rafmacalaba/data-use-mentions-extended huggingface dataset:
16 The fourth relationship concerns health: H = h (NS, Xh, The; E) Good health is assumed to be produced by nutritional status (NS), goods and services (Xh), such as medical care, and time (The). Here again, the production process can be conditioned by environmental variables (E): education of household members as well as community level variables such as safe water, sanitation, etc. These are outlined by the family of lines marked C in figure 1.

Relationships 2.2 - 2.4 outline periodic flows of food consumption and diets and accumulated stocks of health and nutritional status produced over time. The synergistic relationship between health and nutritional status is depicted in relationship 2.3 and 2.4.12 "<sup>1</sup> As NS and H are "stocks", compared with the "flow" of the diet, it is often common to use recursive models where the stock of period t is determined, among other things, by the stock in the previous period, t-1, e.g. Ht = h(Ht-l ..... ). This approach would lead to inclusion of initial endowments, e.g. birth weight in a nutrition status equation, especially of children. See, for example, Chernichovsky and Coate (1980, 1984), Heller and Drake ( 1979), and for more recent approacnes, see Strauss (1986). This approach is not taken here, as we wish to keep the discussion simple without too much loss of generality. Related statistical Issues are beyond the scope of this paper. While most analyses use cross sectional data which are more readily available, panel data, preferably generated under experimental conditions, would be more appropriate for measurement of the relationships discussed here.

1 20ther relationships such as one dealing with birth weight may he added.
, a random walk procedure and thus starting point information are unnecessary. However, complete household listing is not available or hard to obtain in some developing countries, and the household listing operation—visiting each of the selected PSUs and listing all residential households in those PSUs—is costly and time-consuming. So, although not encouraged, some surveys employ random walks as a last resort to select households in the second stage if subject to pressing constraints, and thus, the digital map supports such a procedure.

27
Table 1 presents the key summary statistics of the merged RWI and weather dataset for South Asia. On average, households in urban South Asia have higher relative wealth than rural households, with more wealth variation in urban areas. South Asia’s average maximum temperature is about 30 degrees Celsius, and it is almost 31 degrees in urban areas. The region is also exposed to temperatures above 35 degrees Celsius, a commonly used threshold for extreme heat, for about 15 percent of the year (57 days per year) in urban areas and 18 percent of the year (67 days per year) in rural areas.

Households with lower wealth are more exposed to higher temperatures, in both urban and rural areas in South Asia (table 2). The estimates are similar with and without state fixed effects. The magnitude of the estimated relationship can be illustrated by comparing locations at the mean of the temperature distribution (30 degrees Celsius) to those at its top tercile (34 degrees Celsius). In urban South Asia, within the same state or province, the RWI is 0.5 standard deviations lower in locations with an average temperature of 34 degrees Celsius than in locations with an average temperature of 30 degrees Celsius. This difference is approximately half the gap between urban and rural areas and is statistically significant. In rural South Asia, the RWI is 0.3 standard deviations lower in locations with an average maximum temperature of 34 degrees Celsius than in locations with 30 degree Celsius. This is approximately the difference between the 5<sup>th</sup> and 10<sup>th</sup> percentile in rural areas or the gap between Vasant Vihar and Hauz Khas in New Delhi. While small, this difference is statistically significant. The relationship between RWI and heat is imprecisely estimated at temperatures above 35 degrees Celsius, possibly because some of the major cities in South Asia with higher RWI like Chennai and Karachi have high average temperatures and rural areas with high average temperatures and higher RWI are in more
|Yes|Yes|Yes|Yes|

_Note:_ For the control variables, see table 1. The variable _PDRNI restricted_ is the diet quality index that measures the proportional difference of relative nutrient intake (PDRNI). A one-unit increase expresses a higher distance to the ideal value and thus implies a less nutritious diet. * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clustered by district.

# **6 Conclusion**

Rising obesity rates are one of the most undervalued public health issues in many emerging economies. High rates of excess body weight have detrimental health effects and generate a burden on health systems. A unique representative restaurant survey that includes objective measures of the micronutrients in the most widely consumed meals in Metropolitan Lima allows us to shed light on the relationship between the nutritional quality of FAFH and individual excess body weight among the population in urban Peru. Eating away from home is a frequent occurrence across socioeconomic groups in Peru and represents over a quarter of household food expenditures. We build a new diet quality index, the PDRNI, to account for overnutrition in specific micronutrients that are most relevant in terms of excess body weight. The index overcomes shortcomings of other diet quality indexes.

