rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

In recent years, containerized trade, the backbone of global value chains, has experienced unprecedented disruptions. For instance, the COVID-19 pandemic created unforeseen consequences in a far-ranging array of sectors on a global scale, triggering an unprecedented supply chain crisis from late 2020 to mid-2022. Surging trade demand surpassed shipping capacity, itself affected by massive operational disruptions in key ports. In 2023, there were two events of global relevance. First, a severe drought affected the operation of the locks in the Panama Canal, resulting in a reduction in throughput and restricting the size of vessels able to transit the canal. Later in the year, militant groups carried out attacks in the Red Sea, forcing shipping lines to reroute ships servicing the Asia-Europe and Asia-US East Coast trade routes through the Cape of Good Hope.

In contrast to typical localized events impacting supply and logistics chains, the recent disruptions have been geographically extensive, long-lasting in duration, and multi-faceted – impacting demand patterns, manufacturing, maritime logistics, port operations, and freight distribution in complex ways. For decades, the container shipping network has operated loop services according to a regular schedule, like passenger rail or air services. Schedule predictability helps deal with trade imbalances, where surpluses in some regions offset deficits in others. This container supply/demand mismatch means that about 20 percent of total container movements are empty repositioning voyages. When disruptions hit, delays in repositioning containers may amplify the effect of ship delays (section 4).

A key challenge lies in quantifying the stress levels global container ports are facing through a unified metric that can be precisely measured and utilized as a meaningful indicator. To fill this gap, the Global Supply Chain Stress Index (GSCSI) is proposed as a measure capturing the delayed container shipping capacity across global maritime ports. This stress metric, which has been published in the World Bank’s Trade Watch since late 2021, is inherently port-centric and derived from analyzing container shipping traffic flows. The index is computed monthly and
������� �������� �������� ��� ��� ����� (1) where _Yikt_ is the outcome of interest, i.e. either an indicator variable for whether a household uses improved seeds or fertilizer on crop _k_ or kebele-level yields for _k_ , �� ��� �� are a time trend and woreda fixed effect, **xikt** is a vector of control variables and the _β_ s are parameters to be estimated with main interest in _β1_ and _β2_ . Alternatively, replace _dit_ or _dikt_ by **_A_** _idt_ or **_A_** _idkt_ , a vector with the total commercial farm area cultivated (with crop _k_ ) in a concentric circle around smallholder kebele _i_ with inner and outer radius _d_ as above (0 to 25, 25 to 50, and 50 to 100 km)<sup>8</sup> to obtain an estimating equation of the form We extend this framework in two ways. To assess labor market effects, we use the LSMS-ISA survey to compute an indicator variable of whether household _i_ engaged in full-time agricultural or non-agricultural employment or temporary work by estimating versions of regressions (1) and (2) where _Yikt_ denotes the employment indicator. As a counterfactual we include in _dit_ the distance between the household and the centroid of the nearest town with population greater than 2,000. To explore if large farms establishment can

> 7 See the LSMS-ISA Ethiopia website for more information on sample design and data.

> 8 To illustrate, _Aikt(25-50)_ is the area under commercial farms growing crop _k_ within a 25-50km range of the household living in kebele _i_ . In all cases, yield (obtained through crop-cuts of randomly selected fields in the kebele) is measured at kebele level and input use at household level.

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or not the products are for market and/or own consumption).</mark><sup>9</sup> <mark>Informal jobs are often defined as jobs for which the employer does not pay social security contributions, jobs without benefits such as paid or sick leave or jobs without a written contract (Ruppert Bulmer, 2018). These definitions are currently being revised by the ILO to align them with the narrowed definition of employment and the revised classification of status in employment (ICSE‐18) and the new classification of status at work (ICSaW‐18).</mark><sup>10</sup>

# 3. What do we measure?

While the previous section highlights _what should be measured,_ this section evaluates _what current survey programs do measure_ , allowing us to compare how current survey programs collect data on women and youth employment and related SDGs.

