rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.4 Placebo Tests and Applicant Characteristics**

We use data from In-Tech application forms to examine whether applicants on each side of the cutoff have similar characteristics. Table 1 shows averages of these characteristics for funded and non-funded proposals, as well as RD estimates for the difference in these characteristics at the cutoff, using the different bandwidths discussed above.

The averages show that the characteristics of funded and non-funded projects are very similar. About 25% of projects applied only for the research phase (as opposed to research and implementation), which lowers the average project duration across all projects to 32 months instead of the 36 months that are the standard for research plus implementation. The majority (54%) had two consortium partners, with 33% having three partners and only 13% having four or more partners. In slightly less than half of the consortia the leader was an enterprise, as opposed to an R&D institute, scientific unit or a public university. About 26% of leaders were based in Warsaw and they had on average 73 to 99 research employees and close to 140 technical and administrative staff. The first partner had about 140 research employees on average. Close to 80 percent of project managers had experience abroad.

Leaders had received on average 50 million PLN (US$14 million) in research funds from the government budget for science during the last three years before the In-Tech application. The requested grant size for the In-Tech project was about 2.5 million PLN (US$714,000) on average. For about half of the projects, the application forms stated that they plan to develop a new product or process as opposed to improving an existing product or process. About 14% of projects had the aim of improving a business process. Finally, 44% projects had plans to sell their innovation on the export market.

The RD coefficients in Table 1 show a statistically significant difference in project characteristics at the cutoff for only two variables. First, the specification with bandwidth
on trade come from the intensive and the extensive margins of trade. Importantly, we carefully investigate the endogeneity of trade and deep RTAs through different methods. We use an Instrumental Variable approach based on the domino effect of RTAs (Baldwin and Jaimovich, 2012), a plausible exogeneity test à la Conley et al. (2012), and we employ an event study approach to check the parallel trend assumption and confirm the validity of the key results.

To assess the impact of deep RTAs on the export performances of firms, we adapt the standard gravity model for trade (Head and Mayer, 2014) to firm-level analysis and include a variable capturing the depth of RTAs. Using information from the Deep Trade Agreements database, we construct different measures of RTA depth based on the policy areas covered by the agreements and their legal enforceability. Our specification allows to identify the impact on firms’ exports of a _change_ in the depth of RTAs (i.e. newly signed RTAs or amendment of preexisting ones) between two countries, controlling for any firm-year and country-specific factor that may affect the export performance of firms. The baseline results show that one additional legally enforceable policy area in RTAs boosts the exports of firms by 0.3%; corresponding to a 3.6% increase in firms’ exports when moving from _shallow_ to _deep_ RTAs.<sup>1</sup> This effect is larger for policy areas in RTAs that are not regulated by the agreements of the World Trade Organization (WTO-extra), such as investment or competition. One additional legally enforceable WTOextra provision increases the firm’s exports by 0.7%. The instrumental variable approach and the plausible exogeneity test both suggest the causal interpretation of our results.

This average effect of deep RTAs on the export performance of firms may hide substantial heterogeneity across firms with different characteristics. In New New Trade Theory models à la Melitz (2003) firms are heterogeneous and a reduction in fixed and/or variable export cost is expected to favor exports of
php/about.

> 5 We focus on low-income and middle-income countries because they account for roughly 95 percent of FDP (refugees + IDPs) in the world based on data from UNHCR Refugee Finder and IDMC.

> 6 A representative sample means that when analyzed, the observed characteristics of the sample reflect the true characteristics in the target population that is being researched (Baal and Ronkainen, 2017).
8|Poultry, goats,<br>Swine|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. *Among households practicing agriculture before the earthquake.

# _(g) Displaced people and host households_

Table 7 presents some statistics on mortality and displacements. Overall, 12.4% of households declared having a member dead or missing as a consequence of the earthquake. 12.8% of households declared that at least one member of the household were displaced: this percentage is higher among the richest (14.8%) than among the poorest (8.4%). Among these households reporting at least one displaced member, the mean number is 2.8 people per household, with few variations according to household wealth. In June 2010, the majority of those people were still displaced.

