rafmacalaba/data-use-mentions-extended huggingface dataset:
**Appendix E: Data on Research and Development Expenditures and Sales for the United States in 2004 and 2005.**

|TABLE E1. Funds for industrial R&D and sales for comp|aniesperformingindustrial R&|D in the United Stat|es,byindustry: 20|04 and 2005|||
|---|---|---|---|---|---|---|
||||All R&D||Sales in $million|s<br>Ratio of R&D expenses|
|Industryand companysize|NAICS codes|2004|2005|2004-2005 average|in 2005|to sales (x1,000)|
||||$millions||||
|All industries|21–23, 31–33, 42, 44–81|208,301|226,159|217,230|6,119,133|36|
|Manufacturing industries|31–33|147,288|158,190|152,739|3,998,256|38|
|<br>Food|311|2,254|2,716|2,485|374,342|7|
|Beverage and tobacco products|312|555<br>|i<br>539|547|38,003|14|
|Textiles, apparel, and leather|313–16|570|816|693|51,639|13|
|Wood products|321|D|D|0|27,002|0|
|Paper, printing, and support activities|322, 323|D|D|0|159,608|0|
|Petroleum and coal products|324|1,603|D|802|404,317|2|
|Chemicals|325|D|42,995|21,498|624,344|34|
|Pharmaceuticals and medicines
months of the pandemic in Rwanda in Figure 1a, leveraging fortnightly cabinet announcements and daily COVID-19 updates. On March 14, 2020, immediately following the detection of the first COVID-19 case in Rwanda, MINISANTE recommended the use of masks in health care settings. A compulsory mask mandate for all public settings was introduced on April 19, just before lockdown restrictions were partially lifted on May 4. This mandate was strictly enforced, with deterrence measures including fines in Kigali City Province, and complementary measures to promote compliance, including infomercials by the national broadcaster and education campaigns by the Rwanda National Police. These measures were effective—by early June, according to the Innovations for Poverty Action’s RECOVR survey, 95% of households always wore a mask when they went out in public.

To increase the availability of certifiably high-quality masks, the Rwanda FDA issued licenses to textile manufacturers to produce surgical and barrier masks. We present the timeline of key policy actions targeting the manufacturing of certified masks in Rwanda in Figure 1a. On March 21, 2020, in response to reported shortages of imported masks, the Ministry of Trade and Industry (MINICOM) imposed price restrictions and the FDA issued a public announcement citing concerns about the sale of substandard masks. To combat this shortage and to mitigate concerns about the sale of unregulated low-quality masks, the FDA licensed 21 garment manufacturers that had submitted applications to produce masks on April 17, two days before the national mask mandate.<sup>1</sup> Licensing was coupled with additional incentives to engage in mask production—MINICOM announced the intent to facilitate access to machines and raw materials, and a VAT exemption for domestically manufactured masks was

> 1 By May 26, an additional 65 smaller firms from the textile industry had been licensed. These firms comprise only 3% of the domestic mask production in our data. In Appendix D, we find licensed firms shift production away from textiles and into masks, but do not increase profits; this is consistent with firm entry into mask production until
Policy Research Working Paper 10300

# **Abstract**

This paper examines the welfare impacts of the COVID-19 pandemic, using harmonized data from 343 high-frequency phone surveys conducted in 80 economies during 2020 and 2021, representing more than 2.5 billion people. The analysis focuses on the scarring effects of the initial losses of employment and income by examining their evolution over time across and within countries, as restrictions on mobility and economic activity were introduced and then gradually relaxed. The employment and welfare outcomes of some groups that were impacted to a greater degree initially—including women, informal workers, and those with less education—have been improving at a slower pace. The social protection response in lower-income economies was largely insufficient to protect households from the pandemic shock. Unmitigated welfare losses, as seen for example from the large share of households indicating income losses well into 2021, are highly correlated with food insecurity, which likely led some households to sell physical assets and deplete their savings. Without proper remediation, the uneven welfare impacts associated with COVID-19 may be amplified over the medium to long term, leading to future increases in poverty and inequality.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bbrunckhorst@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction
13 to 17.<sup>6</sup> Kerckhoff (2002) also observes that Americans are more likely than Europeans to shift back from full-time work to full-time school before settling into stable employment. Duryea, et al. (2003) shows the high incidence of working students in Latin America while UCW (2010) suggests that this dual activity is increasing across the world.