To do so, we reviewed the questionnaires of four nationally representative household survey programs: LSMS‐ISA surveys, LSMS‐type surveys, Labor Force Surveys (LFS) and Demographic and Health Surveys (DHS). More specifically, we reviewed the questionnaires of the latest wave of all eight LSMS‐ISA surveys (Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda) and complemented this set with four LSMS surveys conducted in Guatemala, Nepal,

> 9 _ILO, Measuring informality: A statistical manual_ , http://mospi.nic.in/sites/default/files/publication_reports/wcms_182300.pdf

> 10 One key question is how own‐use producers should be classified within the framework of informal employment. The current definition classifies own‐use producers in ‘informal employment’. But, given the narrowed definition of employment, own‐use producers are currently not classified as employed.

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the community data roughly divide households into two groups: those with no possibility of connecting to a network and those who may have a chance of connecting.

22 It is possible that the low connection rates in rural areas is more a reflection of problems with the access data than of the willingness of poor households to connect. Rural communities and primary sampling units cover a larger land area than urban communities. The fact that there is access a particular service somewhere within a rural community does not necessarily mean that it is technically or financially feasible to install a connection at every home in the area. While this is true in urban areas as well, this weakness in the community survey data is especially problematic in rural areas.

23 In Ecuador and Panama, where information on yard taps is available, a greater percentage of urban and rural households of all quantiles chose an in-house or yard tap than a sewer connection (given access to each service).

24 Sewer information is not available for Kazakhstan, and telephone access is not available in Nepal. Therefore, the households with access to all 4 services live in Ecuador, Kyrgyz Republic, and Panama.

**25** Nearly 2500 of the 3000 households with access to all four services live in Ecuador and Panama. In these countries information on yard taps is available. Nearly 95 percent of the 2500 households have an in-house or yard tap.

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16 intensity, an increase in Saigon river levels, the total Ho Chi Minh City population, and so on. These nine factors were identified during participatory scoping discussions in the two workshops and in discussions with Steering Center for Flood Control. Appendix A describes these factors in more detail.

**Table 4.2. Exogenous uncertainties and their range of plausible values.**

|**Uncertain Factor**|**Range of values**|**Relevant sources of data**|
|---|---|---|
|Increase in rainfall<br>intensity over 1980-2000<br>levels|+0% to +60%|IPCC (2012)|
|Increase in Saigon River<br>level over 1990-2000<br>levels|+30 cm to +100 cm|MONRE (2009, 2010); NOAA<br>(2012); Vermeer and Rahmstorf<br>(2009); Discussions with Steering<br>Center for Flood Control|
|Total Ho Chi Minh City<br>population|7.4 M to 19.1 M|Ho Chi Minh City Statistics<br>Office (2011); Ministry of<br>Planning and Investment (2011);<br>World Bank(2010)|
|Geographic distribution<br>ofpopulation|World Bank<br>estimate to current|Ho Chi Minh City Statistics<br>Office(2011);World Bank(2010)|
|Poverty rate|2.4% to 25%|ADB(2010); (Coulthart_et al._<br>2006);UNDP(2010)|
|Average annual<br>economicgrowth|6% to 12%|Hawksworth et al. (2009); ADB<br>(2010);Voice of Vietnam(2001)|
|Geographic distribution<br>of economicgrowth|Equitable to<br>Inequitable|Ho Chi Minh City Statistics<br>Office
hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

Based on these considerations, we first classify the increasing returns to scale sectors of our model into low, medium and high technology sectors. The classification is defined by the share of R&D expenditures in total sales, based on U.S. data. For low R&D intensive sectors, we assume that the elasticity of firms with respect to price is the same for the Africa region as for the EU, but the elasticity is only one-third of Rest of the World elasticity (trade with the Africa or EU regions misses out on trade with China or the U.S.). For medium and high R&D intensive sectors, we assume that trade and FDI with the Africa region is only one-eigth as valuable as trade with the Rest of the World (as discussed above), while trade with the EU is two-thirds as valuable as trade with the Rest of the World. Finally, we allow the elasticity of the Rest of the World to vary depending on the R&D intensity of the sector, where we allow for more technology diffusion in more R&D intensive sectors. The results of these assumptions are in table 6b.

To determine the impact of this parameter on the results, we conduct three types of sensitivity analysis on these parameters: systematic sensitivity analysis, piecemeal sensitivity analysis and a third where we simulate the model 100 times. When we conduct sensitivity analysis, we scale all the elasticities from 0.5 times their central values to 1.5 times their central values.

