rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Introduction**

Natural hazards and the damages caused by them lead to headlines in the news now more than ever. Part of the reason for the increased reporting is due to the focus on climate, but it is also caused by larger economic damages than in prior years. The latter point is due to the growing global population and the increasing economic value in settled places. With this value increase comes a need for updated local data on where people live and the economic activity there. This is particularly true in the wake of a devastating natural hazard, when it can be difficult to assess damages quickly and accurately.

A recent method to assess damages and economic activity is to use remotely sensed data. As a proxy for economic activity one has seen widespread use of nightlights (Henderson, Storeygard and Weil 2012) (Gillespie, et al. 2014) (Skoufias, Strobl and Tveit 2017) (Hodler and Raschky 2014) (Michalopoulos and Papaioannou 2014) (World Bank 2017). The modeling and measurement of natural hazards has also been extensively covered in the literature for different disaster types such as earthquakes (De Groeve, et al. 2008), typhoons (Emanuel 2011) and floods (Wu, Adler and Tian, et al. 2014) to name a few.

The aim of this paper is to provide a refined model of a previous study (Skoufias, Strobl and Tveit 2017) where nightlights were used as a proxy for economic activity and then combined with damage indices to find the economic impact. The novelty is that a new, more spatially and temporally detailed data set for nightlights, the Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA), following the work of Elvidge et al. (2017), is employed. In addition, settlement layers have been used to remove nightlight cells with zero population to reduce the impact that “empty” cells could have on the analysis.

Five countries in Southeast Asia have been chosen as
of the Kilian index. Moreover, I multiply the estimated global GDP factor with the average factor loading in the subsequent analysis. The resulting measure is a moderated world business cycle relative to the world industrial production index. Consequently, the estimates of the impact of U.S. energy shocks on global economic activity will be more conservative.

> 21The variation in each observed variable can be decomposed as _var_ ( _yi,t_ ) = ( _βiGlobal_ )<sup>2</sup> _var_ ( _ft_<sup>_Global_</sup> )+ _var_ ( _εi,t_ ). Thus, the share of variance due to the global factor is given as ( _βi_<sup>_Global_</sup> _var_ )<sup>2</sup> _var_ ( _yi,t_ ( _f_ ) _<u>t</u>_<sup>_Global_</sup> ) _._

> 22Note that IRFs are reported without parameter estimation uncertainty. The median, 16 _th_ , and 84 _th_ percentiles impulse responses are reported from the admissible models. The results, thus, summarize the set of admissible models and focus on identification uncertainty (i.e., value of **B0** ), which is due to inequality restrictions.

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to the conflict. To identify these events and their geographical locations, I used the main Tajik newspapers for 1991-1999, and books and reports about the conflict. The conflict data are given at the raion (district) level as most of the sources referred to raions or groups of raions when discussing events related to the conflict.

The number of incidents reported in newspapers is incomplete by nature, as newspapers may be more likely to report events that occur in major cities, places close to the place of the publication or localities that are easier to access. For example, the two central newspapers published in Dushanbe that I surveyed, reported 124 events related to conflict between 1992 and 1999 for the capital city Dushanbe, while the raions of Gharm (Rashtr) and Tavildara that were heavily affected by the war and often occupied by opposition forces were mentioned only 18 and 19 times respectively. Kolhozabad that was a place of major battle and changed hands in the course of the war six times was mentioned only five times. Thus, my preferred measure of conflict activity is a dummy variable (“Reports of Conflict Activity”<sup>12</sup> ) that is equal to “1” if a raion experienced high exposure to the conflict defined as repeated mentions of the raion with respect to fighting, economic damage, insecurity, presence of military groups and attacks on civilians or military personnel. The raion is assigned a measure of “0” is an exposure was lower. Since the conflict affected most of the country in some way, this measure is likely to lead to underestimation of the true impact of the war on the variables of interest. Several qualitative accounts on Tajikistan (Tadjbakhsh, 1996; Gomart, 2003) mention that conflict first started in areas that were a subject to Soviet forced resettlement policies where population was brought into valleys from the mountains to increase available labor. Since the areas with more resettlement were more heterogeneous, they suffered from a higher level of conflict argues Tadjbakhsh (1996). Many resettled
with the strongest competition for UK manufacturers coming from producers in the EU and US. Freeman and Revenga (1999) report a similar result for Europe, Gaston and Trefler (1997) found significant employment responses to import competition in some sectors in Canada, and Gourinchas (1999a, b) found a significant effect of exchange rate fluctuations on movements of jobs across and within sectors in France, using firm-level job creation and destruction data. In the case of the US, Bernard and Jensen (1999b) find that intra-industry reallocations to higher productivity exporters explains up to 20 percent of productivity growth in US manufacturing. For developing countries, among other studies, Aw, Chung and Roberts (2000) find that exposure to trade forces the exit of the least efficient producers in Korea and Taiwan, while Pavcnik (2002) finds that market share reallocations contributed significantly to productivity growth following trade liberalization in Chile.

