rafmacalaba/data-use-mentions-extended huggingface dataset:
Fasani, F and J Mazza. 2020b., “Being on the Frontline? Immigrant Workers in Europe and the <u>COVID-19 Pandemic,”</u> _IZA_ Discussion Papers 13963.

Friedberg, Rachel M. 2000. “You can’t take it with you? Immigrant assimilation and the portability of human capital,” _Journal of Labor Economics_ , 18(2): 221–51.

Foged, Mette and Giovanni Peri. 2016. “Immigrants' Effect on Native Workers: New Analysis on Longitudinal Data,” _American Economic Journal: Applied Economics_ , 8 (2): 1-34.

Garrote Sanchez, Daniel, Nicolas Gomez Parra, Caglar Ozden, and Bob Rijkers. 2020. “Which jobs are most vulnerable to COVID-19? Analysis of the European Union,” _World Bank Research & Policy Briefs_ No. 34. Washington, D.C.: World Bank Group _._ Giuntella, Osea, and Fabrizio Mazzonna. 2015. “Do immigrants improve the health of natives?” _Journal of Health Economics_ 43: 140–53.

Goel, Deepti, and Kevin Lang. 2017. “Social Ties and the Job Search of Recent Immigrants,” _ILR Review_ .

Green, David A. 1999. “Immigrant Occupational Attainment: Assimilation and Mobility over Time,” _Journal of Labor Economics_ 17 (1): 49-79.

Hao, Lingxin. 2008. “Workplace Nonfatal Injuries Among Immigrants to the U.S.” Mimeo, Johns Hopkins University Department of Sociology. Available online at <u>http://paa2008.princeton.edu/download.aspx?submissionId=80386.</u> Hersch, Joni, and W. Kip Viscusi. 2010. “Immigrant Status and the Value of Statistical Life.” _Journal of Human Resources_ 45 (Summer): 749-771.

Hildebrandt, Nicole, and David J. McKenzie. 2005. “The Effects of Migration on Child Health in Mexico.” _Economia_ 6 (1): 257–89.

Juranek
insignificant throughout. This failure to find impacts of large farms is not surprising for teff where small farmers’ yields already higher commercial ones’. In the case of sorghum, it suggests that, beyond adoption of improved seed, there is little transfer of technology between large and small farms.

# **4.2 Rural labor market participation**

With growing population and limited absorptive capacity in the non-agricultural sector, the ability to create gainful employment is a key concern for policy makers and considerable hopes are being pinned on large farms being integrated into agro-processing value chains. Unfortunately, CSA’s smallholder survey lacks information on households’ off-farm labor supply. To fill this gap, we use individual level data on off-farm labor supply from the 2011/12 and 2013/14 rounds of nationally representative LSMS-ISA surveys. Our two measures are if, over the last 12 months, individuals age 15 and above engaged in paid work or casual (non)-agricultural labor. The bottom panel of table 8 highlights that in both rounds 4% or 5% of sampled individuals engaged in paid work, the share of casual agricultural workers increased from 8% to 16% between the two rounds and only about 1% engaged in casual labor outside agriculture.

To explore if rural dwellers’ proximity to commercial farms affects their labor market participation in ways comparable to those emanating from other employment centers, we compare the impact of individuals’ distance to the next small town, defined as an urban center with at least 2,000 inhabitants, to that of their proximity to commercial farms or total large farm are cultivated in different distance bands. Regressions show that incidence of paid work decreases significantly with distance to towns but not distance to large farms (table 8, col. 1), in line with the notion that large farms fail to significantly contribute to generation of paid jobs.<sup>19</sup> The point estimate suggests that being located 10 km closer to the next town increases the propensity to engage in paid
.174|0.178|-0.005|
|Bicycle|0.157|0.157|0.151|0.006|
|Motorbike|0.306|0.305|0.310|-0.004|
|Car or other vehicle|0.096|0.097|0.094|0.002|
|Mobile Phone|0.760|0.764|0.752|0.008|
|**_Other activities_**|||||
|Owns land|0.669|0.667|0.666|0.004|
|Cropfarming|0.699|0.697|0.696|0.003|
|Owns anylivestock|0.469|0.466|0.464|0.005|
|Operates nonfarm enterprise|0.595|0.596|0.602|-0.007|
|Anymember has financial account|0.493|0.496|0.500|-0.007|
|Income from remittance(int'l & domestic)|0.319|0.321|0.308|0.011|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

# <mark>4.1.3 Non-response bias</mark>

The last source of bias to examine is for non-response. Non-response as considered here includes cases from the HFPS sample that were either (1) not successfully contacted or (2) contacted but not successfully interviewed (refused or breakoff mid-interview). Both of these sources of non-response (non-contact and non-interview) will be considered separately here.

