rafmacalaba/data-use-mentions-extended huggingface dataset:
of infections and deaths amount to 19 and 29 percent of the world’s cases.<sup>15</sup> Still, the low rates of infections, and especially death rates, in low-income countries relative to richer countries are arguably attributed to the younger age structure of the population in the region.

The age structure of the population plays a role in the health consequences of COVID-19. Nations with a large share of older people have typically registered high rates of COVID-19 infections and deaths. Figure 1 plots the percentage of the population 65 years or older vis-à-vis the COVID-19 confirmed cases per million people, while Figure 2 depicts the relationship between the percentage of the population 65 years or older and the COVID-19 deaths per million people. The scatterplots clearly illustrate the incidence of COVID-19 cases and fatalities across the different stages of the demographic dividend —as captured by the cross-country distribution of the population 65+ years for industrial countries, Sub-Saharan Africa (SSA), and non-SSA developing countries. The figures show that developing countries with lower shares of older population tend to have a lower incidence of infections and, particularly, deaths. Consequently, countries with greater shares of older populations tend to have higher infection rates and death rates (Figures 1 and 2).

# **4. Data Description**

This section describes the sources of variables used in the regression analysis to assess the growth impact of: (a) the different channels of health and economic transmission of COVID-19, and (b) the policy responses to combat the pandemic. We collected data for a cross-section of 135 developing countries —of which 47 countries are in Sub-Saharan Africa. Given that the real effects of the pandemic are still evolving, we approximate the dependent variable as the differences between 2020 GDP growth forecasts conducted by the World Bank‘s Global Economic Prospects before and after the pandemic which is January 2020 and July 2020, respectively.<sup>16</sup> We also conduct our regression analysis with alternative measures of our dependent variable:
2012 growth in textile exports also stalled for these countries.

**Figure 2:  Evolution of total exports of textiles and apparel (first year = 100)**

<!-- Start of picture text --> 800 1200<br>700 1050<br>600 900<br>500 750<br>400 600<br>300 450<br>200 300<br>100 150<br>0 0<br>1989 1993 1997 2001 2005 2009 2013 2017<br>Mexico Turkey Malaysia China (right index)<br><!-- End of picture text --> Source: Calculations based on data from UN Comtrade. Nominal export growth rate in current USD. All indices are normalized at 100 in the first year shown. Values for the first three countries are shown on the left scale and for China on the right scale.

6
Quality

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Clinic quality index**(SUR)||0.055|_−_0.002|0.112|254|
|||(0.064)|(0.074)|(0.075)||
|||[0.322]|[0.644]|[0.157]||
|(1) Clinic service provision index|0.000|0.149|0.095|0.203|254|
||(1.000)|(0.153)|(0.180)|(0.175)||
|||[1.000]|[1.000]|[1.000]||
|(2) Clinic aware of free health care|0.798|0.005|_−_0.023|0.033|254|
||(0.404)|(0.052)|(0.060)|(0.058)||
|||[1.000]|[1.000]|[1.000]||
|(3) Employee satisfaction index|0.000|0.003|_−_0.043|0.049|254|
||(1.000)|(0.127)|(0.141)|(0.152)||
|||[1.000]|[1.000]|[1.000]||

_Notes:_ Treatment effects are
increase uncertainty and/or the fixed cost

> 5Lowering the marginal costs may entail switching to a lower quality segment or sourcing from a new supplier.

> 6According to World Bank Doing Business data, during the period of analysis, Indonesia had the most onerous employment termination rules among middle-income and Southeast Asian countries. Indonesian employers had the obligation to both notify the workers’ representatives in case of dismissal and to seek approval for the worker’s dismissal by the judicial bodies in case no agreement with the worker can be found. In addition the severance payment accruing to the worker upon dismissal was particularly generous in Indonesia. For example, a worker with 10 years of tenure was entitled to 95 salary weeks for redundancy dismissal versus 50 in Thailand, 43 in Vietnam and the Philippines, 33 in Malaysia and 21 in India.

