rafmacalaba/data-use-mentions-extended huggingface dataset:
budget. These statistics suggest that there is not a clear expenditure pattern of the Albanian parents by sibling sex composition. Therefore, the instrument seems not to be related to any indirect income effects that might question its exogeneity.<sup>21</sup> Lastly, it can be argued that another possible threat to the exogeneity of the instrument is the fact that having two girls as the oldest children can make it easier for the parents to increase their labor supply as the older girls can do household chores and take care of the younger siblings. However, it is worth pointing out that this may be a bigger concern for the results 18The data used for analyzing the expenditure patterns in Albania come only from the 2002, 2005 and 2012 Albanian LSMS surveys. Unfortunately, there are no available expenditure data on the 2008 DHS survey. The LSMS surveys contain very detailed information on household food and non-food expenditures. These databases have also the advantage of making possible the separation of child clothing and education expenditures from adult’s expenditures.

> 19Cruces and Galiani (2007) make a similar argument for the cases of Argentina and Mexico. They indicate that sex composition in unlikely to have a noticeable effect on clothing and footwear expenditure for these Latin American countries due to the fact that only around 4.8% of the budget was used for clothing and footwear in the year 2000 in Mexico, while 6.7% for the case of Argentinean households (for all members).

20B ̈utikofer (2010) contributes to the debate over the usage of the same-sex sibship as an identifying instrument by analyzing whether families with same-sex siblings composition face larger economies of scale in consumption in a variety of countries. She finds no significant differences between the estimated equivalence scales of families with different siblings sex composition in richer countries including the case of Albania. This analysis provides further evidence that household economies of scale do not crucially differ with siblings’ sex-composition.

> 21Huber (2012) developed a test to assess the validity of an instrumental variable
– – of degree programs: _general_ preparing students for higher education, _technological_ – preparing students both for the labor market and for higher education, and _technical_ emphasizing technical and vocational education (INEE 2017a)).

Although graduation rates and learning outcomes in EMS have experienced an improvement during the last 10 years, their levels are consistently low (see Figure 1). According to the official statistics from Mexico’s National Institute for the Evaluation of Education (INEE for its acronym in Spanish),<sup>2</sup> in 2014 only 67% of students graduated three years after enrolling in EMS, with this share being significantly higher among females (70%) than males (62%). Graduation rates vary across types of degree programs with _general_ schools showing the highest (69%), followed by _technological_ schools with rates very close to the national average and _technical_ schools showing the lowest (54%). According to INEE, more than 60% of the cumulative dropouts throughout the three years of EMS take place during the first year. Household survey data show that enrollment in EMS among youths aged 15 to 18 varies substantially across household income deciles. For instance, in 2012, only 13.5 percent of youth aged 15 to 18 in the poorest households were enrolled in EMS versus an enrollment rate of 95 percent among the richest households. The disparity in enrollment rates across the distribution raises the question of whether liquidity constraints have an important role to play. Moreover, information from the 2011 _EMS School Dropout Survey_ shows that more than a third of the 2,549 EMS dropouts surveyed declared that economic constraints were the main reason for leaving school ((SEP 2012)).

> 2See http://publicaciones.inee.edu.mx/buscadorPub/P1/B/115/P1B115.pdf

5
After the September 2001 terrorist attacks, the United States launched the Operation Enduring Freedom, whose objective was to break apart the Taliban and Al Qaeda groups in Afghanistan. The operation encompassed two decades of intense conflict between the U.S. allies and the Taliban militias until 2021, when the U.S. government withdrew its troops from Afghanistan. Seizing this opportunity, the Taliban abruptly regained complete territorial control of Afghanistan in 2021. Their rule, once again, restricted access to information, and women’s access to education and labor markets (Amnesty International 2023, United Nations 2023a, United Nations 2023b).

