rafmacalaba/data-use-mentions-extended huggingface dataset:
because more men and boys than women and girls died. Further, about 2 million Rwandans, commonly referred to as _new caseload refugees_ , escaped to the Democratic Republic of Congo and Tanzania immediately after the genocide in fear of revenge by the RPF and persecution for their crimes. Among the new caseload refugees were perpetrators of the genocide, who were mostly males, and Rwandan civilians. About 600,000 new caseload refugees returned from Congo to Rwanda and another 500,000 refugees returned from Tanzania to Rwanda in late 1996 (World Bank 2003). A smaller portion of the new caseload refugees, mostly former militias, became involved in the Congo wars and were repatriated to Rwanda in the period 1997-2000 (Verwimp and van Bavel

> 4 Using population data from the province of Gikongoro, Verpoorten (2005) concludes that Tutsi women had only a moderately higher probability of survival than Tutsi men, with the respective probabilities being 0.29 and 0.21.
can be explained by teaching practices (Burchinal, 2018; Leyva et al., 2015), there are several studies that highlight its importance for low- and middle-income countries, especially at the kindergarten level. For example, a study in Ecuador found a one SD increase in teaching practices is associated with a 0.18 SD increase in learning outcomes (Araujo et al., 2016). In Ghana, teaching practices account for a 0.07 to 0.17 SD increase in student learning outcomes (Wolf et al., 2018) – similar results are found in Chile (Leyva et al., 2015). Further, there is evidence that improvements in teaching practices lead to positive effects on student learning. For instance, a meta-analysis of over 60 coaching programs found those designed to improve teaching practices (0.58 SD), also resulted in increased learning outcomes (0.15 SD) (Kraft et al., 2018).

Even when education systems attempt to capture teaching practices, most tools used in low- and middle-income countries fall short on several accounts, as they: (i) measure either the quantity or quality of teaching practices; (ii) do not explicitly focus on teachers’ efforts to develop students’ socioemotional skills; (iii) use tools designed for other contexts, which may include irrelevant items or fail to include important ones; and (iv) use tools that are neither evidence-based nor meet basic reliability criteria.

The _Teach_ classroom observational tool was developed in response to these concerns and to foster the measurement of teaching practices in low- and middle-income countries. _Teach_ measures teacher practices at a primary school level and is intended to be used as a system diagnostic and monitoring tool and for professional development. As a diagnostic and monitoring tool at the system level, _Teach_ helps governments identify bottlenecks in service delivery, monitor the effectiveness of their policies, and focus efforts to improve teacher practices. As a professional development tool, _<mark>Teach</mark
**MONGOLIA'S INFORMAL SECTOR** -

**Overview**

**7**

bureaucratic hurdles, and the ability to self-finance afforded to small scale activities - the greater ease of entry will be argued to be a necessary component of any explanation of informal sector growth. We will also examine the final result of these factors, incomes that are frequently higher in the informal sector than in the formal sector. Taxation of the formal and informal sector sectors will be discussed, and it will be shown that informals of average and high incomes generally pay less tax than formal employees with similar income, but due to the regressive nature of the system of taxation, poorer informals pay very large amounts of tax relative to their incomes. In Section 4 we evaluate the sum of the knowledge presented by the study, and answer the question "what good is the informal sector?" Two appendices provide supplementary information. A narrative about Ulaanbaatar's distribution and transportation sectors, sectors in which informals play a large role, will be provided in Appendix A. The purpose of such a narrative is to provide the reader with a flavor for the informal activities of Ulaanbaatar: the manner in which factors of production are organized, the forms of "ownership," characteristics of participants, treatment by officials, the incomes generated, the level of competition, and the value added. Particular emphasis will be placed on kiosks and taxicabs, two informal occupations that were surveyed in large numbers for the purposes of this study. Appendix B contains the methodological detail related to the informal sector surveys conducted during this study, as well as tables with descriptive statistics derived from those surveys.
that in the 2011 Census of India. However, this similarity hides important discrepancies across administrative types, especially in the midrange between villages and municipal corporations. The exercise also shows that relying exclusively on one type of indicators, such as nighttime light, can yield potentially misleading results for India’s rate and speed of urbanization.

## _The urbanization rate in 2011_

According to the 2011 Census of India, the share of the population living in urban areas was 31.2 percent. But this figure is computed for a set of units broader than our universe because we excluded small states and union territories as well as villages for which information was missing. If only the 564,052 units considered in our analyses were retained, the 2011 Census of India would yield an urbanization rate of 31.6 percent. This is almost identical to the 31.4 percent rate predicted by the benchmark specification of the probabilistic model (figure 7).

Predictions using other specifications vary, but they also fall within a close range of the official estimate by the 2011 Census of India. The urbanization rate varies between 27.0 percent using built‐up share as the only key indicator and 33.9 percent using only population size. The predicted urbanization rate is similar31.8 percent when using the random forests method.

