rafmacalaba/data-use-mentions-extended huggingface dataset:
11. Hao J, Zhu J, Zhong R. The rise of big data on urban studies and planning practices in China: Review and open research issues. Journal of Urban Management. 2015;4(2):92–124.

12. Blumenstock J, Cadamuro G, On R. Predicting poverty and wealth from mobile phone metadata. Science. 2015;350(6264):1073–1076.

13. Kosinski M, Stillwell D, Graepel T. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences. 2013;110(15):5802–5805.

14. Resch B, Summa A, Zeile P, Strube M. Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm. Urban Planning. 2016;1(2):114–127. doi:https://doi.org/10.17645/up.v1i2.617.

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19. Williams S.
context, the provider assessment falls within the framework set out by Miller in 1990, that a holistic assessment of health worker competence should assess their clinical knowledge, if they know how to apply that knowledge, if they can show how to apply that knowledge, and if they actually apply that knowledge in practice.<sup>7</sup> Our vignette data capture a combination of clinical knowledge and their knowledge of how to apply it. Other, complementary methods (specifically, standardized patient studies) demonstrate whether health workers can show how to apply their knowledge and whether they actually apply that knowledge in practice.<sup>8-10</sup> Nationally representative standardized patient data are unavailable in the settings and for the wide array of conditions we study, but limitations revealed in the first aspects of the health worker assessment (what they know and if they know how to apply it) and to one aspect of practice (absenteeism) potentially imply even further limitations on the latter part (whether they can and do actually apply knowledge in practice).

# **Methods**

# **_Data_**

We draw on data from the Service Delivery Indicators (SDI) program—an ongoing initiative to collect informative and standardized measures of what providers know, what they do, and what they have to work with. The SDI program—piloted in 2010-2011—grew out of concern about quality of services in health and education, especially in Sub-Saharan Africa, most clearly (and perhaps most damagingly) manifested at the facility level, in fast-expanding systems of primary health provision.<sup>11,12</sup> To date, the SDI program has collected data from 10 countries: Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013), and has since expanded to countries outside the
six different target variable combinations as outlined in Table 1 and explained in more detail in what follows.

Table 1: Target variables

|Target variable|Approach for estimating poverty|
|---|---|
|(1) Poverty rates|Predicted directly|
|(2) Changes in<br>poverty rates|Apply the predicted change to the most recent poverty rate|
|(3) Mean welfare|Scale the past distribution to the predicted mean welfare|
|(4) Growth in mean<br>welfare|Scale the past distribution by 1+the predicted growth in mean<br>welfare|
|(5) Mean welfare<br>and Gini coeffcient|(i) Assume the distribution is log-normal or log-logistic or (ii)<br>apply a growth incidence curve from the past distribution to<br>match the predicted mean and Gini|
|(6) Growth in mean<br>welfare and Gini<br>coeffcient|(i) Apply the predicted growth in the mean and Gini to the<br>past mean and Gini and assume that the distribution is log-<br>normal or log-logistic or (ii) apply a growth incidence curve<br>to the past distribution with the predicted growth in the mean<br>and Gini|

_Notes:_ The table lists the six different combinations of target variables we will use for the predictions and how poverty rates are backed out based on the target variable(s).

_(1) Poverty rates and (2) changes in poverty rates._ Predicting the poverty rate directly is the most intuitive and straightforward option; the poverty rates are the ultimate objective of this paper. Predicting poverty rates directly at the nowcasting year, _tn_ , has the advantage that it can yield estimates for countries without any previous poverty estimates at all. This is particularly a concern for countries that do not produce or share household survey data. Predicting changes in poverty from the past survey conducted at
## Box 5. Developing domestic catastrophe risk insurance markets in Fiji

TC Winston highlighted a major protection gap: 94 percent of houses are uninsured against tropical cyclones. The building stock in Fiji comprises approximately 240,000 residential homes (Pacific Risk Information System – PacRIS). With only 15,000 homes (or 6 percent of the housing stock) purchase coverage against tropical cyclone, a significant gap in insurance coverage emerges. The supply of TC coverage within the domestic insurance industry is limited because insurers are unable to secure reinsurance capacity. Insurance companies can only supply property catastrophe insurance for houses of a high construction standard and a current engineer’s certificate which is required by the international reinsurers to provide reinsurance.

