rafmacalaba/data-use-mentions-extended huggingface dataset:
weeks, yet learning losses were much smaller (Arenas and Gortazar 2022) than in say, the Netherlands (Haelermans et al. 2021) or Germany (Ludewig et al. 2022), which closed schools for only 10 weeks. But in general, the longer the closures, the greater the losses (Patrinos 2023), everything else constant. There are also differences in the application of distance education. In some countries this was a failure (Agostinelli et al. 2022), while other countries managed to support the online education with parental resources, such as France (Thorn and Vincent-Lancrin 2021).

We contribute to this literature by leveraging a unique individual student-level dataset that assesses student learning outcomes in 2019 and 2021. As the pandemic started, the Government of Uzbekistan announced the closure of all educational institutions from March 18, 2020, for three weeks initially (UNESCO 2020). However, the closure was extended, and the education system operated on a hybrid model the entire 2020/21 academic year. By March 2021, 81 percent of school principals reported school closures for four months or longer (UNESCO 2022). With the rise in Omicron cases, the education system extended the school break in January 2022 for an additional three weeks. This is further confirmed by information from Global Monitoring of School Closures by the UNESCO Institute for Statistics, which reports that schools in Uzbekistan were partially or fully closed for a total of 14 weeks. These school closures and hybrid learning affected around 6 million students in Uzbekistan (UNESCO 2020). We evaluate the impact of the COVID-induced school closures due to the pandemic on learning outcomes of school pupils. Individual-level data for 2019 and 2021 allows us to account for possible pre-existing differences across cohorts, mitigating bias concerns.

2
unemployment and attitudes have long-lasting effects on the employment probability of refugees. The negative effects of initial unemployment are cumulative over time and reach a maximum after more than 15 years, whereas the impact of attitudes is small shortly after arrival, reaches a maximum 3–5 years after arrival, and then decreases over time. Our findings on the role of attitudes differ from Aksoy et al. (2020) and seem to indicate that when natives’ attitudes become more restrictive over time, refugees could feel pressured to increase their integration efforts and search more intensively for a job.

Based on our findings that highlight the importance of an early entry into the workforce in order to ensure an effective economic integration of refugees, we acknowledge the significant challenges that persist and discuss some policy recommendations emerging from our study and some broader policy implications. First, shortening the asylum process is important. In this regard, Switzerland, recently carried out a reform of the asylum process, and starting from March 1, 2019 adopted a reduced asylum procedure, which was approved through a referendum in 2016. Under this policy framework, each stage of the process has a well-defined timeline, and the overall examination of the asylum applications is completed at a faster pace, in most cases within 140 days. Moreover, applicants have the right to receive free legal assistance. In addition to this, temporarily admitted refugees and recognized refugees can work in the canton of residency upon a simple declaration. The rationale behind these changes is to improve the overall efficiency of the system, which includes avoiding a waiting time for asylumseekers of several years, and allowing those entitled to receive protection as soon as possible. The current data availability of the administrative records (i.e., only until 2018) has not yet allowed us to rigorously assess the effectiveness of this reform, which we leave for future research. Second, given the significant variation over time in the way the initial local conditions and other factors affect the pattern of economic integration, we highlight the importance of taking
appearing as a light grey square when all browser extensions were listed. Agents did not inquire about the plugin throughout the duration of the experiment. The plugin worked in the following way. Consider a day when agent _James_ (real name) was assigned the name _Steve_ . Whenever the customer typed “Steve” into the chat, James would only see “Agent” in his chat window. In contrast, the client would still see “Steve.” This name masking included any references to the agent’s assigned name in the chat transcript.<sup>13</sup> The experiment was launched in January 2019 and the name assignment continued until October 2019. The company had full discretion over which agents participated in the study. They included all five agents based in the East African office, and one agent from the West Africa branch. The company informed agents that it was interested in learning more about how customers respond to different agents, and may change agents’ display name in the chat. No further information about the nature or the objectives of the experiment was provided by the company, including the focus on gender. Agents did not ask any follow up questions throughout the duration of the experiment, and the company made no additional requests of agents (in terms of protocols/procedures to follow). It is unlikely that knowledge of the experiment would have affected agents’ behavior as the study was never discussed in

> 12 Of the 1,198 names, there are 1,196 unique full names, and 579 unique first names. Table A1 lists 20 example names, by gender. We do not have access to data sources like birth certificate records or a census to validate how common these names are for men or women.

