rafmacalaba/data-use-mentions-extended huggingface dataset:
available in the input-output table of the Chilean economy for 1986.2 The aggregation first accomplished by dividing these 41 industries into sub-industries using SITC classifications, leading to a total of 113 sub industries (corresponding to the number of nodes at level II in Figure 1). Each of the tariff lines (nodes at level III) was then assigned to a subindustry using an CCCN/ SITC concordance. To allow differing degrees of substitutability between different types of countries, the source countries were divided into three groups: OECD countries, Latin American countries, and the rest of the world. There were thus a total of 3,033 nodes at level IV in Figure 1, resulting from 3 country groups for each of the tariff lines.

At each node, a CES aggregator is used to derive a quantity and price index from the price and quantity indices at the lower level nodes. For example, let A denote a representative node and let Ai be the nodes that are connected to A at the next lower level of aggregation. We want to develop an aggregate index for node A, ZA from the indices **ZA** calculated at the lower level nodes. The aggregate quantity at node A will be where aA E [0, oo) is the elasticity of substitution between imports from the different inputs to node.3 The parameters bA are included to allow for the possibi lity of quality differences between goods from different input sources. The corresponding price index for goods at node A will be <!-- Start of picture text --> A 5 (2)<br><!-- End of picture text -->
Policy Research Working Paper 9815

# **Abstract**

Many developing countries face severe electricity constraints, which are reflected in low electrification rates, frequent and prolonged outages, and high electricity tariffs, all of which result in low electricity consumption that impedes economic development. This study estimates the impact of electricity consumption on value added through reduced form equations for three sectors: agriculture, manufacturing, and services. It uses panel data on 126 countries for 1996–2014 from the International Energy Agency and World Development Indicators databases. To control for endogeneity and reverse causality bias in the ordinary least squares estimators, the study applies two-step difference and system panel generalized method of moments estimation techniques, which improve the ordinary least squares estimates by applying lags of the explanatory variables as instruments that are not correlated with the error term and account for countries’ fixed effects generating bias in the coefficients. The estimation results indicate that electricity consumption has a significant and positive impact on the manufacturing sector’s value added in non-highincome countries (with an elasticity of 0.022). By contrast, the electricity consumption elasticities are insignificant in agriculture and services in non-high-income countries, as the production technologies of these industries vary substantially across income groups compared with those in manufacturing. Finally, using all the countries in the sample produce positive and significant results for all sectors, with the highest elasticity of 0.036 in manufacturing.

This paper is a product of the Sector Economics and Development Impact, International Finance Corporation. It is part of a larger effort by the World Bank Group 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 shovhannisyan@ifc.org and kstamm@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
0.04|
|_4_|1,787|0.06|
|_Richest_|2,026|0.07|
|_All_|6,748|0.22|

_Source:_ Author’s calculations based on 2008 ILCS and 2009 GDP data The lifeline tariffs benefit scheme had several inadequacies as a measure to provide compensation against the impact of the gas tariff increase. First, the amount of lifeline tariffs benefit was only about 15% of the cost of gas price hike to the bottom poorest 20% of households and less than 5% of the total cost to households of the gas price hike (see Table 8). Second, while this compensatory measure provides some relief for the poor and low gas 18
negative for the intensive margin of trade: the change in the value of U.S. goods that are already exported to the EU within surviving trade relationships, e.g., the same firm exporting the same product to Europe throughout the time span. Overall, the impact at the extensive margin outweighs the impact at the intensive margin. The empirical findings suggest that EU product standards harmonization to international norms contributes significantly to explain the export entry patterns observed in my sample of U.S. manufacturing firms.

These results have an important policy implication. The U.S. National Export Initiative laid down in 2010 seeks to double exports over the next five years by enhancing small and medium sized firms’ ability to enter export markets and by actively reducing barriers to trade. The results obtained in this paper suggest that working towards a reduction in the differences of product requirements across markets could be a supportive policy to encourage firm entry into export markets.

The remainder of the paper is structured as follows. The next section develops the theoretical model. Section 3 describes the data set and presents summary statistics. Section 4 presents the empirical strategy and report results from testing the main theoretical results. Concluding remarks are offered in section 5. Finally, appendix 1 contains theoretical derivations and appendix 2 presents details of the data set.

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correct answer at this stage seems to be no: for the present, clarity and simplicity of the suggested measures outweighs any advantages that sophistication could bring.

