rafmacalaba/data-use-mentions-extended huggingface dataset:
Selective fertility and endogenous migration are other major concerns regarding the identication strategy. As discussed in the data section, I limit the analytical sample to those who were already born or in utero during the start of the conict in 1996. Purely on identication prospective, it helps limit the potential confoundedness between the explained variables of interest and selective fertility and migration. For instance, the strategy helps mitigate the scenario in which after grasping the seriousness of the war, couples that are highly concerned about their children's health in high conict areas may choose to delay having children or migrate to low conict areas to start a family. However, it could be true for the periods prior to the start of the conict that in anticipation of the war concerned couples may have delayed having children or may have migrated. Detailed robustness checks are presented in the empirical results section.

# 4.2 Intergenerational Health Impacts

The gap between the start of the Nepal's civil conict and the time of the NDHS 2016 survey is sucient enough that I can explore the impacts the conict had on the children of women who were exposed to the war in their childhood. Anthropometric measures were collected for children under the age of 5 at the time of the survey and hence I limit the second-generation sample to children under 5 in 2016. I employ the same strategy as in Equation (7) and add child specic controls to estimate. Following is the estimation equation: where _Y_ is a health outcome of a child _j_ whose anthropometrics were measured in month _n_ , was born in month _k_ and year _l_ to a woman _i_ who was born in month _m_ and year _t_ , and resides in village _v_ , district _d_ and development region _r_ . Child health endowment is dened as function of all mother's controls and exposure to war as dened in section 4.1 and child specic characteristics. _θl_ are child's birth-year xed eects. As with mothers, child
**Figure 3 - Adult Enrollment across Age Groups**

<!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64<br>Primary Secondary Tertiary<br>Net Enrolment Rate<br>(of qualified individuals)<br><!-- End of picture text --> Source: Encuesta de Hogares, 2000 Note: The high tertiary enrollment at age 20-24 includes the fulltime student population that continues directly from secondary to tertiary education.

The figure shows that there is marginal adult enrollment in primary and secondary education above the age of 25, while there is some enrollment in tertiary education up to the age of 50.<sup>3</sup> Many factors could explain the low enrolment. Cultural and national factors, such as poverty, lack of security, economic instability, educational culture and local labor market conditions, surely affect adult enrolment. Further, the education system could be insufficiently geared towards adult education, Holm-Nielsen, Thorn, Brunner and Balan (forthcoming). However, a pre-condition for the success of lifelong learning policies that better accommodates adult learners into the education system is that there are economic gains for the adult population in re-entering the education system. This next section describes the methodology to estimate those gains.

3 In an international perspective, a small share of Colombians takes advantage of the high pecuniary gains from tertiary education. Only 0.4 percent of adults aged above 30 are enrolled. This compares to 2.1 percent in Sweden, 1.8 percent in the UK, and 1.5 percent in the US. In a regional context, Colombia also appears to be below average. Argentina (1.3 percent), Brazil (0.6 percent) and Uruguay (0.8 percent
Policy Research Working Paper 9294

# **Abstract**

Nearly one in three children under age five in the Philippines is stunted, a key marker of undernutrition. This rate is high for the country’s level of income. This paper provides the first detailed multivariate analysis of potential drivers of stunting in the Philippines, using data from the 2015 National Nutrition Survey. Potential drivers are analyzed individually and grouped in major categories. The analysis finds that stunting between 24–60 months is principally associated with suboptimal prenatal conditions and inadequate food security and diversity. If the results are given a causal interpretation, they imply that if all Filipino newborns had adequate prenatal conditions, the fraction stunted at age 24–60 months would fall by 20 percent. Similarly, providing adequate food security and diversity to all Filipino children would reduce stunting by 22 percent. Other factors—including access to water, sanitation, and environmental conditions—have less strong associations with stunting. The results point to a series of policy priorities to reduce stunting: supporting the nutrition and health of expectant mothers, ensuring access to contraception to reduce adolescent pregnancy, and ensuring that children consume a variety of healthy foods, including protein-dense foods such as milk, meat, and eggs.