7
– – shocks women, urban workers, and the low-educated recovered more slowly during the initial relaxation of policy stringency measures during the summer of 2020 (Narayan et al. 2022). ILO (2021) using labor force survey data finds that the labor market recovery stalled during 2021, with little progress since the last quarter of 2020. Kim et al. (2021) using phone survey data from East Asian countries, find that employment impacts were widespread across the distribution when mobility restrictions were stringent, but it was more difficult for poorer workers to regain employment once stringencies had been relaxed. This pattern is also found in the US (Chetty et al. 2020; Lee et al. 2021), Colombia (Alvarez and Pizzinelli 2021), and India (Kugler and Sinha 2020).

– This paper’s first contribution is the use of a unique data source arguably the most – comprehensive data collection effort during the pandemic to provide an analysis of the initial welfare impact, notably employment and income, and its evolution in the first two years of the pandemic, across and within countries.

Focusing solely on the levels of employment could obscure some of the welfare effects of the pandemic. In particular, transitions into agriculture and self-employment would likely be associated with lower quality informal jobs, yielding lower remuneration vis-`a-vis prepandemic wage employment. For instance, Kugler, Viollaz, et al. (2021) document a fall in wage employment and an equivalent increase in the share of self-employment among younger workers during the early stages of the pandemic. Such coping strategies may be more prevalent among lower-income households, who lack access to safety nets and cannot sustain prolonged unemployment spells for fear of not being able to cover basic necessities. Therefore, another contribution of this paper is the analysis of employment transitions for different population groups during the pandemic to examine not only the scope of employment changes, but also changes in the quality of employment across different population groups.

Despite the uneven impact of the pandemic across population groups, the
0.5 million GRD – 1.25%, 51-250 million GRD – 1.20%, 251-750 million GRD – 1.175%, 7511750 million GRD – 0.205%, 1751 million GRD and above – 0.025%. Inter-bank, insider, illegal and central government deposits are not covered. Membership is mandatory by the commercial and cooperative banks and the Fund covers savings, checking and foreign currency deposits. The coverage limit is EUR 20,000 per depositor per institution with no co-insurance.

_Sources_ : Garcia (1999), Hellenic Deposit Guarantee Fund (2000), IADI Survey: El Salvador (2002), Institute of International Bankers (1996), Kyei (1995).

**Guatemala.** The deposit insurance scheme in Guatemala was established in 1999 and is publicly governed. A private fund is employed although the government may make temporary contributions with the provision of repayment. The premium rates are set at 1% plus 0.5% when the fund falls short of the target. The coverage limit is $2,800 per depositor which can be adjusted to cover between 90 to 95% of the accounts. _Source_ : Garcia (2000).

**Honduras.** The deposit insurance scheme of Honduras was established in 1999 as a response to a major banking crisis and under the Temporary Law of Financial Stabilization, all bank deposits received a full expost guarantee which remained valid until 2002. After September 2003 the government insurance covered insured deposits up to 165,000 lempiras ($9,500). The scheme is publicly administered and jointly funded requiring premiums up to 0.25% of insured deposits. _Sources_ : Industry Canada (2002), Garcia (2000).

**Hong Kong.** ( _Hong Kong Monetary Authority, Companies Ordinance_ ) There is no explicit deposit insurance scheme in Hong Kong but is soon expected to be introduced. The coverage limit of the proposed scheme is HK$ 100,000 (or $12,820
variation in financial literacy has been challenging. For example, it may be the desire to invest in stocks or plan for retirement that causes individuals to invest in financial literacy rather than the other way around. There also may be some omitted variables, such as ability or patience, that affect both financial literacy and financial behavior. One way around this problem is to look at financial mistakes and assess whether they are correlated with financial literacy, as it is harder to argue that the causality goes from mistakes to financial literacy. Interestingly, Agarwal et al. (2009) show that financial mistakes are prevalent among the young and the elderly, which are the groups that display the lowest levels of financial knowledge. Calvet, Campbell, and Sodini (2007, 2009) examine data from Sweden and look at actions of investors that can be classified as mistakes. They find that poorer, less educated, and immigrant households—demographic characteristics that are strongly associated with low financial literacy—are more likely to make financial mistakes.

