rafmacalaba/data-use-mentions-extended huggingface dataset:
exists for a broad swathe of low‐ and middle‐income countries. Existing studies also reveal heterogeneity of the wage premium across the wage distribution, with higher wage premia for lower skilled workers, a finding that is also confirmed in the data used in this study.

The few studies of wage premiums in developing country contexts find generally similar results. For example, Filmer and Lindauer (2001), and Glinskaya and Lokshin (2005) find a public sector wage premium in Indonesia and India respectively. Tansel (2005) finds public sector wage premiums for both men and women in Turkey. Panizza (2001) finds a public sector premium in Latin America, particularly for female workers. By contrast Coppola and Calvo‐Gonzalez (2011) show that in Peru there is a public sector wage premium in monetary compensation which disappears and instead turns into a public sector wage penalty when bonuses and in‐kind payments are included in the analysis. In their estimates of public sector earnings gaps in the Arab Republic of Egypt, Tansel, Keskin and Ozdemir (2018) address issues of unobservable differences between public sector workers using panel data and individual fixed effects. They find public sector earnings penalties for men but premiums for women. Public sector earnings premiums for women are larger for workers from the lower quantiles of the conditional wage distribution. In contrast to much of the literature, in South Africa Kwanda and Ntuli (2018) find that the public‐private earnings gap starts as a penalty for public sector workers at the bottom of the distribution of earnings, increases to large earnings premiums for public workers up to the 80<sup>th</sup> percentile and then falls slightly thereafter.

The analysis in this paper is most similar to the cross‐national analysis of Finan, Olken, and Pande (2015) who estimate public sector wage differentials for 32 countries and find a public sector wage premium in almost every country, which is highest in low‐income countries and then falls with income per capita. This paper differs
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**development of 'catastrophe bonds' to be issued on domestic and intemational capital markets.**

**b. The bonds which would pay higher than average yields have a risk of dropping their coupon rate and principal payments significantly in the event of a catastrophe (hurricane hit, major earthquake) to which bond payments are linked (e.g.: threshold windspeed or earthquake intensity).**

**c. The multilateral institution would guarantee the payments of the bonds to investors, through a partial risk guarantee involving the government and the private bond issuer to ensure liquidity or contractual payments as scheduled.**

**d. The domestic capital market would list the bonds on international exchanges to further transfer the risk into the global capital market. Since these risks are unrelated to typical stock market or other financial risks, they can prove attractive to international investors, and may provide them additional much needed diversity in their global portfolios.**

**e. The bonds would trigger lower coupon interest payments and possibly partial principal losses upon the triggering of key weather events (e.g.: windspeed, earthquake intensity/magnitude).**

**f. Trigger indicators would involve weather or storm indexes to be tracked via the installation in affected countries of weather station equipment with satellite links to 'recording centers', to track rainfall, windspeed, or other direct indicators of natural disaster adversity factors. These would serve as triggers for payout of such risk linked securities.**

Prior to structuring such weather indexed ('parametric') bonds, data on weather events and associated losses would be compiled to ensure that a sufficiently strong correlation was achieved between the index-triggered payment and actual losses based on historical experience. Such data compilation is essential for the structuring and pricing of such insurance contracts. The key element of such contracts' success, however, is the reliability and objectivity of the weather measurement mechanism.

