rafmacalaba/data-use-mentions-extended huggingface dataset:
phenomenon and study the real role of ICT in economic growth, we disaggregate the effects by technology, degree of development, and timing of adoption.

When addressing the impact of ICT adoption on economic growth, there appears to be a distinction in the literature between effects in developed versus developing countries. Dedrick et al. (2003), based on a review of 50 empirical studies, conclude that computer adoption has positive effects in the United States but mixed effects among developing countries. A possible explanation of this evidence could be that IT investment represents a significant share of the U.S. capital stock, which is not the case in the developing world. Niebel (2018) measures the impact of ICT on economic growth on a sample of 59 countries between 1995 and 2010. The results show a positive relationship between ICT and GDP growth. Despite that, the author does not find statistically significant differences in the impacts among developing, emerging, and developed countries. These results, however, might suffer from sample bias since data on ICT capital was mostly available in countries with large GDPs. An analysis including a larger group of countries (especially providing better coverage of smaller economies) would be needed to address this bias. Similarly, Stanley et al. (2018) analyze 58 empirical studies, and find positive and significant impacts of mobile and landline communication technologies on economic growth. Yet, the effects of mobile technologies are twice as large as those of landline. This could explain the technological leapfrogging in developing countries (particularly, in Sub-Saharan Africa) that have passed over landline infrastructure and jumped directly to mobile telecommunications (UNCTAD, 2018). Likewise, using different proxies of ICT infrastructures on economic growth in 28 countries of the European Union between 2000 and 2017, Toader et al. (2018) find that a 1 percent increase in the use of digital infrastructure would increase GDP per capita by between 0.08 percent, if the infrastructure is measured by fixed broadband subscriptions, and 0.4 percent, if it
**during the pandemic.** This question brings together the previous specifications into a single model. Again, we estimate an LPM of the following form:

Model (4) uses the same four outcome variables used in model (3), estimating the correlates of different coping strategies. Since we are interested in the relationship between enterprise income declines and coping mechanisms, the sample is limited to self-employed heads or households with enterprises. The variable _DFhc_ takes a value of 1 if earnings from a farm household enterprise declined compared to the period immediately before the pandemic started, while the variable _DNFhc_ takes a value of 1 if a non-farm household enterprise’s income declined. Thus, the coefficients βF and βNF represent the conditional correlation between an income decline and the prevalence of different coping mechanisms. The other control variables are the same as in model (3).

# **2.2 Evolving impacts of the pandemic**

**The impacts of the COVID-19 crisis varied over time.** We hypothesize that the share of household enterprises, captured by the share of self-employed workers or people working at a family business, fell in the first months of the pandemic when mobility constraints were imposed, and slowly recovered as the pandemic ran its course and mobility constraints were lifted.

**We use the larger sample of HFPS data from 27 countries and 11 waves covering 13 months from March 2020 to April 2021.** We only use the household head definition so that we can estimate how business owners with different characteristics evolved throughout the pandemic. In this exercise, the weights are normalized such that each country is given equal weight, to prevent estimates from being skewed towards populous countries. The results should therefore be interpreted as a simple average across included countries. For comparison purposes, we also estimate the trends of wage earners and of the non-working population.

**The timing of the survey varied by country.** Figure 1 shows the number of countries with available information by month. At the beginning
such contracts. Furthermore, most emerging markets do not have the technological infrastructure or access to commercial credit information necessary to allow this type of financing.

For example, weaknesses in the business environment in the new EU-accession and transition countries gives factoring some key advantages over other lending products and has contributed to its growth in the region. There are several characteristics of factoring that may give it an edge in Eastern Europe. First, factoring removes receivables from the borrower’s estate in bankruptcy, which may be particularly important if the judicial system is less developed or inefficient. Both of these conditions likely apply to most countries in Eastern Europe, as confirmed by the World Bank’s Insolvency and Creditor Rights ROSC reports (Reports on Observance of Standards and Codes) for selected countries in the region.<sup>3</sup> Second, factoring is a type of asset-based financing that has a distinct advantage in providing funding to higher risk and informationally opaque firms, especially SMEs. This is particularly relevant in transition countries whose private sectors are young and continuing to develop and expand in order to catch up to Western Europe. Furthermore, weak accounting standards and a shortage of audited financial statements is characteristic of the region. Factors can base their lending decision primarily on the condition of the underlying accounts (buyers) rather than the creditworthiness of their SME customers (suppliers).

