rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. A Discussion of Energy Subsidies and Tariff Data Sources and Implications for the Level of Disaggregation**

_Energy subsidies rates for calculating ERPs from the I-O matrix: sources._ For this present study the subsidy rates for the various energy sources are derived from the “best-guess” estimates of Abouleinein, El-Laithy and Kheir-El-Din (2009) of the percentage adjustments that would have to be made to observed prices to reach per-unit actual domestic costs (presented in the fourth column of their Table 4, p. 16). These percentage increases – and their translation into the ad valorem subsidy rates used in the above formula (the Si) – are shown in Table 3. In fact these energy subsidy estimates match the fuel energy sectors available in the input-output matrix of 2006/2007.<sup>8</sup> _Applied (formal) tariff levels for selected industries, products and intermediate inputs._ The 2009 tariff levels from the WTO used in the present calculations of the ERPs for the selected sectors, plus those used by Nathan Associates and Al Iskandarani from the late 1990s, are presented in Annex Table 1. Note that the sectors are ISIC codes (rev. 3), while the activities are from the US SIC, as used in the Nathan study. And so, the final column of the Annex Table 1 includes notes and references to the correspondences, which this present study uses, between products (and inputs) and the HS 6-digit tariff schedule as found in the WTO Integrated Database for Egypt.

One alternative for selecting tariff levels for industries is to simply average over the lines associated with some aggregate activity, such as textiles or agriculture. In fact this is what is done in the following section at the HS 2-digit level when assessing protection of industrial groups in the 23-sector input-output table for Egypt (see also Lederman, 2005). Instead of averaging many tariff lines across the 2-digit level, however, in the case of the specific industries available, we have differentiated activities according to
beliefs about the trustworthiness of local government officials rather than a vague notion of trust in government as an institution. Specifically, respondents were asked: “In your opinion, do you believe local councillors or do you have to be careful dealing with them?” By capturing interpersonal trust of specific government officials, these indicators are certainly preferably to generalized trust questions.<sup>6</sup> Also, the longitudinal nature of our dataset provides us with the ability to capture comparable beliefs

> 3From 2005 to 2007, 68 percent percent of the respondents from 2005 were re-interviewed, and in another 20 percent of cases, a different person from the same household was interviewed. From 2007 to 2008, 64 percent of the respondents from 2007 were re-interviewed, and in another 19 percent of cases, a different person from the same household was interviewed. In other cases, the household living at the same address was interviewed or a new household was selected from the census books (IRCBP Evaluation Unit, 2010).

> 4One or two community modules were completed in each enumeration area, depending on whether the area consisted of multiple small villages (IRCBP Evaluation Unit, 2010).

> 5For the next version of the paper, we plan to re-estimate the models using Amelia to impute missing data. For this version, we omitted the responses on our dependent variable that were either missing or coded as “don’t know”, “not enough info” or “neutral”. Only 6.48 percent of the responses fall into one of these three categories. We model responses on our independent variables that our coded as “don’t know” or “missing” (Honaker, King and Blackwell, November 4, 2010)

> 6Despite its advantages, our indicator still suffers from the problem that it is not domain specific. While methodologically stronger, a domain-specific question would have created additional complications in administering the surveys and translating the questionnaires into local languages.

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have been known to be of good quality.

Following the Suez crisis in 1956, Egypt first nationalized British and French schools ard later, in 1958, all foreign schools, and placed controls on the minority schools. Szyliowicz (1973, p. 199) states that

- ... the place of foreign schools in the

- national system of education became increasingly controversial throughout the Arab world as nationalist feelings spread. Missionary schools were criticized for religious proselytizing and some foreign schools, especially the French and Italian, for serving as centers of propaganda for their countries of arigin and for neglecting the teachingof tbe national culture. All foreign schools were accused of contributing to the perpetuation of divisions within the state. Such feelings led to legislation in

every country regdlating the activities of these schools in various ways and bringing them more or less completely under state control....

Such regulations have not been legislated in Lebanon.

Decisions on where students from Arab countries are sent for further training depend upon a number of factors: competence in the language of instruction, financial support from the host country, familiarity -vith the system of education, preferences or biases of governmental or university officials in country of origin, availability of and admission to field of primary interest, and acceptability of foreigners in host country. In order to shed more light on the outlook towards external higher education of the respective Arab countries, an examination was made of the external countries, foreign and Arao, which host students from Arab countries. The data are tabulated in Annex 2, based on the 1986 UNESCO _Statistcal Yearbook,_ indicating the country of origin of foreign students enrolled at higher education institutions in fifty countries selected on the basis of the number of foreign students enrolled. Lack of sufficient information has excluded the Soviet Union, Brazil, Romania, Bulgaria, and Sudan in the Yearbook. In a sense, the information in Annex 2 reflects the major cultural orientation of several Arab countries. The tabulated data are not exhaustive but do represent the higher percentages.