Another strategy has been to rely on instrumental variable (IV) estimation. Several instruments have been used. For example, Lusardi and Mitchell (2009, 2011d) follow Bernheim, Garrett, and Maki (2001) in using high school financial literacy mandates in different states and time periods in the United States. Van Rooij, Lusardi, and Alessie (2011) have used the financial literacy of others, such as siblings and parents. Behrman et al. (2010) use data from the Chilean Social Protection Survey and a set of plausibly exogenous instrumental variables that satisfy critical diagnostic tests to isolate the causal effects of financial literacy on wealth. All these studies show that financial literacy is positively associated with a set of financial outcomes. Moreover, the IV 7
tax payment provides evidence of use and non-payment to be used to cancel legacy titles which would otherwise threaten to undermine tenure security- also clarify records over time and, if all or part of revenues are shared, generate resources for local authorities in an incentive compatible way.

We contribute to three strands of literature. First, strong interest in large-scale land-based investment (LSLBI) following the 2007/08 commodity price boom triggered a wave of investigation that range from case studies of large often foreign ventures (Deininger & Byerlee 2011) to analyses of national land transfer inventories (Glover & Jones 2019) on nature and impact of this this phenomenon. We show that in Zambia -and most likely in any setting where the formal land administration system is characterized by limited outreach and high cost- informal land transfers constitute an important and quantitatively large segment of LSLBI that is not captured well by either of these sources. As this could give rise to yield biased accounts and misleading policy recommendations, generation of complete data on large land acquisitions that can be compared to spatial and textual data from the registry is an important area for future research. Beyond the approach of piggy-backing on census-related data collection efforts taken here, classification of land use

> 4 A land tax on large farms operating on state land would have no negative equity impacts as owners who do not have the means to pay could rent out to generate the resources needed for tax payment.

3
# **_Econometric Evidence on the Saving-Growth Relationship_**

Rodrik (2000) argues that the accumulation of physical capital is the proximate source of economic growth, and in the context of standard growth theory saving is critical in order to maintain a high income per capita (Solow, 1956). In a country-study, such arguments warrant an econometric analysis of the evidence. Building on Brazilian data from IPEA, a vector auto-regression (VAR) analysis is performed in order to study whether gross or private saving positively (Granger) causes economic growth over time. A key reference for this interrelation between saving and growth in a panel setting is Attanasio (2000). The findings for Brazil are compared to those by Attanasio in Table 1.

**Table 1. Private Saving is Found to** **<u>Granger Cause Growth in Brazil</u>**

||Private saving and<br>private investment<br>_(VAR regressions on Brazil)_|Gross saving and<br>gross investment<br>_(VAR regressions on Brazil)_|Gross saving and<br>gross investment<br>_(Multi-country panel analysis)_|
|---|---|---|---|
|_Saving on Growth_|+***[+**]|+[+]|+**|
|_Growth on Saving_|+[+]|+[+]|+**|
|_Investment on Growth_|-**[-**]|-[-]|-**|
|_Growth on Investment_|+*[+*]|+*[+]|+**|
|_Saving on Investment_|+[+]|+[+]|+**|
|_Investment on Saving_|-[-]|-[-]|+**|

Source: Author’s estimations. Results in brackets for difference-estimations.
slowdown in the Indian economy, we restrict ourselves to the pre-demonetization period. Our final sample thereby comprises of a 3 year household panel.

As our explanatory variable of interest is measured on an annual basis, we scale the monthly consumption data by household size and aggregate the monthly consumption data by averaging it across all months in the year. The CMIE also disaggregates household consumption into various food and non-food categories. This allows us to decompose household expenditures into aggregate food, essential food and luxury expenditures.<sup>3</sup> Table 1C shows the summary statistics corresponding to the household survey data. Average per capita monthly household expenditures were INR 2,253 (USD 35) during this period, of which a little under a half went towards food expenditures. Essential food expenditures accounted for 45% of food expenditures while luxury expenditures comprised 13% of monthly per capita expenditures. We see that 75% of the households in the state reside – in rural areas and have low levels of education a third of the households have no adult member who has completed secondary education, while all adults have completed secondary education in only 12% of the households. Over half the households have some member employed in the informal sector while only a third of the households have any member employed in the formal sector.<sup>4</sup> A third of the households have some member employed in agricultural activities and almost half the households have some member employed as a wage laborer.

# **3 Empirical Strategy**

This section describes the empirical strategy used in the paper.

