rafmacalaba/data-use-mentions-extended huggingface dataset:
that a spirit of competition with girls drove some boys to study harder. To the extent that this “gendered” competition was an important determinant of boys’ gains in program schools, it is an open question how large externality gains would be under alternative program that targeted boys rather than girls, or in which they competed against each other. Gneezy et al (2003) provide experimental laboratory data that females and males may react differently to competition, with females performing better when competing against other females than when competing against males, while males perform equally well regardless of the gender of the competition.

cohort 1; however, differences in program impacts across cohorts and years are not statistically significantly different for any of these four school subjects.

22
assumed in this paper) the economic variables of interest are (log)-normally distributed. Finally, the Mexican household survey we use to implement our general approach is a rotating panel that follows individual workers for five quarters over time, which means that the panel dimension of our income data is somewhat limited. Thus, our data do not allow us to assess with certainty the persistence of income shocks beyond five quarters. However, a comparison of our estimates of the income risk parameters with existing results that use data sets with a much longer panel dimension suggests that a large fraction of the income shocks we label “persistent” in this paper last indeed for many years (see Section II.5 for more details). In short, the welfare results presented here do not necessarily show that trade liberalization is costly, but they do provide strong evidence that any comprehensive welfare analysis of trade liberalization ought to take into account the cost of increased labor market risk.

In summary, in this paper we articulate a general framework that allows us to study empirically the impact of trade reform on individual income risk and to evaluate the corresponding welfare effects. We use this framework to study the Mexican economy, which, as we have argued above, seems well-suited for such an analysis. In our empirical implementation of this methodology using longitudinal data on Mexican workers, we find economically significant effects of trade policy on income risk.

We conclude this introduction with a brief comment on some of the earlier empirical literature on the relationship between trade policy and factors related to labor market risk. The impact of trade liberalization on short-run worker displacement has been investigated in the well-known papers of Currie and Harrison (1997), Gaston and Trefler (1994), Levinsohn (1999) and Revenga (1997), among others. More recently, in an innovative paper, Trefler (2004) has analyzed the short-run adjustment costs borne by displaced workers simultaneously with the long run benefits (of higher firm productivity and resource allocation) that accrued in the context of
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increase drivers’ wages. While drivers over 55 years old account for 34 percent of the total labor force in this sector in Europe, only 7 percent are under 25 years old (Ti et al., 2022).

3. Load factor is an important determinant of freight rates. In Europe, the share of road freight kilometers carried by empty vehicles has been increasing especially for international freight ( **Figure 4** ). From the efficiency point of view, holding everything else constant, freight rates may be higher when there are more empty containers. Freight rates are also determined by the road condition. The higher quality of roads generally leads to lower freight rates. In theory, road user costs, including vehicle operating and maintenance costs and drivers’ time costs, can be reduced if roads are well maintained. Road roughness is negatively associated with driving speed (e.g., Alessandroni et al., 2017). However, the relationship among road conditions, traffic speed and freight rates may be complicated by other factors involved, such as the trucking industry’s market structure. Given all these factors, it remains open to argument whether overall freight rates increased or declined, and why.

4. The current paper recasts light on the question of how road freight transport costs are determined, using large sample shipping data from Eastern European and Central Asian countries. Since the early 2010s, the region has made significant infrastructure investments to improve interregional connectivity between Europe and East Asia. Despite our general belief that the enhanced connectivity would facilitate regional integration and trade (e.g., ADB, 2020; Kalyuzhnova and Holzhacker, 2021), there is little empirical evidence to the best of my knowledge. The paper takes advantage of unique micro data, which comprises stop-by-stop shipping data in the Central Asia Regional Economic Cooperation (CAREC) member countries, including Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, collected by the Asian Development Bank (ADB).
acceptably balances restricting estimation to a local neighborhood around the cutoff against having sufficient statistical power to yield informative estimates of treatment effects. Notwithstanding, we examine whether our RD impact estimates and statistical inference results are sensitive to increasing the optimal bandwidth by 50% and 100%, potentially trading off increased bias for reduced variance.