This paper is a product of the Health, Nutrition and Population Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gdemombynes@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
0.010)|
|Implant|Baseline|0.008|0.010||
||Endline|0.011|0.023|0.009|
|||||(0.009)|
|IUD|Baseline|0.007|0.004||
||Endline|0.000|0.005|0.008|
|||||(0.006)|
|Observations|Baseline|871|795||
||Endline|871|795|3332|

_Notes._ Column 1 shows control means for females in ELA only communites and column 2 shows means for females assigned to the _Supply_ treatment. Column 3 shows the simple difference-indifference estimate between the _Supply_ treatment and the ELA Only communities over time. Outcomes are indicators that the female used an injectable, implant, or intrautine device (IUD as contraception with her last partner. These are the primary methods that were promoted by the implementation partners in the _Supply_ treatment. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in column 3. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

69
**<br>**Number of**|**GLP/**<br>**Total**|
|---|---|---|---|---|---|---|---|
||**Providers**|**borrowers**|**Borrowers**|**portfolio**|**(%)**|**poor(%)**|**Credit**|
|Egypt|14|868,315|55.7|165,018,626|1.69|5.8|0.24|
|Iraq|4|27,708|14.7|16,122,827|0.16||0.45|
|Jordan|7|134,037|84.1|121,578,626|3.7|65.6|0.68|
|Lebanon|3|23,678|32.8|20,584,560|0.84||0.1|
|Morocco|9|1,241,957|46.3|695,647,381|5.96|28.1|1.08|
|WB/G|8|34,265|43.2|79,727,913|1.68|||
|Syria|2|22,149|31.4|15,682,015|0.18|7.2|0.04|
|Tunisia|1|94,959|76.7|33,999,508|1.32||0.14|
|Yemen|6|24,976|95.8|3,241,486|0.2|0.2|0.16|

Sources: MIX database, WDI,
to the _changes_ of the explanatory variables over the same period of time. In addition, it is often desirable to control for country or urban fixed effects which may be associated with the level of the dependent variable. This can be done by estimating the first difference regressions in the functional form shown below: log V2 - log VI = bo + E bi (log Xi2 -log Xil) k); where subscripts 1 and 2 denote two points in time; Vt, the dependent variable; X,t, the ith explanatory variable; and bo and bi, parameters. The first-difference regression results are shown in Appendix Table 8 for the national level and Appendix Table 9 for the urban level.

# Analyses with Urban Area Data

In Ingram and Liu (1997), income and population density were often used as explanatory variables in the same regression equation. As Figure 1 indicates, urban per capita income and population density are strongly and nonlinearly related. This crosssection correlation between income and population density at the urban level makes it difficult to interpret the coefficients of these two variables and their effects on urban area motorization when both are used as independent variables. In this study, we attempt to deal explicitly with this problem.

Figure 1 shows that urban population densities vary widely in high income cities. This variation in population density in high income cities reflects the impact of historical paths of development on urban form. Cities that have experienced much of their
||PPPC = PPP / Population|||
|---|---|---|---|
|UVI|Unit value of import|IFS (see note 3)||
|Copperchl|Copper price for Chilean TOT|IFS||
|TOT|Terms of trade|IFS and calculated (see note 4)|1970- 1995|
|DREF|Dummy variable for reforms|constructed (see note 5)|1970-1995|
|DPOL|Dummy variable for political upheaval|constructed (see note 5)|1970-1995|
|EDI|mean years of education for the population<br>age over 15|constructed from<br>(a) Barro & Lee 1997 and<br>(b) Psacharopoulos & Arriagada<br>(see note 6, 7, 8)|1970-1995|
|EDV|variance of education for the population age<br>over 15|constructed|1970-1995|
|EDS|standard deviation of education for the<br>population age over 15|constructed|1970-1995|
|EDZ|coefficient of variability of education for<br>the population age over 15|constructed|1970-1995|
|GINI|GINI index of education for the population<br>age over 15|constructed|1970-1995|
|THL|Theil index of education for the population<br>ageover 15|constructed|1970-1995<br>_|
|SDL.|Standard Deviation of Logs of education for<br>the population age over 15|constructed|1970-1995|