22
# WPS3493

## The Clash of Liberalizations:

## Preferential versus Multilateral Trade Liberalization in the European Union<sup>∗</sup>

Baybars Karacaoval ı<sup>†</sup> Nuno Limão<sup>‡</sup> University of Maryland

### Abstract

There has been an explosion in the number of preferential trade agreements (PTAs) in the last decade. PTAs are characterized by liberalization with respect to only a few partners and thus they can potentially clash with and retard multilateral trade liberalization (MTL). Despite this important concern with PTAs, there is almost no systematic evidence on whether they actually affect MTL. We model the effect of PTAs on MTL and show that PTAs slow down MTL unless they have a common external tariff and allow for internal transfers. Next, we use detailed data on product-level tariffs negotiated by the European Union in the last two multilateral trade rounds to structurally estimate our model. We con fi rm the main prediction—the European Union’s PTAs have clashed with its MTL–and fi nd that the effect is quantitatively signi fi cant. Moreover, we also con fi rm several auxiliary predictions of the model and provide new evidence on the political economy determinants of multilateral liberalization in the European Union.

JEL Classi fi cation: D78; F13; F14; F15.

Keywords: Preferential trade agreements; customs unions; multilateral trade negotiations; MFN tariff concessions; reciprocity.

> ∗We thank, without implying, Stephanie Aaronson, Kishore Gawande, Bernard Hoekman, Patricia Tovar-Rodriguez and Alan Winters for helpful discussions and comments and Marcelo Olarreaga and John Romalis for generously sharing some data. The views expressed here are those of the authors.

> †University of Maryland, Department of Economics, MD 20742. E-mail: karacaov@econ.umd.edu.

> ‡Corresponding Author: limao@econ.umd.edu. University of Maryland, Department of Economics, MD 20742. I acknowledge the fi nancial support of the World Bank Research Group which hosted me
a larger share of O&M [Operation and Maintenance] and donors a larger share of investments). The integration underlines the need to align and harmonize their activities.<sup>12</sup> The government receipts include direct taxes (from households and mining capital), indirect taxes, and non-tax 11 Mining capital earns most of the sector’s value-added (the sector is very capital-intensive) and the elasticity of substitution between capital and labor is kept at a low level (0.3). Given this, the actual rate of output growth closely replicates the exogenous growth rate for the capital stock. FDI to the mining sector is also exogenous, evolving according to the same scenario for the sector. In order to maintain exogenous evolution of both the capital stock and FDI in the sector, the stock and the investment flow were decoupled.

12 The distinction between on- and off-budget aid is important for some purposes. However, keeping the two separate in the MAMS database would impose an additional data requirement and complicate the analysis given that these two activity types are complementary. For example, the government carries out the bulk O&M for infrastructure while donors are responsible for most new investments. At the same time, it would not yield any significant insights, in particular given limited knowledge about how input structures and output quality differ between donor- and government-operated activities.

6
al., 2006). Agents such as the political elite, administration staff, and legislators wield discretionary power if institutions are neither credible nor working well. This has the negative consequence that citizens lose their trust in the authority. In countries where corruption is systemic and the government budget lacks transparency and accountability the obligation of paying taxes cannot be assumed to be an accepted social norm. Institutional instability, lack of transparency and rule of law undermine the willingness of frustrated citizens to be active in the formal economy. Furthermore, there might be a crowding-out effect of morality among the tax administrators when there are a great number of corrupt colleagues. Moreover, regulatory restraints and bureaucratic procedures not only limit competition and the operation of markets, but also provide a better foundation for corrupt activities. If individuals and businesses believe that neither contracts will be enforced nor productive efforts protected, their incentive to be active in the shadow economy increases. Citizens will feel cheated if they believe that corruption is widespread, their tax burden is not spent well, their government lacks accountability, and that they are not protected by the rules of law.

_2. Empirical Evidence on the Impact of Institutions, Governance and Tax Reforms_ Frey and Torgler (2007) explore the determinants of tax morale in Eastern and Western Europe using the European Values Survey (EVS) 1999/2000. The study covers a large set of countries<sup>3</sup> using a micro data set at the individual level (covering more than 32‘000 observations). To investigate whether institutional quality matters they use six proxies of the governance indicators developed by Kaufmann, Kraay, and Mastruzzi (2004). The variables measure the process by which governments are selected, monitored, and replaced (voice and accountability, political stability and absence of violence), the capacity of the government to formulate and implement sound policies (government effectiveness, regulatory quality) and the respect of citizens and the state for the institutions that govern economic and social interactions (rule of law and
15 range in the 2018 HEFPI version to achieve definitory consistency between these survey families and the World Health Survey (WHS) that only collected pregnancy outcome data for women 18 and older. However, as the number of surveys in HEFPI that have maternal and child health data available from age 15 increases (e.g. by adding MICS 5 and 6 surveys) and as the WHS that was conducted in 2002-2004 becomes increasingly dated, the 2019 HEFPI database switches to the 15-49 age-group whenever the raw survey data permit it. Users of the 2019 HEFPI version’s skilled birth attendance and antenatal care use data should be mindful of the new age-group inconsistency in the WHS data points that continue to come from women age 18-49.

