rafmacalaba/data-use-mentions-extended huggingface dataset:
**|
||(0.058)|(0.062)|(0.235)|(0.189)|
|Constant|8.123|9.683|9.659|12.236|
||(0.576)|(0.614)|(1.985)|(1.548)|
|Year dummies|Yes|Yes|Yes|Yes|
|Country dummies|No|No|Yes|Yes|
|R<sup>2</sup>|0.861|0.884|0.975|0.982|
|n|281|281|281|281|

Note: * indicates significance at the 10% level; ** indicates significance at the 5% level; *** indicates significance at the 1% level. Robust standard errors in parentheses.

# **5. Conclusions**

Jobs and labor earnings can be a potential driver of poverty reduction and increased prosperity in developing countries. A channel through which exports can have an impact on poverty is by supporting jobs and increasing earnings for those employed.

In this paper we developed a methodology to measure wage content embodied in exports across countries and sectors. To create this database, we combined different releases of the GTAP database from 1995 to 2011. Although recent years have seen the emergence of different databases of global IO tables, we believe the GTAP data offer a balance between quality and country coverage, making it more suitable than other alternatives for our purposes. The resulting data set includes national IO linkages through trade for a maximum of 120 countries and 57 sectors in 2011. We also use data from the ILO to construct the jobs content of exports, resulting in 66 countries and 11 sectors in 2011. The data sets offer a unique opportunity to examine the value added linkages between different sectors for both skilled and unskilled labor and jobs. We analyze not only the direct contribution of labor to value added
**Table 1: Capabilities and the Indicators Used to Represent Them**

|**Category**|**Capability**|**Indicator**|**Units**|**Source**|
|---|---|---|---|---|
|Fixed|Proximity to markets|GDP‐weighted<br>distance|Kilometers*|CEPII|
||Natural capital|Total value of<br>natural capital|Current $US<br>billion|World Bank Wealth of<br>Nations|
|Long‐term<br>Policy<br>Variables|Human capital|Average years of<br>schooling (>15 years<br>oldpopulation)|Years|Barro and Lee (2010)|
||Physical capital|Capital stock per<br>person|2005 $US<br>thousands|World Development<br>Indicators (WDI) and<br>World Bank staff<br>calculations|
||Institutional capital|Rule of Law|Rating from ‐<br>2.5 to 2.5|World Governance<br>Indicators|
|Short‐<br>term|Logistics/connectivity|Logistics<br>Performance Index|Rating from 1<br>to 5|World Bank LPI|
|Policy<br>Variables|Wage<br>competitiveness|Minimum wage for a<br>19‐year old worker<br>or an apprentice|$US*|World Bank Doing<br>Business|
||Market access|Overall Trade<br>Restrictiveness Index<br>(of Trading Partners)|Uniform tariff<br>equivalent of<br>partner<br>country tariff<br>and non‐tariff<br>barriers*|Kee, Nicita and<br>Olarreaga (2008, 2009)|
||Access to inputs|Overall Trade<br>Restrictiveness
2011 international $ (PPP)|359 (587)|236 (398)|413 (504)|
|per capita, in constant 2011 international $||||
|(PPP)|43 (46)|51 (88)|36 (48)|

_Notes_ :<sup>1</sup> improved building material refers to brick, stones, concrete, or tiles.<sup>2</sup> Improved roofing material refers mainly to zinc (Senegal) and iron sheets (Burkina Faso).

_Sources:_ Senegal (2011), Burkina Faso (2010), Rwanda (2011).

Table 3 presents descriptive household statistics. Senegalese households are the largest with more than 12 members on average, followed by Burkina Faso (eight members), and Rwanda (five members). Most households are headed by men, with the share of female head of households higher in Rwanda (15 percent) than in the other two countries. Senegalese and Rwandan households tend to have slightly higher educational attainment (6 and 5 years, on average), compared to households in Burkina Faso (2 years).

