rafmacalaba/data-use-mentions-extended huggingface dataset:
|||
|Pre-trend (1–4 yrs. before treatm.)||0.00230|0.00196|0.00614|-0.000134|
|||(0.00146)|(0.00147)|(0.00951)|(0.00680)|
|Commune and year FE|No|Yes|No|Yes|No|
|Commune x year FE|No|No|Yes|No|Yes|
|Observations|3,891|49,547|49,547|49,547|49,547|
|_R_<sup>2</sup>|0.207|0.021|0.049|0.100|0.273|

Note: Standard errors are in parentheses, clustered at the commune level. The variable in column (1) is village population size according to the 2012 census. The outcome in columns (2)–(5) is a dummy that equals 1 if the village is the closest to a severe conflict event or within 10 km of a severe conflict event. Results in (2) and (4) follow the specification in equation (1), while columns (3) and (5) follow equation (2).<sup>_∗_</sup> _p < ._ 1;<sup>_∗∗_</sup> _p < ._ 05;<sup>_∗∗∗_</sup> _p < ._ 01

# **3 Results**

## **3.1 Main Results**

Table 2 displays our main findings. As mentioned above, the treatment variable takes a value of 1 during the two years when randomized villages received the cash transfer program and 0 otherwise. As far as the dependent variable is concerned, in col. (1) we start with a definition of conflict capturing whether a given village is the closest to a
company is a popular employment destination for University graduates and professionals. The company offers performance-based bonus plans, yearly salary increments, and employee stock schemes rewards. Umeme’s staff comprise a diversity of professions, including engineering, operations, finance, IT, HR, research and development, corporate affairs, law, communications, sales, customer care and other cross-cutting disciplines. Recruitment into the company is conducted in one of three ways: internships for university students in their second or third years; graduate recruitment, which involves an intensive rotation training program to provide a holistic understanding of the business; and professional recruitment of experienced individuals normally through adverts in national newspapers, radios and online. The company has employed the latest IT solutions to improve its quality of service.

> 15 For the detailed corporate governance index, see the annex.

> 16 For the detailed utility management index, see the annex.

32
Policy Research Working Paper 5561

# **Abstract**

This paper quantifies hurricane damage caused by climate change across the US. A damage function is estimated from historic hurricane data to measure the impacts at each location given the storm’s strength. The minimum barometric pressure of each storm turns out to be a better indicator of damages than the traditional measure of maximum wind speed. A hurricane generator in the Atlantic Ocean is then used to create 5000 storms with and without climate change. Combining the location and intensity of each storm with the income and population projected for each location, it is possible to estimate a detailed picture of how hurricanes will impact each state with and without climate change. Income and population growth alone increase expected baseline damage from $9 to $27 billion per year by 2100. Climate change is expected to increase damage by another $40 billion. Over 85 percent of these impacts are in Florida and the Gulf states. The 10 percent most damaging storms cause 93 percent of expected damage.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Finance Economics and Urban Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at Robert.mendelsohn@yale.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive
**|-11.4**|-29.4***|2.6|-4.6**|313.3***|
|24-hour recall arm|0.3|-57.8***|12.6***|-11.6***|-27.4***|0.5|-2.3|477.7***|

## <u>Notes:</u>

(1) OLS regressions controlling for EA and day of the week FEs; the standard errors are clustered at the household-level. Regressions control for the same individual and household-level characteristics as in eq. (1). Asterisks are indicated on the smartphone diary and 24hour recall estimates if they were significantly different, respectively, from the 7-day module. ***p<0.01, **p<0.05, *p<0.10

# 5. Factors underlying reporting differences across recall and smartphone arms: Intraday variation in time allocation and simultaneity

Apart from average treatment effects, the data collected across both treatment arms in the experimental study allow for a more disaggregated view of time allocation that has been less well-examined in time use surveys. This includes how men and women manage their time within the day — as well as how intensively they use their time, as measured by different activities being conducted simultaneously. As we discuss in this section, some of these nuances can help explain some of the findings we observed in Table 6. For example, despite the 24-hour recall arm’s 15-minute floor for time recording, we find that the smartphone arm reveals greater time across leisure and non-leisure activities during potentially productive times of the day. We also find a much higher incidence of activities conducted simultaneously in the smartphone arm, and across a wider range of activities than the recall arm, including non-leisure activities. In so doing, we