The degree to which labor markets are segmented seems to change over the lifecycle. A segmented labor-market perspective assumes that the informal sector is a disadvantaged one and all informal workers are really queuing for formal sector jobs.<sup>7</sup> However, several authors have found flows of comparable magnitudes between formal and informal jobs at different times of the life cycle: Gong et al (2004) can not reject the hypothesis that formal and informal sector jobs have the same entry and access rates, using 1999 – 2000 panel data in urban Mexico; Bosch and Maloney (2010) find similar conditional flows in Mexico, Argentina and Brazil, without separating young from adult workers; and Egel and SalehiIsfahani (2008) observe that young men switch frequently between formal and informal sectors in Iran.<sup>8</sup> This suggests that the informal sector may play a transitory role for youth rather than being a dead-end career path.

# **3. Methodology and Data**

Following the work of Bosch and Maloney (2007), three methodologies are used to understand the dynamics of youth employment. First, transition matrices<sup>9</sup> are estimated to understand the magnitude of turnover, namely the share of people who move into one state of employment or out of it every period. This allows us to determine, for example, if young people enter the informal sector or leave it at different rates than do adults at any one period of time.

Second, as proposed in Bosch and Maloney (2007), we decompose the transitions matrix into two separate components. The first component
amount.

# **7. Conclusions and Main Messages**

Most SIDS are vulnerable to climate induced disasters, especially tropical cyclones, and are highly exposed to major losses and damages from such disasters because of the small size of their territory and of their geographic location. The EM-DAT database records 185 episodes of tropical cyclone disasters among Caribbean SIDS and 74 among SIDS in Oceania from 1995 to 2022. These disasters caused USD 45 billion in damages in the Caribbean and USD 2 billion in Oceania (in 2022 prices). These figures are almost certainly underestimates because there are no recorded damages in the database for some large disasters. In 22 of the tropical cyclone disasters, recorded damages exceeded 10 percent of GDP: for three of these disasters, the damages exceeded 100 percent of GDP and for a further five, damages were between 50 percent

> 30 The practical difficulties of implementing effective debt alleviation measures are illustrated by the experience of Jamaica (Schmid, 2016). With public debt to GDP of 140 percent in 2010, Jamaica undertook two programs of debt restructuring, in 2010 and 2013, but neither reduced the debt burden significantly. There were two reasons for this. First, the debt restructurings were restricted to domestic government securities, which comprised around 55 percent of the public debt stock in January 2010, but omitted external bonds held by international investors which comprised almost 30 percent of the debt stock. The latter were omitted because the bonds lacked collective action clauses which could have allowed a haircut to be imposed on all bondholders without costly and time consuming legal disputes with individual bond holders. Second, the amount of debt relief which the restructuring of domestic government securities provided was relatively small – it was limited to a reduction in the interest rate and an extension of maturities – but did not involve a write down in the nominal value of the debt (although there was a significant reduction in the PV of the debt) because that would have risked financial fragility among domestic financial institutions – banks, broker dealers and insurance companies –
have little sense of any impact yet. When asked about whether they’d improved on any indicators, they indicated that it is too early to tell. Moreover, we saw less sign of the team making decisions based on evidence or a detailed understanding of the problem. Members of the old PMU claim that tasks have deviated significantly from the previous workplan (for example the QISS component was removed on the basis that it was too expensive, though it has since been reinstated) and that the emphasis has been on large procurements that they believe will have little bearing on the indicators. We were told that four vehicles have been procured at inflated costs for example, two of which are being used by individuals and not for the program at all. We also noted that some procurements the team discussed with us are actually related to the advancement of general health objectives (for example procuring generators) and do not seem to have been made with a clear attention to their impact on the six indicators.

We found that the current PMU does have technically capable people in it, some of whom benefitted from the ADG TA. We also found that their discussion of future interventions showed some appetite for innovation and some attention to outcomes, though this was not so pronounced as their Kogi and Anambra counterparts. Current interventions, such as the installment of a data monitoring chart in all the health facilities, show some promise and an appetite for innovation, but in general, we noted that most of their interventions are still in planning phase, with very little actually delivered. Given that the budget is supposedly over 80% spent, this suggests that a large part of expenditure may have been diverted to inputs that were not directly relevant to SOML.

Unlike the other states, Abia was unable to present us with any current DHIS data on their performance against the six indicators. We also noted that the new PS is fairly unaware of SOML and, unlike his Abia and Anambra counterparts, did not seem to know the various indicators.

# 8
.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>, 94-111.</mark>

- Government of Tanzania. (2017). Agricultural Sector Development Strategy Phase – “Agricultural Sector for Industrial Development.”

