study of Aging (KLoSA); United States: 2008 Health and Retirement Survey (HRS); United Kingdom: 2008/9 English Longitudinal Study of Ageing (ELSA).

32
s financial sector, government interventions to increase access to financial services (including through microfinance), especially for Tanzania‟s rural population, remain largely ineffective.

**Figure 4.1:  Formal and Semi-formal Access13 Lags, Tanzania and Other East African Countries, 2009***

<!-- Start of picture text --> 100%<br>100%<br>80%<br>80%<br>54.0% 56.0% 60%<br>60%<br>40%<br>40% 20%<br>35.0% 27.3%<br>20% 0%<br>4.3%<br>2.0%<br>9.0% 12.4% Kenya Uganda Malawi Rwanda Tanzania Tanzania East<br>0% (2006) (2009) Africa<br>Tanzania (2006) Tanzania (2009)<br>Formal Semi Formal Informal Financially Excluded<br>Formal Semi Formal Informal Financially Excluded<br>Note: *unless otherwise specified<br> Source: FinScope Survey, 2009<br><!-- End of picture text -->

> 13 People with “formal” access include those who hold an account in or have some sort of relationship with a financial institution, such as a commercial bank, community bank, or insurance company that is supervised by a financial services regulator; and also people who use products from formal institutions such as pension fund or hire purchase companies which are not supervised by either the Bank of Tanzania or the Insurance Commissioner. “Semi-formal” access includes people who use products from SACCOs and MFIs which are formally registered, but not supervised by a financial service regulator; and people who use products from institutions offering financial services other than SACCOs and MFIs.  These institutions have a formal character, but are not supervised by a financial service regulator.  This category includes M-Pesa and government loans for housing and
and negative effects of natural resources are larger. This result can be explained by ‘appropriability’ characteristics (i.e. high value, easy storage, easy transportation or smuggling, and quick sale), which make these types of natural resources more prone to rent-seeking behaviour, corruption, and conflict. Easily appropriable point-source resources, such as oil, diamonds, and minerals, are more likely to be harmful to institutional quality and growth than diffuse resources such as agriculture (rice, wheat, and animals), whose rents are spread throughout the economy (Auty 1997; Isham et al. 2005; Mavrotas et al. 2011; Woolcook et al. 2001).

Most of the previous studies are cross-country analyses. A distinctive feature of our paper is that we use panel data on mineral deposits to estimate inequality at district level during 2001– 2012, a period also characterized by relatively high growth in Africa. Gennaioli et al. (2014) argue that there are large regional differences within countries that need to be understood; the use of subnational panel data therefore accounts for intra- and inter-regional differences that can confound estimates. From an estimation standpoint, our study is also free from omitted variable bias emanating from the large unobservable differences that are usually present in cross-country studies.<sup>8</sup>

> 8 Lederman and Maloney (2008) consider cross-country heterogeneity a key reason for the elusiveness of empirical evidence on the resource curse.

6
For households reporting positive medical expenditures, values can be dis-aggregated into three categories of care: inpatient, outpatient and medicine purchases. Panel A.1. of Table 1 also reports descriptive statistics of these outcomes. Medicine purchases were lower after NHIS introduction, and represent 80% of the total medical expenditures among households pre-NHIS and 55% post-NHIS. Outpatient expenditures appear to be larger among households observed after the introduction of the NHIS, possibly as a result of the higher level of care. This difference is small and statistically non-significant. Inpatient expenditures are minimal, as the reported incidence of inpatient care is low in the sample. We opt to omit this variable from our analysis for the same reason.<sup>13</sup> Panel A of Figure 3 displays the OOP medical expenditures (in logarithms) in the months around the introduction of the NHIS. The figure reveals a noticeable drop in OOP payments at the time of NHIS implementation, of similar magnitude when evaluated with a linear fit compared to a kernel-weighted polynomial fit.<sup>14</sup> Our empirical strategy could be underestimating the impact of the introduction of the NHIS if households delay their medical expenditures in expectation to the introduction of the NHIS. If there was anticipation, we might see a dip in the average OOP payments in the months right before the NHIS. Panel A indicates that this does not seem to be the case.

**Health Status.** The GLSS also contains self-reported information on the number of days of illness or injury over the past two weeks. Panel A.2. of Table 1 displays that 53% of the households reported any illness or injury before the NHIS. This share increases to 64% among households observed after the introduction of the NHIS. This may be due to specific reporting behavior because it has been documented that respondents in low- and middle income country settings tend to report their use of medical care instead of their health status (Murray 1996; Bago d’Uva et al.
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Democratic Republic of Congo, where freight often sits for 16 days (World Bank 2010c).<sup>3</sup> In this corridor, transit time accounts for more than 30 percent of the total time required to move imports from ports to landlocked capitals. Along most other corridors, dwell times at ports account for between 40 and 70 percent of the total time, followed by lengthy administrative processes.

**Figure 2.3 Time required to import goods: Road versus rail routes**

# a. Total time (hours)

<!-- Start of picture text --> 1250<br>1000<br>750<br>500<br>250<br>0<br>road rail road rail road rail road rail road rail<br>Port Locomotive Interchange Transport Border Administrative<br>b. By step (% of total time)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>road rail road rail road rail road rail road rail<br>Port Locomotive Interchange Transport Border Administrative<br>Lusaka Durban-<br>Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam- Lubumbashi<br>Lusaka Durban-<br>transport Lubumbashi<br>Durban-Harare Dar es Salaam-<br>along altnernative modes of<br>Shares of time to import goods<br>Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam-Lusaka Durban-Lubumbashi<br>alternative modes of transport Dar es Salaam-Lusaka Durban-Lubumbashi<br>Total time (hours) to import goods via<br><!-- End of picture text -->

# b. By step (% of total time)

_Source:_ Based on data collected from World Bank 2009; AICD ports and railway databases; Nathan Associates 2010; Teravaninthorn and Raballand 2009; and World Bank 2010.