# _Research use of existing surveys_

Also problematic, though it has not been reviewed here, is the degree to which existing data has been used for research purposes, though the position here seems to be changing. Funding for a ramped-up program of research could be considered. This could begin with a first wave assembling experienced applied micro researchers to carry out methodologically innovative work on the income and productivity contribution of access to finance, and on the barriers to access, based on the existing micro data sets. Then in a second phase, post-doctoral research fellowships could be offered to developing country researchers for applying these methodologies to what would by then be a growing set of available micro survey results.

# _Concluding remark_

The list of proposed national basic financial access indicators and the potential for much more research based on micro surveys highlights the desirability of pushing forward on each of the four main modes of data collection. Given comparative advantage, this suggests efforts on the part of several international agencies as well as partnerships with national regulators and private sector financial service providers.

> headcount at the $2-per-day level in an international comparison would tend to indicate the degree to which some countries’ financial access was greater or less than the overall level of development.  However, the underlying reasoning here is not universally accepted and any such normalization risks being seen as a black-box obscuring rather than assisting understanding.

Second, different dimensions of usage or cost barriers could be aggregated in some weighted average. Here again, the absence of general agreement on the appropriate weights argues against attempting, at the present s tate of knowledge, to set an international standard aggregate.

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disaggregated by month in the course of a given year we chose to use only annual aggregates; the reason for this decision is the fact that most capital expenditure is concentrated towards the end of the year and as a result comparing periods other than full years would be misleading. This information is available for a large number of possible cuts, in particular disaggregated by type of spending (i.e. current vs. capital expenditures). Crucially, this information is available disaggregated for expenditures that are financed with resources that have different types of restrictions attached to them. A particularly important distinction in the Peruvian context is that between ‘ordinary’ and ‘defined’ resources. If unspent at the end of the year ordinary resources revert back to the central government. In contrast, if defined resources are unspent at the end of the year the balance gets reallocated to the municipality in the following year’s budget. This is relevant because all revenues related to natural resources shared between the different levels of governments by formula are included among the defined resources. This municipal public expenditure data has become available due to the roll out of the Integrated Financial Management System ( _Sistema Integrado de Administración Financiera, SIAF_ ) in all municipalities in Peru and is currently available on-line – a key element in improving - accountability to citizens (http://transparencia <u>economica.mef.gob.pe/amigable/). The Ministry</u> of Economy and Finance kindly provided the data, including for the years in which SIAF’s information was not made available on-line.

Population data are from the 2007 Census. In addition to the total number of inhabitants per municipality we also know the number of so-called ‘populated centers’ within a given municipality and the number of people living in each of those populated centers. This allows us to exploit the information content in the dispersion of population within a given municipality.

The human resources of each municipality are available through the annual National Registry of Municipalities ( _Registro Nacional de Municipalidades,
by a lengthy spell of default involving payment arrears, according to data from the World Bank’s Debtor Reporting System (DRS). Yet, the observed scope of arrears in this database is likely the tip of the iceberg because missed payments can go unreported, not just toward China but toward other lenders as well. It is a significant shortcoming in the literature that we lack a systematic, publicly available dataset on the start and end date of sovereign defaults towards official creditors as well as supplier credits. “Hidden defaults” will likely remain a challenge in the years ahead.

Third, we show that Chinese lenders only grant limited debt relief, which can result in lengthy debt overhang spells and “serial restructurings”. China has been a major contributor to the 3
**Figure 9: Rates of Increase per Year of Age in COVID-19 Mortality vs. Rate of Increase per Year of Age in All-Cause Mortality by Country**

<!-- Start of picture text --> y y y<br>CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>SWE<br>ESP<br>DEU KOR ITA<br>HUN PRT NOR<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>MYSCOL<br>ZAF<br>AFG PER<br>PAK<br>MEX<br>.06 .07 .08 .09 .1<br>Rate of increase in all-cause mortality rate<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Source: Author’s analysis. LMICs are shown in red and HMICs in blue.

The pattern of mortality risk by age across countries from COVID-19 resembles the pattern for all-cause mortality, matching the observation that the patterns of mortality from COVID-19 resemble those for mortality generally. The disease kills principally those who were already at highest risk of death: the aged and those with comorbidities. Likewise, in the United States African-Americans are at higher risk of death from COVID-19 and have higher mortality risk overall at all ages (Yancy 2020). Similarly, ethnic minorities in the United Kingdom are at highest risk of dying of COVID-19 (Public Health England 2020).

