rafmacalaba/data-use-mentions-extended huggingface dataset:
of being approved for a future loan (58%) as well as the probability of loan approval for the average borrower (62%). In terms of our model, expectations of improved credit access after debt relief are consistent with expecting debt relief to improve future ability to repay, which may improve credit access. It is also consistent with beliefs that lenders are shielded from the costs of debt relief. This is plausible in the context of our survey, where lenders were indeed compensated for their losses in past debt relief programs.

Both with respect to loan repayment and access to credit, we find that respondents’ beliefs about themselves are quite similar to their beliefs about others. Overall, the responses are in line with the belief that debt relief strengthens household balance sheets without causing significant costs to lenders or widespread moral hazard that could offset positive effects on credit supply.

How do expectations differ when borrowers are asked about debt forbearance versus debt forgiveness? In Figure 7, we report estimates of the difference in expectations between respondents of the debt forbearance and debt forgiveness surveys. When asked about forbearance, respondents are no more likely to expect that their repayment will suffer than respondents asked about forgiveness. However, they believe that they will be more likely to apply and be approved for future loans if they receive forbearance, rather than debt forgiveness. One possible explanation is that borrowers anticipate experiencing less stigma from forbearance than a debt writedown because it gives the borrower a chance to prove their creditworthiness. Another possibility is that respondents assume temporary forbearance to be less costly to the creditor than a permanent writedown. Respondents who are asked about forbearance are also more likely to expect future debt relief, compared to borrowers asked about debt forgiveness. This could reflect the belief that forbearance programs are politically less costly than debt forgiveness, or that governments are more willing to impose costs on banks than on the taxpayer.

**Figure 7:** Expectations: Debt Forbearance versus Debt Forgiveness

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# **2.2. Flood Hazards and Impact on WWTPs**

Flood hazards and their impact on China’s WWTP are done in the context of climate change. Firstly, we apply and downscale the CaMa‐Flood model for 21<sup>st</sup> century China for RCP 4.5 and RCP8.5 at return periods greater than 30, 50 and 100 respectively. Based on these results, we then compute how the probability of flooding may change for specified geographical locations in China and superimpose the wastewater assets we collected onto the changing flood hazard maps. This way we are able to pinpoint the exact infrastructures that face increasing, decreasing or non‐changing flooding probabilities due to climate change.

# **2.2.1. Changing Flood Hazards**

We drive a global river routing model – Catchment‐Based Macro‐scale Floodplain (CaMa‐ Flood) – using the daily runoff of the Atmospheric and Oceanic General Circulation Models (AOGCMs) at a spatial resolution of 1° x 1° 1 (Hu _et al_ ., 2017). The CaMa‐Flood model routes the runoff input simulated by a land surface model into the oceans or lakes along a prescribed river network (Yamazaki et al., 2011). It calculates river channel storage, floodplain storage, river discharge, river water depth, and inundated area for each grid cell at a spatial resolution of 0.25° x 0.25°. A recently developed Global Width Database for Large Rivers (GWD‐LR) is also incorporated into it (Yamazaki _et al_ ., 2011).

For the AOGCMs, we adopt a historic (1970–2005) and future period (2006–2100<sup>2</sup> ) at representative concentration pathways (RCP) 4.5 and 8.5 (Stocker _et al._ , 2013). RCPs 4.5 and 8.
## 5. Validation:

- a. Households cross-checked with pension and land cadaster data to confirm they are not wealthy.

6. Entry or waitlisting:

- a. Households enter program if sufficient budget, otherwise go on the waitlist.

7. Reverification:

- a. By law, households file an annual report on the MOLSA website;

- b. Social workers also revisit the households each year to maintain rapport and support case management.

The key to MoLSA eligibility is the PMT model. This model uses welfare proxies to predict consumption. Such proxies need to be generally easy to verify and correlated with consumption. Common categories include: (i) demographic; (ii) education and employment; (iii) housing; (iv) assets; and (v) displacement.

Statistical models are developed giving scoring weights to each proxy using national household surveys. The initial models were based on the 2012 IHSES survey. New models have been developed using the more recent 2017/18 Rapid Welfare Monitoring Survey, also known as the Survey of Well-being via Instant and Frequent Tracking (SWIFT) and are currently being implemented. Each household has an estimated consumption based on these weights.

# 3.2 MPCA Eligibility

The CLCI used a similar but distinct approach to develop a tool for MPCA vulnerability assessment and scoring, endorsed by the CWG and widely used by other MPCA actors including I/NGOs as well as UN agencies. However, the 2016 Vulnerability Model (VM) differs in a number of ways:

1. Both MoLSA PMT and VM use statistical regressions to derive weights for each indicator, but vary in what they are trying to estimate:

- a. PMT estimates household per capita consumption;

- b. VM estimates income to earnings ratio (initially estimated $2 a day poverty status).