# **5.  EXPORTS, INTERMEDIATES, FDI AND GLOBAL PRODUCTION SHARING**

Research that focuses on the differential role of exports as opposed to imports as a source of labor market effects concludes that exports tend to positively and imports negatively affect labor employed in the sectors concerned. Thus Davidson and Matusz (2003) find higher sectoral net exports to be associated with less job destruction and more job creation. Harrison and Hanson (1999), find that trade reforms result in employment expansion in export sectors/firms in Mexico, and Milner and Wright (1998) find the same for Mauritius. None of this is surprising of course, but it is important to bear in mind that greater imports have to be paid for, thus requiring and inducing output and employment in export sectors. More interesting is the relative effects on different types of labor.

Exporters in an industry tend to be more productive than other plants. This finding is by now very well established—e.g., Clerides, Lach and Tybout (1998), Bernard and Jensen (1999a) and Aw, Chung and Roberts (2000). One
000176|-0.000330||-0.000319|-0.000285|||||
||(0.000461)||(0.000459)|(0.000462)|(0.000237)||(0.000238)|(0.000253)|||||
|Share of hhlds with computer||0.282|0.281|0.282||0.147|0.147|0.151|||||
|||(0.201)|(0.201)|(0.203)||(0.117)|(0.116)|(0.117)|||||
|IDN exports per worker *||||-0.000337||||-0.000796|||||
|Share of hhlds with computer||||(0.00185)||||(0.00133)|||||
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|||||
|R-squared|0.942|0.942|0.942|0.942|0.773|0.774|0.773|0.773|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, Philippines STEP survey 2015-2016, and US Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. High-educated workers: workers with at least complete secondary education. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the
<!-- Start of picture text --> a) Correlation of RISE Renewable Energy and Perceived Grid Readiness from Survey Data<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>o 80 @ Ghana South Africa<br>8<br>nw 7/0<br>> R? = 0.1268<br>><br>@ 60<br>Ww » , 8 eo. @ Kenya<br>o Cote d'Ivoire ‘Malawi 8 Uganda -_-"7 -<br>we}FA© 50 Cameroon; Ethiopia fwand\ Zambia - ~- -~-<br>5 Zimbabwe @ a <7”<br>a& 40 NigerDRCOO BiersTogo (sone«= _ Le -- Tanzania/<br>o 3 [Burundi » §— Fritrea @ Nigeria<br>ty Soc<br>MO r) Angola” CON8° \iozambique<br>a 20 liberia ChagudanGuinea @ Madagascar @ Mali<br>Mauritania<br>10 Central African Republic<br>@ South Sudan<br>0 @ Somalia<br>1.0 0.5 00 05 10 15 2.0 25 30 35 40 45 50 5.5 6.0<br><!-- End of picture text --> <!-- Start of picture text --> Perceived Grid Readiness (Generation, Distribution, Transmission) (Net Positive Score)<br><!-- End of picture text --> b) Correlation of RISE Off-Grid and Perceived Off-Grid Readiness from Survey Data <!-- Start of picture text --> 90<br>80 Ethiopia<br>South Africa Ne e<br>o $ ; @ Kenya @ Uganda R2 =0.11<br>fa Rwanda C<br>re) 70 ameroon » --<br>
The range of tasks that may be performed by robots varies across sectors. The industry-specific robotization frontier, relative factor prices and productivity determine the extent of robot use within sectors. Production of each final good variety further requires a composite intermediate good from the same industry. In equilibrium, varieties of intermediate inputs and final goods are sourced from the country that supplies at the lowest price. Thus, there are two layers of competition: (1) between robots and workers in factor markets; and (2) between countries in sector-specific product markets for inputs and outputs. Relative production costs (driven by factor prices and technology) determine country-specific robotization and trade patterns.