Table 4.3a presents the comparison of the weighted mean characteristics for the HFPS sample (columns 1, 6, 11, and 16), sample of
sentiments. We still obtain a positive and significant relationship between famines and the Communist war

> 14 The difference between the CCP and the Nationalist ideology is evident from the official statements that a new party member of each party has to declare joining. See <u>https://en.wikipedia.org/wiki/Chinese_Communist_Party_Admission_Oath.</u> The Nationalist party emphasizes that each member has to obey traditional Chinese values such as courage, filial duty, kindness, keeping promises, peace, courtesy, obedience to supervisors, frugality, tidiness, and helping others. The CCP joining statement, in contrast, focuses on obeying the _principles_ of the party, carrying out orders, accepting disciplines, being loyal to the party, and being willing to sacrifice one’s life for the party, and never be a traitor to the cause.

> 15 The difference in the total numbers of martyrs between the CCP and the KMT largely reflects the CCP’s tradition in collecting the identity of their martyrs (see Hung, 2008). Another factor is that the KMT retreated to Taiwan, China, in 1949 and could no longer access detailed personnel data of its casualties on the mainland.

28
Dewees, P.A., 1994. Trees, Land, and Labor. The World Bank, Washington, DC.

Dewees, P.A., 1995a. Trees and Farm Boundaries: Farm Forestry, Land Tenure and Reform in Kenya. Africa 65, 217-235.

- Dewees, P.A., 1995b. Trees on farms in Malawi: Private investment, public policy, and farmer choice. World Development 23, 1085-1102.

- DiMiceli, C.M., Carroll, M.L., Sohlberg, C., Huang, C., Hansen, M.C., Townshend, J.R.G., 2011. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000 - 2010, Collection 5 Percent Tree Cover, Collection 5 ed. University of Maryland, College Park, MD.

- FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2), FAO, Rome, and IIASA, Laxenburg, Austria.

- Fay, C., Michon, G., 2005. Redressing forestry hegemony when a forestry regulataory framework is best replaced by an agrarian one. Forests, Trees and Livelihoods 15, 193-209.

- Franzel, S., 1999. Socioeconomic factors affecting the adoption potential of improved tree fallows in Africa. Agroforest Syst 47, 305-321.

- Garrity, D.P., Akinnifesi, F.K., Ajayi, O.C., Weldesemayat, S.G., Mowo, J.G., Kalinganire, A., Larwanou, M., Bayala, J., 2010. Evergreen Agriculture: a robust approach to sustainable food security in Africa. Food Sec. 2, 197-214.

- Godoy, R.A., 1992. Determinants of smallholder commercial tree cultivation. World Development 20, 713-725.

- Ickowitz, A., Powell, B., Salim, M.
exchange, and market risks. Other relevant factors are also not considered, including contagion and second-round effects, implications of contingent liabilities, and the strength of the bank resolution framework. Therefore, our framework is inherently limited in scope and should ideally be complemented with additional stress tests and quantitative and qualitative supervisory information.

To illustrate the potential application of this new metric of systemic banking resilience, we explore the value added of the CDBP during the COVID-19 pandemic. The spread of COVID-19 prompted many policy makers to introduce a wide range of policy support measures including debt moratoria, credit guarantees, and regulatory forbearance (Feyen et al., 2021), which has rendered bank balance sheets less transparent in some jurisdictions. In this context, we apply our CDBP framework to offer preliminary evidence of systemic bank resilience based on data of over 1,500 banks in 59 EMDEs.

To evaluate the value added of the CDBP, we investigate its empirical association with commonly used country-level macro-financial and soundness indicators. The results of these preliminary analyses show that this new metric may contain additional information not already encapsulated in these commonly used indicators, suggesting that it could complement them. As such, the CDBP could be used for basic monitoring purposes, particularly in countries with limited availability of reliable financial data.

