rafmacalaba/data-use-mentions-extended huggingface dataset:
S. trade remedy protection.<sup>22</sup> The higher is the share of the value of U.S. industry production exported to the foreign country, the _less_ likely is the foreign country to initiate a formal WTO dispute against the U.S. because it can discipline the U.S. industry directly through its own appeal to a trade remedy investigation. The industry level production data is the value of shipments data taken from the _NBER Manufacturing Industry Productivity Database_ for the 1991-1996 period, while for 1997-2002, the data was taken from individual, industry level reports in the U.S. Census Bureaus’ publication, _2002 Economic Census, Manufacturing Industry Series._<sup>23</sup>

> 22 The U.S. 6-digit NAICS export data is also taken from Feenstra et. al (2002). Blonigen and Bown (2003) have found evidence that the capacity for foreign antidumping retaliation threats affects an earlier stage of the antidumping process, i.e., the question of which foreign countries are to be named in an antidumping investigation. Their results suggest that, ceteris paribus, a U.S. industry is less likely to name a foreign country on whom its market it is particularly reliant for its own exports.

> 23 We have also interacted this variable with an indicator for whether the WTO member has an antidumping law, but this makes little difference as virtually all WTO members in the sample of countries hit with a U.S. remedy had an antidumping law in place.

13
introduce process innovation and invest in

# R&D

<!-- Start of picture text --> Percent Percent Manufacturing<br>40 30 Services<br>EU-26<br>25<br>30<br>20<br>20 15<br>10<br>10<br>5<br>0 0<br>ECA EU-26 Euro area Introduce process innovation Invest in R&D<br>Sources: EBRD (2020, 2021); World Bank; World Bank, Enterprise Surveys database.<br><!-- End of picture text --> Note: CA = Central Asia; CE = Central Europe; ECA = Europe and Central Asia; EE = Eastern Europe; RUS = Russian Federation; SCC = South Caucasus; SOE = state-owned enterprises; TUR = Türkiye; WBK = Western Balkans.

A. SOE data are 2014-16 averages, as presented in Sanja and Tabak (2020). Sample includes 25 of the 38 countries covered by EBRD, of which 17 are ECA EMDEs.

B.-D. Data reflect the scores of transition qualities, which measures each economy’s performance against that of comparator economies in EBRD regions, as presented in EBRD (2021). Scores range from 1 to 10, where 10 represents a synthetic frontier corresponding to the standards of a sustainable market economy.

E.F. Data for the EU-26 grouping and the euro area exclude Germany. Aggregates are calculated as averages. Data are for 2019.
said they were either already or planned to be vaccinated, compared to 43 percent who would “definitely not” be vaccinated (Table 1). In the same month, at 54 percent, the share in Uzbekistan open to vaccination was consistently higher than Kazakhstan, but still below levels needed to minimize future outbreaks. Of the three countries surveyed, Tajikistan’s respondents were on average most open to being vaccinated against COVID-19, with between 80 and 84 percent willing in summer and autumn in 2021. But due to limited supply, Tajikistan struggled with the lowest vaccination rate among the three in 2021 at the time of the survey experiment.

However, despite relatively high rates of hesitancy when campaigns began in early 2021, vaccination uptake and intentions to be vaccinated have consistently improved, and hesitancy has fallen throughout the region. In Kazakhstan, about 80 percent of adult respondents said that they were either vaccinated or “definitely” would be in June 2022. By that time, the share of people who said they “definitely” would not be vaccinated had fallen from 47 percent of respondents in April 2021 to just 15 percent in June 2022.

Very similar trends were clear in Uzbekistan. There, the share who said they “definitely would not” be vaccinated fell from about 20 percent in April 2021 down to 12 percent in April 2022. The share of saying that they were either already vaccinated or would definitely be rose to 74 percent of adult respondents by April 2022.

