rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 2.0<br>1.8<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>2016-01 2016-04 2016-07 2016-10 2017-01 2017-04 2017-07 2017-10 2018-01 2018-04 2018-07 2018-10 2019-01 2019-04 2019-07 2019-10 2020-01 2020-04 2020-07 2020-10 2021-01 2021-04 2021-07 2021-10 2022-01 2022-04 2022-07 2022-10 2023-01 2023-04 2023-07 2023-10 2024-01<br><!-- End of picture text --> Source: World Bank.

By converting the supply chain disruptions into their impact on trade capacity, the GSCSI offers a tangible representation of the scale of the global supply chain challenges faced over the 2020-2022 period and beyond. This supply chain stress metric can serve as a valuable tool for tracking, analyzing, and contextualizing the significant operational disruptions that have reverberated through global maritime and logistics networks in recent years. It underlines that shipping lines and terminal operators must contend with a notable unpredictable variability, in addition to more predictable seasonal and economic cycles.

# **The COVID-19 Pandemic and the 2021-22 Supply Chain Disruption**

The 2021-22 supply chain crisis resulted from two trends: First, containerized trade rebounded rapidly from the initial pandemic drop, overshooting pre-pandemic levels by late 2020. This surge was driven by a shift in consumer spending toward goods needed to accommodate sudden changes in lifestyles (e.g., workfrom-home) in high-income countries. While the shipping industry aimed to meet growing demand, supply was hampered by pandemic-induced port closure (notably in Asia), and operational constraints at other ports that caused multi-week vessel queues (especially on the US West Coast).

By mid-2021, severe pier and yard congestion had become prevalent among the world's major port gateways (Map 1), exacerbating the availability of containers and straining shipping lines’ capacity to move containerized cargo. This
them that includes regular joint activities and information sharing. In contrast, other IPAs were split in their responses and may have had only regular but ad hoc, or occasional contact with subnational IPAs. These results thus seem to imply that close coordination is important for effective IPAs, but due to the sample size of IPAs with sub-national, no statistically significant findings are present.

## **Strategic Alignment and Focus**

Table 9 considers the strategic alignment of IPAs in both groups. Interestingly, this finds that there is no significant difference in the number of sectors targeted between high-performing IPAs and other IPAs. Yet, this first group may be slightly more likely to dedicate at least half of their resources to priority sectors (90 percent) versus other IPAs (70 percent). There also seems to be no significant difference in how both groups select their priority sectors, as

> 6 One exception is that no African IPAs are in our group of effective IPAs. However, this may be partly due to the small number of such countries in our sectoral FDI database.

17
difference in the historical performance of the high-, middle- and low-income countries in terms of licensing Balance of Payments demonstrates this and suggests there is a growing IP commercialization divide.

<!-- Start of picture text --> High-income states: licensing & royalties<br>Figure 4. High-income States: Licensing and Royalties Balance of Payments, 1980-2009<br>balance of payments, 1980-2009<br>$65<br>$55<br>$45<br>USA<br>$35<br>Japan<br>France<br>$25<br>Germany<br>$15 Italy<br>$5<br>($5)<br>Billions<br><!-- End of picture text --> (Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files) Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files Figure 4 shows that high-income countries all exhibit slow growth or a static Balance of Payments – hovering between + $5 billion and - $5 billion - between 1980 and 2007 (the most recent available year). The USA is the obvious exception in this group. With a phenomenal increase in its Balance of Payments from + $5 billion to + $65 billion, it is the clear historical winner.