## **3.1 Rainfall Shocks and Farm Productivity**

We use a very standard empirical framework in development economics exploiting plausibly exogenous variations in district annual rainfall from its long-term mean (see for instance, Jayachandran 2006; Shah and Steinberg 2017; and Kaur 2019). The primary intuition is that in developing economies with much of the workforce (rural workforce in particular) employed in the farm sector, agricultural output is significantly
29 _Source_ : World Bank staff calculations using household survey data.

_Note_ : For each energy category, the number shown is obtained by subtracting the share of the population in the bottom quintile from that in the top quintile. For example, 31% for LPG in Angola represents the difference between 33% of the top quintile and 2.6% of the bottom quintile citing LPG as the primary cooking fuel. Wood combines firewood and charcoal in Botswana. Others = mostly agricultural residues and dung.

Figure A2.5 and Figure A2.6 show expenditure shares for different forms of household energy in urban and rural areas; the national statistics are presented in Figure 4. The expenditures on “others” should be interpreted with caution because they include imputed costs of freely acquired biomass, which is more readily available in rural areas. Urban Gambia and rural Malawi stand out for households spending more than 10 percent on “others,” charcoal and wood in urban Gambia and wood in rural Malawi, with the latter likely to have captured a significant proportion of freely acquired wood.

# **Figure A2.5  Percentage of total household expenditures spent on different forms of energy by urban households**

<!-- Start of picture text --> Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Gasoline and diesel<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The expenditure shares are averaged across all households, including those reporting zero expenditures.
Notes and sources:

- 1/ Transportation subsidies<sup>are incomplete and are based only on subsidies to Cairo and Alexandria Transportation Authorities.</sup> Value of other transportation<sup>subsidies is unknown. All estimates by International Monetary Fund.</sup>

- 2/ Value of food and transportation<sup>subsidies does not sum to total explicit subsidies because of inGcompieiedaia. Toiai expli,ct subusiudies</sup> estimated by International Monetary<sup>Fund.</sup>

- 3/ Water subsidies, based on<sup>operational and investment subsidies for piped water in Cairo and Alexandria, estimated by vvorld Bank,</sup> Arab Republic of Egypt: Social<sup>Welfare Study (Strenathening the Social Safety Net) (Washington, DC: World Bank, 1995), p.17.</sup> Estimates do not include value<sup>of water subsidies to rural farmers.</sup>

- 4/ Electricity subsidies, measured<sup>as the difference between current yield and long-run marginal cost, estimated by World Bank, Arab</sup> Republic of Egypt: Social Welfare<sup>Study (Strengthening the Social Safety Net) (Washington, DC: World Bank, 1995), p.18.</sup>

- 5/ Total implicit subsidies calculated<sup>as sum of all implicit subsidies.</sup>
1832, Greece pursued the goal to unite the ethnic Greek population scattered across the Ottoman territories. After the First World War, backed by the Allies, the Greek Army occupied territories in western Turkey that were heavily populated by ethnic Greeks. However, while the Greek advance stalled as the military support of the Allies faded, the Turkish Army’s counter-offensive at the end of August 1922 was successful. The persecution of Greek Orthodox communities by the advancing Turkish Army, in particular the fire in the Christian neighborhoods of Smyrna in September 13, 1922, triggered a mass exodus of population to Greek islands and mainland. Almost one million of Greek Orthodox, whose ancestors have been living in Asia Minor for millennia, left Turkey within a few months’ time.

> 6Kontogiorgi (2006), p. 241.

> 7As I cannot provide evidence on the second generation of Albanian immigrants, I examine the integration outcomes of first generation migrants who arrived in Greece at different age in order to measure intergenerational trends. Evidence from both the 2001 census and the ESS suggest that Albanian immigrants have not yet experienced any progress in integration outcomes between younger and older immigrants of the first generation.

