rafmacalaba/data-use-mentions-extended huggingface dataset:
||||||
|Basic metals|1.202|0.198|0.165|0.751|0.042|0.056|
|Fabricated metal prods.|1.097|0.184|0.168|0.666|0.194|0.291|
|Machinery and|0.991|0.225|0.227|0.700|0.199|0.284|
|equipment|||||||
|Electrical machinery|1.102|0.230|0.209|0.558|0.223|0.400|
|Furniture|0.877|0.307|0.350|0.709|0.146|0.206|

Notes. Table 1 presents the production function elasticities from estimating a value-added log-linearized Cobb-Douglas production function following De Loecker (2013). In this paper, value-added is a function of labor and capital. The estimating method is based on the Control Function approach by Ackerberg, Caves, and Frazer (2015). However, it departures from the latter by assuming an endogenous Markovian productivity process, which is a function of learning by exporting. WBES data covers a sample of 7,916 manufacturing enterprises from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data fromDe Loecker (2013) come from the Slovenian Central Statistical Office. The correlation coefficient between the K-to-L estimated ratio using the WBES and De Loecker (2013) database is 0.55. It is also statistically significant at the 5 percent level.

Using the production function elasticities from Table 1, we calculate sector-specific factor intensities, defined as the capital-to-labor PF elasticity ratio, and examine the pairwise correlations between the results obtained using the WBES database and those from De Loecker (2013). We found
12

# Discussion

The severity of COVID-19 disease caused by SARS-CoV-2 is highly variable with some individuals and populations being more vulnerable to complications than others. Using information on known factors for probability of severe disease, and mapping it to available data on such factors from the DHS, here we estimate and map this variation in probability of severe disease across Zimbabwe. Results show that the probability of severe disease varies substantially with some districts having a probability of severe disease nearly 30% higher than others. Furthermore, we show that there is a relationship between handwashing and respiratory illness, a potential risk factor for severe disease, and that improving access to handwashing across the country could reduce the probability of severe disease.

From a policy perspective, this study highlights a potential added benefit of improvements to WASH facilities in addition to the already known numerous other benefits improved WASH brings. Hand washing is also an important intervention for gastro-intestinal and worm infections <mark>(Ejemot et al., 2015; Bieri et al., 2013; Aiello et al., 2008; Ejemot et al., 2008; Rabie & Curtis, 2006), in addition to the relationships highlighted here for reducing transmission of respiratory infections. Taken together, this study adds another perspective on why handwashing access, particularly in developing countries, can continue to be considered as an important tool in the fight against COVID-19. Other studies have also shown it to be highly cost-effective in prevention of the spread of the virus (</mark> Bagepally _et al._ , 2021 <mark>), an important consideration in light of the considerable economic and societal costs of the pandemic, and associated restrictions (</mark> World Bank, 2020; Vagliasindi, 2021 <mark>). Indeed, investments in WASH are likely to lead to substantial economic returns in the absence of COVID-19. The pandemic should make this investment all the more urgent.</mark> While the link between handwashing and probability of severe COVID-19 is plausible, there are a number of
PAY FOR COST AND IMPACT INFORMATION**

Our online survey of more than 400 individuals involved in the policymaking process in developing nations presented a hypothetical scenario in which an unspecified education program had generated test score improvements of 0–20 percent in other countries. Respondents were told that they had an opportunity to implement the program for up to 10,000 students in their country, but that they could also choose to do an impact evaluation to learn of the program’s true effects in their setting.

Respondents were asked how many of the 10,000 students they would be willing to have not receive the program in order to be able to pay for the impact evaluation. A similar question was then asked about the number of beneficiaries they would forgo in order to do a Costing study.

Out of concern for framing effects, the order of the first and second of these questions—on impacts and Costs alone—was randomized. Simple t-tests revealed that order did not have an effect on responses for willingness to pay for either Effectiveness or Cost studies.

12
for European exporters.<sup>1</sup> Since 2009, trade liberalization on the Serbian side has been following a gradual and predictable liberalization schedule and by 2013, most EU imports enter tariff-free. The EU is a main market for inputs for Serbian firms, and its importance is further amplified as European foreign direct investment locates in Serbia to produce for the European market, especially after this trade agreement came into force. One key example is the decision of Fiat, an Italian car manufacturer, to locate its worldwide production of the car model _Fiat500_ in Serbia in 2010, which requires them to import components from many countries. Serbia also signed another trade agreement with its neighbors in the Western Balkans regions in 2006 but these markets are less important as input sources.

