rafmacalaba/datause-encoder-data huggingface dataset:
policy constraints including finance, business regulation, and the quality of public goods affect firms’ adaptive capacity, undermining developing countries’ economic resilience. Our results underline the importance of designing local context-specific policies to support climate adaptation in the private sector.

# **2 Literature**

In the last decades, deteriorating climate conditions have encouraged scholars to investigate the economic costs of climate change. Studies find that increasing average temperatures by 1<sup>_◦_</sup> C reduces global output and productivity by about 1-3% in hot regions (Nordhaus, 2006; Stern 2008; Bansal and Ochoa 2011; Graff Zivin and Neidell 2012). In contrast, cold countries may benefit from warmer temperatures (Tol, 2021). Along this line, Burke, Solomon M Hsiang, and Miguel (2015) present evidence that the productivity of countries increases along with temperatures until an annual average threshold of 13<sup>_◦_</sup> C, with countries’ productivity declining significantly at higher temperatures. As hotter regions are on average poorer, climate change is also expected to hit developing regions harder (Dell, Jones, and Olken 2012; Letta, Montalbano, and R. Tol 2018; Newell, Prest, and Sexton 2021). In fact, many developing nations face greater natural disaster risks such as typhoons and are exposed to extreme heat and precipitation (Bakkensen and Barrage 2018; Solomon M. Hsiang and Jina 2018). Moreover, climate risks in the developing world are amplified by weak governance capacity (Acemoglu and Robinson 2013; La Porta, Lopez-de-Silanes, and Shleifer 2008; Allcott, Collard-Wexler, and D. O’Connell 2016; Chong et al. 2014).

A recent study by Bilal and Kanzig (2024) suggests that previous evidence based on country-level temperature data could underestimate the real effect of climate on the economy. The authors propose the use of aggregate global temperature—as it accounts for correlated local temperatures and spillover effects across countries—and find that the peak effect of warming temperatures reduces economic activity by about 12% in the medium term, ap4
# **Table 1.  Intermediate Indicators of Empowerment: Agency**

|**Asset base**|**Indicator**|**Existing sources/ instruments**|
|---|---|---|
|**Psychological**<br>**assets**|•Self-perceived exclusion from community<br>activities<br>•Level of interaction/sociability with people<br>from different social groups<br>•Capacity to envisage change, to aspire|•IQMSC – section 5<br>•IQMSC – section 5<br>•IQMSC – section 6|
|**Informational**<br>**assets**|•Journey time to nearest working post office<br>•Journey time to nearest working telephone<br>•Frequency of radio listening<br>•Frequency of television watching<br>•Frequency of newspaper reading<br>•Passable road access to house (by periods of<br>time)<br>•Perceived changes in access to information<br>•Completed education level|•IQMSC – section 4<br>•IQMSC – section 4<br>•IQMSC – section 4<br>•IQMSC – section 4<br>•IQMSC – section 4<br>•IQMSC – section 4<br>•IQMSC – section 4<br>•SCAT Household Questionnaire –<br>section 2|
|**Organizational**<br>**assets**|•Membership of organizations<br>•Effectiveness of group leadership<br>•Influence in selection of group leaders<br>•Level of diversity of group membership|•IQMSC – section 1<br>•IQMSC – section 1<br>•IQMSC – section 1<br>•IQMSC – section 1|
|**Material assets**|•<br>Land ownership<br>•<br>Tool ownership<br>•<br>Ownership of durable goods<br>•<br>Type of housing|•LSMS – economic activities module<br>•LSMS – economic activities module<br>•LSMS – economic activities module<br>•SCAT Household Questionnaire –<br>section 2|
|**Financial assets**|•<br>Employment history<br>•<br>Level of indebtedness<br>•<br>Sources of credit<br>•<br>Household expenses<br>•<br>Food expenditure<br>•<br>Occupation|•LSMS – economic activities module<br>•LSMS – economic activities module<br>•LSMS – economic activities module<br>•LSMS – housing module<br>•LSMS – food expenditures module<br>•SCAT Household Questionnaire –<br>section 2|
|**Human assets**|•<br>Literacy levels<br>•<br>Numeracy levels<br>•<br>Health status|•LSMS – education module<br>•LSMS – education module<br>•LSMS – health module|

63
both greater privacy and control over their funds. The administrative costs for the social benefits program were 20 percent less for mobile transfers than for cash payments (Aker, et al., 2016).