**End** Notes for the data sources and data calculations

# 1. WDI

World Bank, _World Developm7ent Inidicators (WDI), CD-ROM._ 2. Nehru & Dhareshwar, 1993 Nehru, Vikram, and Ashok Dhareshwar. 1993. "A New Database on Physical Capital Stock: Sources, Methodology and Results." Revista de Analisis Economico 8 (1): 37-59.

Nehru, Vikram, Eric Swanson, and Ashok Dubey. 1994. "
percent the households live in non-owner-occupied housing. A total of 10.5 percent of the households polled live in housing owned by other individuals; 3.8 percent in state-owned or municipal housing; and 1.7 percent in housing units that belong to an enterprise, institution, organization, or other legal entity. These results appear plausible, given that incentives for tax evasion and hence underreporting were reduced recently. The rental sector is relatively evenly distributed among urban areas.<sup>23</sup>

- With an estimate of slightly over one-third of households, the survey arrives at a similarly plausible non-owner-occupied share for the Polish housing market. The 2002 census data indicate a rental sector share of approximately 31 percent, roughly divided equally between public (municipal, state, non-profit TBS) and private (professional, individual, cooperative, and enterprise). Approximately 40 percent of the cooperative housing units in Poland are rented. The professional / formal apartment-building private rental sector is

> 22  The survey was undertaken within the work on national housing strategy supported by the PHRD Japanese government grant administered by the World Bank.

> 23  In the region of Vilnius, the non-owner-occupied sector reaches 19 percent, of which 4 percent are public rental and 13.6 percent private rental. These values are not far above the country average.

17
# **3.1 Sample Size and Sampling Strategy**

Originally, a stratified random sampling from FICCI‘s member database of over 3,000 firms was considered for the Employer Satisfaction Survey. Sample size was calculated based on the following formula.

where _n_ is the sample size. A 90% confidence interval with margin of error 0.05 was applied. _z_ is the abscissa of the normal curve that cuts off at a given significance level, i.e., 1.65, _p_ (in this case 0.6) is the estimated proportion of an attribute that is present in the population, _q_ is 1- _p_ , and _e_ is the desired level of precision, i.e., 0.05. Using this formula, the originally estimated sample size was about 260, and it was further proportionately allocated to FICCI‘s classification of 17 economic sectors.

There were several difficulties in sampling. First, although the sampling method was originally a stratified random sampling, some member companies were directly contacted to participate in the survey, due to a low response rate (convenience sampling). This may have introduced a bias in the representativeness of the sample. Second, the web-based survey was opened in the last few weeks of the survey to all companies that registered. This slightly increased the sample size. This self-selection could also have caused a selection bias. Third, the sample size was not large enough to meet a 90% confidence interval. Due to time constraints, it was reduced from 260 to 157 companies, i.e., an 80% confidence interval with a margin of error of 0.05.

Despite these shortcomings, the study brings value since it is the first of its kind in India. Further, the participation number of 157 is comparable to other employer satisfaction surveys. The employer satisfaction survey is expected to be conducted every two years. Therefore, the sampling methodologies and the survey design are expected to improve in future rounds, and the
traceability throughout the chain up to the level of overseas producers forms parts of many private standards, including the GlobalGAP.

