rafmacalaba/data-use-mentions-extended huggingface dataset:
is over the past six months). The family includes outcomes derived from the household survey, including staff absenteeism and wait times, problems with clinic facilities or staff, satisfaction with services, staff attitude, drug availability, and fees paid. Per our analysis plan, two satisfaction outcomes appear in this family and the satisfaction family (measured at the individual- and household-level respectively). We verify that this redundancy does not affect our familylevel result for health service delivery.

7. **Contributions to clinics** is measured at the community-level. The family includes two outcomes. The first outcome, derived from the survey of village leaders, captures whether the community convened meetings about the clinic and whether it contributed labor to the upkeep of the clinic or well-being of staff (e.g., helping to plant a garden for nurses). The second outcome incorporates responses from clinic staff about whether the community made such contributions or had disputes with clinic staff.

8. **Community development and political engagement (CDPE)** is measured at the community-level. The family includes outcomes related to community members’ participation in meetings in the last six months, contributions to local development projects over the last year, their self-reported ability to address problems collectively over the last year, and turnout for the local and national elections in November 2012.

> 15 As with general utilization, satisfaction with care is asked of individuals who attend the clinic for childbirth in the past year, antenatal or postnatal care, vaccination, any illness or injury in the past month or any other type of consultation in the past month.

13
>(21.1%)<br>0 2,000 4,000 6,000<br>USD billions<br><!-- End of picture text -->

# **LIC: 101.8bn**

_Figure 12. Flood exposed economic activity, disaggregated by countries’ income groups. Computed as exposure headcount per country multiplied by GDP per capita. (LIC: low-income countries; LMIC/UMIC: lower/upper middle-income countries; HIC: high income countries, based on World Bank income classifications)_ <!-- Start of picture text --> United States (HIC) 1,926.4<br>Japan (HIC) 1,342.5<br>India (LMIC) 898.6<br>Germany (HIC) 829.3<br>Netherlands (HIC) 548.9<br>Vietnam (LMIC) 503.0<br>Indonesia (UMIC) 498.7<br>Thailand (UMIC) 442.0<br>Pakistan (LMIC) 413.9<br>South Korea (HIC) 396.8<br>0 500 1000 1500 2000 2500<br>USD millions<br><!-- End of picture text --> _Figure 13. Top 10 countries: Economic value at risk, computed as exposure headcount multiplied by subnational income per capita. Note: no subnational income data are available for several countries, including China._ 17
*|(7.3%)|(8.3%)|(8.9%)|(6.7%)|(8.5%)|(9.2%)|
||1,731|372|96|266,126|67,497|17,512|
|**Muzaffargarh**|(10.5%)|(10.9%)|(10.7%)|(12.4%)|(12.8%)|(12.9%)|
||2,773|165|45|386,011|22,974|5,848|
|**Rahim Yar Khan**|(16.9%)|(4.9%)|(5%)|(18%)|(4.4%)|(4.3%)|
||942|314|80|129,564|60,853|16,441|
|**Rajanpur**|(5.7%)|(9.2%)|(8.9%)|(6%)|(11.5%)|(12.1%)|
|**Total**|**16,438**|**3,400**|**894**|**2,148,670**|**527,738**|**136,181**|

Source: Author’s calculations using the Punjab Annual School Census, 2021.

# **Tools**

The AIM-ECD DA (or AIM-ECD for short) was used to measure early childhood developmental outcomes for children aged 4-6 years old (Pushparatnam et al., 2021; World Bank, 2021). The Teach Early Childhood Education (Teach ECE) tool was used to conduct classroom observations to capture the quality
world‘s population who are international migrants increased only from 2.7 to 2.8 percent (UNDP, 2009). However, while the global migration rate has been quite flat, the migrant flow has increasingly been from less developed to more developed countries, with a reduction in within-region flows. The number of individuals migrating from the ―South‖ to the ―North‖ increased from 14 million in 1960 to 60 million in 2000 (Özden et al, 2010).<sup>2</sup> At the same time, the percentage of migrants with tertiary education increased dramatically. Defoort (2008) uses data from six main OECD destination

> 2 Özden et al (2010) define the ―North‖ as Australia, New Zealand, Japan, Canada, the United States, the EU-15, and other nations of the European Free Trade Association, and the ―South‖ as all other countries. It therefore corresponds to countries which were developed or developing in 1960.