Digitalization can also speed up the collection of social benefits payments. A recent study in Bangladesh on the impact of moving education subsidies from cash to digital channels found that nearly 80 percent of mothers preferred the digital payments because they removed the need to travel and wait for cash disbursements (Gelb, et al., 2019).

As evidenced by the examples above, government initiatives can play and have played a foundational role in expanding financial inclusion. India has been particularly proactive in this regard. A government program launched in 2016 provided every adult in the country a digital government ID, which was leveraged to open more than 250 million financial accounts (Agarwal, et al., 2017). This study also finds in some regions that this increase in account takeup also corresponds with increases in savings balances and the issuance of new loans.<sup>5</sup> The idea that some degree of government engagement helps to fuel financial inclusion is not unique to India. In fact, Findex data finds that 865 million account owners in developing countries (18 percent of adults), including 423 million women, opened their first account to directly receive some type of government payment, including a government wage payment, a government benefit, or a government pension.

# **_3.2 Private sector payments as a driver of financial usage_**

Government payments are not the only type of payment that can deliver benefits through digitalization. Receiving private sector wages directly into an account can also benefit recipients by being more convenient, safer, and sometimes less expensive, if the wage earner must travel to pick up their wages—just as government payments have proven to be more convenient, safer, and cheaper. In developing economies, 61 percent of adults receive their wages into an account, an increase from just 37 percent in 2014 and 45 percent in 2017. Among the unbanked, 12 percent—or about 165 million people—received private sector wage payments in cash.

Research also shows that farmers and other adults who receive payments from wholesale buyers of agricultural goods benefit from the expanded use of financial services. Farmers in Malawi

> 5 Yet about a third of account owners in India report no activity (deposits, withdrawals, or payments) from or to their account. Adults in India with an inactive account report not using their account for three main reasons: distant from a financial institution, lack of trust, and having no need for an account (Demirguc-Kunt, et al., 2022).

9
# **Global Poverty Goals and Prices: How Purchasing Power Parity Matters**

Dean Jolliffe* Espen Beer Prydz _Keywords_ : Global Poverty, International Comparisons Program, Purchasing Power Parity _JEL Codes_ : I3, I32, E31, F01.

* The authors are with the Development Economics Research Group of the World Bank. Contact information: djolliffe@worldbank.org (D. Jolliffe) and eprydz@worldbank.org (E. Prydz). Jolliffe is also a Research Fellow with the Institute for the Study of Labor (IZA) in Bonn and a Research Affiliate with the National Poverty Center at the Ford School of Public Policy, University of Michigan.

_Acknowledgements:_ Please note that this paper does not reflect official estimates of the World Bank Group. The authors wish to thank participants of a “Global Poverty Monitoring and the 2011 ICP Purchasing Power Parity Indices” workshop, and seminar participants from the World Bank’s Poverty Global Practice for useful feedback. They also thank Aziz Atamanov, Shaohua Chen, Andrew Dabalen, Aslı Demirgüç-Kunt, Yuri Dikhanov, Francisco H.G. Ferreira, Nada Hamadeh, Aart Kraay, Christoph Lakner, Peter Lanjouw, Martin Ravallion, Umar Serajuddin, Prem Sangraula and Tara Vishwanath for helpful conversations, comments and advice. The analysis in this paper is based on the publicly available data from the World Development Indicators database and PovcalNet, which is a World Bank research product led by Shaohua Chen.
**Agent Orange and the prevalence of cancer among the Vietnamese population 30 years after the end of the Vietnam War**<sup>*****</sup>

