rafmacalaba/data-use-mentions-extended huggingface dataset:
probability of a sovereign debt crisis but tended to be largely unrelated to banking and currency crises. Larger FDI inflows, a more stable form of finance than portfolio inflows, were associated with a lower probability of a currency crisis.

Magnitude of crisis probabilities. In isolation, some of these probabilities appear small. This is expected since they are based on a sample in which crises are rare (less than 5 percent of observations) and they are associated with individual indicators. However, the probabilities could cumulate rapidly when multiple indicators deteriorate at the same time as has frequently happened prior to financial crises.

Twin crisis probabilities. The probability of the occurrence of twin crises (any two of sovereign debt, banking, and currency crises) is lower than single crisis events (in line with the less than 0.5 percent of the observations with twin or triple crises in the sample). A twin crisis is defined as the occurrence of any two of sovereign debt, banking, or currency crises within two consecutive years. However, the explanatory variables in the baseline model have better predictive ability in predicting a twin crisis one year ahead than in predicting a single crisis. An adverse GDP growth shock, a larger share of shortterm debt, higher debt service burden, lower reserve cover, and larger changes in government and private debt significantly increase the probability of a twin crisis, although the interaction term of government and private debt is insignificant. The estimation results are shown in Table 8.

# 3.3 Robustness tests

Several additional correlates were added to the baseline empirical specification to test the robustness of the results. The baseline results are robust to these alternative specifications. These results are provided in Tables 9 and 10.

First, the quality of institutions may affect the incidence of crises. However, data for meaningful cross-country and over-time comparison, such as the Worldwide Governance Indicators (WDI; Kaufmann, Kraay and Mastruzzi 2010), is only available starting in the early 1990s. As a result, most sovereign debt and banking crises
Figure 4: Percentage of Labor Force in Each Country with Great Job, _k_ = 1 <!-- Start of picture text --> Notes : Authors’ calculations using SEDLAC data. Sample size and years<br>covered differs by country as described in Table 1.<br>Arg Bol Bra Chl Col CriDom EcuGtm Hnd Mex Nic Pan Per Pry Slv Ury<br>.3<br>.2<br>Percentage<br>.1<br>0<br><!-- End of picture text --> 19
# **2.2 Student**

The phenomenon of primary and high school children taking extra classes to supplement in-school instruction in Vietnam is well known, see [Ha and Harpham, 2005] and [Dang, 2007]. Table 2 indicates that while Dev7 students spent roughly 4.7 hours in such classes (total of OUTMATH, OUTLANG and OUTSCIE), the Vietnamese student spends nearly 2 hours more for a total of 6.6 hours per week in such classes, with the difference being highest for OUTMATH. Vietnamese students also spent about 1 additional hour per week doing homework (total of ST57Q01 and ST57Q02) compared to Dev7 students. The highest difference in this set of variables concerns the variable ST57Q04, which relates to extra classes taught by a commercial company. While most of the schools in Vietnam are public or government schools, it is interesting to note that students report nearly 5 hours of commercially provided extra lessons, while the total for Dev7 countries is only about 2 hours per week. Collectively, these variables indicate that Vietnamese students spent about 16 hours per week studying outside of school, compared to 13 hours per week for Dev7 students.

**Table 2: Student studying time out of school**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Weekly out-of-s**|**chool hours per subject**|||||
|OUTMATH _(r)_|weekly out-of-school<br>lessons in math|1.828<br>(2.1539)|23603|3.1305<br>(2.3133)|3227|
|OUTREAD _(r)_|weekly out-of-school<br>lessons in ’test language’|1.2882<br>(1.9623)
conditional on workers’ education level. Earle, Telegdy and Antal (2013) use linked employer-employee data in Hungary and find the wage premium of foreign acquisition to be around 4.5 percent. The wage premium is observed for all education, experience, gender, and occupation groups as well as across wage quantiles. Divestment to domestic owners, on the other hand, is shown to reverse the effects. On employment, Lipsey et al (2013) find that the shift from domestic to foreign ownership tends to raise employment growth in Indonesia. As for productivity, Arnold and Javorcik (2009) use Indonesian plant level data and find that plant productivity increases about 13.5 percent by the third year under foreign ownership. These productivity improvements occur simultaneously with increases in investment in machinery and equipment, employment, wages and output. Similarly, taking into account endogenous acquisition decisions in China, Wang and Wang (2015) find positive impacts of foreign acquisition on firm productivity, export, output, employment and wages.

