rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3. Data

For national statistics, the paper relies on data from the Ministry of Education, Science, and Training (MoEST) Basic Education Statistics (BEST) and the World Bank’s World Development Indicators (WDI). Other data come from three sources.

**1. 2013 school survey undertaken as the baseline of an impact evaluation** (IE) by the World Bank. This is referred to henceforth as the _<u>2013 IE data.</u>_ The study sample includes 396 lower secondary schools from five regions of Tanzania: Simiyu, Shinyanga, Morogoro, Lindi and Geita.<sup>1</sup> Around 16 percent of the interviewed schools are private and the remaining are public. The survey includes information from head-teacher and teacher surveys, along with tests for Form III (Grade 9) students. As a part of data collection, students were tested in independently-created curriculumbased tests on English, Mathematics and Kiswahili. For the purposes of this analysis, we include only schools from the Morogoro region (166 schools), as we can merge it with other data sources, including administrative data on student performance.

**2. 2015 school survey in Morogoro** undertaken by the World Bank. This is referred to henceforth as the _<u>2015 Morogoro data</u>_ <u>. This survey collected information from 142 schools in the</u> Morogoro region of Tanzania, 53 of which were public schools and 89 of which were private schools. Roughly, 62 percent of the schools were at pre-primary/primary level and the remaining (38 percent) were at the secondary level (lower secondary, upper secondary or both). It included interviews with head-teachers on school demographics, operational costs, management and student outcomes. The survey was not designed to be nationally representative.

For private schools, an additional module on possible partnership with the government was included. Private schools were randomly selected to be administered one of
The inverse demand function is assumed to be that of Bulow and Pfleiderer (1983): where _P_<sup>_T_</sup> is the price charged by the traders to the consumers. There is a fixed number ( _Nr ≥_ 1) of refiners who import crude palm oil from the world market at an exogenous price _P_<sup>_w_</sup> and produce edible oil with a Leontief production function; for simplicity, we assume that 1 unit of crude is transformed into 1 unit of refined oil. The refiners incur per unit cost of _C_<sup>_r_</sup> for labor and other intermediate inputs in addition to the crude oil cost. Refiners engage in Cournot competition; prior to the reform they sell to DOTs. The case where the refiners collude with one another is captured by a value of _Nr_ smaller than the true number of refiners.

There is a fixed ( _Nd ≥_ 1) number of DOTs in the market. The DOTs buy DOs for the refined oil from the refiner at price _P_<sup>_r_</sup> , and sell these to wholesalers. DOTs do not take physical delivery of the oil, so incur no storage or transport costs themselves. DOTs engage in Cournot competition with one another in selling DOs to wholesale traders. As with the refiners, the case where the DOTs collude is represented by a value of _Nd_ smaller than the actual number of DOTs.

Evidence from a recent survey of wholesalers and dealers indicates that an important role of the DOTs is to provide supplier credit to the wholesalers (Emran et al. (2015)). DOTs are usually quite wealthy, with access to short term capital at lower costs than is available to most wholesalers. The DOTs sell oil to the wholesale traders on credit. They bear an interest cost of _i_<sup>_d_</sup> , smaller than _i_<sup>_b_</sup> which either wholesalers or refiners would bear.

The DOTs purchase DOs
2016).<sup>11</sup> To examine how urban agglomeration affects firm propensity to undertake innovation-related activities, it is crucial to know the location of firms, ideally measured in a consistent way across countries. Publicly available WBES provide information on the geographic units where each firm is located. However, the scope of the geographic units varies by country and these units do not necessarily correspond to a well-defined urban area. For example, the Kazakhstan data indicate only the region (e.g., north or south) to which surveyed firms belong, while the Indian data reveal only the state where a firm is located. Even if the geographic unit refer to cities, as is the case for some countries, they are not comparable since the official definitions of cities differ greatly across countries (UN 2018). Fortunately, the WBES conducted since 2012 contain the

> 9 Examples of relatively minor product innovations include a shoe manufacturing firm in Bandung, Indonesia introducing shoes made out of nubuck leather with anti-slip and oil resistant soles, or a plastic bag printing firm in Port Moresby, Papua New Guinea printing bags to carry food. Examples of process innovations include a manufacturing firm in Yangon, Myanmar offering a door-to-door sales service or a repair shop running an ad campaign on Facebook. There are more advanced product innovations in the sample as well, including a firm in Ho Chi Minh starting to weld optical fibers for Viettel, a telecom multinational. For process innovation, an example is an auto repair company in Yangon adopting laser-based technology.

