rafmacalaba/data-use-mentions-extended huggingface dataset:
units of analysis and all our models likewise cluster standard errors at the level of PRIOGRID cells. In Appendix C, we also cluster standard errors at the level of subnational administrative regions (admin1), showing that our results remain robust. While the resolution of the grid is a feature that comes with the PRIO-GRID data structure, there is no theoretical reason for choosing a particular degree of resolution, perhaps aside from picking inappropriately large cells that would introduce distances at which the effect of climate on population movements would be improbable. The 0.5°resolution represents a standard adopted by the literature relying on grid structures.

# **3.1 Measuring Drought**

To measure exposure to drought at the grid cell level, we use a metric of the proportion of months out of 12 months that are part of the longest streak of consecutive months ending in a given year with SPEI values of less than _−_ 1.5.<sup>2</sup> SPEI is the _Standardized Pre-_ . _cipitation Evapotranspiration Index_ SPEI is an extension of the Standardized Precipitation Index (SPI) but its advantage is that it takes both precipitation and evapotranspiration into account when determining the presence of drought. The measure we use captures drought in a given grid cell, thus conveying information about inclement weather that is most likely to affect agricultural yields in that cell. All SPEI values are taken from the SPEI Global Drought Monitor which we downloaded from the PRIO-GRID website.<sup>3</sup> As an illustration, the left part of Figure 2 displays the average value of our drought measure (i.e. the mean fraction of the year that a given grid cell experienced drought) for South America, averaging values for the entire period between 1985 and 2014.

To account for spatial correlation in our variables and, therefore, to discern the cumulative effect of drought in the cells that are adjacent to urban areas, we calculate spatial lags for both drought and the relevant control variables.<sup>4<
. “The Teacher-Tool Relationship: Theorizing the Design and Use of Curriculum Materials.” In Mathematics Teachers at Work: Connecting Curriculum Materials and Classroom Instruction, edited by J. T. Remillard, B. A. Herbel-Eisenmann, and G. M. Lloyd. Pp. 37-56. New York: Routledge.

Bruns, B., and J. Luque. 2014. Profesores Excelentes. Docentes Excelentes: Cómo Mejorar el Aprendizaje en América Latina y el Caribe. Washington, DC: Banco Mundial.

http://www.bancomundial.org/content/dam/Worldbank/Highlights%20&%20Features/lac/LC5/Spanishexcellent-teachers-report.pdf.

Cilliers, J., and S. Taylor. 2017. “Monitoring Teachers and Changing Teaching Practice: Evidence from a Field Experiment.” McCourt School of Public Policy, Georgetown University. Department of Basic Education, South Africa. Working Paper. https://sites.tufts.edu/neudc2017/files/2017/10/paper_110.pdf.

Christophel, D. M. 1990. “The Relationships Among Teacher Immediacy Behaviors, Student Motivation, and Learning.” Communication Education 39 (4): 323–40.

Cristia, J. P., P. Ibarrarán, S. Cueto, A. Santiago, and E. Severín. 2012. “Technology and Child Development: Evidence from the One Laptop per Child Program.” Working Paper No. IDB-WP-304, InterAmerican Development Bank, Washington, DC.

Darling-Hammond, L., R. C. Wei, A. Andree, N. Richardson, and S. Orphanos. 2009. Professional Learning in the Learning Profession. Washington, DC: National Staff Development Council. https://learningforward.org/docs/default-source/pdf/nsdcstudy2009.pdf 33
reduced the area cultivated with winter crops by 10% (0.84/8.47) and 24% (1.79/7.45) in 2022 and 2023, respectively, and it reduced the area cultivated with summer crops in the same years by 12% (2.16/17.89) and 10.5% (2.08/19.7), respectively. Although indirect or macro effects accounted for close to 90% (0.75/0.84) of the total for winter crops in 2022, their importance declined to about 61% for 2022 summer (1.33/2.16) and 2023 winter (1.09/1.79) crops, and to less than 25% (0.51/2.08) for 2023 summer crops. This may reflect adaptation to the conflict outside the areas directly affected. Finally, half to slightly less than three-quarters of the direct conflict effects on agricultural area (50% and 71% for winter and summer crops, respectively, in 2022, and 72% and 58% for winter and summer crops, respectively, in 2023) were on territory controlled by Russian forces. This contrasts to an increase of overall economic activity as measured by nighttime lights (Zhukov 2023), highlighting the importance of measuring outcomes in a way that is suited to the question at hand.

