rafmacalaba/data-use-mentions-extended huggingface dataset:
To mitigate the impact of these factors, several methods are used. First, researchers, starting with pioneering work on Indonesia by Alderman and Garcia (1993), attempt to model the observed changes in consumption. They also construct transition matrices by quintile using predicted consumption rather than actual consumption (e.g. Woorland and Klasen or Hyat). These estimated transition matrices put a bound on possible measurement error. Second, some researchers use long panels to distinguish between persistent shocks to income or consumption and transitory ones (Friedman, 1957). For longer panels expanding more than two rounds, it is typical to average several periods and work with the resulting moving averages. By taking an average over a period of time, measurement error and transitory shocks will be partially averaged out (Shorrocks, 1978). Due to availability of just two rounds in Egypt panel we limit ourselves to the first approach.

# **III. Data and methodology**

Living standards in Egypt are monitored with the high-quality large household survey: Household Income, Expenditures and Consumption Surveys (HIECS). Conducted every five years since 1995, these surveys have been the main (and the only official) source for poverty and inequality data in Egypt. In late 2007, faced with multiple policy demands arising from the social policy agenda, the authorities decided to make the data collection more frequent. Since the preparation of a new large survey takes time (including the need to update the sampling frame following the new 2006 census), and the survey itself takes 12 months to collect all data, the decision to proceed without delays resulted in a design requiring less preparation: it was decided to re-visit in 2008 the households interviewed in the February during HIECS 2004/05, applying the same questionnaire. International experts contacted by CAPMAS (Kalton) confirmed the feasibility of such an approach. CAPMAS conducted the field work, revisiting all addresses, completing the survey and matching the new sample to the 2004/05 data. The Household Income, Expenditure and Consumption
(10 percent) and sales (9 percent) in 2004 (the VHLSS 2004).

33 Some economists relate the evolution of relative wages of females to women’s changing comparative advantage as economies develop (Fan and Lui, 2003; Galor and Weil, 1996; Goldin and Sokoloff, 1982). They argue that when economic structure changes, rewarding the attributes in which women have a comparative advantage, this in turn leads to a narrowing gender gap in wages. The common assumption in the literature is that men are relatively better endowed for their performance of physical labor whereas men and women are equally endowed for mental labor. Thus, females have a comparative advantage in sectors which require less physical labor. A historical illustration which supports this view may be found in the early stages of industrialization in the U.S.. Goldin and Sokoloff (1982) argue that rising demand for unskilled labor in manufacturing during the first half of the nineteenth century in the U.S., in particular in female-intensive manufacturing such as was found in the cotton and wool textile industries, drew female workers from agriculture (where women had less comparative advantage), and that both the relative wage of females and labor force participation rates for females increased during the same period. Fan and Lui (2003) develop a model focusing on different endowments in physical labor and mental labor in determining occupational choices and show that the gender gap is smaller in occupations and industries in which physical labor is less intensively used. Using the 1981 and 1991 Hong Kong population censuses, Fan and Lui (2003) suggest that the reduction in the gender gap in Hong Kong SAR, China during this period is attributable to its structural change from a manufacturing- to a service-oriented economy in favor of women’s comparative advantage. Running Mincerian earnings equations separately on the subsets of industrial sectors, they show that gender gaps were much larger in primary and manufacturing sectors (where physical labor is more intensively used) than in service sectors, which are more mental labor
# **<u>Table 2: Model Parameters and calibration</u>**

||**Baseline Scenario**|
|---|---|
|**_Estimated Parameters_**||
|Share of informal job offers (_ψ_)|0.77|
|Bargaining power for the formal sector (_βf_)|0.99|
|Bargaining power for the informal sector (_βi_)|0.99|
|Scale factor in matching function (_a_)|0.19|
|Arrival of self-employment opportunities (_α_)|0.11|
|Job destruction rate in the informal sector (_λI_)|0.11|
|Job destruction rate in the formal sector (_λF_)|0.01|
|Job destruction rate for the self-employed (_λS_)|0.08|
|**_Labor market composition_**||
|Share informal work|0.297|
|Share formal work|0.201|
|Share self-employment|0.278|
|Mean log-wage informal sector|7.48|
|Mean log-wage formal sector|8.01|
|Std log-wage informal sector|6.33|
|Std log-wage formal sector|6.64|

Source: Author’s own estimates using the 2015 MLFS.