4
<!-- Start of picture text --> Figure 9. Change in Household Consumption-to-Income Ratio<br><!-- End of picture text --> <!-- Start of picture text --> 8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>-12.0%<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Bangladesh Ghana Moldova Nepal<br>Peru Romania Thailand<br>Source: Authors’ calculations with data from RIGA, and National Household Surveys.<br>percentage change<br><!-- End of picture text -->

# Figure 10. Decomposition of Changes in Moderate Poverty

<!-- Start of picture text --> 140.0<br>120.0<br>100.0<br>80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>-20.0<br>-40.0<br>US$1.25-a-day US$4.00-a-day US$5.00-<br>a-day<br>Share of adults Employment + earnings Nonlabor Income Consumption-to-income ratio<br>share of total poverty reduction<br>Bangladesh, 2000–10 Ghana, 1998–2005 Nepal, 1996–2003 Argentina, 2000–10 Brazil, 2001–09 Chile, 2000–09 Colombia, 2002–10 Costa Rica, 2000–08 Ecuador, 2003–10 Honduras, 1999–2009 Panama, 2001–09 Paraguay, 1999–10 Peru, 2004–10
|Agriculture|57,504.80|-4.2|765.85|
|---|---|---|---|
|Manufacturing|112,029.02*|-1.8|-653.29|
|Construction|41,013.66|-0.2|77.57|
|Trade|60,340.16|3.7|-594.72|
|Transport|128,421.10*|1.1|576.89|
|Business|144,599.13*|-2.5|-1,738.55|
|Other|57,656.80|3.7|-697.14|
|_Aggregate_|_75,960.34_|_0.0_|_-2,263.38_|

Source: Authors’ calculations based on data from DOSM Note: Asterisks represent values that are larger than the aggregate output per worker for the period.

Similar to the findings of the aggregate decomposition, the sectoral decomposition’s findings for Malaysia are also broadly consistent with the patterns of structural transformation that have been documented for other countries. For example, Merotto (2020) finds that improvements in within-sector labor productivity in the services sector are generally the main drivers of growth in output per capita in upper-middle income countries with above average per capita income growth, followed by within-sector labor productivity improvements in the manufacturing sector.

# **4.3. Dynamic Reallocation and Economic Growth**

Table 14 uses the canonical method introduced in Section 2.1 to decompose the growth of labor productivity into within-sector effects, static reallocation and dynamic reallocation for the period from 1990 to 2018 as well as three sub-periods. The table shows that the contribution of dynamic reallocation to labor productivity has been substantially higher than the contribution of static reallocation. This can largely be attributed to the substantial contribution of dynamic reallocation to productivity growth in the sub-period 1990 to 1997. During
Air Pollution Reduces Economic Activity: Evidence from India<sup>∗</sup>

- A. Patrick Behrer<sup>1</sup> Rishabh Dhruv Sharma<sup>1</sup> , Choudhary<sup>1</sup> ,

> 1The World Bank

JEL Codes: Q52, Q53, Q56, O12, O13.

Keywords: Air pollution, economic growth, environmental health, India

# **1 Introduction**

Exposure to high levels of particulate air pollution, especially PM2 _._ 5, has negative consequences for human health and productivity. Studies show that exposure to elevated levels of air pollution substantially decreases the rate of human capital formation (Persico and Johnson, 2020; Heissel et al., 2019; Levy et al., 2020; Molina, 2021; Zivin et al., 2020), increases mortality (Pope III et al., 2002; Krewski et al., 2009; Lepeule et al., 2012), morbidity (Tong,