To examine the impacts of extremism exposure on refugees’ integration, we utilize a unique census of Afghan refugees living in Tajikistan in 2023.<sup>3</sup> Tajikistan has an open border with Afghanistan, and has accommodated nearly 9,532 Afghan refugees and asylumseekers after the events of 2021 (UNHCR 2023). For this purpose, the census interviewed all Afghan refugee households registered with the United Nations Refugee Agency office in Tajikistan.<sup>4</sup> Our empirical strategy compares the integration outcomes of refugees who experienced to extremism the and sudden dramatic shift varying exposure generated by exogenous in Taliban’s territorial control in their province of birth between 2017 and 2021. During this period, the Taliban’s territorial control abruptly shifted from 19 to 100 percent of the we the outcomes of Afghan territory. Specifically, compare integration refugees living in Tajikistan in 2023, that had similar characteristics before the migration episode, but who were born in provinces that experienced diverse shocks in Taliban territorial control between 2017 and 2021. To do this, our main specification controls for a rich

> 3The census was collected by the project Enhancing Government capacity in hosting temporarily Afghan refugees with focus on socio-economic resilience and gender issues, under the Rapid Social Response Program (RSR), multi-donor Trust Fund of the World Bank. The objective of that project was to identify opportunities for refugee integration in Tajikistan. The
## our model.

The second adjustment concerns the selection of countries from the Amadeus database. Given the known heterogeneity in the degree of coverage of the countries’ firm size distributions, and the importance of minimizing biases in the calculation of average firm sizes, we apply the following selection criteria. Taking Eurostat’s Structural Business Statistics database as the measure of the true size distribution of employment, we keep a country in the sample if 1) the ratio of aggregate manufacturing employment in Amadeus to aggregate manufacturing employment in Eurostat is greater than or equal to 80 percent, and 2) the ratio of manufacturing employment between Amadeus and Eurostat in each bin of Eurostat’s size distribution above the 10-worker threshold (10–20, 20–50, 50–250, 250+) is also greater than or equal to 80 percent. The countries that satisfy these criteria are Belgium, Bulgaria, Finland, France, Hungary, Italy, Latvia, Portugal, Romania, and Spain. Table 2 in Data Appendix B shows the final list of countries together with a brief description of the data sources.

# **4.1 Average Size and Idiosyncratic Distortions**

One advantage of using the firm-level data to compute average size is that it allows us to control for cross-country differences in production structures across broad industries within manufacturing. Thinking of manufacturing as a collection of two-digit sub-industries, average size may then vary across countries due to 1) heterogeneity in average size across firms within two-digit industries under a fixed distribution of production shares across these industries and 2) heterogeneity in production shares for a given distribution of average sizes. Considering this distinction is essential for our analysis given our interpretation of cross-country differences in average firm size as being driven by differences in the degree of allocative distortions and entry barriers. Since, following the literature (Hsieh and Klenow [2009]), misallocation is measured across firms within an industry,<sup>23</sup> our theory is silent about potential misallo-

> 23The reason to restrict
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- MoWRI (Ministry of Water Resources and Irrigation, Government of Southern Sudan). 2007. ―Draft Water Policy.‖ Juba.

- ———. 2010. ―GoSS Water Strategic Framework.‖ Advanced draft, Juba.

- Nathan Associates. 2010. ―Definition and Investment Strategy for a Core Strategic Transport Network for Eastern and Southern Africa: Corridor Review and Performance Report.‖ Draft Document submitted to the World Bank, Washington, DC.

Platts. 2006. WEPP database.

- ———. 2009. WEPP database.

- Southern Sudan Center for Census, Statistics and Evaluation. 2010. _Statistical Yearbook for Southern Sudan 2010_ . Juba.

- WHO (World Health Organization) Joint Monitoring Programme (JMP). 2010a. ―Sudan Estimates for the Use of Improved Drinking-Water Sources.‖ www.wssinfo.org/resources/documents.html.

- ———. 2010b. ―Sudan Estimates for the Use of Improved Sanitation Facilities.‖ <u>www.wssinfo.org/resources/documents.html.</u>

- World Bank. 2007. ―Ethiopia Power Export Project Stage 2: Ethiopia-Sudan Interconnector Project.‖ World Bank Project Appraisal Document, Washington, DC.