- <mark>Mancini, G., & Vecchi, G. (2022). On the construction of a consumption aggregate for inequality and poverty analysis.</mark> _<mark>World Bank Group, Washington, DC</mark>_ <mark>.</mark>

- Ministry of Finance and Planning - Poverty Eradication Division (MoFP- PED) [Tanzania Mainland], National Bureau of Statistics (NBS) and the World Bank. (2020). Tanzania Mainland Household Budget Survey 2017/18. Final Report Dodoma, Tanzania MoFP-PED, NBS and Washington DC USA, and WB.

- National Bureau of Statistics (NBS). (2022a). _National Accounts of Tanzania Mainland (2015 – 2021)._ NBS, Tanzania

- National Bureau of Statistics (NBS). (2022b). _Integrated Labour Force Survey 2020/21_ . NBS, Tanzania.

- <mark>Sharp, M., Buffiere, B., Himelein, K., & Gibson, J. (2019). Effects of data collection methods on estimated household consumption and poverty, and on survey costs: evidence from an experiment in the Marshall Islands. In</mark> _<mark>IARIW-World Bank Conference on New Approaches to Defining and Measuring Poverty in a Growing World, Washington DC, November</mark>_ <mark>.</mark>

- <mark>UNICEF. (2021). The state of food security and nutrition in the world 2021</mark> . <u>https://data.unicef.org/resources/sofi-2021/</u>

-
The elasticity of country c is then given by _γ1 + γ2c,_ where _γ2c_ is the coefficient of the corresponding country interaction.

The resulting elasticities of access are in the expected range and strongly significant throughout. For example, the Latin American average water access elasticity is 0.08, and country-specific values vary between 0.02 for Argentina and Costa Rica, and 0.18 for Peru. The values for toilet ownership and sanitation access are higher on average, at 0.16 and 0.17 respectively, and range between 0.07 and 0.25.

Similarly, the average Latin American electricity access elasticity is 0.06, and it ranges from 0.01 for Argentina, 0.02 for Costa Rica and Ecuador, to 0.16 for Peru. Interestingly, elasticities for appliances ownership are in general quite larger, with 0.15 averages for refrigerators and washing machines, and ranges between 0.08 and 0.24, indicating faster progress on the intensive margin of consumption.

In the ICT cluster, elasticities are high across all indicators. Notably, these are higher for landline phones than cell phones.

Finally, the elasticity of vehicle ownership is strong for cars, with a Latin American average of 0.16, ranging between 0.09 in Bolivia and 0.20 in Mexico, but very small for motorcycles, with an average of 0.01 only.

Gertler et al. (2016), looking at refrigerators, suggested that the elasticities of asset ownership vary along the income distribution. The Latin American data display a rich pattern, with heterogeneity across assets. Figure 4 shows the rate increase for each decile between 2002 and 2012 for refrigerators, washing machines and cars, as well as the related income elasticity computed separately at each decile. While the lowest income groups have experienced the largest increase for refrigerators, a clear inverted U-shaped pattern emerges for washing machine, with the largest increase in the middle of the income distribution. Finally, car ownership displays the opposite
years (2000-2019 inclusive) of interest. The data are then used for panel econometric analysis of program impacts -on owners’ gender for newly registered plots and the number of registered land- and credit-market transactions- with block- and year- fixed effects.

Such analysis yields three key results: First, the likelihood of a newly registered plot having a female coowner increased by 55 percentage points, one-third of which due to policy change and two-thirds to systematic registration. Second, the share of registered parcels sold or pledged as mortgage more than doubled, an effect that can be attributed to policy-induced reductions in the cost of first time (40%) and subsequent (60%) sporadic registration. Third, systematic registration had no impact on credit access, in line with what is found in the literature evaluating systematic registration programs (Agyei-Holmes _et al._ 2020; Field and Torero 2006; Galiani and Schargrodsky 2010). Substantive results are robust to restricting the sample to the areas chosen as treatment and control for the DID specification, suggesting that the lack of measured impact in the DID analysis is not merely due to selection issues. It confirms that in addition to going beyond traditional approaches allowing to measure to what extent policy changes were implemented and to identify their impact, registry data allow to avoid measurement error that has biased estimated program effects towards zero even for econometric models that are properly specified, a notion that is confirmed by comparing registry to survey data for the same households.

