rafmacalaba/data-use-mentions-extended huggingface dataset:
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countries have price elasticities greater than one. The pervasive controls on imports that were imposed in Latin American countries to foster industrialization may explain the low estimated elasticity for those countries. Conversely, in the Sub-Saharan countries a pure foreign exchange constraint may be binding as quantities adjust to price changes in order to keep constant the total expenditure in foreign currency.

A comparison of the literature on price elasticities reveals much less consensus on price than on income elasticities. In their 1985 review article, Goldstein and Rhan suumarize several studies of aggregate import price elasticities.4 The reported cross-country average elasticity varies widely across studies. In Goldstein, et al., (1980), the average price elasticity for the eight countries reported is .57.5 A paper by Geraci and Prewo (1980) on five developed countries finds an average import price elasticity of .734. Other studies find average price elasticities around one.<sup>6</sup> Although this paper's price elasticity estimates for developed countries are lower than the average,7 they are well within the bounds of prior estimates. Evidence for developing countries is more limited. Khan (1974) reports price elasticity estimates for 15 developing countries8 that average (excluding Turkey) .948.9 Bahmani-Oskooee (1986) uses quarterly data to estimate long-run price elasticities for seven developing countries with an average (excluding South Africa) of .395. This study confirms the consensus that the estimated historical long-run price responsiveness in developing countries is less than one.<sup>10</sup>
0.029)|(0.040)|(0.023)|(0.024)|(0.047)|

_Source_ : Authors’ analysis from student surveys.

_Note_ : *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Attrition = 1 for students observed at baseline but not at first follow-up (column 1), second follow-up (column 2), or without working cellphone or email for voucher offer 6 months after intervention (column 3). Columns 4–6 include socioeconomic status variable, columns 7–9 include father’s education variable, and columns 10–12 include mother’s education variable as controls. Standard errors clustered at the school level in parentheses.
>30</sup> As Table 8 indicates, there seems to be a positive spillover to the earnings in freshwater fishery in provinces where _Formosa_ had the strongest impact (i.e. Quang Tri and Hue). This spillover effect is robust to the adoption of both geographic (columns 1-4) and industry control groups (columns 5-8), as well as different levels of added fixed effects. The estimated ATEs range between 20 and 25 percent increase in monthly income for the freshwater fishery workers, and are likely due to the positive demand shock for freshwater seafoods after – the breakout of _Formosa_ prices of the saltwater-substituted products soared when the demand for them elevated. However, unlike the case of freshwater fishery, we do not find any robust and consistent evidence suggesting spillover on the earnings of workers employed in husbandry, restaurant, or lodging.

> 30 Together with saltwater fishery, restaurant, and lodging, the other compensation-eligible sub-industries include salt manufacturing and fishery services. However, we do not observe sufficient sample of workers in these two sub-industries in the labor force surveys.

22
where, _Ai_ 0 is the SNAP participation ratio in each county in the pre-period of 2006, which allows us to exploit county-level variation in the interaction variables.

Fourth, we implement a quadruple-difference model at the household level. In analyzing the consumer panel data, we are able to exploit the richness of the data to further relax the identifying assumptions. Specifically, we expect a rise in SNAP benefits to have the largest effect on SNAP-eligible goods consumed by SNAP-eligible households. Neoclassical theory predicts that if households are inframarginal in food spending, they will treat benefit increases as cash and non-SNAP spending could rise. Nevertheless, HS find that most households are inframarginal and have a low MPC for SNAP-ineligible goods relative to SNAP-eligible goods, and also demonstrate that mental accounting is a channel that could raise the MPCF out of SNAP.

We test these predictions by the following specification in equation (6). Each observation is a household-product group-period and classified into one of four groups based on whether a household is SNAP-eligible as well as whether the expenditures are on products categorized as SNAP-eligible or not. We regress (log) expenditures of household _i_ in group _g_ in period _t_ on the (log) benefits per participant in state _s_ that household _i_ resides in period _t_ , and interact this variable with four group indicators _Tg_ . Household-month-of-the-year fixed effects are included to control for household-specific characteristics that may vary seasonally, along with group fixed effects and period fixed effects: This specification allows us to provide supporting evidence that the SNAP-benefit shocks are exogenous to unobservable recessionary shocks and other contextual factors, because unobservable shocks that affect spending are unlikely to differentially impact only SNAP-eligible households and SNAP-eligible product groups. The level-level specification, as opposed to the log-log specification, provides an estimate of the MPC of a SNAP household.