We show that Serbian importers are increasing in their intensive and extensive margins between 2006 and 2015, using data on Serbian firms from the Business Registries Agency (BRA) combined with data provided by the Serbian Customs Administration. Table 1 presents the total value of Serbian imports between 2006 and 2015 as well as the average imports of a firm-origin country pair and the total number of firm-origin country pairs. Over the period, all three measures are increasing, which suggests that the increase in total imports is due to both increases in the intensive margin (value of firms’ imports from each of their source countries) and extensive margin (number of countries firms source from). Serbian firms are increasingly making sourcing decisions that cross international borders. The sourcing choice of the firm is significant because the basket of inputs and sources it chooses impact production costs, product quality, and subsequently the competitiveness of the firm. Both margins are incorporated in our empirical framework, which we describe in detail below. The increases in the intensive margin are incorporated into firm learning (with a lag, as in Fernandes and Tang (2014)) because changes in other firms’ imports from a source act as a signal to the non-importers about the potential savings and quality of inputs from
.

We apply the double Median Absolute Deviation method for outlier detection and correction to all constructed continuous variables in this analysis, that is, harvest quantity, storage and sales quantity, total, family, and hired labor person-days, organic and inorganic fertilizer quantity applied (Leys et al., 2013).

# Determining recall length

The length of the recall period cannot be determined exactly in the survey data for two reasons. On the hand, we do not observe directly when farmers engage in the activities of interest (planting, fertilizing, working, harvesting, etc.). Instead, some information about the timing of the activities can be retrieved from farmer-reported dates and the cropping calendar. On the other hand, farmers engage in these activities over extended time periods, so that strictly no single recall length exists. We therefore proxy the length of the recall period for each outcome of interest.

During the interview, farmers are prompted to list all plots used for crop cultivation since the beginning of the agricultural season, so that this proxy for recall length seems an appropriate choice. The beginning of the planting season is determined by each country’s cropping calendar and is identical for all households in each data set.

First, for harvest (and storage and sales) quantity, we use as recall length the distance in months between the interview date and the end of the harvest period. In all three surveys, farmers are asked to report harvest dates and this information is used to construct this second recall length variable. Harvest dates are likely recorded with error, being subject to memory decay over time in the same way as the outcome variables. To address this concern, we use the mode of harvest end dates at the cluster (enumeration area) level, rather than each household’s individual maize harvest date, provided that there are at least 10 observations per cluster. Where there are fewer than 10 observations per cluster, the variable is instead based on the next 8
# **6. Conclusions**

Using three waves of Business Pulse Survey data collected in South Asia during the COVID19 crisis, we disentangle the impact of the shock on informal firms, their adjustment response, and the reasons underlying their fragility. We construct a measure of informality that captures its multidimensionality. In our analysis, we also incorporate the various views characterizing informality by comparing the pre-pandemic productivity distribution of these firms – necessity, De Soto, and parasite firms – with that of formal firms. These features allow us to test the heterogeneity in impact, recovery, and response to the pandemic by the degree of informality (our composite index) and the type or motivation for informality.

Before delving into analyzing the impact of the crisis, we compare the stylized facts on informality using our survey data. In line with the existing literature, our data confirm that informal firms, as defined by our composite index, tend to be smaller in size, relatively younger, and more prevalent in sectors with low entry costs. Additionally, informal firms have lower labor productivity, possibly driven by poor management capabilities and digital readiness. Such lack of capabilities potentially makes informal firms more vulnerable to shocks even after accounting for external attributes. That is, informality by itself leads to worse outcomes and, in particular, for firms driven by necessity that remain subsistence. These factors also limit the adjustment response to shocks. For instance, the gap between formal and informal in terms of digital adoption is not only salient prior to the pandemic but also widening.

Given our finding that size cannot proxy for informality due to the multi-dimensional nature of the problem, we propose considering the following aspects in supporting the private sector in the context of informality: : _Targeting – Who to support_ In general, sector or firm selection can be distortionary; however, during shocks the efficiency of instruments can be most effective for the industry or group of firms that are most affected. Based on our analysis, we suggest that considering the severity and type of informality is
purchased (kg) is negative across the same set of countries, but only significant in Tanzania (see Table A21).

# **4. Discussion and Policy Implications**

During the 2020-2024 period multiple factors were in play, and farmers faced unusually high prices as a result. Using nationally representative, longitudinal, and cross-country microdata from household surveys in six Sub-Saharan African countries, we unpack the implications of the price shocks on farmers, and the heterogeneous strategies employed by households with respect to inorganic fertilizer use.

