rafmacalaba/datause-encoder-data huggingface dataset:
**Table S27. Lack of access to basic necessities by consumption** **<u>quintile</u>**

||Unable to Access<br>Medicine|Unable to Access<br>Staple|Unable to Access<br>Soap|
|---|---|---|---|
|Ethiopia|0.221***|0.009||
||(0.037)|(0.033)||
|Nigeria|0.011|-0.034|0.018|
||(0.024)|(0.032)|(0.019)|
|Uganda|0.217***|-0.129***|0.092***|
||(0.024)|(0.030)|(0.019)|
|Quintile 2|0.004|-0.081*|-0.071*|
||(0.043)|(0.047)|(0.039)|
|Quintile 3|0.001|-0.156***|-0.115***|
||(0.042)|(0.043)|(0.036)|
|Quintile 4|-0.039|-0.121***|-0.099***|
||(0.039)|(0.042)|(0.037)|
|Quintile 5|-0.113***|-0.218***|-0.138***|
||(0.034)|(0.039)|(0.036)|
|Ethiopia-Nigeria|0.000|0.107||
|Ethiopia-Uganda|0.919|0.000||
|Nigeria-Uganda|0.000|0.000|0.000|
|Observations|4,604|6,792|5,297|
|R-squared|0.072|0.038|0.030|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent answered “yes” to a question about the inability to access a basic necessity. Inability or ability to access a basic necessity is conditional on the household needing the item or attempting to purchase the item. The binary variable is regressed on indicators for each country, with Malawi as the base case, and each pre-COVID-19 household annual per capita consumption quintile, with the lowest consumption quintile as the base case. Note that the question regarding soap was not asked in Ethiopia. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

54
**THE ROLE OF THE EXTRACTIVE INDUSTRIES IN THE MAURITANIAN ECONOMY**

**Annual real GDP growth averaged just 4 percent during the mid-2000s; in 2006, however, growth shot up to over 11 percent on the strength of rising resource exports, and GDP growth rates have remained solid through 2013.** The rapid expansion in 2006 was driven by the start of oil production, as Mauritania’s first offshore oil wells came on line, although extraction rates fell drastically within a few months due to serious technical difficulties<sup>5</sup> . Mauritania’s overall macroeconomic indicators have continued to improve, thanks to the opening of the first gold and copper mines, and record iron ore prices. Every month the Cabinet authorizes

**Figure 1: Iron, Gold and Petroleum Prices, 1990-2012**

<!-- Start of picture text --> 1600<br>1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>Crude oil, average ($/bbl) Iron ore, cfr spot ($/dry mt)<br>Gold ($/troy oz)<br>USD<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> Sources: World Bank, prices in constant 2005 US$ new permits for mining exploration to foreign and national companies. The threats related to security in the interior parts of the country have not discouraged international mining companies and the total number of permits (to foreign companies) is – to date – approximately 200.

**Large-scale iron mining began in the mid-1960s, when the World Bank, the Mauritanian Government, the French Government and the international iron-mining consortium MIFERMA**<sup>**6**</sup> **financed the country’s first commercial iron mines.** Today SNIM is the majority owner of Mauritania’s iron mines and the sole extractor, with an annual production volume of roughly 11 million tons.<sup>7</sup> Current expansion plans are projected to boost extraction capacity to 25 million tons by 2018 and to 40 million tons by 2025. SNIM will remain the only iron ore company until 2020. SNIM has an important social dimension and has contributed to the achievement of several public goals by co-financing port and railways extensions, telecommunication projects and by creating some 10,000 jobs, both permanent and seasonal. It has even set up its own Foundation, and contributes significantly to several nationally relevant projects. Its participation to national budget has doubled from roughly US$140 million (on average, over the recent years) to US$300 million starting from 2014. Mauritanian iron ore tends to be high-grade, and the country’s proved and probable iron reserves are vast at an estimated 3 billion tons. Prospective partnership agreements between SNIM and international mining firms and

> 5 It quickly declined below 20,000 bl/day after mid-2007 and is now around 9,000 bl/day.  Hydrocarbon reserves are considered modest: proven and probable crude oil reserves at 310 mln barrels in 2008 compared with 2.1 bln for Chad.

> 6 The _Société Anonyme des Mines de Fer de Mauritanie_ was a joint venture by French, British, Italian and German steel firms established prior to Mauritania’s independence from France in 1960.

