rafmacalaba/data-use-mentions-extended huggingface dataset:
source. PAYE directly taxes personal emoluments including wages, salaries, overtime pay, leave pay, commissions, fees, bonuses, and any other payments from employment. However, there are some exempt emoluments, such as pension benefits and gratuities. The design of PAYE tax in Zambia is largely progressive, with a certain proportion of earnings below the defined tax threshold of ZAR4,500 (approximately US$250) taxed at a rate of zero. The top rate for PAYE in 2022 was 37.5 percent, compared to a Southern African Development Community (SADC) average of 30.1 percent and an OECD average of 56 percent.

Zambia introduced VAT as a principal consumption tax on goods and services in 1995. VAT in Zambia is imposed on the final consumers of both imported and locally manufactured standard rated goods. Goods and services are classified into three broad categories for VAT purposes. These include zero- rated supplies, where input VAT incurred is claimable while output VAT is charged at 0 percent. The other class of supplies is exempt supplies, where input VAT incurred is non-claimable and output VAT is not chargeable. The remainder of inputs are taxed at a standard rate of 16 percent of sale value. This standard tax rate is similar to the SADC average.

Filing of tax returns is the principal means by which a taxpayer’s tax liability is determined by the Zambian Revenue Authority. VAT and PAYE returns contain information about formal firms’ revenue and employment levels each month. Further background information about firms is also available, such as their locations, sectors, and registration dates, which can be used to estimate the age of the firm (see examples of VAT and PAYE returns in Appendix A).

Firms that filed VAT and PAYE returns in 2020 were largely based in urban areas and were mainly involved in wholesale/retail trade and manufacturing. Almost two-thirds of firms were in Lusaka (the capital and by far the largest city). Almost half of firms were involved in wholesale and retail trade, and
. based on UNPVSR<br>1984 Special Supplement;<br>1985-90, Bank projection|Based on UNPVSR<br>1984<br>Speciat Supplement|
|erra Leone|Dec 85|3516 F|Bankprojection<br>from census,<br>adjusted for 5unidercount|Based on 1974census|U.N. 1988 revision (prelim.)|
|ngapore|Jun80|2414 F|UNPVSR<br>7/86 (official<br>est.)|Based on U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Solumon Islands|<br>Feb 76|197 F|USBOC<br>1985|Based on USBOC 1985|USBOC 1985|
|Somalia|Feb 75|3253 F|Bank projection from census,<br>adjusted for 201 undercount|Bankest.|Bank est.|
|South Africa|Nay 80|25017 F|U.N. 1988 revision (preLim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (preLim.)|
|Spain|Nar81|37746 F|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|Sri Lanka|Mar 81|14847 F|UNPVSR<br>7/86 (officiaL<br>est.)|1987DHS|Based on UNPVSR<br>4/87|
|St. Helena|Feb 87|6 F|UNPVSR<br>1/88 (official<br>est.)|--||
|St. Kitts<br>and Nevis|may 80|<br>44 F|Official<br>est.,<br>Statistics<br>Office,<br>Planming<br>Unit|Based onofficial<br>data,<br>Statistics<br>Office,<br>Planning<br>Unit|Based on official<br>data,<br>Statistics<
Equation (1) tells us that to arrive at the relative contribution of the two components, one would need a value for _α_ , which we wish to remain agnostic about. Our approach is to compute for each country (in each scenario) the value of _α_ an observer would have to hold so as to judge that Covid-related mortality and additional poverty make identical contributions to the welfare costs of the pandemic, given the observed outcomes. Using a superscript _A_ to denote actual, or estimated, outcomes, this "break-even" _α_ , which we ˆ call _α_ , is given by: ˆ For any _α < α_ , additional poverty is the dominant source of the current welfare costs of the pandemic.

# 3.1 Six countries with high-quality data

We rst look at a restricted sample of six countries for which we have data on age-specic mortality from Covid-19.<sup>16</sup> Three of them are high-income countries: Belgium, the United Kingdom (UK) and Sweden. We selected these three countries because they had the highest number of Covid-deaths per capita as of early June.<sup>17</sup> The three remaining countries are developing countries for which age-specic mortality data are available as of early June: Pakistan, Peru and the Philippines. For each of these countries, we estimate _LY_<sup>_A_</sup> and _PY_<sup>_A_</sup> as follows.

