rafmacalaba/data-use-mentions-extended huggingface dataset:
5. Casual inspection of the data in table 1 shows that there is clustering of the years of civil war produced by the death-based coding rule. This property is true regardless of whether the intensity is “war” (row 1), with its spotty coverage, or the merger of intensities 1–3 (row 2), although it is more pronounced in the latter case. This observation does not require the support of formal statistical analysis. This feature of the data is expected because almost any classification of country-years into civil war or its absence must include the sustained nature of these conflicts. Following the third point above, if country narratives establish that the gaps between years of civil war in table 1 are also years of civil war, the autocorrelation of these series would increase because additional years of civil war would be followed by civil war, and likewise for years of peace. Trite as these observations may be, econometric studies of civil war have ignored their implications.

Proper regard for the statistical properties of the errors in the relationship between the dependent variable and the explanatory variables is key to reliable econometric inferences. The autocorrelation of the dependent variable, whether a country had a civil war in any given year, which is evident in table 1, is not a sufficient condition for the errors for each country in an econometric equation explaining whether a country had a civil war to be correlated across years. It is, however, highly suggestive of autocorrelation in the errors and requires that econometric analysis pay special attention to this possibility. However, econometric studies of whether countries have civil wars have not tested for this property of the error terms or corrected for autocorrelation. Two problems ensue if autocorrelation in the error terms exists but is ignored. First, levels of statistical significance reported in econometric studies are likely to be exaggerated, making it seem that inferences are reliable when they are actually the result of chance. Second, if the specification of the equation explaining civil war includes a variable 15
ing. Nevertheless, as an emerging technology in a developing country setting, we were still unsure how feasible live Zoom training sessions would be.

IPA Mexico monitored 75 training sessions to observe how frequently technology issues occurred. 3 of the sessions had to be rescheduled due to electricity cuts or a hurricane, but otherwise technical issues involving computers, cameras, and microphones were not a major issue. There were occasional connectivity issues due to slow connection networks or to entrepreneurs’ data plans finishing, but these connections were usually reestablished within a few minutes. Participants typically used their mobile phones to join the Zoom sessions. Instructors used the chat and microphone features to get some questions and feedback from participants: in the average monitored session just over 80 percent of participants typed something in the chat, and approximately half turned on their video briefly (they kept it off most of the time to conserve data).

Take-up and attendance were then much higher than has been the case in most voluntary asynchronous courses. 80.7 percent of those assigned to treatment attended at least one session, with a mean of 5.5 sessions attended and 61.4 percent completing the course. This is in line with the average take-up rate for in-person training classes of 65 percent (McKenzie and Woodruff, 2014) and higher than Iacovone et al. (2018) find for CREA’s in-person training, where 69% started the course and 45% completed it. Figure A2 shows attendance rates by session. They fall over the course, but not steeply, from 72% for the first session to 60% for the last session. In contrast, take-up from the control group for the asynchronous materials was low: only 11.3% completed the first module, and only 6.6% completed all 4 modules.

# **3.2 How Much Cost-Saving is there from Zoom Training?**

One motive for considering business training by Zoom is the potential to lower costs. We worked with CREA to collect cost data on
Overall, therefore, as a share of total digital services trade (i.e. also including trade covered by countries with different data models), these numbers tell that, for the component of cross-border data transfers, around 42 percent is governed by the conditional transfers model, around 11 percent by the open transfers model, and only less than 1 percent by the limited transfers model. Considering rules on the domestic processing of data, a share of 42 percent is governed by the conditional processing model, only about 2 percent of trade is among country pairs applying the open processing model, and a similar share of 2 percent is based on the limited processing model.