> 13 The vast majority of interactions occurred in English, limiting concerns about gendered identifiers. Other gendered identifiers like “Sir” or “Miss” are rarely used in 1.3% of chats, and section 4 shows the results are unaffected by excluding any days
our design. In Section 5, we present the results quantifying the extent to which _well-being comparisons_ are altered when accounting for non-monetary dimensions.

# **Section 4.1: Data sources and the Nigerian context**

We compare well-being across the 36 states of Nigeria in 2019. As we explain below, this is a conservative context to probe the answer to question 1.

## **Data sources**

We rely on two main data sources: the 2018-19 Nigeria Living Standards Survey (NLSS) and the 2018 Nigeria Demographic and Health Survey (DHS). The NLSS 2018-19 is a large-scale household survey, focusing on measuring living conditions of the population, collected by the National Bureau of Statistics of Nigeria (NBS) between September of 2018 and October of 2019. The NLSS questionnaire includes wideranging modules, covering demographic indicators, education, health, labor, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators (NLSS Report 2020). The additional data source, the 2018 NDHS is a survey implemented by the National Population Commission (NPC), whose data collection took place from 14 August to 29 December 2018. It provides up-to-date estimates of basic demographic and health indicators, especially, nutrition data relying on records of anthropometry measurements of children under 5 and women between 15 and 49. We complement these databases with mortality information taken from Human Development Indices for the UNDP Nigeria Human Development Report 2016.

## **Monetary poverty is high and heterogeneously distributed across Nigerian states**

Nigeria provides a conservative context for our purpose. Answering question 1 requires contrasting wellbeing comparisons obtained from the monetary dimension alone with well-being comparisons obtained from a multidimensional indicator aggregating the monetary and the non-monetary dimensions. As we show below, Nigeria has a high monetary poverty rate and this rate is very heterogeneous across the 36 Nigerian states. Accounting for non-monetary dimensions is less likely to reverse well-being comparisons in a context where
using data from the 2016 Household Socio Economic Survey, so it refers to the distributional impact of taxes and transfers as of 2016. The third summarizes the main results by type of policy intervention and in total. The paper concludes with a comparison of the distributional effect of taxes and transfers in Mongolia vis-à-vis other countries in the world and a qualitative assessment of the distributional impact of some policy options for the country. The latter gives some basic intuition about the distributional consequences of some policy options currently discussed in the country.

# **II. Description of taxes and social expenditures in Mongolia**

This analysis focuses on taxes and transfers that can be directly traced to have an impact on households’ welfare. It focuses on first order, partial equilibrium, short-term impacts on poverty and inequality measures of welfare across households. Therefore, it analyzes the distributional impact of personal income, value and excise taxes as well as social security contributions (on the revenue side), monetary transfers such as social insurance-assistance programs, and in-kind transfers in public health and education (on the expenditure side). The study also includes quasifiscal expenditures due to subsidized interest rates or energy prices. However, it does not include corporate taxes, custom taxes, royalties or expenditures in infrastructure because, although it is certain that they have impacts on the distribution of households’ welfare, these are more the result of second order, general equilibrium, long-term impacts.

## **<mark>Tax revenue sources</mark>**

General government revenues as a percentage of GDP, have been declining in recent years. They have gone from around 30 percent of GDP in the late years of the past decade, to around 25 percent for the years 2014-2016. However, the main sources of revenue (i.e., corporate, personal, value added, excise and custom taxes, social security contributions and royalties) have remained quite stable over the years. These represent 20 percent of GDP (plus/minus one percentage point) since
applied.<sup>1</sup> We then complement this analysis at the micro level using data from 100 villages across five districts to assess respondents‟ awareness of TASAF II and their likelihood of benefitting from it. Because we have data on every household in these program villages, we are able to examine the relative roles of eligibility, access to information, and political connectedness in exceptional detail.

At both the national and local levels, our data paint a remarkably consistent picture. At the national level, the application process is regressive with project applications significantly more likely to come from richer districts. This pattern is strongly correlated with variation in access to media and information and with political participation across districts, such as voter registration and turnout. The subsequent project approval process is pro-poor, due largely to the pre-determined funding allocations made from the center to the districts. The progressive selection of projects from an initially skewed pool of applications reproduces the familiar finding of mildly pro-poor final funding allocations in the literature that precedes us.

Similarly, at the local level, awareness of TASAF II (which is necessary to apply) is positively correlated with education, ownership of a radio or phone, attending village meetings, and being related to village elites. Among those who heard of TASAF II, program beneficiaries are poorer, but also still more likely to be politically active and live close to the village center. Again, while the eligibility criteria imposed by the center ensures that the program is pro-poor, we find little evidence of pro-poor selection of beneficiaries within villages conditional on eligibility.

