rafmacalaba/datause-encoder-data huggingface dataset:
Each country’s distribution is represented by the average incomes of the ten deciles. This is not dissimilar from other studies in the literature such as Bourguignon and Morrisson (2002) who use 11 quantiles. This ignores any within-decile inequality, thus, as argued by Anand and Segal (2008) understating within-country inequality and perhaps global inequality. Our choice of decile groups was dictated by PovcalNet, where more detailed information is unavailable.<sup>30</sup> Also, because of consistency and ease of discussion, we decided to use deciles also in those surveys where more detailed information was available.

# **2.2** **<u>Survey selection</u>**

The surveys included in the database need to meet two conditions: First, they need to be within two years of a benchmark year. Second, they need to be at least three and no more than seven years from the previous and next survey. The rationale of the second condition is not to allow surveys that are either too close or too far apart from the interval of five years since a lot of the analysis is based on the assumption that five-year intervals hold throughout the sample. Table 1 shows the years between the survey year and the benchmark year.<sup>31</sup> The choice of benchmark years is essentially arbitrary and we followed Milanovic (2012) in choosing 1988, 1993 and 1998. Global household survey coverage is very poor prior to 1988. 2003 and 2008 were chosen in order to obtain equally spaced benchmark years. Compared with Milanovic (2012), we managed to obtain a closer fit to the benchmark year in all years with roughly 3⁄4 of surveys conducted within one year of the benchmark year.

We use a mix of income and consumption surveys, as is customary in this literature. Although there are obviously important differences between income and consumption, we refer to them, as already mentioned, interchangeably.<sup>32</sup> We do not adjust for differences between income and consumption surveys because any such adjustment, applied to deciles, would be arbitrary.<sup>33</sup>

> 30 Using PovcalNet, Segal (2011) obtains more detailed fractiles (limited to represent at most 5 million people) by accessing the detailed estimation code of the Lorenz curves which PovcalNet fits on the grouped data. This is cumbersome and was not feasible given the large number of surveys we deal with, but may be addressed in future work.

> 31 For example, for the benchmark year 2008, 7.4% of surveys were conducted in the year 2006. In the case of PovcalNet, the survey year is sometimes not an integer, which would happen if a survey was conducted over more than one year. In all cases we took the start year of the survey, i.e. we rounded down the non-integer years.

> 32 Inequality tends to be lower in terms of consumption than in terms of income (Deininger and Squire, 1996), and (at least in the USA) has increased by less in consumption-terms (Fisher et al., 2013). In the full PovcalNet data (not the sample used in this paper), the average Gini index of consumption surveys (37.98%) is approximately 10 Gini points

17
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https://www.eia.gov/electricity/data/browser 31
# **Table 8. Middle Eastern Countries' Twenty Largest Three-Digit Intra-Regional Exports, 1970, 1980 and 1990.**

||V|alue of Exports ($0|00)|Percen|t of Total Ex|ports(|
|---|---|---|---|---|---|---|
|Description (SITC)|1970|1980|1990|1970|1980|1990|
|Crude Petroleum (331)|31,684|2,051,579|1,886,796|0.93|12.28|21.25|
|Petroleum Products (332)|29,051|963,226|424,976|0.86|5.76|4.79|
|Fresh Fruit and Nuts (051)|20,727|48,472|331,344|0.61|0.29|3.73|
|Fresh Vegetables (054)|18,887|46,829|288,776|0.56|0.28|3.25|
|Live Animals (001)|32,845|19,021|283,055|0.96|0.11|3.19|
|Plastic Materials (581)|1,867|7,352|279,824|0.06|0.04|3.15|
|Iron and Steel Shapes (673)|3,239|64,125|255,721|0.09|0.38|2.88|
|Aluminum (684)|1,605|16,789|143,153|0.05|0.10|1.61|
|Nonfur Clothing (841)|8,274|92,921|137,907|0.24|0.56|1.55|
|Articles of Paper (642)|902|7,133|95,222|0.03|0.04|1.07|
|Fixed Vegetable Oils (421)|2,563|1,411|93,289|0.08|0.01|1.05|
|Electrical Distributing Machinery (723)|1,562|7,778|85,611|0.05|0.05|0.96|
|Textile Yarn and Thread (651)|4,283|543|84,181|0.13||0.95|
|Machines Nonelectric, nes (719)|12,039|39,853|83,418|0.35|0.24|0.94|
|Road Motor Vehicles (732)|34,194|149,107|82,060|1.01|0.89|0.92|
|Natural Gas (341)|934|88,500|81,831|0.03|0.53|0.92|
|Gold Silverware and Jewelry (897)|2,608|26,234|79,358|0.08|0.16|0.89|
|Copper (682)|207|1,954|72,775|0.01|0.01|0.82|
|Soaps and Cleaning Preparations (554)|1,441|10,985|70,879|0.04|0.07|0.80|
|Medicinal Products (541)|3,198|3,526|69,672|0.06|0.02|0.78|