Fifth, we estimate IV regressions for each of the specifications discussed above. We construct the instrument, denoted as synthetic benefits per population, as illustrated in Section 2.2. For each of the specifications discussed above, we use the synthetic benefits per population
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The experience of the twenty-year period, suggests that the Caribbean countries would be damaged by two tropical storms each year and by a hurricane every two years. In general, climatologists view the 1969 to 1989 period as _below_ average in terms of cyclonic activity in the Atlantic. So far, nine hurricanes have hit the Caribbean islands in the 1990s, or an average of almost one per year, double the rate of the prior 20 years.

There are significant regional differences in exposure, however. Northern and eastern islands are more exposed than southern islands. The Leeward Islands including Antigua, Montserrat, St. Kitts and Nevis are viewed as being most exposed, while Trinidad and Tobago have minimal exposure.

Insurance loss modeling thus, involves three main steps: An assessment is made of the probability of the underlying natural phenomena, either hurricane storms or earthquakes and their intensities. This information is developed from historical meteorological and seismic data. The second step is to assess the exposure level and vulnerability of insured property to differing hazard intensities (as per the graph above). The third step is to combine the information from the first two steps and produce a probable loss distribution in dollar terms.

To illustrate, consider an extremely simple example of a small island, subject to only a category II type hurricane. The probability of such a hurricane striking the island is 10 percent. In other words, it can be expected that such a hurricane will hit the island once every ten years. The value of insured property on the island is $100 million. It is estimated that a category II storm hitting the island will cause $20 million in insured loss. This information can then be used to calculate appropriate annual insurance rates and to decide on the maximum capital ($20 million) needed in any given year to pay losses. Insurance loss modeling develops data on these two main figures -- annual average loss and probabilities of maximum losses -- from data on multiple meteorological or seismological events.

# **_The_
8 rapid poverty reduction and structural change, particularly when models using nonconsumption assets are employed.

Application of the method further lends some credence to a recent literature that argues that official inflation adjustments proposed for Russia during a period of deep crisis in the late 1990s, overstated cost-of-living increases and attendant increases in poverty. The illustration for Kenya indicates that cost-of-living deflators for the early 2000s derived directly from household survey data appear more credible than official CPI deflators, resulting in an assessment that poverty declined moderately during the period between 1997 and 2005/6.

We end with a preliminary meta-analysis in which we examine whether there are country or other context-specific circumstances that are correlated with the success of poverty prediction methods. The analysis provides some useful pointers, highlighting in particular the importance of high explanatory power of the consumption model (as reflected in high R-squareds), which turns out to be a defining characteristic of both the comprehensive asset model as well as the non-staple food and nonfood expenditure models.

The paper proceeds in section 2 with a brief review of the SAE methodology and the theoretical considerations in choosing consumption predictors (i.e. the consumption subcomponents and the non-consumption asset variables). Section 3 describes the case study data in more detail and examines the validity of the approach in Vietnam and China. Application of the method to assess alternative positions regarding the rate of change of prices over time in the two additional case study settings of Russia and Kenya
allocated to one of the four survey assignments. The total sample is 1,344 households, with 336 households assigned to each of the four survey assignments. Although the sample of 1,344 is not designed to be nationally representative of Tanzania, the districts were selected to capture variations between urban and rural areas and along other socio‐economic dimensions.

The basic characteristics of the sampled households generally match the nationally representative data from the Household Budget Survey (2006/07) (results not presented here). Household interviews were conducted over a 12‐month period, but because of small samples, we do not explore the survey assignment effects across seasons (such as harvest time with peak labor demand and dry seasons with low demand). The random assignment of households is validated when examining different household characteristics, as reported in Table 2 Panel A.

Individuals are classified on the basis of the survey assignment they actually received, which is the result of the initial assignment of their household (to one of the four survey assignments), whether the individual is selected to be a proxy respondent or a self‐report, and whether the proxy/self‐report assignment is realized. In the case of self‐report modules, up to two persons over age 10 are randomly selected to self‐report. If persons randomly selected to self‐report are unavailable, an alternative person is selected at random. In the case of proxy assignment, one person in the household over the age of 15 is selected to self‐report and to proxy report on up to two random household members. Thus, in the proxy assignment, one household member actually self‐reports in addition to reporting on other household members. Therefore, the number of self‐reports should be about half the number of proxy reports for households in the proxy assignment. In total, by design, there are more self‐reports than proxy reports. Because the survey experiment highly emphasized the importance of avoiding proxies when not assigned to this treatment,
6. _Aquifer transmissivity_ : for a given power, the greater the transmissivity, the higher the volume pumped by a water pumping system and thus the risk of over exploitation. Therefore, the lowest transmissivity value is allocated a 1 and the highest value a 5.