Bilateral digital services trade is on average higher among country-pairs sharing the open data model for cross-border data transfers, with an average value of 3284 Mln. USD. Countries sharing the conditional transfers model have an average bilateral digital services trade value which is significantly lower (1067 Mln. USD), whereas the average trade value for countries following the limited model is the lowest (911 Mln. USD). The high value for the open model is naturally explained by the size of the US, which is at the centre of all trade that is covered by this model. When using trade per capita as a measure, the conditional model emerges as the one with the highest average value of digital services followed by the open model, and then the limited model. This ranking is explained by the fact that many countries adhering to the conditional model are smaller economies.<sup>14</sup> These differences also become visible by plotting the distribution of the services trade data by data model, as done in Figure A2. It adopts the log of bilateral digital trade. The differences in the average values between the open model and conditional model becomes clearly visible: countries sharing the open model for cross-border transfers of data have a higher average of total digital services trade. But when expressing values on a per capita basis, countries sharing the conditional model shows
0.2|10.9|
||2006m11-2007m2|7.6|12.8|0.4|20.8||2006m9-2006m12|0.5|9.6|0.1|10.2|
||2008m11-2009m3|7.6|13.8|1.0|22.4||2008m10-2009m1|0.9|10.1|0.2|11.2|
|ES|2004m10-2005m1|4.1|13.1|0.5|17.7|SE|2004m10-2005m1|0.4|7.8|0.2|8.4|
||2006m11-2007m2|5.1|14.4|0.9|20.4||2006m10-2007m1|0.5|7.3|0.3|8.1|
||2008m9-2008m11|4.4|13.9|0.5|18.8||2008m10-2009m1|0.8|7.1|0.3|8.2|

_Notes:_ Informal self-employment includes: (i) All non-professional self-employed operating solely; (ii) Employers with 5 or fewer workers. Persons working without a contract for own family‘s business (family workers) are considered separately and not accounted for in the models presented in Tables 3-7. Informal dependent employment includes employees (persons in a dependent employment relationship) without a contract or who is uncertain of their contract. Labor force is extended to include all non-employed persons willing to work. Data collection period is 5% trimmed. _Source:_ Calculation with ESS data.

39
**Figure 2: Relationship between SBM Efficiency and Volatility in Occupancy by hotel size**

<!-- Start of picture text --> Large firms sample (>=20 workers)<br>Small firms sample (<20 workers)<br>-1 -.5 0 .5 1 -1 0 1 2 3<br>Volatility in Occupancy (residuals) Volatility in Occupancy (residuals)<br>coef = .18627309, (robust) se = .03555412, t = 5.24 coef = -.11768041, (robust) se = .04691994, t = -2.51<br>.4 1<br>.2<br>.5<br>0<br>0<br>SBM Efficiency (residuals) -.2 SBM Efficiency (residuals)<br>-.4 -.5<br><!-- End of picture text --> Note: The figures are a partial scatter plot of residuals estimated separately for the sample of hotels with less than 20 full-time permanent workers and for the rest. These figures are obtained after controlling for region dummies (city fixed effects), dummy variables for the star-rating of the hotel (2-star, 3-star, and 4- or 5-star rating), and hotel size (log of number of workers). The lines of fit shown are statistically significant at less than the 5 percent level.

Source: Authors’ own calculations based on ES data for hotels in Malaysia in 2019.

35
: An equilibrium model and some

- empirical evidence from Brazil. _Review of Income and Wealth_ , 57: S8–S26.

- Djankov S., I. Lieberman, J. Mukherjee and T. Nenova (2003). Going Informal: Benefits and Costs. In B. Belev (ed.) ―The Informal Economy in the EU Accession Countries: Size, Scope, Trends and Challenges to the Process of EU Enlargement‖. Sofia: CSD: 63-80.

Ekelund, J., E. Johansson, M.-R. Jarvelin and D. Lichtermann (2005). Self-employment and risk

- aversion - evidence from psychological test data, _Labour Economics_ 12(5): 649-659.

_EURES - the European Job Mobility Portal_ (2011). Living & Working.

- http://ec.europa.eu/eures/main.jsp?acro=lw&lang=en&catId=490&parentId=0

- European Commission (2008). _Employment in Europe 2008_ . Luxembourg: Office for Official Publications of the European Communities.

- European Commission (2009). _New Skills for New Jobs. Anticipating and matching labour market and_

- _skills needs._ Luxembourg: Office for Official Publications of the European Communities.

