rafmacalaba/data-use-mentions-extended huggingface dataset:
paid work (ILO, 2013).<sup>4</sup> This restricted definition of employment means that seasonal and/or irregularly-paid jobs — which by recent ILO estimates across 110 countries constitute about 70 percent of employment in developing and emerging economies, with much of it in agriculture (ILO, 2018a)) — need to be elicited more carefully, so as not to be missed through standard survey approaches. A better understanding of issues around respondent selection and survey measurement is therefore critical, to better design policies that foster economic opportunities, along with monitoring SDG targets around small-scale agricultural and informal employment.<sup>5</sup>

# **3. Country Context and Data**

Malawi is a predominantly rural country situated in southeast Africa, with a little over half of its households below the national poverty line, and 20 percent living in extreme poverty (World Bank, 2019). Agriculture makes up 26 percent of the GDP and is a source of employment for 83 percent of Malawian households (Davis et al., 2017). A high share of households, particularly in rural areas, are engaged in seasonal economic activities, including _ganyu_ — the local term for shortterm, temporary, off-farm labor opportunities (Beegle et al., 2017).

Our analysis is informed by two surveys that were implemented concurrently by the Malawi National Statistical Office: the Fourth Integrated Household Survey (IHS4) 2016/17 and the Integrated Household Panel Survey (IHPS) 2016.<sup>6</sup> The IHS4 was a multi-topic, nationallyrepresentative, cross-sectional household survey that was conducted over the period of April 2016April 2017. The IHS4 followed the business-as-usual approach to individual-level labor data collection, which allows for (1) proxy respondents to be interviewed on behalf of unavailable respondents, and (2) any respondent, whether proxy or self, to be interviewed in group, non-private settings. The Integrated Household Panel Survey (IHPS), on the other hand, was the third wave of
of personal data. Developing states might also engage regionally and bilaterally with other jurisdictions to effectively distribute the costs of enforcement through systems of mutual recognition.

As the above review demonstrates, the cost of data privacy compliance can be quite high, so high that companies avoid certain jurisdictions entirely or simply ignore the laws. The GDPR can be so difficult to comply with that more than half of the EU privacy professionals surveyed in the IAPP/EY study said that their organizations were not “fully” or even “very” compliant.<sup>234</sup> At the same time, the cost of not having data privacy protection can be quite high as well, and results in consumers and other counterparties avoiding beneficial transactions because of the risks that the information they share will be misused. Concerns over costs of compliance or costs of enforcement might be ameliorated if stronger data protection laws make it easier for local businesses to participate in global value chains.<sup>235</sup> The digital economy depends on a proper legal framework that protects privacy.

The study also reveals the need for further inquiry. Private companies are reluctant to publish information about the costs of compliance, which might be perceived as either too little (by consumers) or too much (by shareholders). Might particular data privacy obligations such as the right to data access, to redress, to reasonable cybersecurity, for example, offer particularly cost-effective privacy? Governments should review their own enforcement efforts, including whether the resources they deploy are sufficient to regulate the growing digital economy. How effective are different types of government enforcement efforts (such as audits, sanctions, or guidance regarding best practices)? Governments could gather more data from companies on their compliance expenditures. Understanding costs is a critical step towards achieving privacy.

> 234 Hughes & Saverice-Rohan, _supra_ note 74.

> 235 The World Bank Grp., WORLD DEVELOPMENT REPORT 2020: TRADING FOR DEVELOPMENT IN THE AGE OF GLOBAL VALUE CHAINS (Caroline Freund, Aaditya Mattoo & Pol Antras eds
||Proportion<br>of children<br>|Schooling ra<br>t<br>|te of children by di<br>heirparents (%)<br>|sability of|
|---|---|---|---|---|
|Groups|with parents<br>with<br>disability (%)|Parents<br>without<br>disability|Parents<br>with<br>disability|Total|
|_Consumption quintiles_|||||
|Poorest|7.12|78.15|63.94|77.13|
|Near poorest|5.66|84.45|74.25|83.88|
|Middle|4.65|87.58|71.48|86.83|
|Near richest|4.91|89.35|78.88|88.84|
|Richest|5.34|93.23|88.18|92.96|
|Number of observations|8,790|507|8,283|8,790|

_Source: Estimation from the 2006 VHLSS_ Table 2. Proportion of schooled children by their educational performance

||Childre|n with paren|ts without di|sability||Parents wi|th disability||
|---|---|---|---|---|---|---|---|---|
|Groups|Excellent|Good|Middle,<br>Bad|Total|Excellent|Good|Middle,<br>Bad|Total|
|Total|14.01|37.29|48.7|100|11.02|39.82|49.16|100|
|_Gender_|||||||||
|Girls|17.03|40.51|
at the country level or, at best, at the country-industry level. Indeed, in neoclassical frameworks in which production technologies feature constant returns to scale, there is little hope to generate predictions at a more granular level (i.e., at the firm or plant level). Furthermore, Input-Output tables and publicly available international trade statistics are reported at the industry- (or product-level) rather than at the firm level.

