rafmacalaba/data-use-mentions-extended huggingface dataset:
The analysis in this paper relies on the estimation of production functions and the use of TFP as a proxy measure for technology transfer. Our reliance on TFP as the dependent variable assumes that the adoption of new technologies will, with some lag, lead to an improvement in productivity. A serious problem with this assumption is that the case study literature has documented that such productivity improvements are dependent on the technological abilities of domestic firms. Nelson and Pack (1998) have demonstrated that the production function methodology can underestimate or ignore the role of technological effort at the level of the firm, and thus affect TFP growth estimates. Differences in technological capacity across firms in an industry may be an important determinant of TFP performance, but we do not have information on this at the level of the firm-data on technology-relevant variables such as R&D expenditure or the composition of the workforce are not available at the level of the firrr.. However, the Czech Republic is not a developing country-it has a long-standing industrial base and is well-endowed with engineering and scientific human capital. For the economy as whole, therefore, the capacity to rapidly upgrade productive efficiency through the adoption of best practice techniques (both hard and soft) should be considerable. Note that this makes the Czech case less relevant as a comparator for developing countries that do not have equivalent endowments.

# **III. Profile of Czech Firms**

Information on Czech enterprises was compiled for the 1992-97 period from surveys using a questionnaire prepared by the authors and a database containing financial and ownership information. Financial variables were defined using international accounting standards from the onset of the survey in 1992. The database comprises 513 firms quoted on the Prague stock 8
0.8|
|Other Southern Africa|0.3|0.2|4.7|4.0|
|Other Sub-Saharan Africa|0.3|0.3|2.2|2.1|
|Sri Lanka|0.2|0.2|10.8|10.3|
|OtherSouth Asia|0.2|0.2|8.8|8.6|

a “Without protection” refers to what production would be without subsidies and tariffs in the cotton, textile and clothing markets globally, as generated by the GTAP model b Same list of countries as in Appendix Table A3 and sorted according to region’s share of global textile production in 2001. Regions with less than 1 percent share of the global market are not shown. They are Malawi, Mozambique, Nigeria, Other SACU, Singapore, Uganda, Viet Nam, and Zambia.

Source: GTAP database 6.05 and authors’ GTAP model simulation results
interactions which, in turn, makes conflict more likely. Conflict, on the other hand, destroys trust. The incidence of conflict can be reduced by policies abating cultural barriers, fostering inter-ethnic trade and human capital, and shifting beliefs. In this view peacekeeping forces by themselves or externally imposed regime changes, have no enduring effects.

Rohner et al. (2013a) bring this idea to data on trust and ethnic identity in Uganda. Using individual and county-level data, they document large causal effects on trust and ethnic identity of an outburst of ethnic conflicts in 2002–2005. Using data from the Afrobarometer and ACLED they find that more intense fighting decreases generalized trust and increases ethnic identity. Controlling for the intensity of violence during the conflict, they also document that post-conflict economic recovery was slower in ethnically fractionalized counties. Again, the interpretation of this fact is that trust is needed for economic activity and that this trust breaks down across ethnic groups.

69
8. Calculate simulated total per capita consumption in 2016 that includes non-collected items and express it in 2022 prices, as in equation (9).

9. Calculate poverty and inequality using the new consumption aggregate.

10. Repeat 1,000 times all steps 1 to 9.

This process allows for estimating confidence intervals for poverty and inequality. A new LASSO model is fit for every bootstrap sample of the data, and the whole consumption vector is constructed via the improved LASSO PMM process described above. In this exercise, a thousand bootstrap repetitions were implemented. As a result, both point and interval estimates are calculated for every welfare indicator.

# **III. Limitations**

Despite the strengths of the matching approach implemented in this analysis, several limitations must be considered. First, the imputation process accounts for missing data, as well as for data collection and fieldwork improvements between the survey rounds. These last enhancements can introduce additional sources of variation in the imputed data, which may affect the accuracy of the imputation results.

Another limitation is that certain items collected in both surveys have changed. For example, rice, categorized as fine, medium, and coarse in previous rounds, was divided into six categories in 2022, primarily for capturing quality. This change in categorization introduces complexities in the imputation process and may impact the accuracy of the imputed consumption values for these items. The categorization in 2022 may align differently from the previous categorization, which could introduce discrepancies in the imputed data.

Furthermore, the 2022 poverty lines were defined at the domain level, whereas the 2016 (and 2010) lines were defined at the stratum level. This difference in the level of aggregation poses a challenge in determining the poverty status of each household during the imputation procedure. The imputation process involves identifying donor households in the 2022 survey and assigning their shares and domains to the corresponding 2016 (2010) households. This additional step introduces an element of uncertainty in the imputation and may affect the accuracy of poverty estimates.

