rafmacalaba/data-use-mentions-extended huggingface dataset:
14. Migrant workers arrive in Kuala-Lumpur and are received by the employers in the presence of a representative from the Bangladesh High Mission in Kuala-Lumpur.

# Appendix A.2: Survey finding rates

## **Survey finding rates**

With the field protocol described in Section 3, we were able to find and interview a higher share of T1 group compared to T2 and the control group. As **Appendix Figure 1** shows, the overall interview rates were 94 percent for T1, 69 percent for T2, and 68 percent for the control group. The large follow-up rate for T1 is seen in both the phone-based tracking as well as field-based tracking. While 47 percent of the control group were found through phone calls, conditional on having a phone, or getting phone numbers from fellow applicants, 55 percent of T1 were found and 89 percent of T2 were found. The reason for this discrepancy is that the phone records we got from BMET, albeit incomplete, were more up-to-date, as they kept interacting with the winners for further recruitment processes.<sup>1</sup> Among respondents who we tracked on-field (all those not found by phone), the finding rate for the control group was about 40 percent whereas the finding rates for the treated groups were significantly higher at 89 percent and 64 percent for T1 and T2 respectively. Enumerators found it much easier to track the treated individuals in the villages because their information was more up to date with the local authorities. The winners had to interact with local authorities to submit the necessary information for their recruitment processing. Additionally, the treated applicants also became more well known in the local community as a result of winning the lottery.

## **Impact of differential finding rates**

Common non-parametric bounding approaches, such as the Lee (2009) bounds are uninformative for many of our outcomes due to our particularly high finding rate in the treatment group (94 percent) relative to the control group (68 percent). This means that
it is necessary to ascertain such underlying basic facts as whether certain household groups tend to be precluded from employment in all-inclusive resorts; whether local agricultural products are deemed as suitable for purchase by hotels and restaurants; or whether any obstacles exist to development of handicrafts and local services as hotels are built. To answer these kinds of questions and develop monitoring of local-level impacts, it is necessary to conduct destination level surveys of at least a few selected areas where tourism has already developed.

# **7. Conclusions and Policy Recommendations**

The tourism sector has large significance for the Panamanian economy because of very high and rapidly increasing arrivals of foreign tourists and high levels of spending. This is evident from very large contribution of the sector to GDP—somewhere in the range of 6 to 9 percent; arrivals of foreign tourists that exceed 70 percent of the country’s population over a year; and particularly high economic significance in relative terms in peripheral areas such as Bocas del Toro. Furthermore, the tourism sector has the highest multiplier effects on the economy—that are nearly double the multiplier of Zone Colon and the Canal—because of very high backward and forward linkages. The sector also has a large potential to benefit the poor—most of the gains in household incomes from Zone Colon, the Canal and other sectors of the economy accrue to urban non-poor households 16
alcohol consumption, smoking more, fattier diets).<sup>11</sup> The gender difference in _SAH_ appears to reflect a psychological difference.

The 2002 survey included questions on functional abilities, specific ailments and recent disease histories. The questions covered the level of difficulty incurred in performing various physical activities (running, walking, lifting, crouching etc), weight, height, reported ailments (problems in chest, lungs, liver and so on) and diseases diagnosed (diabetic, heart attack,

> 10 The RLMS data are obtainable from: http://www.cpc.unc.edu/projects/rlms/rlms home.html.

> 11 See Cockerham (2000). Also see the gender differences in vital statistics for Russia reported DaVanzo and Adamson (1997) and Brainerd and Cutler (2005).