> 10 See pages 16-20 of “World Bank Group’s Enterprise Survey: Understanding the Questionnaire”, 2019, World Bank.

> 11 Robustness tests use as dependent variables an indicator of whether the new or improved product or service was also new for the establishment’s main market and an indicator of whether the establishment reported either a product innovation or process innovation. The latter allows for mistaken categorization of innovation as a product or process innovation by respondents. See Section 4.
should come as no surprise that this approach does not prove to be fruitful. However, even without such knowledge, we would not necessarily believe that the cutoff is followed strictly (as in a sharp RD design) because there may be differences between the ENCV data and a household’s characteristics at the time of the Sisben interview. (For instance, the program benefit may have lifted a marginal household across the eligibility line.) However, this would not pose any major problems as long as the probability of program receipt were still to change discontinuously at the cutoff (as in a fuzzy RD design). A couple of unpublished working papers on the predecessor of Colombia Mayor, the Programa de Protección Social al Adulto Mayor, attempt an RD approach based on estimated Sisben scores and thresholds, but do not find any results (Rubio 2014; Rubio, Hessel, and Avendano 2015). Analyzing the effect of a different social protection program for which eligibility is based on Sisben scores (Régimen Subsidiado, a public health insurance program), Miller, Pinto, and Vera-Hernández (2013) use a similar strategy, but find only weakly significant results. We are able to observe the actual Sisben scores implied by the ENCV data and find that the official thresholds are not enforced in practice. Moreover, below, we also provide statistics on municipality-specific prioritization, which would be the principal reason to estimate municipality-specific thresholds, and show that, at least for Colombia Mayor, there is not a lot of variance across municipalities.

In table 5, we present the results of a simple RD-inspired spline regression with standard errors clustered at the municipal level. We regress the binary outcome variable of a household receiving Colombia Mayor according to whether it is Sisben eligible (another binary variable) and measure the absolute value of its distance to the cutoff, which we capture using two separate variables depending on whether the household is Sisben eligible (that is, to the left of the cutoff) or not (to the right of the cutoff).
three studies that provide variable targeting costs for PMT and CBT methods: Alatas et al. (2012) provide variable costs for CBT and PMT-based targeting of a single program in Indonesia; Karlan & Thuysbaert (2019) provide variable costs for CBT and PMT-based targeting of an ultra-poor program in Honduras and one in Peru; and Schnitzer & Stoeffler (2021) provide variable costs for three CBT-based programs and four PMT-based programs in seven countries in Sub-Saharan Africa.<sup>15</sup> Table S10 summarizes the cost estimates from each of these papers; we use the median per-household targeting cost for each method in our analysis ($2.20 per household for CBT and $4.00 per household for PMT). While using these global estimates to inform our model of targeting costs in Afghanistan is not ideal, since no data on targeting costs from the TUP program or other anti-poverty programs is available for the country, these values are the best available estimate on which to base our cost analysis.

We are unable to find any papers that document the targeting cost associated with consumption-based targeting, as consumption data is rarely used as a real-world targeting strategy. We therefore consider the costs of consumption to be strictly greater than the costs of targeting on a PMT, since consumption modules take longer to collect than PMT data in household surveys. In practice, we expect that the cost of targeting on consumption would be substantially greater than the cost of targeting on a PMT.