# **4. Assessing the effects of the conflict on farm profitability**

Beyond changes in area cultivated, the conflict may have affected the profitability of agricultural production. Data on output and input prices point to large price effects, especially in the first year of the war. We combine these with information on input use and profitability from a panel of 1,206 small and

> 11 In terms of the model results reported in table 3, column (6), scenario one involves setting the “any conflict” dummy and “damaged field area” to zero, whereas scenario two involves
Figure 3: Event study: relationship between pre-determined measure of German comparative advantage and the acquisition of Polish firms.

<!-- Start of picture text --> .01<br>.005<br>0<br>Coefficient<br>-.005<br>-.01<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The plot is created by regressing a dummy variable indicating German ownership on a full set of event time indicators interacted with the pre-determined measure of German comparative advantage and controlling for firm and year fixed effects (equation (3)). The omitted baseline is the last pre-treatment year, 2003. The vertical bars indicates 95% confidence interval. Results include only firms established before 2004. Germany’s comparative advantages are measured by average sector-level export shares over the period 1994-2004. Overall difference in differences results are shown in Table A.5.

Figure 4: Event study: the effects of Germany’s specialization over time on productivity <!-- Start of picture text --> .2<br>.1<br>0<br>Coefficient<br>-.1<br>-.2<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. The plots are created by regressing firm-level TFPR on a full set of time indicators interacted with Germany’s initial export share and controlling for firm and year fixed effects (equation (5)). The omitted baseline is the last pre-treatment year, 2003. Estimates are based on the sample of firms with more than 50 employees. TFPR is estimated using the method
24 effectiveness has been based on case studies, anecdotal information, and surveys conducted before and after training sessions. However limited this evidence may be, it nonetheless suggest that these efforts are both constructive and essential (for example, see: Rashid, 2001; Villanueva, 1999).

Training of judges and judicial personnel is also important. A survey of a small number of judicial personnel in El Salvador, for example, revealed that while the vast majority considered the problem of family violence to be important and believed that the existing legislation on family violence was necessary, none had received any training on recent reforms to the family violence law (CLADEM, n/d). This situation is not uncommon in the region.

Judicial training should focus on interpretation and enforcement of domestic legislation on gender based violence; it should also cover the application of international human rights conventions such as CEDAW, Belém do Pará and other broader human rights legal frameworks <u>to which countries are signatories. (See Box 2.3 for an example of training of</u> judges in international conventions.) Other justice-sector institutions where training is necessary include prosecutors, social services, ancillary institutions, public defenders and pathologists (Villanueva, 1999).

Evaluations of training initiatives suggest a number of common themes. First, changing attitudes towards violence against women and children is a challenging, long-term process. The quality of the content and skills of the trainer is essential. Training appears to be most effective when all levels of personnel receive training (including high level officials), and when training is backed up with changes throughout the institution, such as policies, procedures, adequate resources, and continual monitoring and evaluation.