- **Spillover to other domestic firms.** In addition to the direct benefits from foreign acquisition on targeted firms, many studies examine the spillover effect of FDI on other domestic firms in the host country. The literature distinguishes three types of spillover: intraindustry or horizontal spillover, inter-industry spillover through backward linkage, and interindustry spillover through forward linkage. The literature provides little evidence in support of intra-industry productivity spillovers from FDI.  Javorcik (2004) and many subsequent studies show that FDI exerts little or negative effects on the productivity of domestic firms in the same industry. Lu et al. (2017), for example, explore the relaxation of FDI regulations upon China's WTO accession to evaluate the spillover effect of horizontal FDI and find FDI to exert either a negative or an insignificant effect on the productivity of Chinese domestic firms.

One of the potential explanations for the limited horizontal spillover is that domestic producers also face crowding out effects in product and factor markets when foreign competitors enter the market. Such crowding
not statistically significant (see the Appendix for more details). We focus on this panel dataset to assess the change in learning from 2019 to 2021 and to identify student and teacher characteristics associated with the change in learning outcomes.

The effects of the COVID-19 pandemic on the mathematics achievements of children were estimated using a simple difference in average learning outcomes in 2019 (pre-COVID) and 2021. We compare this average change to the benchmark for annual progress as obtained from the literature review to assess the extent to which students learned over time during COVID as compared to non-COVID years.

As different schools were closed for different time periods due to COVID-19 and for other nonpandemic-related reasons, we add a variable for the duration of school closure Zi to analyze the effect of duration of school closure on learning trends and test our second hypothesis (H2). Furthermore, we add a set of student characteristics Xi to test the effect of student characteristics on learning progression and test H3 to H6. The overall empirical equation is represented by: 2021 2019 Where ∆yij = yij - yij is change in learning outcomes of an individual student (i) in school (j) from 2019 to 2021 and εij is an error term clustered at the school level. The constant α captures the average change in learning due to the pandemic. In this setup, we add one variable at a time to assess heterogeneity in the trend in learning outcomes by each student characteristic; Xi includes student sex, parental education, index of student access to and usage of digital resources including internet, index of student-level information on learning continuity during COVID-19 induced school closures, indices of student-reported teachers’ pedagogical and classroom management skills and family support in learning. At the end, we include all variables together to evaluate the strength of the different factors on trends in learning outcomes.

# _Benchmarks for Annual Progress_

As we have data for only two time periods, we compare the learning trend observed from 2019 to 2021 for students in Uzbekistan to the average expected rate of learning per year
|||with perennial crops, or fallowed<br>plots)||
|---|---|---|---|
|_plot_manager_id_obs_|Unique plot<br>manager ID|<br>Only first manager considered<br>when multiple are cited.<br>This ID can be merged with<br>_indiv_obs_id_in the individual-<br>level dataset|UGA: absent when<br>there are multiple<br>managers in wave<br>7|
|_plot_manager_id_merge_||Only first manager considered<br>when multiple are cited.<br>This ID can be merged with<br>_indiv_obs_merge_in the<br>individual-level dataset||
|_formal_education_manager_|Does the plot<br>manager possess<br>any formal<br>education?|||
|_primary_education_manager_|Did the plot<br>manager complete<br>primary school?||“Primary<br>education” consists<br>of:<br>-<br>6 years in<br>NGA<br>-<br>6 years in<br>MALI<br>-<br>7 years in<br>TZA<br>-<br>8 years in<br>ETH<br>-<br>6 years in<br>NIGER<br>-<br>8 years in<br>MALAWI<br>-<br>7 years in<br>UGANDA<br>NGA: not asked for<br>old members who<br>are not in school<br>(w1, w2)|
|_age_manager_|Age (in years) of<br>the plot manager|Capped at 100||
|_female_manager_|Is the plot<br>manager female?||NER: plot manager<br>= hh manager if<br>managed jointly|
|_married_manager_|Is the plot<br>
of the imported commodity in total demand for this commodity in the regions. That is, the imports of commodity _c_ in the region _A_ from region _B_ are distributed across household demand and intermediate use of activities in region _A_ based on the share of households and intermediate use of activities in the total local supply of commodity _A_ . All these calculations result in negative demand for some commodities in some regions by some Armington agents (Armington, 1969). We set those to zero and leave balancing to the SAM balancing algorithm as the imbalances are generally small.

