rafmacalaba/data-use-mentions-extended huggingface dataset:
rate (FDR) described in Anderson (2008). We also conduct placebo experiments to lend further support that any results we find from applying our empirical strategy are due to differences in exposure to COVID-19 and not any other pre-existing factors that differ across municipalities.

# **5. Results**

The results of estimating Equation (1) using the municipal-level warning index are shown in Table 3. Each column reports a different specification, which varies in whether we use controls or not and the level of fixed effects that we employ. The table presents the coefficient of interest, which captures how a change of 1 point in the warning index affects educational outcomes: dropout, effective promotion, switching from private to public school and vice versa (public to private). We also include the pre-COVID-19 means to provide a relative measure of the estimated effects.

Our findings show that higher values of the warning index increased dropout rates between 0.7-0.8 percentage points, an effect consistent across specifications with and without individual and school-level controls and including either municipality or school fixed effects. Compared to the pre-COVID-19 mean of 3.2 percent, this represents an increase of 8.1% in between-year school dropout. We also see a reduction in effective promotion rates of around 2 percentage points, a fall of 2.4% with respect to the pre-pandemic mean. There is an increase in movers from private to public schools of 10% compared to pre-pandemic levels and a reduction in the other of 6.3%. All coefficients are robust to multiple hypothesis adjustments, since they remain significant after adjusting for the 16 hypotheses tested in Table 3. These results suggest that municipalities with greater exposure to COVID-19 infections had greater educational losses in Guatemala.

17
Figure 4 also underscores the importance of examining these relationships by income and age — among poor households, younger women and men are equally likely to want to work additional paid hours. This diverges substantially, however, among groups aged 35 and older, likely due to women’s greater responsibilities within the home, including care of children. Also, some women may want to work more but are time constrained, so may consider themselves as neither available nor willing to work given their circumstances. As discussed further in Section V, asking additional contextual questions on reasons for wanting/not wanting to work more, including factors related to the quality of available economic opportunities as well as time constraints, can help in better understanding the trends in Figure 4.

Figure 4. Uganda National Panel Survey, 2013-14: relationship between age and desire to work more (own-reporting sample) <!-- Start of picture text --> Households below the poverty line Households above the poverty line<br><!-- End of picture text --> Finally, to complement statistics on unemployment, the 19<sup>th</sup> ICLS also introduced the concept of the potential labor force to provide additional data on those out of the labor force, but who could be responsive to employment policy. This includes (a) unavailable jobseekers, who are seeking employment but not available; (b) available potential jobseekers, who are available but not seeking; and (c) willing potential jobseekers, who are neither seeking nor available for employment, but who want employment.

A question on desire to work, in particular, is critical for capturing (c) – and with important implications for women, who may be out of the labor force and not actively seeking work<sup>5</sup> because of unpaid work burdens, lack of suitable economic opportunities, not being allowed to work/other barriers to seeking work, or other social/cultural norms affecting their time allocation (see Section V
and Angola are among the worst performers globally. Logistics in southern Africa also suffer from high import costs, the second highest in the world after West Africa (World Bank 2010a; 2010b).

> 2 LPI is an annual survey of international freight forwarders. The survey aggregates the perception of survey respondents into an index that integrates several factors—transport, warehousing, border clearance, and so on—in order to rank them, and rates global logistics for trade.
# Appendix 4 Estimation results

Table A.4.1 Sensitivity of long-term inflation expectations to inflation shocks

## A. 1990H2-2018H1

||Dependent va<br>|riable: Change in lo<br>|ng-term inflation e<br>Add|xpectations<br>|
|---|---|---|---|---|
||All countries|All countries|vance<br>economies|EMDEs|
|All countries|0.282***<br>(0.021)||||
|Advanced<br>economies||0.159***<br>(0.028)|0.154***<br>(0.032)||
|EMDEs||0.425***<br>(0.030)||0.425***<br>(0.030)|
|Observations|2,408|2,408|1,344|1,064|
|R-squared|0.069|0.086|0.019|0.169|

## B. 1995H1-2004H2

||Dependent va<br>|riable: Change in lo<br>|ng-term inflation e<br>Advanced|xpectations<br>|
|---|---|---|---|---|
||All countries|All countries|<br>economies|EMDEs|
|All countries|0.423***<br>(0.035)||||
|Advanced<br>economies||0.284***<br>(0.049)|0.278***<br>(0.052)||
|EMDEs||0.554***||0.558***|
|||(0.048)
and concludes that firms experiencing fewer credit constraints are more likely to own a private generator in the areas where public power supply is unreliable.