# **5. Data and sample**

# _Data_

_Baseline data_ : The baseline data come from the program application records and SLQAT test records collected and maintained by PEF. In phase 3, properly filled-in application forms received by PEF before the announced deadline of April 2007, as well as forms filled out by schools solicited by PEF inspection teams on their way to inspect applicant schools were considered. In phase 4, only properly filled-in forms received by PEF before the announced deadline of July 2007 were considered. PEF collected 1,070 and 1,430 properly filled-in application forms in phases 3 and 4, respectively.<sup>20</sup> PEF constructed a school-level application electronic database for each phase which contains information on school characteristics (location, gender type, level, physical infrastructure, and registration status), total school enrollment separately by gender, total number of teachers and administrative staff separately by gender, and the minimum and maximum monthly teacher salaries in the school.<sup>21</sup> These databases serve as the source of baseline data on the following outcomes measured at the school-level: number of students, teachers, classrooms,

> 20 The total number of unique applications received by PEF is unknown as all rejected applications were discarded. 21 As part of this inspection, the data provided by the school in the application form are verified. These inspection data would have been useful for checking the accuracy of the application data; they were however collected in paper form and not entered into an electronic database. Consequently, these data are unavailable for the purposes of this study.

23
of those without a permit (Stave, Kebede, & Kattaa, 2021).

Although the number of Syrians with work permits has increased, they still mainly find jobs in the informal sector since there is reportedly a mismatch between their skills profiles and the sectors in which they are allowed to work (Barbaret, Hagen-Zanker, & Mansour-Ille, 2018). Interview and survey data of Syrian refugees further revealed that a majority of workers, including those with higher skill levels, felt unsafe in their jobs due to frequent harassment, low bargaining power, harsh labor conditions, and little agency to change their situation due to fear of the authorities (IRC & Airbel Center). In this context, women are still disadvantaged. Given the direct and indirect costs of acquiring work permits (such as the direct cost of the transaction, the costs of providing all required documents at different institutions, transportation costs to complete those transactions, time and resources forgone that would have been used to work or care for children), it is often only male family members that are in a position to apply for work permits. Refugee women’s options in the labor 7
assessments through simulations to predict the direct distributional effects of changes in market concentration could serve as a useful starting point for inputs into a proposed reform program.

The simulation tool presented in this paper, the Welfare and Competition tool (WELCOM), has minimum data requirements: a representative household survey with detailed expenditure data, along with explicit parameters that define the market structure of the industry of interest. This simulation approach builds a set of flexible theoretical models (monopoly, oligopoly with a homogeneous good, and partial collusive oligopoly) into a user-friendly interface that can be used to assess the impact of market concentration on household welfare, poverty, and inequality. WELCOM is a Stata-based package conceived as part of a larger effort by the World Bank to improve the understanding of the links among competition, growth, and shared prosperity as well as to promote policies that foster competition, especially in developing countries.<sup>10</sup> This simulation approach contributes to development policy in three ways. First, it provides a simple tool for practitioners and scholars to implement a first-stage analysis of the distributional effects of changes in market concentration. Second, the simulation approach intends to attract the interest of and use by practitioners to motivate discussions on procompetition policy reforms, as well as to improve the understanding of the likely distributional impacts by identifying potential winners and losers of policy changes. Third, the technique contributes to the emerging literature on the effects of competition on household welfare described above, as well as to the literature on simulation methods, which, to the best of our knowledge, does not include other tools to measure the distributional effects of market concentration.

As with any microsimulation tool, WELCOM is not without limitations. First, the tool follows a partial equilibrium approach that relies on the expenditure of households on the products expected to be affected by the policy reform. It thus only captures the first-order effects of competition on welfare. Second, the current version of WELCOM only analyzes the distributional effects of changes in market concentration through the price
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Efficient market gap for mobile telephone services in West Africa**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source_ : Mayer and others (2009) using GSM coverage figures for 2005.

_Note_ : Existing access represents the percentage of the population covered by voice infrastructure in the third quarter of 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. The coverage gap represents the percentage of the population for whom services are not viable without a subsidy. SSA = Sub-Saharan Africa.

**Table 16. Senegal’s skyrocketing mobile teledensity**

|Subscribers/100 people|2005|2006|2007|2008|Average annual<br>growth (%)|
|---|---|---|---|---|---|
|Benin|14.10|19.20|23.7|27.7|25.50|
|Burkina Faso|5.00|7.60|11.5|14.4|42.84|
|Ghana|12.10|19.30|29.4|34.6|43.17|
|Mali|6.70|12.90|21.1|35.4|74.63|
|Niger|2.70|4.40|6.7|8.5|47.37|
|Nigeria|14.
in _R_ 0 is probably due to the lockdown policies implemented immediately after the outbreak.

> 18A corner solution in this step occurs for the four countries that we have dropped (Denmark, Sweden Switzerland, and Norway). If the country imposes a mild lockdown, the implied _μ_  ̄ for this country will be low, which makes it impossible to reach the target _R_ 0 even with _ξ_ = _∞_ .