In light of the significant and quantitatively large impacts of regulatory reform, it is puzzling that, beyond elimination of the Ministerial consent requirement, implementation of reforms remained limited: contrary to Rwanda which moved from the 137<sup>th</sup> to the top rank in less than a decade, Lesotho still stands at 114<sup>th</sup> and registering a land transfer is estimated to cost 8.5% of property value. In addition to posing challenges for sustaining the (gender) gains made under the
where _nsc_ denotes the number of households selected in cluster _c_ within state _s_ (10 in this case) and _ρs_ is the intra-cluster correlation of per capita income. Given the above, we can calculate the number of clusters needed to achieve the minimum sample size for each state and then multiply this number by 10 to find the final household sample size. Taking these sample size requirements as given, each of our samples is then drawn in accordance with the two-stage design.

Specifically, the clusters within each state are selected with probability proportional to size (without replacement), where the measure of size is the number of households within each cluster. The households within each cluster are then selected via simple random sampling. According to this design, the inclusion probability for a given household is then approximately: where _n_ ̃ _sc_ is the total number of households in cluster _c_ within state _s_ , _Ns_ is the number of clusters ̃ selected in state _s_ , and _ns_ is the total number of households in state _s_ .<sup>8</sup> The sample size across the 500 samples is roughly 23,500 households. Under this sampling scenario, not all municipalities are included, and the number of municipalities included varies from sample to sample, ranging between 951 to 1,020 municipalities. The median municipality included in a given sample is represented by a single cluster and thus its sample size is 10 households.

In addition to the Intercensal Survey, INEGI has also released geo-referenced data that is similar to the remotely-sensed data often used in machine-learning applications. These geographic variables were calculated by INEGI for the year 2015 (the same year as the Intercensal Survey) and are only available at the municipality level. The geo-referenced data includes 105 different indicators. Specifically, the dataset includes information from 21 different dimensions and each dimension is associated with five different summary measures. For example, we summarize the atmospherically resistant vegetation index for each municipality using the minimum, maximum, mean, sum, and standard deviation of the index. The
Nations Statistics Division (UNSD). This dataset provides detailed information on global merchandise trade, encompassing the import and export of goods and services between countries and regions. In this study, we limit the analysis to trade in merchandise only, since data on trade in services are very limited. Moreover, following the standard convention in empirical trade studies, we use the value of imports reported by Jordan’s trade partners to capture the value of Jordan’s exports to its partners.

Trade trends provide insights into Jordan's economic health and growth prospects. Analyzing trade data helps understand how Jordan is integrating into the global economy and which sectors are driving export growth. Moreover, understanding trade patterns helps Jordan identify opportunities to diversify its economy. Over-reliance on a few key exports or trading partners can make the economy vulnerable to external shocks. By analyzing trade trends, Jordan can strategize to broaden its export base and reduce dependency on specific markets.

Jordan has pursued a policy of trade liberalization in the past few decades. Between 1997 and 2024, Jordan signed eight trade agreements (see Table 2). The most important is the PanArab Free Trade Area, signed in 1997 with the Middle East and North Africa (MENA) countries. In 2021, it accounts for a third of Jordan’s total exports.

**Table 2. Jordan’s Trade Agreements**

|**Agreement Name**|**Signature**|**Entry**<br>**into**<br>**force**|**Partners**|
|---|---|---|---|
|Pan-Arab Free Trade Area<br>(PAFTA)|01/19/1997|01/01/1998|MENA|
|EU - Jordan|11/24/1997|05/01/2002|EU, Jordan|
|United States - Jordan|10/25/2000|12/17/2001
# Figure 9. Regional trade agreements

A. Share of members of major RTAs in global GDP and trade B. Share of intra-RTA trade in members’ total trade <!-- Start of picture text --> Percent Percent of members' total trade<br>GDP Trade<br>60<br>40<br>30 45<br>20 30<br>10<br>15<br>0<br>EU 0<br>EU<br>USMCA RCEP ASEAN MERCOSUR CPTPP AfCFTA SAPTA Pacific Alliance USMCA RCEP ASEAN MERCOSUR CPTPP AfCFTA SAPTA Pacific Alliance<br><!-- End of picture text --> Sources: Comtrade (database); World Bank; World Trade Organization.

Note: RTAs are reciprocal trade agreements between two or more partners and include both free trade agreements and customs unions. Data for 2019.

A.B. AfCFTA = African Continental Free Trade Area; ASEAN = Association of South East Nations; CPTPP = Comprehensive and Progressive Agreement for Trans-Pacific Partnership; EU = European Union; MERCOSUR = Southern Common Market; RCEP = Regional Comprehensive Economic Partnership; SAPTA = South Asian Preferential Trading Arrangement; USMCA = United States-Mexico-Canada Agreement.