> ∗We thank Martin Heger, Teevrat Garg, Namrata Kala, Sam Heft-Neal, E. Somanathan and CEM workshop participants, Somik Lal, Pablo Cesar Benitez, and participants at EEA 2023 for helpful feedback and comments. Tanay Raj Bhatt provided excellent research assistance in the construction of the district data. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
The firm is an exporter.|
|Foreign Share|ES|The share of foreign ownership of the firm.|
|Foreign Firm|ES|Whether the share of foreign ownership of the firm is not zero.|
|_City-industry level_|||
|||City-industry average of the firm’s answer on whether corruption constitutes an<br>obstacle. The original ranking is from 0 (no obstacle) to 4 (very severe|
|ES Corruption Obstacles|ES|obstacle). 2 (moderate obstacle) and above are treated as “having obstacle”(the<br>logic is the same for skilled labor availability, access to land and informal<br>competition).|
|ES Security Cost|ES|City-industry average of the share of a firm’s sales paid for security.|
|ES Web|ES|City-industry share of firms that answer that they use websites to conduct business.|
|ES Power Outage|ES|City-industry share of firms that had ever experienced a power outage in the survey<br>year.|
|ES Skill Obstacles|ES|City-industry average of the firm’s answer on whether skilled labor availability<br>constitutes an obstacle.|
|ES Overdraft|ES|City-industry share of firms with an overdraft facility.|
|ES Land Access<br>Obstacles|ES|City-industry average of the firm’s answer on whether access to land constitutes an<br>obstacle.|
|ES Informal Competition<br>Obstacles|ES|City-industry average of the firm’s answer on whether competition with informal<br>sectors constitutes an obstacle.|
|_Country level_|||
|DB Contract<br>Enforcement|DB|Country-level Contract Enforcement score.|
|DB Get Electricity|DB|Country-level Getting Electricity score.|
|DB Get Credit|DB|Country-level Getting Credit score.|
|DB Construction Permit|DB|Country-level Dealing with Construction Permits score.|
|WB HLO|Angrist
The last term of equation (4.2) captures the instructional loss due to health shocks. Formally, where _q_ is infection of one of the household members with COVID-19, and _d_ is death of a household member due to COVID-19. The probabilities _P_ ( _q_ =1) and _P_ ( _d_ =1) are estimated based on the number of COVID-19 infections and deaths per inhabitant in the country multiplied by the household size.<sup>9</sup> Parameters <sup>_q_</sup> and <sup>_d_</sup> are the respective days of schooling lost due to the occurrence of the two events. Like NLT we set τ<sup>q</sup> to the average days of symptom duration (5 days of schooling), and τ<sup>d</sup> to a three-week loss of instructional time (15 days).<sup>10</sup> Finally, we follow NLT in computing α as one minus the parental factor of substitution, which is a function of parental education. Formally, where _ei_ measures parental educational background in the household of student _i_ and max(e) its maximum value in the sample ( _i.e_ . the years of education associated with tertiary education). Parental educational background is defined as the years of education of the parent in the household with the highest level of education.

# **4.2. Predicted educational losses**

In this section we illustrate the results for 13 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador Mexico, Peru, Paraguay, and Uruguay. These countries represent 89% of the total population of the region, and 87% of the population at

> 9 In this point we depart from NLT who take the average country-level household size and use data for 2020 to estimate the average household size for each country.

> 10 The results are robust to
to stress these results, especially as there might not be much relevant difference in institutional quality across countries in the sample. As for the role of military heads of state, the results are different whether I use SIPRI or COMTRADE data. The latter suggests that discoveries trigger arms imports only in countries where the head is a military officer, while SIPRI data suggests the opposite. I guess it is cautious not to make any conclusions about the role of military heads here.

In Tables 4 and 5 I check if giant discoveries trigger arms imports from all or only some countries among the major arms exporters. I estimate both the baseline model and the model where the discovery dummy is interacted with the oil price to check for effects when the oil price is high. According to SIPRI data, arms imports from the United States, the Russian Federation, Germany, France, and China account for around 75% of all imports during 18
Annex B provides technical details of the modeling framework.

The GIDD is a top-down macro-micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks obtained from the CGE model. Transferred shocks are distributed in a predefined set of linkage aggregate variables that communicate between the two models. The GIDD model relies on a collection of 130 nationally representative household surveys with harmonized variables on demographics, education, labor, welfare and consumption. The sample covers more than 10.5 million individuals from countries constituting 83.4% of global GDP and 86.3% of global population. The data is described with more detail in Ahmed et al. (2020). Additional details regarding the GIDD microsimulation framework can be found in Bussolo et al. (2010).

The ENVISAGE-GIDD model linking process is performed in a sequence of steps, where the two models are pre-calibrated to the same demographic and macroeconomic baseline assumptions. ENVISAGE and GIDD models are connected through changes in labor supply, skill formation, and real earnings. In terms of labor supply, the macro and micro models incorporate projections for skilled and unskilled labor over time. The GIDD framework, in addition, reallocates workers across sectors based on aggregate general equilibrium conditions. After reallocation, the GIDD simulates impacts on real earnings, income growth, and changes in relative prices for food and non-food expenditures. The shocks are transmitted through a set of linkage variables that connect the macro and micro models. Figure 1 provides an overview of the general approach. For additional details on the model linkage implementation an interested reader is referred to Chepeliev et al. (2021).

