rafmacalaba/datause-encoder-data huggingface dataset:
**Policy Research**

**Macroeconomic Adjustment and Growth**

# WPS 799

This paper -a product of the Macroeconiomic Adjustment and Growth Division, Country Economics - Department is part of' a larger effort in the Bank to understanid why growth rates diff'cr. This research was funded by the World Bank's Research Support Budget under a preparattion grant for the since-approved research project "How National Policies A'ffect Long-Run Growth?" (RPO 676-66). Copies are available f'ree from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Rebecca Martin, room N11-043, exicitsioin 39065 (54 pages). November 199 1.

Rebelo surveys recent growth models lhait try to explain the diversity amilon-g countries in rales of' economic growth.

He finds that these models catn generate differences in growth rates onl inl thc absence ol' international capital markets. Under these models, if there were 1'ree initenaitionlal capital mobility, the growth rate ol'conisumiiptioni and GNP would quickly be equalized all over the world.

Rebelo describes a simple modification of standard prefeireices that eliminates this implausibie cqualization of growth and is consistent with the flct tihat the savings rate is lower in poor countries tiha in ricih countries.

Policy Rcsearch Workling Papers N di .'niiiate nldings of s ork undcr **\a!** in the Bank. An objecti\ **c** of **the scrics** is to get thcsc Findings out quickl\. e% en **if** preser,itions are **le-:** than fully polished Tht findings, inicrpretations. and conclusions in these papers do not necessarily **reprte.ent** official B1ank policy Produced hv the Iihoicv Research Disseniination Ceniter
This paper uses the historical military routes of Armenia during the Russian Empire times to account for the endogeneity of modern day road quality.

The research focuses on Armenia to fill the gap in the literature for several reasons. In Armenia the agricultural employment rate is still high, has reduced from 40.4% in 1991 to only 33.6% in 2016.<sup>2</sup> Moreover, most people engaged in agriculture in Armenia, like in many other low- and middle-income countries, are best characterized as engaging in subsistence or quasi-subsistence agriculture, meaning that they consume most of the goods they produce. Given this, identifying mechanisms of structural change is very important. In addition, Armenia, has been receiving funding for rehabilitating and improving existing deteriorated roads, as well as for building new ones. Therefore, it is policy relevant to study the impacts of these road projects. And lastly, the unique dataset on road quality and the historical setting of Armenia give an opportunity to identify impacts of road quality on rural employment outcomes.

# **2 Conceptual framework**

In regional research, the importance of transportation costs has long been recognized as one of the major factors of economic development. Von Th ̈unen’s land rent model and its subsequent modifications predict concentric circles of specialization in agriculture surrounding cities (Von Th ̈unen, 1826). Building on this, regional economic models predict that improving rural connectivity would facilitate moving from agricultural to nonagricultural employment. For example, the Alonso-Muth-Mills model predicts an urban perimeter beyond which agriculture would be the primary employment sector. The urban perimeter takes into account that urban wages, deduced with transportation costs, would be lower than agricultural wages (Brueckner, 1987). Therefore, if we consider road quality improvements as a source of decreasing commuting costs in a given location, we expect improved road quality to expand the urban perimeter.

Starting from Lewis (1954) model, researchers have argued that labor market imperfections prevent people employed in the agricultural sector from relocating towards

> 2Source: International Labor Organization (ILO), Key Indicators of the Labor Market `https://www. ilo.org/ilostat` . Accessed April, 2019. Note: The recently updated ILO methodology somewhat differs with the one of Armenia’s Statistical Committee.