A fall in the price of industrial robots initially induces robotization in the North, where the cost of labor is relatively higher to begin with. This shift impacts relative production costs between countries, and therefore trade patterns. Robots replace domestic labor within sectors, leading to lower costs of production in the North, and hence to an increase in exports to the South. The effect of Northern robotization on imports sourced from the South in the same sector is theoretically ambiguous. On the one hand, robotization makes Northern producers more competitive, which implies that some varieties that were previously imported from the South are now sourced domestically. On the other hand, robotization leads to an expansion in the scale of production, which raises the demand for inputs sourced from the South.

Simple OLS regressions using country-industry panel data on trade and industrial robots over the last two decades—including country-pair-year and industry-year fixed effects—point to a robust positive relationship between robot intensity in own production and imports sourced from less developed countries. They also reveal an even stronger association with exports to these countries. Robotization thus leads to a significant reduction in net imports from less developed countries within the same sector. However, there are reasons to be concerned that these relationships reflect various forms of endogeneity. Robotization may in part be a response to changing trade patterns—for instance, trade may alter the incentives of
.5|
|Open defecation|% pop|23.1|23|24|38.3|
|<br>|<br>|||2007<br>||
|Domestic water consumption|liter/capita/day|29.6||40|50.9|
|Revenue collection|% sales|94.6||77|94.1|
|Distribution losses|% production|31.5||40|34.8|
|Cost recovery|% total costs|57.2||61.4|89.5|
|Operating Cost recovery|% operating costs|80.1||85.8|125.2|
|Total hidden costs as % of revenue|%|169||297|67|
|||Sierra Leone<br>2007|Countries wi<br>scarce w<br>resourc|th non-<br>ater<br>es<br>Oth|er developing<br>regions|
|Average effective tariff|U.S. cents per m3|22||80|3.0 – 60.0|

_Source_ : AICD water supply and sanitation database downloadable from **http://www.infrastructureafrica.org/aicd/tools/data** .

Access figures calculated by AICD using data from the 2000 Multiple Indicators Cluster Survey as published by the JMP in March 2010 and the 2008 Demographic and Health Survey Note: A country is considered non- scarce water resources is the renewable internal freshwater resources per capita is greater than 3,000 mm Domestic water consumption, revenue collection, distributional losses, cost recovery, and total hidden costs only reflect the values for GWVC as there is not data available for SOWALCO — = data not available.

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grids displaying within grid elevation differences of more than 1,000 meters. To exploit this largely neglected insight (Dubois and Shilbi, 2003), an elevation difference weighted rainfall index based on the ten nearest stations is also constructed.<sup>12</sup> The relative importance of both the IDW and IEW indices in determining rainfall in a grid is subsequently estimated through an ordinary least square regression of the log of the observed monthly rainfall patterns in the 166 stations during 1980‐2006 on the log of their IDW and the log of their IEW index, monthly dummies and their interactions to allow for monthly variations in the better than this for IDW (6.3 mm), which is negligible compared to the average rainfall of 18.51 mm among the 367 omitted data points for which rainfall was interpolated from the remaining 100 rainfall stations. 9 The average distance between rainfall stations is 32 km.

10 While the satellite based rainfall maps produced by CPC/NOAA could also be used in principle, they only dated back to 2001, preventing us from constructing disaggregated historical rainfall patterns, necessary to explore patterns of adaptation. There is an additional concern as the satellite based CPC / NOAA data for Vietnam appear calibrated on a rather small number of rainfall stations (10 or so) from within Vietnam.