# **Deregulation:**

Our first set of simulation assume the full abolishment of payroll taxes and of the minimum wage. One would expect that abolishing such regulations would decrease the labor costs for formal employment. As such, in general, such policy would contribute to a lower unemployment and to increase the share of workers with jobs in the formal sector. Simulations confirm such results, with a much higher impact for youth and women than for the average worker. Results are presented in Table 3. Changes in the composition of employment are presented in percentage points while changes in wages are presented in percent. In order to assess the statistical significant of the results, standard errors for every scenario
# **List of acronyms**

|AIS|Automatic Identification System|
|---|---|
|API|Application Programming Interface|
|BDI|Baltic Dry Index|
|CES|Constant Elasticity of Substitution|
|CPPI|Container Port Performance Index|
|FOB|Free on Board|
|GSCPI|Global Supply Chain Pressure Index (FED New York)|
|GSCSI|Global Supply Chain Stress Index|
|IT|Information Technology|
|LPI|Logistics Performance Index|
|MDST|MDS Transmodal|
|PMI|Purchasing Managers’ Index (IHS Markit (S & P))|
|SITC|Standard International Trade Classification|
|TEU/MTEU|Twenty-foot Equivalent Units / 1 million Twenty-foot Equivalent Units|
|UNLOCODE|United Nations Code for Trade and Transport Locations|
|USD|United States Dollar|

21
tend to exhibit the larger numbers of exporters. Among developing countries, the largest numbers of exporters are found in Turkey and Mexico followed by South

> 31 These matrix tables are available only for the developing countries for which we have the customs exporter-level data. We are unable to provide matrix tables for the developed countries in our sample.

> 32 For countries for which data is available for only 1 or 2 years within the period 2006-2008 we compute the average based on those years. For Kuwait and Portugal the data coverage does not include that period. Hence for Table 2 and for Figure 1 as well as for the Figures in Appendix 3 we use averages for the period 2009-2010 for Kuwait and averages for the 2003-2005 period for Portugal.

18
our results mirror how much, for example, the UK average science score changed between 2009 and 2015 (5 points), and how much the Brazilian average science score decreased over the same period (4 points).

Unlike PISA, the data in Prova Brasil includes school identifiers that allow for a one-to-one match with the schools surveyed in the 2013 WMS wave.<sup>14</sup> In total, we have 262 matched schools in the public sector. We use this matched sample in Figure 3 where we show a school-level binned scatter plot of WMS management score against the Prova Brasil-based management score. There is a positive and significant correlation of 0.19, suggesting reasonable internal validation of the Prova Brasil index. As with the PISA index, we repeat the exercise of correlating the new index with student performance in secondary schools and find the same pattern (see Figure 4).

In Table 1 we formalize these relationships by reporting the average correlations between student learning and our management indices. For the student-level PISA dataset, we run OLS regressions via the OECD’s repest Stata command, which uses the five available test score plausible values for each student and subject. We report the standard errors in parentheses and _p_ -values in square brackets. The standard errors are clustered at the school level and use the appropriate survey weights.<sup>15</sup> In the PISA specifications we include country fixed effects, and successively introduce school controls (dummies for school location, student-teacher ratio, log of the number of students, share of government funding relative to total funding the school receives, and ratio of computers connected to the web used as a proxy for school resources) and then student controls (gender, grade, socio-economic status and immigration status). All panels use the same sample but have different subject outcome variables. The R-squared for each set of regressions is reported within each panel, while the common sample characteristics and controls included are reported at the bottom of the table. Column (
suspending federal transfers, not guaranteeing new loans, or explicitly determining situations in which local governments can default on their liabilities. Overall, the FD Law implicitly reduced the incentives of banks to lend to local governments. Evidence displayed in Figure 3 is consistent with this hypothesis—after the FD Law is introduced, banks reduce their share of lending to the government (Panel A) and the risk premium of loans to private firms vis-a-vis loans to the government drops (Panel B).

# **3. Data**

The datasets used in the analysis come from seven sources. The first data set is the _Single Public Registry_ , provided by the Ministry of Finance. This data set reports the debt of subnational governments (at the state and municipal levels) decomposed by credit sources on a quarterly frequency. In addition to total public liabilities, the data set also includes the lending terms contracted by local governments such as interest rates and maturity. Based on this data, we construct the ratio of public debt to freely disposable income of local governments in March 2016—one month prior to the enactment of the Law—to measure the ex-ante indebtedness of subnational governments. This ratio is highly persistent over time and strongly associated with the public-debt-to-net-income financial health indicator that local governments were required to report in the alert system from 2017 onwards (correlation of 0.88).

The second data set consists of a state-year panel containing the annual public expenditures and public revenues of each state in Mexico from 2014 to 2018. This data set is publicly available from the National Institute of Statistics (INEGI). On the expenditure side, we use information on the total expenditure of state governments as well as on certain public expenditure categories. The first category— _transfers, subsidies, and other aid_ —is the largest expenditure group of state governments and represents almost 60 percent of expenditure of states. 76 percent of expenditures within this category comprise funding for health, education and social security programs and institutions. The second category corresponds to _public infrastructure_ spending
We repeat the above analysis for current grade, grade at entry, and rate of promotion.