In the remainder of the paper, we first cover methodological aspects and describe assumptions, caveats, and limitations. We then apply the framework to bank data covering the pandemic episode and relate the CDBP measure to several macro-fiscal indicators and financial soundness indicators. The last section concludes.

2
Table A.23: Under-5 EFM: Controlling for State x Year x Female FE

|**Outcome: Death before age 5**|(1)|(2)|
|---|---|---|
|_Firstborn girl_ * _Female_|2.780***<br>(0.575)|2.796***<br>(0.580)|
|_Firstborn girl_ * _Female_*_Post1_|-1.566**<br>(0.758)|-1.576*<br>(0.764)|
|_Firstborn girl_ * _Female_*_Post2_|-1.976**<br>(0.740)|-2.004**<br>(0.740)|
|N|227,129|227,129|
|_Xijt_|x|x|
|_Firstborn Girl_ x Birth year FE|x|x|
|_Female_ x Birth year FE|x|x|
|_Female_ x State FE|x|x|
|_Female_ x Birth order FE|x|x|
|Birth order x Birth year FE|x|x|
|Birth order x State FE|x|x|
|State x Birth year FE|x|x|
|_Firstborn girl_ x State FE|x|x|
|_Firstborn girl_ x Birth order FE|x|x|
|_Female_ x _Xijt_||x|
|_Firstborn girl_ x State x _Female_ FE|x|x|

NOTES: This table checks the robustness of our findings to the inclusion of State x Year x Female FE. Columns (1) and (2) correspond, respectively, to columns (3) and (4) in Table 3. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

69
?‖ Policy Research Working Papers, World Bank, forthcoming.

Drèze, Jean and P. V. Srinivasan. 1997. ―Widowhood and Poverty in Rural India: Some Inferences from Household Survey Data,‖ _Journal of Development Economics_ 54: 217-234.

Duflo, Esther. 2003. ―Grandmothers and Granddaughters: Old Age Pension and Intra-household Allocation in South Africa.‖ _World Bank Economic Review_ 17(1): 1–25.

Dutta, Puja, Stephen Howes and Rinku Murgai. 2010. ―Small but Effective: India‘s Targeted Unconditional Cash Transfers,‖ ASARC Working Paper 2010/1, Canberra, Australia.

Ezememari, Kene, Nazmul Chaudhury and Janet Owens. 2002. ―Gender and Risk in the Design of Social Protection Interventions,‖ Social Protection Discussion Paper Series No. 0231, World Bank, Washington, DC.

28
completed projects, no useful inferences can be drawn about the appropriate sequencing of activities in administration reform projects.

Box 6. Implementation Plan for VAT **Introduction** **_Mongolia Fiscal TechnicalAssistance_** _Project,_ **_19981_** The introduction of a VAT in Mongolia is part of a major tax _reform_ program of base broadening and rate lowering. Key requirements for successful VAT implementation have been identified as: (a) a sound tax administration and (b) well-informed taxpayers that know how to apply the new law.

To implemen **t** he reform. (a) a VAT management committee has been set up headed by the Director General of the General Tax Department (GDNT). (b) An interagency working group has been created for day-to-day project implementation with staff from the Ministry of Finance (MOF), the GDNT and the Customs General Administrationi (CGA). (e) Implementation is to be supported by an IMF-supervised lonig tenn VAT advisor.

I The project became effective in September 1998. Impleme-ntation started in March 1998. and will continue until March 2001. The detailed implementation plan has eight phases with a detailed plan of action having been prepared for each phase. The project has a detailed timeline and specifies implementation responsibilities for each component. The phases are:

- I . _Legislation and PublicAcceptance."_ To draft and pass a VAT Law through Parliament and start a public education program: MAOF and GDNT.

2. _Policy Interpretat_ **_ons:_** To develop interpretations of each provision, initiating contact with industry sectors on those issues and establishing a permanent policy interpretations unit in headquarters: GDNT.

3. _Registration:_ To develop registration forms and procedures, a strategy for identifying potential taxpayers and training for inspectors: GDNT.

4. **_Tax_** _Accounting:-_ The design, reporting procedures, control, and information required on VAT returns: GDiNT.