To estimate a country's number of lost-years, we start from the age-specic mortality information: the number of Covid-related deaths distributed by age categories. Where available for individual countries, this information is obtained from Oces of National Statistics, Ministries of Health or other government oces. A specic list is included in Table A1 in the Appendix.<sup>18</sup> Then, for each death we assume that the number of lost-years is equal to the residual life-expectancy at the age of death, as computed from the country's pre-pandemic
The number of exporters is clearly very low, but it is hard to compare with other countries because similar administrative data is rarely available. As a basis of comparison, we can compare the export propensity of Rwandan and other African firms in the World Bank Enterprise Surveys (WBES), see Van Biesebroeck and Mensah (2019) for details on that dataset. In the case of Rwanda, 9.0% and 9.5% of surveyed firms report to be exporting in, respectively, the 2006 and 2011 surveys. The average across other sub-Saharan African countries is 10.6% in both years, but 15.5%, which is quite a bit higher, over the full 19922018 period. However, limited to firms in the manufacturing sector the export propensity is 22.9% in Rwanda, which is barely distinguishable from the full sample average over all African countries of 22.1%. However, the two survey years count respectively 13 and 19 exporters for Rwanda. It would suggest that either almost all formal firms are included in the WBES survey or the administrative data misses some exporters.

# **4.2 Trade in manufacturing products is becoming relatively more important**

Not only the number of firms has increased over the sample period, the same is true for the absolute export volumes. The solid lines in Figure 7 show rising export values for all three sectors. The absolute growth is most pronounced for the mining and agriculture sectors, but both sectors also experienced large downturns. We only plot the absolute exports to 2015 as the aggregates are lower in 2016 and we cannot rule out entirely that this is at least partly due to incomplete recording at the time when we received the data.

Comparing average exports in the first two years (2008-2009) to the last two years (20142015), exports have increasing by 67 million USD for mining and by 32 and 26 million USD for agriculture and manufacturing. The percentage increase shows a rather different pattern as the initial export volumes differed a
by Russia, Belarus, and Kazakhstan, which form a customs union. By 2010, none of these countries was a World Trade Organization (WTO) member, therefore we focus the analysis on the simple average applied tariff including preferences (instead of the most favored nation (MFN) tariff), which varies between 6.012% for Russia and 6.778% for Belarus (Table 1).<sup>3</sup> The MFN tariff is the border (import) tax rate that one WTO member imposes on imports from another WTO member, unless the country is part of a preferential trade agreement (PTA). The MFN tariff is calculated as a simple and weighted average of the rates available at each product-destination combination. The weights are the share of imports on total imports. The average applied tariff including preferences is the effectively applied tariff. To conduct the analysis, we employ data from UNCTAD and the WTO accessed through the World Bank’s World Integrated Trade Solution (WITS).

The highest level of protection imposed by Russia, Belarus, and Kazakhstan is observed in the sector of motor vehicles for the transport of goods. The import duties corresponding to the products that belong to this category range from 22.5% (HS 870423) to 25% (HS 870422). The weighted average applied tariff ranges between 2.241% for Belarus and 3.728% for Russia. The difference between the simple and the weighted average applied tariff is explained by the particular import structure of the country. The lower weighted average applied tariff for Russia is the result of imports of medicaments, the most important category, which accounts for 3.07% of total imports, and has tariffs that vary between 0% and 10%, depending on the trading partner. Motor cars and motor vehicles is the second most important category, representing 2.67% of total imports, and having no tariff. Electrical apparatus for line telephony or line telegraphy, the category that explains 1
ratio C4. These concentration ratios were taken unadjusted from the Census listings. For each four-digit industry, the Census also reports the values of the truncated Her...ndahl index H, representing the sum of squared market shares of the largest 50 ...rms in the industry or the entire universe, whichever is lower. The census also reports the values of the 50-...rm concentration ratio C50. From the values of H and C50 we can derive V (s); the variance of market shares of the largest 50 ...rms or the entire universe, whichever is lower. This will permit us to estimate equation (18). It can be easily shown that<sup>10</sup>