The passing of the Bayh-Dole Act in 1980, which enabled US universities, SMEs and non-profit organizations to appropriate control of IP resulting from federally funded research, is often regarded as an important factor in the increase of patenting and licensing activity by these actors. This has led many other OECD and developing nations to consider enacting similar policy measures to stimulate licensing revenue. Clarifying IP ownership in public-private partnerships is a useful policy measure to reduce operating uncertainty and stimulate the commercialization of IP. However, critics remark that it is difficult to disaggregate the effects of the Bayh-Dole Act from other factors that influenced the growth of licensing revenue in US universities post-1980 (David C
# _1.3_ Description of Sample and comparison with other localities in States

Table 4 uses 2010 census data to compare all localities in Oaxaca and Yucatán versus the localities sampled for the study. The team also included a comparison with rural localities in the states that have forest coverage. An important caveat of this analysis is that the census does not include subgroup information of the localities’ population (e.g., the population of men versus women, population that speaks the local indigenous language) for localities with only one or two houses. This affects 30 percent of the localities of the selected states. In the census data, these localities appear as missing values when subgroup information is used as variables for comparison. Additionally, filtering for localities with more than ten women excluded any locality without this information.

Localities in the sample are, on average, larger in terms of inhabited houses and population than rural localities with forest coverage. This is likely due to the aformentioned exclusion of localities with one or two houses. Those in the sample are also marginally smaller than the states’ average size, but not significantly.

In addition, localities in the sample are similar to rural localities with forest coverage – both feature agriculture as the main economic activity and a similar proportion of people above three years of age speaking indigenous languages.<sup>2</sup> These characteristics are less prominent in the selected states as a whole. The sample is slightly more educated than other rural localities with forest coverage, and marginally lower than the state, although these differences are not statistically significant.

According to available demographic variables, females make up about half of the population in the localities, on average, for all comparison groups. But localities in the sample have fewer womenheaded households than the state average, and about the same as rural localities with forest coverage.

Finally, for localities with data on the marginalization index of 2010<sup>3</sup> , we show descriptive statistics in Table 4 using the official groups
that assess how school attendance and other education outcomes respond to conflict-related shocks in real time. Yemen is no exception, where the poor security situation (e.g., Sundberg and Melander 2013), the budgetary problems of the government and the Central Statistical Organization (e.g., World Bank 2017b), and the significant upheaval in population movement since the last census conducted in 2004 all make it very difficult to conduct traditional household surveys (e.g., TFPM 2017). Furthermore, given the large and frequent shocks that could potentially affect school attendance (e.g., Tandon and Vishwanath 2020), high-frequency data collection would be necessary to differentiate between potential mechanisms affecting school attendance.

Given the substantial constraints on traditional data collection and the need for high-frequency data, we turn to rare, non-traditional survey data. This paper utilizes the most geographically complete survey covering Yemen following the start of the conflict- the mobile Vulnerability and Assessment Mapping Survey (mVAM) conducted by the WFP. The monthly survey began in August 2015 and has been conducted nearly every month since. However, the survey was originally focused on food security, and only began incorporating school attendance in March 2019 (e.g., WFP 2019b). The data collected in the monthly survey since March 2019 provide a rare opportunity to analyze schooling decisions in an active and volatile conflict, with important implications for governments, and humanitarian and development organizations.

On average, each mVAM interview takes about 15 minutes to complete. The survey is conducted via mobile phone and respondents are reached via random digit dialing. The survey is stratified by governorate/capital city, where potential respondents continue to be contacted until a sufficient number of completed responses have been met in each governorate aside from the island of Socotra.13 The number of minimum responses per governorate are determined by the governorate’s share of the total population, and the total number of respondents surveyed each month is approximately 2,400.

The survey is a rolling panel,
I use the bias-yield relationship observed in Figure 6 to estimate district TFPs.<sup>11</sup> The principal assumption is that the dependence continues to hold for districts in Bihar. I also assume that the relative yields in the initial year reflect differences in relative TFPs. Table 7 reports the convergence results after adjusting for the bias. Panels A and B correspond to the two 20-year periods shown in Figure 6. The patterns of convergence remain broadly consistent with the baseline results where rice and wheat productivities are moving from convergence to divergence. Like in the case of yields, the pace of divergence in maize TFPs has been increasing. Therefore, under the above assumptions, the exercise suggests that the baseline results are robust to the choice of productivity measure. Still, future research should try and bring first-hand data on inputs under analysis, which will help establish the yield-TFP relationship at smaller geographical units.