In the Caribbean, the Miami Hurricane (Tropical Prediction) Center
||
|||[0.379]|[0.283]|[0.886]||
|Satisfaction index|0.000|0.108|0.106|0.109|3183|
||(1.000)|(0.055)<sup>_∗_</sup>|(0.068)|(0.061)<sup>_∗_</sup>||
|||[0.188]|[0.235]|[0.144]||
|Health service delivery index|0.000|_−_0.056|_−_0.033|_−_0.064|1819|
||(1.000)|(0.066)|(0.088)|(0.070)||
|||[0.329]|[0.459]|[0.397]||
|Clinic quality index|0.000|0.311|_−_0.023|0.565|160|
||(1.000)|(0.236)|(0.301)|(0.264)<sup>_∗∗_</sup>||
|||[0.205]|[0.459]|[0.144]||
|CDPE index|0.000|0.264|0.232|0.288|320|
||(1.000)|(0.108)<sup>_∗∗_</sup>|(0.155)|(0.132)<sup>_∗∗_</sup>||
|||[0.188]|[0.235]|[0.144]||
|Contributions to clinic index|0.000|0.
sometimes, even to keep them above the poverty line: consider that the basic consumption basket for a family of four in 2011 cost US$6,400 per year, and the poverty line for a family of four was around US$3,300.

# **2.4 Access to finance**

By and large, access to credit for firms in the EMES sample is low. Only 22 percent of firms borrowed funds at their inception, and only 31 percent of them obtained a loan in the last year. This is substantially lower than the share of formal urban firms of all sizes who currently access credit in Ecuador (49 percent) and formal firms with 50 employees or less in all of Latin America (42 percent; LAC Enterprise Survey 2010). Female entrepreneurs tend to be more successful in obtaining loans, and smaller firms are also more likely to obtain a loan either at the start or in the last year, compared to their larger counterparts. This is likely explained by the fact that most of the credit –and thus the liability—is given to the entrepreneur, not to the firm (this was confirmed by focus group participants), which increases the risk and difficulty for larger firms to obtain loans (that are typically also larger). As a result, larger firms are more likely to use their own earnings to finance their operations, although retained earnings alone are not likely to be sufficient to consistently finance business expansion.

**Figure 4: Uses of credit**

<!-- Start of picture text --> Construction<br>Food vendors<br>Restaurants and hotels<br>Auto repair<br>Transport<br>Groceries retail<br>Manufacturing others<br>Textiles manufacturing<br>[11-50]<br>[6-10]<br>[1-5]<br>Males<br>Females<br>0% 10% 20% 30% 40% 50% 60% 70% 80%
context of the two crises considered here. Though dual agency problems are likely (to some degree) behind market responses, this hypothesis is difficult to test empirically.

A second explanation lies in the information content of fundamentals during crises. More precisely, to the extent that fundamentals are not independent from systemic factors, rapidly changing systemic innovations may detract from the predictive power of bank fundamentals that are released at lower frequencies and with a delay. This second explanation is easier to test in the data. To see how systemic factors can influence standard fundamentals it is enough to consider the case of non-performing loan ratios, which are likely to deteriorate due to the increase in borrowing costs and the downturn in economic activity associated with a systemic crisis.

To illustrate this point, consider the case of a depositor that attempts to assess the future evolution of a bank’s portfolio (summarized here by its non-performing loan, NPL, ratio) over different time horizons, based on the most recently released ratio and readily available measures of systemic risk.<sup>17</sup> Assuming, as before, that balance sheet data are released with a four-month delay, this exercise amounts to predicting future values of the non-performing ratio using its four-month lag and systemic risk indicators that are currently available.

We replicate this exercise by estimating regressions of the type illustrated by the equation below, where _NPL_ refers to the non-performing loan ratio, _S_ is a matrix of systemic factors (country risk and exchange rate risk), α _i_ refer to the individual bank effects, and ε _t_ is the error term. _k (j)_ denotes the number of lags in the systemic indicators ( _NPL_ ) used for predicting the evolution of bank’s non-performing loan ratio.

> 17 We focus on the ratio of non-performing loans to total loans, since we expect this variable to reflect bank conditions better than bank capital (which can be increased at any time by bank shareholders) and return on assets (which tends to be a more volatile measure of
disease outbreaks. The shock of this pandemic needs not only demand management but also the combination of monetary, fiscal and health policy responses. Therefore, there is a critical role for the government to ease real and financial stress. Quarantine and social distance measures also work to reduce the spread of viruses. Maintaining better hygiene practices is highly effective with a low cost (Levine and McKibbin 2020). In the long term, global cooperation is necessary to strengthen public health and economic development as the pandemic can kill many people in any society if those diseases are generated in poor countries due to overcrowding, poor public health and interaction with wild animals. Consequently, public investment in health is crucial to improve the quality of life as a driver of economic growth.