There are also a number of additional tax, legal, and regulatory challenges to factoring in many developing countries. For instance, the tax treatment of factoring transactions often makes factoring prohibitively expensive. For example, some countries

> 3 For country-level Insolvency and Creditor Rights ROSC reports, see: http://www.worldbank.org/ifa/rosc.html.

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# **Table 4. Chronic and transient poverty by adult scale factors, Jalan-Ravallion**

# **decomposition, RLMS 1994-2017**

||<br>**E**<br>|**quivalent in**<br>|**come is com**<br>|**puted using**<br>||
|---|---|---|---|---|---|
||ea**_=0.3_**|ea**_=0.4_**|ea**_=0.5_**|ea=**_0.6_**|ea**_=0.7_**|
|**_Headcount Poverty_**||||||
|Total Poverty|0.085|0.1|0.119|0.142|0.17|
|Transient Poverty|0.036|0.041|0.045|0.05|0.054|
|Chronic Poverty|0.049|0.059|0.074|0.092|0.115|
|_Share of chronic poverty (%)_|_57.3_|_59.4_|_62.1_|_64.9_|_67.9_|
|**_Poverty Gap_**||||||
|Total Poverty|0.03|0.035|0.042|0.05|0.06|
|Transient Poverty|0.015|0.017|0.02|0.023|0.026|
|Chronic Poverty|0.015|0.018|0.022|0.027|0.035|
|_Share of chronic poverty (%)_|_49.6_|_50.7_|_52.3_|_54.5_|_57.2_|
.474|2,582|
|<br>Assess lethargy|0.13|0.12*|0.058|2,582|
|Check for mouth ulcers|0.27|-0.00|0.963|2,582|
|Measure radial pulse|0.39|0.31*|0.052|2,582|
|<br>Pinch skin|0.63|0.17*|0.057|<br>2,582|
|Stridor|0.25|0.13|0.146|<br>2,582|
|Temperature|0.91|0.05|0.391|2,582|
|Check for visible wasting|0.19|0.11|0.131|2,582|

Note: data from direct clinical observations conducted at the rural health centers. The PBF impact is estimated using a propensity score weighting specification as only follow-up observations are being included in the analysis. Standard errors are clustered at the district level.

a Data from consultations of adults aged 40 and above. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, age and gender of patient, gender age and position of provider, and a binary indicator for experience of over 20 years. b Data from consultations of children under-5. Regressions include controls for facility’s exposure to the collaborative quality improvement and citizen scorecards interventions, Region, gender age and position of provider, and a binary indicator for experience of over 20 years.

Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 35
If market failures are not associated with imitation, then the second _α_ in equation (1) would not have an effect on _D_ , and barriers would have an unambiguously negative correlation with discovery. Also, the absence of market failures associated with imitation would imply that the effect of an increase in returns to discovery is not affected by barriers to entry.

If imitators can benefit from spillovers emanating from the original discovery, we would observe an ambiguous relationship between discovery and barriers to entry. Furthermore, we would expect that the relationship between returns to discovery and the frequency of discovery would be affected by variations in barriers to entry. The effect of higher returns on export discoveries would be _greater_ in countries with _higher_ barriers to entry, as first movers would be able to appropriate a larger share of the export returns. That is, in this scenario pioneers would have a greater incentive to experiment for a given level of expected returns. Conversely, if barriers to entry are low, then an increase in exogenous returns to exporting will have a smaller effect on discovery, as much of those returns will leak to imitators. This, then, is our test of the market failure hypothesis: how does the relationship between exogenous returns to discovery and the frequency of observed discovery vary with barriers to entry.

# **3. Data and Methodology**

To test this hypothesis, we require a metric of inside-the-frontier innovation (when a good enters a country’s productive basket for the first time), which has not yet been developed. Production data highly disaggregated at the product level would be the ideal source of such a metric, but are unavailable for most countries. As such, we exploit export data, which for reasons of customs reporting are available for a broad crosssection of countries at a high level of disaggregation by product.