7
reflected in regional average yields, which are as high as 1.7 in West Africa and 1.5 in East Africa, but only 1.1 in Southern Africa. From 1961-2008, area growth accounted for two-thirds of the overall 3 percent annual production growth in Sub-Saharan Africa; yield growth has averaged only 1 percent annually.

Growth rates vary considerably in each region of the continent and by decade (depending on endpoints chosen and the incidence of droughts), sometimes appearing negative but also much higher during episodes of success. In some cases, such as Zimbabwe and Zambia, trends in maize production have changed abruptly with policy shifts, and in other countries such as Angola and Mozambique, prolonged civil wars depress trends. The yield gap between countries in Sub-Saharan Africa and those with comparable production conditions is large, although it narrows if only rainfed areas are considered. It is important to recognize, however, that yield variability is much greater in Sub-Saharan Africa than elsewhere on a world scale (Box 1).

## **Box 1: How do yields in Sub-Saharan Africa compare to those of other tropical regions?**

Average yields and yield growth rates in other countries in tropical rainfed environments provide points of contrast. From 2005-2008, average maize yields were estimated at 3.8 t/ha in Brazil, 3.1 t/ha in Mexico, 2.5 t/ha in the Philippines, and 3.9 t/ha in Thailand, compared to 1.4 t/ha in Sub-Saharan Africa. Annual yield growth from 19612008 averaged 2.4 percent, 1.8 percent, 2.8 percent and 1.6 percent respectively in Brazil, India, Philippines and Thailand, on average about double the 1 percent growth in Sub-Saharan Africa. However, a careful analysis of subnational data, suggests that netting out irrigated area which is important in all but Brazil, the gap between yields in Africa and rainfed areas elsewhere is smaller although still sizeable. For example, rainfed maize yields
Table 5: Impacts of program on forest cover loss, 25-point and 10-point windows

||(1)<br>**Full**|(2)<br>**2011-2012**|(3)<br>**2013-2014**,|(4)<br>**Full**|(5)<br>**2011-2012**|(6)<br>**2013-2014**|
|---|---|---|---|---|---|---|
|||**Perc. defore**|**sted**||**Defor** _>_**2 ha**||
||||**Panel A: 25-**|**point wind**|**ow**||
|Above threshold|-0.125|-0.285|-0.016|-0.029|-0.042|-0.028|
||(0.109)|(0.230)|(0.054)|(0.032)|(0.049)|(0.038)|
|Control Mean|0.619|0.956|0.319|0.062|0.093|0.033|
|Control SD|3.385|4.138|2.495|0.240|0.291|0.179|
|MDE|0.304|0.644|0.151|0.089|0.138|0.106|
|Observations|14119|7019|7127|14119|7019|7127|
||||**Panel B: 10-**|**point wind**|**ow**||
|Above threshold|-
_Non-ed M Head_|-|-|_72.2_|_80.0_|_73.1_|_67.8_|_70.1_|
|Ed F Head|-|-|4.1|10.0|5.5|5.3|6.6|
|_Non-ed F Head_|-|-|_95.9_|_90.0_|_94.5_|_94.7_|_93.4_|
|Extr. poor|78.4|54.4|55.6|54.3|52.4|48.5|53.4|
|_Non-extr. poor_|_21.6_|_45.6_|_44.4_|_45.7_|_47.6_|_51.5_|_46.6_|
|Observations|2912|4622|5848|5614|1187|3102|3165|

Source: MECOVI.

Table 2: Enrollment, Attendance and Working

||1999|2000|2001|2002|2005|2006|2007|
|---|---|---|---|---|---|---|---|
|Enrollment (%)|90.8|91.1|90.9|91.1|94.6|94.2|95.6|
|Attendance (%)|53.2|71.2|49.5|55.0|62.1|75.4|50.6|
|Working (%)|26.5|19.1|22.2|20.3|17.04|19.5|21.9|

Source: MECOVI.