2. They vary in how they do this:

- a. PMT uses a continuous regression with variable selection (stepwise in 2012, Lasso in 2017);

- b. VM uses a binary regression with
approximately 4,000 middle schools for girls in stipend and non-stipend districts. These are the schools that could be eligible for the program at both baseline and follow-up.<sup>12</sup> In order to extract contextual information and construct covariates, this analysis uses several household surveys. The household surveys include the Pakistan Social and Living Standard Measurement survey (PSLM) collected in 2004-05, the Punjab Multiple Cluster Survey (MICS) collected in 2003, and the Pakistan Integrated Household Survey (PIHS) collected in 2001. They use multi-topic questionnaires focusing on a comprehensive set of social and economic indicators as well as information on infrastructure supply and quality, thus allowing for construction of some covariates at the district level. These surveys are representative at the district level and disaggregated by urban and rural areas.<sup>13</sup> Other sources include educational censuses and administrative data that provide information on the supply of educational services and other programs. This evaluation uses the National Education Census collected in 2005 on both public and private schools to construct additional covariates. It also relies on the administrative data of other elements of the educational reform implemented in Punjab at the same time as the FSSP, such as distribution of free textbooks, recruitment of new teachers, construction of school facilities, and improvement of school councils.

The main data source for the estimation of impacts at the individual level is two waves of the MICS collected in 2003 (pre-program) and 2007-08 (post-program). This household survey covers a wide range of social and economic indicators, all repeated in both rounds for comparability. The sampling design follows the sampling frame of the previous population census (1998) and provides estimates for the province, for urban and rural areas, and for each of the districts in Punjab and the Cantonment of Lahore. Sampling for 2003 was stratified by rural, urban, and large city areas. In the follow-up survey in 2007-08 the sample size was vastly

> 12 The sample of eligible schools includes only girls’ schools that offered
**Table 1. Summary statistics of workers and employers in Rozee.pk and the entire labor market**

||Data from R|ozee.pk|Labor Force Surve<br>Enterprise Surv|y 2014‐15,<br>ey 2013|
|---|---|---|---|---|
||Mean|Std|Mean|Std|
|**Workers**|||||
|Female|0.21|0.40|0.25||
|Age|26.02|6.46|34.91|12.89|
|Education|||||
|Secondary|0.10|0.30|0.32|0.46|
|Postsecondary|0.86|0.35|0.09|0.29|
|Salary (2012 PKR)|25,362|31,389|12,271||
|**Employers**|||||
|Size (# employees)|||||
|Micro: 1‐10|0.48|0.50|0.28|0.45|
|Small: 11‐50|0.34|0.47|0.45|0.50|
|Medium: 51‐100|0.09|0.28|0.09|0.29|
|Large: 101‐600|0.07|0.25|0.13|0.34|
|Mega: 601+|0.03|0.18|0.05|0.21|
|Business age (years)|9.72|15.26|18.70|11.30|

_Source_ : Authors’ calculation using Rozee.pk data, Enterprise Survey 2013, and Labor Force Survey 2014 – 15, except for the female proportion and salary in
# Appendix A7: Sentiment and FDI Inflows

The following section investigates the effect of international sentiment and discussions on FDI inflows to the GCC countries. The analysis is particularly important today as more and more GCC countries engage in reforms targeted toward improving FDI.

We perform our analysis using a panel of all six GCC countries over the period 2000-2020. We include a set of macroeconomic and structural variables in our estimation based on the existing literature. Data on GDP growth, inflation, FDI inflows, and trade openness are obtained from the World Development Indicators database; data on six institutional quality indices are obtained from the World Governance Indicators database.

Using the data described above, we estimate the following equation: R R The dependent variable in all specifications is FDI inflows as a percentage of lagged GDP. Our main FFDDIIiiIIRRvvRRwwppii,tt = ααii + ββtt + λλR λλRRIIiiaaii,tt + XXii,tt + εεii,tt variables of interest are a group of reform media variables (international reform chatter, international reform sentiment, Twitter reform intensity, and Twitter reform sentiment). Each of these variables is standardized to have zero mean and unit variance in the regression. The construction of these variables is described in the main text. In addition, we control for GDP growth, inflation, trade openness, and institutional quality in all regressions. We consider the latter to be a good proxy for the attractiveness of a country for FDI investors. Finally, we include country and time fixed effects—the former control for unobserved, country-specific, and time-invariant differences in FDI attractiveness across the GCC countries. Time fixed effects capture the effects of any large shocks (e.g., Global Financial Crisis, Arab Spring) on FDI attractiveness across all countries.