> 3 The baseline for
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE and agricultural resources and are investing heavily in the transport and energy facilities needed to support their operations. Because of Liberia’s policy of ensuring that such investments benefit the broader national economy, they could potentially contribute to meeting the country’s overall infrastructure spending needs.

Despite the potential for private investment, Liberia will likely need more than a decade to reach the illustrative infrastructure targets outlined in this report. Under business-as-usual assumptions for spending and efficiency, it would take at least 40 years for Liberia to reach these goals. Yet with a combination of increased finance, improved efficiency, and cost-reducing innovations, it should be possible to significantly reduce that time.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Liberia. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas—including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Liberia, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Liberia is a low-income state richly endowed with natural resources; therefore, both low-income and resource rich benchmarks will be used to evaluate its performance. Detailed comparisons will also be made with immediate regional neighbors in the Economic Community of West African States (ECOWAS) and other states that have emerged from conflict such as the Democratic Republic of Congo and Rwanda.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2004 to 2009. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to ensure consistent comparisons across countries, the indicators and analysis were standardized
tered.<sup>1</sup> Our final data set includes 8,748 firm observations. However, management practice information for manufacturing firms was collected only if the firm had over 20 employees, which reduced our sample size to about 4,000 firms.<sup>2</sup> The overall or general management score is calculated using eleven questions from the management practice module. These questions map to eight questions from the United States Management and Organizational Practices Survey (U.S. Census Bureau, 2015). They cover four categories of practices: target-setting, monitoring, incentives, and operations. Firms’ responses to questions on management practices are aggregated into an overall management score following Bloom et al. (2019) in two steps. First, the responses to each of the management practice questions are normalized on a 0-1 scale: the response associated with the most structure and prevalence is normalized to one, and the one associated with the least is normalized to zero. Second, the overall management score is calculated as the unweighted average of the individual question scores. We use these scores to compute descriptive statistics. For regression analysis, we take the z-score of the overall management score for ease of interpretation. Summary statistics for management practices and other firm characteristics are shown in Appendix Table A2.

# **3.2 Descriptive Analysis**

In this paper we focus on six outcomes reported in the WBES COVID-19 surveys, including: (i) change in sales (%), (ii) closure (temporary and permanent), (iii) number of employees furloughed or laid off (as a share of total pre-COVID-19 initial level), (iv) partial or full adjustment or conversion in product or service mix (“pivoting”), (v) an increase in online work arrangements, and (vi) probability to fall into arrears.<sup>3</sup> In our descriptive analysis,

> 1We dropped observations for the Turkish Cypriot Community (TCC)
The program has impacts beyond the women in the treatment group. We find evidence of significant, positive spillover effects to women in the control group through business linkages. Interestingly, these spillovers appear to be driven by economic activities between businesses: we find little evidence that business skills also get transmitted from women in the treatment group to women in the control group. The social network data that allows us to measure and adjust for these spillovers provides a novel way for studies limited to individual-level randomization to assess spillovers.

Although our study highlights the importance of skills-based barriers for women’s economic inclusion, the women in our sample are self-selected in contrast to studies on graduation from poverty programs, which typically evaluate on representative samples of ultra-poor women (e.g., Banerjee, Duflo, Goldberg, et al. 2015, Blattman et al. 2016, Bandiera, Burgess, et al. 2017, Banerjee, Duflo, and Sharma 2021). This difference raises important directions for future policy-relevant work. Are the returns to skills lower when self-selection is not permitted, or do self-targeting errors limit returns to skills-based programming? Can the skills-based components of multifaceted programs allow for self-targeting, even if cash and asset transfers require alternative targeting methodologies? Understanding how to maximize the effectiveness of skillsbased interventions is central to building effective and sustainable programs for women’s economic inclusion.

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in a road network is challenging. First, it is difficult (and sometimes impossible) to predict the frequency, severity, and duration of the potential disruptions to which a road segment is exposed. Large data sets and sophisticated modeling are needed to accurately estimate the probability of an extreme event such as extreme rainfall or earthquake to occur in a particular place. In addition, climate change adds considerable uncertainty to the magnitude of mean and extreme changes in precipitation that may occur in a country or region. This uncertainty is even more pronounced at the fine scale needed for road projects – since downscaling climate data tends to amplify rather than reduce uncertainty.

Moreover, the economic impact of natural disasters on a road depends on many local variables that are sometimes unknown to analysts. The impact worsens if the road is not well maintained, if deforestation makes the terrain prone to landslides, or if emergency responses are not dispatched in a timely fashion to make the road operational again in a short time. Whether a critical road can be repaired in a few weeks or months can dramatically change the economic consequences of a disruption, as well as the type of interventions required.

Finally, the economic impact of a disruption in a road network is exacerbated if the road is critical to an economic sector, to a certain community, or to the entire transportation network. Traditionally, economic 2
Firms (%)<br>10<br>0<br><!-- End of picture text -->

**Section 3: Critical Firm Capabilities: Management in Focus**

# _3.1 Why do we need a management survey?_

Although managerial and organizational practices have been conceived to be critical in affecting firm outcomes since the time of Adam Smith’s pin factory and Walker’s 1887 paper on business performance, these practices are not well measured in administrative data on firms or the commercial data on companies’ accounts. Some studies have used

> 10 The lowest participation rate was observed in Dalmatia (47 percent).

11