The findings indicate that lower diet quality in FAFH in restaurants located in the individual’s food environment is significantly associated with excess body weight. This relationship holds after accounting for a number of factors that correlate with body weight, including socioeconomic fixed effects which control for unobserved factors that correlate with body weight and the nutritional quality of FAFH. Among the various nutrients, the main drivers of poor nutritional quality appear to be sodium, especially in poorer areas. Sodium intake is positively associated with the consumption of energy-rich sugar-sweetened beverages, increasing higher total daily energy intake and contributing to weight gain.

Furthermore, the lower quality of readily available food is found to explain part of the 19
- Energy Sector Management Assistance Program (ESMAP) (2010a). Regional Electrcity Trade: Lessons from Global Case Studies and a Literature Review. World Bank, Washington, DC.

- Energy Sector Management Assistance Program (ESMAP) (2010b). (2010). Potential of Energy Integration in Mashreq and Neighboring Countries. Report No. 54455-MNA. World Bank, Washington, DC.

- GCC Interconnection Authority. (2014). Developing Power Trade Through the GCC Interconnector.

GCCIA. (2017). Gulf Cooperation Council Interconnection Authority Annual Report, 2017.

International Energy Agency (IEA). (2014). World Energy Outlook, 2014.

International Energy Agency (IEA). (2019). Electricity Information 2019 Edition. OECD, Paris. International Hydropower Association (IHA). (2017). International Hydropower Status Report. SolarGIS. (accessed 2/1/2018). PVOut (https://solargis.com/) Timilsina, Govinda R (2019). Regional Electricity Trade for Hydropower Development in South Asia. International Journal of Water Resources Development.

DOI:10.1080/07900627.2018.1515065 Timilsina, Govinda R. and Mike Toman (2016). Potential Gains from Expanding Regional Electricity Trade in South Asia. Energy Economics, Vol. 60, pp. 6-14, 2016

- Timilsina, Govinda R. and Mike Toman (2018). Carbon Pricing and Cross-Border Electricity Trading for Climate Change Mitigation in South Asia. Economics of Energy & Environmental Policy, Vol. 7, No. 2, pp. 1-14.

- UN Framework Convention on Climate Change. (2015). INDCs as communicated by parties (http://www4.unfccc.int/submissions/indc/Submission%20Pages/submissions.aspx)

- US Energy and Information Administration (EIA). (released 7/5/2018). Data - Petroleum and other liquid ( https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm)

- World
was starting to use or increasing the use of digital platforms (41% of the sample) while comparatively lower fractions of businesses invested in new equipment, software, or digital solutions (14%); increased home-based work (12%); or reported higher fractions of online sales (13%). Of the firms in our sample, 47% have increased the use of digital technologies for at least one of the four dimensions previously. Still, more sophisticated businesses were relatively more likely to report each individual digital response.

> 10We describe the technologies in a firm with a level of sophistication of 1.78 using the data from the 19 businesses with an index between 1.77 and 1.79 in our sample.

> 11The technologies with increasing levels of sophistication associated with each business function are described in Figure A1.

9
2021. Limiting the sample timeframe to 2000-2019, we identify fourteen instances of such events in the dataset, which we refer to as budget balance rule modification events and are reported out in the appendix, in Table A14. Following Davoodi et al. (2022b), we measure a country’s fiscal balance using the country’s specified budget balance target, which can differ across countries. For example, Chile’s budget balance rule pertains to its structural balance, whereas Israel’s budget balance rule pertains to its overall balance, and Uruguay’s rule pertains to its primary balance. Each of these variables are identifiable in the IMF’s Fiscal Rules and World Economic Outlook (WEO) datasets. A country’s baseline (pre-suspension/revision) fiscal balance is calculated as the three-year average target balance prior to the suspension or revision of a fiscal rule. Our summary statistics (reported in Table A15) show that following a budget balance rule modification event, the median duration for a country to return to its baseline fiscal balance is 3.5 years. The median deviation of a country’s fiscal balance from its baseline following a modification is 4.4% of GDP. In the sample analyzed, two countries, Argentina and Russia, which both suspended their budget balance rules in 2009 as a result of the global financial crisis, never returned to their presuspension baseline in the years following the suspensions, and ultimately abolished their rules.<sup>20</sup> While the sample of countries that have previously suspended, revised, and/or enacted an escape clause for budget balance rules is not large, we employ an event study analysis as an initial step to empirically answer the question of how long a country should be expected to take to return to compliance. After controlling for year and country fixed effects, our estimates indicate that following a budget balance rule modification, a country is expected to take approximately three years to return to its baseline fiscal balance. This implies that for
lights, we use the monthly version of the cloud-free DNB composites, which excludes data impacted by stray light between April 2012 and December

> 5In the working paper version, Hu and Yao (2021) also provide an elasticity for VIIRS data. However, different from this paper, they aggregate the data to annual frequency, which leaves very few observations for their estimation and requires estimating the model in levels, which weakens the

> 6Miguel and Mobarak (2021) provide a good overview of this literature with a focus on low- and middle-income countries.