Annex
# **Figure 2 Percent of SOEs generating losses before receiving subsidies, by sector, 2009–18**

# **a. Power companies**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>c. Roads<br>100<br>80<br>60<br>40<br>20<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Percent of firms<br>Percent of firms<br><!-- End of picture text -->

# **b. Airlines and airports**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>d. Railways<br>100<br>80<br>60<br>40<br>20<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Percent of firms<br>Percent of firms<br><!-- End of picture text --> _Source_ : World Bank Infrastructure SOE Database.

_Note_ : Losses were calculated using adjusted net income, which subtracts operational subsidies from total profit (loss) for the year.

Part of the fiscal risk we examine stems from the fact that infrastructure SOEs have very little slack to deal with shocks. They use most of their revenues to cover payroll or maintenance expenses, leaving little net income for retained earnings—a key capital buffer for writing off the losses from large shocks. Figure 3 displays the variation across sectors of key expense items as a percent of operating income. Employment costs are extremely large in the railway
standardized survey instrument and methodology allows for comparisons across economies. Sampling weights are provided to correct for over-sampling and are used throughout the analysis that follows. The surveys cover a range of topics including access to finance, infrastructure, competition, crime, labor, business environment obstacles, and firm performance.<sup>1</sup> The sample used below consists of a cross-section of 10,326 firms in 22 UMICs and 4,618 firms in 11 HICs for which information is available in the ES. Table 1 provides the list of countries along with the sample size by country. The year the survey was conducted varies across countries, ranging from 2009 to 2017.

We complement the ES with country-level data obtained from World Development Indicators (World Bank), Worldwide Governance Indicators (World Bank), Transparency International, and International Country Risk Guide (ICRG).

_Mean differences between firms in Malaysia and the comparators_ Table 2 provides the mean value of labor productivity and several of its potential determinants for the UMICs and the HICs, and whether the difference between the two income groups is significant. Labor productivity is defined as total annual sales of a firm during the last fiscal year (in 2009 USD) divided by the total number of workers at the firm at the end of the last fiscal year. The mean level of labor productivity in HICs equals USD 213,200 and USD 80,741 in UMICs. The former is about 2.6 times the latter, and the difference between them is significant at the 1 percent level. One worry here might be that the mean labor productivity may be unduly affected by extreme values. However, comparing median labor productivity provides a qualitatively similar picture. That is, the median labor productivity for firms in UMICs equals USD 25,327 compared with a

> 1 Details of the methodology can be accessed through the following link: http://www.enterprisesurveys.org/methodology

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deflated by a real exchange rate index.

In most papers, the real exchange rate has the expected effect: an appreciation of the origin’s currency increases bilateral tourism while the appreciation of the destination reduces it, and an increase in income tends to increase demand more than proportionately. If tourism is inelastic; that is, the price elasticity of tourism is less than one, then increasing its price will reduce tourist arrivals and that will result in a higher spending per tourist (total revenues increase more than the reduction in tourist arrivals). If, on the contrary, tourism is elastic; that is, price elasticity is more than one, implying that tourism is a luxury good, then increasing the price will produce a reduction of the average spending per tourist. The estimated elasticity is in general large and is highly sensitive to the estimation techniques and measures of the real exchange rate. Rosensweig (1988) finds elasticities of substitution of Caribbean islands with each other is 1.33 to 2.45, with Mexico 1.85 to 1.00 and with Europe 1.7, depending on whether the origin is restricted to the United States or all countries. Rosensweig’s estimates are broadly in line with those of Papatheodoru (1999), who estimates a model in levels for the Mediterranean region for tourists from France, Germany, and the United Kingdom and finds price elasticities greater than 1; however, Garín-Muñoz and Pérez Amaral (2000) estimate a dynamic model in levels and a non-dynamic differences specification on the determinants of tourist flows to Spain and obtain price elasticities of only 0.30 and 0.25 respectively. Song et al. (2000), working in an error correction framework on single country data, obtain price elasticities that range from 0 to 2 for the principal destination countries of English tourists. Estimated income elasticities tend to confirm the intuition of the sector as being a luxury 4
, South Asia, and sub-Saharan Africa with a selected sample of countries from the Amadeus database, the selection being driven by the extent to which the size distribution portrayed in these data is comparable with the one reported in Eurostat’s Structural Business Statistics database. More details are provided in Section 4.