> 17 The results are available
Based on this literature, alongside the intuition that the economic shock from the Great Recession would affect migrant more than non-migrant households, we define migrant households for this analysis based on child age, highlighting whether a child lived in a household with a migrant to the United States during their early, most vulnerable years. Specifically, a child under three years old measured in 2008 is defined to be in a migrant household if the household had a migrant to the United States (including the head, spouse of head or children of either as described above)<sup>vii</sup> in at least three of the four calendar years 2005– 08, and not in a migrant household otherwise.<sup>viii</sup> This definition includes migrants who left prior to 2005 and remained away for at least three of the four years 2005–08.<sup>ix</sup> Since the baseline household survey was implemented from April to July of 2008, the four-year window covers the entire lifespan of any child under three years of age at the time of the baseline, including part or all of the in-utero period. Similarly, a child under three years old in 2012 is defined as living in a migrant household if the household had a migrant to the United States in at least three of the four calendar years 2009–12. For older children, living in a migrant household is defined in parallel fashion, i.e., based on having a migrant in the United States during the years corresponding to when the child was under three years old, adjusting the window based on exact age at measurement (see Appendix Figure 1).<sup>x</sup> _3.2 Cross-sectional cohort comparison model_ Categorizing cohorts of children under three years old in 2008 or 2012 by migration status as defined in Section 3.1 results in four groups. These are used to set up a difference-indifference (DD) analysis in which outcomes for children in migrant versus (comparison) nonmigrant
August (which corresponds to the agricultural season for most crops grown in Nigeria) in 2019 and 2020. We then merge these data with the anonymized GPS coordinates with a random offset of the Enumeration Areas (EAs) where individuals reside, as provided in the public use GHS-Panel 2018/19 datasets.<sup>15</sup> Thus, following Vicente-Serrano et al. (2010), individuals are affected by drought when they reside in an EAs whose SPEI is less than ́1. Finally, we used the reports by IFRC to define flood disaster. Specifically, our flood measure takes the value 1 when the individual resides in the states affected by

# **2.2.5 Summary Statistics**

Table 1 displays summary statistics of the sample we use for the main analysis. This ́ sample includes children aged 5 18 years, and we exclude the respondents for whom the schools are still closed at the time of the survey in October 2020, those who are still on leave or refuse to go to school because they are afraid of contracting the coronavirus and those who are waiting for admission. This table indicates that 90% of children attended school before the schools’ closure compared to 82%, who are attending school during COVID-19, i.e. after the schools reopen in 2020.

In 2019, the average age of respondents in the sample is 9.92, and they are mostly the children of the household head (83%). Among the household heads, 41% have never been to school. The average household size is approximately 9. Slightly less than half of the individuals in the sample are girls (48%), and it is observed that 74% of the respondents live in rural areas. More than half (52%) of the individuals have been in contact with their teachers during the school closure period. Individuals were more affected by drought in 2019 than in 2020. Specifically, 56% of respondents were affected by the drought in 2019,
Facto Crawling Peg). In this case, 59% of the observations in the sample are classified as fixed ER, and 52 countries have at least one year in this regime.

**Control variables.** Our specification in equation (5) allows the MPS to vary with other covariates. In addition to a generic control for the level of development, measured as the log of Gross Domestic Product Per Capita (GDPPC; constant dollars, from World Development Indicators (WDI)), we include two other controls from the literature.

The first control variable is motivated by Calderón and Schmidt-Hebbel (2008), Frankel et al. (2013), Cespedes and Velasco (2014), and Carneiro and Garrido (2016) who argue that the ability of countries to conduct countercyclical fiscal policy depends on the quality of their institutions. Following this literature, we construct a country-specific institutional quality (IQ) index as the average of four indicators: (i) government effectiveness, (ii) rule of law, (iii) regulatory quality, and (iv) control of corruption. The indicators are provided by the International Country Risk Guide for 146 countries from 1984–2016. The IQ index ranges from zero (lowest institutional quality) to one (highest institutional quality).

Second, Gavin and Perotti (1997), Riascos and Vegh (2003), and Caballero and Krishnamurthy (2004) argue that imperfect credit markets prevent developing countries from borrowing during economic downturns and hence affect their ability to conduct countercyclical fiscal policy. To assess the development of domestic financial markets, we use Loayza and Ranciere’s (2006) measure of financial depth as liquid liabilities as a share of GDP (data from the World Bank WDI).

**Outliers.** As commodity prices and fiscal policy in developing countries are volatile, we need to be careful to make sure that our results are not being driven by a small number of influential observations. We check for the presence of outliers and influential observations by
composition, wealth index, religion, and caste; and the availability of community services and infrastructure. The descriptive statistics for these variables are presented in table 3.

Tables 4 and 5 show the coefficients of the Weibull proportional hazard estimation for children younger than age 36 months using three rounds of the NFHS.<sup>10</sup> Table 4 includes the child‘s weight at birth estimated by the mother; Table 5 uses weight measured at birth. The weight at birth is one of the important factors affecting neo-natal child mortality that combines the unobserved information about pre-natal conditions and shocks experienced by the mother and her child (i.e., Claeson, et al. 2000). The results of the two estimations are not directly comparable as one is a categorical variable (mother‘s assessment of weight at birth) and the other is a continuous variable (measured weight), but the implications are similar<sup>11</sup> . The remainder of the discussion focuses on the estimations based on the specification using mother-assessed weight at birth because the sample sizes are much larger.

