rafmacalaba/data-use-mentions-extended huggingface dataset:
The second step determines the share of health expenditure attributable to pollution. For this step data on the disability adjusted life years (DALY),<sup>17</sup> for each disease “d” associated with air pollution (chronic obstructive pulmonary disease, lower respiratory infections, stroke, diabetes mellitus type 2, ischemic heart disease, tracheal, bronchus, and lung cancer), is divided by total disability adjusted life years in each year and then multiplied by the proportion of the population with disease d. � dd,cc,2017 DDAAWWYYss cc,2017 In the model nominal spending on health care is assumed to rise linearly in line with GDP and the dd TTCCS llDDAAWWYYss In the model nominal spending on health care is assumed to rise linearly in line with GDP and the increase in the concentration of 2.5mm particles in excess of the previously discussed threshold level.

## Thus, health expenditure due to pollution in year t is given by

The 2017 HEAP value is calculated as <u>, ,2017</u> . ∗� II EE DDAAWWYYss PPVVKK,2017 PPVVKK,2017 PPVVKK,2017 II,2017 DDXXAAPP = GGDDPP ∗TTDDXX EE,2017 ∗PPAAFF are disability adjusted life years, for disease d, country c, year t and II TTCCS llDDAAWWYYss = . (population attributable fraction) represents the share of all people suffering II,EE,tt EE,2017 DDAAWWYYss TTCCS llDDAAWWYYss from the disease in year , that contracted it because of pollution.<sup>18</sup> Data for the PAF are sourced dd II,EE,2017 II,tt ∑DDAAWWYYss PPAAFF from the CPAT model (World Bank 2020b). dd SS

# 3.8.3.2 Mapping working days lost to economic outcomes

Pollution impacts the economy on the one hand by reducing the labor force due to pollution-related deaths, and reducing total labor supply (measured in hours worked) by the hours lost by those workers who suffer from pollution related disease but do not die
and A.4 in the Appendix for descriptive statistics). We include enumeration areas fixed effects ( _μEA_ ) or household fixed effects ( _μHH_ ) to control for unobserved characteristics that are constant within EAs/households. Second, we implement the double LASSO method of Chernozhukov et al. (2017) using the original list of potential controls augmented with fourth-degree polynomials along with all first-order interactions and the inverse hyperbolic sine transformation of continuous variables (Knaus et al. 2020). We also estimate specifications without control variables.

– Three main methodological challenges could afflict this OLS regression and more gen-

> 10 _PHH_ and _PEA_ are standardised to facilitate interpretation. Before standardisation, the variable _PHH_ is set as equal to 0 (= the mean of the perception index _Pi_ ) in households with only one adult.

> 11When describing host perceptions across all sites in the later OLS analysis without a measure for interaction (Table 4), the neighbourhoods are defined by similar Enumeration Areas from the Kenyan census for respondents in Nairobi, by villages around Kakuma, by the five main towns close to each of the five Dollo Ado camps, and by specific sublocations to the official woredas in Addis Ababa.

15
consumer durables), PCE inflation, and the yield on the US 10-year treasury. The VAR is estimated between 1953:Q2 and 2018:Q3, with the starting date constrained by the availability of the 10-year treasury yield data. All data are from the FRED database produced by the Federal Reserve Bank of St. Louis (see Appendix 9.5 for further details).

While the IRFs provide qualitatively similar results (Figure 11), the forecast error variance decomposition (FEVD) shows important differences between the identifications. The Max-Share and Spectral identifications, which are both susceptible to low-frequency contamination, have the highest explained variance at each horizon (Table 6). The NAMS approach, which is less biased by this type of contamination as shown in section 3.3.2, has the lowest. This suggests that the Max-Share and Spectral approaches are also capturing confounding low-frequency shocks in the data.

There appear to be few confounding business frequency shocks in the productivity levels data. In part, this is observable in the spectral densities shown earlier, which show an overwhelming domination of the variance of productivity at low frequencies (Figure 1). This can also be seen by the similarities between the MaxShare and limited spectral FEVD identifications. One way of thinking about the relationship between the two methodologies is that the limited spectral approach is simply a Fourier transform of the Max-Share identification, with the additional feature of excluding certain frequencies. The forecast error variance matrix with which the maximization takes place for each can be written as: and Therefore, medium and high-frequency volatility must be a sufficiently small proportion of the variance at this horizon so that the maximization problems are essentially equivalent. Under the standard Spectral identification (using the longrun representation of VAR coefficients), confounding low-frequency shocks will be given less weight relative to highly persistent shocks such as technology, providing an explanation of the lower proportion of explained variance in the standard Spectral relative to the limited Spectral identification.