# **_GBV in the context of transition from inquisitory to accusatory legal systems_**

While training of judges and judicial personnel can improve the quality of judicial services for survivors of GBV, it does not address the structural and systemic problems that plague many judicial systems of the region; corruption, lack of transparency, procedural
)||||
|Other|172|230|354|325|6.74|10.78|−2.05|
||(0.346)|(0.416)|(0.619)|(0.930)||||
|Ramo 33|449|942|1,291|1.506|21.96|7.41|4.16|
||(0.576)|(0.484)|(0.573)|(0.859)||||
|Municipal Strengthening Contribution Fund|189|360|440|501|18.10|4.44|3.47|
||(0.208)|(0.113)|(0.073)|(0.104)||||
|FAIS|259|582|851|1,004|24.94|9.24|4.49|
||(0.451)|(0.438)|(0.567)|(0.842)||||

_Source:_ Calculations based on SIMBAD (State and Municipal System Databases), National Institute of Statistics and Geography (INEGI). _Note:_ In years in which the municipality does not report financial statements, these were proxied by financial statements from the nearest year within a two-year period either before or after. All monetary variables are expressed in the values as of August 2014. The data cover 2,120 municipalities on which indicators are available on poverty and public finance in around 2000, 2005, 2010, and 2014. Averages are not weighted by population. Own municipal resources include taxes, rights, and land use.

12 Through the reform to the Fiscal Coordination Law in 2013, the allocation formula for the FAIS changed to align the distribution of resources with the new multidimensional poverty measurement. The new formula distributes resources in a two-step process
yn e wayr = (immigrant premium)x (skill premium)x wry wn e wor = (immigrant premium) x(US age premium)x (skill premium) x wry7

Productivity and preference parameters. When employment levels (qsaj) and wage rates (ws) are determined, we sequentially use Eqs. (13), (14) and (15) to calibrate the relative productivity parameters. First, we insert wage and employment data into Eq. (13) and calibrate (Cyyv,lHom,l tym, tom). This allows computing the composite qsa using Eq. (6). Second, we compute average wage for cach SA group as ws, = ~~SANA SAN TESARISAR~~ and substitute these wage rates into Eq. (14) to calibrate (Ty4,Tra). This allows computing gs using Eq. (5). Third, we compute the average wage rate by skill group as wg = ~~Ss~~ <u>dsO</u> and plug them into Eq. (15) to calibrate ['y. This allows computing q using Eq. (4), and calibrate the TFP to match the GDP data using Eq. (3). We know that a fraction 1 — a of GDP serves to remunerate labor. Using 1 — a = 2/3, we calibrate the level of our reference wage, wry yn, to match the national wage bill.

As for preference parameters, we calibrate ds, as a residual from Eq. (11). This gives sam = Weiv (dsam/Qsam). In the extension with involuntary unemployment, we use 18
Nevertheless, we replicate the ML approach, as outlined in section 4.2.1, with the nationallyrepresentative EACI sample. Figure 7 summarizes the results for the first sample split out of the 100 replications. The right top-panel shows that ML yields 40 percent of the variation in CC yields, and the level of explained variation is in fact higher than the comparable result based on the ERIVaS sample. Even though we continue to see (in the left-top panel) a higher concentration of values around the mean for ML yields, there is a better tracking of the large values of CC yields. The coefficients for the key inputs in the regressions of ML yields are also comparable to those obtained from the regressions of CC yields, as shown in the bottom two plots of Figure 7. While the size and heterogeneity of the EACI sample are among the factors that improve the predictive accuracy of the ML model vis-`a-vis the results achieved with the ERIVaS sample, the EACI rounding errors in CC yields may also be creating a scenario that ML tools are better equipped to accommodate.

To verify whether we can expect the same results from the ML approach done with ERIVaS (selecting randomly a third of the plots) using the EACI 2017 and make inferences to the population of sorghum plots in Mali, we compare in Table A5, plots and households characteristics of plots selected for crop cutting with those that were not. The statistically significant differences appear to be seldom providing confidence that the selection of plot for CC is analogous to the simulation conducted with the data from the experience.

Finally, Table 12 reports the results from the yield regressions estimated with ML, and separately SR, yields using the entire EACI sample, and the results from the yield regressions estimated with CC yields using the sub-sample of EACI plots that were subject to crop cutting. The coefficient for plot area is negative but not statistically significant. This may be because the mean difference in plot area between the crop cut plots and the rest of the sample was statistically significant
# **Eliciting Illegal Migration Rates through List Randomization**<sup>*****</sup>

David McKenzie, _World Bank, BREAD, CEPR, CREAM, and IZA_ Melissa Siegel, _Maastricht Graduate School of Governance and UNU-MERIT_ _Keywords:_ Illegal migration; List Randomization; Item Count Method; Survey Techniques _JEL codes:_ F22, C83, J61, K42.

