rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Incidence of COVID-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands**

### **Bo Pieter Johannes Andr ́ee**

bandree@worldbankgroup.org _Strategy, Analytics, Financing Solutions & Knowledge Unit d’I ́ena, 66 Avenue d’I ́ena, 75116 Paris, France_

## **Highlights**

**Background:** Research on viral respiratory infections has found that infection risks increase following exposure to high concentrations of particulate matter. Several hot-spots of Severe Acute Respiratory Syndrome Coronavirus 2 infections are in areas associated with high levels of air pollution.

**Approach:** This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands using data on confirmed cases and hospital admissions coded by residence, along with local PM2 _._ 5, PM10, population density, demographics and health-related pre-conditions. The analysis utilizes different regression specifications that allow for spatial dependence, nonlinearity, alternative error distributions and outlier treatment.

**Results:** PM2 _._ 5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. Taking the WHO guideline of 10mcg/m3 as a baseline, the estimates suggest that expected COVID-19 cases increase by nearly 100% when pollution concentrations increase by 20%.

**Conclusion:** The findings call for further investigation into the association between air pollution on SARS-CoV-2 infection risk. If particulate matter plays a significant role in the incidence of COVID-19 disease, it has strong implications for the mitigation strategies required to prevent spreading, particularly in areas that have high levels of pollution.

**Keywords:** COVID-19, SARS-CoV-2, Coronavirus, Air Pollution, Particulate Matter.

**JEL Codes:** O13, F64, Q51, Q52, Q53

## **Acknowledgments**

Thanks to Aart Kraay, Antonio Gasparrini, Urvashi Narain, Sameer Akbar, Michael Toman, Nadia Piffaretti, Phoebe Spencer and participants of the March 9
--|---|
||(1)|(2)|(3)|
|Guerrilla control|0.0402*|0_._0399<sup>_†_</sup>|1.28e-05|
||(0.0217)|(0.0267)|(0.0267)|
|Observations|2,266|1,913|1,963|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|0.530|0.420|0.460|

_Note:_ The table presents the results of equation 1 for the Simpson’s Index calculated for all plots, commercial plots, and subsistence plots in the Agrarian Census of 2007. The calculation of the Simpson’s Index is _S_ = 1 _−_ (<sup><u>�</u></sup> <u>�</u><sup>_N_</sup> _iNi aai_<sup>2</sup> _<u>i</u>_ )<sup>2where</sup><sup>_ai_</sup> refers to the size of each plot. The unit of observation is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, _†_ p _<_ 0.15.

88
4 Under these assumptions. it is clear that the welfare changes due to a change in trade policy are the usual production and consumption costs of protection referred to in the literature on the costs of protection. Our measure of the welfare cost to the US of departures from free trade is given by the equivalent variation (EV) measure associated with the utility function selected for the representative consumer, i.e., where C is the cost function corresponding to the selected utility function, superscripts o and 1 refer to the equilibrium before and after the counterfactual trade policy experiment, p is the vector of final goods prices, and IU is indirect utility which depends on prices and income. 3/ The structure of the ten-sector model is described in table 1. 4/ To best capture the trading possibilities at a relatively aggregated level for an economy like the US, we have treated commodities supplied (or purchased) abroad and domestic commodities sold on the domestic market as imperfect substitutes. This assumption of product differentiation, which has found considerable support at the disaggregate level (e.g. Isard, 1977), is commonly used in applied general equilibrium analysis and is also adopted in most of the partial equilibrium estimates of the cost of protection in the US cited above. The assumption is particulirly suitable for the relatively high level of aggregation in our btudy and allows for the observed cross-hauling in trade statistics. On the export side, the assumption of product differentiation is reflected in the constant elasticity of transformation (CET) funct.Lon between domestic and foreign
However, it is important to note that this paper is primarily concerned with the _measurement_ of disaster crop losses as opposed to identifying their causal drivers. In as much as our identification strategy for _measuring_ losses is thus unaffected by these factors, they do not pose a major challenge for this study.

