rafmacalaba/data-use-mentions-extended huggingface dataset:
part of annual industrial census and survey collection efforts, so that the appropriate policy response can be better attuned to the needs of particular types of firms within industries.<sup>27</sup> Further, in cases where such expenditures are not formally recorded by firms (such as spending on product development), we can at least improve the accuracy of methodological assumptions by collecting basic information on spending patterns through small, targeted surveys. Given the potentially huge significance of intangible investment, there are high returns from spending resources on such data collection.

> 27 As an illustration of already-ongoing efforts to collect intangible asset data at the firm level, the Brazilian development bank BNDES has begun to collect and evaluate measures on intangible asset investment by its borrowing firms as part of an initiative to improve credit analysis. See Almeida and Braga (2011) and Mendes and Braga (2011).

35
and loss accounts for private enterprises, positions Orbis as one of the most robust datasets available for analyzing financial performance at the firm level.<sup>67</sup> The Orbis database provided by the Wharton Research Data Services (WRDS) includes latitude and longitude information at the firm level, enabling the precise determination of each firm’s geographic location. This aspect is essential for correlating firms with their respective regional weather data. However, not all firms in our dataset have this geographical data. Almost 20% of firms in our sample do not have the geographical information. To address this, we employed the Google Maps API, using available country, region, and city data for these firms. The methodology for this process is detailed in Appendix A.

For our study, we extracted firm-level information from 2000 to 2019, focusing on medium, large, and very large companies. The primary variables extracted included the firm’s location, fixed assets, and the NACE code for sector identification. While other financial variables were sparsely populated, fixed asset data was more consistently available. Consequently, we used fixed asset growth as a proxy for firm size growth.

Grid-level weather data. In our study, we utilize the temperature gridded data for global domains, which is derived from the Berkeley Earth Foundation dataset (BERKEARTH).<sup>8</sup> This dataset is sourced from the Climate Data Store, as part of the Copernicus Climate Change Service, managed by the European Centre for Medium-Range Weather Forecasts on behalf of the European Union. The BERKEARTH dataset has comprehensive coverage of global temperature changes, encompassing both land and ocean regions and providing historical temperature data spanning over two centuries. The methodology employed in the processing of this dataset is detailed in Rohde & Hausfather (2020).

The BERKEARTH dataset offers data in three distinct grid formats: 1 × 1, 2 × 2, and 3 × 3 degrees. Each ‘1 degree’ in this context refers to a geographical measure, where 1
GDP growth occurred at income levels corresponding to those of the wealthier emerging economies and poorer high-income economies.

The analysis of high-frequency IP data is also important because the timing of the collapse and the recovery does not coincide with the calendar year and because it suggests that the post-crisis is one area where emerging economies displayed better performance. In particular, emerging economies were typically under recessionary pressures for a fewer number of months than advanced economies. For example, by December 2010, IP in emerging economies had returned to its geometric trend, whereas in advanced economies it was still more than 13 percentage points below trend. This analysis of the post-crisis period complements the growing literature on the 2008–2009 global crisis that has concentrated mainly on the collapse and has typically analyzed low frequency GDP measures.

One important difference between emerging and developed economies is the degree of volatility in economic activity. Business-cycle fluctuations in the former are typically twice as large as in the latter. For example, the average annual change in GDP growth of emerging economies between 1980 and 2007 was more than twice as large as that of advanced economies. Hence, it is possible that the large contraction and bounce-back effect observed in emerging economies might just be reflecting their usual volatility in economic activity and not necessarily a different behavior during the crisis. Nonetheless, we show that fluctuations around the 2008– 2009 crisis were not usual at all. The collapse in the growth rate of emerging economies’ GDP was almost twice as large as the average growth deceleration observed in tranquil times and so was the recovery. One important difference this time is that, whereas the collapse and recovery in GDP growth rates were not very different between emerging and advanced economies, 4
second stage, households from chosen census enumerations are sampled. Every quarter, 1,020 households are part of the survey each month. These 1,020 households are interviewed every quarter. Each household completes a diary that collects information on its respective expenditures twice a month. Interviewers visit each household twice a month to collect this information. They also gather additional data on a limited set of household characteristics and a broad measure of household income. The sample is generally representative at the national level and at the residence level (urban-rural) but not at lower levels of geographic disaggregation.