> 19Infection fatality rates calculated with data from China might not be generalizable to other countries because of factors such as prevalence of comorbidities and quality of health services (Ghisolfi et al., 2020; Demombynes, 2020). More recent meta-analyses found mortality rates in line with those reported by Verity et al. (2020) (Levin et al., 2020; Meyerowitz-Katz and Merone, 2020). It is important to note, however, that these analyses overwhelmingly rely on studies from high-income countries.

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225|113|140|
|||**Mo**|**zambique**|Coun|tries with|Other|
|||||scar|e water|developing|
|||**Mid-2000s**|**Late 20**|<br>res<br>**00s**|<br>ources|<br>regions|
|Residential tariff|U.S. cents per m3|3|2|**64**|60.26|3.0–60.0|

_Source:_ Demographic and Health Survey and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). Access figures from DHS surveys (1997 and 2003) and MICS Survey (2008).

Utilities figures are the weighted average by water production of the following utilities: Beira, Maputo, Nampula, Pemba, and Quilimane. — = Not available.

Mozambique’s reforms of the water and sanitation sector attracted about $350 million in investments between 2007 and 2008. This has allowed for enhancing the level of service in cities served by the holding company. Hours of supply increased from 11 to 16 on average between 2002 and 2006, which has led to an increase in the domestic water consumption from 33.3 to 37 liters per capita in the same 27
at marriage is consistently declining in Gujarat relative to its neighboring states, 2 years after the riots. According to Appendix A, figure A.2, the probability of women getting married before 18 increased after the Gujarat riots.

# **7.2 Synthetic Controls Approach**

The results from the event study approach (Figures 1(a) and 1(b)) suggest that there were some differential trends between Gujarat and the control states even before the riots of 2002 for a few years. To address this concern, we examine the effects of the Gujarat riots by employing the synthetic controls approach suggested by Abadie and Gardeazabal (2003). Using a combination of other Indian control states or the donor pool, we first construct a synthetic Gujarat that best approximates the characteristics of women in Gujarat before the riots of 2002. More specifically, synthetic Gujarat is constructed as a weighted average of the donor pool, with weights chosen so that the resulting synthetic Gujarat best reproduces the values of a set of predictors of women’s age at marriage in Gujarat before the riots in 2022. We then use the post-riot outcomes for synthetic Gujarat to estimate the outcomes that would have been observed in Gujarat in the absence of the riots of 2002. Next, we compare the outcomes of synthetic Gujarat to actual Gujarat after 2002.

We collapse the individual-level data in a state-year panel for the synthetic controls method. To approximate the outcomes (age at marriage and proportion of child marriages) at the state level, we use the proportion of women who reside in rural areas, reside in households with male heads, belong to rich or poor households, are Hindus, and are scheduled castes/scheduled tribes/other backward classes. Appendix E, table E.1 shows that Gujarat and synthetic Gujarat are similar across several demographic characteristics.<sup>3</sup> The results from the synthetic control methods are presented in Appendix B, figures B.1 and B.2. Figure B.1 suggests that the
tenure in Poland, Russia, and the United Kingdom in the 1990s. They develop a model that describes the process of labor mobility in a transition economy that yields testable hypotheses about the differences in tenure and wage profiles between labor markets in transition and developed economies. They report that tenure-turnover rates were higher in Russia and lower in Poland than in the United Kingdom.

<u>Orazem, Vodopivec, and Wu (2005) analyze the process of job displacement in Slovenia during</u> the 1990s. They find that the probability of job displacement and quitting decreased with job tenure.

Using longitudinal data from Poland, Jackson and Mach (2009) examine the labor transition from the state to the private sector in the late 1990s. They show that many workers went through spells of unemployment or exited the labor force before obtaining private sector jobs. <u>Newell and Socha (2007)</u> show that the returns to years of tenure rose and steepened in Poland between 1998 and 2002, increasing wage inequality and reducing relative wages for younger workers. Labor mobility declined with job tenure in Poland, as it did in Slovenia.

<u>Noorkôiv et al. (1998)</u> and <u>Lehmann, Philips, and Wadsworth (2005)</u> investigate wage and employment dynamics during Estonia’s transition to a market economy (1989–95). They document a significant redeployment of labor from traditional manufacturing and agriculture to modern private sectors. The wage-job tenure profiles did not change significantly over the period analyzed. Returns to experience increased for most cohorts; they grew rapidly for the youngest groups of workers.