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**Exhibit 24:  Growth and Contribution to growth of Taxes and Taxable Employment by Subsector**

|**2008 ‐ 2012**|**Taxes**|||**Employ**|**ment**||
|---|---|---|---|---|---|---|
||CAGR|Contri‐<br>bution to<br>Sector|Contri‐<br>bution to<br>Total|CAGR|Contri‐<br>bution to<br>Sector|Contribution<br>to Total|
||(i)|(ii)|(iii)|(i)|(ii)|(iii)|
|**_AGRICULTURE_**|||||||
|**Crop and animal**<br>**production**|2.83|0.97|0.00|2.30|0.98|0.00|
|**Forestry**|‐|0.00|0.00|‐|0.00|0.00|
|**Fishing**|‐|0.02|0.00|‐|0.01|0.00|
|**Multispectral – Agriculture**|‐|0.01|0.00|‐|0.00|0.00|
|**Total – Agriculture**|2.86|1.00|0.00|2.31|1.00|0.00|
|**_INDUSTRY _**|||||||
|**Mining, quarrying**|1.44|0.18|0.01|2.02|0.44|0.03|
|**Manufacturing**|1.28|0.30|0.01|1.48|0.22|0.01|
|**Electricity gas and water**|1.24|0.02|0.00|0.48|0.01|0.00|
|**Construction**|2.23|0.42|0.02|1.29|0.32|0.02|
|**Multispectral – Industry**|3.31|0.07|0.00|0.63|0.01|0.00|
|**Total ‐ Industry**|1.58|1.00|0.05|1.43|1.00|0.06|
|**_SERVICES_**|||||||
|**Wholesale and retail trade**|0.79|0.34|0.07|0.72|0.27|0.02|
|**Hotels and restaurants**|0.96|0.04|0.01|0.93|0.17|0.01|
|**Transport, storage and**<br>**communications**|1.28|0.22|0.04|1.21|0.13|0.01|
|**Finance, insurance**|2.23|0.06|0.01|1.01|0.05|0.00|
|**Real estate, business**<br>**services**|1.44|0.09|0.02|1.05|0.20|0.01|
|**Education and health**|‐|0.01|0.00|‐|0.04|0.00|
|**Other personal services**|1.16|0.01|0.00|0.59|0.03|0.00|
|**Multispectral – Services**|2.42|0.24|0.05|1.51|0.12|0.01|
|**Total – Services**|1.09|1.00|0.21|0.92|1.01|0.07|
|**Multispectral**|2.23|‐|0.01|2.54|‐|0.03|

_<u>Note:</u>_ Refers to companies registered after 2008 and active as of 2012 (dubbed for simplicity as “Relevant”). The table shows:

- (i) Compound Annual Growth Rate (CAGR) in tax declarations and employment from 2008 to 2012 for relevant companies by subsector;

- (ii) Contribution, by subsector, of relevant companies to growth in tax declarations and taxable employment within the sector; and

- (iii) Contribution, by subsector, of relevant companies to overall growth in tax declarations and taxable employment.

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# **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 World Bank “Data Lab Clinic: COVID-19 and Air Quality” for valuable comments and stimulating remarks on an earlier version of the analysis, and Andres Chamorro for managing several of the used data sources. This work has been funded under the World Bank DT4D program. This paper reflects the views of the author, and does not reflect the official views of the World Bank, its Executive Directors, or the countries they represent.

.
prices and costs increased.<sup>2</sup> As oil prices increased on average from US$43/bbl in 2016 to US$100/bbl in 2022, subsidies covered an increasing share of the market price. Relative to the size of the economy, the total subsidy expenditure also increased consistently from 1.5 percent of GDP in 2019 to 3.7 percent in 2023.

Figure 5. Subsidy expenditure as a share of GDP in Angola, 2019-2023 <!-- Start of picture text --> 4% 3.8%<br>3.7%<br>0.3% 0.3%<br>3% 2.8% 0.9% 0.9%<br>0.5%<br>2% 1.7%<br>0.8%<br>1.5%<br>0.5%<br>0.2%<br>2.6% 2.6%<br>0.3%<br>1% 0.4%<br>1.4%<br>1.0% 0.8%<br>0%<br>2019 2020 2021 2022 2023<br>Diesel Gasoline LPG Kerosene<br><!-- End of picture text --> Source: authors’ estimations based on IGAPE and INE In June 2023, the Government of Angola raised gasoline prices, and in April 2024, it increased diesel prices. These price increases removed around 40 percent and 9 percent of the subsidy per liter, respectively. On June 1, 2023, a government decree laid out a multiphase approach to fuel subsidy reform, for the first time since 2016. Retail prices remained constant until May 2023, when the retail gasoline price stood at about US$0.31 per liter, the fourth lowest price in the world. Then, the government decided to implement an adjustment in the price of gasoline of 87.5 percent from 160 to 300 kwanzas per liter. Gasoline subsidies are being prioritized in the short term as they are the most regressive and their removal has relatively limited direct and indirect impact on the poor. The second phase of the reform in April 2024 targeted diesel, increasing its retail price from 135 to 200 kwanzas. The statement from the Petroleum Derivatives Regulatory Institute said this price change is part of “the gradual adjustment until 2025 (...) towards market levels” initiated on June 1, 2023.