**Expenditure data in the HBS follows the classification of COICOP, developed by Eurostat, and allows us to identify energy-related spending components clearly.** We refer to the variables detailed in Table 2 to measure energy spending. To distinguish between different types of energy expenditures, we rely on the 4-digit categories of spending components, which is the most disaggregated category detailed in the HBS codebooks. Table 2 indicates that we can identify spending on electricity, natural gas and town gas, liquefied hydrocarbons, butane, propane and similar, liquid fuels, coal, other solid fuels, and heat energy.

**We define energy spending shares, our target variable, as the ratio of energy spending over household income, as observed in the HBS.** To calculate the energy expenditure share, we aggregate the different energy spending components at the household level and divide the resulting overall energy expenditure by household income. We do not scale energy spending or household income by the number of household members or the adult equivalent of household members, as they would cancel out in any case. The resulting indicator is the energy spending share in overall income at the household level. Table 2 reveals that the average energy spending share observed in the HBS 2019 in Bulgaria is 13.1 percent. The largest energy spending component is on electricity (59.3 percent of all energy expenditure), followed by other solid fuels (27.5 percent) and heat
widely dispersed in the market. Having arrived at the location, identifying maize selling households necessitates distinguishing and locating households that are ready to sell from those that have already sold their maize, and those that have chosen to postpone selling. Hence, we consider buyer search costs to be concentrated in this latter segment. The costs of this search process will be directly associated with the proximity of the household from the local market. Therefore, by using distance to markets as a proxy for search costs, we are making the assumption that all of the costs associated with the second segment are related to search.

The benefit of this approach is that it allows us to use Household Budget Survey data in measuring the effects of search costs on producer prices. Given the general standardization of such surveys, this approach may facilitate comparative analyses of estimated search costs in cross sectional and longitudinal analyses.

# **4.0 The data**

Our empirical analysis is based on the Tanzania Household Budget Survey (HBS) of 2007. The most recent round of the National Bureau of Statistic‟s HBSs was undertaken in 2007. It collected information on a wide range of household and individual characteristics. They include consumption (income) poverty and trends in productive and social sector indicators. The particular content that is of interest to us relates to maize growing households and variables such as maize prices, distance to markets and household characteristics.

One of the main findings of the 2007 HBS is that rate of poverty reduction in Tanzania has been modest. Figures 1 and 2 show the development in the food and basic needs poverty incidence from the 1991/92 HBS, 2000/01 HBS and 2007 HBS.

10
the role of external finance for faster growth in countries with better developed financial systems. These papers focus on only a small set of obstacles that firms confront without discussing the motivation for choosing that set.

It is essential to explore the relationship between the business environment and firm growth not only across countries but across regions and by firm characteristics within countries—by firm size, age, sector, and ownership type. In examining this relationship, the literature has focused largely on the effect of difficulties in access to finance by firm type, particularly firm size. Generally the finding is that smaller firms are more constrained (Love and Mylenko 2003; IDB 2007). Beck, DemirgüçKunt, and Maksimovic (2005), using the World Business Environment Survey data set, include measures of corruption and property rights. Based on firms’ perceptions of potential constraints, they also find patterns across countries, with small firms benefiting the most from greater financial and institutional development.