12
among members of a society. Resilience thus refers to the ability of communities and groups in both fragile and nonfragile environments to cope with shocks such as climate change, pandemics,

> 7 <mark>Small area layers (SALs) were used as primary sampling units, from urban formal, urban informal, rural formal and rural informal settlements.</mark>

> 8 <mark>The modules on opinions and perceptions are collected on adults aged 18 years or older.</mark>

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# **8. Conclusions**

This paper analyzes the relationship between intergenerational educational mobility and income levels using panel data covering 68 countries from across the world over the period 2000-2020. The dataset relies on data from the Life in Transition Survey and the Global Database on Intergenerational Mobility. It introduces and applies a new oriented distance-based measure of educational mobility - the upward mobility gap - which improves over the standard absolute mobility measures and provides unambiguous mobility rankings. It compares the patterns of educational mobility over generations and across regions observed by this measure with those observed by other absolute and relative mobility measures. Countries with high upward mobility gaps may have low mobility as measured by relative mobility indicators.

The empirical analysis shows that the relationship between intergenerational educational mobility and country income is region-specific. For instance, the indicators of relative mobility in Europe and Central Asia are uncorrelated with a country’s income levels over time, and only a specific dimension of absolute mobility—upward mobility in higher education—shows a positive and statistically robust association with country income across different specifications. In Latin America and the Caribbean countries, higher relative mobility is associated with _lower_ income, while higher absolute mobility is associated with _higher_ income. These findings suggest that the relationship between social mobility and long-term development may be context-specific, with some aspects of mobility more relevant for economic growth in some contexts but not others.

The reasons behind the heterogeneous relationship between intergenerational mobility and country income over time are potentially manifold. In contexts where growth is driven by investments in capital -be it physical or human- higher absolute mobility may be associated with a greater success in increasing the human capital stock across generations. When growth is driven by increases in total factor productivity, a higher human capital stock across generations will only be associated with higher income levels if such capital leads to the adoption of new technologies or to generate innovations (Akcigit, Pearce, and Prato, 2024; World Bank, 2024). This could be the case in countries with higher upward mobility in higher
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Growth in seats for travel between Sudan and South Sudan tripled between 2001 and 2007. The Diio SRS shows that in 2007, while there were 219,741 seats for travel within Sudan, there were only 696 seats advertised for South Sudan (table 8) These figures may mask the fact that traffic between the two regions has actually grown rather significantly—from 24,452 seats in 2001 to 87,191 in 2007. How much of that traffic is hub traffic through Khartoum traveling between points in South Sudan is not known.

**Table 8. Benchmarking air transport indicators for Sudan and select other countries**<sup>**4**</sup>

|Country|Sudan|Ethiopia|Chad|CAR|Kenya|Egypt|
|---|---|---|---|---|---|---|
|Traffic (2007)|||||||
|Domestic travel (‘000 seats per year, excluding south)|218|729|n.a.|n.a.|2,093|5,959|
|International travel within Africa (‘000 seats per year)|302|1,837|110|21|3,145|1,886|
|Intercontinental travel (‘000 seats per year)|2,052|2,005|89|24|2,755|15,793|
|Seats available per 100 people|9.8|5.8|1.8|1|21|27.5|
|Herfindahl-Hirschmann Index—air transport market (%)|15.29|70.61|36.35|50.26|39.47|24|
|Quality|||||||
|Percent of seat-km in newer aircraft|71.9|98.5|99.5|100|80
from a Cobb-Douglas production function in capital and labour, with a capital share of 0.5.<sup>18</sup> Each panel also shows the estimated relation between the variables assuming both a simple linear specification and a cubic polynomial to allow for the nonlinearities required to generate multiple equilibria. In the first panel we observe a linear relation between (log) TFP and (log) capital per worker. It is apparent that the polynomial specification does not offer a significant improvement on the fit with respect to the linear specification. Overall, the estimated relationship suggests a constant elasticity γ of about 0.2. Both the finding of a constant elasticity and its magnitude are inconsistent with the requirements of multiple equilibria. A constant elasticity γ implies that the function _Af_ '( _k_ ) is either always decreasing or always increasing, which implies a unique equilibrium (or none). Also, as determined above, the lower bound for γ (obtained by assuming a very large capital share) is 0.4, which is almost twice the estimated value. The relation of TFP with total capital, shown in the second panel, does not fare better. In fact, the figure shows practically no relation between the two variables. Even if we take the econometric results at face value, they also suggest a constant and small elasticity (about 0.06). As explained above, a constant elasticity is inconsistent with multiple equilibria, and the implied value of the elasticity is way below the magnitude required for multiplicity. Finally, the bottom panel shows the relation of TFP with total output. It is apparent that the situation is almost identical to that obtained for total capital.