Most low‐income households do not meet the standard required to purchase an engineer’s certificate which demonstrates that there is market failure creating a major contingent liability for the GoF. Uninsured households create an implicit liability or social obligation for the GoF to provide financial assistance in the aftermath of a disaster, as was seen in the case of TC Winston where GoF has spent FJ$80 million to support the reconstruction of housing.

The World Bank and the IFC have been developing two pilot catastrophe risk insurance schemes to test the viability of market‐based insurance solutions to provide household coverage against tropical cyclones. The first product is property insurance for houses of low‐build quality that are still deemed insurable with some basic reinforcement, such as roof strapping. Such houses are estimated to represent half of the housing stock, or approximately 121,000 households, in Fiji. The second product is a parametric‐based livelihoods (protection) insurance product for households with non‐insurable assets, which account for approximately 68,000 households.

# Empirical Assessment of Linkages

This section reviews countries’ exposure to macro‐financial and climate related risks. We start with a discussion of physical and transition risks and then seek to identify countries that are subject to “double jeopardy” due to the simultaneous presence of both risks. The
# **8.  Summary and Conclusions**

This paper has identified high-priority sites for methane emissions reduction and introduced a global monitoring approach that employs data from the ESA’s Sentinel-5P (S5P) satellite platform. Drawing on geolocated S5P observations that typically number over 200,000 per day, we compute monthly mean methane concentration anomalies for each cell of a 5-km global grid. Our data span the period from January 2019 to November 2022. To highlight local accountability, our monitoring approach estimates trends in S5P atmospheric concentration data for districtlevel (GADM2) administrative units and scale-equivalent areas within national exclusive economic zones (EEZs) where offshore oil and gas extraction occur. These global geographic units (GGUs) comprise 51,976 areas. The spatial distribution of methane emissions is so highly skewed that 2.2% of these areas account for 50% of anthropogenic methane emissions in “bottom-up” estimates provided the Emissions Database for Global Atmospheric Research (EDGAR). We focus on these 1,131 high-priority GGUs, drawing on EDGAR to identify their dominant sources of emissions from seven sectors (Agricultural Soils, Livestock, Gas, Oil, Coal, Landfills and Wastewater).

We illustrate our monitoring approach with three GGU case studies: landfills in Spain (Madrid); irrigated rice production in India (Karnal district, Haryana state); and oil production in Iraq (Al Amarah district, Maysan governorate). Each case employs S5P data from the GGU subregion that includes all EDGAR emissions points in the point set that accounts for 50% of global emissions. These points are often spatially clustered, and our approach ensures that our trend models focus on areas where emissions are most highly concentrated.

In each case, we estimate two trend models by fixed effects. The first model estimates the monthly trend for the period January 2019 – November 2022, while the second focuses on recent history by estimating the difference between mean concentrations in 2022 and the previous three years.

We extend this approach to 775 high-priority GGUs that have sufficient S5P observations for
_Figure 6_ - Net effects after taxation on processed and ultra-processed foods <!-- Start of picture text --> 16.00% 14.61%<br>14.00%<br>12.00%<br>10.00%<br>8.20%<br>8.00%<br>5.70%<br>6.00%<br>4.43%<br>3.17%<br>4.00%<br>2.31%<br>1.48%<br>2.00% 0.80%<br>0.49%<br>0.00%<br>0.00%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>Processed and ultra-processed foods<br>Net effect<br><!-- End of picture text --> Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019; and SIH/SUS. Estimates assume a price shock of 20%.

22
<!-- Start of picture text --> 100<br>86<br>80<br>62<br>60<br>40 32<br>20 13<br>6<br>1<br>0<br>Down Same Up<br>Experienced Expect<br><!-- End of picture text --> Source: World Bank staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014 Households in urban areas, in the Western Development region, in the hills, with heads employed in semiskilled, non-agriculture sector, in the top 40 percent of the wealth distribution and with both domestic and overseas migrants have the highest odds of perceiving upward mobility. In contrast, households in rural areas, in the Far Western Region, in the mountains and the Terai, and the bottom two quintiles are likely to have higher odds of lower mobility. Expected upward mobility, on the other hand, is highest in MidWestern region, mountains and lowest in the Terai and among semi-skilled workers in non-agricultural sectors. The fact that this latter group has experienced the highest upward mobility among all other occupation categories and yet is also the group that has the lowest expectation of upward economic mobility underscores an important aspect of the nature of structural transformation that has underpinned Nepal’s living standards improvement. The growth in labor income that has driven the improvements in living standards at the bottom of the distribution have come from expansion of opportunities as well as the rise in low productivity service sectors in rural as well as urban areas. But given the high degree of informality as well as the low prospects for productivity growth in these sectors, there is a dimmer outlook for continued improvements in living standards into the future. This is the population that constitutes Nepal’s growing vulnerable class.