## **Classifying the datasets between each other’s**

The next step of the AHP is to classify the datasets based on their impact on the pumped volume (i.e. abstraction level) and thus on the risk of over exploitation. We choose to consider the annual average of GHI as the most important data as this study focuses on PVWPS and because GHI directly impacts the pumped volume (Meunier, 2019). Then, we consider the renewable groundwater resources as it provides information about the sustainably extractible resource. We consider that storage is coming next as it is the amount of groundwater that is still available in the event of over-abstraction of renewable resources. Then comes the population density as it significantly influences the water demand and thus has a potentially important impact on groundwater abstraction. Subsequently, we consider the static water level because it is directly linked to the resource accessibility. Finally, we consider the transmissivity, which is less directly linked to the pumped volume than the static water level. To assess the robustness of our choices, we compare the outcomes derived from these choices with the results obtained when assigning equal importance to each dataset (see Section 4).

## **Calculating the weights**

Following the AHP approach from (Saaty, 1977) and the classification detailed in the previous paragraph, the chosen pair wise comparison matrix is presented in Table 2. The consistency ratio for this pair wise comparison matrix is 0.027, which is acceptable (Saaty, 1977). The weights for each dataset drawn from this pair wise comparison matrix are summarized in Table 3.

||**Annual**<br>**average of GHI**|**Renewable groundwater**<br>*
years. The only identified impact on supply chain and interdependencies are when demand shifts and the water used for cooling. For demand shifts, nuclear power is not as quickly able to adjust power production as other technologies including natural gas. Generally, this is accounted for with forecasting and planning.

The largest vulnerability of nuclear power facilities is the intake water used for cooling. High temperatures can make it difficult for water to reach temperatures cool enough to be discharged and, due to permitting restrictions, may require generation curtailment (Galbraith 2011). A case study on the 2011 Texas heat wave was performed and augmented with facility-specific data for curtailments at several facilities, including a study finding 25 water-related curtailments in the United States between 2000-2015 (McCall et al. 2016). The impacts on power generation were similar across all data reviewed: water availability and temperature affected generation systems that rely on cooling water. The majority of curtailments occur due to water intake and/or discharge issues related to water temperature (>90%) with many of these (79%) occurring during heatwave/drought conditions. One example occurred for winter weather, where snow and ice restricted the amount of intake water available. Specific design and operation parameters associated with environmental regulations and permitting were the major cause of curtailment (water temperatures exceeding permitted levels and/or water availability). This was also seen in France in August 2018. Here four nuclear reactors were shut down because river temperatures were too high to allow for water discharge (Shugerman 2018).

_Costs, Impacts and Resilient Design Considerations for Nuclear-Fired Generation._ The key vulnerability of existing nuclear power plants is the reliance on intake water for cooling. The design guidelines for constructing a facility include seismic considerations (where applicable) and resilience to withstand high windspeeds, flooding, and other hazards, Table 11.

**<u>Table 11 – Nuclear Power Plants, Costs and Resilient Design Considerations</u>**

|Hurricane|Drought/Heat Wave|Earthquake|
|-
. Next, we estimate separate regressions for each country again regressing cell-level average PM2 _._ 5 on cell-level relative wealth (Methods). This exercise provides us with countrylevel estimates for the within-country correlation between wealth and pollution and allows us to assess which factors contribute to pollution being progressive or regressive in different settings. We next restrict our sample to large urban areas and estimate city-specific regressions in order to assess how pollution varies with wealth across neighborhoods within cities. Finally, we explore potential drivers of heterogeneity in both country- and city- level relationships.

# **2 Results**

While wealthier LMICs tend to have lower pollution on average (Section SI-1.1), we find that the average within-country correlation between wealth and ambient pollution is positive. This relationship is seen both in the raw data and in our regression results. Areas with the lowest average PM2 _._ 5 concentrations have the lowest average RWI levels while areas with the highest PM2 _._ 5 concentrations have the highest average RWI (Figure 1a). Results from our preferred 3
The modern responsibility of developing countries to produce statistics is well established. The United Nations’ Sustainable Development Goals (SDGs) emphasize the need for countries to generate socioeconomic indicators within the limits of each country’s capacity. The costs of such efforts are not trivial, and the trade-offs between investing in data capacity and other pressing needs are presumably difficult for developing country governments to ascertain.<sup>2</sup> The long-run benefits of transparency, however, could be considerable.