For phone-based targeting, we associate no cost with the collection and analysis of phone data. While in some cases phone data may require purchase from the operator, partnerships between mobile network operators and governments for social protection and public health applications have not, to date, involved payment (Milusheva et al., 2021). The fixed cost of mobile data analysis is non-negligible but its contribution to marginal cost is close to zero as the number of screened households increases. A phone-based targeting method that collects informed consent from program applicants to analyze phone data would have
to plan and prepare for their departure may have all their documents together and they may move to a location with better economic opportunities and denser social network. Contrast that to households with little time for preparation because their property was damaged or destroyed. They might lose access to the PDS because of missing documents, and at the same time have fewer options to work or informal support network. Families’ choice of destination itself, therefore, can have important implications in having access to the PDS. A household’s ration card is linked to a specific local ration agent. When moving to a new location, the household is required to establish an official residency, and only then can it start the process of transferring the linkage to a new agent in a new location and start receiving the assistance. A household that values the PDS highly may choose a destination where it is easy to establish a residency, and which has uninterrupted supply of rations. To control for both the observed and unobserved geography-specific association with PDS accessibility, we modify equation 1 as follows: where, subscripts i , j , and m index a household i , resident in census block j and district m . The SWIFT survey covered 106 qhadas (districts), which are added as fixed-effects, ηm , to account for qhada-level variation in the availability of ration and other characteristics that influence households’ welfare and the PDS accessibility. Census blocks (EA) served as the primary sampling units for the survey which were selected with a probability proportional to size. The standard errors, hence, are clustered at this level to allow for the correlation among error terms within these blocks.<sup>10</sup> Equation 2 teases out the confounding factors between the PDS and outcomes of interest at the specified geographic level. The non-random assignment of the treatment, however, entails that the heterogeneity in a household’s selection into the PDS program within qhadas, if present, will bias the coefficient estimates. We try to overcome such biases by employing the propensity
exposure to climate policy impacts, and income level, as well as observed pledges, strategies, and behavior (for example, level of ambition of their nationally determined contribution). Each country’s assignment to one of the two clubs also includes a subjective element—the judgment of the authors—and is not necessarily consistent with its ranking based on the index of preparedness for an LCT (Peszko et al 2020).

# **2.3. Scenarios**

The robustness of different diversification and climate policy strategies available to FFDCs is stress-tested by simulating multiple exploratory policy scenarios. These scenarios are designed as combinations of plausible (not necessarily probable) alternative external LCT impacts on FFDCs (over which they have little control) as well as the strategic choices that FFDCs can make to prepare for these impacts (figure 1):

- LCT Pioneers choices of climate policies (unilateral carbon taxes);

- LCT Pioneers choices of trade measures (border adjustment taxes);

- The FFDCs choices how to diversify their economies; and

- The FFDCs choices whether and how to cooperate on global climate action.

1 Recursive, dynamic CGE models are useful tools with which to understand both the relative scale of the shock and the subsequent economy—with adjustments of prices, wages, profits, outputs and trade flows. The recursive nature of the model simulates decision making under uncertainty and with myopic behavior of decision makers. For example, it allows for accumulation of vulnerable capital stock before the policy or technology shock occurs, even if some economic actors divest in response to preannounced or expected policies. Such models also have well-documented limitations.

5
. Moreover, because the most recent crisis episode began in advanced economies rather than in the ECA countries themselves, we offer a fresh look at the role of foreign banks when their parents are under financial pressure.

The empirical results in this paper show that firms that had access to financing were better able to weather the crisis than firms that did not. Larger and older firms were less likely to

> sample of all syndicated loans to private borrowers from 2005 to 2009, de Haas and Van Horen (2011) find that geographic proximity, establishment as a subsidiary (rather than a branch), integration into a network of domestic co-lenders, and a history of syndicated lending to a particular country all enhanced the stability of foreign lending.

> 4 Evidence is from bank-level regressions for 1,275 banks in ECA in de Haas et al (2011) and from a survey of central bank governors in Mihaljek (2010).