> 7For more details on different versions of VIIRS nighttime light data, please visit `https: //eogdata.mines.edu/products/vnl/` .

5
and post-1991 periods. Therefore, while there is starting age information contained in the 1984 census, the level two administrative information does not match with the zones used in the study. To adjust the 1984 data to the 1994 geographical boundaries, shapefiles from each time period provided by Minnesota Population Center and the Ethiopian Central Statistical Agency (2017) are overlaid, and new start values for the post1991 boundaries are calculated as the weighted averages of the start age value from the 1984 area and the portion of the post-1991 zone that is made up of that 1984 area. Unfortunately, this requires an unrealistic assumption of a consistent distribution of population within geographic area, and introduces a significant amount of measurement error into the start age calculations.

A.44
To circumvent the problems that arise from applying a threshold without regard to a household’s income, the alternative is to use a financial margin (FM) threshold. The FM is defined as where _DI_ and _BLC_ are, respectively, disposable income and the basic living costs of household _j_ , and _DSE_ is their debt service expenditure. A household is considered at risk if the financial margin is negative.<sup>11</sup> But how is the _BLC_ calculated? Two options are possible. The first option is to use the data provided by statistical offices on subsistence consumption levels and apply this to each household using the composition characteristics of individual households. Specifically, subsistence consumption is adapted to each household using an equivalized household size, which is the sum of the first adult plus 0.5 times the number of other adults in the household plus 0.3 times the number of children, where adults are defined as people aged 14 years old and over and children are those aged 13 years old and younger. The _BLC_ is calculated on a annual basis and per person, and is converted into annual household _j_ expenditures by and where _EQ_ is the equivalized household size in household _j_ . The other option is to estimate _BLC_ for all households in the HBS using an equation given by and where _BLC_ * is drawn from the HBS questionnaire (i.e., households are asked what is the minimum income they need), _DI_ is household disposable income, _A_ and _C_ are the number of adults and children in each household, and _R_ is an indicator variable that distinguishes between urban and non-urban regions; this is to control for regional differences in price levels. The third term on the right-hand-side is defined as _β_ 2 ( _A_ -1) and captures household adults other than the head of household; the constant in the estimated equation captures the head of household effect. While this econometric estimation was pursued, it did not materially change the paper’s conclusions; thus, to ease the exposition
Nonetheless, it is still possible that positive trade shocks make the signing and depth of RTAs more likely, creating reverse causation.

The panel structure of our data helps to mitigate concerns about the endogeneity of trade policy, as transitory trade shocks should not affect the likelihood of permanent changes in trade arrangements and permanent trade shocks are well captured by pair-specific fixed effects. Additionally, the inclusion of the<sup>L2015</sup> _t_ =2001 _Globijt_ -terms also helps to reduce endogeneity concerns as they account for all time-varying policy covariates that affect all international trade partners in the same way such as trends in trade policy (i.e., increased probability of successfully signing RTAs in the first place). Moreover, since WTO law obliges RTAs to cover “substantially all trade”, uncorrelated sector-specific shocks are unlikely to trigger signing an RTA that covers all sectors. Having said all this, we are aware that some endogeneity bias may still bias our estimates upwards. Given that there are no convincing instruments for RTAs, our (almost) saturated model eliminates that concern as far as possible.

Furthermore, identification relies on the assumption that RTAs do not change deep preferences such that consumers prefer sourcing from an alternative trade partner even if trade costs have not changed. As preferences are unobservable, this assumption cannot be properly tested but it is crucial for the validity of the “indirect” approach to measuring changes in NTBs.

Tariffs are set at the tariff-line level, which corresponds in most countries to eight- or even ten-digit-level products. Unfortunately, trade data are only available for higher levels of aggregation.<sup>4</sup> Therefore, in virtually all gravity settings, tariffs need to be aggregated to match the level of aggregation that can be found in the trade data. One might be worried about aggregation bias of the tariff variable. Particularly in cases when tariffs are only reduced for a subset of tariff lines, the aggregated tariff variable might be measured very noisily—in the extreme, it might
11 Ukraine had received HUS subsidies. Because eligibility is based on both expected consumption and income, HUS subsidies are by design more targeted than the implicit subsidies received by natural gas users when the tariffs were artificially kept below the price of gas on the international market, transmission through pipeline and distribution costs.