> 11 � ����� To be precise, ���������∑��� ∑�� ����� �� with r(x) the inverse distance weighted rainfall at interpolated point x, based on observed rainfall values rk at k=0,1, ..., N known interpolating (known) points xk, with � ������ ���,���<sup>�</sup> as weight, x _k_ an interpolating (known) point, d a given (Euclidean) distance from the known point xk to the unknown point x and p a positive real number
domestic servant, ownership of agricultural land proxied using whether any member of a household has undertaken any farming activity on its household’s land in the last seven days, ownership of 14 different assets (radio, TV, telephone, refrigerator, electric stove, and so forth), main source of drinking water (piped into dwelling, piped into yard/plot, public tap/standpipe, tube well, and so forth), toilet facility type (flush toilet, ventilated pit latrine, composting toilet, and so forth) and whether the facility is private or shared, and main cooking fuel type (wood, charcoal, crop residue/leaves, and so forth). I also include the main construction material in the household’s main dwelling’s floor (mud/dung, bamboo/reed, wood planks, and so forth), walls (wood and mud, wood and thatch, wood only, and so forth), and roof (corrugated iron sheet, cement/concrete, thatch, and so forth) in the construction of the score.

I further construct and use two alternative measures of household poverty status from the two measures of welfare. Specifically, I use household sample weights, household size, and each of the measures of welfare all from the ESS2<sup>17</sup> to construct the consumption or wealth poverty line as the value of consumption expenditure per capita or wealth score, respectively, on the 40th percentile of the distribution in the population of individuals.<sup>18</sup> Then, the two measures are constructed as binary variables indicating whether the round-specific value of a household’s consumption expenditure per capita or wealth score is at most as large as the value defining the respective poverty line.

Additionally, I use five individual-level variables reflecting salaried labor market engagement in the 12 months immediately before the data collection in each round of the survey. Two of these are binary variables indicating whether an individual has had permanent or temporary salaried employment
the crisis. Allowing for differential productivity-survival dynamics for entrants does not reduce the attenuation effect. Thus, the attenuation effect documented in this paper is not simply an artifact of reduced entry.

# _6.2.4 Connectedness and Creative Destruction_

Another possible explanation of the attenuation effect is that the fall of Suharto may have hurt firms affiliated with the Suharto regime disproportionately (see Fisman, 2001, and Mobaraq and Purbasari, 2008). If these firms were highly productive, their exit may result in an attenuation of the link between productivity and survival. Table 10 examines this possibility. The data on political connectedness are available for firms in 1997, constructed using JSX and board membership information from 1995-1997. Thus, to test the role of political connections, we look at survival patterns from 1997 to 2001, controlling too for factors in our baseline specifications presented in Table 3.<sup>14</sup> Also note that relatively few firms are identified as being politically connected to Suharto; 210 firms that are connected based on the stock market response to, and 97 firms have a Suharto family member on their Boards, which may make it hard to estimate the effects with precision. Row A uses the connectedness measure that is based on the response of the Jakarta Stock Exchange to news about Suharto‟s health and the business networks of those adversely affected by this news, while Row B uses as a proxy for connections whether a firm had a Suharto family member on its board. Row C combines these two definitions. The baseline regressions presented in columns 1 and 3 merely control for connectedness, while the extended specifications presented in columns 2 and 4 additionally include interactions between connectedness and productivity.

The baseline regressions demonstrate that firms with political connections to Suharto were not, on average, more likely to exit over the period 1997-2001 ceteris paribus. However, the extended models suggest that, amongst firms with connections to the Suharto regime, the least

> 14  Since the proxy for connectedness is in part based on stock market reactions over
Indonesia (March 1980) and Egypt (1981), are highly comprehensive and show great depth of analysis. Both these reports also provide a good secondary analysis of survey data.

Most sector reports are strong in demographic and population analysis but weak in analyzing program effectiveness issues, especially those which are salient at the program-client interface level. Typically, sector reviews do not go beyond description of the service delivery and IEC system and preliminary assessment of the management structure at the central government level. Since management issues are likely to be sensitive and sector reports need government clearance, the reluctarce to deal with certain issues is understandable, but since chese issues lie at the heart of effective program and project development, it is important that a mechanism be found to address them. The Mauritius (1983) and Egypt (1985) reports are exceptions, as they focus on management aspects in some detail.