Except for the dependent variable, the model specifications and decompositions are the same and so need not be repeated.

An issue we are unable to deal with because of lack of data is sample selection bias." This bias could potentially come from two sources. The universe of staff who are currently active is the result of hiring choices and staff attrition. To assess the existence and extent of bias in pay and grade levels and promotion, it would be necessary to examine time series data. Full histories for the staff who are currently employed at the Bank are not sufficient, because they leave out others who have already left the Bank. Although potentially a problem, we think that it is not a major one: attrition tends to be low. For example, excluding the last three years, attrition at the Bank has been around 3 percent per year.

# _Variables_

This section summarizes the variables used in the analysis and described more fully in Appendix Table 1. The variables comprising X are dummy variables for the year of entry to Bank employment (this captures tenure as well as any circumstances specific to the year of appointment), age at entry (entered in linear and squared forms), dummies for maximum completed level of education (doctorate, masters and bachelors degrees), dummies that interact the education degree with its country of location and discipline, dummies for the university where economics PhDs wvere obtained, number of years of work experience prior to joining the World Bank, current marital status, dummy variables for current nationality, and current "This bias might affect both the salary and grade analysis.

10
As a result, the welfare costs from increased poverty relative to those from increased mortality are much higher for poorer countries and tend to fall markedly with income per capita. There is, of course, non-trivial variation at each level of GDP per capita, reecting dierences in demography, inequality, and policy choices. But the evidence we present makes it dicult to escape the conclusion that the optimal policy response to the pandemic is not one size ts all. It will almost certainly dier across countries, with policy makers in poorer countries being justiably more concerned with the poverty costs faced by their populations than those in richer countries.

We have not sought to identify the optimal position countries should take when facing policy trade-os. We do not even believe that there necessarily is a trade-o between lives and livelihoods for every policy response to Covid-19. Developing a vaccine, for example, would clearly reduce both lost-years and poverty-years in the future. The same is likely true for early contact-tracing and large-scale testing. Imposing social-distancing early likely dominates doing it late (Demirguc-Kunt et al., 2020), and so on.

What we hope to have contributed is a simple and transparent approach to estimating the relative welfare costs of Covid-induced increases in mortality and poverty. The evidence we have assembled suggests that there are very large poverty costs across the world, and particularly in developing countries. A concern with these poverty eects is clearly ethically legitimate and economically important. We hope our approach can contribute to more informed public debates on policy choices aimed to mitigate the welfare costs of the pandemic.

# References

- Abay, K., T. Kibrom, and A. Woldemichael (2020). Winners and Losers from Covid-19: Global Evidence from Google Search. World Bank, mimeo.

- Acemoglu, D., V. Chernozhukov, I. Werning, and M. D. Whinston (2020). A Multi-Risk SIR Model with Optimally Targeted Lockdown. NBER, Working
which appears to have been due to low demand for the program.<sup>15</sup> Khandker et al. (1998) assess the village-level impacts of the three programs using a regression model applied to village level data, with program placement indicators for whether a particular village has a particular kind of program. The data used came from a household survey administered in 1991-1992. A limitation of the analysis is that it does not allow program placement to be based on unobservable village attributes, which could lead to bias in the estimated program impacts. Also, the study does not distinguish between impacts on male and female borrowers within the village.

Khandker et al. (1998) find that all of the programs have positive impacts on income, production and employment, especially in the non-farm sector. For example, average household income increased by about 20-30% in villages with the programs. However, only the Grameen Bank, which on average provided the largest loans, increased household labor supply (by 7%); the other programs appear to have reduced labor supply (by 11-12%). Also, only the Grameen Bank had an effect on the average village level wage, which the authors hypothesize was due to a general equilibrium effect stemming from the decrease in the supply of wage workers and an increase in self-employment. For this reason, the Grameen Bank appears to have had important positive spillovers on wage workers not directly participating in it.

# **4.1.2  Gender-targeted Grants in Sri Lanka**

A recent study examining the differential impacts of providing grants to women and men is de Mel, McKenzie, and Woodruf (2007), which estimates the returns to capital for Sri Lankan microenterprises owned by women and men using a randomized social experiment. The experiment randomly provided cash or equipment grants in the amount of 10,000 or 20,000 rupees. 10,000 rupees was equivalent to about three months of median profits. The study finds that about 75% of the grants were
# 8 Appendix: Data

Table A1 lists all the countries in our main sample of Figure 1. The total is 189 countries, comprising 152 EMDE and 37 AE or 94 commodity-exporters and 95 noncommodity exporters.