20
Nardi, Mariacristina, Eric French, and John Jones, “Why do the elderly save? The role of medical expenses,” Journal of political economy, 2010, 118 (1), 39–75.

Mahmood, Naushin, and Zafar Mueen Nasir, “Pension and social security schemes in Pakistan: Some Policy Options,” No. 22211. East Asian Bureau of Economic Research, 2008.

<mark>Paxson, Christina. “Saving and growth: evidence from micro data,” European Economic Review, 1996, 40(2), 255-288.</mark> 21
training start at the end of the rainy season as the majority of them were engaged in farming activities, and were unable to leave their farms to engage in the training program. 105 participants (12 percent) enrolled in training. In feedback from monthly surveys of beneficiaries participating in the training program, they complained about financial pressures in sustaining themselves while studying, and so we added a monthly stipend of 1,000 GMD conditional on regular attendance. The COVID-19 pandemic resulted in the closure of all educational programs in the country as of 18<sup>th</sup> March 2020. This resulted in a nine-month suspension of the GTTI training program partway through the program. The training program was then resumed in January through July 2021. Only half the participants returned, with 49 respondents participating throughout training and 31 of them being certified by the program. Youth in this treatment group indicated several reasons for not taking up the training offer. The main reason was due to too many responsibilities in their home villages (33 percent), while the other 20
intuitively, on the relative importance of trade distortions and the other policy-induced distortions.

The obvious but significant corollary is that trade liberalization will not, in general, have an unambiguous effect on productive efficiency. In this model, in fact, trade liberalization will reduce productive efficiency if the labor market distortion is pronounced, but it will increase efficiency if the labor market distortion is mild. This indicates the need to include a term for the interaction between trade opening and labor market distortions in assessing the empirical connection between trade opening and growth.

The discussion in this section has focused on the links between trade liberalization and labor market reforms, but it should be apparent that the essence of the analysis can be extended to analyze the complementarity between trade opening and other reforms. Keeping this in mind, we now turn to an empirical evaluation of the complementarity of trade reform and other reforms.

# **3. EMPIRICAL ANALYSIS**

The objective of the empirical section is to examine how the growth effect of openness may depend on a variety of country characteristics, including some that can be changed by policy. For this purpose, we work with pooled cross-country and time-series data, focusing on comparative information from within-country changes. We start with a linear growth regression specification and then extend it to account for interaction terms between an openness measure and proxies for various country characteristics. These are educational investment, financial depth, macroeconomic price stability, public infrastructure, governance, labor-market flexibility, ease of firm entry, and ease of firm exit. We build on the panel-data growth regression literature that uses a GMM procedure to control for endogeneity and unobserved country-specific factors, as presented for example in Dollar and Kraay (2004) and Levine, Loayza, Beck (2000). Further details on the methodology are given below.

15
and in country-years where and when political institutions are weak. In a theoretical contribution, Chang, Hevia and Loayza (2010) develop a model in which expropriations and privatizations reflect a tradeoff between more redistribution and better incentives for effort, and argue that this helps to explain why expropriations are more likely in resource-rich economies when commodity prices are high.

The only other study we are aware of that looks at sovereign default and expropriation at the same time and the interrelationship between them is by Tomz and Wright (2010). They find that, for the world as a whole, defaults and expropriations have occurred in alternating, rather than coincident waves. They suggest that one possible reason is that punishment spillover for failure to honor contracts across these two types of foreign investment is small. If -- to the contrary -- foreign investors and creditors cooperated fully in punishing countries for either form of sovereign theft, countries that defaulted would also have incentives to expropriate as well, since they have already incurred the full punishment for expropriation (and vice versa). This would imply a coincidence of default and expropriation events which is not observed in the data. We expand on their analysis by documenting in more detail the patterns of association between the two types of events, and their correlates, at the country level.