> 8Since the Chilean manufacturing data cover the universe of manufacturing plants with 10 or more workers, we apply the same truncation to the US data to arrive at the average size of 118 workers in the US. We explain data coverage details for all countries in the data section.

> 9The actual estimate of the elasticity is 0.32. However, we take into account that there is correlated misallocation in the US, our benchmark of efficiency. By subtracting the US’ estimate of the distortion elasticity of 0.15 from the estimate for Chile, we reach the value of 0.17. We proceed in this fashion for all of the countries.

5
## **2.3 Additional data**

All our firm gravity equations include a measure of bilateral tariffs that controls for tariff liberalization taking place between origin countries and destination markets over the period, whether unilaterally or in the context of PTAs. We rely on a global tariff database covering tariffs at the HS 6-digit product level for 197 importing countries and their trading partners from 1988 to 2017 from Teti (2020). The database addresses two major issues with tariff data from TRAINS, missing data and misreporting. Additionally, some of our specifications control for a measure of GDP in the destination market, taken from the World Development Indicators. Summary statistics on these variables are provided in Appendix Table C.4.

# **3 Empirical framework**

To examine the effects of SPS and TBT provisions in PTAs on firm exports, we consider the following two specifications: and where _y_ are exports, _f_ is a firm (in origin country _i_ ), _j_ is a destination market, _k_ is an HS 6-digit product, _t_ is a year, and _epsilon_ is an independent and identically distributed error term. Our key variable of interest in Equation (1) is _Prov_ which is either (i) the number of 12
improvements in product, process, organization and marketing technologies). In this context, we examine wherever possible _which_ forces constrain productivity and innovation in the manufacturing sector and _to what degree_ these constraints matter relative to each other.

For the cluster-level productivity analysis, we combine two established data sources in a novel way by importing average ‘industry-firm size-location’ cluster-level business environment variables from the World Bank Enterprise Survey (WBES) to the comprehensive Census of Industry productivity data available for similar clusters of enterprises. We use a novel empirical approach by comparing the relative importance and distance to a ‘productivity frontier’ given the potential relaxation of constraining business environment indicators. For the firm-level TFP

> 2 The importance for growth of three main classes of intangible assets has been explored recently, namely firm investment in computerized information (software and databases), innovative property (R&D spending, product development in the financial industry, architectural, engineering and other designs), and economic competencies (reputation and branding, employee training, and business process improvements). Based on the quantification of these classes of intangible assets, intangible investment is actually greater than tangible investment in the UK, US and Sweden,; and in Canada, Denmark, Finland, France, Germany and Japan, it comes close (OECD 2010). See Dutz et al. (2012a) for a recent application to another middle-income country, Brazil.

3
pandemic’s distributional impact. For 26 countries that do not have any of the above three sources of data, we rely on estimates from simulations published in the literature or provided to us by World Bank teams. For the rest of the world, covering roughly 18% of the global population, we utilize growth in national accounts (disaggregated by economic sector where possible). We combine these various data sources to estimate the global income distribution in 2020.<sup>1</sup> To isolate the impact of the pandemic, we compare our actual 2020 distribution with a counterfactual 2020 distribution which assumes that countries in 2020 experienced no distributional change and the economic growth that was forecasted before the pandemic spread.

This paper provides the first estimates of the impact of the pandemic on global inequality and poverty that explicitly account for the distributional impact of the pandemic. Earlier work on global inequality (such as Deaton 2021) and global poverty (such as Lakner et al. 2022, Valensisi 2020, Sumner et al. 2020) did not account for within-country inequality or used general equilibrium models (Laborde et al. 2021). Several cross-country studies estimate the impact of the

> 1 Our global distribution combines income and consumption data. For simplicity, we refer to it as the global income distribution throughout.

2
Underpricing of power and water is less burdensome in Sierra Leone than in comparator countries**

Financial burden of underpricing as percentage of GDP <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>Sierra Leone LIC-Fragile<br>Percenatage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia and others 2009.

How affordable are cost-reflective tariffs? With a tariff of $0.31 per kilowatt-hour and a monthly subsistence consumption of 50 kilowatt-hours, the associated utility bill comes to $15.50 per month. Based on the distribution of household budgets in Sierra Leone, monthly utility bills at these levels would be affordable by less than 40 percent of the population (figure 24). However, the share of the population that could afford the service is much higher than the share of the population that already has the service, suggesting that Sierra Leone has scope to increase coverage before affordability would become a serious impediment. Strikingly, a more limited subsistence-consumption level of 25 kilowatt-hours per month for power—which is sufficient to meet the most basic needs—would cost $7.80 per month and would be affordable to half of the population. Subsistence consumption of four cubic meters per month for water would cost $2.60 per month and would be affordable for most of the population.