New firm-level evidence on the impact of COVID-19 on sales and employment by the World Bank’s Business Pulse Surveys (BPS)<sup>10</sup> shows that firm sales dropped by 32 percent among highincome countries and by 48 percent among low- and middle-income countries. The decline in sales amid the pandemic varied widely across countries, sectors of economic activity and firm size. For instance, South Africa, Bangladesh, Nepal, Honduras, India and Jordan registered an average drop in sales that exceed 60 percent. Across sectors, firm activity in tourism as well as businesses that require face-to-face interactions were the most affected by the pandemic.<sup>11</sup>

# **3. COVID-19 in Developing Countries**

The COVID-19 pandemic has spread rapidly across the world, taking a human toll of nearly five million fatalities among the more than 240 million confirmed cases across 210 countries and territories.<sup>12</sup> As of mid-October, North America and South America have the largest number of infections by the COVID-19 pandemic, with 46 million and 45.5 million cases, followed by Western Europe and South Asia (38.4 million and 38.3 million cases), and Eastern Europe and Central Asia (33
Figure A4: Stringency of lockdown restrictions and Google mobility trends around transit stations. Selected countries.

<!-- Start of picture text --> BGR BRA CIV<br>100 20 100 20 100 20<br>80 0 80 0 80 0<br>60 -20 60 -20 60 -20<br>40 -40 40 -40 40 -40<br>20 -60 20 -60 20 -60<br>0 -80 0 -80 0 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>PAK VNM ZAF<br>100 20 100 20 100 20<br>80 0 80 0 80 0<br>60 -20 60 -20 60 -20<br>40 -40 40 -40 40 -40<br>20 -60 20 -60 20 -60<br>0 -80 0 -80 0 -80<br>01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08 01/01 01/02 01/03 01/04 01/05 01/06 01/07 01/08<br>Stringency of lockdown restrictions<br>Mobility around transit stations<br>Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0) Stringency of lockdown measures Google mobility index (baseline = 0)<br>Stringency of lockdown measures Google mobility index (
Policy Research Working Paper 10674

# **Abstract**

Affirmative action is a promising solution to the crucial challenge of bridging the gap in women’s access to higher education in low- and middle-income countries (LMICs). This paper uses public universities’ matriculation data from 2013–2018 and difference-in-differences estimators to examine the causal impact of a gender quota on women’s educational opportunities in Afghanistan. The quota increased the proportion of women in the treated concentration group by nine percentage points and the share of women from low socio-economic status by three percentage points. The expansion was associated with a 0.04-unit decline in the average score ratio of female-tomale applicants, driven by a reduction in the score threshold needed for women’s admission. The effects were condensed in competitive concentrations, where the overall share of women and women with low SES increased by 17 and four percentage points, respectively. The findings suggest that affirmative action is a viable option for addressing the gender gap in fragile settings..

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at rnajam@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of
variables more accurately. Although not a perfect substitute, these methods can also address the challenges posed by survey costs or implementation and enable back-casting of consumption from a more recent survey for better comparison with older surveys (Dang, 2021; Edochie et al., 2022).

One of the main advantages of matching-based imputation methods is that they are relatively simple to implement and require minimal assumptions about the underlying data-generating process. Another advantage is that these preserve the structure of the original dataset (Andridge and Little, 2010). In other words, the imputed data in the receiver survey will have similar distributions and relationships to the original data in the donor survey.