> 9 It should be noted that dropping r leads to a change in units and could a¤ect the testing of H01 in (17) and H02 in (19) . Moreover, established ...rms are more likely to buy their own capital, so (1 �<sup>RK</sup> rK<sup>)islikelytooverstimatesunkenness.</sup>

...rms, then we compute the variance of the market shares of the largest 50 ...rms from the values of the truncated Her...ndahl index H50 and the 50-...rm concentration ratio C50 that are reported by 50 the census, i.e., V (s) = 50<sup>1(H50 �C</sup> 50<sup>2).If,ontheotherhand,anindustrycontainsN...rmswhere</sup> N < 50, then CN = 1 and V (s) =<sup>1�1</sup> N<sup>(HN</sup> N<sup>):</sup> 12
accidents (Um 2020). Just as the region’s challenges are diverse and complex, so are investment needs in infrastructure, education, health, and green technology.

Responding to the pandemic. The COVID-19 pandemic has highlighted inadequacies in the health and education sectors in MNA, and the urgent need to invest in them. Most MNA economies were ill-prepared for the pandemic, with public o ffi cials overcon fi dent about health system capabilities (World Bank 2021e). Even prior to the pandemic, achieving universal healthcare coverage would have required countries globally to increase spending on primary healthcare by at least 1 percent of GDP (WHO 2019). Despite signi fi cant progress in MNA over the last two decades toward achieving universal healthcare—meaning access to health services, when and where needed, without fi nancial hardship—the region still ranks below other EMDE regions and the advanced economies in this regard. In some of the region’s economies, public spending on healthcare, per capita, is among the lowest in the world, resulting in limited access and large out-of-pocket expenses for citizens. Insu ffi cient investment in health services, particularly in non-Gulf Cooperation Council (GCC) economies means inadequate numbers of health workers, insu ffi cient hospital beds per capita, and limited ability to provide essential health services The World Bank’s Human Capital Index has risen over the past decade in almost 80 percent of MNA economies with much of this gain coming from educational improvements.<sup>10</sup> Nonetheless, a child born in MNA in 2020 was expected to achieve only 56 percent of its future productivity on average based on the Human Capital Index. The pandemic has reversed some of the gains to education with pandemic-related school closures averaging 48 weeks in 2020-21 in MNA, above the global average of 38 weeks. The resulting outsized damage to human capital accumulation could signi fi cantly impair the lifetime earnings of many (Azevedo et al. 2021). Returns to education are also the lowest of any EMDE region, re
is generally a reservation value because workers can engage in informal work, help at home (home-production activities), perform odd-jobs and so on. Also, the reallocation may be itself costly (beyond forgone income) if workers need to retool themselves to accumulate sector-specific skills. More recent work on trade adjustment costs attempts to better model and quantify these factors and to provide foundations to these issues.

The papers in this literature are heavily structural and the foundations vary from paper to paper. Each of the papers reviewed here focus on different foundations. First, I look at a strand of literature on _labor_ adjustment costs. The drivers of those costs are labor market frictions, sector-specific skills (human capital) and training costs, hiring and firing costs, and labor mobility costs. Second, I look at capital adjustment costs and capital market imperfections. All these paper rely of variants of dynamic stochastic general equilibrium models that are partly calibrated and partly estimated using firm and worker data. In this section, I review the main examples in this literature emphasizing both the size of the cost of adjustment and the role of the driver of those adjustment costs. Table 1 summarizes the main results.

# **4.2.1. Labor Mobility Costs**

I begin my review of the literature with a series of papers on labor mobility costs, namely Cameron, Chaudhuri, and McLaren (2007), Chaudhuri and McLaren (2007), Artuc, Chaudhuri, and McLaren (2010), and Artuc, Chaudhuri, and McLaren (2008). I begin here because the definition of labor mobility costs is broad enough and encompasses many of the channels that I will explore in more detail below.