Aterido, Hallward-Driemeier, and Pagés (2007, 2009, 2010) analyze the effect of several aspects of the business environment—access to finance, corruption, and regulations—on the growth of firms. Their findings show that the business environment affects small, medium-size, and large firms differently. The reason is that small firms are exposed to a different set of constraints than large firms are. Access to electricity, for example, has heterogeneous effects: small and medium-size firms are often affected by power cuts, while large and micro firms tend not to be. The main reason is that micro firms use less energy-intensive tools and large firms are more likely to secure their own energy supply (Gelb and others 2007). Thus infrastructure such as the electricity grid affects the growth rate of small and medium-size firms directly, but has only an indirect effect on the growth rate of micro and large firms. Micro firms are much more credit constrained and must rely less on external funds to finance investment. Improving access to finance might boost the entry rate and the growth of small
including some of the possible longer-term effects. Second, juxtaposed with other research examining the effects of aggregate shocks, our results illustrate conditions under which an aggregate shock can have large and persistent heterogeneous effects on a group of young children. The findings point to the possibility that effects may have been large and persistent in the specific study context because of its initial high levels of poverty and undernutrition. And third, the analysis highlights that gains from migration and remittances can be fragile. In Guatemala, this fragility may be related to the high cost of migration to the United States and insecure position of most migrants, hindering recovery from the shock.

# **2. Data**

# _2.1 Household panel survey 2008–12 and qualitative data in context_

The study is in the department of Huehuetenango, located in the Western Highlands along the Mexican border. Populated predominantly by indigenous Guatemalans, this remote, rural region is characterized by substantial outmigration and high levels of poverty and food insecurity (World Bank 2003, 2008). Based on the 2006 Guatemalan National Living Standards Survey ( _Encuesta Nacional de Condiciones de Vida_ or ENCOVI), 76 percent of the population in the Western Highlands was poor, compared to 51 percent nationwide. In 2008, household-level poverty in the study sample was 72 percent, and 40 percent of households had an international migrant, with more than one-third of them in the United States. A school census in 2008 indicated that although undernutrition had declined modestly since the prior 2001 census, 63 percent of children 6–10 years old in the Western Highlands were still stunted (HAZ < -2), compared to 46 percent nationally, among the highest in Latin America ( _Ministerio de Educación_ and SESAN 2008). Child undernutrition in the study region also was severe, with 60 percent of children under five years old stunted and 23 percent underweight (weight-for-age Z-
# **ACKNOWLEDGEMENTS**

We are grateful for valuable advice and support received from Johannes Herderschee, Paul Brenton, Shiho Nagaki and Olivier Cadot.

The survey of trading firms in Kinshasa and Brazzaville was carried out by Alexander van Hoeken, with support from Daniel Mberi. Retail prices in Kinshasa and Brazzaville markets were collected by Aimé Mpamfi Schina. Yves Birere carried out the Kisangani-based research. We thank these collaborators for their excellent contributions.

For the night light maps, we thank Christophe Mager.

Additional useful advice was received from Claude Baissac, Christophe Bösch and Markus Scheuermaier.

Finally, we would like to thank all our interview partners in Kinshasa and Brazzaville for their availability and expertise (see Appendix 1).

4
‐2016‐measuring‐impact‐public‐sector‐wage.</u>

> 6 Prior to the conflict, 85 percent of the population resided in a household that owned a mobile phone and would be able to be reached by the household‐level survey conducted by the WFP.  Furthermore, the Data section will discuss a wide range of evidence that suggests mobile phone access of households has remained high even among the population most affected by violence.

> 7 The average number of monthly fatalities in a governorate over the time period under analysis is approximately 70.

2
# 3.1.2 Production of energy

Section 2 described how sectors are modeled. This holds true for the electricity sector too in the absence of data on fixed and variable factors of production. Electricity production is a translog cost function, where the share in output is explicitly modeled where _ωt_<sup>_E_istheshareofelectricityintotalvalue-addedand�</sup> _i_ =1<sup>_β_</sup> _i_<sup>ln</sup> � _Pt_<sup>_SRV_</sup> _Pt_<sup>_i_</sup> <u>�</u> represent the coefficients on relative prices (cross and own-price elasticities), while ln � _YYt_<sup>_F CST_</sup> _<u>t</u>_<sup>_IO,E_</sup> � measures the impact of final demand (approximated using the input-output table) on electricity production. If the IO tables reflect that household consumption is the main user of electricity, then the household weight in _Yt_<sup>_IO,E_</sup> will be larger.