4
21

**Figure 2:** Migration stock among country-pairs with different income levels.

<!-- Start of picture text --> South-North migration (in millions) North-South migration (in millions)<br>1990 2000 2010 2020 1990 2000 2010 2020<br>North-North migration (in millions) South-South migration (in millions)<br>1990 2000 2010 2020 1990 2000 2010 2020<br>140 7.5<br>7<br>120<br>100 6.5<br>80 6<br>60 5.5<br>40 5<br>90<br>40<br>80<br>35<br>30 70<br>25 60<br><!-- End of picture text --> Source: United Nations Population Division.

**Figure 3:** Density of bilateral migration stocks among country-pairs with and without visa provisions.

<!-- Start of picture text --> 1990 1995 2000<br>2005 2010 2015<br>0 5 10 15 0 5 10 15<br>2020<br>0 5 10 15<br>Log of migration stock<br>Migration stock density with visa provision<br>Migration stock density without visa provision<br>.15<br>.1<br>.05<br>0<br>.15<br>.1<br>.05<br>0<br>Density<br>.15<br>.1<br>.05<br>0<br><!-- End of picture text --> Source: United Nations Population Division and World Bank Deep Trade Agreement database.
empowerment than the type of employment captured with standard, individual questions. Disaggregation of employment and understanding the relationships between types of employment and women’s outcomes has been shown to be important in other research (Assaad, Krafft, and Salemi 2023; Assaad, Krafft, and Selwaness 2022; Selwaness and Krafft 2021), and should be pursued with better measures of employment as well.

Additional research is needed to continue to improve the detection of women’s employment. The same types of employment that are under-detected contemporaneously tend to be left out of retrospective recall (Assaad, Krafft, and Yassin 2018). Piloting and testing project-based detection in retrospective questions is an important area for future research. Similar projectbased measures of earnings are also available with the LMPSs (Krafft and Davis 2021) and research on what additional earnings are detected, and gender gaps in earnings, merits future research.

Additional qualitative and quantitative work to understand reasons for underreporting women’s employment could be helpful. It could, for example, be that women disproportionately engage in informal businesses and are concerned about reporting a business to a government official for fear of legal or tax implications. It could also be that strong female homemaker/male breadwinner norms lead to the underreporting of employment (Muller and Sousa 2020; Franck and Olsson 2014; El-Feki, Heilman, and Barker 2017; Hoodfar 1997). Potentially, similar problems could arise due to perceptions of norms (Bursztyn, Gonzalez, and Yanagizawa-Drott 2020; Bursztyn et al. 2023; Barnett 2023). These problems may occur particularly for certain types of employment, such as home-based businesses (Muller and Sousa 2020).

25
share of processing exports in total exports, and especially when destined for far away countries. These results are in line with Yi’s (2003) theory that intra-GPN trade is more sensitive to changes in trade costs than regular trade.

In Section 6, we use the processing trade data to analyze the impact of the recent Great Recession on intra-GPN trade. This analysis has become particularly relevant in light of the great collapse in trade during the crisis, which was significantly larger than the drop in world GDP. While there is a general consensus among trade economists that GPNs played an important role in the great trade collapse, there continues to be a heated debate through which channels. Analyzing China’s processing trade data, we find evidence for the existence of three channels: a compositional effect, a trade cost effect and a bullwhip effect. First, in line with the _compositional effect_ , we find that the sectors that contributed most to China’s exports collapse are those where processing trade is more prevalent. Second, we show that, within industries, processing exports consistently dropped more than non-processing exports during the Great Recession. This is consistent with the _trade cost effect,_ since intra-GPN trade should be more sensitive to trade costs than regular trade. Third, in line with the _bullwhip effect_ , we show that in virtually all industries, the drop in demand for China’s processing exports led to a magnified drop in processing imports.