In the real world, however, it is not countries or industries that participate in international trade, but rather firms. In line with this simple observation, economic research in international trade has undergone a dramatic transformation in the last twenty years, one that has placed firmlevel international strategies at the center stage. This intellectual revolution was fueled by the increased availability of longitudinal plant and firm-level datasets that permitted researchers to unveil a series of new facts that challenged the validity of existing models (see Bernard and Jensen, 1995). At the theoretical level, the seminal paper in the literature is that of Melitz (2003), which focuses on the exporting decisions of heterogeneous firms within an industry. In Melitz’framework, firms are assumed to produce differentiated products under technologies featuring increasing returns to scale. Product differentiation confers market power to firms, while scale economies are associated with firms facing fixed cost of production and of distribution. The decision of a firm on whether

> 10 In the presence of inventories, GVCs are also expected to generate differential volatility at different stages of the chain (see Ferrari, 2019).

14
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economic activity fell sharply as sectoral GDP estimates have shown. Sectors such as tourism, hospitality and recreational activities were particularly hit hard as consumers reduced their exposure to the virus. We observe that jobs that have greater income risks are more likely to have experienced a reduction in demand too; so, there is a strong positive correlation between the supply and demand shocks. However, the demand shock may have continued for after government lockdown measures ended and other supply restrictions were lifted as the result of lower household spending capacity, and higher levels of uncertainty about the economy.

To capture the uneven impact of the pandemic experience on consumer spending across sectors, we follow Rio Chanona et al. (2020) who base their analysis on an earlier model developed by the US Congressional Budget Office (CBO, 2006) to generate scenarios to examine the possible impacts that could occur in the event of an influenza pandemic. The most-severe scenario, modeled after the SARS episode in east Asia, estimated that an 11.5 percent reduction in consumer spending would occur (CBO, 2006). For comparison, private consumption in 2020 in the Euro Area fell by 8.4% in real terms compared to 2019, and by 12.8% in the United Kingdom.<sup>6</sup> The CBO provides estimates of the size of the change in demand across two-digit categories of the North American Industry Classification System (NAICS); we convert these to two-digit categories used in the statistical classification system of the EU (NACE). Under a scenario that assumed an outbreak similar to the one that occurred during the Spanish flu pandemic of 1918-1919, the US Congressional Budget Office estimated that consumption in the hospitality and in arts, entertainment and recreation sectors would plummet by 80 percent (CBO, 2006). Under this scenario, it estimated that demand for healthcare services would rise by 15 percent, and that no major consumption changes would occur in other sectors, such as utilities, professional activities, financial and ICT services, public
shares on export intensity (exports/sales). The instruments are the weighted average the real exchange rate of a firm export partners and the weighted average of the real gdp of a firm export destinations. Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

14
tables, have been distributed according to the proportion of the total regional imports from abroad of each product.

More recent works for Italy are contained in Gesualdo et al. (2019) and Severini et al. (2019). The first (Gesualdo et al., 2019) presents Italy’s multisectoral computable general equilibrium tax model. It shows a methodology for modeling and accommodating the full range of direct and indirect taxes. The authors provide a powerful tool for acquiring new insights into fiscal policy analysis through a commodity tax matrix and a production tax matrix. Simulations are performed to evaluate a value added tax rates reform, finding that a budget-neutral uniform tax rate reform would improve GDP and welfare. The second paper (Severini et al., 2019) focuses on gender disparity in Italy, showing another powerful aspect of CGE models. The authors present a multisectoral analysis to bridge gender disaggregation within income formation. A gender-aware SAM is used to evaluate the impact of different fiscal policies to reduce female labor costs.

> 2The WIOD database provides annual time series of world input-output tables (WIOTs) from 1995 to 2011, including 35 industries and 59 products.

5
29 regressed on different measures of grid‐electricity poverty, the poverty gap at 250 kWh and headcount at 100 kWh, shown in Figure 7, are the two best predictors of access. However, although countries do not overlap, the correlation coefficients are not as high as between access and the economic poverty gap at $3.10 or $1.90. The share of total household expenditures required to consume different amounts of electricity is another predictor of access, but is not as good as the two measures of grid‐electricity poverty.