Bank accounts are held by less than 20 percent of Senegalese and Burkinabe 16
functionality. In fact, among water points that are one-year old, hydrogeological factors such as groundwater productivity, groundwater storage and depth to groundwater have the greatest effect among other factors on their functionality.

# **4. Data and Methodology**

# _(a) Water Point Mapping Data_

This study uses the WPM data compiled in November 2016)<sup>15</sup> . The analysis made use of data on 40,917 water points out of a total of 83,295. This final figure was derived after we 1) restricted our sample to those between 1 and 20 years old; 2) removed other water schemes, defined as a water point with multiple taps; 3) identified other additional duplicates; and 4) dropped water points with incomplete information<sup>16</sup> . Information pertaining to the settlement type, types of pump, types of promoters and water source is found in the WPM dataset, in addition to the functionality status of the water points. Following Andres et al., (2018) and Fisher (2015) a number of hydrological information were combined with the WPM dataset which were obtained from the British Geological Survey (BGS) (MacDonald et al., 2012). The hydrological database included groundwater productivity<sup>17</sup> , groundwater storage<sup>18</sup> and depth to groundwater<sup>19</sup> . Each data were presented in the form of a map with a 5km resolution grid and they were geospatially matched to each water point available in the WPM dataset where possible. The water points were matched such that only about one percent of water points in the dataset did not have at least one of those measurements.

# _(b) Methodology and Empirical Strategy_

First, <mark>Locally Weighted Scatterplot Smoothing</mark> (“Lowess Smoothing” of running-line least squares) is employed to estimate a locally weighted non-parametric regression of ‘non- _functionality’_
in the fight against energy poverty should reflect the aspect of energy poverty that the government aims to address.** Suppose the government aims to implement long-term approaches to address energy poverty via renovation programs, for example. In that case, observational indicators about the square meters renovated or the energy efficiency of buildings and consensual metrics on the share of households experiencing leakages might be most appropriate. If governments are most interested in tackling the income component of energy poverty via social protection programs, for example, a measure incorporating total household income might be most appropriate. Similarly, if policymakers wish to address the dimension around low access to high-quality energy, measures that track connectivity to energy might be most suited.

**Regarding expenditure-based approaches, we recommend relating energy expenditure to the measure used to define monetary poverty.** This means that countries should use total household expenditure to scale energy expenditure in the case of relative measures when they also use consumption to define poverty. If their welfare measures rely on income-based approaches, they should relate energy expenditure to household income. Nevertheless, this approach should only be applied if income information from expenditure surveys is reliable. If income is unreliable, researchers and statisticians can explore alternative approaches to combine information on expenditure and

> 46 For more information, see the website: <u>https://kbs-frb.be/fr/barometre-de-la-precarite-energetique-2009-2017</u>

> 47 The European Energy Poverty Observatory (EPOV) initiative receives funding from the European Commission and is executed by a consortium of 13 diverse organizations, including universities, advocacy groups, think tanks, and representatives from the business sector. The University of Manchester leads this consortium. EPOV has several objectives, including enhancing information transparency and policy effectiveness by consolidating diverse data and knowledge sources existing across the EU. Additionally, it aims to offer an easily accessible resource that fosters public engagement and supports well-informed decision-making for local, national, and EUlevel authorities. The Observatory strives for a comprehensive understanding of energy poverty in the European Union and intends to aid
10 However, source countries’ FDI is not originating from a single resident location in practice. To reflect this fact in our model, we incorporate costs to treaty-shopping. Specifically, we assume that the MNE incurs costs C� if the share of funds provided from location i deviates from this location’s actual funding potential ~~α~~ � ~~.~~ In the empirical application, we use GDP weights to proxy for funding potential. Following the literature on international profit shifting (see e.g. Huizinga and Laeven,2008), we assume treaty shopping costs take the simple quadratic functional form: where the parameter γ determines the overall costs of treaty shopping.<sup>23</sup> A deviation between real funding potential and reported financing is costly as it requires the establishment of new organizational structures, which may need to have sufficient substance for an entity to qualify for treaty benefits.<sup>24</sup> As γ→0, treaty shopping costs tend to infinity, in which case the actual funding share in each location is no longer a choice variable.