# **VIII. Results for Preferential Reduction of All Services Barriers—Central Elasticity Case**

We execute several scenarios to assess the impacts of Tanzania entering into a bilateral free trade agreement that includes services with the European Union, and similarly with the Africa region. In these scenarios we assume that Tanzanian ad valorem
.<sup>6</sup>

> 5 Kerala and West Bengal restructured their institutions of local government before the passing of the 73rd Act (in 1991 and 1992, respectively), but elections implementing these reservations were not held until after enforcement in 1993. Bihar was prevented from implementation due to legal issues regarding certain provisions of the Amendments (Iyer et al. 2012).

> 6 Data on the timing of political reservations comes from several publications documenting the implementation and progress of the reservations (Mathew 1995, 2000; GOI 2008; Iyer et al. 2012). Appendix Table 1 lists years. Beginning with Bihar in 2006, several states increased their reservations for women above the 33% required by the Amendments. These changes occur after our sample period ends in 2005.

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coresidency bias in intergenerational mobility analysis. They suggest house rental cost for identication, as house rental is the largest cost for children planning to leave parental home. Since house rental rates are not available for our study period, we use the recently available data on built-up density. Built-up density in 1975 in a district is used as a measure of house rent: a higher built-up density in a district implies lower rent and a lower coresidency rate (borne out by a negative coecient on built-up density in the selection equation). The IPW corrected estimates are similar to the unweighted estimates reported above. We provide an explanation for the evidence that selection correction does not alter the estimates substantially. This apparently surprising nding can be understood better when we 20
Weck-Hannemann 1996, Loayza 1996, Johnson, et al. 2000, Dabla-Norris, Gradstein and Inchuauste 2008).

> 28 However as Dollar _et al_ . (2005) also point out that even the city-sector averages won’t be exogenous if more efficient firms self-select to better climate locations and the analysis does not fully control for all the forces driving the self-selection behaviors.

25
- ——— (1962): “Employment Status, Weeks Worked, Occupation, and Industry for the Population of the United States: 1960,” 1960 Census of Population, Supplementary Report.

- ——— (1972a): “Industry of Employed Persons for the United States: 1970,” 1970 Census of

- Population, Supplementary Report.

- ——— (1972b): “Weeks Worked, Class of Worker, Last Occupation of the Experienced

- Unemployed, and Labor Mobility for the United States: 1970,” 1970 Census of Population, Supplementary Report.

- ——— (2018): “Consumer Population Survey, ASEC,” United States Census Bureau.

- World Bank (2023): “World Development Indicators, WDI,” World Bank.

- Yeh, C., C. Macaluso, and B. Hershbein (2022): “Monopsony in the US Labor Market,” _American Economic Review_ , 112, 2099–2138.

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Ecuador|2003<br>2010|ENFT|ENFT|
|El Salvador|2000<br>2009|EHPM|EHPM|
|Honduras|1999<br>2009|EPHPM|EPHPM|
|Mexico|2000<br>2010|ENIGH|ENIGH|
|Panama|2001<br>2009|EH|EH|
|Paraguay|1999<br>2010|EIH|EPH|
|Cons<br>|umption-Based Pove<br> <br>|rtyMeasures<br>|<br>|
|Bangladesh|2000<br>2010|HIES|HIES|
|Ghana|1998<br>2005|RIGA|RIGA|
|Moldova|2001<br>2010|HBS|HBS|
|Nepal|1996<br>2003|RIGA|RIGA|
|Peru|2005<br>2009|ENAHO|ENAHO|
|Romania|2001<br>2009|HBS|HBS|
|Thailand|2000<br>2009|SES|SES|

35
especially that provided by friends and relatives, is positively associated with the sales growth of rural microenterprises that have at least one employee.<sup>7</sup> Similarly, Zhang (2008) finds that proxies for reputation and relationships play important roles in explaining usage of formal finance by small firms in Chengdu (one of China’s largest inland cities), even though those factors tend to be more pivotal for access to informal finance. While those studies have focused on the firm as their unit of observation, we examine reliance on informal sources of finance by Chinese households.