> 7 Source: International Trade Centre (ITC), Trademap database.

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- 1019 ISSN 0028-0836, 1476-4687. doi: 10.1038/s41586-023-06153-x. URL `https://www.nature.com/` 1020 `articles/s41586-023-06153-x` .

- 1021 K. Gugler, A. Haxhimusa, and M. Liebensteiner. Effectiveness of climate policies: Carbon pricing vs. 1022 subsidizing renewables. _Journal of Environmental Economics and Management_ , 106:102405, Mar. 1023 2021. ISSN 00950696. doi: 10.1016/j.jeem.2020.102405. URL `https://linkinghub.elsevier.com/` 1024 `retrieve/pii/S0095069620301285` .

- 1025 M. Ha-Duong, M. J. Grubb, and J.-C. Hourcade. Influence of socioeconomic inertia and uncertainty 1026 on optimal CO2-emission abatement. _Nature_ , 390(6657):270–273, Nov. 1997. ISSN 0028-0836, 14761027 4687. doi: 10.1038/36825. URL `http://www.nature.com/articles/36825` .

- 1028 S. Hallegatte, C. Godinho, J. Rentschler, P. Avner, I. I. Dorband, C. Knudsen, J. Lemke, and P. Mealy. 1029 _Within Reach: Navigating the Political Economy of Decarbonization_ . The World Bank, Nov. 2023. 1030 ISBN 9781464819537. doi: 10.1596/978-1-4648-1953-7. URL `http://elibrary.worldbank.org/` 1031 `doi/book/10.1596/978-1-4648-1953-7` .

- 1032 W. W. Hogan and D. W. Jorgenson. Productivity trends and the cost of reducing CO2 emissions. _The_ 1033 _Energy Journal_ , 12(1):67–85, 1991. URL `https://www.jstor.org/stable/41322403` .

- 1034 H. Hotelling. The Economics of Exhaustible Resources. _Journal of Political Economy_ , 39(2):137–175, 1035 1931. ISSN 0022-3808. URL `https://www.jstor.org/stable/1822328` .

- 1036 A. Ihsan, D. E. Abriningrum, B. Suharnoko, A. Rahmawati, and S. F. Giannozzi. In1037 donesia’s Fuel Subsidies Reforms. Report, World Bank Group, Washington, DC, 1038 May 2024. URL `https://openknowledge.worldbank.org/server/api/core/bitstreams/` 1039 `1d693935-3fad-55e1-91f4-3feae60dd23c/content` .

- 1040 Intergovernmental Panel on Climate Change. _Climate Change 2021: The Physical Science Basis_ . 1041 Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel 1042 on Climate Change. Cambridge University Press, 2021.

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**Table 2.7 Allocation of total bank loans outstanding by infrastructure sector**

Year-end 2006; <u>US$ millions, unless otherwise specified /a</u>

|Country|Transport|Telecoms|Transport,<br>communications &<br>storage|Transport,<br>commication, energy &<br>water|Electricity, water &<br>gas/public utilities|Construction of public<br>works /b|Total, infrastructure<br>sectors|Total, all economic<br>sectors|Infrastructure loans as<br>% total bank loans|
|---|---|---|---|---|---|---|---|---|---|
|Benin|||82.4||41.2||123.6|1,009.0|12|
|Burkina Faso|||74.9||9.6||84.5|817.9|10|
|Cameroon|—|—|—|—|—|—|—|—|—|
|Cape Verde|||28.3||58.0|21.3|107.6|239.6|45|
|Chad|0|0|0|0|0|0|0|270.7|0|
|Congo, Dem. Rep.||||5.8|||5.8|70.0|8|
|Côte d’Ivoire|||271.7||63.0||334.7|2,267.3|15|
|Ethiopia|||243.8||4.1||247.9|3,314.2|7|
|Ghana||||177.9|||177.9|2,156.6|8|
|Kenya|||574.5||||574.5|6,438.7|9|
|Lesotho||||20.9|||20.9|48.7|43|
|Madagascar|67.6||||||67.6|2,164.4|3|
|Malawi|||14.0||3.4||17.4|186.7|9|
|Mozambique|||60.7||||60.7|986.7|6|
|Namibia|||93.1||24.0||117.1|3,003.7|4|
|Niger|||49.0||17.5||66.5|329.2|20|
|Nigeria|||1,360.8||1,082.8||2,443.6|19,765.9|12|
|Rwanda|||24.8||0.8||25.6|311.4|8|
|Senegal|||193.1||92.8||286.0|2,301.8|12|
|South Africa|||5,011.0||1,263.8||6,274.9|249,020.2|3|
|Sudan|||5.2||||5.2|55.3|9|
|Tanzania|93.0||||||93.0|1,222.7|8|
|Uganda|71.9||||3.0||74.9|997.5|8|
|Zambia|3.5|33.1||5.5|30.7||72.9|1,088.7|7|
|Total AICD|236.0|33.1|8,087.5|210.2|2,694.7|21.3|11,282.7|298,066.8|4|
|Chile|||2,900.3||2,085.9||7,213.4|63,063.8|10|
|Malaysia|||2,790.6||1,322.8|910.3|5,023.8|155,153.6|3|