5
Remittance intensities ( _Iij_ ) were calculated as the ratio of remittance outflow from country j to migrant-origin country _i_ ( _Rij_ ) to the nominal GDP of remittance-source country _j_ ( _Yj_ ): where _Ij_ is the share of remittance outflows _Rj_ to the GDP _Yj_ of country j. _rij_ , the share of country _j_ ’s remittance outflows received by country _i_ , was calculated using the bilateral remittance matrix of Ratha and Shaw (2007).

Two approaches were followed to forecast remittances for country _i_ . The first assumes that remittances from remittance source country _j_ to a migrant-origin country _i_ grow at the same rate as migrant incomes in the host country. The second approach recognizes that remittances may grow faster (or slower) than the incomes in the destination country.

# **_(a) Remittance matrix-based approach_**

The first approach assumes that remittances grow (or decline) at the same rate as migrant incomes in the host country. Remittance outflows from country _j_ were forecasted using estimated remittance intensities ( _Iij_ ) and the projections of nominal gross domestic product for each source country _j_ ( _Yj_ ) from the World Bank’s global macroeconomic forecasts.

The forecasts for remittance inflows for country i were calculated by adding up the share of remittances to country _i_ in the remittance outflows from country _j_ ( _rij_ ) for all remittance-source countries.

7
by using the Spatial Analyst Zonal method.

# **4.2 Household Survey Data**

The primary economic data we use come from a repeated cross-section of the Nepal Living Standards Survey (NLSS) which is a nationally-representative survey of households and communities conducted by the Nepal Central Bureau of Statistics with assistance from the World Bank. The first round of the household survey, NLSS-I covered 3,373 households in 274 wards sampled from four geographic and ecological strata: Mountains, Urban Hills, Rural Hills and Terai. Between 12 and 16 households were interviewed in each ward between June 1995 and May 1996.

The NLSS-II, on the other hand, was conducted between April 2003 and April 2004 and its cross-sectional sample was constructed using a stratified design similar to NLSS-I. It covered 73 districts of Nepal (excluding the Rasuwa and Mustang districts) and enumerated 3912 households in 326 wards. Both rounds collected detailed information on household consumption of a wide range of food and non-food items, data on socio-demographic composition of the interviewed households, labor status of household members, health and educational status, information on various sources

> 12Kappa values in this range are considered ‘excellent’.(Monserud and Leemans (1992))

> 13Village Development Committee is administrative name for villages in Nepal

19
rate*US consumer price index/<br>consumerprice index|exchange rate from IFS; CPI<br>from WDI|
|Inflation|CPI inflation calculated as % change overpreviousyear.(%yoy)|IFS|
|Inflation targeting|dummy variable takes a value of 1 after a country moves to an<br>inflation targetingregime and 0 before that||
|External liabilities|External liabilities includeportfolio equity,FDI and debt liabilities.|Lane and Milesi-Ferretti(2007)|
|G4-moneysupply|Sum of US,UK, Japan and Euro area moneysupply (M2)|Haver|

32
-2.6|6.7|-1.5|2.7|-1.0|7.9|-34.2|
|Imports|4.1|6.9|3.6|-4.0|-2.2|-37.0|-33.4|
|Demand of domestic<br>goods|0.7|6.9|3.4|-0.7|-4.2|-17.6|-23.0|
|Price of domestic<br>good|5.7|3.6|4.7|-5.5|6.2|-36.1|40.5|
|Real exchange rate|5.7|0.1|8.1|-5.5|5.5|-36.1|-43.7|
|Real GDP at market<br>price|0.1|6.7|2.0|-0.2|-2.7|-2.6|-25.9|

Source: author simulations and observations from Lebanon National Accounts 2004-2020 (CAS, 2022) and WDI, 2023.

Figure 4. Population in Lebanon 2004-2022 (thousands, right hand axis) and share of ages 15-64 (percent, left hand axis) <!-- Start of picture text --> 70% 6,500<br>69% 6,250<br>68% 6,000<br>67% 5,750<br>66% 5,500<br>65% 5,250<br>64% 5,000<br>63% 4,750<br>62% 4,500<br>Total Population Share Ages 15-64<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br
<mark>our sample, labor informality is a pervasive phenomenon that may operate as a buffer mechanism</mark> during a crisis.<sup>7</sup> <mark>Having estimated the sectoral elasticities, we project the 2020 employment level for each country and sector. To obtain these projections, we use ILO data for 2019 sectoral employment levels and estimated sectoral GDP changes from the World Bank's Macro Poverty Outlooks. We then add the sectoral projections within each country and obtain an estimation of the total employment level for 2020 that we compare with the observed employment level reported by National Statistical Offices.</mark><sup>8</sup> Figure 1 presents, for each country and method, the percentage difference between our employment level projection for 2020 and the observed employment level.