- ———. 2008. ―Revitalizing Sudan’s Non-Oil Exports: A Diagnostic Trade Integration Study (DTIS).‖ Prepared for the Integrated Framework Program, World Bank, Washington, DC.

- ———. 2009a. ―Sudan: The Road toward Sustainable and Broad-based Growth.‖ Poverty Reduction and Economic Management Unit, Africa Region. Washington, DC.

- ———. 2009b. ―Sudan Investment Climate.‖ Finance and Private Sector Development Unit, Africa Region, Washington, DC.

- ———. 2010a. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Northern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, Washington, DC.

- ——
each climate model. Altogether 8 sets of 17,000 tropical cyclones are generated. For each track, we follow where the tropical cyclone makes landfall. Tropical cyclones that land on continents are assumed to terminate there. Tropical cyclones that pass over islands are assumed to continue and can strike multiple locations. The minimum barometric pressure and the maximum wind speed at landfall of each storm are recorded. The models are also used to predict the expected frequency of tropical cyclones in each ocean basin.

A damage function is then used to predict the damage that each storm will cause. The coefficient for storm intensity was estimated using aggregate damages per storm and storm characteristics at landfall from US storms since 1960 (NOAA 2009). Several storm characteristics were tested including maximum wind speed and minimum barometric pressure. Vulnerability measures were used to control for population density and income at each impacted coastal area. These data were inferred for each year from decennial Census data by county (US Census of Population 1960, 1970, 1980, 1990, 2000).

A separate damage analysis was then conducted of tropical cyclones around the globe (EMDAT 2009). The international data set was used to estimate the coefficients for vulnerability (income and population density). The observations in this data set are damages and fatalities per country and event. These were matched with national income and population. All dollar values were adjusted for inflation to USD 2010.

Some storms were predicted to cause so much damage that damages exceeded the capital stock in the path of the storm. Storm damages were truncated at a maximum where all the capital ashore is destroyed. A value for US storms was calculated that assumed a highly destructive storm would destroy five coastal counties completely (Mendelsohn et al. 2010). The average damage for the US for these highly destructive storms was $172 billion per storm in 2100. This average national value was then transferred to other countries in proportion to their GDP in 2100. Countries with higher future GDP have higher maximum values.

The model calculated damages for
students’ performance, but different in that the risk associated to class completion or college continuity is not fully exogenous as in these papers but depends on an endogenous variable—student effort.

While the literature has placed much attention on the role of ability in performance and college outcomes, a growing line of research highlights the role of effort. Zamarro, Hitt, and Mendez (2019) use data from the Program for International Student Assessment (PISA) to show that different effort measures explain about a third of observed cross-country test score variation. Stinebrickner and Stinebrickner (2004) rely on time use surveys to estimate the effects of study time on grades. Ariely et al (2009) show that using incentives can help the average student improve her test performance, though the effect is limited on high-ability students. Beneito et al (2018) provide evidence that the tuition increase implemented by Spanish colleges in 2012 boosted student effort. Ahn et al (2019) model effort in response to grading policies. We contribute to this line of research by explicitly modeling the role of effort and embedding it in a dynamic setting, where it affects class accumulation and risk mitigation.

In an efficient and equitable world, college enrollment would depend on student ability rather than parental resources (Cameron and Heckman 1998 and 1999, Carneiro and Heckman 2002). In Colombia, as in other countries, parental resources matter greatly to college enrollment even controlling for ability. This provides strong evidence for credit constraints limiting college access, as discussed in a large literature. Lochner and Monge-Naranjo (2011) develop a model that helps explain the rising importance of family income for college attendance in the U.S. even in the presence of credit. Solis (2017) finds that relaxing credit constraints in Chile had a positive impact on enrollment and college years completed, particularly for low-income students. Parental resources and background, however, may be of limited importance. Hai and Heckman (2017) show that equalizing initial ability has larger effects on college outcomes and 6
# 4. **The** Peruvian **VTS Data** Set