28
these outcomes are well balanced across treatment arms. Out of the six schools that dropped out of the sample, two schools were in the control group, two were in the _Original_ group, and two in the _Plus_ group. The _p_ -values of the Komolgorov-Smirnoff statistic for the equality of the distributions of work experience in the school of the community instructors in each treatment arm and the control group are 0.773 and 0.892, respectively. The _p_ -value of the _Plus_ - _Original_ difference in the share of mentors who drop out from the program during the experiment is 0.957. There is no evidence of composition changes between the _Original_ and _Plus_ groups induced by mentor turnover (see Table B-3).

The effective sample size of the second experiment is 1,045 children/parents and 224 schools (see Appendix A.2 for further details on the sampling design). We use the Early Grade Reading Assessment (reading score) and the Early Grade Math Assessment (math score) as our main measures of children’s cognitive achievement. Those are individually administered student assessments that have been conducted in more than 40 countries and in a variety of languages (Dubeck and Gove, 2015; Platas et al., 2016). While these instruments are typically applied to students in first, second, or third grade, we administer them to third through six grade students to account for the large learning gaps of the children in our sample. The school-average standardized scores in math and Spanish as measured in the school year prior to the introduction of the second experiment are, respectively, 0.5 and 0.7 standard deviations below the national averages.<sup>4</sup> To measure the impact of the intervention on socio-emotional skills, we consider a collection of thirty-two behavioral issues as reported by a caregiver, which resembles the questionnaire in the Children section of the National Longitudinal Study of Youth (CNLSY-79), such as antisocial behavior, anxiety/depression, headstrongness, hyperactivity and
investment competitiveness to better attract and retain FDI. To that end, policy makers need to identify the drivers of FDI and specific market entry and operational constraints that may deter foreign investors. Via a new survey covering over 2,400 foreign investors across 10 major middle-income countries (MICs), this paper responds to this need by assessing the importance of various policy factors—including FDIrelated regulations, political risk, and investment promotion—as well as studying heterogeneity across different types of MNE affiliates.

The remainder of the paper is structured as follows: Section 2 reviews the literature on policy factors that affect FDI. Section 3 describes the survey data and presents descriptive analysis of variables. Section 4 presents the descriptive statistics. Section 5 details the analytical methodology. Section 6 discusses the results. Finally, Section 7 concludes and discusses policy implications.

# **2. LITERATURE REVIEW: THE ROLE OF POLICY IN DRIVING FDI**

> 1 Recent projections show remittances exceeding FDI for low- and middle-income countries in 2019, although they are not projected to do so for the 10 surveyed MICs given their relatively higher FDI compared to remittances (Global Knowledge Partnership on Migration and Development [KNOMAD] database: https://www.knomad.org/data/remittances). Foreign bank lending is also a significant source of private external finance and represents about half of all external liabilities of emerging-market countries (Bräuning and Ivashina 2019).

> 2 Although FDI inflows as a share of GDP have declined in most of the surveyed MICs, experiences have varied. For example, from 2008 to 2018, FDI inflows to China fell sharply (from 3.7 percent to 1.5 percent) but increased in Brazil (from 3.0 percent to 4.7 percent). As discussed in the Overview, a mix of economic factors are plausibly shaping global FDI trends, including declining rates of return on FDI, changes in U.S. tax policy, increasingly assetlight forms of international production on the backs of digital technologies
41.5|18.8|30.9|

a Author's calculations for 1986-1992 from the Penn World Table Mark 5.6, described in Summers and Heston (1991) and available on the website: http://pwt.econ.upenn.edu. Post-1992 data are unavailable. b/ Authors' calculations from the peak to the trough of the depression. This is 1986-1994 for Cameroon and C6te d'Ivoire, and 1929-1933 for the United States. Data are from the World Bank (1999) and U.S. Bureau of the Census (1975).

c Not Available.

Governments tried especially hard to rein in wage costs, but without success. In fact, wages actually rose relative to GDP. In addition, the high real exchange rate hurt the export and formal manufacturing sectors, undermining the domestic tax base and widening budget deficits. Furthermore, the economic slowdown called for by internal adjustment caused a change in the composition of imports, to lower-value items, and increased the level of informal imports so that tariff revenues also fell. This meant that at a time when internal adjustment called for the CFA zone governments to decrease their fiscal deficits, these deficits actually increased since the governments could not reduce '1 For example, the incidence of poverty doubled in Cote d'Ivoire from 30 percent to 60 percent between 1985 and 1992.