* We thank Emily Beam, Miriam Bruhn, Gabriel Lara, and Dean Yang for their collaboration on the related surveys that we added these questions to; the World Bank’s Research Support Budget for funding the Philippines survey; the Russian Trust Fund for Financial Literacy for funding the Mexico survey; the Dutch Ministry of Foreign Affairs for funding the Ethiopian and Moroccan surveys; the field teams in each country for their excellent work in conducting these surveys; and Matthew Groh for valuable research assistance. All opinions expressed in the paper are those of the authors alone and do not necessarily reflect those of their respective institutions.
non-listed) subsidiaries to make issuance data consistent with the level of aggregation of the income and balance sheet data. Listed subsidiaries with income and balance sheet data are considered as separate firms with their own matching issuance activity. Our final sample includes separately both listed parent firms and listed subsidiaries, in addition to the many other firms that are not part of conglomerates. To make sure that our results are not affected by the inclusion of parent firms, we repeated the analysis by excluding parent firms that have subsidiaries with issuance activity in capital markets over the 1991-2016 period. Our main findings are robust to this change, implying that firm performance around issuance is not driven by the capital raised through subsidiaries. This is not surprising because the number of issuances through subsidiaries is 14,725 (less than 10 percent of the total) and the number of firms with issuing subsidiaries is 4,232 (less than 7 percent of the total).

We exclude all firms with a primary industry classification in the financial sector (SIC codes 6000-6999) or the public sector (SIC codes 9100-9999). Moreover, we exclude both countries with less than 10 firms issuing in capital markets between 1991 and 2016 as well as offshore financial centers.<sup>7</sup> Firm-level attributes (total assets, sales, tangible fixed assets, intangible assets, and R&D expenditures) are all measured in logs in constant 2011 U.S. dollars (except for the number of employees, which is just measured in logs). Their growth rates are thus calculated as the difference in logs. All data on firm-level attributes are winsorized by country at the 1 percent level.

Our final matched dataset comprises 62,653 firms from 65 countries, of which 45,306 issued equity and/or bonds at least once during the sample period (Appendix Table 1). We examine 150,165 issuance events: 102,161 equity issuances, 38,487 corporate bond issuances, and 9,517 mixed issuances (when a firm
Figure C18: Theil indexes at the national and across urban versus rural areas, sorted by GDP per capita <!-- Start of picture text --> (A) DOU (B) DB<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>Urban Rural National<br>NERMWITCDETHGNBBFAUGAGINTZALSOSENBGDGHACIVMRTAGOVNMCOLGAB NERMWITCDETHGNBBFAUGAGINTZALSOSENBGDGHACIVMRTAGOVNMCOLGAB<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method.

Figure C19: Theil indexes at the national and across urban versus rural areas, sorted by Theil index <!-- Start of picture text --> (A) DOU (B) DB<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>National Urban Rural<br>MWIAGOCOLLSOUGAGHABFATZANERGABSENVNMETHCIVTCDBGDGNBMRTGIN MWIAGOCOLLSOUGAGHABFATZANERGABSENVNMETHCIVTCDBGDGNBMRTGIN<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method.

82
link between human capital and migration. Section 4 examines the level of expenditure and structure of the economy, section 5 constructs measures of the Balassa-Samuelson adjusted real exchange rates for these economies and explores whether they are under or over-valued and section 6 looks at export performance. Section 7 concludes.