- _!5 Taxpayer information: GDNT._

- l6. _"
older firms. Following similar analysis in Bloom et al. (2019b) on Mexico and the work of Hsieh and Klenow (2014) regarding the life cycle of firms, this finding appears to be indicative of poor allocative efficiency among firms in Croatia where clearly, market selection is not operating perhaps due to the lack of pro-competitive conditions, especially in services. Nevertheless, the finding in Croatia is somewhat similar to that in Russia, where firms also do not improve with age (Grover and Torre), although in Croatia it is very clear that this negative association is driven by the services sector.

_Third_ , exporters have higher scores in adopting structured management practices, and significantly so in manufacturing. More precisely, the management score of a manufacturing exporter in Croatia is likely to be higher by 8 percent than an average firm. The fact that this result is mainly driven by high-tech manufacturing exporters in regions other than Croatia, where management score is likely to be 16 percent higher, points to the potential for improving management capabilities among other firms and sectors. In Eastern Croatia, this relationship between exporting and higher managerial capabilities is not significant even for manufacturing. To design interventions for improving management capabilities in Eastern Croatia, it may be crucial to examine the export composition and destination markets for firms in Slavonia and more specifically in understanding why exporters do not find it worthwhile to improve management capabilities.

_Fourth_ , firms with more than 20% of their employees having a college degree are more likely to adopt structured management practices. Education of employees matters for managerial score in all regions except in Slavonia. This is a surprising finding and may need to be explored further. It may perhaps be related to the quality of education among workers in Slavonian firms.<sup>24</sup>

> 23 In Bloom et al. (2019b), only four questions from the U.S. MOPS were selected for microenterprises. The correlation between the management score calculated with this subsample of questions and the overall management score for SMEs and
governorates is the primary focus of the analysis here.

20For each survey, the 95 percent confidence interval for the average number of mobile phones owned by the household is contained in the 95 percent confidence interval prior to the conflict (based on data from the 2014 Household Budget Survey). Despite the large shock to household incomes and energy sources needed to charge mobile phones, the confidence intervals overlap in approximately 52 percent of the governorate-month observations. However, in the cases where there was not broad overlap between the WFP survey and preconflict data, much of the lack of overlap is in part due to the changes in the composition of the populations due to large-scale migration of IDPs (e.g., TFPM 2017), and also due to the five years that has passed since the last nationally-representative survey.

21All estimates are qualitatively identical when the analysis only focuses on regions where mobile phone ownership has not changed since the last nationally-representative estimate.

11
,_ the restructuring and downsizing operations have produced better capitalized banking systems. Among the _CEEs, Bulgaria_ had the highest capital adequacy ratio in 1998 of around 37 percent due to revaluation gains.

# **c. Banking Sector Efficiency**

The effects of bank crisis resolution on banking sector efficiency were mixed. One measure, interest rate spreads<sup>45</sup> , indicates that the banking sector has become more efficient in the _CEEs_ and _Baltics,_ while it remains far from efficient in the _CIS_ countries. Another measure - central bank credit to commercial banks as a share of GDP<sup>46</sup> - indicate that there were improvements in this area for all three country groups.

In the _CEEs_ and the _Baltics,_ interest rate spreads have, by and large, been declining in the latter part of the 1990s, suggesting increasing banking sector efficiency during that period. In 1998, the average interest rate spreads of the two country groups were around 8 percent, which was still twice the level in the OECD countries. By contrast, in the four _CIS_ countries, interest margins were extremely high, averaging 33 percent in 1998, reflecting the lack of competition and inefficiency in their banking systems.

For all three country groups, central bank credit to banks as a share of GDP generally declined through the 1990s, reflecting a decrease in the reliance of the banking system on the central bank, and suggesting an increase in efficiency. By 1998, this ratio had fallen to around I percent or smaller in the

> **45** See Appendix Table 9 for data on interest rate spreads for the twelve countries and for OECD countries.

> **46** See Appendix Table 10 for data on central bank credit to banks as a share of GDP for the twelve countries and for OECD countries.