Our original contribution is to document systematically the effects of both electricity deficiencies and credit constraints on the decision to invest in mitigating technology, i.e., a generator, and to analyze how their impact varies across firm types (in particular, size) and technological characteristics of the industrial sector they belong to. The use of a theoretical model of firms’ responses to power outages allows us to derive precise predictions that are borne out by the data. Using a dataset with a wide coverage across 87 countries and 28 twodigit (ISIC) industry classifications, we show that electricity-related constraints have nonlinear effects and that they also interact with credit constraints. Moreover, the marginal and threshold effects vary according to sectors’ degree of reliance on electricity and firms’ size. Finally, we discuss potential policy implications from our results. Credit constraints appear to be the priority in sectors very reliant on electricity to spur entry and convergence to the technological frontier, while in other sectors, firms would benefit more widely from marginal improvements in electrical supply. We discuss how the power-sharing versus pricing policies issues highlighted above may be used to address these implications.

The paper is structured as follows. Section 2 presents our dataset and provides descriptive statistics on the extent of electricity deficiencies and credit constraints, as they emerge from the enterprise surveys. In Section 3, we then develop a model of investment by firms when infrastructure and credit constraints are present. Section 4 spells out the econometric specifications to be estimated with our data. Section 5 presents the results, and Section 6 discusses the policy implications and concludes.

# 2. Data and Stylized Facts

We use data from the enterprise surveys for 104 countries covered a total of 70,192 firms over the period 2002-2006.<sup>8</sup> Of these, 87 countries have data on number of power outages, 77 countries have data on generator and
not include data for ages below the legal working age, which makes it impossible to estimate the rate of return to primary education, although many children in developing countries work and therefor have foregone earnings if in school. For this reason, the Mincerian method is more convenient than the full discounting method.

# <u>Estimation comparison between these two methods.</u>

The two methods, and as demonstrated below, compute different estimates of the returns to education. The Mincerian function method produces only private returns, and only in special cases are these returns similar to the marginal internal rate of return in particular conditions (Heckman et al. 2008). The Mincer regression may underestimate the private returns (downward bias) (Bhuller et al. 2017; Heckman, et al. 2008). In this sense, the discounting method is a precise empirical procedure to estimate private and social returns, as it allows to incorporate true direct and indirect costs of schooling.

# **Data**

We use data from Azerbaijan’s 2015 Monitoring Survey for Social Welfare (AMSSW), covering nearly 38,700 individuals. The survey is nationally representative. We use wage and other information for individuals aged 15-65 in full-time dependent employment. We estimate the number of years of schooling of the individual based on the highest educational level completed and considering the changes in the required years of schooling for a degree over time. Table 1 presents summary statistics of the sample of the AMSSW.

8
# **Annex B: Malaysian firm‐level data**

Table B1: Selected questions from Malaysian firm‐level survey

## **Survey Question 9.12: Research and development expenditure**

Refers to expenditure incurred on Research and Development (R&D) activities. R&D is the systematic study of new processes, techniques and applications & products.

- (a) In‐House: The percentage of expenses incurred by the establishment itself for the purposes of research and development.

- (b) Outsource: The percentage of expenses paid to other establishment for the purposes of research and development.

**Survey Section C2: Does your establishment has any Intellectual Property (IP) Protection System?**

Intellectual Property is the exclusive rights provided by law for a certain period of time to the creators of the works to control the use of their work. Intellectual property refers to patents, trademarks (including: brand registered / insured), copyright and related rights and others.

1. Patent is an exclusive right granted by the Government for a new invention, whether it is a product or a process. The protection of a patent is 20 years from the filing date.

2. Trademark may consist of words, logos, pictures, names, letters, numbers or a combination of such elements. It is a marketing tool that allows users to recognize and associate a release with certain dealers. Also known as mark, brand, logo or trademark is a sign placed on goods or services produced by a manufacturer to identify and distinguish it from goods or services produced by other parties.

3. Copyright in Malaysia is a work automatically protected when it meets the following conditions:

- Sufficient efforts have been made to make the work original in nature;

- The works were written, recorded or made in the form of materials and the creator is a qualified person;

- The works were made in Malaysia or the first publication of the work, is in Malaysia.

- Industrial design was
10.67||**_IGME6 _**|0.222|0.37|
|**_ES9_**|11.63|11.78||**_IGME9 _**|0.298|0.37|
|**_ES16_**|14.41|14.37||**_IGME16_**|0.474|0.37|

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
|enous regressors||1.68e-09|0.000952|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets.*** p<0.01, ** p<0.05, * p<0.1.Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.
rather than higher productivity compared to other firms (Shleifer, 1998; Harrison et al., 2019) and they are connected firms aiming to shield their market position (Colonnelli & Prem, 2022). On the other hand, unreported results show no employment insulation for non-state-owned large firms, suggesting that the connectedness and lack of competition felt by state-owned firms is what drives the results rather than their large size.