The estimated hazard odds ratios on the control variables in the estimations of proportional hazard models reveal the expected relationship between child mortality and characteristics of the child, mother, and household. A child‘s gender has no effect on mortality hazard: mortality rates for boys are higher than for girls in the six months after birth and lower after that. A higher birth order has a negative impact on survival probabilities (Miller et al. 1992). Weight at birth, whether assessed by the mother or measured at birth, is a strong predictor of mortality. Children whose mother‘s assessed their weight as small (with an odds ratio greater than 1 on the dummy variable reported in table 4) and children with low measured weight (with odds ratios less than one on the continuous measured weight variables in table 6) are significantly less likely to survive than are children who weigh more at birth.

Children
: 63%), neurological (headache: 60%; dizziness: 53%), ocular (eye

> 2 Hospital admission records in Vietnam attribute 11% of all poisonings to pesticide misuse: approximately 840 poisonings in 53 cities and provinces in 1999 (Poison Control Center, Vietnam Ministry of Health, 2000).

> 3 The World Health Organization estimates that there are 50 cases of poisoning for every case reported and registered (WHO, 1990).

> 4 The survey was designed by the World Bank team.

6
-> Table 7 presents the variables that are listed in the 2006 dataset. The more institutional variables that we included in our analysis, the more interesting were the findings that emerged. Among other things, firing teachers, which is an indicator of school autonomy, was significant. Also, if a school was having to compete with others in the vicinity, then the effect was large, positive, and significant. Parental involvement in formulating the school budget was also positive and significant. Public schools retained their large advantage. An adequate supply of math teachers played a positive role in the determination of test scores, while grade repetition had a small negative effect. The level of the mother’s schooling was significant. Doing math work in class was also important.

16
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _8_ gaps, and projecting future values. To accomplish this, a predictive model is constructed for severe food insecurity that uses covariates that describe macroeconomic drivers of food insecurity and structural vulnerabilities. The model is of a simple contemporaneous form: where _yit_ is the prevalence rate of severe food insecurity for country _i_ at time _t_ , and _Xit_ is a vector of length _d_ that describes several attributes of country _i_ at time _t_ . For estimation purposes, the individual country data can naturally be stacked as vectors so that the prediction function can be estimated from _Y_ = _f_ ( _X_ ) where _Y_ is a vector of length _T × N_ , and _X_ is a _d_ -column matrix with _T × N_ rows. The annual predictions at the national level are optimized for out-of-sample squared correlations. The optimized model _f_<sup>ˆ</sup> can then be used to reconstruct historical food insecurity databased on official macro-economic statistics, fill missing observations, or produce preliminary estimates and projections by leveraging preliminary assessments of the macro-economic figures.

# _C. Covariates_

Since the outlook is intended to inform broad-based economic policies that can be defended in front of a non-expert audience, parsimony and straightforwardness in input variables carries a premium. A critical constraint for specifying _X_ is that credible outlooks or plausible scenarios for the future values _XH_ : _{Xt_ +1 _, t_ +2 _,..., t_ + _h}_ across a long horizon _h >_ 3 will be required to produce meaningful food insecurity outlooks. The empirical application here applies the model to the WEO database to project food insecurity through 2027, and considers the following variables for model estimation purposes.

- 1) The poverty rate at US $ 1.90

- 2) GDP per capita, ppp adjusted

- 3) The 3-year average real GDP growth rate

- 4) The 3-year average population growth rate

- 5) The 3-year average CPI (Consumer Price Index) inflation
# L I S T O F F I G U R E S

|Figure1|FDI Infow into Developing Countries<br>. . . . . . . . . .|. . .<br>2|
|---|---|---|
|Figure2|Assets and Sales by US Affliates,2008<br>. . . . . . . . . .|. . .<br>9|
|Figure3|Illustration of the Impact of Restricting FDI<br>. . . . . . .|. . .<br>14|
|Figure4|Randomness of Country Samples<br>. . . . . . . . . . . . .|. . .<br>18|
|Figure5|Quality of Institutions and Arbitration Regimes . . . . .|. . .<br>21|
|Figure6|Correcting for Country and Firm Selection Biases<br>. . .|. . .<br>25|
|Figure7|FDI and Statutory Openness<br>. . . . . . . . . . . . . . . .|. . .<br>31|
|Figure8|Reported and Unreported FDI Data . . . . . . . . . . . .|. . .<br>35|
|Figure9|Sorting of Firms by Productivity . . . . . . . . . . . . . .|. . .<br>53|

# L I S T O F TA B L E S

|Table1|Main Regressions: FDI Stock in2007-2008<br>. . . . . . . . . . .|29|
|---|---|---|
|Table2|Regressions: OECD Source Countries Excluded . . . . . . . .|30|
|Table3|Addressing the Zero Problem<br>. . . . . . . . .
32.7%) compared to New York (22.7%) and Los Angeles (27.8%) (HUD 2013; US Census Bureau Fact Finder), which further complicates the picture of privilege and marginalization along the line of race.