The share of farming households utilizing inorganic fertilizer does not appear to have been harshly dampened during this period, with statistically significant negative time trends in Burkina Faso and Malawi but with moderate magnitudes, a finding potentially contrary to expectation. Though incidence of use was relatively stable, those that did not apply fertilizer were on the poorer end of the wealth distribution and reported affordability as a key barrier to adoption, potentially hinting at longer-run issues of input affordability beyond this crisis period.

While we do not find large changes in fertilizer use at the extensive margin, important insights emerge with respect to the intensive margin. A significant share of fertilizer-using households, up to 70 percent in Malawi and more than 45 percent in Burkina Faso, Ethiopia, Malawi, Nigeria, and Tanzania, reported a shortfall in the quantity of fertilizer acquired. These shortfalls were predominantly due to affordability as opposed to availability-related constraints, and tended to affect poorer households more than those at the upper end of the wealth distribution. In light of price shocks and related fertilizer shortfalls, farming households employed a variety of coping strategies. Some farmers reduced the intensity with which they applied fertilizer (i.e., reduced the application rate) while others reduced the area in which it was applied. Some households, in the face of price shocks, sold assets or borrowed money from friends and relatives to purchase the input. These strategies threaten to reduce agricultural productivity, while also indebting households and/or depleting their savings.

Many farming households, in the
than 100,000. West Bengal departs from the norm with a higher proportion of CTs, which are located close to a larger city, especially Kolkata. However, a closer look at the location of the new CTs and the analysis that has already been conducted in West Bengal indicate a shift in the spatial distribution of urban settlements in the state (Chatterjee, 2014). We also classify CTs by contiguity of built-up area, which we call settlement agglomerations (SA) (Denis and Marius-Gnanou, 2011). The results, as shown in Table 3, differ slightly between the two approaches.

**Table 3: Census Towns close to Large Urban Areas by Number and Population**

||Bihar|Jharkhand|Odisha|West Bengal|All India|
|---|---|---|---|---|---|
|**Proximity meas**|**ured by distance**|**buffer**||||
|Number|38.3|34.6|11.2|48.7|41.0|
|Population|37.6|44.6|13.8|52.5|49.0|
|**Proximity meas**|**ured by contiguit**|**y of built up area**||||
|Number|31.7|13.3|3.4|38.1|37.3|
|Population|29.6|32.7|6.2|42.0|51.4|

**Note** : Large Urban Areas are those with more than 100,000 in population

Source: Authors’ analysis As indicated in the discussion below on specific case studies, the study includes CTs that are part of an agglomeration, close to larger towns, as well as CTs that are stand-alone and those that are part of agglomerations with other
Policy Research Working Paper 8603

# **Abstract**

This paper explores the link between exports and the demand for skilled tasks. Using the Chilean _Encuesta Nacional Industrial Anual_ (ENIA), an annual census of manufacturing firms, the analysis first shows that Chilean exporters utilize more skills than Chilean non-exporters. More importantly, there is a distinct pattern of task differentiation among exporters both within skilled and unskilled tasks. Exporting firms demand the services of skilled specialized workers (engineers) as opposed to skilled administrative workers and managers. In addition, exporters demand less unskilled labor, especially blue-collar operatives. This suggests that exporters substitute skilled engineers for unskilled blue-collar workers to perform export-related tasks.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at dlederman@worldbank.org and guido.g.porto@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
affected Dar es Salaam in April 2018 was not a particularly severe event in terms of casualties and did not receive much attention outside Tanzania. Nonetheless, it affected 38% of the population and cost households an estimated US$110 to US$228 million, representing about 2-4% of the city’s GDP.

On average, affected households lost about 23% of their annual income due to the flood in April 2018. Asset losses represent 77% of total losses. The second largest source of losses were housing repairs. Consequently, to understand the impact of floods on households, it is necessary to assess asset losses and not only housing damage, highlighting potential flaws in the traditional modeling of disaster losses.

Losses caused by indirect impacts, including health and labor, are monetarily small but have large effects on well-being. A conclusion from the focus group discussions with affected households is that the welfare effects of illness and labor are not well-captured in monetary terms. For households living in areas affected by flood risk, health impacts tend to be highlighted as the worst, and missing work is associated with increased stress and fear of losing current and future income. Women are more likely than men to miss work due to floods. No support from the government is available to the households to deal with these losses that for many households happen repeatedly.