Source: United Nations Series D Trade Tapes. Data are based on import statistics of the regional countries. Information on Iran, Bahrain and Lebanon's imports are missing from the 1990 data. The 1980 statistics do not include Lebanon and Iran while UAE data are not included in the 1970 totals. See the notes to Table 7 for details on how the totals were compiled.

co
# **1.1 Features of Taxation in Africa**

Tax collection in Africa is low, but similar to other regions at a similar income level (Figure 1). In 2018, the most recent year with wide data coverage, Sub-Saharan African countries collected 14 percent in taxes as a share of GDP (UNU-WIDER Government Revenue Dataset, 2021). This continent-wide average masks significant variation across countries. High- and upper-middle-income countries like Seychelles, Namibia and South Africa have rates as high as 28 - 33 percent whereas low-income countries like Chad, Democratic Republic of Congo and Ethiopia have rates as low as 7 percent. These numbers have remained stagnant over the last 3 decades, with African countries collecting an average of 12 - 15 percent of GDP as taxes from 1990 to 2020. Both the South Asia region and the Middle East and North Africa region have similarly low rates as Sub-Saharan Africa of about 14 percent. In contrast, the Europe and Central Asia region has the highest rate of 32 percent (UNU-WIDER Government Revenue Dataset, 2021). Across all countries, on average, higher income countries collect a higher share of GDP as taxes.

The composition of taxes in Africa is also roughly similar to that of countries at similar income levels. Figure 2 presents evidence on the reliance of countries on different tax instruments: taxes on consumption, personal income, corporations, payroll, trade and property. As in almost all regions, the greatest component of taxes in Sub-Saharan Africa is consumption taxes (49 percent), which consist of sales tax, value added taxes and excise taxes. Some striking differences of African countries, even compared to countries at a similar income level, are that African countries collect a minimal amount in payroll taxes, possibly a reflection of the low share of the economy employed in the formal sector.<sup>1</sup> In contrast, African countries collect more trade taxes than countries at a similar income level.

With regards to tax rates, the tax rate on income, profits and capital gains in African countries range from 3 percent to 48 percent. On average, African countries have a higher

> 1According to Medina and Schneider (2018), the shadow economy amounted to about 40 percent of Sub-Saharan Africa’s GDP in 1991-2015, compared to 17 percent of OECD countries’ GDP.

3
and rising anti-trade and anti-globalization sentiment associated with a persistent slowdown in trade flows are, however, changing the discussion towards the potential impacts of increasing protectionism.

An important part of the existing body of literature concerns the economic impact of trade wars. The seminal work of Johnson (1954) first conceptualized the beggar-thy-neighbour motive behind increases in tariffs and showed that in a trade war a country can gain by imposing an optimal tariff even when others retaliate. Subsequent literature generalized Johnson’s (1954) findings using more general assumptions such as imperfect competition (Brander and Spencer 1984; Eaton and Grossman 1986; Broda et al. 2008), specific tariffs (Horwell 1966) and quotas (Tower 1975).

Ossa (2014) was the first to empirically estimate the impact of introducing optimal tariffs and the impacts of a global trade war. Using a multi-country, multi-sector general equilibrium model with inter- and intra-industry trade and special interests, the author models tariffs that 1) manipulate terms of trade; 2) shift profits away from countries and 3) protect politically influential industries, and estimates that world trade war tariffs average 63 percent which if implemented would translate into welfare losses of 2.9 percent.

More recently, Noland et al. (2016) used a traditional, multi-equation econometrically estimated macroeconomic model developed by Moody’s Analytics to analyse the impacts of different trade war scenarios between the US and China/Mexico. The authors conclude that in a full trade war scenario in which the US imposes a 45 percent tariff on nonoil imports from China and a 35 percent tariff on nonoil imports from Mexico followed by China’s and Mexico’s symmetric response on US exports is expected to push the US economy into recession by 2019 with an associated plunge in investment of 9.5 percent and unemployment rate of 8.4 percent. In the same vein, Bouet and Laborde (2017) use a static computable general equilibrium model called MIRAGRODEP to measure the impact of potential trade wars between the United States, on the one hand, and China and Mexico, on the other. An innovative feature of their is that tariffs are en- approach computed dogenously considering different objective functions such as to minimize welfare losses, minimize terms-of-trade losses, to generates the same amount of collected revenue or tariffs that replicate a Nash equilibrium. The authors conclude that out of the 18 potential scenarios considered, there is none in which the US can benefit by neither an increase in domestic welfare nor GDP. Using econometric modeling techniques and a new database on the content of trade agreements, Mattoo, Mulabdic and Ruta (2016) argue that undoing US trade agreements would result in a decline in US exports by up to 4.3 percent and real income by 3.2 percent. This in turn would lead to the decline in the exports of the US’s trading partners by 0.1-7.2 percent.