# **3.  Data on Sovereign Defaults and Expropriations**

Our main dataset on sovereign defaults and expropriations is taken from Tomz and Wright (2010). This is a dataset of country-year observations spanning 191 countries since 1929, which identifies country-years in which the two types of sovereign theft occurred. In their dataset, sovereign defaults occur when a country fails to make required payments to foreign private creditors within the required period. It therefore does not include defaults against foreign official creditors, such as bilateral official lenders and multilateral aid agencies. The data records each year that a country is in default, since default episodes often last for multiple years. In fact, the typical length of a default episode in their dataset is
**Table 7 - Reduction in Medical Costs (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|1.13|0.71|0.50|0.41|0.34|0.34|0.23|0.18|0.14|0.05|
|Medium elasticity|1.60|1.05|0.76|0.65|0.55|0.56|0.40|0.33|0.24|0.10|
|Upper-bound elasticity|2.07|1.39|1.02|0.89|0.76|0.78|0.56|0.47|0.35|0.15|

_Source:_ Estimates based on the Russia Longitudinal Monitoring Survey 2016. _Note:_ The table reports the share of total consumption for each decile.

**Figure 3 – Income Gains: Medical Costs of Tobacco Taxes (Reduction in Medical Expenditures), by Decile**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price
.6|0.0|683|683|0.0|87|
|Other High<br>Income|0.7|0.8|0.1|7|8|1.0|1|
|Latin America &<br>Caribbean|3.8|4.4|0.6|22|26|3.7|5|
|South Asia|9.7|10.6|1.0|170|187|16.9|27|
|East Asia &<br>Pacific|3.7|5.1|1.4|24|33|8.9|11|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table reports poverty estimates for the 2017 reference year. For countries with no surveys conducted</u> exactly in 2017, the poverty estimates are extrapolated from the latest survey if conducted before 2017, otherwise extrapolated or interpolated from the closest surveys before and after 2017. The extrapolation and interpolation rules applied are the same ones the World Bank applies when “lining-up” global poverty estimates for every year (see the - Poverty and Inequality Platform (PIP) Methodological Handbook via <u>https://worldbank.github.io/PIP Methodology/lineupestimates.html). The table is arranged in ascending order of change in poverty (column 4). These</u> results are based on the full sample of 162 countries covered in the paper.

33
the cholera Case Fatality Rate should be below 1%. Nonetheless, in 2020, in Sub-Saharan Africa the annual Case Fatality Rate was 1.6% - the highest in the world (WHO 2020) and in 2009, for instance, 98% of the 221,226 notified cases worldwide were from Africa. Further, in recent years, remarkable epidemics struck various African regions located far from the coast. For instance, in 2008–2009, Zimbabwe experienced the largest cholera outbreak ever recorded in Africa, with more than 100,000 cases and more than 4,300 deaths (World Bank 2016). These examples stress the need to better characterize cholera outbreaks in non-coastal regions of Africa and how to use various measures to reduce the cholera risk in susceptible environments.

The analysis presented in this paper uses cholera case location data and geospatial covariates to include the location of networked and non-networked WSS infrastructure as well as groundwater data to generate a high-resolution map of cholera risk across the city. It has sought to explain patterns of risk in relation to known risk factors for the disease from the literature, identify priority areas and compare alternative strategies for improved infrastructure. The analysis presents scenarios of standalone or combined investments across sewerage coverage and maintenance; on-site sanitation improvements; piped water network coverage and quality; and ensuring the safety of point source water and the relative costing estimates for each, and number of population it would affect (i.e. cost efficiency). It identifies the investment most strongly correlated with the largest reduction in cholera risk would be the provision of flush to sewer infrastructure citywide. However, such an investment would also be disproportionately the most expensive. It identifies the next most impactful stand-alone investment would be the expansion of piped water city-wide, followed by addressing water quality in the existing network. Finally, the analysis was carried out in the context of a considered restructuring of an existing World Bank investment called the Lusaka Sanitation Program (LSP), and identifies the most efficient combined initiative was partial sanitation investment scale-up
added 65 deposit accounts per 1,000 adults but the number of outstanding loans remained more or less the same.<sup>2</sup> At the same time, global retail networks, consisting of financial institution branches, ATMs, and POS terminals expanded. Per 100,000 adults, there are 167 new POS terminals, five ATMs, and one bank branch.

The analysis of data on financial inclusion mandates under the purview of financial regulators indicates that financial inclusion is high on policymakers’ agendas and reforms are widespread. However, implementation capacity is often limited. Countries that have financial inclusion strategy documents also have more topics under the purview of financial regulators with more resources. Low-income countries have more topics under the purview of financial regulators, but fewer resources.