On the methodological side, a strand of literature relevant for this paper is concerned with estimating and forecasting the climate change impacts on agricultural yields. For this, three approaches are common (Challinor _et al._ , 2014; Liu _et al._ , 2016; Zhao _et al._ , 2017). The first two approaches use process-based crop models in conjunction with meteorological models and can be split into grid-based models (e.g. Rosenzweig _et al._ , 2014; Webber _et al._ , 2018) and less coarse but more geographically scattered site-based models (e.g. Asseng _et al._ , 2013).<sup>6</sup> These models are also becoming increasingly popular as in-season, real-time monitoring tools for early crop loss prediction and swift relief (e.g. the World Food Program’s LEAP tool employed in Ethiopia and documented in Drechsler and Soer (2016)).

As discussed previously, crop models vary in their data requirements but usually require a rich set of sensor data at high frequency and sufficient spatial resolution. They are thus demanding for accurate, large-scale, and micro-level measurement in LMICs where smallholder agriculture with small and irregular plot sizes, intercropping, and large idiosyncratic variations in yields are the norm. Despite recent advancements in high resolution yield estimation (e.g. Burke and Lobell, 2017), survey data may thus provide complementary information. Survey data are typically the data source for a third approach in the climate change and crop yield literature that employs statistical modeling based on observed harvest data at varying levels of aggregation (e.g. Lobell, Schlenker and Costa-Roberts, 2011; Imran, Zurita‐Milla and Stein, 2013;
Nonfinancial firms cannot own any equity investment in a commercial bank|1<br>(11)|0<br>(0)|1<br>(15)|
|**D. Maximum Percentage of a Bank's Equity That Can be Owned by a**<br>**Single Owner**|**Median**|**Median**|**Median**|
|2010 (%)<br>2005 (%)|100<br>(100)|100<br>(100)|100<br>(100)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

26
For high income OECD economies, Chile, Japan, and Israel have the highest proportions of legal gender disparities while the Slovak Republic, Australia and Ireland have the lowest (figure A7). While Chile and Japan have the top two highest proportions of legal gender disparities, the composition of legal disparities differs. For Chile it is mostly due to legal disparities in Getting a job, while in Japan it is due to lack of domestic violence legislation. In South Asia, Afghanistan and Pakistan have the highest proportions of legal disparities while Bhutan and India have the lowest. Afghanistan is the only economy in the region with 100 percent legal disparities in three subcategories - Building credit, Going to court, and Violence (figure A8). In MENA, the Islamic Republic of Iran has the highest proportions of legal gender disparities followed by Saudi Arabia, while Malta and Morocco have the lowest in the region (figure A9).

# **4. Correlations with Other Gender Indices**

The WBL composite measure of legal disparities provides a clean measure of differential treatment of men and women before the law. There are two potential critiques of this measure. On one hand it may be unrelated to gender equality where the law on the books does not reflect the realities on the ground. Under this scenario, the WBL composite measure of legal disparities would be uncorrelated with other measures of gender inequality. On the other hand, the measure could be perfectly correlated with other gender indices, thereby making the WBL measure of legal disparities redundant. To address both critiques, the correlations between the WBL measure of legal disparities and other gender-related measures are explored. The following gender-related measures are considered: CIRI Women's Social Rights, CIRI Women's Economic Rights, CIRI Women's Political Rights, WEF Gender Gap Index, Social Watch Gender Equity Index, Ratio of 13
more disaggregated level, we can identify the industries with 64 two-digit divisions based on the Korean Standard Industrial Classification.<sup>18</sup> In the manufacturing sector, our focus of interest, there are 24 two-digit industries.