# **References**

**<mark>Avila, Antonio Flavio Dias, and Robert E. Evenson.</mark>** <mark>“Total factor productivity growth in agriculture: the role of technological capital.”</mark> _<mark>Handbook of Agricultural Economics</mark>_ <mark>4 (2010): 3769-3822.</mark>

**<mark>Ball, V. Eldon, Jean-Christophe Bureau, Jean-Pierre Butault, and Richard Nehring (2001).</mark>** “Levels of farm sector productivity: An international comparison.” _Journal of Productivity Analysis_ 15.1: 5-29.

**Ball, V. Eldon, Charles Hallahan, and Richard Nehring (2004).** “Convergence of productivity: An analysis of the catch-up hypothesis within a panel of states.” _American Journal of Agricultural Economics_ 86.5: 1315-1321.

**Bandyopadhyay, D. (2009).** “Lost opportunity in Bihar.” _Economic and Political Weekly_ : 12-14.

**Banerjee, Abhijit (2004
clined in India over the past two decades, not even during the rapid growth period since the early 1990s. In the DHS, where 55.3 percent of Indian households defecated openly in 200506, 63.7 did in the earlier 1998 survey round, and 69.7 did in 1992. This is particularly true for poor people: the joint UNICEF and WHO report concludes that “the poorest 40 percent of the population in Southern Asia have barely benefited from improvements in sanitation.” In 2010, 86 percent of the poorest quintile of South Asians defecated openly.

Therefore, it is already well-known that open defecation is bad for children’s health; that early-life disease leads to lasting stunting; and that open defecation is exceptionally widespread in India. The contribution of this paper is to quantitatively assess the importance of the link among these facts. The results indicate that sanitation is a statistically important predictor of differences in the height of children in developing countries and can explain differences of interest to economists and of significance to human development.

# **2 Evidence from country means: 140 DHS surveys**

Across countries, observed in different years, how much of the variation in child height is explained by variation in open defecation? This section uses 140 DHS surveys, each collapsed into a single observation, to show that sanitation alone explains more than half of the variation across country-years.

The analysis proceeds in several steps. First, section 2.2 documents that, across country means, height is associated with open defecation, with little change after controlling for GDP. Next, section 2.2.1 uses country fixed effects and replication on sub-samples of world regions to show that no geographic or genetic differences are responsible for the result. Then, section 2.2.2 verifies that other dimensions of infrastructure or well-being do not similarly predict child height. Section 2.2.3 observes that children would be more exposed to fecal pathogens where population is more dense, and finds that open defecation interacts with population
# **Table 2 Descriptive statistics for the Colombian Stock Exchange**

The table displays yearly market stock returns. For Colombia, returns are calculated using the COLCAP, a value weighted index. The MSCI Latin America index captures large cap and mid cap firms across five countries: Brazil, Chile, Colombia, Mexico, and Peru. On December 2015, the country weight of Colombia in the index was 3.15%. Market capitalization (market cap) is the end-of-year total stock market value. Foreign flows and number of stocks are from the CSE dataset.

|Year|MSCI Latin<br>America|Colombian Stock<br>Market|Market Cap<br>($ billion cop)|Foreign Flows<br>($ billion cop)|Number of<br>stocks|
|---|---|---|---|---|---|
|2006|43.2%|16.0%|11,161|642|68|
|2007|50.4%|-4.3%|10,694|275|67|
|2008|-51.4%|-34.7%|7,561|631|66|
|2009|103.8%|42.8%|11,602|943|62|
|2010|14.7%|28.9%|15,497|122|63|
|2011|-19.4%|-20.2%|12,666|-13|71|
|2012|8.7%|15.0%|14,716|-44|72|
|2013|-13.4%|-11.9%|13,071|479|70|
|2014|-12.3%|-11.6%|11,635|-370|66|
|2015|-
- Shorrocks, A.F. (1980) ‘The class of additively decomposable inequality measures.’ _Econometrica_ 48(3), 613–626

- van Doorslaer, E., A. Wagstaff, H. Bleichrodt, S. Calonge, U. Gerdtham, M. Gerfin, J. Geurts, L. Gross, U. H ̈akkinen, R.E. Leu, O. O’Donnell, C. Propper, F. Puffer, M Rodr ́ıguez, G. Sundberg, and O. Winkelhake (1997) ‘Income-related inequalities in health: some international comparisons.’ _Journal of Health Economics_ 16(1), 93–112