To investigate how social capital affects financial usage and whether it matters for consumption, and how specific components of consumption are affected differently by financial usage, we rely on a comprehensive new national data set, the Chinese Household Finance Survey (CHFS) collected in 2013 by the Chinese Household Finance Research Center of Southwestern University of Finance and Economics. It is representative of Chinese households and contains detailed information on household consumption, structure, income, and access to various types of finance.

For access to finance, we rely on variables describing access to and amounts of formal and informal loans. Another advantage over other studies of finance in China afforded by our data is that we can directly compare the effects of informal finance in rural versus urban settings. And because we have information on household income and wealth, we can directly compare the consumption effects of financial usage in poor versus non-poor households. As noted above, a further advantage is that we are able to use information on household structure and social networks to construct variables to instrument for our informal finance variable, thus enabling us to better identify the causal effects of informal financial arrangements on consumption. A final advantage is that we are able to examine the effect of access to two distinct types of social capital— access to the traditional family-based social network and access to the modern, partybased political network—and contrast how they affect access to finance and the private welfare of households, and how those
elicit the willingness to pay for social insurance.<sup>4</sup> The approach follows Eriksson and Kristensen (2014), who studied the trade-off between wages and alternate employment benefits among Danish workers. In their set-up, employment benefits included internet and a home computer, work-related training, health insurance, work-time flexibility, and an annual bonus. Out of all of the different employment benefits, they found that work-time flexibility was most highly valued, followed by health insurance (Eriksson and Kristensen 2014). Wiswall and Zafar (2018) employ a discrete choice experiment to estimate preference for work flexibility, job stability, and earnings among high-performing undergraduates at the New York University for hypothetical future jobs. Other studies that have employed a discrete choice experiment include Mas and Pallais (2017), who analyzed the willingness to pay for different work arrangements – with their main focus being flexibility – as part of a hiring process for a national call center in the US, thus allowing them to replicate a real market transaction. When comparing their findings through the hiring process and a vignette-based survey experiment that they conducted to test for validity, their results were broadly consistent. In turn, this suggests that vignette-based experiments, when designed properly, can generate responses that are similar to market choices (Mas and Pallais 2017).

# **3 Data**

Our sample consists of 1,038 gig workers, including digital freelancers and location-based workers. The data was collected through an online survey administered by the Malaysian arm of a global market research firm, Ipsos. To gather respondents, we partnered with the Malaysia Digital Economy Corporation (MDEC) to tap into their network of digital freelancers via their Global Online Work Force (GLOW)<sup>5</sup> members, as well as an online panel respondents of gig workers. The data collection period is February 2022 – May 2022.

The online survey questionnaire is divided into four parts: (i) respondents’ demographic background, which asks details on age,
22

- Kilic, Talip, and Thomas Pave Sohnesen. 2019. ”same question but different answer: experimental evidence on questionnaire design’s impact on poverty measured by proxies”. _Review of Income and Wealth_ 65 (1): 144–165.

- Lanjouw, Jean O., and Peter Lanjouw. 2001. How to compare apples and oranges: poverty measurement based on different definitions of consumption. _Review of Income and Wealth_ 47 (1): 25–42.

- Mathiassen, Astrid. 2013. Testing prediction performance of poverty models: empirical evidence from Uganda. _Review of Income and Wealth_ 59 (1): 91–112.

- Pape, Utz, and Johan Mistiaen. 2018. _Household expenditure and poverty measures in 60 minutes: a new approach with results from Mogadishu._ Working Paper Series 8430. The World Bank.

- Ravallion, Martin. 1994. _Poverty comparisons._ Vol. 56. Taylor & Francis US.

- Watmough, Gary R, Charlotte LJ Marcinko, Clare Sullivan, Kevin Tschirhart, Patrick K Mutuo, Cheryl A Palm, and Jens-Christian Svenning. 2019. Socioecologically informed use of remote sensing data to predict rural household poverty. _Proceedings of the National Academy of Sciences_ 116 (4): 1213–1218.