8
and 36 primary schools were randomly selected into the control group. Selection was stratified by island group, school ownership (public or private), whether the school received students from communities included in the school readiness intervention, and the number of children in the first 6 grades. One school dropped out of the intervention after the second year, and this was replaced by a randomly selected school. Not all schools were eligible to be selected into the control or treatment groups: two other primary school interventions were being piloted in several schools which are excluded from the evaluation’s population. This included all schools on the island of ‘Eua. Note that schools in the treatment and control groups could include students from the school readiness intervention’s treatment communities, control communities and unassigned communities.

Data were collected on both child outcomes and behaviors that the interventions were trying to influence for both interventions. For the school readiness intervention, the Tongan Early Human Capabilities Index (TEHCI) survey measured school readiness and collected data on home activities, and surveys of treatment and control communities were conducted to measure community activities that supported school readiness. For the CLRW intervention, classroom observations were conducted to measure teaching practices and the Tongan Early Grade Reading Assessment (TEGRA) measured students’ early grade reading skill. These data are discussed in more detail in the sections on the respective interventions. Table 1 presents a timeline for the interventions and data collection by birth cohort. These cohorts are approximate, as children may enter first grade later and sometimes earlier than age 6.

As noted above for both interventions, there is a distinction between intervention and treatment. For the school readiness intervention, we are measuring the effect of providing community leaders with support to establish and maintain CPBAs, not the effect of a child’s exposure to a CPBA. For the reading intervention, we are measuring the effect of providing teachers with training, materials and coaching, not the effect of exposure to the new pedagogic approach. In both cases, the effect size of
559|
||Owner cum tenant|0.00|8.29|50.71|1.63|3.78|2,780|
||Pattadar|4.81|5.84|3.90|59.44|8.25|6,817|
||Landless|25.01|29.56|19.47|28.44|73.44|41,714|
||No of obs.|29,794|2,003|1,767|2,584|44,379|80,527|

_Source:_ Own computation from 2008/9 West Bengal listing survey.

24
Foreign banks, as the above analysis indicates, have not been in the retail business and their impact on concentration has been minimal. While concentration ratios and indices are lower now than in 1980, this does not necessarily mean the system is more competitive. An important issue here is the bank branch network of the banks in the system before 1980. These banks set up their networks when the financial system was repressed and it was relatively cheap to do so, simply because the price of capital was distorted. With the freeing of interest rates in 1980 this was corrected, but existing banks had their large networks by that time giving them advantage over new entrants. In fact, branch concentration ratio remained fairly stable For this reason we take market structure and branch concentration factors into account in the empirical work presented below.

# **IV. DATA AND VARIABLES**

All bank data used in this study comes from the annual publication of the Banks Association of Turkey titled "Banks in Turkey". This publication includes income statements, balance sheets and other information on a yearly basis and on every bank operating in Turkey.

Using this data the following variables were constructed: (i) net interest margin/ total assets, (ii) overhead costs/total assets; and (iii) before tax net income/total assets _average lending rate = interest and non-interest income received._ _average loans (after adjusting for loan loss provisions)._ _average deposit rate = interest paid/average deposits._ _net margin = average lending rate-average deposit rate/total assets (ta)._

**11**
and Sources**

Our empirical analysis relies on geo-referenced Afrobarometer surveys data for the period of 2005 to 2014. The Afrobarometer constitutes a large pool of public opinion surveys and a reliable source of information on various political and socio-economic characteristics of residents and local communities in Africa. The surveys capture the availability and residents’ appraisal of their perceived quality of public goods and services in local communities -- these latter identified by the enumeration areas (primary sampling unit). The geospatial coordinates of the enumeration areas facilitate the merging the Afrobarometer surveys data with the records of the Raw Materials Database (RMD) of SNL Metals and Mining by S&P. Each enumeration area and their corresponding respondents in the surveys are matched with its closest mine based on a cut-off distance value (Picard, 2010).

6
<mark>materials were read aloud in the participant’s desired language (Haoussa or Djerma). Enumerators were blind to the hypotheses.</mark> <mark>An endline survey was conducted approximately one year later among the full sample. Female enumerators were blind to condition assignment and administered the survey to female respondents in a private space in their homes or near their homes. Enumerators asked respondents a series of economic, psychological, social, and program-related outcome measures in the participant’s desired language.</mark> _Post-intervention Survey Measures._ Responses to self-reported psychosocial measures and hypothetical economic scenarios were collected among those in intervention conditions only. Details on the wording and construction of each measure can be found on the AEA pre-registration: <u>https://www.socialscienceregistry.org/trials/3570.</u> Note that many of these measures were original, meaning that they were developed to be specific to this economic and cultural context. Given time constraints, we were unable to assess the properties of these measures before the survey, and several were found post-hoc to have poor properties (e.g., low internal reliability, low variation, frequent ceiling effects). While we preregistered a split sample analytic approach due to the exploratory nature of these measures and to help control the rate of false discovery (Anderson & Magruder, 2017), we ultimately found no significant effect on any outcome measure and thus present analyses on the full sample.