The problem with using export data seems obvious: a product emerging as a new export may have been produced domestically for some time, and therefore would not represent a true inside-the-frontier innovation. However,
, like ours, combined both a differences-in-differences approach and a propensity score matching estimator (see De Aquino and Kassouf 2011; Marín 2006; Xerxenevsky 2012).

In Chile, for instance, most studies found positive impacts of the Chilean program of extension of the school day ( _Jornada Escolar Completa_ ) on student performance (Bellei 2009; Marín 2006; Valenzuela 2005).<sup>16</sup> Arzola (2010) is an exception.<sup>17</sup>

> 14 Some international studies on this topic are Baker _et al_ . (2004) and Lavy (2010). Both studies use crosscountry data to investigate the relation between instructional time and student performance. However, as mentioned before in our review, we will focus on studies that use microdata.

> 15 Some studies investigate effects on other socioeconomic outcomes such as crime rates, adolescent motherhood and labor market outcomes (see Berthelon and Kruger 2010; Contreras, Cabreara, and Sepúlveda 2010; Pires and Urzua 2011).

> 16 Pires and Urzua (2011) use a retrospective survey applied to individuals aged 25 to 30 to assess the impact of the Chilean full-time school program on learning outcomes. Their findings show that the extension of school day during high school increased student academic achievement, measured by tests taken during adulthood.

> 17 Although all studies exploit data from Education Quality Measurement System (SIMCE)Arzola (2010) analyzed a different time-period than the other two studies (focusing on the period 2005–9 instead of 2001–3). Llambí (2013) also evaluated the Chilean program of extension of the school day and found mixed results, depending on the econometric method used.

6
cross-borders traders were examined through a survey in mid-2010 at four key border-crossing points in the Great Lakes region: at Uvira-Bujumbura (between DRC and Burundi); BukavuCyangugu (between DRC and Rwanda); Goma-Gisenyi (DRC and Rwanda) and Kasindi-Mpondwe (DRC and Uganda) (Brenton et al., 2012). In general, the survey results showed that cross-border traders were mostly poor young women (85% of the 181 surveyed traders were female and the average age of a trader was 32) engaged in trading small quantities of cereals, pulses, vegetables, and fruits, typically carried by head. The household income of most traders (77% of respondents) relied primarily on cross-border trading activity. As indicated in Garc ́ıa Mora and Roshan (2013), approximately 60 percent of cross-border traders in DRC and Rwanda were officially registered with the Government, implying that they were not seeking to hide from or evade government agencies. Nevertheless, many border officials held the view that cross-border traders were illegal traders, and treated them with abuse at the border. From the survey of 181 cross-border traders in the Great Lakes region, it was reported that 85% of traders were asked to pay bribes; 60% of traders received fines; 54% of traders were subject of acts of violence, threat and sexual harassment; 38% of traders were faced with rude border official’s behavior, verbal abuse, and insults; 38% of traders had their goods confiscated; and 19% of traders were forced to wait for a long period before crossing the border (Brenton and Isik, 2012). Payment of bribes followed a usual phrase from border officials, known by all traders: ” _sans argent, on ne passe pas_ ” (no money, no passing).

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# **VII Figures**

Figure 1. Registry and Program Rollout: RAMV Census Registration, PEP Application, and Data Collection <!-- Start of picture text --> July 25, 2018<br>Residency permit (Permiso Especial de<br>Permanencia – PEP) for irregular<br>refugees registered in the RAMV is<br>announced<br>April 6, 2018 June 8, 2018 August 2, 2018 December 21, 2018 October 2020 February 2021<br>RAMV registry RAMV registry PEP program PEP program Survey Survey<br>starts ends starts ends collection starts collection ends<br>2017 2018 2019 2020 2021<br>SAMPLE<br>442,462 refugees registered in Around 281,307<br>395 municipalities in Colombia people received the<br>(35% of the territory) PEP document<br><!-- End of picture text --> 38
of the SAM is done in a separate process and is combined of two components: regionalization and balancing. The first component prepares the proto-SAM using regional data, while the latter balances it. The second component is the standard SAM balancing algorithm cited above (see Section 3.10 for a further description of cross-entropy with HPD estimator).

Regional Input-Output coefficients are estimated using Augmented Flegg Location Quotients (Flegg et al. (1995); Flegg and Webber (1997); Flegg and Webber (2000)). We used the

> 3The national SAM was built in a previous project between the Italian Department of Finance, the World Bank, and the European Commission under the project "Improving the evaluation of VAT and excise tax policies in Italy."