Our main results on FDI inflows using news media are presented in Table A7.1. The results from Column 3 suggest that sentiment about GCC economic reforms in international media is significantly and robustly correlated with the level of FDI inflows. Moreover, the size of this effect is economically significant: one
> 0.04<br>44 EU11<br>42 0.03 Rest of EU<br>40 0.02<br>38<br>0.01<br>36<br>0<br>34 2015 2020 2025 2030 2035 2040 2045 2050 2055<br>-0.01<br>32<br>-0.02<br>30<br>-0.03<br>-0.04<br>Median age<br>Growth in labor force (%)<br>Cyprus Ireland Luxembourg Malta United Kingdom France Spain Denmark Netherlands Sweden Portugal Belgium Greece Austria Finland Italy Germany Slovakia Poland Romania Lithuania Czech Republic Estonia Hungary Latvia Croatia Bulgaria Slovenia<br><!-- End of picture text --> _Source:_ UN World Population Prospects (2010) _Source:_ Loichinger (2012) 18 The method combines age-, sex-, and educational attainment-specific participation rates obtained from the European Labor Force Survey with population projections by age, sex and education calculated using the probabilistic population projection methods described by KC et at (2010) 20
quality improvements through hypothetical reallocation in the presence of excess capacity is new to the health care quality improvement literature in low-income countries. It is motivated by the fact that even though patients seeking care for common conditions are frequently misdiagnosed and incorrectly managed,<sup>11, 15–17</sup> current approaches of training existing providers have yet to generate substantive and consistent impacts across contexts. A comprehensive review in LMICs that documented 499 studies showed enormous variation in the effects of any given strategy, and found no “silver bullet” that reliably replicated across studies.<sup>18</sup> Our allocation-based analysis suggests that, at least in half the countries in our sample, the losses due to allocative inefficiency may be large, and that improved utilization of competent providers could lead to substantial quality gains. In the other half, because the fraction of providers who are highly competent is small, neither reallocation nor upskilling using the current infrastructure improves quality. In this case, a substantial rethinking of the entire medical training architecture may be required.

# **2 Methods**

## **2.1 Data Source**

Our analysis was based on data collected through the Service Delivery Indicators (SDI) surveys, conducted by the World Bank, in 10 Sub-Saharan African countries with a total population of 515 million, or 43% of the Sub-Saharan African population. Within each of these countries, facilities were first sampled from a list of facilities that provided primary care services, and survey data were collected on facility caseloads and rosters of clinical staff. Then, in a second stage at an unannounced visit, present providers were interviewed, with clinical vignettes administered for up to seven common medical conditions, assessing their medical competence (knowledge). The final dataset that was fully matched with caseload data included vignette data from 14,367 health care providers practicing at 7,915 health facilities. A complete description of the vignette sample selection and administration process is presented in Daniels _et al_ . (2024)
-6-

# Crosi-sectional Analysis of Factors Affecting Growth in Developing Countries: Examination of Methodological Problems

A large number of studies of international cross-sectional data have been conducted during the lost 20 years in an attempt to explain the growth rate or other variabies in the developing economies. The purpose of these studies is to permit scientific comparison of national experiences, as the basis for policy recommerdations. In the absence of systematic and scientific international comparisons, judgments are likely to be based only on partial comparisons and subjective evaluations. Moreover, if international cross-sectional studies are to serve for economic policy, their conclusions must be solidly established. Yet it seems that, in the present state of the literature, these conclusions are relatively fragile and sometimes contradictory.

The conclusions depend on the methodological choices made. What definition is used for economic growth? Is it measured over a long or a short period? Does the investigation concern all countries or a homogeneous subset? Does it seek to evaluate all factors affecting growth or, on the contrary, to bring out the impact of some particular factor? Is the aim to measure the overall impact of one or another factor or to identify the mechanisms through which it affects growth? Is this done using a reduced form model or a structural model?

The purpose of the present work is obviously not to resolve all the methodology problems posed by cross-sectional studies. Nor is it to present an exhaustive balance sheet of the cross-sectional analyses that contribute to knowledge of factors affecting growth and of the efficacy of economic policies. Our analysis is based on some 30 cross-sectional studies selected using various criteria. The reference studies used here are recent studies (since 1982), written in English or French, that directly explain growth (excluded are studies designed solely to explain certain factors affecting growth, such as the saving rate, the investment rate, the rate of export growth, etc.). The reference studies, moreover, either use a large range of independent variables or display originality in the choice of factors affecting growth or in
cases to judges and prosecutors.<sup>4</sup> We believe our approach can fruitfully be adapted to other contexts.

Second, we contribute to the nascent literature on the performance of bureaucrats as a determinant of state effectiveness and tax collection (Olken and Pande 2012; Dincecco and Ravanilla 2017; Pepinsky et al. 2017; Xu 2018; Xu et al. 2018), by highlighting the granularity of tax evasion and showing how the behavior of a select few actors has macro-fiscal ramifications. While corruption was systemic and enabled by most inspectors (10 out of 16 in a typical semester), IT staff, and the port manager, the tax revenue losses were very concentrated among a select few inspectors and brokers. In any given semester the top 2 most corrupt inspectors accounted for 55 percent of the tax revenue losses associated with the corruption scheme we document.