- Victora, C. (1992) ‘The association between wasting and stunting: An international perspective.’ _Journal of Nutrition_ 122(5), 1105–1110

- Wagstaff, A., and E. van Doorslaer (2000) ‘Income inequality and health: What does the literature tell us?’ _Annual Review of Public Health_ 21, 543–567

- (2004) ‘Overall versus socioeconomic health inequality: a measurement framework and two empirical illustrations.’ _Health Economics_ 13, 297–301

- Wagstaff, A., E. van Doorslaer, and N. Watanabe (2003) ‘On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam.’ _Journal of Econometrics_ 112, 207–223

- Waterlow, J.C., R. Buzina, W. Keller, J.M. Lane, M.Z. Nichaman, and J.M. Tanner (1977) ‘The presentation and use of height and weight data for comparing the nutritional status of groups of children under the age of 10 years.’ _Bulletin of the World Health Organization_ 55(4), 489–498

- WFP (2001) _Identifying Poor Areas in Cambodia: Combining Census and Socio-Economic Survey Data to Trace the Spatial Dimensions of Poverty_ (Phnom Penh: World Food Programme)

- WHO Working
agricultural and food tariffs are more substantial, being nearly halved on average (15.9 percent to 8.2 percent), as are those for textiles and clothing (from 7.3 percent to 4.1 percent).

The implied tariff reductions for developing countries are similarly modest, but for different reasons. The average tariff drops from 8.4 percent to 7.5 percent in the case of the central Doha scenario, and further to 6.8 percent when developing-country cuts (including those of LDCs) are fully reciprocal (Doha-all). The principal reason for the modest reductions lies in the huge gap between applied tariffs and tariff bindings. Average bound tariffs in agriculture are 2.4 times as high as applied tariffs, and this ratio is 5 in the case of the LDCs.<sup>16</sup> Thus, when bound tariffs are reduced, there is often no real liberalization because they remain above the currently applied tariffs.<sup>17</sup>

# **4. Global Merchandise Trade Reform**

## **4.1 Welfare Impact**

Table 1 presents the standard welfare impact, or change in real income.<sup>18</sup> Global merchandise trade reform that includes elimination of domestic support would generate US$287 billion in additional income in 2015 at the global level compared with the baseline scenario.

Almost 70 percent of the global gains would accrue to the high-income countries. However, as a share of income, developing countries do somewhat better, with an average increase of 0.8 percent compared to 0.6 percent for high-income countries. The results vary widely across

> 16. The scenarios, though assessed at an aggregate level, are based on formula cuts at the six-digit level thanks to the MAcMaps database that also incorporates the existing tariff bindings.

> 17. Francois and Martin (2004) have explored potential gains from setting bindings at applied levels even in the absence of actual liberalization. It could
of this research. Respondents should be asked to estimate the number of purchases made over a fixed reference period, anchoring questions on consumption rather than on acquisitions (as in the 2016 Barbados Survey of Living Conditions and the 2017/18 Jordan Household Income and Expenditure Survey). If information on purchasing behavior is available, simple models can be used to estimate the effects of using diaries on inequality measurements.

A third implication of our findings is that surveys should be designed to elicit repeated measurements from the same respondents. In our case, for example, a recall module was administered to all households before starting the diary week. The same setting can be found in other important family expenditure surveys, such as in Canada and the United States. The methodology we have presented can be applied to any of these contexts to study how consumption inequality has evolved over time, raising the problem of how our approach can be extended to allow for longitudinal information. We hope to address this and some of the related problems in future research.

29
inant analysis shows that management practices, some business characteristics and skills of the entrepreneur can be used to separate both groups, but the distinction based on these observables is only partially successful, which suggests the existence of unobservables determining both the decision to set up a business, based on a perceived opportunity, and firm performance. Finally, our instrumental variables results confirm that opportunity entrepreneurs have higher performance measured as profits and management practices, even after controlling for the observable skills of the entrepreneur.