Equation (2) suggests that _marginal_ tax avoidance costs are linear in the avoided tax base. In the context of our model, this implies that variation in a country’s bilateral tax treatment of income flows provides an identification device for treaty-shopping. In contrast, van’t Ried and Lejour (2018) employ a network analysis and assume that _total_ tax avoidance costs are linear in the avoided tax base. As a result, the minimal bilateral tax burden determines income flows in their framework, rather than the entire distribution of a country’s bilateral tax rates. Note that all investments should be rerouted through a single conduit country if total treaty-shopping costs are linear in the avoided tax base. This assumption is easier to reconcile with empirical data when using bilateral FDI data, such as van’t Ried and Lejour (2018), than when using aggregate FDI data as we do.<sup
# **Figure 20. Long-term growth and inflation forecasts**

<!-- Start of picture text --> A. 2020 GDP growth forecasts over time B. 2020 inflation forecasts over time<br>Percent Percent<br>7 Forecast in 2010 Forecast in 2010<br>5<br>6 Forecast in April 2016 Forecast in April 2016<br>4<br>5<br>4 3<br>3<br>2<br>2<br>1<br>1<br>0 0<br>World EMDEs United Euro Japan World EMDEs United Euro Japan<br>States Area States Area<br><!-- End of picture text --> Sources: Consensus Economics, World Bank.

Notes: A: GDP-weighted average growth rates for World and EMDEs (emerging market and developing economies). The World aggregate is based on 45 countries for which long-term consensus forecast data are available. EMDEs consist of 22 economies. B: GDP-weighted average CPI inflation rates for World and EMDEs. From these two aggregate groups, Venezuela, RB, is excluded due to high inflation forecast.

An additional and perhaps more controversial step might involve so-called “helicopter money,” a concept initially floated by Friedman (1969). The practical implementation of such a policy was first suggested by Bernanke (2002) to address persistent deflation in Japan. The idea is to stimulate aggregate demand by increasing public spending and/or cutting taxes through monetary financing. The implementation of such policy might involve infrastructure spending or direct tax rebates to households financed through an increase in money supply. The growth in aggregate demand, all else being equal, could eventually inflate prices. However, more work on the logistical aspects and implications of these types of policies is necessary before their implementation.

**_Why is a broader mix of policies needed?_** The benefits of unconventional monetary policy might diminish over time, and the risks associated with their aggressive use point
18

**Figure 11  Percentage of people living in households that cited LPG as the primary cooking fuel who are estimated to have abandoned polluting fuels**

<!-- Start of picture text --> Urban<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The analysis is confined to those areas—nationally or urban areas—where more than one-fifth of the population lived in households citing LPG as the primary cooking fuel. Only national results were available for Mauritius. In Burkina Faso, Kenya, and Nigeria, the one-fifth threshold was met only in urban areas and hence national results are not shown.

Analysis of LPG expenditure shares shows that the 5 percent threshold suggested in the multi-tier framework as an upper limit for affordability was not breached in any of the sampled areas. In Figure 12, the blue bars represent LPG expenditure shares of all households citing LPG as the primary cooking fuel, whether or not they had abandoned polluting fuels. The green bars take the segment of the households in the blue bars who had abandoned polluting fuels. The differences are small. This finding seems surprising at first. First, abandonment of polluting fuels would be expected to increase the quantities of LPG purchased, everything else being equal. Second, measurement errors caused by asking households how much they had spent on LPG in the previous month would be expected to be smaller for those using only LPG: many more of those who had not abandoned other fuels would be expected to purchase LPG less frequently than once a month, resulting in under-estimation of expenditures on LPG, although overestimation by those who happened to have purchased LPG in the previous month might have offset such under-estimation. In countries where a large fraction of those using LPG as the primary cooking fuel had not abandoned polluting fuels despite spending little on LPG—Cameroon, Sudan, and urban Burkina Faso—it is not clear that financial considerations had prevented these households from switching entirely to LPG (and
# **1. Introduction**