To explore the effect of the abrupt minimum wage increase announcement in August 2017 following a series of events associated with the political scandal that started in late 2016, we restrict the sample to the years 2013 through 2019, a seven-year horizon that includes four pre-period years and three post-period years.<sup>19</sup> Our key conjecture is that, faced with the minimum wage hikes, Korean firms with foreign affiliates adjusted their workforces differently than firms with no foreign affiliates. We therefore divide Korean firms into two groups: (i) firms with at least one foreign affiliates in the period 2013–2016 (MNCs); and (ii) firms with no foreign affiliates (non-MNCs) during the same period. Note that a sizable portion of Korean firms operated in foreign countries in 2016, which provides a good setup for our empirical analysis.<sup>20</sup> Table 1 summarizes the distribution of included in our geographic foreign affiliates data set in 2016 by region and country of destination.<sup>21</sup> In Korean manufacturing, other Asian countries were the top destination with 3,237, or 78.9% of the 4,103 in total, followed by North America with 356 (8.68%) and Europe with 315 (7.68%). At the country level, China accounts for 1,851 subsidiaries (15.11%), followed by Vietnam with 488 (11.89%), and then the USA with 343 (8.36%). Korean MNCs’ global production sharing is most pronounced in emerging Asian countries such as China and Vietnam.<sup>22</sup> the division of Korean into two new Although firms groups may provide insights into the
is the risk-free rate, denominated in the same currency as bond _i_ . Appendix A provides the sources for these risk-free rates (total return series on UK and US bonds). For completeness, we also compute excess returns vis-à-vis UK or US bills, which is useful when comparing the risk-return properties of external sovereign bonds with those of other asset classes (Section 6).

Further methodological details are discussed in Appendixes B2 and B3, including a discussion on how we deal with historical bond features such as sinking funds.

# 4.3. Main results on bond returns

Table 3 summarizes our main results. The table shows average yearly sovereign bond returns for our global portfolio of foreign-currency bonds over 200 years. Henceforth, we do not show returns for the period 1974 to 1994 due to a lack of representative bond pricing data in the 1970s and 1980s.<sup>39</sup> This means that the time series for the global portfolio ends in 1973 and starts again in 1995. Appendix B5 shows a number of robustness checks, such as on selection (survivorship) bias and the inflation series used.

Table 3: Returns on a global portfolio of external sovereign bonds, 1815-2016

||Re<br>|al Retur<br>|n<br> <br>|Nomi<br>|nal Ret<br>|urn<br> <br>|Above<br>US/UK|<br> <br>Sharpe<br>rti|
|---|---|---|---|---|---|---|---|---|
||Arithm.<br>Mean|Geom.<br>Mean|<br> <br>SD|Arithm.<br>Mean|Geom.<br>Mean|<br> <br>SD|<br>gov.<br>Bonds|<br> <br
The effect can be broken to two components: the decline in the stock markets of emerging markets, and the appreciation of the U.S. dollar. Indeed, as the WTI oil price declines, the dollar index appreciates in value (Table 5.2). A 10% decline in the WTI oil price leads to 0.41% increase in the U.S. dollar. We use the tradeweighted dollar index that the U.S. has against its major trade partners.<sup>16</sup> The appreciation of the U.S. dollar is usually a worrying sign to emerging markets (Shin, 2016).

Table 5.2 Dollar index (broad)

|VARIABLES<br>∆log (Index)|∆log (Index)|
|---|---|
|∆_Log( Oil Price)_<br>-0.0406**|-0.0414**|
|(0.0200)|(0.0176)|
|Lags<br>No|Yes|
|Constant<br>0.000236**|0.000258**|
|(0.000111)|(0.000115)|
|Observations<br>689|674|
|R-squared<br>0.133|0.148|
|Robust standard errors in parent|heses|
|*** p<0.01, ** p<0.05, * p<0|.1|