Analyzing longitudinal data from Russia and Ukraine, <u>Gordnichenko and Sebirianova-Peters (2005)</u> and <u>Lehmann, Pignatti, and Wadsworth (2005)</u> find no significant increase in returns to job tenure between 1998 and 2002. Lehmann, Pignatti, and Wadsworth report the heightened risks of job displacement for workers
equals one if the womans spouse works. Takes the value 0 for women<br>who are not married or living with a partner.|Individual|
|Income Stability|Dummy variable equals one when there is at least one wage earner in the household and is<br>zero otherwise.|Household|
|At least one person receives remittances regularly|<sup>Dummy variable equals one if at least one household member receives remittances on a</sup><br>regular basis.|Household|
||Index capturing the type of dwelling (equal to 1 if it is a house or apartment), dwelling<br>property (equal to 1 if the family owns it), wall materials (equal to 1 if they are of materials<br>||
|Household Home & WASH index|such as bricks or concrete), roof materials (equal to 1 if they are of materials such as<br>concrete or cement), access to water (equal to 1 if through a pipeline) and type of toilet<br>(equal to 1 if connected to a sewerage system or to a septic tank). Index is created using a<br>factor model.|Household|
|Urban|Dummy variable equal one if woman lives in an urban area.|Individual|
|Use internet in the last 3 months|Dummy variable equal one if woman the woman used the internet in the last three months<br>regardless of the place of use.|Individual|
|Washing machine|Dummy variable equal one if the family owns a washing machine.|Household|
|Female Decision Making|Dummy variable equals one if the main decisions in the household are taken by a woman or<br>by a woman jointly with a man and is zero otherwise|Household|
|High Wage Female Workers in the Public Sector|Share of women among all Central Government employees earning a high salary (at least<br>10,000 Quetzales per
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Average fixed capital per enterprise coomonly ranges between $500 and $4,000 in African rural nonfarm activities. Enterprises such **as** grain Dills, sawmills and wholesaling require on the order of $1,000 to $10,000 per enterprise; while traditional crafts, personal services and repairs require capital in the range of only $50 to $600 (see Haggblade, Hazell and Brown, 1987; Haggblade, 1982; Wilcock and Chuta, 1981; Freeman and Norcliffe, 1985; and Liedholm and Chuta, 1976).

B. Comnooltion Aggregate employment data for rural Africa remain in short supply because most studies have focused on limited portions of the rural economy, often on farm households; others, although interested in the nonfarm economy, rarely include commercial and service establishments, concentrating instead on manufacturing and repair enterprises. Nonetheless some available data, coming mainly from population censuses do allow a comprehensive view of the rural economy (Table 3, Panel A). Amid wide vaviation, these data indicate that commercial establishments typically predominate, employing 30-40 percent of the rural nonfarm workforce. Services and manufacturing comprise about 25 percent each, with construction and mining accounting for the remainder. The data in Table 4 indicate that, within manufacturing, food preparation, tailoring, carpentry and metal working activities predominate.

# C. Emloyment Density

While a steady stream of micro research has greatly increased our understanding of the African rural nonfarm economy over the past 25 years, differences in survey coverage complicate cross-country comparisons. They make it difficult to meaningfully compare employment percentages across surveys, because activity shares are computed as percentages of different bases depending on the comprehensiveness of the survey coverage. One solution to this problem--a solution adopted by Steel (1979), Kilby (1987)
populous subnational regions within a given country. We obtained subnational estimates of the 2018 population counts from WorldPop.<sup>5</sup>

# 2.2 Methodology

First, we look at broad trends in the real-time indicators. Using our current knowledge of the evolution of the crisis throughout 2020, we identify key patterns in trends using purely descriptive graphical representations of those indicators that we believe to be the most reflective of the COVID-19 crisis in most parts of the world. We start with using both satellite-derived variables– nighttime lights and NO2–to track economic activity. We continue with the Google mobility indices and the Google Search data set. Finally, we use food prices restricted to the following six food items commonly consumed in many countries: bread, chicken, eggs, milk, onions, and rice.

Second, we estimate simple cross-section regressions with three separate dependent variables: GDP growth, deviation of actual GDP growth from the pre-pandemic expected growth, and the stringency index presented in Hale et al. (2021), which we refer to as the “Oxford Stringency Index.” We obtained country-level GDP – as well as values for predicted GDP from prior to the crisis – for a large group of countries for the entire year of 2020 from the IMF. We then constructed deviations of actual GDP from predicted GDP to analyze how well the real-time indicators predict these deviations. There are 62 countries for which we have information on the 2020 growth rates plus the complete list of real-time indicators described above. Table A1 lists the countries included in this analysis. We first present regressions including all candidate predictors. Then, to create a more parsimonious model, we also employ lasso to select features. We use the cross-validation (CV) method to select the value of that minimizes the mean squared error (MSE) in the validation sample and present the resulting estimates. We include information on 2019 GDP per capita, total population, and primary school enrollment rate as candidate covariates taken from the World λλ Development Indicators database.