Figures 1 through 16 plot the country correlations between the HP filtered cyclical components of real government spending and real GDP (WEO, IMF, April 2022). The sample period is 1980-2020, but the panel is unbalanced since some countries do not have data for the whole sample period. The institutional variables (political risk, bureaucracy quality, control of corruption, government stability, and law and order) are taken from the International Country Risk Guide (ICRG). The threshold to distinguish countries with better institutions from those with worse institutions was computed by taking an average score for each country between 1984-2020 and comparing it to the cross-country median score. For capital controls, the Chinn-Ito index was used to calculate the financial openness of an economy for the period 1980-2019. The Ilzetzki et al. (2021) database was used to calculate the average exchange rate regime of an economy between 1980 and 2019.

In the regressions of Section 3, the size of the public sector is defined as government spending as a proportion of GDP, from WEO (IMF). WEO is also the source for GDP per capita. For openness (defined as exports plus imports as a proportion of GDP), the source is the World Integrated Trade Solution (World Bank). In the regressions of Section 4, fiscal rules are taken from the fiscal database in Davoodi et al. (2022). This database distinguishes between budget balance rules, debt rules, expenditure rules, and revenue rules. The variable fiscal rules is a dummy-variable that takes the value 1 if any rule is being enforced and 0 if none is. We then take the average over the country sample. Sovereign wealth funds (SWF) is a dummy-variable that takes the value 1 if a fund is in existence and 0 if it is not. We then take the
The individual NTB effects for each RTA are summarized in Figure 3, Panel (a). On the x- axis, the 258 RTAs that entered into force between 2000 and 2015 are displayed and ordered by the size of the NTB effect, the y-axis shows the corresponding estimated NTB effects. For the significant agreements, 95% confidence intervals are included. As a robustness check, we estimate Equation (6) using the data starting in 1989 (only manufacturing and agriculture). Panel (b) plots the two sets of NTB coefficients against each other. If all of them were identical, they would perfectly align on the 45-degree line. Eyeballing already tells us that the two estimates are very similar. In fact, the correlation between the two distributions of the NTBcoefficients is 0.85 indicating that the results are robust to extending the period of observation. The comparison between the two sets of coefficients provides an additional interesting insight as it highlights the role of services trade: The NTB effects of the short panel, which includes services trade, are more often larger than the NTB effects of the long panel (only agriculture and manufacturing).

# **5.2 Predicting NTB Effects Using the Lasso Method**

Next, we ask whether we can find persistent patterns in the content of TAs that help predict the NTB effects—positive as well as negative ones. We would like to answer the question of whether there is a systematic difference in the content of the TAs that generate high NTB effects compared to those that have no or even a negative NTB effect. To do so, we need detailed information on the content of TAs. The World Bank has put together a new dataset that analyzes in detail 18 key policy areas for roughly 200 RTAs (Mattoo, Rocha, et al. 2020). For each agreement and policy area, the database provides granular information on the specific provisions covering stated objectives and substantive commitments, as well as aspects relating to transparency, procedures, and enforcement.<sup
The prevalence of stunting among all children in the EGSF survey (61.6%) is slightly higher than in the 1987 DHS survey and quite a bit higher than in the 1995 DHS survey (57.9% and 49.7% respectively). This higher prevalence reflects the EGSF restriction on rural areas. The prevalence of stunting in rural areas of Guatemala is 62.1 % and 56.6% respectively in the 1987 and 1995 DHS surveys.<sup>5</sup> More than half (50.4%) of the stunted children in the EGSF are extremely malnourished (as determined by height-for-age z-scores below -3.0).

# **4.2 Age Pattern of Height-for-Age**

Figure 1 shows the mean z-scores for height-for-age by age group and sex in the EGSF sample. The height-for-age pattern does not seem to vary significantly by sex, a usual finding in Latin America. Following the typical age-pattem observed in developing countries, the average z-score decreases up to age 24 months and then tends to level off (Martorell and Habicht 1986). The negative z-scores observed for children at birth indicate that the malnutrition process leading to deficits in height-for-age is likely to have begun prenatally, when inadequately nourished pregnant mothers failed to provide a satisfactory nutritional intake to their fetuses.

# **4.3 Ethnic, Education and Income Differentials in Height-for-Age**

Table **1** displays the mean height-for-age and the percentage of children stunted among children of different ethnic, education and income categories in the EGSF sample. The figures confirmn the existence of a very marked socioeconomic gradient in child health and nutrition, which was found in previous research in Guatemala (Pebley and Goldman 1995). Note that the prevalence of stunting of children of ladino mothers is less than _50%_ while it is almost 80% among children of indigenous mothers who do not speak Spanish. Differentials are also very important in terms of both mothers' and husbands' education. Note the very low prevalence