# **5 Possible Explanations for Variation across Countries**

Rules and practices for classifying deaths by particular causes vary by country, and the same is true for classification of COVID-19 deaths. The extent to which deaths are attributed to COVID-19 varies depending on the overall effectiveness of the country’s vital statistics system in generating cause of death information, the country
We focus on the symmetric Cournot equilibrium, for which existence and uniqueness are discussed in Amir and Lambson (2000) for the oligopoly case. We adapt these sufficient conditions for the oligopsony case in Online Appendix B.1. In a symmetric equilibrium, **_L_** _mi_ = _L × Nmi_ .

The number of active firms is determined endogenously by the free-entry condition: where _χi_ represents a fixed entry cost that entrant firms need to pay to produce and sell their product in the respective product market.

Online Appendix B.2 shows that a sufficient (but not necessary) condition for the profit function to be monotonically decreasing in _Nmi_ is _Wm_<sup>_′′_(</sup><sup>**_L_**</sup><sup>_mi_)</sup><sup>_≥_0.Assumingthat</sup> condition to hold and denoting by Π(<sup> ̃</sup> _·_ ) the inverse profit function, Equation (3) can be written as: If _Wm_ ( _·_ ) is a constant elasticity of substitution function (CES), then _εm_ is constant and the impact of product market regulation on markdowns is unambiguously positive.<sup>9</sup>

# **3 Data**

We test empirically the insights of the model on Indonesian manufacturing plants. To that end we exploit two main sources of data: an extensive panel of manufacturing plants and data on product market regulation over time, which we manually code.

## **3.1 Manufacturing Data**

Plant-level data are taken from the Indonesian survey of manufacturing plants with at least 20 employees ( _Statistik Industri_ , SI) administered by the _Indonesian Statistical Office_

> 9 The impact of product market regulation on markdown is ambiguous in the model extension with homogeneous labor within a commuting zone, as shown in Online Appendix E.

8
previous decade by applying the same growth rate to household consumption and mapping the sectoral information to the household’s head sector of employment. Number of poor (panel b) is estimated using WDI population data.

Using the same methodology and data we can also estimate poverty at the US$3.20 poverty line. This is a relevant poverty measure for Nigeria as it reflects the typical standards of living in lower-middle-income countries (see Jolliffe and Prydz 2016, World Bank 2018, 2020).<sup>17</sup> Using this higher line, the backcasted series is much closer to the 2009/19 HNLSS poverty estimates. Figure 2 shows that the backcasted poverty rate for 2009 at the US$3.20 poverty line is estimated to be 76.5 percent assuming a pass-through rate equal to one between sectoral GDP consumption and household welfare. This estimate is just 3 percentage points lower than poverty rate estimated directly from the 2009/10 HNLSS (see Table _7_ for full backcasted series).

> 17 This follows the World Bank income group classification of economies based on a country’s GNI per capita. The latest thresholds and classification can be found at <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.</u> Nigeria is classified as a lower-middle-income economy since 2009.

12
secular increase in _both_ exports and imports, it cannot possibly have been the root cause of large global imbalances in 2000 and onwards (Figures 22 and 23).

**Figure 22: Ratio of Trade Balance to GDP in Asian countries**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>Japan<br>10%<br>5% Korea<br>0%<br>Taiwan<br>-5%<br>-10% China<br>-15%<br>-20%<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Sources: International Monetary Fund, World Economic Outlook database; Organization for Economic Co-operation and Development National Accounts data files; Taiwan Statistical Databook; and World Bank national accounts data.

**Figure 23: Decomposition of Ratio of Trade Balance over GDP**

<!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>Japan<br>0.2<br>Korea<br>0<br>-0.2 Taiwan<br>-0.4<br>China<br>-0.6<br>-0.8<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>Imports over GDP Exports over GDP<br><!-- End of picture text --> Sources: International Monetary Fund, World Economic Outlook database; Organization for Economic Co-operation and Development National Accounts data files; Taiwan Statistical Databook; and World Bank national accounts data.

27
panel a 153,038 firm-quarter observations in the left plot and 579,123 firm-quarter observations in the right plot in 20 countries and in panel b 617 exporting firms in the left plot and 1,024 importing firms in the right plot in 13 countries.

The BPS data neither covers the universe of globally engaged firms nor is stratified by the degree of global engagement of firms. Yet the fact that the results based on BPS data are consistent with those obtained for the universe of globally engaged firms in the customs data gives us confidence that BPS data provides a credible understanding of the impact of the pandemic on globally engaged firms.