The panels of **Figure 10** repeat the exercise performed in **Figure 9** , but this time using data from Colombian plants.<sup>19</sup> The graphs plot the relations between the log of TFP at the plant level and the log of the different measures of activity at the
immigrant stocks: East Asia and Pacific (17.9 percent), Japan (15.5 percent), Sub-Saharan Africa (15.4 percent), and Latin America and the Caribbean (6.9 percent). In absolute terms, all regions of the world sent more women abroad in 2000 than in 1960. The largest proportional increase was from Latin America and the Caribbean (up 10.9 million or 630 percent), followed by the highincome Middle East and North Africa region (500,000, 290 percent), the low-income Middle East and North Africa region (3.3 million, 250 percent), Japan (330,000, 210 percent), East Asia and the Pacific (6.3 million, 180 percent), and Sub-Saharan Africa (4.4 million, 180 percent). In 2000, the countries with the highest proportion of women in their emigration stocks were Ukraine (61 percent), the Philippines (60 percent), and Singapore (60 percent).

# _V. CONCLUSION_

This article draws on the largest collection of censuses and population registers providing information on international bilateral migration and constructs consistent square matrices for the last five completed census rounds (1960 to 2000). Problems in the underlying data that confound meaningful comparisons include differences in recording and recoding practices among destination countries and missing and omitted data.

The main contribution of this article is in recognizing and overcoming these obstacles by making a series of simplifying assumptions. Tradeoffs between pragmatism and accuracy are inevitable, and one of the largest hurdles is establishing a set of rules for achieving a fixed set of countries. Researchers face daunting challenges when working with migration data, and any attempt to resolve them will inevitably fall short of the 24
# **Quantifying war-induced crop losses in Ukraine in near real time to strengthen local and global food security**

# **1. Introduction**

More than four months into the war that started with Russia’s attack on Ukraine on Feb. 24, 2022, the human and economic toll is already vast: 6 and 8 million individuals are estimated to have left the country or be internally displaced requiring support and GDP is estimated to contract by 45%. Implications for regional and global food security will be far-reaching as, with 41.5 million ha of highly fertile land–larger than the agricultural area of France (18. mn ha), Germany (12 mn. ha) and Poland (11 mn. ha) combined– Ukraine has traditionally been a breadbasket and a main exporter of wheat and sunflower oil. After the start of the war, grain prices rose well above the levels experienced in the 2007/08 food crisis, highlighting agriculture’s <mark>geostrategic role (von Cramon-Taubadel 2022).</mark> As the blockade of its Black Sea ports and the lack of alternatives with comparable capacity prevents Ukraine from exporting most of its 2021 harvest, countries such as Egypt, Indonesia, and Lebanon that are highly dependent on Ukrainian food imports already experienced shortages. The dearth of storage space for the 2022 harvest this creates, together with war-induced shortages of inputs such as fuel, fertilizer, seed, or labor, may affect farmers’ planting and crop management decisions in the 2022 season. This would depress not only local welfare but also imply that, instead of realizing its vast potential for intensification of wheat production (Swinnen _et al._ 2017), production losses in Ukraine will exacerbate global food insecurity.