# **VI. Effectiveness of Compensatory Mechanisms**

Workers may adjust to changes in employment and earnings by seeking government support. This support normally takes the form of unemployment insurance, need-based cash transfer programs and/or job training. While the Brazilian government provides unemployment insur-

> 42It would be preferable to measure the Herfindahl index of concentration based on firm output shares since heavily concentrated sectors in terms of employment may simply reflect one large and highly productive firm rather than the lack of competition. Unfortunately due to restrictions in access to the Brazilian firm-level PIA data, we can only use firm employment shares. The competition variables are measured based on the worker’s firm and sector as of 2008.

> 43The result for state-owned firms is weak statistically but that is expected given the very small percentage of the sample that is accounted for by workers in state-owned firms (1.5%).

22
own. We have also included in the analysis an indicator variable for workers below or at the national minimum wage in a given year. Minimum wage information is collected from the ILOSTAST Database. The description of trends in earnings and other variables in the next section is based on this dataset and variable definitions.

# **3. Falling Inequality in Brazil and Its Five Potential Drivers**

As noted earlier, Brazil has experienced a non-trivial reduction in income inequality since the macroeconomic stabilization of 1994-1995. This decline was particularly pronounced since 2003, a period during which average incomes also grew relatively rapidly (Figure 1, Panel A) and poverty fell sharply.<sup>9</sup> Brazil was not alone: similar trajectories were observed in a number of other Latin American countries over the same period. A comprehensive discussion of the trends in Argentina, Brazil, Mexico and Peru can be found in López-Calva and Lustig (2010).<sup>10</sup> Much of the popular discourse on falling inequality in Latin America has stressed the role of growing fiscal redistribution, through new social protection instruments such as conditional cash transfers (CCTs). Indeed, Brazil’s federal government launched a conditional cash transfer program named _Bolsa Família_ in 2003, which has since reached over 50 million people, and become one of the world’s largest CCT programs. Although _Bolsa Família_ and other fiscal redistribution programs did contribute to the reduction in household income inequality, the best available estimates put this contribution at between 30-40 percent of the overall decline (Barros et al., 2010; Azevedo et al. 2013). Another 10 percent or so has been attributed to demographic factors, chiefly the rapid decline in family sizes, which has been most pronounced among poorer households. The remaining 50-60 percent of the decline in inequality in household incomes has been attributed to changes in the distribution of labor earnings. It is these changes that are our subject here.

Figure 1 provides a visual description of the basic income trends in
in Panels B and C are daily averages from self-reported diaries. There are more observations than IDs in Panel C, Column 3 because 17 women took part in 2 rounds of diaries. Sickness is an indicator variable equal to 1 if the household or respondent reported having a cough, fever, malaria, typhoid, diarrhea, cuts or burns, or any other illness.

The exact number of observations differ for some variables due to missing values. Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period. Standard deviations in parentheses.
15 half of those using LPG used it as their primary cooking fuel. At the opposite end of the spectrum, Angola, Gabon, and Mauritius stood out as those in which the uptake of LPG was not only high but was also essentially synonymous with use of LPG as the primary cooking fuel.

**Figure 8  Comparison of households using LPG in any capacity and those citing LPG as the primary cooking fuel**

_Source_ : World Bank staff calculations using household survey data and RIM LPG Intelligence Daily. _Note_ : LPG prices average Saudi Aramco contract prices for propane and butane in the month that is halfway through the survey period and converted to 2022 U.S. dollars using the U.S. GDP deflators.

Because global LPG prices varied several-fold during the period covered by the surveys, the average Saudi Aramco contract prices for propane and butane in 2022 U.S. dollars per kg are also shown in Figure 8. Because the transportation, storage, and bottling costs also vary several-fold from country to country, compounded by price subsidies in some countries, the free-on-board LPG prices merely give indications of the global LPG price movements and some idea of end-user prices in countries with no subsidies at the time, which include southern African countries except Angola, Kenya, Liberia, Nigeria, Tanzania, and Uganda. As an extreme example of a very large difference between end-user prices and the Arab Gulf prices, the average of maximum retail prices of LPG established by the government of South Africa in 28 regional zones was higher than the Arab Gulf price by nearly US$1.30 per kg,<sup>7</sup> tripling the retail price from the Arab Gulf price and markedly higher than US$0.30 assumed in the calculation of annual household income needed for monthly purchases of 12.5 kg of LPG. By contrast, in Nigeria, where LPG prices have been deregulated for a long time, the difference was US$0.30 per kg.<sup>8</sup
The initial situation for food trade (trade in raw food from agriculture and processed food from industry) is of particular importance for the simulations in this paper. Drawing on data in Tables 2.3-2.5, for the net food exporter, exports, imports, and the trade balance for food (raw agricultural and processed industrial) represent 7.2, 4.4, and 2.8 percent of GDP, respectively; for the food net importer, the same shares are 2.8, 7.7, and -4.9 percent.<sup>15</sup> At a more disaggregated level, exports and imports represent only a few percent of domestic output or domestic demand for food agriculture (unprocessed food) but are more important for processed food for which the correspond shares range between 8 and 27 percent.