To assess the relative returns to VTE and general education in Peru, we **make use** of the Peruvian Living Standards Survey (rLSS) national probability sample of hiuseholds and individuals surveyed in 198511986.5 From the full PLSS sample we drew **a** subsample (n - 2,210) of all urban males between the ages of 15 and 69 who were either employ,ed in the private wage sector or were "self-employed," that is, working for pay in a family enterprise. Rural workers were excluded from the present study because of the small number of such individuals with VTE.<sup>6</sup> The breakdown of the sample between those who are self-employed and those who are wage-employed, and between those living in Metropolitan Lima and those living in all other urban areas (OUAs) is presented in table 2. The distribution across the four cells is nearly equal, the largest group being the wage-employed in Lima (756), and the smallest the self-employed in Lima (427).

S The PLSS was implemented between June 1985 and July 1986 by Peru's Instituto Nacional de Estadistica (INE). The survey questionnaire was developed by the World Bank in collaboration with INE and the Central Bank of Peru and adapted from a prototype questionnaire used earlier in Cote d'Ivoire. A parallel study to this one comparing the returns to VTE and general education in Cote d'Ivoire is available (Grootaert 1988). For details about the PLSS survey and sample, see Grootaert and Arriagada (1986).

6 Analyses of the earnings of similar categories of Peruvian workers, without the focus on VTE, are described elsewhere -- see Stelener, Arriagada, and Moock (1987), which look at the returns to education in general, and Arriagada (1988), which focuses on the returns to training. These two studies are confined also to males, but they
on the other hand, have greater access to agricultural resources, which helps offset partly their initial productivity disadvantage.</mark> <mark>The contribution of this paper to the literature is threefold. First, this is the first study to analyze the agricultural productivity gender gap using nationally representative data in Sri Lanka, thereby contributing to the scarce literature in South Asia on this topic. While the data primarily captures smallholder farmers due to the nature of the household survey, the setting is particularly interesting as it starts out from an unconditional productivity gap in favor of women which, to the best of our knowledge, has not been observed in any other country. Second, a particular focus is on analyzing how the gendered pattern of crop mix would influence the productivity gender gap among smallholder farmers. While paddy, the main staple crop in Sri</mark>

> 22 One exception is Mahajan (2019), which studies the agricultural gender gap using a nationally representative survey in India.

3
being employed is not significantly different from zero for females. Also, the impact on labor supply does not significantly differ across genders (Table7).

Finally we analyzed how the effect of chronic diseases changes among people employed in the informal sector, where workers do not benefit from any employment protection, disability benefits or health insurance. In our data there is no explicit information on which sector a worker belongs to, though extensive information on health insurance coverage is available. In Egypt only employees of the formal economy are entitled to a health insurance which typically does not extend to other members of the family. Indeed our indicator of informal sector is based on whether people are covered by a health insurance. We attribute individuals to the informal sector when they report that in their household no one is covered by a health insurance and those who report of having a health insurance provided by the school, which covers only the children<sup>9</sup> . According to this classification about 51 percent of the sample belongs to the informal sector, a figure not dissimilar from the 49 percent in 1998 which is estimated using the Egypt Labor Market Survey 1998.

In column (5) of Tables 6-8 we have reported estimates of the specification including the interaction between chronic diseases and the informal sector indicator. Not surprisingly, chronic conditions reduce employment probability much more among workers employed in the informal sector (37 percent compared to a statistically insignificant 13 percent of those belonging to the formal sector) (Table 6). Similarly, labor supply falls by 36 hours per week in the informal sector compared to less than 13 in the formal (Table 7).

From Table 8 we notice that the absence of relation between chronic diseases and wages persists also within each sector. This result is further confirmed by Figure 3 which plots log hourly wage distribution by sector for the sample of siblings. There is no evidence that chronic conditions influence wages differently in the informal sector.