Focusing the impact of oil price fluctuations on oil exporters, Table 5.3 examines the impacts of oil price fluctuations on the dollar-denominated MSCI Gulf States Index **.** The impact here reflects the reactions of oil-exporters’ stock markets to changes in oil prices. Quite surprisingly, a lower WTI oil price hurts the dollar value of the Gulf States’ stock markets but the impact is statistically insignificant: every 10% decline in WTI oil price, MSCI Gulf State
To illustrate the importance of allowing the effect of commodity prices on revenue to vary across countries, Figure 4 displays on the vertical axis the point estimates of an univariate regression of the change in commodity revenues (as a share of MAGDP) on the log change in the commodity price index. The predicted impact on resource revenue of a 100% increase in the commodity price index ranges from 0% to 45% of MAGDP. This range is also wide within SSA countries (0% to 40%). On the x-axis, we plot the 2008–2012 average commodity revenues as percent of 2010 GDP, ������� . The estimates are almost proportional, suggesting yy that 1 is close to 1 and is stable across countries (0.92, on average; 1.01 for SSA). ii CCRRC We get a very similar estimate of 1 in our first-stage estimates. δδ δδ

# _C. Data_

To estimate equations (4)–(6), we use annual panel data on government expenditure and commodity resource revenues over 1980–2016 for 56 commodity exporters (22 in SSA). The criteria to add a country in the panel is to have sizable commodity exports (more than 5% of GDP or one-third of total exports, on average, in 2008–2012) and to have at least two consecutive years of data for all of the variables included in the regression.<sup>46</sup> Appendix Table 5 displays the list of countries included in the panel as well as the data sources, and the sample period for each variable. Appendix Table 7 provides summary statistics of all variables in the regressions.

To construct our spending variable (LHS of equation (4)) we obtain data on “General Government Total Expenditure, Percent of GDP” (GGEY), “Gross Domestic Product, Current National Prices” (NGDP), and “Inflation, Average Consumer Prices” (CPI) from the International Monetary Fund (IMF) World
11 Market prices are also to some extent indicative of the quality of housing in Moscow, e.g., the Stalin-era and the elite brick buildings are the most expensive, whereas the small-sized apartments of Khrushchev period and the 1970s are the cheapest. The former are high-quality, while the latter are low-quality housing. Average value and, consequently, average quality in Moscow corresponds to standard apartments with an improved layout in the new panel multistory buildings located mainly on the outskirts of the city.

**Table 1.6.** Average price of housing unit by quality and location, Moscow, April, 1995 (thousand dollars).

|Type of Apartment||Locati|on fro|m Cen|ter*|Average|
|---|---|---|---|---|---|---|
||1|2|3|4|_5_||
|Communal apartments|65|39|33|32|30|41|
|Separate apartments, average<br>of which:|116|83|59|58|52|64|
|Khrushchev-era buildings|60|49|42|43|27|42|
|Panel buildings (1970s design)|71|49|48|52|33|51|
|Brick buildings (1970s design)|80|62|51|46|33|56|
|Modem panel buildings|81|83|71|68|56|67|
|Old low-rise stock|130|69|72|72|35|85|
|Modemrbrickbuildings|112|118|89|72|-|103|
|Stalin-era buildings|147|114|95|-|-|114|
|Average|105|<br>77|57|57|51|62|

Sources: The Moscow Longitudinal Household Survey, December 1994; author's estimates. *Location: 1- city
term.

# **5.2 Identification**

The granularity of our data allow us to identify causal economic effects of temperature shocks. Our key identifying assumption is that changes in temperatures in a firm’s geocoded location (relative to the location’s long-term mean) are exogenous to the firm’s output other than through the direct impact on its performance, after conditioning on our set of fixed effects and controls. The geographical granularity in our data enables us to account for unobserved heterogeneity across countries, economic activities, and years through country by sector by year fixed effects. Controlling for historical temperatures and firm size further restricts the comparisons we make. We thus exploit within-country, within-sector, within-year fluctuations in temperature, conditional on historical temperature and firm size. In this section, we consider the plausibility of our identifying assumption.

Reverse causality is unlikely to apply in our setting. Although economic activity at more aggregate levels can influence temperature, it is unreasonable to expect that this would happen at a firm’s geocoded location level since an individual firm’s output only has a negligible impact on the climate.