# **2. Mapping Global Production Networks**

Empirical evidence on vertical specialization and the role of trade costs in GPNs is remarkably scant, largely because concrete evidence is difficult to obtain. To measure vertical specialization, one would like to know the number of countries involved in the production process of a specific good, the value added created in each country, and the sequential supply chain linkages between production activities. However, this information is hard to come by. Firms are generally not willing to provide data on the cost structure of their own supply chain activities, and often
, and the public sector earnings premium across the earnings distribution. We use the country‐level regional and income classifications from the World Development Indicators (WDI) database. A selected list of variables and their description is given in Annex 2.

We applied a variety of criteria to determine which surveys to include, to ensure sufficient sample sizes for the main variables. We excluded from the sample surveys with more than 30 percent missing observations on sector of employment (public sector or private sector), wages, or occupation. We also excluded from the analysis countries with odd data; for example, in Haiti 100 percent of the respondents claimed to be working in the private sector, implying that Haiti has no public sector, which is clearly wrong. We used the most recent acceptable survey available for each country.

The resulting data set contains surveys covering the period from 2009 to 2016, for which we have 91 countries with data on public employment and 68 countries with data on wages. These include 31 countries in Sub‐Saharan Africa (SSA), 16 in Latin America and the Caribbean (LAC), 15 in Eastern Europe and Central Asia (EECA), 8 in East Asia and the Pacific (EAP), 10 in high‐income Western Europe and North America, 6 in the Middle East and North Africa (MENA) and 5 in South Asia (SA). See Annex 1 for the list of countries.

> 1 <u>https://datacatalog.worldbank.org/dataset/worldwide‐bureaucracy‐indicators.</u>

> 2 Where data exist for a country in the LIS but not in the I2D2, we use the LIS; where data exist for a country in both the LIS and I2D2, we use the I2D2.

4
33 the trigger point, leading to increasing foreign labor inflows that are mutually reinforcing and facilitated through the formation and accumulation of social capital.

# **VII. Policy Failure**

Foreign labor management has been largely successful in Singapore and Malaysia for a variety of reasons, not least of which is effective immigration policy. The news is not all good, however. The shortcomings of immigration policy are evidenced by the existence of a large informal sector of illegal foreign workers. Anecdotal evidence indicates that employment of foreign workers in the informal sector has grown rapidly in Singapore in recent years, concurrent with the liberalization of restrictions on legally imported labor. Malaysia's legal foreign labor force more than doubled between 1990 and 1996, and currently exceeds a million workers, equivalent to 13 percent of the total labor force according to official statistics. Unofficial estimates that include illegal workers put the figure closer to 2 million, however. Illegal workers are found primarily in the construction sector, but also work in services, manufacturing and hotels and restaurants. In 1991, nearly 30 percent of all foreign labor in Malaysia was employed in the construction sector, while almost half of non-Malaysian workers were employed in agriculture and forestry where they constituted 30 percent of sectoral employment (World Bank, 1995).

Restrictions and permit fees have given rise to evasion through illegal recruitment and forged documents (a thriving industry) as well as informal trading of permits. Firms employing workers on unofficial contracts can pay lower wages and fewer benefits than stipulated by law, and can impose harsh working conditions on illegal workers who are powerless to complain for fear of deportation. Most evidence suggests that employers retain the premium saved by avoiding levy payments and compensate illegal workers below their legal counterparts; there is some countervailing evidence, however, that savings are passed on to the illegal workers, whose wages are effectively higher than their legal counterparts (Wong, 1997).

It is important to recognize the disadvantages associated with informal employment. Workers on informal contracts do not benefit from protective legislation
of wetlands from the Global Lakes and Wetlands Database (GWLD v3) from WWF; and the presence of ethnic homelands of ethnicities whose historic economic activity is identified as being agricultural (see Michalopoulos and Papaioannou, 2013 for data description).

# 5. Empirical Approach and Results

Building on our conceptual framework, this section analyzes the relationship between water and fragility in the context of the Sahel region. We first show evidence that the region is historically at a higher risk of conflict related to variations in water availability. We argue that this is due to the high degree of water 7