**Figure 3  Wages in formal and informal sectors**

<
non-financial sector, again normalized by the GDP level in a given country. _Total credit_ represents the ratio of all credit to the private sector over GDP. The variable thus includes credit provided both by deposit-taking banks and by other financial institutions.

Our main measure of the depth of financial markets in the exporting country is called _Stock markets_ . It represents the ratio between stock market capitalization (the market value of all listed stocks) and GDP of a given country, and it is a standard measure of well-developed stock markets. _Stock market turnover_ (the ratio between the value of stock market transactions and the stock market capitalization) serves as a proxy for an active and liquid (rather than large) stock market. In additional tests, we also use _Stock market value traded_ (the value of stock market transactions divided by GDP).

The data are from the Global Financial Development Database that builds on the earlier work by Beck et al. (2000) and has been substantially extended by Cih ́ak<sup>ˇ</sup> et al. (2012). The database contains various indicators of financial development across countries and over time and is regularly updated.

All measures representing financial institutions and markets vary over time. We measure them at the beginning of a given export spell in order to capture the impact of the predetermined financial environment on the subsequent export performance. The direct effects of these time-varying country-level financial variables are thus captured in our regressions by the included country-time fixed effects ( _δc∗t_ 0), with time referring to the beginning of a given export spell ( _t_ 0). Consequently, the variables described in this subsection enter the regressions explicitly only as part of interaction terms with the industry-level measures of financial vulnerability.

# **3.3 Product-Related Trade Variables**

The export survival rate in the US market and all trade-related control variables at the product level (initial export, total export, number of suppliers, a multiple spell dummy) are computed using the BACI dataset developed by the
Policy Research Working Paper 10559

# **Abstract**

Capabilities to track fast-moving economic developments re-main limited in many regions of the developing world. This complicates prioritizing policies aimed at supporting vulnerable populations. To gain insight into the evolution of fluid events in a data scarce context, this paper explores the ability of recent machine-learning advances to produce continuous data in near-real-time by imputing multiple entries in ongoing surveys. The paper attempts to track inflation in fresh produce prices at the local market level in Papua New Guinea, relying only on incomplete and intermittent survey data. This application is made challenging by high intra-month price volatility, low cross-market price correlations, and weak price trends. The modeling approach uses chained equations to produce an ensemble prediction for multiple price quotes simultaneously. The paper runs cross-validation of the prediction strategy under different designs in terms of markets, foods, and time periods covered. The results show that when the survey is well-designed, imputations can achieve accuracy that is attractive when compared to costly–and logistically often infeasible–direct measurement. The methods have wider applicability and could help to fill crucial data gaps in data scarce regions such as the Pacific Islands, especially in conjunction with specifically designed continuous surveys.

This paper is a product of the Development Data Group, Development Economics, the Agriculture and Food Global Practice; and the Poverty and Equity Global Practice. 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 authors may be contacted at bandree@worldbank.org and upape@worldbank.org.

_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
3.0|5.0|3.9|3.3|2.5|
|**Luxembourg**|5.2|2.8|2.9|5.7|1.7|2.9|3.3|
|**Latvia**|7.6|8.8|5.4|10.2|3.3|4.0|10.9|
|**Malta**|2.5|2.3|5.5|3.6|-|-|-|
|**Netherlands**|0.8|0.7|0.4|1.0|1.0|0.1|0.5|
|**Poland**|13.2|12.4|-|14.7|10.4|10.5|15.7|
|**Portugal**|7.3|7.7|7.7|10.5|8.1|5.9|6.8|
|**Slovenia**|14.2|13.8|13.3|30.3|12.5|7.0|11.2|
|**Slovak**<br>**Republic**|4.4|5.9|4.3|6.7|3.1|2.4|8.2|
|**Total**|6.3|6.2|5.0|7.1|5.6|3.9|6.9|

Source: Our elaboration on HFCS data.

* Age of the reference person, defined according to the UN/Canberra definition.