**Table 7. Dead/missing and displacements due to the earthquake, by pre-earthquake quintile of wealth**

|Pre-<br>earthquake<br>wealth<br>quintile|Number of<br>households|% with<br>dead/missing<br>people|% with<br>diplaced<br>people*|Number<br>of<br>persons<br>displaced<br>(mean)|Among ho<br>Number<br>of<br>children<br>displaced<br>(mean)|useholds w<br>Number<br>of adults<br>displaced<br>(mean)|ith displac<br>Number<br>of<br>elderly<br>people<br>displaced<br>(mean)|ed persons<br>Some<br>people<br>still<br>displaced|<br> <br>All<br>people<br>still<br
the household and by electronic stove-use monitors (SUMs) attached to each stove to record time and temperature readings. Interviewers returned to the households to record SUM measurements four times throughout the first year following the randomization. Adoption was high and remained high throughout the year—12 months after the intervention use rates were approximately 90 percent with no statistically significant differences across the treatment arms. The frequency of use remained high as well, with treatment households averaging 2.5 cooking events per week on the _Mirt_ stoves, a rate consistent with the traditional frequency for baking and storing _injera_ (Beyene et al., 2015).

Important for interpretation of the health results presented below, baseline results suggest households chose to continue using traditional three-stone open-fire stoves for all non- _injera_ cooking. _Mirt_ stoves are highly specialized for cooking _injera_ , with the main burner approximately 50 cm in diameter, leaving it necessary to have a second stove to cook other foods such as stews and coffee. The _Mirt_ technology was adopted for its specific use, with overall pollution and health improvements constrained by the continued use of open fires by both treatment and control households.

# **2.3 Three-year follow up and health outcomes**

This study reports on the three-year follow-up investigation conducted in late 2016, approximately 40 months after the establishment of the baseline and the initial distribution of the stoves. Given the initial high adoption rates, we added collections of household indoor air pollution and health outcomes in the follow-up survey to assess the secondary outcomes potentially associated with adoption of the improved biomass stoves. We focused on children and adult cooks—the two groups thought to be most at risk from exposure to indoor air pollution from biomass cooking.

Households in the follow-up survey were re-interviewed with minimal and balanced attrition across treatment and control arms. Of the original 504 households, 480 were re-interviewed, yielding a 95 percent re-contact rate.<sup>5</sup> Uptake of the original stoves remained quite high as 60 percent of the treatment households continued to use their
The paper, in comparing the quantitative data with that gathered from a large-scale ethnographic World Bank study on conflict, has also hinted at some of the weaknesses of the PODES data. In particular, the comparison would suggest that PODES significantly underreports levels of conflict (both violent and not), and that this is true across the archipelago. The paper sets out two reasons for this: unclear definitions of conflict, and lack of incentives for respondents to be honest about conflict levels. We argued that the key question with regard to the usefulness of the data is whether under-reporting is biased (i.e. levels of under-reporting differ across locations) or whether it is systemic, with levels of underreporting similar across sites. We suggested that the latter is more likely to be the case, but that more research (perhaps by comparing different data sources on more areas) would need to be conducted to reach firm conclusions.

We conducted a quantitative analysis to find out which locally-specific factors are correlated with the presence of conflict. We found that poverty by itself has very little correlation with conflict. Changes in economic conditions, on the other hand do. Unemployment is universally closely associated with higher conflict rates; high in- and outward migration for work/school have similar positive associations with conflict. Our land variables indicate that the share of converted land in rural areas is associated with higher levels of conflict; the fraction of titled land has no correlation with conflict. The presence of a natural disaster is also clearly associated with higher conflict.