Additionally, the
# **Remote learning with the World Bank's High-Frequency Phone Surveys**

The World Bank’s High-Frequency Phone Survey (HFPS) is one of the most ambitious data sets collected immediately after the onset of the pandemic. The surveys were carried out in over 100 countries, including 13 in Latin America. They offer real-time information on the situation and behavior of households during the first months of the pandemic, including information on remote learning strategies followed during school closures, such as online sessions and assigned homework.<sup>12</sup> Using microdata from the first three rounds of these surveys—carried out between May and August 2020—, Marchionni _et al_ . (2022) show the estimated probability of children engaging in different remote learning activities by parental education. Unfortunately, due to the small samples, estimates are available only for the pool of Latin American countries.

In this section we use the estimated probabilities in Marchionni _et al_ . (2022) to help estimate instructional losses. Specifically, we compute _k_ as: where  _i_ is the predicted probability of student _i_ engaging in remote learning estimated with data from Marchionni _et al_ . (2022). Results are similar to the benchmark case discussed above (second column in Table 3). The educational loss is strongly decreasing in the socioeconomic status of the household. As expected, the gradients are somewhat smaller when using HFPS data, due to a higher degree of aggregation in the information we use for the calculations.

> 12 Variables in the HFPS were harmonized by the World Bank, which helped foster a growing literature (Ballon et al. 2021; Cucagna and Romero 2021; Khamis et al. 2021; Kugler et al. 2021; Mejia-Mantilla et al. 2021).

15
(0.059)|(0.043)|(0.044)|(0.038)|(0.038)|(0.020)|
|Sub Saharan Africa|-0.096**|0.060|-0.008|0.047|-0.086*|-0.055**|
||(0.042)|(0.056)|(0.050)|(0.047)|(0.049)|(0.025)|
|Observations|4,454|7,427|7,427|7,427|7,427|5,952|
|Pseudo-R<sup>2</sup>|0.1301|0.0576|0.1919|0.1432|0.0549|0.0953|

Note: *** 1%, ** 5%, and * 10% level of significance.

15
084|2,084|2,084|2,084|2,084|
|R-squared|0.163|0.296|0.310|0.312|0.309|0.310|0.309|0.312|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is log consumption of household. Data used are from the three waves of the GHS and telephone survey for conflict. The conflict recall is one year. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xxii
For each product section, we also show the changes in value transported by air versus by sea. For many products, the quantity effect is larger by air, excepting chemicals, foodstuffs, and transportation equipment. Our story can be further elaborated by returning to the heterogeneity across trading partner income, but restricting the sample to manufactures (table 7). We additionally consider China and Sub-Saharan Africa, two regions where researchers have hypothesized that the trade fall was unique. As mentioned above, it has been theorized that the trade finance constraint could have been either much more severe or much less severe in SubSaharan Africa. China has been unique both for its more rapid recovery and because it has been a target for protectionism (Bown 2009).

For all four income levels, and for both China and Sub-Saharan Africa, quantity effects were large and negative. This is not surprising; the demand effect hit all countries. The price effect is positive for all regions, with the exception of exports from Sub-Saharan Africa to the US and the EU. The consistent positive association between price changes is striking.

# **3.3 Evidence on Credit Constraints and Trade Collapse**

This section presents the only direct evidence linking credit constraints with upward pressure on manufacturing prices during the crisis. We adopt the classification scheme of Bricongne et al (2009) to separate products according to sectoral dependence on external finance. The Bricongne et al index is based on recent firm data from France and combines two variables: cash flow over value (as a proxy for self-financing capacity) and financial charges over turnover (as a proxy for dependence on external finance). Accordingly, we separate all products into two categories: high dependence on external finance (taking a value of 6 to 10 in the index) and low 20
</sup>||||0_._122<sup>_∗∗∗_</sup>||
||||(0_._012)||||(0_._012)||
|Employed in Industry|||0_._308<sup>_∗∗∗_</sup>||||0_._276<sup>_∗∗∗_</sup>||
||||(0_._012)||||(0_._011)||
|Employed in Services|||0_._382<sup>_∗∗∗_</sup>||||0_._349<sup>_∗∗∗_</sup>||
||||(0_._008)||||(0_._008)||
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Adjusted R2<br>|0_._509<br>|0_._655<br>|0_._671<br>|0_._747<br>|0_._530<br>|0_._663<br>|0_._676<br>|0_._758<br>|
|Nr. of countries<br>Nr. of hh<br>1|16<br>34202<br>1|16<br>34202<br>1|16<br>34202|16<br>59863<br>1|16<br>34202<br>1|16<br>34202<br>1|16<br>34202|16<br>59863|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1 . Robust standard
two measures of implementation efficiency at the village-level. According to qualitative interviews with beneficiaries, local program staff, school teachers, and health staff (see <u>Adato et al. 2000),</u> one major source of inefficiency in program delivery was the delays in the delivery of the form for school attendance monitoring (E1 form), and the associated delays in the payment of scholarships. First, we use program administrative data on the monetary transfers (for scholarships and school supplies) delivered to eligible households to compute the number of months from incorporation until the first disbursements were made to the beneficiary 27
OPTIMAL SIMPLE FISCAL RULES 16 <!-- Start of picture text --> Welfare Loss Consumption of the Ricardain HH<br>6 0.3<br>BBR<br>5 SSR 0.25<br>4 0.2<br>3 0.15<br>2 0.1<br>1 0.05<br>0 0<br>0.45 0.9 0.45 0.95<br>annual persistence of oil-price shock<br>annual persistence of oil-price shock<br>0.3 Consumption of the Hand-to-Mouth HH 4 Public Assets<br>0.25<br>3<br>0.2<br>0.15 2<br>0.1<br>1<br>0.05<br>0 0<br>0.45 0.9 0.95 0.45 0.95<br>annual persistence of oil-price shock annual persistence of oil-price shock<br>annual std deviation<br>% of steady state consumption<br>annual std deviation annual std deviation<br><!-- End of picture text --> Figure 2. **Welfare loss and shock persistence with BBR and SSR:** baseline calibration and constant variance of commodity price shocks.