# <u>3. SIMULATIONS</u>

Initially we analyze the _base_ and world price shock simulations for each of the two LIC archetypes, starting with the net exporter. Given that the impact of world price shocks on the net exporter LIC are positive or only mildly negative, not warranting major domestic policy responses, we limit our domestic policy simulations to the net importer archetype, for which we address the impact of domestic subsidy and transfer policies aimed at mitigating negative effects of world price shocks.

BASE AND WORLD PRICE SHOCKS: THE CASE OF THE NET EXPORTER The _base_ scenario is designed to represent a plausible projection for an LIC economy for the period 2009-2025; it also serves as a benchmark for comparisons with alternative simulations.

For the _base_ scenario (but not for the other scenarios), growth in GDP at factor cost is exogenous. The growth rate in 2010 is set at 6.3 percent, the observed aggregate rate for all LICs. Starting from 2011, we impose the average annual growth rate for the same group of countries for the period 2000-2010, 5.2 percent.<sup>16</sup> Other key assumptions may be
de Leyes Especiales de Reparación_ . Other benefits in cash include: _Bono bodas de oro_ , _Bono de invierno_ , _Bono marzo_ , _Asignación Familiar_ , _Subsidio empleo joven_ , _Aporte estatal Fondo de Censatía Solidario_ , _Descuento Cotizaciones de Salud_ , _Beca Indígena_ , _Beca Retención Escolar_ , and _Beca Presidente de la República_ .

> 9 The near-cash transfers included in the analysis are: Progama Nacional de Alimentación Complementaria, Progama Nacional de Alimentación Complementaria para el Adulto Mayor, Programa de Alimentación Escolar, Yo elijo mi PC, and Útiles Escolares.

> 10 Chile’s tax burden of 16.7 percent of GDP, as reported by administrative data, does not necessarily coincide with the figures published by multilateral organizations due to differences in concepts and definitions. Using revenue statistics of the OECD (2016b), the tax burden in Chile in 2013 is placed close to the Latin American average, but well below (by about 12 percentage points) the tax burden of Argentina, Brazil and the OECD average.

> 11 The FCT is levied on income from capital and from enterprises that undertake commercial, industrial, and other activities. The FCT paid by an enterprise can be used as a credit against the CGT to which its owners, shareholders, partners or managers are liable when they receive dividends.

5
. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

Moreover, previous works using the same firm-level dataset have emphasized the role of state-owned firms relative to private companies in India (Topalova, 2004; Alfaro and Chari, 2009). One could argue that state-owned companies might have a greater lobby power to induce the government to reduce tariff on those goods that they use as intermediate ones in the production of final goods. In order to address 43
<!-- Start of picture text --> Share of Informal Employees in the Labor Force vs. EPL Strictness<br>in European countries, 2004-2009<br>Eastern and Southern Europe Western and Northern Europe<br>GR<br>IE<br>IE<br>IE<br>PT UK<br>BG UA PT<br>UK UK<br>SK HUHUBGCZPLRURUEE LVITLVSISIEESILT ROES PT CH DK DK NLATAT NLNL NO FRFR<br>BE BE<br>HUSK EE FI DE SE<br>1.5 2 2.5 3 3.5 1 1.5 2 2.5 3<br>EPL strictness EPL strictness<br>East South West North<br>Fitted values, GR excluded Fitted values<br>Not included: Israel (EPL strictness 1.46, informal employees 25%)<br>Sources: Share of Informal Employees - calculation with ESS data<br>EPL strictness (version 2): OECD and Muravyev (2010)<br>.<br>Figure 1 Share of informal employees vs. EPL strictness<br>Share of Informal Employees in the Labor Force vs. Min Wage<br>in European countries, 2004-2009<br>Eastern and Southern Europe Western and Northern Europe<br>IL<br>IE<br>IE<br>GR GR<br>IE<br>UK<br>UA LV UA PT PT SI BG UK UK DK ATAT<br>RO E EEE ES SKPL PL ES ES HU CZITBGPTLVHULT SISI CH NODK NLNL NLFRFR<br>SK DE BE BE<br>EE HU FI SE<br>30 35 40 45 50 55 35 40 45 50<br>Minimum wage, Minimum wage,<br>% of average wage in industry & services % of average wage in industry & services<br>Countries without a min wage Countries with a min wage<br>Fitted values Countries without a min wage