45
# **BOX 4: Adoption of Tax E-services in Africa**

**Problem:** Traditional tax processes involve paper-based filing and extensive in-person interactions between taxpayers and tax officials. This system creates several challenges such as large compliance costs, collusion with tax officials or extortion by them, and poor tax records that limit the potential of tax agencies to conduct analyses for compliance monitoring and forecasting. Whereas new technology to digitize taxpayer services may hold a lot of promise, important barriers to adoption of these technologies may severely curb the potential for revenue mobilization.

**Technology intervention:** Many African governments have been heavily investing in e- services as a way to move from paper-based systems to fully digital platforms. As an example of such e-services, e-filing and e-payment facilities enable digital filing of returns and digital payment of tax liabilities. These e-services often live within integrated and automated tax administration systems. The associated web portals also provide a range of benefits in terms of clear information on deadlines, access to assistance, and better record-keeping.

**Research design:** A number of studies from Africa attempt to understand the barriers to access of such e-services. These studies are typically based on surveys of representative samples of taxpayers, collecting detailed information around taxpayer experiences with e-services. In the case of Rwanda, Santoro et al. (2022) originally link survey data with tax administration data, in order to gain more information around taxpayers and their filing behavior.

**Results:** Lack of awareness and lack of training are key factors that have been identified in both Nigeria (Mas’ud, 2019) and Zimbabwe (Obert et al., 2018) as barriers to adoption of e- services by taxpayers. In Nigeria, Efobi et al. (2019) conclude that internal firm characteristics like owner’s education, age of the company, and use of an external auditor and computerized accounting system, are most strongly correlated with the use of e-filing and e-payment by firms.

Partial take-up can
, as well as advantages. Perhaps the most important limitation is that the z-scores are based purely on accounting data. They are thus only as good as the underlying accounting and auditing framework. If financial institutions are able to smooth out the reported data, the z-score may provide an overly positive assessment of the financial institutions‘ stability. Also, the z-score looks at each financial institution separately, potentially overlooking the risk that a default in one financial institution may cause loss to other financial institutions in the system. An advantage of the z-score is that it can be also used for institutions for which more sophisticated, market based data are not available. Also, the z-scores allow comparing the risk of default in different groups of institutions, which may differ in their ownership or objectives, but face the risk of insolvency.

For other indicators, such as the regulatory capital to risk-weighted assets and nonperforming loans to total gross loans, the Global Financial Development Database cross-refers to financial soundness indicator database available on IMF‘s website (fsi.imf.org). Variables such as the nonperforming loan ratios may be better known than the z-score, but they are also known to be lagging indicators of soundness (e.g., Čihák and Schaeck 2010).

One alternative indicator of financial instability is ―excessive‖ credit growth, with the emphasis on _excessive_ . A well-developing financial sector is likely to report expansion in credit growth. Without credit growth, financial sectors would lack in depth or would not be able to provide good access to financial services. Credit growth is important, and indeed may be necessary even
were collected at the beginning of the second wave of COVID-19 extending to the height of it.<sup>5</sup> EIFBS firms comprise a multistage stratified sample drawn from the 2017 economic census sample of 33,331 establishments, which is itself drawn from a sample of 117,149 establishments. The EIFBS sample design is based on three parameters to ensure that the sample produces representative and precise estimates at the national level. These parameters are number of employees, region (urban governorates, lower and upper Egypt) and economic activity level (2 digits). The sample frame, however, excludes firms with less than 5 employees and thus is only representative of small, medium (SMEs) and large manufacturing enterprises. This also implies that informal firms – albeit present – are underrepresented in our sample. We expect that the results pertaining to informality would be stronger with the inclusion of more informal firms. More detailed information on sampling can be found in Annex 1.

Two questionnaires were administered, one for firms that are still in operation, and another, very similar one<sup>6</sup> , for firms that have exited the market or have temporarily shut down operations. The response rate is 75%, meaning that we successfully interviewed 2,383 establishments of which 2,338 are in operation and 45 firms that have either exited the market or are temporarily closed. Of the 766 firms we could not interview, an unknown number, and presumably a much higher proportion, have also exited the market. The issue of representativeness is one of sample selection bias, which arises if firms that refuse to respond have different characteristics to those that did respond. In principle, the presence of such bias can be checked for by comparing characteristics of firms in the sample with those not taking part in the interview. But since data are, by definition, not collected from firms that refuse/didn’t take part, it is not possible to compare the characteristics of firms who responded with