Much of the headline conflict in Indonesia is reported as clashes between ethnic groups. Only a small proportion (3.3%) of conflict reported in PODES was described as being ‘ethnic’. The 2000 Census for the first time collected self-reported ethnicity and we used this data to investigate the correlation between conflict and ethnicity. We find no evidence that increased 31
probability curves. Section 6 offers some conclusions.

# 2. An Empirical Framework for an Ex Ante Assessment of the Welfare Costs of Drought

A catastrophe risk modeling framework uses three components to generate an ex ante assessment of the costs of a catastrophe: hazard, exposure, and vulnerability function. Hazards capture data on the possible weather outcomes that may occur in a given place, such as possible tropical cyclones. Exposure captures what could be affected by the hazard in that location, such as the number and type of buildings. A vulnerability function provides an estimate of the cost of damage that will occur as a result of a given hazard and a given exposure. In the example of tropical cyclones and buildings, it would indicate the cost of damage for a given windspeed affecting a given type of building. Combining the three components allows development of a probabilistic impact curve, which indicates how the cost of hazard varies with the probability of the hazard for a specific place, given the expected hazards and the exposed assets.

This section of the paper shows how this framework can be applied to generate a probabilistic impact curve for drought in SSA. For drought in SSA, the hazard is rainfall deficits during the growing season. As noted in the introduction, there are different ways of measuring this hazard, and the next section discusses the relevant data in more detail, including the time period for which historical data are available and use of the data to generate an estimate of the probability distribution of hazards in the near future.

Exposure in the current case is not about physical infrastructure and its location and characteristics, but about households that may be impacted differently by rainfall deficits depending on their location and characteristics. The data on households are also described in more detail in the next section.

The vulnerability function in this case is the income or welfare losses among households engaged in different livelihoods, which result from lower yields, lower demand for labor, and increased prices that may result from that. The rest of this section focuses on the estimation
# **I Introduction**

There is substantial variation in intergenerational mobility (IGM) in education–measured as the likelihood of children completing at least primary education when parents did not complete primary school–across provinces and districts of 24 countries in the Latin America and the Caribbean (LAC) region (see Munoz, 2021a).<sup>1</sup> This variation in upward mobility can be the result of sorting of families (i.e., the fact that different families choose to live in different places) or, alternatively, can be the result of the existence of causal effects of regions (i.e., the place where people live affects the level of upward mobility independently of the characteristics of the family).

In this paper, I use 21 census samples that span 11 countries in LAC to investigate how much of the variation in upward mobility can be attributed to the effect of being exposed to certain places during childhood. In particular, I use families that moved across districts/provinces and exploit variation in children’s age at the time of the move to isolate sorting from regional childhood exposure effects. This empirical strategy that relies on observational data was first proposed in Chetty and Hendren (2018a) to study the effect of commuting zones on IGM in income in the US and adapted by Alesina, Hohmann, Michalopoulos, and Papaioannou (2021) to the context of educational IGM in developing countries.

I find evidence of regional childhood exposure effects as well as significant sortingselection. I estimate a convergence rate of 3.5% per year of exposure between the ages 1 to 11, which implies that children who move at age 1 would pick up 10 _×_ 3 _._ 5 = 35% of the observed difference in permanent residents’ outcomes between their origin and destination regions. In addition, I find significant selection effects of approximately 42%, which implies that individuals who move to a region where permanent residents have 1 percentage point higher upward mobility have
respectively time, NACE 4-digit sector, and county fixed effects. Equation (13) is estimated separately for each 1-digit sector (that is, manufacturing, services, and mining) and for the whole economy, while the equation parameters are estimated by OLS, always using heteroscedasticity-robust standard errors.

**Figure 14. Productivity (TFPR) growth dividends from a 10 percent reduction of average markup, by sector**

<!-- Start of picture text -->
1.6<br>1.47<br>1.4<br>1.2<br>1<br>0.82<br>0.8<br>0.6<br>0.48<br>0.4<br>0.2<br>0<br>Manufacturing Services All<br>Percentage points per year<br><!-- End of picture text -->

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ Results from year, county, and 4-digit sector fixed effects. Coefficient for mining was not statistically significant.