**Countercyclical Transfers** Kumhof and Laxton (2013) find that countercyclical fiscal policy — — where a fall in output leads to an increase in transfers leads to a substantial increase in welfare. Here we add counter-cyclical transfers to both BBR and SSR in Column 4 and 5 of Table 2, and find only a small improvement in welfare (by 0.03-0.05).<sup>19</sup> The reason is that shocks to non-resource GDP are relatively small and not very persistent, and so even with a sub-optimal policy they generate little welfare loss. Countercyclical transfers are optimal because the temporary nature of the non-resource shocks means that
respectively. The 2018 survey reported that 80 percent of the violence experienced by teenagers was perpetrated by their peers in the school and play community.

A field visit interview with a district education officer in Banjarmasin, South Kalimantan, confirmed the finding that bullying is an issue, mostly for boys, and it can lead boys to drop out of formal school and enter non-formal programs. Though bullying may be a larger issue for boys, violence against girls is a noted issue. In Sampang District, an education official noted that girls are given more care because they need physical protection, particularly during junior secondary when they are maturing. The care includes supervision at school and on the way home, as girls may be harassed on the roads in rural areas. In Sampang, teachers at a junior secondary school noted that the graduation rate of boys is lower than girls at primary and junior secondary levels. At a junior secondary _madrasah_ in Sampang, the principal noted that boys drop out at higher rates due to social influences that contribute to increased absences and that, in their perspective, girls are better at resisting such influences.

During field visits conducted for this study, principals and district education officers noted that boys drop out of school for financial reasons at the senior secondary level, and an array of other forces contributed to dropout, including bullying, an unwillingness or lack of interest in attending school, a focus on video games and drug use, as well as a lack of parental oversight. Lack of parental oversight was cited more often in rural areas as part of this survey and was often coupled with complaints of parents not understanding the importance of education due to low levels of educational attainment in the parents’ generation.

Records from MoWECP’s Simfoni PPA ( _Sistem Informasi Online Perlindungan Perempuan dan Anak_ ), a national online information system dedicated to the protection of women and children, show that in the first three months of 2020 there were already 1,489 victims of violence, where more than three-fourths are women (78 percent)
As a potential extension of the exercise presented herein, the authors recommend that a full probabilistic catastrophe risk model be used to replace the example approach applied above for the hazard module. This would entail development and application of a stochastic model of rainfall that could be applied into the LEAP framework to produce values for actual evapotranspiration in the index calculation. The model would need to be compatible with the time and geographic resolution of the derivation of the vulnerability/damage functions (see Appendix) in order for these to be applied. Sensitivity analyses could be applied within the hazard modelling to consider potential outcomes in the longer term under climate change scenarios. For example, increases in the rates of occurrence of extreme rainfall variability could be used to look beyond the near term view. Similarly, projections of population increase and composition change could be applied to the exposure dataset to demonstrate different future outcomes.

Data paucity is a restricting factor in the methodology and analysis applied, and also in the interpretation of results. It would also be interesting to see how the drought‐consumption relationship, and the attenuation of this by household and community characteristics, differs for other geographies with sufficient data to support this type of analysis.

29
randomly distributed, we expect that accounting for the informal sector will systematically alter the structure of the observed network. A first piece of suggestive evidence is that regions with higher levels of informality have fewer observed firm-to-firm links in the VAT data, even after controlling for population and travel time to metropolitan areas (Table A1).