# **2. Trends in Migration and Remittances**

Data on annual net migration, per thousand of the population, for the eight FSU countries in Central Asia and the South Caucasus, in five-year periods beginning at the turn of this century, are shown in table 1. In the first decade of this century, net migration for Armenia and Georgia was negative (more migrants left than arrived) and averaged more than 1 percent of the population per annum; hence they incurred a drop of more than 10 percent of their population over the course of the decade. Since 2010, net out-migration from Armenia has reduced markedly, to less than a quarter of the levels in the previous decade. Net out-migration from Georgia accelerated in the first half of the second decade of the century, but then fell back sharply after 2015.

**Table 1 Net International Migration Rates per 1000 population (annual averages)**

||2000‐05|2005‐10|2010‐15|2015‐20<br>projection|2000‐20|
|---|---|---|---|---|---|
|Armenia|‐10.6|‐10.5|‐2.1|‐1.7|‐6.2|
|Azerbaijan|0.9|1.2|0.0|0.0|0.5|
|Georgia|‐12.1|‐13.7|‐14.9|2.5|‐9.6|
|Kazakhstan|0.6|‐0.4|1.9|0.0|0.5|
|Kyrgyz
per capita, such as Indonesia and Malaysia, also had much smaller public sectors, accounting for 8% and 15% of total employment, respectively.<sup>8</sup> The size of Egypt’s public sector is more in line with other Arab states, where public sectors tend to account for between 20% to 30% of employment.<sup>9</sup> Figure 2 shows a breakdown of employment in Egypt. Most notable is the very large informal sector, which had grown over the six years, mostly at the expense of the public sector. Also notable is the small size of the private formal sector, approximately a quarter of the informal private sector.

**Figure 2: Share of employment in Egypt by sector, 2012 and 2018**

<!-- Start of picture text --> 2012 2018<br>25.21<br>30.58<br>54.14<br>14.18 60.61<br>15.27<br>Private informal Private formal Public formal Private informal Private formal Public formal<br><!-- End of picture text --> _Notes_ . The analysis relies on cross-sectional data from the Egypt Labor Market Panel Survey (ELMPS) in 2012 and 2018. The analysis is restricted to individuals aged between 20 and 59 years old in each survey round. A job is defined as being formal if the individual had both a work contract and social security in the primary job. A job is defined as informal if the individual did not have a work contract, a social security, or neither. Expansion weights are used.

# **4. Labor Market Transitions: Methodology**

Employment profiles provide a useful snapshot of individuals' labor market positions in a given point in time. But labor market positions are dynamic, i.e. they change and evolve over time. Such evolution is as important as an accurate description of a specific point in time
“can be beneficial to a developing country borrower under a range of circumstances”, but also points to risks and discourages this practice when the proceeds are not spent on assets which can be used to repay the loans, when loans are excessively large and when the details of such borrowings are opaque. The policy paper also touches on a number of considerations, many of which are also stated in this paper, including the fact that such loans are often over-collateralized (they use excessive amounts of collateral) to enhance the borrower’s creditworthiness. They also highlight how the secured nature of certain RBLs might run afoul of negative pledge clauses in loan contracts, including loans made by multilateral development banks.

There are also multiple case studies focused on a single or small number of RBLs. These include Alves (2013) who compares the experience of Angola and Brazil; Gillies and Quaghe (2018) who discuss a proposed deal in Nigeria; and Landry (2018) who looks at the Democratic Republic of Congo’s (DRC) Sino-Congolaise des Mines (Sicomines) case.

The current paper adds to the literature by providing a more detailed large-scale empirical review of existing RBLs based on data that we reviewed. Our work builds on earlier findings published by the Natural Resource Governance Institute (NRGI, 2020), and extends it significantly using novel data and further analysis.

# 3. Data

The analysis in this paper is based on an extended database of 30 major resource-backed loans. These loans cover the period 2004-2018 across Sub-Saharan Africa, covering 11 countries. This database builds on a data set of RBLs first published by NRGI (2020) which itself built primarily on the Johns Hopkins SAIS China-Africa Research Initiative’s (CARI) data set on Chinese lending to Africa.4

> 4 CARI-BU (2021) https://chinaafricaloandata.bu.edu/

4