This paper presents a two-step random hot deck imputation methodology, expanding upon the approach delineated by Fernandez, Olivieri, and Sanchez (2023). This strategy, initially grounded in the framework established by Lucchetti et al. (2020), aims to reconstruct the evolving poverty and inequality trends in Bangladesh. This method utilizes LASSO PMM (Predictive Mean Matching) and resampling techniques to predict a comparable consumption aggregate for 2010 and 2016 to the data collected in the 2022 HIES.

> 5 Two refresher training sessions were conducted during the 12-month survey period: the first refresher training took place on March 22 – 24, 2022, and the second on August 28 – 30, 2022. This is in addition to the initial training sessions conducted before the survey: the Enumerators Training session from December 4 to 24, 2021, and the Field Officials Training from December 27 to 29, 2021.

3
# Cargo iQ (air cargo data)

In addition to the postal flow dataset, the LPI 2023 includes indicators on air cargo logistics. The operational framework of the air cargo industry, including cargo handling by carriers and airports, is rooted in the same EDI protocol as the postal network: the main events of the supply chain follow a similar logical ordering (Figure 2). The dataset was provided by Cargo iQ – a nonprofit and affiliated with the International Air Transport Association (IATA) created in 1997 to develop a system of shipment planning and performance monitoring for air cargo based on common business process and milestone definitions.<sup>12</sup> Cargo iQ’s focuses on the digitalization and exchange of e-AWB (electronic airway bill).

Cargo iQ includes over 60 participants including forwarders, air carriers, ground handling companies, road carriers and airports to work together to define the standards for shared processes and planning to control and to evaluate performance of cargo shipments. Most major carriers are participating in the initiative. Annually, Cargo iQ collects over 110 million data lines, of which 12 million are airportto-airport shipments. These records, covering information for about 650 airports and 184 countries and accounting for 45 percent of global air freight volume, were used to construct the aviation pillar of the LPI 2023.

_Figure 2: Cargo iQ events sequence_ <!-- Start of picture text --> NFD<br>RC<br>DEP DL<br>AR Notification of<br>Cargo<br>Shipment Shipment Received in Readiness for Cargo<br>Departure from Arrival at Warehouse at Delivery of Delivered to<br>Origin / Last Transit / Transit / Cargo to Consignee/age<br>Departure Point Destination Destination) consignee/age nt<br>nt<br><!-- End of picture text --> Cargo iQ’s reporting system captures the path of air cargo shipments defined by a master operating plan (MOP) consisting of 19 milestones.<sup
Figure B21: Decomposition of consumption difference between urban and rural areas <!-- Start of picture text --> (A) DOU (B) DB<br>150<br>100<br>100<br>50<br>50<br>0<br>0<br>Endowments Returns Endowments Returns<br>Contribution to the mean consumption difference (%) Contribution to the mean consumption difference (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distribution of percentage contribution of (1) endowments and (2) returns to the mean differences in the log per capita consumption expenditures between urban and rural areas in 16 SSA countries based on the Oaxaca-Blinder decomposition. The share of interactions is not shown for the sake of presentation. GHSPOP 1km is used for the DOU and DB methods.

59
84.5|87.4|88.4||

_Source: International Monetary Fund, "International Financial Statistics. "_ /I Data for high-income OECD countries except for Germany (1990) and Luxembourg (1993), for which data is not available.

/2 Data for high-income OECD countries except for Germany (1990), Sweden (all years) and UK (all years) for which data is not available.

result of bank crises and bank liquidation. On the other hand, the _CEE_ countries did not experience as large an increase or decline (see section 3).

3
that have been superseded by a deeper one are not included. For example, the association agreements of the EU with the new member states that preceded the Eastern Enlargement are not included. As we do not have any information about their depth, we assume that they are shallow ones.

As an alternative measure for depth, we use on the DESTA database provided by Dür et al. (2014), which focuses on only seven policy areas (c.f., Hofmann et al. (2019) for a discussion of the two methodologies). We classify an agreement as deep whenever the depth index is larger than three. DESTA provides for almost all agreements information about their depth; if no information is available, we assume that the trade agreement is shallow.