Figure 7: Comparison of access with grid‐electricity poverty gap and poverty headcount <!-- Start of picture text --> Electricity poverty gap for 250 kWh/month<br>Electricity poverty headcount for 100 kWh/month<br><!-- End of picture text --> <!-- Start of picture text --> Electricity poverty gap for 250 kWh/month<br>Electricity poverty headcount for 100 kWh/month<br><!-- End of picture text --> <!-- Start of picture text --> Electricity poverty gap for 250 kWh/month<br>Electricity poverty headcount for 100 kWh/month<br><!-- End of picture text --> <!-- Start of picture text --> Electricity poverty gap for 250 kWh/month<br>Electricity poverty headcount for 100 kWh/month<br><!-- End of picture text --> <!-- Start of picture text --> Electricity poverty gap for 250 kWh/month<br>Electricity poverty headcount for 100 kWh/month<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on utility information and household survey data.

As mentioned above, because extending grid electricity to rural households is costly and off‐grid electricity will be cheaper for low‐density, low‐consumption areas
ambulatory facility in the West. In Singapore, for example, ambulatory clinics offer X-rays, clinical laboratory tests, and some rehabilitative services.<sup>27</sup> In Japan, many physician offices (one third of the total) even have a few outpatient beds.<sup>17</sup> And in Japan, as in Hong Kong (China)<sup>28</sup> , clinics offer specialist care, not just primary

> * See Hong Kong (China)’s ‘domestic’ health accounts at <u>http://www.hwfb.gov.hk/statistics/en/dha.htm.</u>

> †  No national health accounts are available for Singapore26, and I have been unable to establish the share of hospital costs borne by the taxpayer.

> ‡ Data on doctors and beds from WHO <u>http://www.wpro.who.int/information_sources/databases/core_indicators/, except Taiwan</u> (China) where the numbers are from Taiwan (China)’s statistical yearbook <u>http://ecommerce.taipeitimes.com/yearbook2004/P243.htm</u> and the OECD averages which are from OECD Health Data<sup>1</sup> .
from the DHS 2003 and 2373 observations from the DHS 2008 correspond to actual live births that took place in Kenya in April 2000-August 2002 and November 2005-February 2008, respectively.

Child mortality is measured at individual level by a dummy variable which takes the value 1 if a child died in a given period and value 0 if it did not die. Depending on the type of mortality, the relevant time period changes. Postneonatal mortality covers the time period after 28 days of age (inclusive) and before the age of 12 months (exclusive). Infant mortality takes into account the
. At the same time, shock recording in microdata sources is subject to the design of the survey questionnaire such as the list of shocks covered, and the number of shocks recorded.

## **Population coverage**

Disaster inventories are not limited to a specific population of interest and can, in principle, cover any event affecting any population group so long as this is reported in a source the disaster inventory can draw on. The lack of an explicit focus and reliance on reports from underlying sources, in turn, means that coverage for poor and marginalized population groups is more likely to be incomplete.

Microdata from household surveys focuses on (stratified) samples from a well-defined target population. In the case of the LSMS-ISA surveys, the samples are typically drawn to be nationally representative of the general population at the national and sub-national levels, and of urban and rural areas. This means that even poor and marginalized populations can be explicitly covered. At the same time, coverage is limited to the sample and affected by gaps in coverage wherever the sample is not fully representative of the population affected by a shock.

## **Temporal coverage**

42
lacked spatial representativeness, and the resulting RAIs were not necessarily comparable between countries. Ten years later, (Iimi et al., 2016) proposed a new method based on GIS data and applied it in eight countries. They assessed the RAI using WorldPop data for rural population distribution and digitized road network data, including road condition, provided by national road agencies.

However, to be able to inform global policy for improving road access in rural areas, global-wide estimates are required. This is the first global study that attempts to provide such estimates. We go on to explore how rural access might be optimally increased via a sequential road upgrading strategy.

In this paper, we build on the approach developed by (Iimi et al., 2016) and use road networks extracted from OpenStreetMap (OSM) for 166 countries, to assess road access globally. Population data for these countries is, similar to Limi et al. (2016), taken from WorldPop.<sup>1</sup> We find that while most European countries have an RAI close to 100 percent, most developing countries are below 60 percent, all SubSaharan countries are below 51 percent, and 24 countries are below 20 percent.

We then explore the cost of increasing rural access in 19 countries with good quality data, using an algorithm that prioritizes investment in individual rural road segments based on their impact on rural access and connectivity. The results show that costs quickly balloon as the RAI increases, but also that they depend on geography, road network connectivity, population distribution, and road unit costs.

Finally, as a thought experiment, we assess the cost of providing regular food and medicine supply by drones to remote areas in Sierra Leone and compare the cost of doing so with the cost of paving roads. We find that even though drones cannot generate the same socio-economic benefits as roads, they can be a good short-term solution to increase rural integration until roads become affordable.