Quy‐Toan Do<sup>†</sup>

# **_JEL Classification:_** N42, N45

**_Keywords:_** Agent Orange, herbicide, cancer, epidemiology, Vietnam, war.

* The invaluable help of Jeanne and Steven Stellman in computing the measures of herbicide exposure is gratefully acknowledged. Le Dang Trung and Phung Duc Tung provided assistance to construct the final dataset. Discussions with William Clark, Jack Colford, Jishnu Das, Quoc‐Anh Do, Jed Friedman, Lakshmi Iyer, Peter Lanjouw, Carl Mason, Axel Pustan, Biju Rao, Jeanne Stellman, Steven Stellman, Carrie Turk, Adam Wagstaff and Damien de Walque benefited the study. The findings, interpretation and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, its Executive Directors, nor the countries they represent.

† Development Economics Research Group. The World Bank. MSN MC3-306; 1818 H street, NW. Washington DC 20433. Tel: +1 202 473 9452; Fax: +1 202 522 1153; Email: qdo@worldbank.org
**Table XIII Hazard Estimates for Duration of Unem ment Spell**

|Variables|Baseline|I|II|III|
|---|---|---|---|---|
|Receivesunemployment insurance||0.783||0.643|
|<br>(UI)||(0.098)||(0.088)|
|Receivessocial assistance/support||0.481||0.470*|
|(SA)||(0.064)||(0.069)|
|Duration ofUI(months)|||0.815'<br>(0.029)||
|DurationofSA(months)|||0.852<br>(0.043)||
|ReceivedUIinthepast||__________|___**_**<br>_<br>(0.248)|0.950|
|Female(-1)|1.007|0.971|0.948|1.045|
||(0.129)|(0.125)|(0.121)|(0.134)|
|**Married(=I)**|1.070|1.117|1.147|1.111|
||(0.167)|____<br>___|(0.171)|(0.171)|
|**26-35**|0.966|1.034|0.988|0.993|
||(0.161)|(0.167)|(0.160)|(0.158)|
|**36-45**|0.977|1.016|0.959|1.003|
||(0.205)|(0.203)|(0.191)|(0.203)|
|**46-55**|1.075|0.992|1.014|1.059|
||(0.270)|(0.240)|(0.246)|(.254)|
|Apprenticeship|1.457|t.454|1.|1.318|
||(0.272)|(0.273)|(0.271)|(0.249)|
|**Secondary**|1.299|1.266|1.318|1.203|
||(0.254)|(0.248)|(0.262)|(0.239)|
|**College(+)**|3.522|3.504|3.565|4.104|
||C(0.972)|(3.9650|(0.992)|4.l0|
|Nopreviousexp.|0.505"|0.580|0.514|0.682|
||(0.115)|(0.133)|(0.118)|(0.154)|
|Headofhousehold|0.727|0.764|0.730|0.769|
||(0.148)|(0.147)|(0.140)|(0.151)|
|Spouseofheadofhousehold|0.657'|0.672|0.659|0.599|
|___________________________________|(0.152)|(0.148)|(0.142)|(0.133)|
|Number of**childrenunder6 in**|0.962||||
|household|(0.124)||||
|**Household dependencyratio**|1.562<br>(0.496)||||
|Otherhouseholdmemberreceives||1.252|1.283|1.115|
|UI||(0.202)|(0.208)|(0.187)|
|Otherhouseholdmemberreceives||0.966|0.927|0.891|
|SA||(0.146)|(0.141)|(0.133)|
|Occupation|YES|YES|YES|YES|
|<br>Sector|YES|YES|YES|YES|
|Region|YES|YES|YES|YES|
|<br>Year|YES|YES|YES|YES|
|Quarter|YES|YES|YES|YES|
|**Numberifindividuals**|2,465|2,465|2,465|2,465|
|Number ofspells|2,465|2,465|2,465|2,465|
|Numberofpseudo-spells|2,465|2,465|2,465|2,680|
|<br>Prob>Chi<sup>2</sup>|0.000|0.000|0.000|0.000|

Source: Labor Force Survey 1996:1-1996:4, 1999:1-1999:4, and 2000:1-2000:2.