Subnational geography such as proximity to the coast or mountains may influence temperatures as well as economic activity. For instance, coastal regions may have more stable temperatures and potentially more economic opportunities due to their proximity to ports or better transport infrastructure. Comparing the impact of temperatures across firms may thus suffer from omitted variables bias since a location’s fixed geographical characteristics could be correlated with other factors affecting firm performance. Our estimation addresses this by leveraging the impact of temperature changes relative to each location’s long-term mean, which differences out fixed geographical characteristics. Combining granular geocoded climate data with rich firm data is thus key to identifying the economic impact of climate change because it allows us to control for the impact of variation in local geographical char14
emerges from the restricted sample. Together, the IHS4-based estimates suggest that bequest rights have either no effect or negative effect on investment, indicating systematic misreporting of bequest rights.

# **5. Conclusion and policy implications**

We use the example of Malawi to show that, even in customary settings where formal documentation is absent, the nature and extent to which women and men have rights to land vary widely. Variation in bequest and to some extent sale but not in other rights systematically affects land-based investment. Yet, standard household surveys may fail to capture such variation due to measurement error arising from reliance on proxy respondents rather than interviewing land owners themselves. We conclude by drawing out implications from our findings for research and policy.

An important area for follow-up research is to assess the extent to which the findings presented here can be generalized beyond Malawi. Doing so would allow exploring if a failure to either include information on bequest rights or to collect such information directly from (co-)owners can explain the lack of clear results on the impact of individual land rights in the literature. Also, if results similar to what was presented here are found in other countries, getting information on the right to bequeath or sell land from concerned individuals at least for a subsample of national household surveys will be important. Beyond identifying the impacts of specific land rights on socio-economic outcomes, this would provide a basis for understanding of changes in individuals’ rights in response to exogenous factors.

Our findings are relevant for Malawi in two respects. First, as most of our data were collected before the 2016 Land Act was assented to by the President in January 2017 and awareness of the specific provisions of the Act remains low, subsequent IHPS (or IHS) waves can help ascertain if passage of the Land Act, in particular its far-reaching modifications of inheritance patterns, affected individuals’ land rights and investment in agricultural and residential land. Second, defining customary estates to implement the Land Act will require adjudication of rights. Our analysis suggests that
. Nearly half of the assets disclosed were foreign stock and other investments (10% of GDP). Almost one-quarter represented deposits in foreign bank accounts and currencies (5% of GDP). About 6% corresponded to undeclared cash (1% of GDP).<sup>3</sup> Lastly, almost one-fifth came from real estate (4% of GDP), corresponding to 167,000 previously hidden properties. The penalties raised US$9.5 billion in revenue, equivalent to 1.8% of GDP (AFIP, 2017). The amount disclosed and revenue collected from penalties place Argentina’s 2016 program as one of the world’s most successful tax amnesties.

# **2.3 Data and methodology**

We use information from statistical yearbooks provided by AFIP, representing detailed tabulated data from tax returns for the country’s wealth, income, value-added, and payroll taxes for FYs 2002–21. Our primary analysis uses data from the wealth tax, including the tabulations with information on the number of filers and taxpayers, the wealth value,

> 3 Participants deposited this money in a special bank account and reported it to the tax authority.

7
# **Estimation of Poverty in Somalia Using Innovative Methodologies**

**Utz Pape and Philip Wollburg**<sup>1</sup>

Keywords: Consumption Measurement, Poverty, Questionnaire Design JEL: C83, D63, I32 1 Authors in alphabetically order. Corresponding author: Utz Pape (upape@worldbank.org). The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. Gonzalo Nunez contributed to the survey design and poverty analysis and provided inputs to this manuscript. The authors would like to thank Kristen Himelein and Wendy Karamba for discussions. In addition, the authors thank Véronique Lefebvre, Sarchil Qader, Amy Ninneman, Dana Thomson and Tom Bird from Flowminder and WorldPop for designing the population sampling frame using quadtrees and producing fieldwork maps, and for modelling and imputing poverty from spatial data, in collaboration with the authors.
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