> 4We consider the two autonomous provinces of Trento and Bolzano, which are counted separately in the NUTS-II regional classification, as a single region labeled Trentino Alto-Adige, to be consistent with the Italian regional framework.

6
(63.9% meet standard versus 84.6% for the diagnostic research tool data) and classroom management tools (41.6% meet standard versus 77.1% for diagnostic research tool data).

Turning now to the measures of agreement; it is important to keep in mind that exact agreement is often high by chance when items are rare or universal (e.g. desks, 90.2% exact agreement, as desks are 94.8% at standard in the QAS tool and 91.9% in the diagnostic research tool). We therefore focus our discussion of the agreement results on the kappa.

Kappa is substantial (0.6 to <0.8) or almost perfect (0.8-1.00) for a limited number of specific items and only among overall and the sub-dimensions for teacher supports at the school level (0.625) and classroom level (0.636). The only items with almost perfect agreement are the class size and the student-teacher ratio. The items with substantial agreement were other hazards, being trained on the curriculum, and the three foundational skills (letter recognition, name writing, and counting to 10).

On the other end of the spectrum were the items with poor kappa (indoor space, kappa=-0.043), and slight (0 to <.0.2) strength of agreement (writing utensils and manipulatives; individualized teaching, correcting misbehavior, discussion, open-ended, relevant pedagogy, and hardworking children). These items were all based on the classroom observation except for children being hardworking, and also would have been potentially time variable (unlike hazards) and subjective. There was fair (0.2 to <0.4) strength of agreement for desks, seating, washing, light, ventilation, 14
**|62.5|64.4|
|**Middle East & North Africa**|62.6|112.3|
|**South Asia**|59.4|113.8|
|**Sub‐Saharan Africa**|100.1|136.4|

# **d. Reality‐check: How do our pre‐BRI estimates compare with other sources?**

In the previous subsections, we presented the method and assumptions we used to construct estimates of shipping time between city‐pairs across the world. We now ask the following question: how do our estimates before the BRI compare with other sources of information regarding shipping time? This allows us to verify the adequacy of our working hypothesis, in particular regarding the speed along different network elements, the border delays and the port processing time. While those estimates cannot be

> 10 For any border, we use the data on “Border Compliance” and the total delay is assumed to be the sum of export time from the exporting country and the import time from the importing country. We do not include documentary compliance as it does not relate to travel time. All data are available at: <u>http://www.doingbusiness.org/data/exploretopics/trading‐across‐borders</u>

9
Figure 9: Passenger cars and GDP per capita <!-- Start of picture text --> .8<br>ISL<br>LUX<br>NZL<br>MLT CAN<br>.6 POL USA<br>ESTSVN DEUAUTAUS CHE<br>ESP BEL NOR<br>LTUGRCPRT JPNFRAGBRFINNLDSWE<br>CYP DNKIRL<br>.4 BGR HRV BHR<br>HUNLVA KWT<br>BLR ISR<br>ROU<br>GEO SUR<br>MEX<br>.2 KAZ CRI CHL<br>MDA<br>TUR<br>TUNMNGAZE GUYZAFTHA CHN SGP<br>MAR COLDOM<br>0 MOZAFG MMR CMR PAK INDNIC LKAGTMNAMPRYPERECU<br>6 8 10 12<br>Log of gdp per capita<br>Number of cars per capita<br><!-- End of picture text --> Source: IRF data for passenger car ownership. Most data points are for 2017, when missing they are replaced by the latest available year between 2014 and 2016.

**Fact 7: As households get richer, they are more likely to own a car.** Figure 10 uses LSMS household data to plot the fraction of households that own at least a vehicle, being a motorcycle or a car, against household expenditures per capita within country. The dashed lines show the density of households by expenditure level. As households get richer, they are more likely to own a vehicle. For Burkina Faso, Mali, Cameroon, Nigeria, and Pakistan, most of the growth in vehicle ownership comes from the acquisition of a motorcycle. For most countries in the LSMS sample, the probability to own a car is almost null for most of the population, except for the richest. The probability to own a car is increasing in a non-linear way, in line with Gertler et al. (2016) who provide evidence of a nonlinear relationship between income and asset ownership as credit-constrained households become more likely to
|-0.0910<br>(0.126)|-0.00385<br>(0.0753)|
|Constant<br>0.203***<br>(0.0386)|0.108***<br>(0.00177)|0.505<br>(0.289)|
|Year Dummy|X|X|
|Region Dummies|X|X|
|Labor and Demographic Controls||X|
|Observations<br>52|52|52|
|R-squared<br>0.091|0.944|0.992|
|Notes: * p<0.10, ** p<0.05, *** p<0.01.<br>_Source_: LFS 2011 and 2013. World Bank author’s ca|lculations.||