This paper describes several methods to estimate poverty in India in 2017. India is likely the country with the largest number of people living below the international poverty line of $1.90 and its latest publicly available household survey dates to 2011/12, giving rise to considerable uncertainty over the recent trend in global poverty. Because of the decision by the Government of India to withhold the most recent household survey (National Sample Survey 2017/18), we use a range of methods to derive a poverty estimate for India in 2017, which can be incorporated in the global poverty counts.<sup>1</sup> We focus on estimating poverty at the international poverty line of $1.90 (using 2011 purchasing power parities).<sup>2</sup> We use two main methodologies. The first method uses a survey-to-survey methodology to impute a consumption aggregate into the 2017/2018 Survey on Social Consumption (SCS) on Health. While this survey collects information on covariates that predict consumption, it does not collect a comprehensive consumption aggregate that could be used to measure poverty directly. Our approach is closely related to Newhouse and Vyas (2019) who impute consumption into the 2014/2015 National Sample Survey.<sup>3</sup> This approach builds on the small area estimation methods developed by Elbers et al. (2003), who impute a welfare aggregate into a census. More recently, Douidich et al. (2016) impute a consumption aggregate into a labor force survey to estimate quarterly poverty rates.

The second method assumes that household survey consumption follows the growth in national accounts, adjusted downward by a pass-through factor.<sup>4</sup> The adjustment factor accounts for the fact that survey growth is systematically lower than growth in national accounts, e.g. see Ravallion (2003), Deaton (2005), Pinkovskiy and Sala-i-Martin (2016), Lakner et al. (Forthcoming),
Hence, by joining their consignments, small-scale traders can reach an entirely new class of customers that they individually could not approach because of their lack of scale.

Moreover, group trade frees up traders’ time, as they do no longer need to accompany their merchandise across the border, queue at border offices, and wait for clients. This makes it possible to trade more frequently, thus handling overall larger sales volumes, or engage in other activities.

Another advantage of group trade is that border agencies are getting a better control over imports and exports. On the regulatory side, veterinary and plant health inspections might be undertaken on aggregated shipments, while individuals crossing the border with only a few animals, or a small amount of produce might entirely escape SPS controls and thus transfer health risks into the destination country.

On the Customs side, it would reduce border tax evasion and extend the coverage of Customs-recorded trade statistics to better reflect the full extent of imports and exports. To date, only a few countries monitor SSCBT on a systematic basis, while most miss out on these important regional trade flows in their import and export statistics (World Bank, 2020a). Better statistics would mean a better information base for decision makers on issues of food security, regional integration, and macroeconomic management.

# 2.2 Challenges of group trade

On the other hand, group trade faces several challenges. With clustering, individual traders must rent the vehicle and driver, pay for the loading and unloading, and cover the fee for coordination and sales services. In addition, there are the trade taxes and other border fees that 8
All four countries had implemented lockdown measures by the end of March 2020; however, the levels of stringency varied across countries. Malawi had the least stringent measures, as the government suggested that people stay home but did not implement a full lockdown, and public transportation was still running as usual throughout 2020. Uganda had the strictest measures, while Nigeria started with stringent measures, but eased restrictions relatively early.

In each of the four countries, the government took steps to support citizens through new or existing cash transfer or public works programs. An example is seen in the expansion of social programs, frontloading payments, and supporting in-kind food distribution programs (Gentilini et al. 2021). Job protection or business support policies were very limited, especially in comparison to similar programs implemented in high-income countries. This is due to the informal nature of the majority of jobs and businesses in these economies. In a study of households’ coping strategies between April and September 2020 in the same four countries, Furbush et al. (2021) show that households mostly relied on savings and reduced food consumption to cope with health and economic shocks. Very few households reported receiving any kind of social assistance. Only in Uganda, only every fourth household reported received assistance (mostly inkind) by August 2020.