43
Policy Research Working Paper 9854

# **Abstract**

What does the macroeconomy look like in the aftermath of tariff changes? This paper estimates impulse response functions from local projections using a panel of annual data that spans 151 countries over 1963–2014. Tariff increases are associated with persistent, economically and statistically significant, declines in domestic output and productivity, as well as higher unemployment and inequality, real exchange rate appreciation and insignificant changes to the trade balance. Output and productivity impacts are magnified when tariffs rise during expansions and when they are imposed by more advanced or smaller (as opposed to developing or larger) economies; effects are asymmetric, being larger when tariffs go up than when they fall. While firmly establishing causality is always a challenge, the results are robust to a large number of perturbations to the baseline methodology, and hold using both macroeconomic and industry-level data.

This paper is a product of the Development Policy Team, 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 dfurceri@imf.org, sahmed@imf.org, jostry@imf.org, and bizaro@nus.edu.sg.

_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._
The analysis uses both consumption and income aggregates (crop income, livestock income, wage income -agricultural and non-agricultural-, and income from non-farm self-employment) calculated from the UNPS data. The consumption aggregate used is the aggregate constructed by the Uganda Bureau of Statistics to generate the official poverty statistics. The majority of the income aggregates come from the Rural Income Generating Activities (RIGA) database, which uses standardized protocols to generate gross income aggregates across waves. Where inconsistencies were noticed, they were communicated to the RIGA team and corrected in the protocols we used. The RIGA aggregates calculate crop and livestock income using information on the amount of goods produced by the household in the last 12 months and the price of these goods reported by the household. Prices are imputed from other sampled households when they are not collected. The measures of crop and livestock income calculated are gross income not netting out costs of land, labor or purchased inputs. Wage income is generated from data collected on wages earned in the last 12 months. The selfemployment income aggregate calculated all self-employment income earned in the last 12 months and nets out the cost of purchased inputs. Purchased inputs can be quite a large share of the income from self-employment activities such as petty trading or handicrafts and netting out the cost of these inputs is important. For more details on how these aggregates are constructed, see Carletto et al. (2007).

In order to assess real changes in incomes and consumption across time the analysis converts the nominal consumption and income aggregates into real aggregates. The national Consumer Price Index (CPI) is used to convert the consumption aggregate across years. Differences in the cost of living across space in Uganda are also accounted for by using a combination of the consumption aggregate provided by the Uganda Bureau of Statistics and the regional poverty lines. The regional poverty lines reflect differences in the cost of non-food requirements across regions and urban and rural locations in Uganda. Differences in the cost of food items are already incorporated in the consumption aggregate provided. For this
cyclones such as hurricanes, are more prevalent. In South America, floods occur more frequently, and droughts have an important impact affecting people. Following EM-DAT, we found that the five most common and impactful natural disasters in the region are floods, tropical storms (including tropical cyclones such as hurricanes), droughts, earthquakes, and landslides.<sup>2</sup> Thus, we use objective measures of these five hazards in the model, but unlike Hill and Porter (2017), we do not model or draw the distribution of these future shocks. Our cross-sectional measures indicate the potential damage from natural hazards based on a probabilistic analysis which is interpreted as a proxy of future aggregate shocks. We also do not include idiosyncratic shocks in the model. Although, we explore them when we perform the Chaudhuri procedure as a robustness exercise.

Finally, this study aims to provide disaggregated data to policy makers to build policies to reduce vulnerability to poverty due to natural hazards in LAC. In this paper, we address the effects of climate risk in the region, which is a relatively unexplored aspect in the region. While there have been some macro-level papers relying on simulations such as Jafino et al. (2020), Solano-Rodríguez et al. (2021), Banerjee et al. (2021) or on agricultural and livestock productivity such as Reyer et al. (2017) and Lachaud et al. (2022) simulations; our main contribution lies in a micro-level perspective focusing on households. Caruso (2017) explores natural disasters’ long-run effects in the region in the 20th century at the micro level exploiting census data. And other studies focus on individual countries within the region or specific types of natural hazards (e.g., Albert, Bustos, and Ponticelli, 2023; Acevedo et al., 2023; Rude and Robayo-Abril, 2023; Aguilar-Gomez et al., 2022). However, there is a lack of comprehensive understanding regarding climate risk in the overall region in the present context.