# **3. The Data**

# _3.A. Construction of the Sample_

We have five waves of Ukraine’s Household Budget Survey (from 2014 to 2019). A summary of the sample size for each wave is displayed in table 1. The expenditure and income information is provided in each HBS data set for the full year, even though Ukrstat, the statistical service of Ukraine, collects the HBS information from each household on a quarterly basis. We appended the national as well as oblast-specific consumer price indices,<sup>15</sup> information about utility tariffs from NERC and Ukrstat, and oblast-specific heating and cooling degree days in each year, since the HBS does not indicate exactly in which city, town or village the household lives.

Heating and cooling degree days are important because the demand for heating and cooling, and the related fuels, depends crucially on outside temperature (Summerfield et al., 2015). Ukraine averages about 4,000 heating degree days a year (base: 18 ° C). The heating

> to commingle industrial and residential customers.  Dodonov et al. (2004) consider non-payment widespread and cite official estimates that households pay for only 70%-80% of the electricity they consume. Fankhauser et al. (2008) examine the “stock” and “flow” of arrears for all utilities in the various regions of Ukraine in 2003 and 2004. Fankhauser and Tepic (2007) report that “payment discipline” among residential customers has improved over recent years, and that “many countries now have collection rates close to 100%.”

> 15 We normalized the CPIs so that it is equal to 100 in 2014. With the
To address endogeneity concerns, we take advantage of the information collected about the year of adoption of more sophisticated technologies - when adopted - and merge the data with a longitudinal dataset that includes data on export status from Brazil’s Ministry of Trade by firm and year. Moreover, to capture longitudinal information on firms’ number of employees and average wages, we combine the dataset with the census of formal workers in Brazil (RAIS). The combined dataset allows us to use a quasi-experimental design to explore the effect of entering export markets on the adoption of sophisticated technologies.

Advancing our key results, we find that entering export markets increases firms’ likelihood of adopting advanced technologies linked to Business Administration (such as Enterprise Resource Planning (ERP)), Supply Chain Management (such as SRM), and Quality Control. The paper is structured as follows. Section 2 describes the data. Section 3 provides some initial correlations between exporting and technology use. Section 4 describes the methodology used to identify the impact of exporting on technology. Section 5 shows the main results. The last section concludes.

# **2 The data**

## **2.1 The Survey**

The Firm-level Adoption of Technology (FAT) survey collects detailed information for a sample of firms about the technologies each firm adopts and uses to perform key business functions necessary to operate in its respective sector (see Cirera et al. (2020)). The survey is composed of five modules. Module A collects information on the general characteristics of the firm.<sup>3</sup> Module B focuses on technologies used for general business functions regardless of the sector where they operate, and sector-specific business functions (module C) focus on technologies that are relevant only for firms in a given sector.<sup>4</sup> Module D focuses on barriers and drivers of technology adoption, while module E gathers information about the firm’s balance sheet and employment.

> 3The survey is designed, implemented,
# **Estimating the short-run poverty impacts of the 2010-11 surge in food prices**

Global food prices rose rapidly between June 2010 and early 2011, taking the World Bank Food Price Index above its peak of the 2008 ―food price crisis‖ by early March. Like the 2008 price spike (Ivanic and Martin, 2008), this rise in food prices seems likely to have increased poverty substantially. There is clearly a need for timely-estimates of the severity of the impacts of such price surges on poverty as a guide for policy responses. A key purpose of this study is to explain the approach used to prepare the indicative estimate of 44 million additional people thrown into poverty first released in the World Bank‘s (2011) March issue of _Food Price Watch._ A key difference from the 2008 food crisis is that the more recent price surge is much more broadly-based across food groups, which implies a different impact on poverty relative to the 2008 episode, since commodities such as rice have seen more moderate price increases while prices of items such as edible oils have increased considerably more than in 2008. Another potentially important difference between the two episodes is in the extent to which changes in world prices of key staples have been transmitted into domestic markets—an issue on which we have much better information than in 2008 (World Bank, 2011).

The poverty and nutritional implications of even short-lived price spikes can be serious as poor people spend very large fractions of their incomes on food, and hence are likely to experience large declines in their real income levels, and poor producers do not have time to increase their output in response to the price change. Even short run price spikes both reduce calorie intake and compromise dietary diversity (Skoufias, Tiwari and Zaman, 2011), and may have adverse long run consequences especially when infants are affected (Alderman, Hoogeveen and Rossi, 2006).

In 2010, a confluence of weather shocks in large producing countries, followed in some cases by export restrictions, curtailed supply and