Sources: National central banks, finance ministries, and other national financial authorities.

a. Data for end-2006 with the exception of the Democratic Republic of the Congo (end-2003), Madagascar (end-2004), Namibia (end-June 2005), Tanzania (end-2005), Ghana (June 2006), South Africa (end-September 2006), Zambia (May 2007), and Chad and Malaysia (March 2006).

b. Breakdown by type of public works financing (for infrastructure versus other public works) is not available.

— = Not available.

20
**Consumption and trade shares (** p p **):** The National Accounts Main Aggregates database of the United Nations Statistics Division (UNSD) (UN, 2019) contains long-term data on final CC<sup>�</sup> , NN<sup>�</sup> consumption, exports, and imports at the economy-wide level. The sectoral splits of any of the three variables are, however, not available. On the other hand, the UNSD database does report the sectoral composition of value-added. Hence, given the values of other variables in equation (5), it is possible to infer the sectoral composition of final consumption p . To execute this strategy, I first aggregate the value-added series to the three sector level using the CC<sup>�</sup>

To execute this strategy, I first aggregate the value-added series to the three sector level using the earlier taxonomy. Like raw employment data, the value-added sectoral shares computed using the UNSD data also feature spurious jumps. Therefore, I refine the raw series before using it in the analysis (see data appendix for details). Next, I source data on the sectoral exports and imports shares from a different source, which I use to estimate the net export shares p . I exploit the Atlas of Economic Complexity (Center for International Development, 2018) database that reports a country’s exports and imports across 11 NN<sup>�</sup> sectors. Agriculture and services trade data in the database is presented at the aggregate level and is consistent with ISIC Rev. 3.1. The database reports the industrial data disaggregated into nine subsectors, and I sum across them to construct sectoral exports and imports. Once I have the estimates of p and p (and the estimates of and ) from the above steps, I employ equation (5) to recover final consumption shares VV<sup>�</sup> NN<sup>�</sup> p . BBpp MMpp **Labor market wedges (** p **):** Data on value-added shares CC<sup>�</sup> p and employment shares p allows expressing sector 's labor productivity relative to any arbitrary sector . As agriculture is almost always TT VV<sup>�</sup> LL the sector with the lowest value-added per worker, I measure productivity in industry and services ii jj relative to agriculture and set . Specifically, the wedge applicable to sector in period equals the ratio of its value-added per worker aappaa to agricultural labor productivity iippaa . ττ = 0 ∀ pp, tt ττ ii **Labor share of income** ) **and sectoral linkages** iippaa ) **:** I obtain the labor share of value-added aappaa tt ww ww at the sectoral level from the Global Trade Analysis Project (GTAP) database (pp pp Center for Global (BB (MM Trade Analysis, 2015). The GTAP data are available for three overlapping years over which I perform the quantitative analysis using the time-series on employment, consumption, and trade shares. I estimate the labor share of value-added for each of the three years, the average of which serves as the benchmark labor share for the entire period under analysis.

10
constant equal to 1 is added to the trade per worker variable. We assess three types of trade variables – exports, imports and GVC participation.

Ordinary least squares (OLS) estimates of β may be biased if there are omitted variables correlated both with labor market outcomes and trade. More specifically, municipalities with higher labor incomes or employment may be more likely to attract foreign direct investment and thereby become a trade hub. This would lead to OLS estimates that are upward biased. Conversely, policy incentives that encourage trading firms to locate in areas with lower employment or labor incomes would downward-bias OLS estimates. To overcome this challenge, an instrumental variable (IV) estimation is used to isolate changes in Mexican trade that are not driven by local factors.