This study explores the partial correlation between data transparency and economic growth. Data transparency, as measured by the Statistical Capacity Indicator (SCI), entails the production and availability of credible statistics. Figure 1 illustrates the unconditional correlation between SCI and GDP-per-capita growth. Country-year observations are ranked in terms of their SCI scores and grouped by terciles along the horizontal axis. Annual growth rates are measured along the vertical axis. In general, higher terciles of the SCI are correlated with faster economic growth for both the sample of economies that are included in the econometric estimations sample and the larger data set that includes economies excluded from the econometric analyses due to missing data on covariates. It is noteworthy that Figure 1 suggests that the positive correlation between SCI and growth is stronger in the larger sample of country-years than in the estimation sample, thus possibly producing a downward bias in the econometric estimates of the partial correlation between SCI and growth.

# **Figure 1: Data Transparency** **<u>and Growth</u>**

|**Data Transparency (SCI) and Log Difference of GDP per Capita (Panel)**|
|---|
|All Economies<br>SampleEconomies|

> 2 It is worth noting here that it is not obvious that setting up minimum standards of data generation and transparency are relatively costly when compared to the public-sector wage bill in developing countries. Furthermore, as discussed later in the paper, the costs of funding national statistical plans seem to be far less than education.

4
# **1. Introduction**

This paper brings enterprise-level empirical evidence to bear on the important policy debate regarding whether innovation-driven growth is inclusive.<sup>1</sup> The conventional view is that the force of economic innovation mainly creates and commercializes sophisticated new-to-theworld frontier products. As such, the benefits of innovation are traditionally perceived to flow disproportionately to the investors in and managers of larger, technically sophisticated corporations; highly skilled workers; stakeholders with control over channels of distribution of inputs and outputs that are needed by technically sophisticated enterprises; and ultimately higherincome households as consumers of innovative products. From this perspective, innovationdriven growth is not inclusive, at least not until that growth generalizes to portions of the economy beyond the sectors involved directly in innovation.

We empirically explore an alternative view that innovation, especially in the context of development, should be recognized as applying to a broader range of non-replicative entrepreneurial accomplishments. Value and productivity-enhancing activities that commercialize ideas embedded in product, process, and organizational and marketing technologies that are new-to-the-firm and possibly new to the local economy, are apt to drive enterprise growth, even if they are not new-to-the-world. Such innovation-driven growth is indeed far more likely to be inclusive, in the sense of providing new employment and consumption opportunities for the segments of the population that are without secure prior participation in the organized developing economy. Local innovation and its consequent inclusive growth are apt to be enabled and spurred by the type of market competition that ensures opportunities for grass-roots entrepreneurs to access essential business services, as well as other required local inputs and distribution outlets. A secure, competitive market environment is especially important for vulnerable young firms, which may well have the most powerful collective potential for fast growth and job creation that is genuinely inclusive.

This paper analyzes key linkages between competition, innovation, productivity and inclusive growth, both conceptually and empirically, using firm-level data across OECD and developing countries. The paper‘s principal empirical finding is that innovation and the resulting increases in productivity do lead, when spurred
method consists of leveraging income or consumption distributions from more than 2,000 household surveys available in the World Bank’s Poverty and Inequality Platform (PIP) for 168 countries covering the period between 1991 and 2000. We combine these distributions with more than 1,000 predictors at the country-year level, including remote sensing variables, across various databases. We develop a simple model which predicts distributions for all countries in the world from a regression on country-level data. Guided by variable-selection techniques, we try various versions of such regressions, each time sequentially excluding all surveys available for one of the 168 countries available in our dataset to predict the distribution for the excluded country using the remaining 167 ones. We then calculate the absolute deviation between the true welfare and predicted welfare at 99 points on each distribution. Once we have these deviations for all surveys in the 168 countries, we calculate the mean absolute deviation across all surveys and all countries.

We find that when predicting distributions with GDP per capita (or income groups if GDP/capita is unavailable), under-5-mortality, life expectancy, and rural population shares, no additional feature significantly reduces the error further. Our preferred model predicts log welfare off by around 30% on average. Though this may sound high, we show that a random forest using all 1,000+ predictors is unable to reduce the error, suggesting that much of the remaining error is likely to be noise.

19
0.0273|0.00988|
||[0.0601]|[0.0632]|
|Constant|-0.744|-0.818|
||[0.114]***|[0.235]***|
|Observations|1,696,260|1,696,260|
|R-squared<br>|0.255<br>|0.256<br>|
|Department fixed effects|No|Yes|

Sources: 2018 National Census of Guatemala and 2018 municipality statistics (FUNDESA and INE). Notes: Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves. Robust standard errors clustered at the municipality level between brackets. *** p<0.01, ** p<0.05, * p<0.1.

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