The sharp depreciation of the kwanza in May and June 2023 led to a further increase in subsidies, including those on petrol. Lower oil prices and production in early 2023, coupled with substantial debt service, reduced the supply of foreign currency, and triggered a 40-percent depreciation of the kwanza in May-June 2023. As a result, subsidies on diesel, kerosene, and LPG rose from 69, 84 and 82 percent of market prices in May to 86, 92 and 88 percent by October, respectively. Although the June price adjustment reduced the gasoline subsidy from 63 percent of the market price in May to 39 percent in June, the currency depreciation increased it to 65 percent by October. When measured in nominal

> 2 The subsidy is calculated as the difference between the logistic market price and the fixed market price. The former fluctuates with international oil prices, the exchange rate, and transport and storage costs. The latter is set by the government. The subsidy is normally transferred from the central government to Sonangol, Angola’s national oil company.

7
on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production.

As we reported in Balestreri, Rutherford and Tarr (2009), the MFN tariff rates, multiplied times the trade flows, exceed the collected tariff rates. That is, using MFN tariff rates for all trade, the weighted average tariff rate exceeds the collected tariff rate of 8.4 percent for the economy as a whole. Thus, they exaggerate the protection received by Kenyan industry and agriculture. This is due to tariff preferences to regional partners and due to other preference items or tariff exemptions. We assume that zero tariffs apply on all imports from the East African Customs Union and from COMESA.<sup>24</sup> Thus, we apply the MFN tariff rates only on the trade flows from outside of these African regions (EU and Rest of World in our model) and take a weighted average tariff rate of the MFN rates on the non-East African regions. The resulting weighted average tariff rate on non-East African imports still exceeds 8.4 percent. We then equiproportionally reduced all the MFN tariffs in our model so that the estimated collected tariffs on imports from the EU and Rest of World divided by the total value of import is 8.4 percent.

# **Share of Market Captured by Multinational Service Providers**

It was necessary to calculate the market share of multinational firms in the services sectors by region of the model. Take the banking sector as an example. We need to know the share of the market captured by Kenyan, EU, African and Rest of the World firms, where the countries in the regions are defined in table 1. This entailed acquiring a list of all banks operating in Kenya along with their market share, and, when the bank is owned by multiple parties, allocating the ownership across the regions of our model. The database Bankscope was sufficient for this task in most cases, but websites of the banks had to be consulted to allocate ownership shares in several cases. The results, by region and sector, are presented in table 6. Documentation

> 24 Kenya agreed to implement zero tariffs on East African Customs Union imports as of January 1, 2005. See Michael-Stahl (2005).

23
the outbreak of the crisis, but the top five importers combined have gained market share from 52.6% in August 2008 to 55.4% in September 2009.

A similar pattern holds for the EU import market. While imports from the US have continued to fall through July 2009, Chinese imports were already rising for four consecutive months (see Figure 8 _,_ right-hand panel). Since March 2009, Chinese imports have gained market share at the cost of the second biggest importer, the US. China’s import share reached 17.6% in July 2009, while the US market share was only 12.4%. The total market share of the top five importers was 50.9% in July 2009, compared to 48.1% in August 2008.