Coefficients represent hazard ratios (Note: A hazard ration greater (smaller) than I indicates that such variable decreases (increases) unemployment duration).

Robust standard errors in parentheses (errors clustered at the individual level).

Significant at the 5 percent level. Significant at the 10 percent level.

Baseline comparison: Single male, 16-25 years old, with primary studies and previous employment experience - low-skilled occupation in agriculture. Not head of household, nor spouse of head.

54
households” in 68 blocks in 17 districts in the state. It encourages participating farmers to shift production towards higher value crops which can be sold at more remunerative prices, and improving access to irrigation is an important element of this aim. More specifically, JOHAR seeks to improve access to irrigation through community owned and managed lift irrigation systems. This access is the purpose of the JOHAR Community Led Lift Irrigation Scheme (JCLIS).

The JCLIS was assigned to install 2,310 solar pumps by June 2022. Each 5 to 7.5 horsepower pump is connected to a buried pipe distribution network that delivers water to a command area covering between six and eight hectares. All but a few of the systems are powered by solar energy, and 1,310 of the pumps are fixed and 1,000 are mobile. Together, they are used to irrigate 10,600 hectares of land belonging to 23,580 farming families across 39 blocks in 13 districts. Of the fixed systems, 572 were already operational in the state as of September 2021, and 242 more were nearing completion. The project provides farmers with improved access to affordable irrigation by i) creating a new water source or augmenting an existing one; ii) installing a solar pump with very low operating costs to lift the water, and iii) building a network of buried pipelines to deliver water to the fields. In addition to physical infrastructure, the project actively encourages the creation and training of a water users’ group (WUG) with the capacity to operate and manage the irrigation system. The JCLIS schemes are all surface or subsurface micro-irrigation schemes, and are not groundwater schemes, which extract water from aquifers. The source of water for these schemes are dug wells or ponds, which get recharged by rain-fed perennial and seasonal streams.

This paper presents the findings of an independent study of the community led irrigation systems created under the JOHAR project. The findings are described in Section 1 and based on qualitative and quantitative data collected from the farmers using these irrigation systems, their neighbors, and those responsible for operating and managing the systems. Section 2 of the report presents the objectives of this study. The authors explain the sampling and data collection methods used, as well as the limitations of overall methodology in Section 3. Section 4 focuses on the impact of the community irrigation systems on the project participants and Section 5 on the sales of high value crops by farmers in the study area. Strategies to increase the benefits of the project’s JCLIS Lift Irrigation Scheme are considered in Section 6. Section 7 concludes with a discussion of opportunities and challenges which affect the prospects of expanding the area of irrigated land in Jharkhand.

3
_Access to improved drinking water sources_ . Figure 2 shows that access to improved drinking water also rose notably across all three countries during this period. In Ghana and Nigeria, access at the MV intervention site rose more quickly than the surrounding area, but areas outside the intervention site also experienced substantial gains. (Again, MVP 2010c does not report this statistic for the MV in Kenya.) _Measles immunization_ . The fraction of children immunized against measles rose in all three countries during the period (Figure 3). In Ghana, vaccination rates rose at the MV intervention site less than in the surrounding area. In Kenya and Nigeria, vaccination rates rose at the MV intervention site more than they rose in the surrounding area.