Del Carpio and Wagner (2015) find some negative impacts, displacement effects, from the increase in foreign-born from 2011 to 2014. The displacement effect is largest among low-skill, agricultural, and loweducated workers. There are various reasons why results in this paper are neutral compared with those of DCW (2015). Their analysis is based on the 2014 LFS, which exhibits a much larger foreign-born population than the 2013 survey that is used in this paper. Unfortunately, due to changes in survey collection 15
<mark>the “Government Effectiveness” measure from the Worldwide Governance Indicators;</mark> and the <mark>annual rate of inflation (Consumer Price Index).</mark><sup>5</sup> Given the dearth of literature on which firm characteristics best capture the broader determinants of bribery mentioned above, we use several proxy measures. We begin with control for industry choice, as this can significantly affect firms’ profitability and growth (Rand and Tarp 2012). We do so by using dummy variables for the industry to which the firm belongs (Industry fixed effects). In our baseline sample, there are 29 different industries defined as the 3-digit ISIC Rev 3.1 level. Next, we control for (log of) number of full-time workers employed at the end of the last fiscal year ( _Firm Size_ ) and (log of) age of the firm. Larger and older firms may be more susceptible to paying bribes due to their greater visibility/exposure, higher ability to pay bribes due to bigger profits and better access to finance, and fewer outside options due to larger sunk investments. Countering these effects, larger and older firms may be less reliant on publicly provided utilities and services, have stronger political connections and managers who are more skillful in negotiating with the public officials. The net effect of these forces will determine how the age and size of the firm affects the level of corruption it experiences.

A firm’s profitability and, therefore, its ability to pay bribes depends on several additional factors such as management quality, exporting status, foreign ownership of the firm, financial condition, infrastructure availability, and the quality of institutions. Financial condition of the firm and the experience of its top management could affect its ability to deal with corrupt public officials as well its outside options. Exporting firms and firms that have foreign ownership may be more prone to paying bribes, as these firms tend to be more productive and profitable, more visible,

> 5 All
study for this experiment – in which enumerators were randomly assigned - has an R2 for treatment status on observable and personality enumerator characteristics of 0.020 and on fixed effects of only 0.024. This indicates a minimal - if any – bias due to the enumerator effect.

> 24 Other strategies that could improve the quality of enumerator data collection include using alternative sources of data (i.e. administrative data) (Baird and Özler, 2012) and employing list experiments (see for instance Blair and Kosuke, 2012). Yet, the former it is not always a viable option while the latter does not allow for individual level analysis. Future work on how to incorporate private reporting of information, such as handing an electronic data collection device to a respondent to complete a question, could be fruitful.

20
Table 5: EFFECTS ON MATCHING IN TRANSFER NETWORKS

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Panel A:_|_Transfers r_<br>ultrapoor|_eceived by Ul_<br>otherpoor|_tra-Poor Househ_<br>middle class|_olds from ..._<br>upper class|
|Treatment|0.056***|0.036***|0.018|-0.008|
||(0.007)|(0.012)|(0.012)|(0.008)|
|Mean in control|0.128|0.345|0.701|0.260|
|Adj. R-squared|0.439|0.415|0.327|0.407|
|Observations|13464|13464|13464|13464|
|_Panel B:_|_Transfer_|_s given by Ul_|_tra-Poor Househ_|_olds to ..._|
||ultrapoor|otherpoor|middle class|upper class|
|Treatment|0.052***|0.032**|0.054***|0.016***|
||(0.008)|(0.013)|(0.019)|(0.005)|
|Mean in control|0.108|0.252|0.394|0.078|
|Adj. R-squared|0.368|0.306|0.254|0.145|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households. All