- Yoshida, N, R Munoz, A Skinner, C Kyung-eun Lee, M Brataj, W Durbin, D Sharma, and C Wieser. 2015. Swift data collection guidelines version 2. _The World Bank._
mobility data of Akbar et al. (2021) offers several advantages over alternative sources, such as INRIX and TomTom data. First, the mobility indices are based on millions of trips on Google Maps, simulated over the same period of time using the same trip sampling strategy for cities all around the world. This was done precisely to facilitate cross-country analysis of the kind undertaken here. Second, Akbar et al. (2018) show that their mobility indices are robust to various alternative trip sampling strategies (such as with different weights on trip destinations, directions of travel or time of day). The indices also condition out the effect of trip characteristics that may vary systematically across cities and would bias any speed estimates based on actual trips. Third, Akbar et al. (2018) show for India that the predicted speeds on the simulated trips are still

> 9 Akbar et al. (2021) design the aggregate distribution of departure times (i.e., the time of query) across trip instances to mimic the distribution of departure times in the US National Household Travel Survey, except that they oversample sparse late night departure times in order to estimate travel times when there is no traffic. 10 The naive approach would be to divide the sum of trip lengths by the sum of trip durations to get an average speed for each city. However, this would ignore the fact that characteristics of trips can differ systematically across cities. Another attractive feature of these indices over raw means of travel times (such as from travel surveys) is that they are not confounded by trip characteristics that may be important determinants of speed but may also vary systematically across cities, such as trip length, departure times and weather conditions.

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.8|3.9|48.0|0.02|0.02|0.15|
|Medium-Low Risk|0.04|77.0|5.7|3.5|29.6|0.01|0.03|0.20|
|Low Risk|0.03|72.1|2.1|3.0|0.0|0.00|0.03|0.22|
|No Risk Data|0.10|82.5|18.2|4.4|62.4|0.06|0.04|0.26|
|Total: Formal Firms|0.27|76.4|7.4|3.5|40.0|0.11|0.16|1.07|
|_Panel B: Formal Firms + Natural Persons_|||||||||
|Formal Firms|0.27|76.4|7.4|3.5|40.0|0.11|0.16|1.07|
|Natural Persons|0.19|82.5|18.2|4.4|62.4|0.12|0.07|0.47|
|Total: Formal Firms + Natural Persons|0.45|77.8|9.8|3.7|45.0|0.22|0.23|1.54|

This table shows the distribution of the aggregate expected loss borne by the government and the banking system as a result of the credit program, for natural persons and the formal firms sample. Panel A reports statistics across the firms’ risk distribution. Panel B reports statistics separately for formal firms and natural persons. Column 1 shows the total expected loss as a share of GDP. Columns 2–4 show the guarantee, the default probability of
increases the indicator slightly. The greater reliance on indirect taxes in most IDA countries is likely driven to a significant extent by administrative capacity constraints that result in greater emphasis being placed on indirect taxes that may be easier to collect than direct taxes. Additionally, the low income levels of a significant proportion of the population can significantly limit the scope for personal income taxation.

Comparing the direct-to-indirect and income tax-to-total tax ratios, shows the tempering impact that the inclusion of income taxes in the base has. As a result the variation in indicator is lower across countries than for the direct-to-indirect tax ratio. Beyond this, the absence of property tax revenue results in some differences in ranking of countries. For example, Grenada, which raises 1.2% of GDP from property taxes, ranks 40<sup>th</sup> based on the direct-to-indirect tax ratio, but 47<sup>th</sup> equal under the income tax-to-total tax ratio.

The benefit of presenting the income tax-to-GDP ratio is also illustrated when looking, for example, at Lesotho, which ranks only moderately according the direct-to-indirect tax and income tax-to-total tax ratios. But the likely redistributive effect that the income taxes have in Lesotho is likely significantly higher than many other countries as it raises significantly more tax revenue from these taxes (as a percentage of GDP). Mozambique, meanwhile, both relies significantly on income taxes and also raises significant revenues implying both significant progressivity and redistribution. Other countries that rely heavily on direct taxes, such as Bhutan, Chad, and the Democratic Republic of Congo, may not achieve significant redistribution as a result of the comparatively low aggregate tax level.

> 32 In some cases, total tax revenue was available for 2019 or 2020, but income tax revenue figures were significantly outdated: Republic of Congo (1994), Côte D’Ivoire (2005), Ethiopia (2007), Guinea (2006). In Mali, 2020 data is available for the direct-to indirect tax ratio, but income tax data is only available