Third and related, we contribute to the literature on the determinants of tax enforcement (Kleven et

> 2In practice, such bypassing appears to have been operationalized through the temporary disabling of a randomization trigger, such that all declarations registered during specific time intervals when this trigger was deactivated were withheld from being sent to the third party to be randomized (including those that were the subject of a corruption agreement).

> 3Excess interaction was not observed for declarations handled by inspectors whose assignment was randomized.

> 4More than 100 customs agencies have adopted the customs clearance IT system used by Madagascar (Automated System for Customs Data (ASYCUDA)) for which the default option for assignment of declarations to inspectors is random assignment (according to workload). Customs agencies that do not use ASYCUDA also typically use random inspector assignment. Random assignment of cases to judges to deter judicial corruption has been adopted by 162 countries (Doing Business, 2020).

3
Savings Account w ith special aw ards 3.0<br>Personal Credit 3.8<br>Use of telephone for banking services 3.8<br>Current Account 7.2<br>Credit Card 8.0<br>Debit Card 17.9<br>31.7<br>Savings Account<br>0 5 10 15 20 25 30 35 40<br>% of population that use each product<br>Type of product<br><!-- End of picture text --> _Source: Colombian Association of Media Investigation ACIM, General Media Survey EGM for the year 2000_ 46
Ellsberg et al. 2000).

Interviewees who were mothers cited the fear of losing the relationship with their children as a significant barrier to seeking help. Women not only feared being distanced from their children but also, they feared being held responsible for ‘destroying the family’ if they were to separate and/or to take action to protect themselves. Additionally, some noted that their family of origin did not support a potential separation from the abuser. In these cases, losing the connection and bonds to their families created additional disincentives for seeking help (see also Section 3.3.2).

Interviewees described how emotional violence produced and reinforced low self-esteem and feelings of hopelessness and negatively affected their ability to seek help or exit a relationship. Threats of aggressors and verbal attacks -- "you are nothing" or "no one else will want you" – paralyzed survivors. Women feared reporting their experiences or telling others would lead to an escalation of violence or retaliation.

Women exposed to emotional violence reported not believing in themselves, in their capabilities and their ability to take action to change their lives by themselves. Narratives presented by interviewees are consistent with dynamics described by international research, which links physical abuse with the abuse of power and control over a woman’s daily life, including her social contacts and financial autonomy. This body of research acknowledges that domestic violence creates feelings of shame, fear, isolation,

> 14 For example, the share of women who agreed that a wife should obey her husband even if she disagreed with him ranged from just over one-fourth of women in urban Paraguay 2008 to nearly three-fourths of women in rural Guatemala 2008/9. Findings from a 2005 WHO study demonstrate the remarkable degree to which women in some settings have internalized social norms that justify abuse.  Similar to the surveys analyzed for PAHO 2012, the share of women who agreed with statements justifying wife-beating was higher among women who had experienced abuse than among those who had not, and may indicate either that women experiencing violence learn
14 births, it will cost US$20 x 125/0.4, or US$6,250, to avert one maternal death.

The roughness of these estimates of the cost of averting maternal deaths deserves emphasis. The validity of the estimates depends critically on the assumptions used in the hypothetical illustrations. Of greater interest than the actual estimates is the fact that similar levels result from two quite different methodologies, which increases the plausibility of the results. And the order of magnitude of the cost of preventing a maternal death, which is a relatively rare event even in developing countries, is not high given the likelihood that the cost of preventing an infant or child death is several hundred dollars, and the cost of preventing an adult death from other causes is likely to be several thousand dollars (please see below). Finally, there is a pressing need for more research into the cost-effectiveness of alternative means of averting maternal (and other) deaths in develoriAng countries.

The cost of preventing a maternal death, whether by family planning or other means, seems high when compared to that of saving an infant and child, which for an infant is about US$700 in developing countries (Walsh and Warren, 1979). The main reason for the lower cost or preventing child deaths is their larger niuber, which produces economies of scale in health programs. Preventing adult deaths from other causes is also relatively costly. For example, the estimated cost per death averted from cancer of the cervix by a screening prog;am is US$501,000 in the U.S. (Barnes, 1981). Data from developing countries are scarce and show costs ranging from US$250 per malaria death averted (Walsh and Warren, 1979), to several hundreds of dollars per person effectively treated and several thousand dollars per death averted, for tuberculosis in Botswana (Barnum, 1986).

The cost-effectiveness of family planning, as well as other interventions, is greatly increased by the fact that for every death