# **5.2 Resilience and Recovery in Firm Performance**

Business recovery from the pandemic is shown based on the latest round of the BPS data by (Cirera et al., 2021a), but the extent to which such recovery is felt by firms with global engagement is not known. The following key patterns emerge when comparing recovery for globally engaged firms with that of other firms. _First_ , the immediate impact of the pandemic on total sales of globally engaged firms is not significantly different from that on other firms, but globally engaged firms are certainly recovering faster. For example, while non-exporters experienced a decrease in total sales by 14
*

The results of Dahl (2011b), based on a global survey of electricity demand including more than 450 studies for about 60 countries for the period from 1951 to the present, indicate that the mean price elasticity in the short run is -0.21 and -0.48 in the long run, and that the mean income elasticity is 0.32 (in the short run) and 1.08 (in the long run). The earlier results by Espey and Espey (2004) based on a meta-analysis of residential electricity demand elasticities from 36 studies (mostly from developed countries) found the mean price elasticity equal to -0.35 and -0.85 respectively in the short and long run and the mean income elasticity equal to 0.28 and 0.97 respectively in the short and long run.

Estimates of income elasticity for countries with lower income than the US are higher. As income increases in developing countries, electrification, ownership of appliances, and demand for electricity increase at a fast rate. However, as the access to electricity reaches 100% and ownership of appliances approaches saturation, increases in income have a smaller effect on demand for electricity.

15
activities and challenges during disasters have aggravated the sociological (Whittle 2010) and epidemiological effects and increased psychological and physical distress (Reacher 2004).

Other post-flood effects, for example increased levels of poverty and famine, can be cited from the Pakistan 2010 event, as indicated in a report by UNESCO on the flood’s impact on the Millennium Development Goals which demonstrated that 5 million people were affected by undernourishment, and that severe damage of crops has led to higher food prices which caused lower food security for the affected people and increases in poverty. Other indirect effects, like closure of schools and damage to educational buildings, also have a long term effect after flood events, which lengthen the duration of the activity of resettlement and restoration to normal life.

Another post-flood impact which directly or indirectly affects already suffering people is the burden of debt for restoration of the economy. According to the country’s economic survey 2009-10, about 34% of Pakistan’s GDP is being used for debt reduction after the 2010 flood. This puts extra pressure on people and reduces their financial ability to cope with the changed situation, making them in turn more vulnerable.

# **1.8 Summary**

**The foregoing analysis of the trends in flood impacts across the world concludes that**

**there is evidence that the impact of flooding is increasing globally** . Trends in number of events, numbers affected by those events and financial, economic and insured damages have all risen over the period since 1950. Immediate loss of life from flooding seems to be increasing more slowly or even decreasing over time which can be attributed to successful warning, evacuation and emergency actions, as well as increased investment in flood defenses.

The need to mitigate the devastation caused by flood events across the world is justified by the cost in human life, massive damages and impact on the lives of millions of people annually. As recent events in Pakistan, Australia and Brazil show, floods can occur in widespread locations and can sometimes overwhelm
One limitation of the EIS database is that it only covers registered firms and workers. In Turkey, about a fifth of wage workers are informally employed. This means that workers drop out of the registry when they either lose their job, or when they accept jobs that are “off the books” in the same firms or in another firm. Workers who transition into informal employment or unemployment are no longer observed in the database. Similarly, firms are no longer observed in the database if they shut down or, alternatively, if they no longer employ registered workers.

# 3.2. The Turkish Household Labor Force Survey

To assess the impacts of the minimum wage on informal employment and total employment, we complement our causal analysis (which relies on the EIS registry) with data from a nationally representative survey. This allows us to gain insights into whether workers who lost their formal jobs due to firm destruction following the raising of the minimum wage remained formally employed, transitioned into informal employment, or became unemployed. We use data from the Turkish Household Labor Force Survey (HLFS), a nationally representative household survey conducted annually from 2000 to 2018. The survey collects detailed information on labor-market outcomes of the Turkish population, including representative samples of those in formal and informal employment, and those who are unemployed or out of the labor force. The survey distinguishes the formality status of each worker by the response given to a question on whether employed individuals are registered with social security institutions – the definition of formality also used by the EIS registry. In addition, the survey records the wages of both formal and informal workers, allowing one to look at potential “lighthouse” effects of the minimum wage on the wage of informal workers who are outside the scope of formal labor regulations.

# 3.3. Descriptive statistics

Table 1 reports summary statistics from the EIS data on the composition and characteristics in the population of registered firms in Turkey. Microenterprises and small firms represent the large majority of formal firms in Turkey. Half of
. Mimeo. March 1997.

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