As a result, exporting adequate quality products requires producers to invest in production infrastructure, revise their practices through sta¤ training and implement traceability systems. A signi...cant body of literature provides evidence that these compliance costs are non trivial and may be su¢ cient to disrupt ...rms trade relationships. Maskus et al., (2005) use ...rm-level data from 16 developing countries in the World Bank Technical Barriers to Trade (TBT) Survey Database. Their ...ndings indicate that standards do increase short-run production costs by requiring additional inputs of labor and capital. They show that a one percent increase in investment to meet compliance costs in importing countries raises variable production costs by between 0.06 and 0.13 percent in the exporting country. Additionally, they ...nd that the ...xed costs of compliance are signi...cant and amount approximately $425,000 per ...rm, or about 4.7 percent of value added on average. Other attempts to quantify the compliance costs include the recent study by UNCTAD (2005). The costs of compliance with SPS for tropical fruits in three sub-Saharan African countries–including United Republic of Tanzania, Mozambique and Guinea–are estimated using the GlobalGAP protocol as a case study<sup>2</sup> . Appendix 8:B reports results for Tanzania. These ...ndings illustrate the importance of both recurring operating and ...xed investment costs, suggesting that compliance is both a costly and lengthy process. While these costs may be born publicly, in practise, the lack of technical and administrative capacities in developing countries implies that the cost burden fall onto individual ...rms. Their ability to secure su¢ cient liquidity to insure ongoing production as well as to make the necessary investments is essential to their pro...tably remaining
expensive and most strictly enforced class of informal debt.<sup>21</sup> Could the observed shift towards informal sources of financing be driven by changes in the supply of credit? Evidence from new loan applications after the program suggests that this is unlikely to be the case. To verify that there was in fact no differential discrimination against beneficiary households applying for new loans, Table D.6, Panel A, presents summary statistics on new loan applications after the program. Despite the fact that all households in the 100% relief category qualified for new bank credit, only 376 of the 1,181 households in the treatment group applied for new loan after the program. However, at 2.5% versus 1.92%, households in the treatment group were not significantly more likely to be denied credit than households in the control group. The regression results in Table D.6, Panel B, confirm this finding: beneficiary households were no more likely to be turned down for a loan and, conditional on a loan being approved, interest rates did not differ between treatment and control. While the statistical power of the test is limited by the relatively small number of new loan applications after the program, the results provide strong suggestive evidence that the documented shift in the composition of borrowing is not driven by changes in the supply of credit or differential discrimination against debt relief recipients.

# **6.3 Debt Relief, Investment and Productivity**

An important economic argument in favor of large-scale debt relief programs is their potential to increase productivity by reducing disincentives for investment arising from high levels of pre-existing debt. The testable implications of this debt overhang argument are two-fold: _First_ , debt relief should lead to an increase in investment expenditures because households, once freed of their debts, are now the residual claimants of their investment returns. _Second_ , debt relief should reduce investments in risky production technologies, since the risk of project failure is no longer borne by debt holders. As I show in this
# **Nature, socioeconomics and adaptation to natural disasters: new evidence from floods**

# **1. Introduction**

Recent studies have analyzed the role of socioeconomic factors in determining fatalities from natural disasters (Kahn 2005, Kellenberg and Mobarak 2008, Cavallo and Noy 2010),<sup>1</sup> earthquakes in particular (Anbarci et al. 2005, Escaleras et al. 2007, Cavallo et al. 2010, Keefer et al. 2010). An appealing and intuitive general finding from these studies is that countries that are richer and have stronger institutions suffer fewer deaths from natural disasters, as they are evidently better able to agree on, invest in, and enforce zoning regulations, building codes, and other preventive measures.

In this paper, we analyze new data on 2,194 flood events in 108 countries during 1985-2008, and our results tell a very different story. Although we find that income is negatively associated with the frequency of floods and, conditional on their magnitude, the fatalities they cause in developing countries, we find that it increases flood frequency, magnitude, and fatalities in developed countries. Also in contrast to the literature, we find that the effect of governance is not uniformly salutary. Its effect on flood frequency and fatalities in developing countries is U-shaped, with improvements in governance reducing the number of floods and deaths when governance is weaker but raising the number when governance is stronger. We find no evidence that governance affects flood magnitude in developing countries or flood frequency, magnitude, or fatalities in developed countries.