**<u>Figure 8: US and EU27 Goods Imports of Top Five Importers</u>**

<!-- Start of picture text --> US Top Five Importers EU27 Top Five Importers<br>35 28<br>30<br>CHN 24<br>25<br>CAN 20 CHN<br>20<br>15 MEX 16 USA<br>10 JPN 12 RUS<br>5 DEU 8 CHE<br>0 4 JPN<br>Billion USD Billion Euros<br>Aug‐08 Sep‐08 Oct‐08 Nov‐08 Dec‐08 Jan‐09 Feb‐09 Mar‐09 Apr‐09 May‐09 Jun‐09 Jul‐09 Aug‐09 Sep‐09 Aug‐08 Sep‐08 Oct‐08 Nov‐08 Dec‐08 Jan‐09 Feb‐09 Mar‐09 Apr‐09 May‐09 Jun‐09 Jul‐09<br><!-- End of picture text --> Source: Own illustration. Data: US International Trade Commission; Eurostat, External Trade. NB: Top Five Imports in August 2008.

China’s outstanding performance in US and EU markets appears to have been matched by some other countries in late 2009, as seen in _Figure 9_ where goods export growth on a YTY basis are compared across all countries for which data were available. While most countries saw smaller declines in exports in Q3 2009 compared to Q2, a small number of countries began to see export increases (on a YTY basis) in November, including Indonesia, Taiwan (China), Thailand,, Chile, Bosnia-Herzegovina and Thailand. Still, many countries showed considerable export declines in Q3 2009 of over 25% compared to a year earlier. This confirms that even a delayed recovery in world trade is likely to take place at very different rates across countries, again, depending on country specialization patterns and trends in GVC consolidation.

17
Tables 5 and 6 present results of regressions between child health outcomes and flaring, after various characteristics of the parents and household (and also of the child, in Table 6) are controlled for. The cluster-level results in Table 5 use as control variables: the cluster averages for wealth quintile and the average person per room, proportion of households in the cluster with poor wall and poor roof, and the proportion of mothers who read a newspaper (as a proxy for access to information).<sup>13</sup> After controlling for the cluster level characteristics, we find a positive association between flaring exposure and incidence rates of all diseases considered except diarrhoea. Our results are in line with evidence from Gobo et al. (2009) where analysis of medical records showed a greater frequency of disease types such as asthma, cough, breathing difficulty, eye/skin irritation in areas with a long history of gas flaring compared to areas with no flaring. Our results also imply a varying relationship between wealth and incidence of diseases. Richer clusters on average have significant less fever and diarrhoea, while cough and respiratory symptom incidence show no similar association, suggesting a lesser role for income/wealth as a mitigating factor for respiratory diseases in this context. Aside from flaring exposure, we find overcrowding significantly associated with cough and respiratory symptoms but not with fever and diarrhoea.

For child anthropometrics, we find significant positive associations between gas flaring and rates of stunting, wasting, and under-weight. These effects are larger than what is reported in Table 3, with no control variables included. Across these three anthropometric indicators, a standard deviation higher volume of gas flaring is associated with up to a 0.1 standard deviation higher rate of stunting, wasting or under-weight. However, the data reveal no significant association between flaring exposure and child mortality outcomes.

Our results at the individual level (in Table 6) are consistent with the cluster level findings. We find a positive association between flaring exposure and incidence of all the diseases considered except diarrhoea. A standard deviation higher flare volume is associated with a three-percentage point higher probability that a child has cough, a respiratory symptom, or fever. Among our control variables, only child age, the education of the mother’s partner, and persons per room (an indicator of crowding) had a significant association with flaring.

For child anthropometric characteristics, we find significant positive associations between flaring and the probability that a child is under-weight or wasted but no association with stunting. Weight-based

> 13 Poor wall is defined as households where the main wall materials are cane/palm/trunk, dirt, bamboo, stone, plywood, cardboard, reused wood, and metal zinc. Poor roof is defined as households where main roof materials made of thatch/palm leaf, rustic mat, wood planks, palm/bamboo main materials.