This point is supported by evidence from other sources of data. Gallup World Survey, for example, also asks respondents similar questions about their perceived position in an
schemes<br>schemes schemes<br>percentage of prevailing percentage of prevailing<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database.

About half of the sample countries have adopted two-part tariffs (figure 1b), combining fixed charges with block tariff pricing. Among countries practicing linear tariffs, the use of two-part tariffs is more common than where IBTs are applied. For African countries the fixed charge tends to be relatively high: between $1.00 and $3.00 per month (table 1). As a reference, the average fixed charge in Latin American countries is $0.70 (Foster and Yepes 2006).

5
Policy Research Working Paper 6394

# **Abstract**

This paper examines whether and how rainfall shocks affect tariff setting in the agricultural sector. In a model of strategic trade policy, the authors show that the impact of a negative rainfall shock on optimal import tariffs is generally ambiguous, depending on the weight placed by the domestic policy maker on tariff revenue, profits and the consumer surplus. The more weight placed on domestic profits, the more likely it is that the policy maker will respond to a rainfall shortage by reducing import tariffs. These findings are robust to alternative assumptions about market structure and the timing of the game. Using detailed panel data on applied tariffs and rainfall for 70 nations, the authors find robust evidence that rainfall shortages generally induce policy makers to set lower tariffs on agricultural imports.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at pbastos@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 and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
James Manor based upon his work in India and Africa concludes considerable promise for decentralization in promoting citizen participation, increasing information flows between governments and citizens, enhancing transparency and accountability, integrating society with the state and reinforcing and invigorating democracy at the national level (see Manor, 1996). Recognizing this, India has recently amended its constitution to strengthen local government institutions (see Paul, 1996). The same results are reaffirmed by Garman and Haggard (1995) based upon a comparative study of Latin America. They conclude that "greater the political sensitivity of central level politicians to subnational political outcomes, the more decentralized the system is likely to be " (p.2).

**Table 3: Correlation of the Decentralization Index with Governance Quality Indicators** (sample size: 80 countries)

|**_Pearson_**|**_Correlation Coefficients_**|
|---|---|
|_Citizen_**_Participation_**||
|Political Freedom|0.599**|
|Political Stability|0.604**|
|**_Government Orientation_**||
|Judicial Efficiency|0.544**|
|Bureaucratic Efficiency|0.540**|
|Absence of Corruption|0.532**|
|**_Social Development_**||
|Human Development Index|0.369*|
|_Egalitarianism in Income_|0.373*|
|_Distribution_||
|_(inverse of Gini coefficient)_||
|**_Economic Management_**||
|Central Bank Independence|0.327*|
|Debt Management Discipline|0.263|
|(Inverse of Debt to GDP Ratio)||
|Openness of the Economy (Outward|0.523**|
|Orientation)||
|**_Governance Quality Index_**|0.617**|
|*significant at the 0.05% level (2-tailed test)<br>** significant at the 0
terminated by the employer.

Improvements in job finding rates and retention translate into sizeable gains in work experience. After two years, people in the treatment group who graduated from vocational schools have spent 1.03 more months (14.8%) in formal employment, driven by a 1.23 month (25.7%) increase in experience with permanent contracts. Importantly, when people switch jobs, even involuntarily, they do not experience a drop in wages, suggesting that the skills workers learn on the job are not firm-specific (Becker, 1964). Especially in LMICs where youth turnover rates are high (Donovan et al., 2020), obtaining general skills is important for helping workers maintain positive wage growth, even as they transition between firms.

Fourth, we provide evidence for the underlying mechanisms of these treatment effects and for why not a larger share of secondary school graduates pursue formal employment. We show that high reservation wages are binding and affect graduates’ career choices. The shift from informal to formal employment and transition from temporary to permanent contracts among the formally employed is driven by job seekers with reservation wages just above formal sector starting salary levels. This raises the question of why reservation wages are so high given the rapid wage growth for youth with entry-level formal jobs.