All other indicator definitions of the 2018 HEFPI database remain the same.<sup>15</sup> Table 3 shows the newly changed definitions of the pap smear, skilled birth attendance, and antenatal care indicators.

<u>Table 3: HEFPI indicators with changed definitions</u>

|Indicator|Definition|Main Data<br>Source<sup>16</sup>|
|---|---|---|
|Pregnancies with 4 or more<br>antenatal care visits (% of total)|Percentage of most recent births in last two years with at least 4<br>antenatal care visits (women age**15**-49 at the time of the survey)|DHS, MICS,<br>WHS|
|Births attended by skilled health<br>staff (% of total)|Percentage of most recent births in last 2 years attended by any<br>skilled health personnel (women age**15**-49 at the time of the<br>survey). Definition of_skilled_varies by country and survey but<br>always includes doctor, nurse, midwife and auxiliary midwife).|DHS, MICS,<br>WHS|
|Pap smear in last 3
20 only in 2007. This overall ATT uses the ATT for households receiving remittances in 2007 and compares it to the counterfactual that would occur if these households did not receive remittances in 2007. Results for the overall ATT in column (11) in Table 5 show that all three of the poverty measures – poverty headcount, poverty gap and squared poverty gap -- show a large and statistically significant decrease. According to column (11), the poverty headcount declines by 26.7 percent, the poverty gap falls by 55.3 percent and the squared poverty gap falls by 69.9 percent. On the basis of these findings, international remittances appear to have a large, statistical effect on reducing poverty in Indonesia However, Table 5 shows that international remittances increase income inequality in Indonesia. For households receiving remittances in 2007, but not in 2000, the Gini coefficient of inequality falls by 3.5 percent with the receipt of remittances, while for households receiving remittances in both years (2000 and 2007) the Gini coefficient rises by 1.7 percent. In column (11) the overall ATT for the Gini coefficient shows that the Gini increases by 2.3 percent, and that this increase is statistically significant.

# 6.  Estimating the Marginal Expenditure Behavior of Households

Since we want to examine the impact of remittances on expenditures, it is important to present the type of expenditure data contained in the IFLS Survey (2000 and 2007). Table 6 shows that the survey collected detailed information on five major categories of expenditure, and on several subdivisions within each category. While the time base over which these expenditure outlays were measured varied (from last 7 days for most food items, to last year for most durable goods), all expenditures were aggregated to obtain yearly values. For household durables (stove, refrigerator, automobile, etc), annual use values were calculated to obtain an estimate of the cost
answered positively. The second question asked whether new technology (machinery, equipment) or related know-how had been obtained in the previous two years. Foreign investment was again associated with substantially greater positive responses-in over 70 percent of the FDI cases and 50 percent of the joint ventures, the partner acquired some kind of new technology, as opposed to 35 percent for firms without foreign linkages. Note that the relative difference between the two sets of firms on the training variable ("software") is greater than on the technology ("hardware") variable. These figures illustrate that although we do not have firm-level data on technological effort, foreign partnerships are associated with greater investments in training and knowledge acquisition.

**Figure 3: Training and New Technology**

<!-- Start of picture text --> 70-<br>60<br>400 .. : l t .. ~~~~~~~~~~~~~~~~~~~~~~~~~~JOiflt Venturesl<br>40 ___ _ __ _ _ __ _ __ CFDI<br>30 .<br>20 OOOOOO<br>Tr,l.ing T-ehnology<br>Survey Questlons<br><!-- End of picture text -->

# **IV. Estimation Procedure**

We estimate production functions for the firms included in the sample. Each firm i has a production function for gross output: (1) Y; = FI(Li, Mi, K, T;) 12
Australia‘s missing data, did not experience the same influx of migrants from Asia that Australia did. In other words, Australia represents such a large share of immigration in Oceania that when it is removed, the remaining countries (mostly small island countries that are origins, not destinations) are not particularly accurate predictors of migration to Australia.

The U.S. case is similar. Using interpolation to fill in the missing years proves effective, while the results from simulation 1 are also reasonable. The results from simulations 4 and 5 are less accurate. The problem with using regional shares for calculating missing coefficients for the United States is similar to that for Australia. The poor results are due to the differences in the migrant profiles of the United States and Canada, which provides the weights for filling in the missing U.S. values. This methodology significantly underpredicts the numbers of migrants from U.S. dependencies, since Canada hosts very few of them, and overpredicts the numbers from former British colonies, populations that are more prominent in Canada.