- European Foundation for the Improvement of Living and Working Conditions (2007). _European_

- _Working Conditions Survey, 2005_ [computer file]. Colchester, Essex: UK Data Archive [distributor], May 2007. SN: 5639

46
’ age partly explains this group’s lower cognitive scores relative to the youth’s. While in the cognitive tests no differences are observed by gender, the memory score is higher for males in all cases.

The average years of schooling of providers, 3.6 for males and 3.4 for females, is consistent with the data registered by the 1995 Population and Housing Census in rural villages.

> 12 National Institute of Psychiatry Ramón de la Fuente Muñiz (INPRF), and the National Commission against Addictions (Conadic).

16
the structure of its pay system. Indeed, a growing body of evidence suggests that the skills of top executives are important for management practices, employee selection and firm performance (Bertrand and Mullainathan, 2003; Bertrand and Schoar, 2003; Bastos and Monteiro, 2011; Bender, Bloom, Card, Van-Reenen, and Wolter, 2018). However, there is little evidence on whether and how managers matter for how revenues from exogenous demand shocks are transmitted to workers.

## **5.4.1 Measuring managerial practices and managerial skills**

Following Bender, Bloom, Card, Van-Reenen, and Wolter (2018), we first use the 2016 management survey to compute firm-level management _z-scores_ —an index of adoption of advanced management practices. We then link these data to the other data sets on workers and firms. This allows us to relate measured management quality to worker and firm observables, including 13
R&D and low NBER) and Germany<sup>7</sup> (high expenditure on R&D and high NBER).

Table 3. Average of variables of production frontier model 2002-2005 by technological level

|**Sample**|**GDP**|**GFKF**|**LF**|**R&D**||**Entrepre**<br>**capi**|**neurship**<br>**tal**|
|---|---|---|---|---|---|---|---|
|**2002-2005**<br>All|**Total**<br>475990|**Total**<br>96681|**Total**<br>21188|**Total**<br> <br>10703.65|**% GDP**<br>1.49|**NBR**<br>54698|**NBER**<br>8.83|
|High-Medium Tech<br>countries<sup>1</sup>|996684|199474|19562|24907.70|2.82|91304|9.91|
|Medium-Low-Tech<br>countries<sup>2</sup>|114397|25298|22317|839.73|0.565|29277|8.07|
|Notes:||||||||

1 High-Medium Tech countries: R&D (% GDP) > 1 (year 2002) 2 Medium-Low-Tech countries : R&D (% GDP) < 1 (year 2002) Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

With a medium level on R&D and NBER, Italy has an robust small and medium enterprise sector
Policy Research Working Paper 9497

# **Abstract**

This paper introduces a new global database and a policy classification framework that records the financial sector policy response to the COVID-19 pandemic across 154 jurisdictions. It documents that authorities around the world have taken a diverse array of measures to mitigate financial distress in markets and for borrowers, and to support the provision of critical financial services to the real economy. Measures that focus on the banking sector constitute the majority of policies taken and aim to take advantage of the flexibility embedded in the international standards. However, emerging markets and developing economies tend to rely more on prudential measures that go beyond this embedded flexibility compared with advanced economies, which may reduce bank balance sheet transparency and increase risks. Using Cox proportional hazards and Poisson regressions, the paper takes initial steps to analyze the determinants of policy makers’ responsiveness and activity in emerging markets and developing economies, respectively. The results indicate that policy makers have typically been significantly more responsive and have taken more policy measures in emerging markets and developing economies that are richer and more populous. Countries with higher private debt levels tend to respond earlier with banking sector and liquidity and funding measures. The spread of COVID-19, macro-financial fundamentals, and fiscal and containment policies appear to play a limited role. In a substantially smaller sample, the paper explores the role of banking characteristics and finds that emerging markets and developing economies with higher private credit levels and that have adopted Basel III features have taken fewer policy measures. Future work is necessary for better unerstanding the country determinants of the policy response as well as the effectiveness and potential unintended consequences of the measures.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at efeijen
Policy Research Working Paper 10452