**Results shows that on average, reducing markups would have a positive impact on aggregate productivity expansion.** Results displayed in table A1.8 in annex 1 suggest that lower (past) markups are associated with higher (current) productivity growth: all coefficients for lagged markups are negative and statistically significant at conventional values for all sectors except mining. Overall, this negative correlation between past markup level and productivity growth is an indication of a positive association between competition and productivity growth, which would be driven by the escape competition effect described by Aghion et al. (2008).<sup>34</sup>

**The magnitude of the economic effect of markup reduction on productivity growth is sizable** . Figure 14 shows that a 10 percent decrease in the average markup of 6.1 percent for the overall economy in the 2008–17 period
Ganimian et al. (2021) to project what benefits might be and assess whether these projected benefits exceed reported costs.

For each study, we gather information about the country’s labor force participation, average wages, and real wage growth to calculate the total expected lifetime earnings of a child.<sup>19</sup> We assume children will work between the ages of 22 and 65, which should be a conservative assumption for the number of active years in the labor market, given that labor market participation tends to start much earlier than age 22 in low- and middle-income countries. Because most studies reported program costs in US dollars, we first convert wage data into US dollars using official exchange rates from the International Financial Statistics of the International Monetary Fund.<sup>20</sup> We discount this lifetime stream of earnings to the year that preprimary program costs were incurred, using two different discount rates – 3 percent and 5 percent. While 3 percent reflects the standard in the literature on economic evaluation in high-income countries, some researchers have argued that the higher economic growth rates in low- and middle-income countries would make rates as high as 5 percent more appropriate (Haacker et al, 2020).

Next, we rescale the present value of earnings to account for the benefits of preprimary interventions, combining study-specific estimates of the improvement in cognitive skills following a preprimary intervention with estimates from the literature on the extent to which improvements in cognitive skills observed during the preprimary period translate into improvements in earnings. To be conservative, we select each study’s lowest estimated treatment effect that is significantly different from zero. To translate average effects on cognitive skills into increases in earnings, Kline and Walters (2016) use a value of 13 percent per standard deviation increase in cognitive skills in their study of the Head Start program in the United States, while Galasso and Wagstaff (2018), focusing on low-income countries, use a value of 4.3 percent. Thus, for example, we
**|0.631|
|**Gasoline****_q_**|0.000|**Gasoline****_q_ **|1.000|
|**Coal****_p_**|0.000|**Coal****_p_ **|1.000|
|**Coal****_q_**|0.000|**Coal****_q_ **|1.000|
|**Diesel****_p_**|0.000|**Diesel****_p_**|1.000|
|**Diesel****_q_**|0.000|**Diesel****_q_ **|1.000|
|**GDP/cap**|0.000|**GDP/cap**|1.000|

_Source:_ Pesaran 2015 _Source:_ Pesaran 2003 _Notes: p_ = fuel price (U _S_ $ PPP) ; _q_ = quantity of fuel consumed per capita (toe) ; GDP/cap = GDP per capita (2017 US$ PPP).

9
disbursements decelerate in downturns and accelerate in upturns making disbursements more procyclical than commitments.<sup>17</sup>

# **5.2 Investment Cyclicality and Financial Performance**

As noted above, countercyclical investing may be beneficial from the perspective of macroeconomic stabilization, but IFC operations are underpinned by financial and institutional imperatives to maintain prudence and profitability. Investment cyclicality must therefore be assessed in the context of the IFC’s business performance. Since 83 percent of disbursements and 87 percent of original commitments in the sample countries were in the form of debt instruments, debt-portfolio metrics<sup>18</sup> were used to proxy IFC asset quality and profitability.<sup>19</sup> Examining country-level NPL and RAROC statistics enables a comparison of average debtportfolio performance across two categories of IFC investment cyclicality (Figure 4); (i) procyclical commitments and disbursements and (ii) countercyclical commitments and disbursements. This analysis covers 14 sample countries from the third quarter of 2008 through the second quarter of 2020. Countries are grouped under the two categories according to the orientation of IFC investment during the period.