> 1 Destructive natural events occur regularly across the world, although most do not cause enough damage to be considered natural disasters. To be included in the widely used EM-DAT global disaster database, an event needs to fulfill at least one of the following criteria: (i) 10 or more people killed, (ii) 100 or more people reported affected (typically displaced), (iii) a declaration of a state of emergency, or (iv
**Figure 3.2: Extreme rural poverty (P0) in Mexico by region,** **<u>selected years 1992-2002</u>**

<!-- Start of picture text --> 80.0<br>1992<br>70.0 1996<br>2002<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Mexico Norte Capital Golfo Pacifico Sur Centro- Centro<br>Norte<br>Source: Calculations based on ENIGH 1992, 1996 and 2002. Rural: areas population with <15,000.<br><!-- End of picture text --> The distinction between regions seems to matter more for rural poverty than does that of rural and urban. As Figure 3.2 clearly shows, the Capital, Center, and CenterNorth regions experienced a considerable reduction in the headcount poverty rate over the past decade. The share of poor people in the total population fell by more than 10 percentage points in each region of these regions. This compares to the Gulf region where poverty increased by more than 10 percentage points during 1992-2002.

During 1992-2002, the pattern of overall poverty changes has closely followed the macroeconomic cycle and the associated rhythm of the labor market. The overall Mexican poverty was not stable, nor was rural poverty stable over the last decade. In rural areas P0 fell from 36 to 32 during 1992-94 and increased during the crises in 199596 where it hit an all-time high of 52 percent poverty rate. More populated rural areas (with between 2,500-15,000 inhabitants) have been more affected by the economic instability than dispersed rural areas (localities with less than 2,500 people). Poverty increased by 51 percent in the former and 27 percent in the latter areas during 1992-96. The crisis of 1994-95 constituted to a
# **1. Introduction**

The link between rising energy costs and firm performance has garnered increasing attention in academic and policy discussions due to the broader adoption of carbon pricing mechanisms and the gradual elimination of energy subsidies. As these policy changes elevate nonrenewable energy prices—a crucial input in the production processes of many industries—firms may experience notable performance implications. Higher energy costs generally affect profitability and operational efficiency by increasing production expenses, particularly in energy-intensive sectors. Firms with greater energy dependence often face heightened stock market volatility due to increased uncertainty and lower expected cash flows (Sadorsky 1999; Henriques & Sadorsky 2008). Moreover, rising energy costs may constrain corporate investment strategies, leading to reduced capital expenditures, especially in industries reliant on energy inputs (Kilian 2008; Bloom 2009).

link between energy price fluctuations and firm performance. Cali et al. (2022) show that, in Indonesia and Mexico, increases in electricity prices harm manufacturing plants’ performance. However, fuel price hikes result in higher productivity and profits of manufacturing plants. Fuel prices incentivize plants to replace inefficient fuel-powered with more productive electricity-powered capital equipment. Their results help to re-evaluate the policy trade-off between reducing carbon emissions and improving economic performance, particularly in countries with large fuel subsidies, such as Indonesia and Mexico. Cali et al. (2023) studied firms from 12 sectors and 11 middle-income countries during 2002–2013, using World Bank Enterprise Survey data. The study did not consistently find that higher energy prices negatively affect economic performance; they may even enhance it in some cases. Firms might be able to offset increased energy costs through innovation or new market strategies. The impact of energy prices on firms is a multifaceted issue, depending on several characteristics, including energy intensity, firm size, ownership type, and previous experience with electricity outages—which made companies less sensitive to price increases, likely due to their familiarity with managing energy and input shortages.

Andre et al. (2023) analyze firm-level data from 21 OECD countries (