These papers present a method for estimating the costs of labor mobility based on a dynamic rationalexpectations model of labor adjustment. The procedure also explains how to use the model to simulate 9
An important characteristic of Malawi’s land tenure system is the co-existence of matrilineal and patrilineal inheritance systems. As the ability to decide on long-term transfer of land rights has been shown to affect investment decisions (Deininger and Jin 2006), individuals’ ability to influence decisions on intergenerational land transfers is likely to affect their willingness to make non-observable investments. Place and Otsuka (2001), Lunduka (2009), Lovo (2016) and Bhaumik _et al._ (2016) indeed argue that in matrilineal and matrilocal systems, the discrepancy between females’ land ownership and males’ social position and superior access to markets for inputs and outputs impedes land-related investments.

To the extent that they give individuals control over assets, inheritance regimes may also affect spouses’ outside options.<sup>5</sup> Women have been documented to be more vulnerable to temperature shocks in patrilineal systems, possibly due to lower levels of tenure security (Asfaw and Maggio 2018). Moreover, higher household consumption in matrilineal as compared to patrilineal systems has been attributed to differences in women’s tenure security across inheritance regimes (Telalagic 2014).

With the objective of integrating customary and statutory rights in innovative ways, Malawi passed a new land law in 2016. The law aims to demarcate territories controlled by individual traditional leaders as a precondition for documented rights to individuals.<sup>6</sup> In addition, it introduced sweeping changes of inheritance practices that have been highly controversial with scholars who argue that such change could disenfranchise women on a massive scale (Peters 2010).

# **3. Data, descriptive statistics and empirical approach**

The descriptive analysis of our data shows that (co)-owners often do not have the full bundle of rights and that women lag men in all rights. Comparing descriptive statistics between surveys relying either on selfreporting (IHPS) or on responses by a proxy (IHS4) suggests that similar figures in the aggregate conceal important discrepancies between surveys. Compared to the data provided by
strategies in such environments. Third, leveraging diversification of crop production and income sources as resilience strategies is effective only for rural households with a large family workforce (Brock and Coulibaly, 1999; Toulmin and Gueye, 2003; Lawson et al., 2015; Banda et al., 2016). Fourth, in drought-prone village economies, there may therefore be a vested interest in the preservation of traditional institutions that promote a large family, such as polygyny, to harness household size and composition to the effectiveness of resilience strategies against drought. If this is indeed the case, then communities where polygyny is highly prevalent should be more resilient to drought shocks compared to those where monogamy is highly prevalent. In other words, diversification of crop production and income sources in the aftermath of a drought should be more successful in polygynous than in monogamous communities.

To identify the resilience effect of polygyny, we regress households’ crop yields on the interaction of polygyny with current and lag droughts using data on Mali. Several challenges confront our identification of the causal effect of the interaction of drought and polygyny. First, there is the potential presence of spatial correlation in the error term, due to the of both and to be clustered in tendency drought polygyny space. Although droughts can happen randomly over time, they often tend to be clustered across neighboring geographic units (Hsiang et al., 2011). Likewise, polygyny, as a cultural practice, is often clustered in geographic units that share similar cultural values. Second, there is the potential serial correlation of our drought series within each geographic unit. To account for these two sources of correlation, we follow Conley (1999); Hsiang et al. (2011) in adjusting standard errors for both serial and spatial correlation. Third, there is the potential presence of spatial correlation due to omitted weather variables. We account for this problem by constructing a measure of drought during the agricultural season, using the Standardized Precipitation-Evapotranspiration Index (Vicente-Serrano et al., 2010; Miao and Popp,
while the data from Chen et al. (2020) shows that across different SSP scenarios, urban extents could in fact decrease in some parts of the world, in part due to declining urban populations, we assume that these urban contractions will not result in land being reclaimed for agricultural use. Urban contractions may have implications for crop and livestock production. This assumption is based on historical trends, while shrinking cities often leads to vacant land and deteriorating infrastructure, rather than reconversion to land uses prior to urbanization (Florentin 2019). Therefore, once an area for crop and livestock production becomes urbanized, the production there is assumed to be part of the estimated production loss permanently. The urban expansion projection data, available in 10-year intervals, are used as proxies to estimate the cumulative urban extent around the world.