_Births delivered by skilled personnel_ . Figure 4 shows that the fraction of live births in the previous 2 years delivered by a doctor, nurse, or community health worker rose in Kenya and Ghana, while showing little change in Nigeria. At the MV intervention site in Kenya, skilled birth attendance fell while it was rising in the surrounding region. In Ghana, skilled birth attendance rose at the MV site more than in the surrounding region. In Nigeria, it increased only slightly at the MV intervention site relative to the surrounding area, despite the construction of a new, doctor-staffed, $174,000 clinic within Pampaida locality (Boyd et al. 2009).

_HIV testing_ . Figure 5 shows that HIV testing rates rose greatly across Kenya and Ghana during this period and fell in Nigeria. In Kenya and Ghana, HIV testing at the MV intervention sites rose more than in the surrounding areas. In Nigeria, HIV testing at the MV site rose while falling in the surrounding area. Kenya faces a major HIV epidemic while HIV is present but much less prevalent in Ghana and Nigeria.

_Bednet usage_ . Figure 6 reveals that insecticide-treated bednet usage by small children increased enormously across Kenya and Ghana and by a small amount across Nigeria during the period. At the MV intervention sites in Kenya and Ghana, bednet usage also rose, somewhat more than in the surrounding area in Kenya, and notably more than in the surrounding area in Ghana. In Nigeria, it rose much more than in the surrounding area.

13
- **Establishing a clear priority scheme for secured creditors.** Granting secured creditors a predictable level of priority is an essential feature to promote lending to firms. Secured creditors should be able to predict their priority vis-à-vis other creditors at the moment of granting a loan to a business.  This increased predictability and transparency could lead banks to provide longer term credit and at better interest rates.  Currently, according to the Doing Business Report 2010, the MENA region is the second worst region worldwide when it comes to a clear and favorable priority scheme for creditors.

- **Improving enforcement mechanisms.** Enforcement and collection of debts upon defaulted loans is possibly the major impediment for increasing access to credit in the MENA region. Speedy, effective, and inexpensive enforcement mechanisms are essential to realizing security interests. Enforcement is most effective when parties can agree on rights and remedies upon default, including seizure and sale of the collateral outside the judicial process. An efficient procedure is particularly important in the context of movable property, which in most cases depreciates in value over time. Despite the importance of this part of the lending process, enforcement/execution is often the weakest link in the chain in the system of secured transactions. In the case of MENA, enforcement mechanisms are also, along with collateral registries, the weakest link in the chain. According to the Doing Business Report 2010, only 3 countries in MENA allow for out of court enforcement mechanisms. **The MENA region ranks last in the easiness for enforcing debts out of court** . More than 100 countries worldwide allow in their legislation to enforce security interests on movable property out of court. In 2010, almost 60% of financial institutions in the region consider the enforcement of security interests in movable property as a major impediment to granting credit to firms.<sup>46</sup> Both the seizure and sale of assets are currently very challenging processes in MENA.

- **It is highly recommended that MENA countries introduce reforms in the enforcement procedures of the different jurisdictions to align the region with international best practices.** The reform of enforcement procedures and in particular the introduction of out of court enforcement procedures has had a considerable impact in lending in a number of countries (India, Romania, Slovakia), sometimes by introducing private enforcement agents and regulated out of court enforcement. A variety of policy options are available to the governments, ranging from: (i) introducing new extrajudicial mechanisms to enforce debts; (ii) introducing fast track or expeditious procedures in court for debt collection processes; (iii) improving the capacity of existing institutions through the use of ADR or strengthening execution agencies; (iv) improving the mechanisms available to financial institutions to sale the secured assets, by granting more freedom in the disposition method (private sale or public auction).

> 46 According to the 2010 World Bank MENA SME Lending Survey to Financial Institutions.

36
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE month for water, the associated utility bill would come to $8 and $8.2 per month, respectively, or $16.2 for both services. Based on the distribution of household budgets in Niger, monthly utility bills for power and water at these levels would be affordable for less than 20 percent of the population (figure 25). A more limited level of subsistence consumption, at 25 kWh per month for power and 4 m<sup>3</sup> per month for water—which is capable of meeting the most basic needs—would cost $4.0 and $3.3 per month, respectively, and would be affordable for half of the population.