# **3. Data and Descriptive Statistics**

Our analysis combines Living Standards Measurement Study Integrated Surveys on Agriculture (LSMSISA) pre-COVID-19 surveys with several rounds of HFPS conducted during the pandemic in Nigeria, Ethiopia, Malawi, and Uganda. In this section, we describe the data sources and present summary statistics of key employment and income indicators.

## **LSMS-ISA pre-COVID-19 surveys**

The LSMS-ISA project is an initiative funded by the Bill and Melinda Gates Foundation (BMGF) that supports governments in eight African countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, Uganda, and Burkina Faso. The goal is to generate several rounds of nationally representative panel surveys with a multi-topic approach, designed to improve the understanding
- <mark>Dreze, J., & Srinivasan, P. V. (1997). Widowhood and poverty in rural India: Some inferences from household survey data.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>54</mark>_ <mark>(2), 217-234.</mark>

- <mark>Dunbar, G. R., Lewbel, A., & Pendakur, K. (2013). Children's resources in collective households: identification, estimation, and an application to child poverty in Malawi.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>103</mark>_ <mark>(1), 438-471.</mark>

- El-Laithy, Heba. (2001). “The Gender Dimensions of Poverty in Egypt.” ERF Working Paper 127.

- ESCWA, 2021 “Targeted social protection in Arab countries before and during the Covid-19 crisis”. Economic and Social Commission for Western Asia.

- Ferreira, Francisco H. G., Julian Messina, Jamele Rigolini, Luis-Felipe López-Calva, and Renos Vakis. (2012). _Economic Mobility and the Rise of the Latin American Middle Class._ Washington DC: World Bank.

- Fuwa, Nobuhiko. (2000). “The poverty and heterogeneity among female-headed households revisited: the case of Panama.” _World Development_ , Vol. 28(8), pp. 1515-1542.

- Gammage, Sarah. (1998). “The Gender Dimension of Household Poverty: Is Headship Still a Useful Concept?” International Center for Research on Women (Washington DC)

- <mark>Ghomi, M. (2022). Who is afraid of sanctions? The macroeconomic and distributional effects of the sanctions against Iran.</mark> _<mark>Economics &
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exchanges on which the firm is traded, ownership, and industry classification. Bloomberg’s industry classification is based on the Industry Classification Benchmark.

To examine corporate recovery, we use **Net Sales** defined as total operating revenue. We also use alternate measures including Output defined as Sales + Changes in Inventories and Corporate Profits computed as Earnings before Interest, Taxes, Depreciation and Amortization (EBITDA). Our main measure of credit is **Short Term Borrowings** that includes bank overdrafts and short-term debts and borrowings.

In our analysis of firm recoveries, we focus on both the balance sheet positions (that provide a snapshot of the firm’s financial resources and obligations at a given point in time) and the statement of cash flows which provides a breakdown of cash flows from operating, investing, and financing activities that helps determine the short term viability of the company. All the accounting variables are converted to real terms by deflating by CPI Index. All our results are robust to using GDP deflator instead of CPI.

# **3. Summary Statistics on the Existence of Phoenix Miracles**

# **_3.1 Macro Evidence_**

We first focus on the behavior of GDP and private sector bank credit covering a five-year window centered on troughs in output, in our sample of 3S collapse episodes. GDP is the real GDP in constant local currency units from World Development Indicators and credit is the Claims on the Private Sector by Deposit Money Banks from the International Financial Statistics (line 22d).

Figure 1A reproduces the picture of Phoenix Miracles from Calvo et al. (2006a) for 22 3S episodes where the drop in average GDP from t-2 to t was 7.8% and the drop in average credit was 15%. After t (the crisis trough), there is a steep V-shaped recovery in output to previous levels where as credit still stagnates and begins to recover only at t+1. Looking at our sample of nine 3S episodes over the 1990s in Figure 1B,