The coverage and depth of sector reports vary considerably. Basic demographic analysis is handled with reasonable consistency, but the discussion of program management, other government departments and NGOs does not conform to any se,pattern. It seems that the composition of the review team may account for.some of the variance in scope and auality.

# 4. Impact of Sector Work

It is very difficult to assess the impact of sector work on Bank activities and the development of population policies. The Health Sector Review concluded that sector work had had a considerable impact in terms of both these criteria. Our interviews also brought out that the operations staff find the sector work useful in project development and policy dialogue.

We have already discussed sector work in relation to policy dialogue in the previous section. We found that sector reports have been an important input in policy dialogue in Africa and Latin America. It is not clear that the same holds for some of the mature programs in India, Indonesia, Egypt, Pakistan and Bangladesh. In fact, it should be mentioned that sector work regarding mature programs hai been extremely limited.

At the aggregate level, there seems
# 1. Introduction

<mark>In April 2018, large parts of Tanzania were affected by severe flooding. Especially in the country’s commercial capital of Dar es Salaam, flooding caused the loss of lives and widespread damages and disruption. By one estimate, the flooding in Dar es Salaam affected between 900,000 and 1.7 million people, either directly or indirectly (Erman et al. 2019). These resulting economic losses to the population were equivalent to 4 percent of the city’s GDP. On average, affected households lost 23 percent of their annual income. And these economic figures do not reflect some of the hidden consequences of disasters, including impacts on people’s health and children’s educational attainment.</mark> <mark>Yet, the 2018 floods were by no means an isolated incident. Changing precipitation patterns, urban expansion into high-risk flood zones, and a lack of effective drainage infrastructure are all contributing to frequent and intense flood events. Recurring floods also mean that firms and households are constantly recovering from and bracing for flooding, which in turn affects their livelihoods and socio-economic prospects. Impacts on public services and infrastructure systems can also have substantial repercussions on those that rely on these services. For instance, this study shows that Tanzania’s power outages increase from an average of 18 hours per month in the dry season to 57 hours in the rainy season – with substantial knock-on effects on household well-being and firm productivity.</mark> <mark>Much progress has been made in developing our understanding of the impacts of natural shocks on households—not least through rich and dedicated household surveys that shed light on the drivers of exposure, vulnerability, and resilience (Erman et al 2019). However, firm surveys that explore</mark> the <mark>business costs of natural hazards and infrastructure disruptions are rare in most developing countries, and virtually nonexistent for Tanzania. As a result, there is only limited understanding of how firms are affected by and
# **Figure 1. Relative size of Telework, Essential and Non face-to-face jobs in EU-LFS**

<!-- Start of picture text --> Telework 6% Essential<br>12% 17%<br>6%<br>14% 9%<br>17%<br>19%<br>No face-to-face<br>Total<br>employment<br><!-- End of picture text --> Source: Own elaboration based on EU-LFS data (2018) and O*NET surveys (2019).

There is a strong and positive correlation between the relative intensity of communication and manual tasks required in occupations and the share of teleworkable jobs (our Measure #2) at the three-digit ISCO level (Figure 2). The Pearson correlation coefficient is 0.87 and statistically significant at the 1% level. As the O*NET survey shows, jobs classified as teleworkable also tend to require a higher intensity of communication-oriented tasks. The correlation between incomesafe jobs (teleworkable _or_ essential) and the relative share of communication to manual tasks is also positive, although slightly weaker than for telework jobs alone.<sup>12</sup> This is due to the fact that certain essential jobs, such as low-skilled delivery jobs, require fewer communication tasks. Similarly, there is also a positive correlation<sup>13</sup> between health-safe jobs and the relative share of communication tasks.

> 12 The correlation is 0.45 but still statistically significant at the 1% level.

> 13 This correlation coefficient is 0.31 and again significant at the 1% level.

13