The policy relevance of these findings is twofold. First, as mentioned before, identifying the observable characteristics that define opportunity entrepreneurs could potentially improve the targeting of business support programs, by concentrating them on the entrepreneurs with the highest growth potential. This by no means implies disregarding low-performing necessity entrepreneurs. On the contrary, the latter might be better served by interventions that help ease their transition to salaried employment. Second, our focus on female entrepreneurs is also of great relevance given that their businesses seem to be at a particular disadvantage when trying to scale-up. For instance, in Latin America, micro-firms led by women have been found to have an even smaller size and lower productivity, compared to those led by men (Bruhn, 2009). Thus, the evidence provided in this paper can be readily applied to those in more need of targeted support.

# **2 Data and descriptive analysis**

Our data come from a baseline survey conducted in 2014 in eight urban areas in Mexico: Aguascalientes, Dolores Hidalgo, Irapuato, Le ́on, Mexico City, Quer ́etaro, San Juan del R ́ıo and Toluca. For the sampling design, a number of census tracks (called AGEBs by the Mexican Statistical Institute, INEGI) with relatively high concentration of commercial activity were chosen.<sup>2</sup> The questionnaire was applied only to women who are business owners or partners, i.e. who take managerial, financial and marketing decisions, and the sample included firm with no more than 5 employees
Finally, corporate data might be noisy and imprecise. Implied interest rates for each company are calculated as interest expense over total debt. These are very rough approximations of actual interest rates paid by the company. Many companies record low implied interest rates, even below 1 percent. Some companies report zero interest expense with non-zero debt. In addition, corporate statements do not allow for precise identification of total bank debt; total debt is approximated as total liabilities minus accounts payables

# **5. Conclusion**

The results of the corporate stress tests suggest that both earnings and interest rate shocks have significant impacts on corporate vulnerability. For Jordan, the results of the tests show that there are a number of firms in the construction, financial and real estate sectors, and in the industry and trade sectors that will be negatively affected by shocks to interest rates or earnings. These stress tests highlight the importance of assessment of corporate vulnerability and including it in bank stress tests.

Because different banks have different sector exposures, and different sectors exhibit different vulnerability at different times, the stress tests that include corporate exposure will provide a more precise evaluation of bank soundness. The estimated NPLs can be used in bank stress testing to evaluate the capital adequacy of the banks.

23
|West Bengal|1,628,708|2,733,376|2,694,214|0.17|0.23|0.20|0.30|0.30|0.29|0.69|0.63|0.63|
|Totals and wtd averages|10,928,081|16,517,785|16,307,628|0.26|0.30|0.30|0.40|0.41|0.41|0.64|0.59|0.56|
|Unweighted averages|620,971|933,266|918,930|0.36|0.38|0.37|0.47|0.47|0.47|0.64|0.60|0.60|

Notes: Indian descriptive statistics taken from Annual Survey of Industries and National Sample Statistics. Totals and weighted averages at the bottom of the table are for the full manufacturing sector, including states and industries not in the final sample. Urbanization rate in establishments is measured as the share of establishments in urban areas. Urbanization rates for employment and output are similarly defined.
|0%|0%|0%|
|8. Financial|24%|24%|39%|22%|22%|27%|

Overall, the stress testing analysis shows that the most vulnerable sectors are construction, finance, industry and trade.

# **4.3 Assumptions and Caveats**

As in any empirical exercise, there are a number of assumptions and caveats that must be kept in mind when applying the results for policy analysis. Below we discuss key assumptions that were made in the study and some caveats.

First, financial statement data only available on publicly listed firms. Thus, it is assumed that all firms in the sector behave similarly to publicly listed firms. In fact, smaller companies might behave differently. However, the analysis of ASE listed companies by size shows higher vulnerability in smaller listed companies. Therefore, the results obtained from large listed companies might be conservative estimates of the overall impact (as the impact on smaller companies might be larger).

Second, sectoral classification may not be precise (i.e. how ASE is classifying companies and how banks are classifying the same companies might be different).

22