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b. Bilateral trade with other developing East Asian countries (excl. China) Exports to dev. East Asia (% of gross exports) Imports from dev. East Asia (% of gross imports) Imports from dev. East Asia (% of gross imports) <!-- Start of picture text --> 60<br>LAO<br>50<br>40<br>MMR<br>30 SGP<br>MDV THA<br>20 BEN<br>IDN MYS BRN<br>10 CHN KOR JPN VNM<br>AUSGNB PHL KHM<br>TGO MLI<br>OMN<br>0 BFAZMB<br>0 10 20 30 40 50 60<br>MNG<br>1995<br>Exports to other East Asia (% of gross exports)<br>80<br>70<br>MAC<br>BRN<br>60<br>TON<br>50<br>PNG PHL QAT<br>40<br>KWT<br>MYS<br>IDN<br>30 SAU AUS CHN<br>MMR<br>SDNGNQ SGPJPN THA VNM<br>20 MLT ARE<br>KHM IRNCHLNZLKOR OMN<br>USA<br>10 ZMBMRTVUT YEM SLB<br>LAO COG<br>0 NER MNG<br>0 20 40 60 80 100<br>1995<br>2015<br>2015<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>70 LAO<br>60<br>50<br>40<br>30<br>BRN<br>KHM<br>THA<br>PHL SGP<br>20 IDN<br>MYS SLBPNG MMR<br>CHN AUSMDV JPN<br>10 VUT NZL BENLKAPAK VNM<br>GNB<br>NPL<br>0 MNG IRQ DJI<br>0 10 20 30 40 50 60<br>1995<br>2015<br><!-- End of picture text -->

# c. Bilateral trade with other East Asian countries

Imports from other East Asia (% of gross imports) <!-- Start of picture text --> 60<br>LBR<br>50<br>40<br>GUY FJI PHL MMR VNM<br>IDN BRN<br>30 MYS<br>SDN CHN<br>THA<br>PNG<br>WSMLKASGPHKG MAC<br>20 MDV KHM<br>AUS<br>NZL KORBGD VUT<br>SLB<br>MNG<br>USA<br>10 OMN<br>LAO SYC<br>ARE PRY<br>AFG<br>DJI CAF<br>NPL<br>0<br>0 20 40 60 80<br>1995<br>2015<br><!-- End of picture text --> 17
# **VIII. Productivity-Proximity Tradeoff and the Efficient Frontier**

As discussed in HHR, there is a tradeoff between product income and distance. Countries are more likely to produce and export goods that are close to what they already produce but those products might not be of high value. There is a choice between producing the goods that are close to the production frontier but with lower product value, and producing high product value goods that are further away from the production frontier. Hence, there is a tradeoff between exporting goods that are relatively “easy” for the country to export even though they have less impact on economic growth, or producing and exporting goods that will induce greater economic growth but are more “difficult” to produce. However, HHR also argue that there is an efficiency frontier where some goods are close to the export basket but still show high product income. In the subsequent analysis we apply these concepts to the Argentine case and look at the particular goods that could reflect the tradeoff as well as those close to the efficiency frontier.

In order to illustrate this tradeoff, in Figure 20 we plot the product income of each product exported by Argentina minus the corresponding Argentine export income against their distance to the world export basket for 2004. The idea behind subtracting export income from the product income of each product is to give a sense of the attractiveness of each good with respect to the trade-weighted average product currently exported. For example, the greater this difference the more attractive is the product and the smaller the difference the less attractive it is. Using HHR terminology, we will apply the term “upscale products” those whose difference is positive and “downscale products” those whose difference is negative.

**Figure 20**

<!-- Start of picture text --> Price-Proximity Tradeoff and the Efficient Frontier, 2004<br>20<br>18<br>15 U<br>13 p<br>s<br>10 c<br>a<br>8<br>l<br>5 e<br>3<br>0<br>D<br>-3 o<br>w<br>-5<br>n<br>-8 s<br>c<br>-10<br>a<br>-13 l<br>e<br>-15<br>0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75<br>Distance (Log of Inverse Density)<br>2000 PPP Internat. $<br>Product Income - Argentinean Export Income,<br><!-- End of picture text --> 24