These results point to the importance of political involvement and access to information in determining who is able even to be considered by TASAF II for potential support. Communities must mobilize to agree on and file applications, and households must typically participate proactively in order to claim assistance for which they are eligible. Being marginalized or poorly educated is likely to make program awareness less likely and navigating the application process more difficult.

Several empirical papers have similarly attempted to measure the
|Clerks|0.140**(0.077)|0.069**(0.034)|0.118*(0.048)|0.106**(0.052)|
|---|---|---|---|---|
|Service,shopand market sales workers|0.079**(0.079)|-0.088**(0.039)|-0.033**(0.049)|0.044**(0.044)|
|Skilled agricultural and fisheryworkers|-0.079**(0.078)|-0.148**(0.033)|0.319*(0.142)|-0.109**(0.129)|
|Craft and related trades workers|-0.263** (0.159)|0.217* (0.097)|0.016** (0.060)|0.114* (0.035)|
|Plant and machine operators and<br>assemblers|-0.105**(0.078)|-0.024**(0.026)|0.161(0.054)|0.143(0.036)|
|Militaryofficers|-0.149**(0.079)|-0.177**(0.036)|.|0.394(0.083)|
|Constant|8.886(0.193)|9.091(0.147)|8.890(0.191)|9.440(0.155)|
|R-squared|0.530|0.362|0.532|0.380|
|Observations|2,152|2,775|2
commercial lands are of great importance as it provides greater value added to the land as reflected in the value of industrial/commercial land compared with agricultural land. The minimum price of industrial land per mu of 0.5 million RMB (i.e., 1.07 million USD per hectare) is 25-fold of the price of agricultural land of 20,000 RMB (i.e., 42,000 USD per hectare). Moreover, since non-agriculture accounts for an everincreasing share of the economy--the average scale of output from the non-agricultural sector of OECD countries is around 98% of their total GDP—it becomes increasingly important to

> 6 See the data from http://internationalpropertyrightsindex.org/country?c=CHINA

9
group perceptions using data from Uganda.<sup>3</sup> Based on this, the paper complements the existing literature on contact theory by being among the first to explore the role of interaction in shaping host community attitudes towards refugees in a developing country context.

OLS regressions identify a positive and significant correlation between refugee-host interaction and the perception of hosts towards refugees. However, the results of OLS regressions could be biased by reverse causality in the relationship between attitudes and refugee-host interaction, by omitted variables, and by mismeasurement issues. To address endogeneity issues, we use a technique called an ‘instrumental variable approach’. We instrument the measure of host interactions with refugees by 1) the share of refugees living in the locality and 2) the intensity of refugee-host interactions among other household and community members. Effective F-tests and Sargan-Hansen tests for over-identifying restrictions suggest that these instruments are valid.

With IV regressions, the association between attitudes and refugee-host interaction dis-

> 3This is because the Refugee Economies Dataset only has data relating to host community interactions with refugees for Uganda, although it has data relating to refugee and host community perceptions and refugees’ interactions with the host community for all three countries.

2
(2.486)|
|Dummy_Mais Educação_* year 2009|1.275|–4.202*|–2.744|
||(1.474)|(2.510)|(2.682)|
|Dummy_Mais Educação_*_ME_student coverage|–1.855|–1.448|0.516|
||(1.495)|(2.589)|(2.903)|
|_R_ <sup>2</sup>|0.736|0.755|0.741|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
more interested in preventing the build-up of market-compatible institutions that would threaten their hold on the economy.

# **III. Institutions and Growth in Transition Economies**

This section tests the importance of institution building for economic growth across transition economies. Using our findings from the previous section, we relate the exogenous component of institution building, explained by natural resource endowment and socialist entrenchment, to measures of economic development.

## _1. Measures of Economic Development and Methodology_

In line with the empirical growth literature, our main indicator of economic development is GDP per capita growth, averaged over the period 1992 to 2002. Growth in GDP per capita over the sample period varies between –4.9% in Tajikistan to 6.4% in Albania, with an average of 1.2% and a standard deviation of 3.2% (Table 1). As an alternative indicator, we use growth over the period from T to 2002, where T is the first year after the beginning of political transition. As in the case of Initial Executive Constraints, we follow Falcetti et al. (2002) to define the beginning of the transition period. Using country-specific growth periods allows us to focus on the period after the start of transition and abstract from the initial output slump that could be observed across all transition economies. On the other hand, this country-specific timing might be controversial and subject to interpretation.