> 17 This may offer another avenue for future research – empirical evidence supporting this proposition can examine historical commitment cancellations trends; comparing cancellations in the recession years with those in upturn periods.

> 18 A higher NPL ratio indicates diminished profitability due to lower rates of interest and/or principal recovery, which affect current revenue inflows and future lending. Similarly, a lower RAROC implies diminished profitability due to riskier investments resulting in higher expected loss and a greater allocation of economic capital.

> 19 To examine equity-portfolio performance, this analysis could be extended to cover return on equity via capital gains and losses.

15
developed by the CEQ Institute and presented in the CEQ Handbook (Lustig, 2022). CEQ Assessments have been implemented in over 60 countries at the time of writing this report.

# **The first step of fiscal incidence analysis is allocation of the main taxes and transfers to households in**

**the survey.** The main modeling techniques in this report include direct identification, inference, imputation, simulation, and prediction. These concepts are described in Box 3.1, below. Typically, fiscal interventions such as direct taxes, indirect taxes, and indirect subsidies are modeled based on simulation, combining tax rules and characteristics of individual’s employment and consumption patterns, respectively.

**Box 3.1: Tax and transfer allocation methods**

<mark>We allocate each tax and transfer by drawing from a set of methods described in detail in Chapter</mark> 3 of the CEQ Handbook, namely:

- **Direct identification:** The survey asks whether a tax (transfer) was paid (received), and the amount.

- **Inference:** Whether a tax (transfer) was paid (received), or the amount, is determined based on administrative data of the taxes (transfers) and other relevant secondary information self-reported by the household.

- **Imputation:** Taxpayers (beneficiaries) are directly identified in the survey, but the amount of a tax (subsidy) paid (received) is imputed based on program rules.

- **Simulation:** Both the identification of taxpayers (beneficiaries) and the amount of a tax (subsidy) paid (received), is estimated based on program rules.

- **Prediction:** A regression model is applied to predict who receives benefits or pays taxes, and/or the amount.

Adapted from Chapter 3 of Lustig (2022).

**The building block of fiscal incidence analysis is the construction of income concepts.** Conceptually, the
# **2.1 Sample**

We begin our sample construction by obtaining financial information of all the firms covered by Compustat-Global. To obtain governance information, we match these firms to the ISS database of RiskMetrics by using the international securities identification number (ISIN) and CUSIP. This data set contains information about governance attributes of the US and international firms from 2003 to 2007.<sup>3</sup> It covers firms from major stock indices which include but are not limited to Standard and Poor’s (S&P) 500, S&P Small Cap 600, Russell 3000, MSCI Europe, and FTSE All Share Index. Our final sample consists of 8,453 unique firms in 23 countries. This translates to a maximum of 33,714 firm-year observations. Our observations are from 26 different industries and the industry definitions come from RiskMetrics.

Panel A in Table 1 provides a summary of the country coverage of our sample. The most represented country in our sample is the United States (71.67% of the observations) with 6,090 firms, followed by Japan (7.91%) with 596 firms and the United Kingdom (5.33%) with 531 firms. The least represented countries are Portugal (0.21%), New Zealand (0.22%) and Austria (0.28%). Panel B shows the distribution of sample firms over the 26 industries. Banking, Capital Goods, and Software & Services are the largest industries representing 10.17%, 8.55% and 7.73% of the sample, respectively. The smallest industries in our sample are Household & Personal Products (0.93%), Food & Staples Retailing (1.08%) and Hotels Restaurants & Leisure (1.12%).

> 3 The country coverage of the ISS governance database has dropped to only a handful after 2008, which limits its ability to provide cross-country comparisons after this date.

7