> 1 <mark>www.worldpop.org</mark
# **_6.2 Limitations_**

There are several important limitations to be mindful of when considering the implications of our results. We are examining only one country, one set of standards, and two tools. Results could differ substantially with different countries’ standards and tools. In particular, the types of items included in standards and tools appear to have an important relationship with consistency, so variation in the emphasis of standards, e.g., whether they focus on child development outcomes, a quality environment, child-teacher interactions, or other aspects of quality will shape consistency.

Our measures of consistency also embed a number of different issues. We cannot separate testre-test differences<sup>18</sup> from inter-rater reliability or that questions were, in some cases, asked or scaled differently across the tools. Enumerators were also only sometimes observing the same period, although visiting around the same time. Implementing QAS at scale, this problem is likely to be worse, as children and classes at different schools could be observed at time points that are even further apart. We also do not and cannot know whether the QAS tool or diagnostic tool data, or neither, are ultimately “right or wrong.” Although the underlying tools, such as the MELQO tools, have undergone extensive design, testing, and adaptation (Raikes et al. 2019), measuring ECD and quality are fundamentally difficult tasks (Burchinal 2018).

# **_6.3 Policy implications_**

Our findings on the limited consistency of QAS tool results have important implications for the design of QAS, and especially subsequent quality enhancement actions. One potential approach to QAS, which may be a good starting point for fiscally constrained countries working to initially develop a QAS system, would be to start the QAS as a national-level system, using a random sample of schools to identify quality problems, nationally. The diagnostic research tool and QAS tool data were most consistent at the national level in identifying similar strengths and weaknesses in KG quality in
# **6. REFERENCES**

<mark>Azevedo, Joao Pedro; Atamanov, Aziz. 2014. Pathways to the middle class in Turkey : how have reducing poverty and boosting shared prosperity helped?. Policy Research working paper ; no. WPS 6834. World Bank Group. http://documents.worldbank.org/curated/en/2014/04/19354560/pathwaysmiddle-class-turkey-reducing-poverty-boosting-shared-prosperity-helped</mark> Beck, Demirguc-Kunt, and Levine 2007. Finance, Inequality and the Poor. <mark>Journal of Economic Growth Volume 12, Issue 1 , pp 27-49 Kluwer Academic Publishers-Plenum Publishers</mark> <u><mark>http://link.springer.com/article/10.1007%2Fs10887-007-9010-6</mark></u> Brunori, Paolo, Francisco H.G. Ferreira, Vito Peragine. 2013. Inequality of Opportunity, Income Inequality and Economic Mobility WPS6304 <mark>World Bank Group.</mark> <u><mark>http://elibrary.worldbank.org/doi/pdf/10.1596/1813-9450-6304</mark></u> Claessens, Stijn and Perotti, Enrico C. 2007., Finance and Inequality: Channels and Evidence, Available at SSRN: http://ssrn.com/abstract=998468 or <u>http://dx.doi.org/10.2139/ssrn.998468</u> de Mel, Suresh, Craig McIntosh, and Christopher Woodruff. 2013. "Deposit Collecting: Unbundling the Role of Frequency, Salience, and Habit Formation in Generating Savings." American Economic Review, 103(3): 387-92.

Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.

Johnston, Don Jr., and Jonathan Morduch. 2008. “The Unbanked
time to haul water as a function of collection time as measured in 2008, separately for rural and urban communities.

The estimated impact also varies significantly by region (Table 6). The median effect of a halving of collection time on school attendance is small (one percentage point or less) in the following regions: Western, Central, Greater Accra, Volta, Eastern, Ashanti and Brong Ahafo. In these regions hauling times per round trip are low, less than 17 minutes on average. In the north of the country (Northern, Upper East, and Upper West regions), where hauling times per round trip are longer, 25 to 30 minutes on average, the estimated impact on girls’ school attendance is in the range 5-6 percentage points.

The magnitude of the effect may seem strong in the Northern regions, in particular when compared to what was found by Koolwal and van de Walle (2013). By their estimation, a onehour reduction in time to the water source would lead to a 10 percentage point increase in school enrollment in Yemen and Pakistan. In our case, we find that a 50% reduction in hauling time (corresponding to roughly 15 minutes reduction in time to the water source in the Northern, Upper East and Upper West regions of Ghana) would increase median school attendance among girls in these regions by 5-6 percentage points. Both methodological approach and study area differ between the two studies, though. Importantly also, our measure of hauling time is a per-trip measure as our data contain no information on households’ numbers of daily trips to their water sources.

# _6.4. The case of boys_

Water fetching is usually described as an activity undertaken primarily by women and girls. However we test if there is any impact of water infrastructure also on boys’ school attendance. We consider boys aged 5 to 15, and use hauling time as the main variable of interest, using as instruments the one-period lagged hauling time, the dummy variable indicating whether the household head is a