In value-added terms, we can then get total energy value-added: The price of electricity (in this case the deflator) is an aggregation of renewable prices ( _Pt_<sup>_REN_</sup> which will be a function of the levelized cost of electricity ( _LCOE_ )), and fossil fuel prices ( _Pt_<sup>_FOSS_</sup> which will be a function of LCOE estimates, or a commodity price proxy in cases where _LCOE_ estimates are not available), all indexed to a value of 1 in the base year. Given that fossil fuels and renewables are substitutes we express the price aggregation as: Fossil fuel prices are a weighted sum of oil prices ( _Pt_<sup>_OIL_</sup> ), coal prices ( _Pt_<sup>_COL_</sup> ) and gas prices ( _Pt_<sup>_GAS_</sup> ): Renewable price is
Policy Research Working Paper 10427

# **Abstract**

This paper articulates and, using newly-assembled data, explores how international taxation affects aggregate tangible cross-border investment. Spillovers from statutory tax rates abroad seem: As sizable as effects from the host’s rate; larger than previous consensus values (attributed to a systematic bias from FDI data); and consistent with ‘implicit’ profit shifting through real investment (rather than ‘paper’ profit shifting). Contrary to much policy discussion, the results also imply that: Host countries’ marginal effective tax rates have at best a weak effect on real investment; those elsewhere have none; and, applied to the prospective global minimum tax, inward tangible investment in most sample countries will increase.

This paper is a product of the Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hpallan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
-61-

# Levels of Value Added in 17 Industries

An important advantage of the direct binary comparison between Mexico and Brazil is the possibility of comparing levels of value added for sample industries at the "national accounts" concept. This concept is much netter than the "US census" concept used in the previous chapter, because it excludes substantial non-industrial inputs (see chapter II).

Our preference is for the former over the present "national accounts" concept. In the present (1968) System of National Accounts, value added of particular industries, and indeed for major sectors of the economy, such as manufacturing, is calculated before deduction of bank service charges. The latter are deducted only globally (without being allocated by industry or sector) to arrive at the total GDP figure for all industries combined. In the former national accounts system, bank service charges were deducted for each industry.

Table 4.5 shows value added (former national accounts concept), both in national currencies and converted at the official exchange rate. Table 4.6, - which presents the value added gross output ratios, shows that the difference between value added at the US census concept and value added at the national accounts concepts is bigger in Mexico than in Brazil (for the present national accounts concept of value added, see table 1 for each industry in the Statistical Appendix).

As explained in chapter III we are unable to apply the "double indicator" approach, i.e. separate comparisons for gross output and inputs. We - applied the value added gross output ratios in table 4.6 to the gross value of output figures in table 4.4 to arrive at the real value added comparisons at the former national accounts concept in table 4.7.
# **6. References**

Achebak, H., D. Devolder, and J. Ballester. 2019. “Trends in temperature-related age-specific and sex-specific mortality from cardiovascular diseases in Spain: a national time-series analysis." _The Lancet Planetary Health_ 3.7: e297-e306.

- Adebola, O. G. 2021. "The Synergy between Gender Socialization and Women Unpaid Care Work in Nigeria: Implication for the Sustainable Development Goals." _International Journal of Social Sciences_ 11.1.

- Afridi, F., K. Mahajan, and N. Sangwan. 2021. “The gendered effects of climate change: Production shocks and labor response in agriculture.” _IZA Discussion Paper 14568_ . Bonn: Institute of Labor Economics.

- Agamile, P., R. Dimova, and J. Golan. 2021. “Crop Choice, Drought and Gender: New Insights from Smallholders’ Response to Weather Shocks in Rural Uganda.” _Journal of Agricultural Economics_ 72(3): 829-856.