Informal support and financing are helping households to cope with the costs of floods – but it is not enough. Poorer households are less likely to have recovered from being affected by floods. However, access to informal sources of finance, including community saving groups, seems to help households recover. While community saving groups are a positive tool to assist households with recovery, the instrument is not ideal for dealing with aggregate shocks. When many members are affected at the same time, it puts strains on the

> 36 PreventionWeb, accessed 19 February 2019.

29
using the Washington Group Short Set on Disability.<sup>11</sup> Sharing a home is common, likely as an economic coping mechanism, with on average of 1.3 families living in the same house. Housing quality is low, with most houses in some state of disrepair or incomplete construction.

Table 1 also illustrates descriptive statistics for the treatment versus control households to assess balance across groups, where the third column presents a mean difference test. Respondents across the study arms are largely balanced by age (with an average age of 34 years), marital status (84% married), and incidence of a disability (Panel A). There is a slight imbalance in the share of respondents who are female, with the proportion slightly higher in the control group. Moreover, average household size is just above five members, with nearly three children on average, and these are balanced between treatment and control, as is the number of families per residential unit (Panel B). Refugee households in the sample face challenging and precarious housing conditions, but these characteristics are generally balanced across groups (Panel C). For example, only 66% of households have access to piped water, 22% have functional windows, and 44% completed floors. While the majority of refugees plans to stay in the same housing unit (92%), a large share have moved shelter in the last year (with an average of 0.44 and 0.47 moves in the control and treatment groups, respectively).

Across all baseline characteristics presented in the table, the p-value on the hypothesis that all differences are zero is 0.102 (using a Chi-squared test), not quite significant at traditional confidence levels. This finding together with the fact that there are few economically meaningful differences across treatment arms in terms of respondent, household and shelter characteristics – out of the 18 covariates examined only two exhibit a statistically significant difference at 95% confidence – indicates that the study randomization generated largely comparable
# **Supplementary Information**

# **SI-1 Supplementary text**

## **SI-1.1 Country level correlations**

To provide context for the relationship between wealth and pollution globally we draw on data from the World Bank to estimate the country-level correlation across the entire world The general intuition that pollution is higher in lower income countries is borne out by the country-level data. Two results stand out: first, as has been well documented, countrylevel average pollution is lower on average in higher income countries (Figure SI-1a). Only two countries with a per capita income above $50,000 have average pollution levels above 20 _μg/m_<sup>3</sup> (Macau and Qatar). Countries with low levels of per capita income (less than $15,000) span the full range of pollution levels but the majority have pollution concentrations above 25 _μg/m_<sup>3</sup> . Simple cross-country regression confirms this correlation, every $10,000 increase in per capita income, is associated with a decline in pollution of 1.8 _μg/m_<sup>3</sup> ( _t-stat_ : 4.3).

Second, consistent with the negative relationship between income and pollution the most polluted countries in the world are concentrated in Central and West Africa, the Middle East, and South Asia (Figure SI-1b-c). With the exception of the Middle East, these are among the lowest income regions in the world. Of the four countries that are notable outliers with pollution levels higher than would be expected given their income levels (labeled in Figure SI-1a), three are highly urbanized city states (The United Arab Emirates, Macau, and Monaco). The fourth, Qatar, has a significant portion of air pollution coming from natural pollution sources like dust storms.

## **SI-1.2 Heterogeniety by per capita income and pollution source**

The spatial pattern of ambient particulate pollution, and hence its correlation with wealth
**Table 7.** Knowledge: Network Spillover and Reinforcing Interaction Effects

||**Knowledge of**<br>**symptoms and causes**<br>**of STIs subindex**|**Sexual violence**<br>**knowledge**<br>**subindex**|**Prevention of STI**<br>**knowledge**<br>**subindex**|**Pregnancy**<br>**prevention**<br>**knowledge subindex**|**Condom use**<br>**knowledge**<br>**subindex**|**General**<br>**knowledge index**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Treatment student|0.132*|0.083|0.377**|0.201**|0.135*|0.278***|
||(0.067)|(0.062)|(0.153)|(0.090)|(0.075)|(0.081)|
|Spillover student|0.082|−0.023|0.137|0.065|0.035|0.022|
||(0.055)|(0.058)|(0.147)|(0.082)|(0.062)|(0.082)|
|Treatment student * % of friends treated|0.