**Figure 24. Water subsidies that reach the poor**

<!-- Start of picture text --> Senegal<br>CAR<br>Gabon<br>Congo<br>Nigeria Kaduna<br>Togo<br>DRC<br>Nigeria FCT<br>Cameroon<br>Côte d'ivoire<br>Niger<br>Chad<br>Cape Verde<br>Burundi<br>Malawi Blantyre<br>Ghana<br>Guinea<br>Uganda<br>Malawi Lilongwe<br>Burkina<br>Rwanda<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9<br>Omega<br><!-- End of picture text --> _Source_ : Banerjee and others 2008a.

_Note_ : Omega is a measure of distributional incidence, or the share of subsidies received by the poor as a percentage of their share in the population. The higher the value of omega, the better the distributional performance of the subsidy. Values of omega below 1 denote a regressive subsidy and values above 1 denote a progressive subsidy. CAR = Central African Republic; DRC = Democratic Republic of Congo Operational inefficiencies of the power and water utilities cost Niger $20 million a year, equivalent to 0.64 percent of GDP. The annual value of collection inefficiencies and losses in the power sector (at $16 million) is substantially higher than for the water sector (at $4 million). The burden of utility inefficiencies in Niger is considerably lower than for the benchmark countries in both the power and water sectors (figure 26).

40
# Table 8: Analysis of classroom quality, teacher, and structural characteristics on children’s <u>communication and general knowledge</u>

||D<br>no|V: Communi|cation & Gen|eral (Raw sco<br>w/ teacher|re)|
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br> <br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.151**|0.073|0.085|0.080|0.120|
||(0.062)|(0.064)|(0.062)|(0.062)|(0.075)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.935**|0.926**|0.908*|
||||(0.465)|(0.470)|(0.476)|
|% of teachers w/ post secondary edu.|||1.206**<br>(0.473)|1.196**<br>(0.477)|1.183**<br>(0.484)|
|Mean years of teaching|||<br>-0.044***|<br>-0.043***|<br>-0.045***|
||||(0.011)|(0.012)|(0.012)|
|% of teachers w/ no ECED experience|||-0.002<br>(0.139)|-0.009<br>(0.139)|-0.023<br>(0.139)|
|% of teachers w/ no training|||0.074<br>(0.178)|0.073<br>(0.178)|0.072<br>(0.178)|
|**Structural characteristics**||||||
|Student-to-staff ratio||||-0.005|-0.004|
|||||(0.012)|(0.012)|
|Hours of operation per week||||0.005<br>(0.018)|0.005<br>(0.018)|
|Constant|5.543***|0.976**|0.041|0.084|0.015|
||(0.188)|(0.437)|(0.644)|(0.675)|(0.684)|
|Observations|7,981|7,981|7,981|7,981|7,981|
|R-squared|0.005|0.140|0.151|0.151|0.149|
|No. ofclusters (centers)|565|565|565|565|565|
|First-stage F-statistic of excluded<br>instrument|||||647.668|
|**Control included:**||||||
|Child characteristics|N|Y|Y|Y|Y|
|Kindergarten dummy|N|Y|Y|Y|Y|
|District dummy|N|Y|Y|Y|Y|

Note: Robust standard errors clustered at the center level in parentheses. Child characteristics include age, agesquared, gender, whether child has special needs (reported by teacher), and mother's highest education level. Kindergarten dummy is 1 for kindergartens or Islamic kindergartens and 0 for non-project playgroups or project playgroups. District dummies are included for the 8 districts where centers and children are located. Models 1-4 are OLS regressions. Model 5 is a two-stage least squares model to correct for measurement error in the ECERS-R. The F-statistic of the first stage is reported.

* p<0.1, ** p<0.05, *** p<0.01

25