Most of the data are from ISTAT (Italian National Institute of Statistics), such as domestic wages and salaries, consumption of general government, final consumption expenditure of households, employment in persons, domestic compensation of employees, gross value added, gross fixed capital formation, and imports/exports. Tax data are administrative data from the Italian Department of Finance (DF) database. As already mentioned, interregional flows are estimated from DF administrative data (REF), and other data are from the national SAM built under the previous project.

For the sake of reproducibility, we present in the following subsections a detailed description of calculations for each account of the new Italian regional SAM.

# **3.1 Regional Market Output**

The activities row represents the regional market output. The dimensions are activitycommodity-region, resulting in a 400x400 account. We follow three steps:

1. to calculate regional output tables using the demand components:

where _HH_ , _GOV_ , and _RegGov_ are the households, government, and local governments’ consumption, respectively. The subscripts _r_ and _c_ indicate the region and commodity, respectively. IC is the intermediate consumption, _INV_ the investment, _EXP_ the exports, and _IndTax_ the indirect taxes; 2. to distribute national output ( _NatOutc_ ) across the region using shares: 3. to distribute regional output among commodity and sectors ( _a_ ) through a bridging 8
firm cuts its annual payroll by 6.7% (26.8% of quarterly payroll). In aggregate, 5.8% of the annual payroll (23.2% of the lockdown quarter payroll) is lost.

Third, we use the panel dimension of the data to predict plausible exit rates. We measure the pre-COVID exit rates among loss-making and profit making firms as an average across years, and find these rates to be remarkably stable across countries and over time. We then apply these rates to the post-COVID profit distribution, allowing for the revenue shock and for firms’ material and payroll adjustments. The results suggest that the share of firms exiting would on average double, with large variation across countries. Aside from the exit rates, the firm-level results are relatively homogeneous across the countries we study. However, firms in poorer countries might fare better than we predict, as they could join the informal sector instead of shutting down completely, and because their true profitability might be higher than observed in the data due to more misreporting.

The second part of our analysis focuses on the implications of our firm-level results for governments’ policies and public finances. First, we estimate to what extent wage subsidies—a widely discussed policy tool, common in many European countries (Giupponi and Landais, 2020)—can mitigate formal employment losses. We find that, on average, even a 90% wage subsidy can only prevent 30% of the predicted payroll cuts. To understand this result, note that in our simulations, wage subsidies affect formal employment only

> 3Replication codes remain live and will continue to be updated with extensions based on feedback from governments and researchers. World Bank Policy Notes that summarize the results for each of the 10 countries will be published on the authors’ websites in August.

3
# Local Economic Shocks and Human Capital Accumulation: Evidence from Rwandan Coffee Mills<sup>*</sup>

Andrew Dabalen, Justice Tei Mensah, Aimable Nsabimana

## **JEL classification:** I28, Q13, J24

**Keywords:** Local economic shocks, Coffee Mills, Human capital accumulation, Rwanda

> *Dabalen: Office of the Chief Economist, Africa Region, The World Bank. Email: adabalen@worldbank.org; Mensah: Office of the Chief Economist, Africa Region, The World Bank. Email: jmensah2@worldbank.org; and Nsabimana: UNU-WIDER, Pretoria, South Africa, Email: aimable.nsabimana@wider.unu.edu; We wish to express our profound gratitude to the Rwandan National Examination and School Inspection Authority (NESA), Rwanda Basic Education Board (REB) for granting access to standardized students’ test score datasets, and to the Rwanda Ministry of Education (MINEDUC) for sharing official documents related to the education sector in the country. We are grateful to Kathleen Beegle, Heidi Kaila, Dean Karlan, Konrad Burchardi, Moussa Blimpo, and participants at the STIAS 2024 Nobel Symposium in Economic Sciences, and the CSAE 2024 Conference for their contributions and comments. We also thank Marijke Verpoorten for sharing the data on the Rwanda coffee census with us. All errors and views expressed in this paper are those of the authors. They do not necessarily represent the views of the World Bank or UNU-WIDER
extensive search of land records, allows the use land as collateral. This will, in turn, be critical to facilitate financial market development and the emergence of more sophisticated financial instruments that draw on the abstract representation of land rights provided by titles (de Soto 2000).