8
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National Health Insurance Scheme, 11 percent of households in Ghana spent over 5 percent of their income on health in 2005 and 2006 (Akazili et al 2017).<sup>2</sup> Moreover, it is also likely that the illness of a worker represents a serious income shock -- even absent spending on health care -- in an environment where few workers have steady wage employment. Indeed, households appear to lack access to effective financial tools to smooth income shocks; for instance, only 47% of respondents at baseline reported having a bank account. These results are likely not unique to urban Ghana. Many developing countries have sizeable urban populations which are, by and large, less well insured against income shocks than their rural counterparts (Wagstaff 2007), face more frequent health shocks (Heltberg and Lund 2009), and frequently lack access to financial means to smooth income even when they live near banks (Banerjee and Duflo 2007).

Our results sit at the intersection of two literatures: household-level responses to health shocks in developing countries and labor supply responses to income shocks. Aside from the direct cost of treatment, negative health shocks also decrease household income by reducing workers’ productivity (Pitt and Rosenzweig 1984; Thomas and Strauss 1997; Thomas et al 2006). Previous research has documented that households respond to health shocks by selling or consuming assets (Asfaw and von Braun 2004; Islam and Maitra 2012), borrowing (Islam and Maitra 2012; Mohanan 2013), or receiving transfers from other households (Asfaw and von Braun 2004; De Weerdt and Dercon 2006; Genoni 2012). The extent to which total consumption is smoothed varies by setting; Genoni (2012) and Mohanan (2013) cannot reject the null hypothesis of full consumption smoothing, while Dercon and Krishnan (2000), Gertler and Gruber (2002), Asfaw and von Braun (2004), and Sparrow et al (2014) find significant decreases

> 2 This spending could come because an individual is not enrolled in the NHIS or is enrolled and does not have an insurance card (Akazili et al 2014), some conditions are not covered (Alhassan et al 2017), premia are charged, and user fees may be charged at health facilities (Akazili et al 2017).

5
|IDP|_Male_|16.50|2,911|0.250|_(0.226,_<br>_0.274)_|42.9|_(38.9,_<br>_47.0)_|58.3|_(57.6,_<br>_58.9)_|
|---|---|---|---|---|---|---|---|---|---|
|Sudan<br>Population|_Female_|16.05|2,832|0.260|_(0.236,_<br>_0.283)_|44.2|_(40.4,_<br>_48.1)_|58.7|_(58.0,_<br>_59.4)_|
|<br>Non-IDP|_Male_|33.53|5,916|0.056|_(0.036,_<br>_0.077)_|9.8|_(6.3,_<br>_13.3)_|57.4|_(55.6,_<br>_59.3)_|
|Population|_Female_|33.93|5,986|0.051|_(0.029,_<br>_0.073)_|9.0|_(5.2,_<br>_12.8)_|56.6|_(55.0,_<br>_58.2)_|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

There is large variation in the MPI incidence and intensity within and across the five countries, both in terms of gender and displacement status. In Sudan, for example, male and female IDPs report an incidence of poverty nearly five times higher than their nondisplaced counterparts, while in Nigeria, the differences are far smaller in magnitude. Still, the displaced populations always experience higher _incidence_ of multidimensional poverty, although in Nigeria, Somalia, and South Sudan, the intensity of poverty is higher among the non-displaced population. Table 6 checks the significance of the differences using an adjusted Wald test of means on the female populations by displacement status (Table 6).

_Table 6: Adjusted Wald test of means by displacement status and gender_

||Fem|ale||IDPs|||Non|-IDPs||
|---|---|---|---|---|---|---|---|---|---|
|Country|IDPs v non-|IDPs||Female v|Male||Femal|e v Mal|e|
||MPI|H|A|MPI|H|A|MPI|H|A|
|Ethiopia|63.75***|63.39***|2.46|10.52***|8.60***|5.97**|0.87|0.87|2.46|
|N.E. Nigeria|2.00|2.78*|4.31**|9.05***|8.27***|0.70|0.34|0.45|4.31**|
|Somalia|7.47***|8.25***|1.20|1.04|1.03|0.00|3.29*|3.04*|1.20|
|South Sudan|2.61|2.55|0.67|1.34|1.22|0.72|1.22|1.20|0.67|
|Sudan|163.27***|162.53***|5.67**|1.50|1.09|4.31**|0.74|0.61|5.67**|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

In both Ethiopia and Somalia, women and girls who are forcibly displaced experience a significantly higher rate of multidimensional poverty, on average, than their nondisplaced counterparts, both in MPI and incidence; however, intensity of poverty is not significantly different between these two populations in either country. In Sudan, forcibly displaced women and girls see significantly higher rates of multidimensional poverty – 16
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21
# Examples

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everything else being equal, the value of investment between US states in the post-shock period, compared to the EU, is more than double than investment between the US and other countries.<sup>11</sup> Similarly to the event study approach, we do not find evidence of an increase in nearshoring, proxied by distance, or friend-shoring, proxied by NATO. If anything, investment by US multinationals in countries that are military allies of the US decreased over time.

> 11 (0.795) −1 = 1.214. yye

13
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