# **Abstract**

This study investigates the effects of public transfers and taxes on the wellbeing of children in Ethiopia. It applies the Commitment to Equity for Children methodology to examine the burdens of taxation and the benefits from government transfers and spending, and their differential wellbeing impacts on children. The study integrates data from the 2018/19 Ethiopia Socioeconomic Survey, which also collected data on taxes and transfers, with administrative data. Measuring its distribution by child monetary and multidimensional wellbeing, the study finds, on average, a progressive, poverty-reducing and equalizing fiscal system. However, there are important differences in the distribution of some of its elements. Indirect taxes, comprising of value-added and excise taxes, are regressive. Similarly, primary education spending, the largest of in-kind transfers, is only progressive in urban areas. On poverty and inequality, the fiscal system reduced the monetary child poverty headcount by 21 percent and the poverty gap by 33 percent. The effect is stronger for girls and children in rural areas than for boys and children in urban areas, therefore reducing inequalities in poverty rates. However, this is only the case when in-kind transfers for education and health are considered. Without the inclusion of in-kind transfers, the study finds that the fiscal system is not well calibrated to reduce poverty. This highlights the essential role of public services, not only in delivering fundamental child rights, but also in reducing poverty among children.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aambel@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is
credit institutions. Overall, communities in Chimaltenango and Totonicapan are the least equipped and communities in Suchitep6quez the best equipped. Another indicator of the economic structure of the community is represented by the total number of people living there as of the 1994 Census. The average community has 2587 inhabitants, the range being between 507 and 9928. Communities in Totonicapan tend to be bigger and communities in Jalapa tend to be smaller than the remaining communities.

Evidence related to the effects of market prices on child height-for-age is ambiguous. Barrera (1990) found no effect of the price of rice, cooking oil, kerosene or milk on child height in the Philippines. Foster (1990) finds that child height is negatively correlated with changes in rice prices in Bangladesh. Thomas and Strauss (1992) found that, taken together, prices have a significant effect on

> 16 In the analysis of the proximate determinants, we will add the availability of piped water and flush toilets as household-level covariates.

> **'7** The presence of a public dumpsite in the community is meant to capture a conscious public health effort by the local authorities.

15
the ENEU survey (third quarter).

_Figure 2.2 Cumulative Educational Distribution by Gender, 1997_ <!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>University Uppeer Sec Lower Sec Prim Comp<br>Educational Level<br>Male Female<br>%<br><!-- End of picture text --> _Source:_ Calculations based on ENEU data.

13
# **Migration and Unemployment**

Unemployment is lower among migrants (6.2 percent) than non-migrants (9.5 percent) and lower still among those who migrated for work (2.6 percent). What is striking is that unemployment rates among migrants and migrants for work are consistently lower in destination governorates with the highest migration inflows (Tables 19 and 20), and even those experiencing high unemployment, such as Port Said with 25% unemployment rate. This might suggest that migrants, especially those migrated to work, have skills that enable them to find jobs.

Furthermore, migrants earn higher wages (after controlling for education level) compared to non-migrants. This may be due to migrant’s matching their skills to the demand for jobs. It also reflects the significance of labor mobility and internal migration as means of achieving higher living standards.

Table 19 – Unemployment Rates of Non-migrants, Migrants and Migrants for Work

||**Overall**|**Non-migrants**|**Migrants**|**Migrants for work**|
|---|---|---|---|---|
|2008|9.9|10.2|3.6|0.2|
|2009|9.4|9.7|5.1|1.1|
|2010|9.3|9.5|6.2|2.6|

Source: Authors’ calculations using Labor Force Survey Table 20 - Unemployment Rates in Governorates with Highest Net Migration Inflows

|Unemployment rate|Overall|Non-migrants|Migrants|Migrated for Work|
|---|---|---|---|---|
|Port Said|25|41.82|8.28|0|
|Ismailia|10.4|11.95|7.8|1.59|
|6 October|10.2|9
# **2.4.1 West Manggarai**

Data on West Manggarai spans from January 2019 to December 2022. It was extracted from administrative data from more than 400 restaurants with a local tax registration number (NPWPD), which reduced to a balanced panel dataset comprising 399 restaurants (NPWPDs) for 48-month period post-cleaning. In the sample, a total of 30 restaurants were given POS units with each of them receiving only one type of POS (RPOS, TMD, or WS)—14 received RPOS, 9 received TMD, and 7 received WS. All of them obtained the units at separate times throughout the observed timeframe. The control group in each estimation is identical and comprises of 369 restaurants (NPWPDs) that did not receive any POS devices.