**Figure 1. Percentage difference between 2020 employment projections and actual 2020 employment levels**

<!-- Start of picture text --> 30%<br>Average Average without outliers<br>25% Mean Median<br>Mean + GDP Mean + GDP*Informality<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>ALB VNM MKD IDN TUR BGR ROU ECU COL MEX PHL BRA ZAF CRI PER<br><!-- End of picture text --> Notes: The mean regression method controlling for the logarithm of GDP interacted with the informality rate is not available in Bulgaria, Mexico, the Philippines, Romania, and <mark>Türkiye.</mark> Actual 2020 employments levels come from National Statistical Offices. The exact data of actual 2020 employment data is shown in Table A1 in the Appendix.

> 7 Bulgaria, Mexico, the Philippines, Romania, and <mark>Türkiye</mark> are excluded from
less reliably the indicator considered yields cross-states comparisons in line with those of H<sup>ˆ</sup> .

**Table 2:** Assuming the joint distribution yields better comparisons than wasting the data on one partial distribution.

<!-- Start of picture text --> ˆ ˆ ˆ ˆ ˆˆ ˆˆ ˆˆ<br>HM HOD HM + HOD H(0.8) H(1.7) H(2.7) max( H ˆ M , H ˆ OD)<br><!-- End of picture text -->

||ˆHM|ˆHOD|ˆHM + ˆHOD|ˆˆH(0.8)|ˆˆH(1.7)|ˆˆH(2.7)|max(<sup>ˆ</sup>HM, <sup>ˆ</sup>HOD)|
|---|---|---|---|---|---|---|---|
||||�<br>∼<sup>ˆˆ</sup>H(0)<br>�||||�<br>∼<sup>ˆˆ</sup>H(∞)<br>�|
|Bias|72|194|32|18|16|22|60|

<!-- Start of picture text --> ∼ H ˆˆ (0) ∼ H ˆˆ (∞)<br>� � � �<br><!-- End of picture text --> The key insight from Table 2 is that, in this context, the proposed method yields a ˆ smaller bias than the classical “wasteful” approaches for all possible values of c. Recall the classic approach consists in using an indicator coming from a single survey, which at global level essentially means using either H<sup>ˆ</sup> M or H<
Self-reported food price inflation is also negatively associated with consumption per capita. Although model (3) of Table 2 does not fully remove transfers from consumption per capita, we choose to use this specification for the simulations to benefit from the use of the three rounds of the IHS.

# **3.2 Vulnerability Estimates**

Building on the consumption model presented in Table 2, we predict consumption in 4,000 states of the world. This simulation is then used to calculate the vulnerability measures discussed earlier. The vulnerability estimates, along with the static poverty indicators, are presented in Table 5.<sup>15</sup> The static poverty figures are not identical to the official poverty statistics since the underlying consumption aggregate does not include cash transfer and food aid. They are relatively close to the official statistics in 2010/11 (IHS 3) since households received less assistance in that relatively affluent year but are higher in 2016/17 (IHS 4) when several households experienced drought.<sup>16</sup> Malawi experienced a severe drought before the 2016/17 survey. Therefore, it is expected that the recorded poverty in this year would likely be higher than the vulnerability to poverty. The 2004/05 round was preceded by a growing season comparable to the historical average. As expected, the recorded moderate headcount poverty is comparable to the share of the population with more than 50 percent chance of falling below the moderate poverty line. After the major drought in 2016, the vulnerability rate (52 percent) was lower than the moderate poverty rate (53.7 percent) recorded in IHS 4. In other words, the magnitude of drought in 2016 was so large that the chance of falling below the poverty line as a result of an even higher magnitude shock was low. On the other hand, during a good weather year such as 2010/11, the vulnerability rate (53.8 percent) was higher than the static poverty rates recorded in that year (