To guide our analysis of underlying mechanisms, we adapt a dynamic job search model with segmented labor markets and job seeker beliefs (Meghir et al., 2015; Mueller and Spinnewijn, 2021). We find that youths in our sample have very high discount rates, which reduces the benefit of future wage growth and dampens its negative effect on reservation wages.<sup>3</sup> In addition, we collect incentive-compatible data on beliefs and find that job seekers have fairly accurate beliefs about job separation rates but underestimate formal wage growth. Even two years after graduating, the median youth believes that formal wages increase by less than 10% over the first six months compared to the actual growth rate of about 25%
.02]|17.76|(18.39)|1,418|
|Other Members Childcare & Chores (Hours)|-0.50|(2.15)|[1.00]|18.54|(18.49)|1,390|
|Total Land Owned (Donum)|4.21|(9.55)|[1.00]|14.36|(80.11)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified housing quality and housing-related finances using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Overall Housing Quality is defined as a normalized housing quality index that includes indicators for quality floors, roofs, and walls, indicators for access to grid electricity and piped water, and the number of people per room. HousingMaterial Quality is defined as the summation of three indicators for high-quality floors, roofs, and walls. Water Access is defined on a scale from 1-5 where 1 is very inaccessible while 5 is very accessible. Clean Water is defined as an indicator for households having treated drinking water (such as by a filter). _Total_ Monthly Housing Expenditures is defined as the total of Rent paid, mortgage, and upgrade cost, and _Per capita_ Housing Expenditures is divided by household size. They do not include the subsidy payments by the implementing organization. Total ID Cards is defined as the sum of the ID cards possessed by the respondent (MOI card, Passport, Residency permit, Work permit, Family book, Syrian ID, and UNHCR file). Hours spent on childcare and chores are measured over the last week and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- Monetary values are
quintiles.

# **2.2.1 A geographic or genetic artifact?**

Perhaps people who live in certain countries or regions tend to be tall or short, and this is coincidentally correlated with open defecation. Is the result driven by certain countries or regions, or fixed differences such as genetics?

Figure 3 presents initial evidence against this possibility. The sample is restricted to countries with more than one DHS observation, and country means across collapsed DHS surveys are subtracted from the height and sanitation survey averages. The figure plots the difference in a country-year’s height from that country’s mean across DHS surveys against the difference in sanitation. The slope is similar to the undifferenced plot. Moreover, panel (b) continues to demonstrate an association despite not including any data from India.

Returning to table 1, column 4 adds country fixed effects. A control is also added for the average height of mothers of measured children; this is in anticipation of a possibility observed by Deaton and Dr`eze (2009) and considered in more depth in section 4.3 that Indian stunting is not caused by current nutritional deprivation or sanitary conditions, but is instead an effect of historical conditions that stunted the growth of women who are now mothers, restricting children’s uterine growth. DHS surveys are categorized into six global regions;<sup>5</sup> column 5 adds six region-specific linear time trends<sup>�</sup> _r_<sup>_δryeary_, to rule out that the effect is driven by</sup> spurious changes in specific parts of the world. Neither of these additions importantly change the estimate of the coefficient, although adding so many controls increases the standard errors.

Table 2 further confirms that no one region is responsible for the results. The association between height and sanitation is replicated in regressions that omit each of the six world regions in turn. The coefficient near 1 notably remains when South Asian observations are

> 5The regions are sub-Saharan Africa, South
results of the disclosure policy. For example, other factors could be driving these trends in the data, such as the entry or exit of less‐compliant firms into the program before or after the policy announcement.

Trends over time in average non‐compliance rates in compensation, contracts, occupational health and safety, work time, and to a lesser extent freedom of association mimic the overall trends – increasing a few years prior to the program, and decreasing in the years after the program announcement. On the other hand, non‐compliance with child labor began to decline prior to program announcement, while non‐ compliance with forced labor shows no clear trend patterns. These results suggest it will be important to differential the empirical results of the Public Disclosure Programme by compliance cluster, given the differences in trends around announcement.

As identified in earlier literature, compliance also tends to improve with program participation. We check this with the Vietnam data, and find similar results. Figure 2 shows that participation in the program appears to be correlated with compliance overall, where noncompliance goes down linearly with participation. Therefore, it will be important to control for duration of program participation for the empirical analysis. Work time, occupational health and safety, freedom of association, and contracts all exhibit steady and declining non‐compliance alongside program participation. Non‐compliance within the compliance clusters of child labor and forced labor appear to drop off relatively quickly after a firm joins the program, within a few cycles. Non‐compliance with the compliance cluster of discrimination shows less clear trends.

18