# **4 The NTB Effects of Deep Trade Agreements Revisited**

Next, we show how accounting for misreported tariffs and globalization trends affects the finding of the literature that deep trade agreements reduce NTBs. We also discuss potential treatment heterogeneity and perform numerous robustness checks.

## **4.1 Baseline Results**

Table 1 shows the baseline results. We start by estimating Equation (4) without controlling for (1 + _τijkt_ ), which yields the combined effect of tariff reductions and NTB effects without accounting for globalization trends. The results can be found in column (1). To tease out the pure NTB effect, we control for tariffs, first using information downloaded from WITS (column (2)), then from the cleaned GTD that accounts for misreporting (columns (3) and (4)). Lastly, we add the border dummies to control for globalization trends (columns (5) and (6)). All specifications include importer-sector-time, exporter-sector-time, and pair-sector fixed effects.

Panel (A) includes data for the three broad sectors of agriculture, manufacturing, and services and the years 2000 to 2015. When using all provisions of the World Bank’s DTA data to determine the depth of
<br>0.090<br>inflow<br>0.080<br>0.070<br>0.060<br>1997-98 1998-99 1999-00 2000-01 2001-02<br><!-- End of picture text --> _Notes_ : 1997-2001: flows between Mays of corresponding years; 2001-2002 – annual average. Flows exclude the impact of migration, mortality, and new entrants who were younger than 15 in the first of the two periods. In this way, the impact of economic change is identified. _Source:_ LFS data and calculations (see Appendix Table 14).

While in 1997-1999 the larger part of the outflow from employment went to unemployment, in 20002002 flows to unemployment and nonparticipation were roughly equal. From 1999 to 2000, however, flows out of employment were predominantly to inactivity, likely because many job-seekers were discouraged and did not seek new jobs. Other reasons for non-activity, such as child-rearing and retirement may also play a role.

**The persistence of unemployment has declined but is still substantial.** As discussed in the previous chapter, the proportion of unemployed who find themselves in the same situation one year later is very substantial. Still, it has declined from 58 to 46 percent in five years (see Appendix Table 6, middle panel). As Figure 15 shows, the declining persistence of unemployment is due to increasing flows both to

20
university after the reform, this did not translate into an increase in university completion, largely due to the insufficient higher education places (Malamud and Pop-Eleches, 2011).

- Abolishing tracking yields mixed results. Kerr, Pekkarinen, and Uusitalo (2013) evaluated the effects of the school system on mathematical, verbal, and logical reasoning skills using data from the Finnish comprehensive school reform that abolished the two-track school system in the 1970s with newly obtained cognitive test score data. On average, they found that the reform had a small positive effect on the average verbal test scores and no significant positive or negative effect on the average arithmetic or logical reasoning test scores.

What is clear among the countries that have vocational upper secondary education is that workbased learning (WBL) design is a more effective approach in linking students with the labor market (European Commission 2013, 2015; Kis 2016; OECD 2014; World Bank 2018). WBL is defined as learning that takes place through some combination of observing, undertaking, and reflecting on productive work in real workplaces (Kis 2016). When implemented effectively, the technical skills acquired through WBL are better aligned with actual labor market skill demand than the skills that are acquired through other learning methods, such as classroom teaching or workplace simulations. In addition, it is believed that WBL helps equip students with essential nontechnical skills, such as the ability to work in teams, problem-solving, and communication skills, to an extent that is not achieved through other learning approaches. WBL can only take place when there is a strong partnership between employers and vocational education providers, and whether or not employers are motivated to engage in such a partnership depends on the extent to which they consider overall vocational education and training provision can meet skill demand. Consequently, only programs that meet employer needs will be able to identify WBL placements for their students.

There has been increasing focus on the trade-off between immediate employment gains of vocational-oriented education and general skills required for lifelong learning. Hanushek et al. (2017)