Following Rodríguez-Castelán et al. (2020), we take advantage of sectoral patterns of other developing-countries’ trade with the United States, excluding Mexico. In particular, we apply the following instrumental variable for exports: where Xm,s,t∗ is the predicted level of exports by sector, defined as follows: and φs,t is the growth rate of exports at the sectoral level s from other low- and middle-income countries (excluding Mexico) to the United States between the initial year and year _t_ . Thus, changes in the instrument are not driven by factors at the municipality level, but only by trends in the exports of other low- and middle-income countries to the United States. These Bartik-type instruments are widely used in the empirical literature to estimate the local impacts of trade.<sup>5</sup> For imports, we mirror this methodology and focus on imports of other low- and middle-income countries (excluding Mexico) from the US as follows: where Mm,s,t∗ is the predicted level of imports by sector s, defined as follows: ′ and φs,t is the rate of growth of sector s imports of other low- and middle-income countries from the United States between year t0 and year t. Note that since our GVC participation measures draw on Mexican firm-level data (for more details, see section 2.3), we are unable to create similar instruments for them.

> 5 For example, Autor, Dorn, and Hanson (2013) estimate the impacts of import competition from China on local US labor markets by combining the initial local distribution of employment across sectors and nationwide changes in imports from China by sector. They instrument this variable using changes in Chinese imports by other high-income countries.

9
respondent’s tenure in his or her public sector position.<sup>29</sup> We do so to account for the possibility of “late” (and “early”) treatment. That is to say, if the receipt of World Bank assistance occurred towards the end of the period of time in which a respondent evaluated the World Bank’s level of policy influence (i.e., one of the last years when his/her job required interacting with the World Bank), then one would need to measure treatment exposure over the entire course of the treatment period.<sup>30</sup> When we replicate our baseline models with these alternative treatment and control variables, we obtain similar results. Although some of the estimates are no longer statistically significant, the _n_ _~~e~~ sw_ _~~m~~ ean_ remains positive in sign and statistically significant in the most rigorous model specification reported in Table A3.

Second, we test whether our results are sensitive to the inclusion of controls that measure a potentially significant confound: client country demand for reform assistance. If the World Bank finds and works with “sympathetic interlocutors” in client countries who share their policy preferences, then it is possible that these government officials would have implemented the World Bank’s policy ideas in the absence of ESW. To address this potential threat to inference, we exploit two unique features of the _2014 Reform Efforts Survey_ : a question that asked each respondent to report how frequently he or she interacted with the World Bank officials via phone, email, video, or face-to-face communications during his or her period of public service,<sup>31</sup> and a question that asked each respondent to reveal the intensity of his or her preferences for status-quo challenging reform.<sup>32</sup> We use the first variable ( _freq_ _~~c~~ omm_ )

> 29We calculate these mean values by using all observations between and including the respondent’s start year and end year.

> 30We also set our (country-level) control values to their mean values of the duration of a respondent’s tenure in his or her public sector position to account for potential confounds over the full period of treatment. By way of illustration, consider the possibility that GDP per capita increased sharply between a respondent’s start year and end year. We know that GDP per capita negatively correlates with World Bank policy influence, so if our control variable does not capture the fact that GDP per capita increased between the respondent’s start year and end year, we will have omitted a potentially important confound. The two principal disadvantages of this approach are endogeneity and measurement error (if respondent assessments of the World Bank’s policy influence are based on actions taken by the World Bank early in their tenure while the measurements of the treatments and controls take into account data from later years).

> 31Respondents were allowed to select from one of six response options: once a year or less, 2 or 3 times a year, about once a month, 2 or 3 times a month, about once a week, or daily.

> 32Respondents were asked to indicate if they thought that their governments pursued “too much reform,”

24
- 31 -

43. Two other kinds of personal taxes were also assessed. Low wage workers in the urban economy were subject to a two tier graduated tax and males over the age of 18 who were not subject to any other type of income tax had to pay a nominal poll tax. These two taxes, however, were not important sources of revenue. Together they contributed only about 12 percent of total personal income tax collections which, in turn, constituted only 16 or 17 percent of total tax collections. As a result, it was estimated that as few as 10 percent of urban households were responsible .jr as much as 80 percent of the roenues collected through all types of personal income taxes.