Why do such statistics matter for understanding the impacts of climate change on poverty? They matter because, in comparison with burgeoning case studies and comparative analyses on poor people in poor countries, the climate change literature and debates on poor people in developed countries and their experiences with a changing climate have been astoundingly mute. The poor in upper MICs (e.g. China, Brazil, South Africa) and HICs (e.g. OECD countries) have been largely invisible, insufficiently accounted and represented in international poverty metrics and in‐depth climate change inquiries. The same is true for growing inequalities behind cloaked processes of impoverishment (‘falling into poverty’) and marginalization. Even progressive poverty measures, such as the Multidimensional Poverty Index (UNDP, 2010) do not capture poverty in HICs. In fact, Chapter 13 on Livelihoods and Poverty in the AR5 noted the limited research on climate change impacts on poor people in more affluent nations as a major gap in knowledge (Olsson et al. 2014a). The exception stems from extreme events, especially hurricanes and heat waves, during which the ugly face of western/northern poverty all of a sudden becomes shockingly visible. Vignette 1 exposes differential merit in protecting socially vulnerable versus affluent people and regions against sea level rise in the United States. A closer look at the 1995 Chicago and 2003 European heat waves, provided in Vignette 4, reveals disproportionate and premature death among poor, disadvantaged, and socially isolated segments in society.

One of the prime reasons why poverty and inequality in MICs and HICs have been understudied is related to the narratives that encapsulate the essence of ‘the poor’ in affluent nations. In the United States, the dominant narrative about poor people in general, including single mothers on
# **II Lack of quality in public goods and individualized solutions to collective problems in the middle class**

In this section we illustrate some of the behavioral patterns of the middle and upper classes in the Dominican Republic<sup>1</sup> , such as an observed tendency to adopt individual solutions to solve collective problems. We draw from household surveys to illustrate to the propensity of using private services (health, electricity, transportation) by citizens of different socioeconomic strata.

Besley and Coate (1991) argue that schemes for universal provision of public goods (education, health, etc.) can redistribute income from the rich to the poor. They develop a discrete choice model where some individuals can afford to consume a higher quality service in the private sector. Redistribution does occur, but in some cases it might involve a deadweight loss. Concretely, if the quality of the public good supplied is low, both low-income and highincome individuals may be worse off, as the poor effectively receive a public service, but they do not value it, whereas the rich pay taxes for nothing in return (as they use private solutions).

The Dominican Republic seems to have fallen in one of these inefficient equilibriums for political economy reasons: it is too costly to create both the political and the civil society coalitions that would lead to achieve greater government accountability and an improvement in public services. As we will see in the next section, citizens ́ trust in institutions is low and those who can afford it have already decided to respond individually to the problems. In this section we introduce some examples of sectors or activities in which the middle and high classes in the Dominican Republic have opted for private solutions to substitute or complement poor public service provision.

## **II.1 Electricity supply**

The Electric sector in the Dominican Republic has been characterized by large energy losses caused by deficient infrastructures and maintenance, poorly targeted subsidies and widespread no-payment of bills. Reforms have proven to be slow and not always
Heckman selection model and rely either on functional form or exclusion restrictions to identify the insurance effect. Both have been used in the insurance literature, but neither is altogether satisfactory.<sup>7</sup> In the present context, with a two-wave panel conducted before and after the insurance scheme was introduced, we are able to make a much weaker exogeniety assumption (cf. Blundell and Costa Dias 2000). Instead of (5), the regression analogue of what we have is: and so that if ∆ denotes the difference between period _t_ =1 and _t_ =2, we get

> 7  Studies relying on the non-linearity of the Heckman model to secure identification include the studies of Waters (1999) and Jowett et al. (2003).  A number of studies explore exclusion restrictions to identify the effects of insurance in a cross-section context.  Gertler and Strum (1997) consider various possibilities, but in the end conclude they have no plausible instruments. Harmon and Nolan (2001) exclude education from the utilization equation, which they acknowledge is problematic, and resort to non-linearity as the alternative identification strategy.  Guariglia and Rossi (2004) in their analysis of the impact of health insurance on savings in the UK use as identifying instruments occupation, workplace size, whether the individual has an occupational pension, and whether he or she works in the private sector.  It is not at all obvious why any of these ought to be excluded from the savings equation.

6