3
This model is referred to as the sampling model, and is assumed to have heteroscedastic error variance var( _ωi|yi_ ) = _σω,i_<sup>2.Thesevariancesareassumedtobeknownalthoughinrealitythese</sup> are estimated using the survey data. The best linear unbiased predictor (BLUP) is obtained by predicting _yi_ through the model, _y_ ˆ _i_<sup>_d_=</sup><sup>_xi_ˆ</sup><sup>_β_+</sup><sup>_ε_ˆ</sup><sup>_i_.The</sup><sup>_β_ˆaretheweightedleastsquaresestimator</sup> ˆ ˆ of _β_ under the Fay-Herriot model and, _εi_ = _ψi yi_<sup>_d−xi_ˆ</sup><sup>_β_</sup> is also the BLUP of the area effect _εi._ � � Additionally, _ψi_ = _σu_<sup>2</sup><sup>_/_</sup> � _σu_<sup>2+</sup><sup>_σ_</sup> _ω,i_<sup>2</sup> �. In essence, the final estimates for areas contained in the survey are the weighted average between the direct estimate _y_ ˆ _i_<sup>_d_and the synthetic estimator</sup><sup>_xi_ˆ</sup><sup>_β_, where the</sup> weights, _ψi_ and (1 _− ψi_ ), are determined by the quality of the model and the quality of the direct estimator. For the non-sampled entities, the area-level model simply uses the synthetic estimates to produce predictions. As Molina et al. (2022) notes, normality assumptions are not required for the estimates to be BLUP, but assumptions are needed for estimation of the mean squared error (MSE). Finally, similar to the machine-learning approach, the area-level model can be implemented at the PSU or municipality levels.<sup>15</sup> Regarding unit-level models,
8 the investor of this risk or should they be permitted to use riskier means to trade without their client’s knowledge and if so who bears the burden of any loss that may result.

**3. Distributed Denial Of  Service (DDOS) attack**<sup>12</sup> —DDOS attacks could take several forms. First a hacker may attempt to flood (overwhelm the network with vast amounts of data) a brokerage company’s network just before the close of market, to prevent it from placing fresh quotes before the end of the day. This could result in penalties and subsequent “reputational risk”. In a delivery vs. payment scenario the stock is not transferred to the buyer until payment is received. If payment orders are kept from being delivered then the stock will not transfer. In an Straight Through Processing (STP),TP, T + 0 environment this scenario carries significantly more risk.  This scenario is particularly plausible in the New York Stock Exchange’s new wireless trading model. Although the Exchange uses fiber optics as its primary transfer channel, it has installed a WLAN (wireless local asynchronous network) to serve as the redundant backup for those lines in the event of another terrorist strike.  Unfortunately using the WLAN poses a significant vulnerability to the integrity of the stored market data. Or the hacker may cause the system to fail to respond to a time-based transaction(s) or a trade(s). For example, a system overload/failure, could delay the response time and cause a Call Option to be placed after its expiry date. Who is liable for a failed trade in that situation?

**4. Market Data Sabotage** —Market participants are heavily reliant upon the market data they receive from entities like Bloomberg’s and/or Reuters as well as online chat rooms and discussion forums. Hackers have the potential to corrupt the data or insert erroneous data once it leaves the perimeters of financial information disseminators and aggregators such as
**Heterogeneous Agglomeration Economies in the Developing Countries: The Roles of Firm Characteristics, Sector Tradability, and Urban Mobility**

Martijn Burger,<sup>∗</sup> Elena Ianchovichina,<sup>א</sup> and Prottoy Akbar<sup>♦</sup>

**Keywords** : agglomeration economies, developing economies, tradables, congestion, urban mobility

**JEL Codes** : D22, E26, O18, R00, R1

> ∗ Martijn J. Burger is a professor in the Department of Organization, Open University of the Netherlands and academic director at the Erasmus Happiness Economics Research Organisation (EHERO), Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, the Netherlands, Tel: +31 (0) 10 4089579, Fax: +31 (0)10 4089141. E-mail: mburger@ese.eur.nl.

> א Elena I. Ianchovichina is a lead economist and the deputy chief economist for the Latin America and the Caribbean Region of the World Bank, 1818 H Street NW, Washington, DC 20433, USA, Tel: +1 202 2803576. E- mail: eianchovichina@worldbank.org.

> ♦ Prottoy A. Akbar is an assistant professor at Aalto University and Helsinki Graduate School of Economics. Email: <u>prottoy.akbar@aalto.fi.</u>

We thank Frank van Oort, Martin Rama, and Bob Rijkers for useful comments on earlier drafts of the paper. We are grateful to Victor Couture, Gilles Duranton, and Adam Storeygard for sharing the mobility data. The findings, interpretations, and conclusions expressed in this paper are entirely ours. 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 or the governments they represent.