44. The tax study made several recommendations aimed at broadening the tax base by closing loopholes and making the personal income tax fairer. To remove the burden of the tax from low income households, the study recommended either that the absence of personal exemptions be compensated for by introducing a zero rate income bracket or that the personal exemptions be reinstated. To broaden the base, the study recommended repealing the interest deduction on housing mortgage loans as housing did not generate any taxable income. The study also recommended repealing the exemption on interest income earned at Post Office Savings Banks even though in inflationary conditions taxing interest income is equivalent to taxing capital. The rationale for this recommendation was that this exemption was originally intended to encourage greater savings on the part of low income households but was increasingly being used by corporations for purposes of tax arbitrage. It was also recommended that greater use be made of creditable taxes on repatriated earnings from Malawi. Thus, a 10 percent withholding tax on all forms of remittances to foreigners, including interest, dividends, rents and royalties, was suggested to
Taking the average and standard deviation across simulations, we arrive at the point estimate of poverty rate for province _p_ and its associated standard error. The aggregate welfare estimates derived in this manner serve as the baseline information for each province or any geographic units. We shall refer to the poverty estimates before the trade liberalization created in this way as the ex ante poverty estimates.

The next step is to simulate how much change would occur across different sectors of the economy. As with Bourguignon et al. (2005), we need to find error terms for each _surve_ y record. It is straightforward to find μ _hi_ and η _h_ , because they are just the observed value minus the predicted value. When the wage data is missing, μ is drawn from the normal distribution. We also draw _u_ ˆ _hiw_ , _u_ ˆ _his_ and _u_ ˆ _hin_ from a Gumbel distribution in a way consistent with the observed employment status. Therefore, combining Eq(1)– Eq(4) and Eq(5)–Eq(8) , we have the following relationship: The macroeconomic CGE provides us with aggregate wage and non-wage household incomes in each segment of the economy, as well as the aggregate labor supply from wage-earners and self-employed individuals. In other words, we obtain aggregate macroeconomic account after the trade liberalization, which we shall denote with tilde � (e.g. _WGI l_ ). To maintain the consistency between the left-hand-side and the righthand-side of the system of equations above, we need to change at least one of the parameters in each equation. Following the method outlined by Bourguignon et al. (2005), we assume that the macroeconomic changes are channelled through the intercepts in the above equations. Bourguignon et al. (2005) show that this assumption implies a neutrality of the change with regard to individual or household characteristics.

16
willingness to get vaccinated by around 50 percent (Hoy et al, 2021).

# **2.1.2 Intra-household dynamics and the acceptance of vaccines**

Clustering of opinions and exchange of information is a common phenomenon in social networks, particularly households (Christakis and Fowler, 2013). The sources of such information can be external agents, such as health or government authorities, or internal agents, such as people in one’s social circle ranging from friends or co-workers to people living in the same household. The conditions under which intra-household decisions are made, such as gender roles in the household, status within the household and cultural norms, impact household outcomes (Ashraf, 2009). For example, studies on food security have shown that gendered aspects of intra-household decision making impact production diversity and dietary quality in Ethiopia (Sariyev et al., 2020) and Malawi (Jones et al., 2014).

Social networks and intra-household dynamics can play an essential role in influencing attitudes in vaccination uptake. A review of eleven studies on vaccine uptake and social networks found that individuals held more positive attitudes and had a higher probability to get vaccinated if their network consists of people, especially family and friends, with positive attitudes towards vaccines (Kontantinou et al., 2021). In a similar way, negative attitudes and lower acceptance were transmitted within households (ibid). A cross-cultural study by Taylor et al (2016) on household influence on flu vaccine decisions found that household members recommend one another to vaccinate, albeit at different levels, and that advice is congruous with an increased vaccination uptake.

Recent studies related to COVID-19 have also emphasized the role of social networks and households in impacting behavior. There is evidence that closely knit communities have reported relatively fewer COVID-19 cases and deaths because of the spread of information about preventive measures (Fraser et al., 2021; Makridis and Freeman, 2021; Pitas and Ehmer, 2020). In a study on COVID-19 vaccine uptake among Americans, Hao and Shao (2022) found that people are more likely to get vaccinated if a higher proportion of their family or close friends had already been vaccinated. An IMF study by Khan et al. (2021), 8