Simulations 4 and 5 perform extremely well for Switzerland: the deviations from the actual data are less than 1 percent. This is due to the fact that several large Western European nations have similar migrant profiles to Switzerland, unlike the case for Australia and New Zealand and the United States and Canada. The data for 1970–2000 prove better for interpolating the missing data for Switzerland for 1960, while the data for earlier years are somewhat less effective at predicting the missing data for 2000.

The results for Chile are also good. Using the data for Chile in other years and the propensity measures yields a margin of error that is under 6 percent (simulation 1). Interpolation proves accurate when data for either 1960 or 200 are removed. With subregional shares, the differences in the log ratios are small, but the correlation coefficients are not as high as in other cases because Chile‘s immigrant profile is bimodal. Chile has a small number of large immigrant stocks
percentage change in male and female employment AC. That is for males = (M empl. AC- M empl.)/M empl. BC; for females = (F empl. AC- F empl.)/FempBC. This value differs from the % change given in columns 3 and 6 of Table 1.

While low levels of female participation in manufacturing persist, the focus of this paper is on the moderate changes that COVID-19 has brought about in terms of the employment gap and the underlying explanatory factors in Egyptian manufacturing. On account of the vast use of lockdown measures, COVID-19 has induced both a demand and a supply side shock. In this paper, we look at these separately as well as the effect of the combined demand and supply shock on the gender gap. The demand shock is inferred from reported declines in domestic and foreign quantities demanded, and the supply shock from reported declines in production and in capacity utilization. The combined shock combines the two. Figure 2 shows that the demand shock (Figure 2a. as opposed to the supply one has hit men harder resulting in relatively larger improvements in the gender employment gap. The overall effect of the combined shock is also to improve the gap (Figure 2c).

<!-- Start of picture text --> Figure 2: The Gender Gap (median) pre- and post-COVID-19 by type of shock<br>a. Demand shock b. Supply shock c. Combined shock<br><!-- End of picture text --> Source: Authors’ own elaboration using the EIFBS. Weights are used. Gender gap is the difference of the number of male and female employees before COVID (BC) and after COVID (AC).

Despite the overall improvement in the gender gap, it is possible to find heterogenous effects of firm characteristics. ‘Status variables’ such as formality or export status, size and sector could have varying effects on the gap. For example, while the gap is considerably larger
patterns.<sup>13</sup> _Weather shocks_ : Data on weather shocks is taken from the Livelihoods, Early Assessment and Protection project (LEAP). LEAP combines ground and satellite rainfall data collected throughout the year to provide rainfall data for each district in Ethiopia.

We use a weighted average of the rainfall during the growing season for the analysis. The relevant rainfall measure for this analysis is not necessarily the total rainfall that falls in the season as the timing of the rainfall also matters for crop production. In order to aggregate decadal rainfall data into one number which accurately reflects both the quantity and the timing of the rainfall experienced in the district in that season, we use a crop model, chosen based on the crops usually grown in a given place. The crop model measures how sensitive the crop is to rainfall losses at that moment in the production cycle. Rainfall losses in that 10-day period are given more weight. The

> 12 Occasionally there were months in which prices of certain commodities were not collected on certain markets. This data is treated as missing.

> 13 Specifically, the prices are expressed in log of Ethiopian birr per kilogram, and birr per day for wages.

10
# Informality, Consumption Taxes and Redistribution

Pierre Bachas, Lucie Gadenne & Anders Jensen<sup>_∗_</sup>

## JEL: E26, H21, H23, 023

Keywords: Household Budget Surveys, Inequality, Informality, Redistribution, Taxes.

> _∗_ Pierre Bachas: World Bank DECRG Macro & Growth, pbachas@worldbank.org. Lucie Gadenne: University of Warwick, Institute for Fiscal Studies and CEPR, l.gadenne@warwick.ac.uk. Anders Jensen: Harvard Kennedy School and NBER, Anders ~~J~~ ensen@hks.harvard.edu. The findings and conclusions are those of the authors and do not represent the views of the World Bank. We would like to thank Michael Best, Anne Brockmeyer, Roberto Fatal, Xavier Jaravel, Michael Keen, Joana Naritomi, Henrik Kleven, Wojciech Kopczuk, Joel Slemrod, Mazhar Waseem and numerous seminar participants for helpful comments. We thank Eva Davoine, Elie Gerschel, Mariana Racimo, Roxanne Rahnama and Alvaro Zuniga for excellent research assistance. We gratefully acknowledge financial support from Weatherhead Center for International Affairs at Harvard University, the World Bank Development Economics’ Research Support Budget, and the IFS’s TaxDev center. Replication codes for the paper are available here.