Differentiating elements of property rights in this way illustrates two points. First, although they do not form a necessary evolutionary sequence, different elements build upon each other and the institutions to support them become increasingly more sophisticated and costly to implement (Sjaastad and Bromley 2000b). Second, any land tenure regimes defines a multi-dimensional bundle of rights, the content of which often differs depending on local circumstances. More disaggregated information on the individual “sticks” in this bundle will be required to accurately assess the impact of specific rights. In fact, the high variability in the results from empirical studies aiming to explore the impact of title (Pinckney and Kimuyu 1994, Migot-Adholla _et al._ 1994, Roth _et al._ 1994) can be interpreted as resulting at least partly from omission of “lesser” rights to transfer land with or without permission by the lineage or a finer gradation of tenure security linked to this arrangement in specific contexts. For example, formal title will have maximum impact on increasing tenure security if owners and potential buyers are aware of the 3
Northern Cape<br>2.9%|0.813**|0.828**|
||(14.76)|(14.13)|
|Northern Province<br>18.2%|-0.019<br>|-0.003<br>|
||(0.76)|(0.13)|
|Northwestern Province<br>14.8%|-0.028|0.013|
||(0.96)|(0.40)|
|Western Cape<br>3.5%|0.790**|0.788**|
||(17.73)|(16.07)|
|Household has electricity<br>28.9%||0.147**|
|||(7.73)|
|Household owns cattle<br>17.5%||0.145**|
|||(6.14)|
|Household owns sheep or goatt<br>16.4%||0.005|
|||(0.22)|
|Houeshold owns poultry<br>28.3%||0.054**|
|||(2.64)|
|Household owns pigs<br>8.5%||0.015|
|||(0.54)|
|Pseudo R'|0.4645|0.4914|
|Log-likelihood|-1285.73|-1220.33|
|No. of observations<br>4256|4256|4255|
|Absolute value of robust z-statistics in parentheses|||

* significant at 5%; ** significant at 1%; + significant at 10% Source: Land reform M&E survey plus SALDRU sample for non-whites in rural areas only.
# **Table A1 – Summary Comparative Table of Subsidy Reforms (2010-2014)**

|||**North**|**Africa**|||**Middle**|**East**||
|---|---|---|---|---|---|---|---|---|
||**Morocco**|**Tunisia**|**Libya**|**Egypt**|**Jordan**|**Yemen**|**Djibouti**|**Iran**|
|Income level (World Bank Income<br>Data)|Lower-middle|Upper-middle|Upper-middle|Lower-middle|Upper-middle|Lower-middle|Lower-middle|Upper-middle|
|Net energy importer/exporter<br>(World Bank Energy Data)|Importer|Importer|Exporter|Exporter|Importer|Exporter|Importer|Exporter|
|Initial trigger of reforms|Removal of indexation<br>mechanism, budget<br>deficit|Budget deficit|n.a.|budget deficit; change of<br>regime|budget deficit; pressure<br>from IFIs|<br>budget deficit; IFIs<br>pressure|n.a.|Budget deficit; Political<br>strategy; International<br>sanctions|
|Reforms 2010|n.a.|n.a.|no reforms|n.a.|n.a.|Prices on gasoline,<br>diesel, and kerosene<br>gradually increased by<br>about 30 percent, and<br>prices of LPG by 100<br>percent|no reforms|Mjor increase in food<br>and energy prices|
|Reforms 2012|Diesel prices increased<br>by 14 percent, gasoline<br>by 20 percent, and<br>industrial fuel by 27
.2|110|
|Share in EU25 external|0.6|1.2|1.8|2.5|2.9|3.4|289|
|of which: Parts (%)|0.6|0.8|1.2|1.5|1.7|1.8|222|
|Finalgoods(%)|0.7|1.6|2.4|3.3|3.8|4.6|293|

Source: Computations based on UN COMTRADE Statistics.