Using GDP data for transition economies is often seen as problematic since the statistical capacity to measure market output was only being developed after the start of the transition process, so that GDP numbers especially in the early years of transition are rather unreliable 27
of a project financier, a privatization commission or a transport economic regulator. There are a few very good recent surveys of these models (see for instance Small and Winston (1999) in English, Quinet (1998) in French) and several comprehensive textbooks on the subject (Ortuzar and Willumsen in (1994) in English or Ortuzar (2000) in Spanish). They reveal impressive improvements over the years to model demand and increased accuracy in identifying and assessing the relative importance of the various factors explaining demand, including modal shifts resulting from policy changes. Although there is a lot we still don't know, many countries can now count on useful results on the value of time and the willingness to pay for various types of transport service users. These are relevant indications when pricing new services to be provided by private operators.<sup>2</sup> It makes sense to compare the tolls or tariffs calculated from the cost side with these rough estimates of the willingness to pay for some services or with the value of time revealed by the post-mortem analysis of comparable projects increasingly found in the literature (see Quinet on Marseille (1998), Small and Winston (1999) on the Dutch data base).

Unfortunately, this improved knowledge does not seem to be spreading to the toolkits used by transport planners or privatization teams in developing countries--and for that matter... in many developed countries-because few are willing to invest in the data needed to make the IThis "overselling" and the common resulting renegotiation of the terms of the contract typically allows th eprivate operator's to obtain extensions of contract duration, new or larger subsidies and/or lower investment requirements. All these changes alter the fundamental financial terms of the contracts which often end up being significantly less attractive than anticipated from the viewpoint of the government--and often also from the view point of the users. On the other hand, new fact sometime do arise and renegotitiation is not necessarily a bad thing, as long
<!-- Start of picture text --> F IGURE V-8: P ROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF TRANSFERS AND SUBSIDIES IN L ATIN A MERICA<br>Panel A: Direct transfers Panel B: Subsidies<br>1 0.024 1.0000 0.009<br>0.9 0.021 0.9000<br>0.8 0.8000<br>0.018<br>0.7 0.7000<br>0.006<br>0.6 0.015 0.6000<br>0.5 0.012 0.5000<br>0.4 0.009 0.4000<br>0.003<br>0.3 0.3000<br>0.006<br>0.2 0.2000<br>0.1 0.003 0.1000<br>0 0 0.0000 0<br>Kakwani Coefficient Marginal contribution Kakwani Coefficient Marginal contribution<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the Commitment to Equity<br>Methodology (CEQ), 2017. Colombia World Bank Group staff estimates using the ENPH 2017<br>c) In-kind transfers: Education and health care<br>As regards transfers for health and education services, the results presented must be interpreted carefully<br>given that in this analysis, the quality of the services provided was not taken into account. For the analysis,<br>only the amount transferred per student in the case of education spending, and the amount transferred<br>per user in the case of health expenditure were considered. That is, the analysis assumes that the benefit<br>received is equal to the amount spent per student or person belonging to the health care system.<br>FIGURE V-9: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF EXPENDITURE ON EDUCATION AND HEALTH IN LATIN AMERICA<br>
(2’) which relates the number of hours optimally supplied to .

## **Wage equation**

Finally we state a usual Mincer equation to model the relationship between log wages and human capital inputs.

This specification is usually adopted to represent the fact that a worker’s productivity (and hence his wage) depends on his own human capital and the fact that wages are dispersed also among people holding the same occupation.

# **Empirical specification**

So far we have defined the model in very general terms. Nonetheless important hints about which variables to include have already emerged. For instance, the fact that wages are a function of individual characteristics, wages can be substituted out in both the employment and the labor supply equations. Moreover, there is a clear indication that the (only) set of variables to be included in our reduced form equations are those influencing individual wages (productivity) and those influencing the disutility from labor ( and ). We stress here that both observable and unobservable/non measurable variables do enter these sets.

As mentioned earlier, it is reasonable to assume that poor health reduces worker productivity and increases the disutility of labor. Specifically, we are interested in the role of chronic diseases and disabilities and the presence of such conditions is our measure of health conditions. In the dataset we use, Egypt Household Health Utilization and Expenditure Survey 2002 (EHHUES2002), a representative sample of people is asked “During the last 12 months have you been complaining from any persistent health problem for at least 3 months (including disability, disease, injury) or any other chronic disease?”. About 25% of the subsample of people aged 16-64 (working age) answer affirmatively (27% of females and 22% of males). Chronic conditions are also the most important single factor influencing people’s negative health conditions, according to EHHUES2002. For instance the probability of reporting bad health increases by 25% in case of chronic diseases, after controlling for age, gender