The rest of this paper is organized as follows. Section 2 describes the methodology and results of counting the number of unbanked adults across the world. The state of and changes in access to deposit and credit services, as well as in the extent of retail networks, are analyzed in Section 3.

> 2 These are calculated for a subset of countries with comparable data in 2008 and 2009.

3
few observations per _jati_ -state-year cell.<sup>20</sup> Thus, the variation in our key explanatory variable, expected dowry, is at a more aggregate level (caste-state-year) than our savings data, which is at the household level. With 17 states and 22 years, we have 1,285 caste-state-year groupings or marriage markets in our sample.<sup>21</sup> Figures A.2 and A.3 show the expected dowry variation by year of birth of the firstborn child and by caste groups.<sup>22</sup> Although our caste categories are broad, our objective of using them is simply to distinguish between marriage markets. While it is true that the SCs, for example, do not operate as one marriage market, it is also true that the likelihood of an SC individual marrying another SC individual is substantially and significantly higher than an SC individual marrying an upper-caste individual, making our categorization still useful for a marriage market analysis. To illustrate, in REDS 2006, 91 percent of SC individuals were married to another SC individual. The corresponding numbers for STs, OBCs, and other castes were 93 percent, 95 percent, and 92 percent respectively, implying that our caste categorization captures the marriage markets reasonably well.

Does our expected dowry variable <u>predict</u> actual dowry? Our expected dowry variable is a significant predictor of the actual dowry paid at the time of marriage. Using data on individuals whose year of birth and year of marriage fall within our data, we run an ordinary least-squares (OLS) regression of the actual dowry paid at the time of marriage on the expected dowry in the year of birth (using our definition). Table A.3 shows that the “first stage” relationship between our constructed expected dowry measure and actual dowry that the household pays, including state fixed effects, year-of-birth fixed effects, caste fixed effects and their interactions, is highly significant.<sup>23</
Figure 1: Cumulative productivity distributions Note: Survey weights are applied in the computation of the data presented above In section 1, we outlined concerns that have been raised at the macro level that productivity growth has been lagging in the region, and the micro data from the WBES provide further justification of these concerns. Table 3 shows the evolution of labor productivity and total factor productivity across the seven countries covered in our analysis, for the three rounds of WBES data that are available for these countries. While average labor productivity increased from 2006 to 2010, it subsequently declined in 2017; for the median establishment, there was a minor increase between 2010 and 2017. Total factor productivity also increased from 2006 to 2010, but subsequently declined—this holds at both the mean and the median values.

11
the effect of a hurricane on household income (or poverty) might be underestimated if the interval is bigger. Noy (2009) discusses the importance of a weighting function to evaluate impacts on GDP since events observed earlier in the year may have higher impact on GDP than events that occur towards the end of the year.

# 4.3. Results

Table 5 presents the main results for this section using OLS. Standard errors are clustered at the regional level. Results are standardized in order to render all the indexes comparable. Column (1) – (4) show the non‐weighted results, while column (5) – (8) show the (linearly) weighted results. Weighted results are smaller than non‐weighted effects. All results include the control variables (maximum level of education, access to water and sanitation, crowding, subsistence capacity, and precarious materials).<sup>28</sup> Our results show that major hurricanes have substantial adverse impacts on both income and poverty. Based on the non‐weighted results, an increase of one standard deviation in the intensity (wind speed) of a hurricane leads to a decrease in total and labor income by 2 – 4 percent, while moderate and extreme poverty increases by 1 – 2 percentage points.

To illustrate these results, we will focus on Hurricane Matthew for Honduran Region 5.<sup>29</sup> According to these parameters, and taking MSWS as a measure of hurricane damage, Hurricane Matthew had a

> 24 The ratio of members of the household to the number of exclusive rooms occupied by the household.

> 25 Households are considered to have unsatisfied basic needs in subsistence capacity based on two conditions: (i) the ratio of members of the household to the employed members is higher than 3; and (ii) the household head has at most elementary school education.

> 26 Where the construction materials of the dwelling are of low quality.

> 27 Notice that Hurricane Beta discussed in the second