Other features setting apart automotive from ICT network are, first, that there is a large number of local suppliers of automotive parts and components and, second, the network appears to be moving rapidly towards the status of a net exporter. Large increase in their exports notwithstanding, the surge in exports of final products failed to trigger similar growth in imports of parts and components. While the value of total exports of the automotive network increased 417 percent in 2000-04, imports of parts increased only 95 percent. Furthermore, the import intensity—as measured by the ratio of imports of parts to total automotive exports—of the automotive sectors has been falling rather steeply since 1996 and is lower than in the ICT network trade.<sup>19</sup> In trade with the EU the ratio fell from 3.04 in 1996 to 1.49 in 2000 and 0.68 in 2004, while in total trade of this network from 1.35 in 1995 and 2000 to 0.62 in 2004 (Table 22). Overall, exports as a percentage of imports increased steeply from 32 percent in 2000 to 87 percent in 2003. In other words, while in the 1990s imported parts were used among others to 19 Based on network trade intensities of EU-8 that successfully have entered into supply chains, the value of the share of imports of parts in network’s exports of parts and final products below 100 percent
the in‡ows of migrants into Europe. Second, we account for the potential lower quality of schooling for migrants who completed their education in their home countries or for the "downgrading" of their skills in the host countries’labor market. Both corrections reduce the e¤ective percentage of highly skilled immigrants. While the corrections make some di¤erence, the general picture described above remains unchanged.

We consider two other extensions. First, we introduce the possibility of crowding e¤ects of immigrants on productivity due to the presence of a ...xed factor or the presence of a positive density externality, such as in Ciccone and Hall (1996), on productivity. While their presence either attenuates or increases slightly the e¤ects but leaves the basic results unchanged. Second, we account for the short-run impact of immigrants by including in the simulations sluggish capital adjustment. We distribute net immigration into yearly ‡ows of immigrants and assume a speed of adjustment of capital that is consistent with estimates from the macro literature. While such sluggish adjustment generates a small negative contribution to average wages in the short run, the overall short-run e¤ects of immigration are still positives in four of the ten considered European countries. When negative, these short-run e¤ects are very small, with the largest negative impact achieved in Spain in the order of -0.35%. Emigration turns out still to exert a negative wage e¤ect on non-movers in the short run and usually larger, in absolute value, than the e¤ect of immigration.

The rest of the paper is organized as follows. Section 2 presents the simple aggregate production framework from which we derive wages as marginal productivity of di¤erent types of workers. Section 3 describes the data, their construction and their sources and shows some simple summary statistics about the educational structure of labor force data and migrant data. Section 4 presents the basic results of the simulated wage e¤ects of immigrants using our model and the range of parameters
sexual violence is prevalent and the large majority of cross-border traders are women and most border officials are men. According to the most recent Demographic and Health Survey data from DRC, 28% of women aged 15-49 in North Kivu and 35% of those in South Kivu have experienced sexual

> 1In 2011, the DRC Government issued a decree officially banning the collection of taxes on the import of foodstuffs (including vegetables and fruits) and addressing the perceptions of cross-border trade as being illegal (Government of the DRC, 2011). While most of the traders engaged in cross-border trade are not smugglers, some smuggling of banned products (e.g., charcoal from Rwanda) does indeed occur along the border (Titeca and Kimanuka, 2012).

> 2This study took place during a period of heightened tension between the DRC and Rwanda against the backdrop of the “March 23” rebel group’s armed conflict with the DRC Government (Rono, 2013), with temporary curfews and border closures also occurring.

5
leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our Tamasheq Arab survey, had already returned to Northern SonghaiBella PeulhOther Mali. In fact, as pointed out in (Etang-Ndip Analytic weights used Source: LDPS 2014-15 et al., 2015), IDPs and returnees have a similar ethnic composition because 94% of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali.

6