To quantify war-induced direct and indirect impacts on area and expected yield of winter crops, this paper uses a 4-year (2019-22) panel of 10,125 village councils (VCs) for which reliable crop cover information based on a machine learning model using ground-truthed training data
Figure 2: Agriculture’s Contribution to Sudanese Economy <!-- Start of picture text --> Agriculture Value added (% of GDP) Agriculture Labour Share<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> _Source:_ ‘Agriculture Value Added’ from World Development Indicators (WDI 2018).<sup>2</sup> ‘Agriculture Labor Share’ from ILO (International Labour Organization) (ILOSTAT 2018).<sup>3</sup> Since the early 1990s, the relative rate of assistance (RRA)<sup>4</sup> (a measure of pro-agricultural policies), has been increasing for Sudan (Kym and Signe 2013). This measure has increased from being negative (indicating an anti-agricultural bias) to being slightly positive (indicating a pro-agricultural bias). Policies that restrict the export of agricultural commodities and promote the industrial sector by taxing the agricultural sector contribute to negative values for the RRA, as was observed in the early 1990s. As Sudan has reversed these policies, much like other African and developing countries, this indicator has steadily increased. However, since the 2007–08 world food price crisis, the increasing trend in this measure has reversed slightly. The increase in the RRA since the early 1990s, indicating an improving policy environment for the agricultural sector, may be a reason for the significant declines in rural poverty (as shown in Section II).

> 2 <u>https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS?locations=SD.</u>

> 3 <u>https://www.ilo.org/ilostat/.</u>
# **1. Introduction**

<mark>There is strong agreement that teachers matter a lot for student learning;</mark><sup>2</sup> <mark>but little agreement on which specific teacher factors matter most. Studies document wide variation in teacher effectiveness that is not well explained by observable teacher characteristics (e.g., McCaffrey et. al 2004; Jacob and Lefgren, 2005; Rivkin, Hanushek, and Kain, 2005; Gordon, Kane, and Staiger, 2006; Kane, Rockoff, and Staiger, 2008). Specifically, observable and widely available teacher characteristics such as teacher qualifications, test scores, training, and experience appear to be weak predictors of teacher contributions to student learning in high-income countries (Rockoff 2004; Rivkin, Hanushek and Kain 2005; Aaronson, Barrow and Sander 2007, Staiger and Rockoff 2010). This finding is mirrored in recent studies from low- and middle-income countries. Research from Pakistan and India does not find a strong relationship between teacher qualifications and teacher value-added in either government or private schools (Bau and Das 2020; Azam and Kingdon 2015); in Ecuador teacher entry-exam performance explains a small fraction of the variation in student learning (Cruz-Aguayo et al. 2017). Bau and Das (2020) find in Pakistan’s context that observed teacher characteristics account for less than 5 percent of the variation in teacher value-added.</mark> Using machine learning methods on a rich student-teacher data set from Tanzania, this paper identifies key predictors of teacher effectiveness, proxied through student learning gains linked to a teacher over an academic year, closely linked to the concept of teacher value-added (TVA).<sup>3</sup> Specifically, it explores which aspects of a teacher – who teachers are, what teachers know, what teachers do, or what teachers believe – are most predictive of student learning gains.

Machine learning (ML) is a well-suited (albeit novel) approach for identifying key predictors of
.8]|970|4635<br>[99.9]|954|4723<br>[111.6]|-87.88|
|F-test joint signifcance (F-stat)|||||||1_._203|
|F-test joint signifcance (p-value)|||||||0_._216|

_Notes:_ Source: Baseline survey. The last column reports differences in the means across the groups. *, **, *** indicate t-test significance at the 10, 5, and 1 percent level. Standard errors are robust. Household vulnerability score is based on the national Mexican AMAI measure which considers the number of bathrooms, bedrooms, cars, and employed persons in the household, as well as internet access and head of household education level.

16
, and the innovation cost function, to match salient macro and firm-level properties of the United States. Then, we estimate idiosyncratic distortions from Ghanaian firm-level data and feed alternative estimates of entry barriers from the World Bank’s Doing Business Indicators and from (Fattal-Jaef, 2022).

In the first step, we require the relative entry costs, _fe_ 1 and _fe_ 2, and the relative sector-wide productivity levels _A_ 1 and _A_ 2, to be consistent with an informal employment share of 8% in the U.S. and an average firm size in the U.S.’s formal and Ghana’s informal manufacturing sectors of 116 and 3.5 workers respectively.<sup>20</sup> The innovation cost function in the formal sector is parameterized to replicate the life-cycle growth and the employment share in the top 10 largest firms in the U.S. manufacturing sector. The growth and exit rate in the informal sector, in turn, are set to replicate the small share of employment accounted for by large firms in Ghana’s informal firms’ size distribution.