Based on the above assumptions, we adopt the following method for our analysis: First, raster calculations are performed to derive the areas of urban expansion for 2040 and 2100 for all five SSP scenarios. Since we assume that the potential impact on crop and livestock production due to urbanization is irreversible, we use cumulative urban extents for each time period. For example, to calculate the urban expansion for 2040, we overlay urban projections for all previous decades (2020, 2030, and 2040). Any area that has ever been marked as urban is considered part of the cumulative urban extent, regardless of whether the area subsequently deurbanizes or not.<sup>10</sup> Following this, we calculate the percentage of the urban extent that is _newly urban_ compared to

> 9 Table A1 in the Annex displays the overlap percentages between food production and urban extent in grid cells. 10 This assumption holds true for any of the years preceding the 10-year interval from 2010 to 2100. Only 3 years of dataset are extracted to analysis for this working paper; 2010, 2040 and 2100.

8
: cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at a spatial resolution of 1/12 degree degrees (approximately 10 sq km at the equator). Missing values in the crop production dataset are specifically distinguished from crops that are not grown in a particular area. Crops not grown in an area are coded as zero, while crop data that is not available is coded as a missing value. This distinction is important as it helps to differentiate between areas where crops are not grown and areas where data is simply not available (Yu et al., 2020).

_Table 3. Baseline values for crop production and livestock production_

||**Global**|**Africa**|**EAP**|**ECA**|**LCR**|**MENA**|**SOA**|**Other**|
|---|---|---|---|---|---|---|---|---|
|**Food crops 2010**|||||||||
|**, ,**<br>**metric tons (mt)**|7,510.9|575.0|2,263.5|636.7|1,435.5|226.6|1,049.9|1,323.7|
|_Fruits and Vegetables_|1,611.6|102.2|742.8|120.3|150.3|102.3|195.7|197.8|
|_Cereals and roots_|3,207.0|350.9|948.9|385.7|232.3|82|430.9|776.4|
|_Sugars and oils_|2,625.0|108
shows the community covariates variables of the expected variance equations of the model. Finally, the assessment is conducted at the household level using the most recent household survey NNii year available in each country, even though the estimations are based on the five rounds. The poverty rate and the vulnerability to poverty incidence are computed over households, not individuals. Henceforth, the main results will be presented as household incidence. To get the number of people involved in the analysis, we make some extra computations based on the number of members of each household. In the cases where the results pertain to individuals, we will explicitly specify it.

# **4 Results**

The vulnerability to poverty varies widely across countries, ranging from 2 percent in Chile to 64 percent in Guatemala when analyzing household incidence. Additionally, approximately one-third of the countries have a household incidence of vulnerability to poverty between 25 percent and 45 percent, and about half of the countries have an individual incidence in that range. The percentage of the population vulnerable to poverty ranges from 4 percent in Chile to 73 percent in Guatemala. In addition, the rates of vulnerability are fairly heterogenous within most countries. Table B3 in the Appendix presents all the vulnerability to poverty measures at the national level, further stratified by rural/urban areas and the country’s regions. Figure B2 shows the vulnerability to poverty incidence by state.

> 13 While it is possible to perform LASSO using many variables, we have limited the number of variables to the ones mentioned above to ensure the comparison between the countries. However, more household characteristics can be added if needed for a specific country study.

15
national level.<sup>6</sup> A secondary objective of the HIES 2016/17 is to understand the rapid urbanization occurring in Bangladesh, and to

> 6 The poverty headcount ratio is defined as the proportion of people living below the official national poverty line. In Bangladesh, there are two official government poverty measures: the poverty headcount ratio and the _extreme_ poverty headcount ratio. The poverty headcount ratio is based on a set of division-level poverty lines, defined separately for urban and rural areas, called “upper poverty lines”. In contrast, the extreme poverty headcount ratio is based on a set of division-level poverty lines, also defined separately for urban and rural areas, called “lower poverty lines”. Both upper and lower poverty lines include a food and non-food component, but they differ in how the non-food component is defined. For details on how the poverty lines and poverty estimates are produced, see World Bank (2013).

7