- Aizer, A. 2010. "The gender wage gap and domestic violence." _American Economic Review_ 100(4): 1847-59.

- Akresh, R., P. Verwimp, and T. Bundervoet. 2011. "Civil war, crop failure, and child stunting in Rwanda." _Economic Development and cultural change_ 59(4): 777-810.

- Akter, S. 2021. “Do catastrophic floods change the gender division of labor? Panel data evidence from Pakistan.” _International Journal of Disaster Risk Reduction_ 60(102296) _._

- Almond, D., and B. Mazumder. 2011. "Health capital and the prenatal environment: the effect of Ramadan observance during pregnancy." _American Economic Journal: Applied Economics_ 3(4): 56-85.

- Anglewicz, P., and T. W. Myroniuk. 2018. “Shocks and migration in Malawi.” _Demographic Research_ 38: 321-334.

- Anttila-Hughes, J., and S. Hsiang. 2013. “
households for participation in RMGs? Given the extensive baseline surveys administered to the RMG-eligible and the RS, we can compare them on a wide range of dimensions, including income, assets, land holding, etc. Notably, the baseline questionnaires did not include a comprehensive consumption module, which elsewhere is commonly used for assessing poverty status. Table A1 in the Appendix shows 127 variables describing the participants’ basic demographic characteristics, household composition, income, possession of durables and livestock, housing conditions, nutrition, and experience of economic shocks, available in our survey.

To reduce dimensionality and address the likelihood of Type I errors in our numerous possible comparisons between the RMG-eligible and RS groups, we follow Katz, Kling and Liebman (2007) and Andersen (2008) by constructing composite z-scores and adjusting the p-values for multiple hypothesis testing. The z-scores of the individual variables are calculated and then averaged by category to construct composite z-scores. The z-scores of variables assumed to be negatively correlated with wealth are inverted. Altogether, we construct 7 composite z-scores to encompass broad dimensions of welfare: land and buildings ownership, household composition, durables ownership, livestock ownership, housing conditions, nutrition, and shocks. We also test for differences in household per capita total income. We apply Bonferroni correction to adjust p-values for the differences in means tests, to further reduce the

> 12 Sixteen villages did not have enough RMG, WL, and RS members to rank 15 individuals because of either smaller RMG-eligible memberships or participant absence at time of survey. In these cases, the maximum number of households available for ranking was specified in the instructions. Eight villages had 14 households ranked, 5 villages had 13, 2 villages had 12, and 1 village had 9.

8
of 1985<br>intercensal survey|
|an, Islamic|Nov 76|33709 F|U.N. 1984 assessment|Based on U.N. 1984|Based on U.N. 1984|
|Rep. of||||assessment|assessment|
|aq|Oct 77|12000 F|U.N. 1984 assessment|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Ireland|Apr 81|3443F|UNPVSR 4/87 (official<br>est.)|Eurostat<br>1987|Eurostat<br>1987|
|Isle of Man|Apr 86|<br>64 J|UPVSR 7/86||--|
|Israel|Jun 83|<br>4038 J|UNPVSR 4/87 (official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR 4,87|
|Italy|Oct 81|56557 F|Eurostat<br>1987|Eurostat1987|Eurostat<br>1987|
|Jamaica|Jun 82|<br>2206 F|U.N. 1988 revision<br>(pretim.)|U.N. 1988 revision<br>(preLim.)|U.N. 1988 revision<br>(prelim.)|
|Japan|Oct 85|121049 F|UNPVSR 4/87 (official<br>est.)|Based on INED 10/87|Based on UNPVSR<br>4/87|
|Johnston<br>Islan|d Apr 70|<br>1 F|UNPVSR 4/87 (official<br>est.)|||
|Jordan|Nov 79|<br>2133 F|U.N. 1988 revision<br>(pretim.);<br>11/79 census covered<br>East<br>|<br