The elasticity of substitution between formal and informal goods is drawn from Bachas et al. (2020), while we adopt a standard value for the elasticity of substitution across varieties from the misallocation literature.

In the second step, we appeal to Ghana’s National Industrial Census of 2003 to calibrate the idiosyncratic distortions and the entry barriers. The National Industrial Census is an alternative database covering industrial production only for registered establishments with ten workers or more, providing rich balance sheet information with which to measure idiosyncratic distortions following the approach in (Hsieh & Klenow, 2009).<sup>21</sup> We follow (Fattal-Jaef, 2022) in measuring idiosyncratic distortions directly from the firm-level data and inferring the entry barrier so that the model’s average firm size for the Ghanaian equilibrium matches the average firm size in the formal-sector data. Given the idiosyncratic distortion

> 20 The average size in U.S. manufacturing
We supplement the survey data with administrative information on program tuition and highquality accreditation (above and beyond a regular license), and institution governance and type. Some of these variables are available in administrative sources such as higher education censuses, whereas we collected others from HEI and program websites.

# **3.2 Descriptive statistics of program-level data**

Tables 1 and A6 show descriptive statistics of quality determinants (panel A) and student, program, and HEI characteristics (panel B) using program-level data from the WBSCPS and administrative sources. We begin with the description of quality determinants. On average, the programs have desirable traits but also substantial variation. In terms of _infrastructure_ , most programs (72 percent) have enough equipment for practical training given enrollment. Online teaching was rare prior to the COVID-19 pandemic, suggesting that the adjustment to provide it must have been challenging. As for _T&C_ , most programs (70 percent) teach a fixed curriculum with structured pathways (last updated, on average, about three years ago), with substantial emphasis on practical training. They tend to teach both cognitive (e.g., numerical) and socioemotional competencies (e.g., working under pressure), and about half of them provide remedial education, before and/or during the program. Most programs (86 percent) analyze student performance more than once a year to address problems. About 60 percent of the programs require an internship outside the institution, and less than half of the programs have special graduation requirements such as specific exams, theses, or second language tests. Regarding _costs and funding_ , annual tuition is $2,244 on average; it varies between zero and $25,515 (note that all monetary variables are expressed in 2019 PPP dollars except where noted). Less than a third of the programs receive outside funding, and about 40 percent of programs report having some students who take on bank or government loans.

14
22 use such inputs. This suggests another area for attention in designing future voucher programs and their implementation modalities.

The findings bear out the lingering concerns in the literature about elite capture in decentralized targeting, with voucher coverage and allocation in high trust environments emerging as counteracting forces when overcoming poverty related market constraints, while the presence of extension agents may further exacerbate targeting inefficiencies, when increasing volume is the objective. To further see how the political, social and other factors indirectly affect targeting performance through the channel of elite capture, the number of vouchers received by elected officials and VVC members is regressed directly on these factors in Table 6, again using the tobit estimator. First, elite capture increases with the number of vouchers distributed, but at a rate of less than one to one, consistent with the earlier observation that voucher coverage _can_ increase targeting performance (at least among the poor). Second, elite capture of the vouchers also increases when intra-village land inequality goes up, providing support for the use of within village inequality indicators as proxy for elite capture. Finally, the further away from the rural towns, the more prone the local political dynamics are to elite capture.

# **4 Concluding remarks**

Input subsidy programs have once again become a popular instrument in the policy toolbox to increase agricultural productivity across Sub-Saharan Africa. Given that their fiscal burden can be high and typically trends upward over time, they only carry broad support to the extent that they address market failures, such as credit and insurance market failures, and to the extent that they generate multiplier effects by increasing aggregate output and reducing staple food prices. This presumes proper targeting, with decentralized targeting of input vouchers currently the preferred tool of choice.