The data set also includes 225 hotels with 6 of them being facilitated with WS, thus leaving the remaining 219 in the control group. Unlike restaurants, hotels in West Manggarai were not subject to distribution of RPOS and TMD (Table 3). As the enactment of POS distribution program in West Manggarai came in waves, our data naturally carries varying treatment times, which we accommodate in the form of panel data analysis.

# **2.4.2 Gorontalo**

Compared to West Manggarai, Gorontalo’s data is more comprehensive. It spans longer from January 2018 to December 2022 and covers more businesses—up to 1,081 restaurants in total. Among them, 92 received RPOS, 14 received RPOS with added connection to Grab’s system (RPOS-Grab), and 7 received TMD. With 5 restaurants using 2 types of POS, the treatment group reduced to 107 restaurants and left the remaining 974 as controls. In contrast to West Manggarai, we found no information regarding the WS assignment to restaurants in Gorontalo.

There are 400 hotels in the districts included in our Gorontalo observation. Two of them were given access to the PAC Hotel application and 3 were equipped with TMD devices. This boils down to 5 being in the treatment
**Bolivia**|n.d.|25.7|22.8|20.3|32.8|
|**Brazil**|6.7|7.5|10.1|8.7|14.4|
|**Chile**|13.7|19.3|27.5|28.8|40.5|
|**Colombia**|12.5|15.5|15.0|16.6|17.1|
|**Costa Rica**|25.1|29.4|31.8|40.6|46.1|
|**Ecuador**|16.3|24.8|25.7|28.9|30.3|
|**Mexico**|7.7|9.1|17.2|24.9|27.3|
|**Peru**|18.2|16.6|16.7|13.4|22.6|
|**Uruguay**|14.3|15.7|22.6|18.4|26.6|
|**Venezuela**|25.3|26.0|25.5|28.2|32.6|

_Source: Own calculations using WDI primary data._ To complement this analysis, Figure 2 presents the deviations of the forecasted versus actual export/GDP ratio for a sample of Latin American countries for two years (2000 and 2008), in which 6
# **2.1 Aid Data**

Data on foreign aid are sourced from World Bank projects in the period 1995–2014, geocoded by AidData. Other geocoded datasets on aid exist, but this one covers the whole African continent (and beyond) for the longest period. The World Bank dataset contains projects from both the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA), totaling 1,900 projects in Africa split across 16,553 different locations. The IBRD provides low or zero interest rate loans to sufficiently creditworthy countries, whereas the IDA gives loans to poorer and less creditworthy countries; 12 percent of IDA funds are given as grants not to be paid back. Both types of lending are accompanied by technical assistance from the World Bank, and projects are monitored by World Bank<sup>12</sup> The data contain information on all locations in which a given project has been implemented. Locations are classified into categories 1 to 8 according to the level of geographical disaggregation of their coordinates, with the categories called, somewhat misleadingly, “precision categories.” Precision 1 locations correspond to a specific place, that is, a populated place of some kind (such as a village, town, or city), in approximately 80 percent of the cases, or to a third-order administrative division (ADM3, i.e., a neighborhood or suburb) in approximately 15 percent of the cases.<sup>13</sup> Precision 2 locations are similar to precision 1 locations, but their reported coordinates, being within 25 km of the exact location, are not as accurate. Precision 3, 4, and 6 locations correspond to second-order administrative divisions (ADM2), firstorder administrative divisions (ADM1), and countries, respectively. Note that precision 3, 4 and 6 categories refer to projects intended to serve the whole administrative division (e.g., training for all public employees in a 12The information available at the project