**_Economic outcomes._** Three composite variables ( _approach behaviors_ , _approach feelings_ , and _budget allocation_ ) comprise an ‘economic composite index’, which relates to business intentions and investment behaviors. _Approach behaviors_ and _approach feelings_ assess response to four hypothetical business decisions that relate to: reinvesting in a profitable activity following an intrahousehold disagreement, becoming a traveling saleswoman, seeking information on business development, and asking trainers for advice on their business. Different options were given for each scenario but approach behaviors were classified into binary indicators
_Table A3. Parametric values in India in 2019 for 1/θ when the poverty lines is $2.15 (2017 PPPs) per day. (1/θ =Number of years spent in monetary poverty yielding the same well-being loss as one year of life lost)_

|**y0 ($ a day)**|**0.5**|**0.75**|**1.0**|**1.25**|
|---|---|---|---|---|
|**ɛ (CRRA)**|||||
|**2.5**|7.8|4.1|2.6|1.8|
|**2.0**|4.7|3.0|2.1|1.6|
|**1.5**|3.0|2.2|1.7|1.4|
|**1.0**|2.1|1.7|1.5|1.3|

Note: According to the PIP platform, in India in 2019, mean consumption is 5.13 $/day (2017 PPPs). For the $2.15 a day line, mean consumption among the poor is $1.75 a day and mean consumption among the non-poor is $5.51 a day. The utility function considered is CES.

# **Appendix 3: Imputation model for nutritious outcomes**

Given the lack of available information on individual health in the Household Living Conditions Survey (NLSS), the main source of data used in this study, a related survey, the Nigeria's Demographic Health Survey (DHS) 2018, was exploited to infer information on the nutritional status of individuals. The general approach used was to build a model from DHS
0.67|0.69|0.68|0.66|0.69|0.67|0.69|0.69|0.69|
|None, literate|0.06|0.06|0.06|0.11*|0.08*|0.10|0.11|0.12|0.11|
|Less than complete primary|0.20**|0.16**|0.18|0.18*|0.15*|0.16|0.14**|0.11**|0.12|
|Complete primary|0.03|0.04|0.03|0.03|0.04|0.03|0.02|0.04|0.03|
|Complete lower secondary|0.03|0.03|0.03|0.02|0.03|0.02|0.02|0.02|0.02|
|Complete upper secondary|0.01|0.02|0.02|0.01|0.02|0.01|0.01|0.01|0.01|
|Highereducation|0.00|0.01|0.00|0.00|0.00|0.00|0.00**|0.01**|0.00|
|**N (observations)**|744|743|1,487|744|744|1,488|744|744|1,488|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ Tests for equality of means across treatment and control estimated in each round with *p < 0.1; **p < 0.05; ***p < 0.01
Table 14: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose sports activities field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|–0.126|–5.468***|–3.804**|
||(0.702)|(1.952)|(1.820)|
|_R_ <sup>2</sup>|0.732|0.825|0.836|
|Observations|1,476|1,476|1,476|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|0.120|–4.185*|–2.121|
||(1.401)|(2.204)|(2.000)|
|_R_ <sup>2</sup>|0.728|0.773|0.787|
|Observations|1,238|1,238|1,238|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined sports and activities field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout
agreement, as follows: where the _CAFTAij_ , _t_ indicator switches to one on the year when the CAFTA-DR agreement is enforced in both countries _i_ and _j_ , which ranges between 2006 and 2009. In some specifications, we also interact the _CAFTAij_ , _t_ variable with country dummies for capturing country or country-pair-specific effects. As shown by Baier and Bergstrand (2007), the effects of trade agreements tend to evolve over time, phasing in over several years. For this reason, we include a set of lags, _CAFTAij_ , _t_ + _s_ . In the same way, the literature calls for the inclusion of leads to capture the anticipation effects of trade agreements (Egger et al., 2022). Also, we use consecutive-year data instead of estimating gravity equations with interval or averaged data. Egger et al. (2022) show that relative to interval or averaged data, the use of consecutive-year data avoids downward-biased effect estimates due to the distribution of trade-policy events during an event window as well as due to anticipation (pre-interval) and delayed (post-interval) effects, and it improves the efficiency of effect estimates due to the use of more data. These adjustments yield the following model: where ∑ _k βsCAFTAij_ , _t−k_ and ∑ _s βsCAFTAij_ , _t_ + _s_ capture the lags and leads of the CAFTADR effect, respectively.

We also have additional specifications. First, while Equation (6) controls for world average globalization dynamics, it does not control for those that are country-specific. Given that these controls may be important for obtaining unbiased estimates, we use the following specification: 10