# **Informal-sector shares correlate negatively with regional economic size and income.**

First, we explore the spatial distribution of informal firms, which we find predominantly reside in smaller markets. Figure A4 plots the distribution of formal sector shares across counties, measured using both value-added and employment metrics. The graph shows that in most counties, the formal sector accounts for less than 20% of economic activity.

We find a strong correlation between a county’s formal sector share and both its economic size (measured by Gross County Product) and income level (measured by Gross County Product per capita). As shown in Figure A5, economic size alone explains between 35% and 52% of the variation in formal sector shares across counties. This pattern is consistent across all three measures of economic activity: employment, value added and the number of firms.

To validate that this positive correlation between market size and formal sector share is not merely an artifact of the administrative data, Figure 5 presents correlations between Gross County Product and three additional employment-based formality measures that do not rely on the administrative data. Notably, while more stringent definitions of informality yield flatter slopes, the _R_<sup>2</sup> remains stable. This consistency suggests that economic size explains similar proportions of county-level informality variation regardless of the measurement approach.

# **The incidence of informality systematically varies across sectors.**

Beyond geographic patterns, informality also varies systematically across sectors. Figure 6 compares a sector’s value added (from administrative data) with its contribution to Kenya’s GDP (from national accounts). Manufacturing and business services show the closest alignment between these measures, which suggests that the bulk
25. Differences in the economic performance of East Asia and Latin America can also be illustrated using EXPY and PRODY indicators.<sup>9</sup> EXPY is a measure of the export sophistication of the product basket for each country. Technically speaking, it is the sum of the country's export share of each product weighted by the PRODY measurement for that product. The PRODY index is the sum of weighted per capita GDPs of all nations for each product, with the weight being the country's export share for the product divided by the aggregate of all nations' export share for the product.<sup>10</sup> 26. Figure 7 shows that China has been steadily upgrading their export basket during the past twenty years. The EXPY of Mexico, the highest in Latin America until 2010, slowly increased during the 1990s but stagnated in the past decade. Costa Rica, thanks to the establishment of the Intel semiconductor assembly and test plant in 1996 (Box 1), has leap-frogged all of Latin America to become the nation with the highest EXPY in the region. Colombia has also upgraded its export basket, achieving the same increase in EXPY as China and India but in less time. Argentina and Brazil‘s EXPY‘s, however, have stagnated during this period. On the bottom of the spectrum is Chile, whose EXPY has actually declined in the past decade, despite strong economic growth, owing to excessive reliance on copper and other commodities.

<!-- Start of picture text --> Figure 7: EXPY Trends for Latin America and China<br>20000<br>Costa Rica<br>18000<br>Mexico<br>16000<br>Argentina<br>14000 Brazil<br>Chile<br>12000<br>Colombia<br>10000<br>China<br>8000<br>Source: World Bank calculations based on Comtrade.<br>EXPY<br>1991 1992 1993 1994 1995
1980-89**|**1990-99**|**2000-04**|**2005-08**|**2009-13**|
|---|---|---|---|---|---|
|Turkey|0.2|0.4|0.8|3.0|1.6|
|Eastern Europe and<br>Central Asia median|0.6|1.7|3.6|6.2|3.0|
|Brazil|0.7|1.5|3.4|2.5|2.9|
|China|0.6|3.9|3.2|4.7|4.0|
|Malaysia|3.2|5.8|2.7|3.9|3.3|
|Mexico|1.2|1.9|3.1|2.4|2.3|
|India|0.0|0.4|0.9|2.1|1.8|
|Russian Federation|-|0.7|1.5|3.7|2.9|
|South Africa|0.0|0.6|1.7|2.1|1.6|

Source: World Development Indicators using World Bank MacroStats to Go 2
9 T|
|Part I|-1.7 T|-4.6 T|-7.7 T|
|IBRD|-2.4 T|-4.9 T|-7.5 T|
|IDA/Blend|-0.3 T|-0.5 T|-0.7 T|
|Grand Total|-4.3 T|-10.0 T|-15.9 T|

Source: Authors’ calculation. Aggregate economic cost of foregone earnings at present value (2017 PPP $). Simulation 1 results based on latest available LAYS of 157 countries (unweighted average), with the change in LAYS expressed as the global aggregate economic cost at present value of students’ foregone earnings.

50
who mistype web addresses or those who stumble on a broken link. While we are unable to quantify this bias, its direction is unclear. While those who access the Internet regularly are more likely to click on broken links, those who access the Internet sparsely are also likely to mistype addresses because of their unfamiliarity.</mark>

> 19 A possible reason why respondents in the Internet surveys have higher access to education than others is that those with access to Internet itself are more educated than those without access (Schonlau et al., 2009).

13
that non‐participants present a target group that potentially has the most marginal added value to participation but are at the same time the most difficult group to entice into these types of events.

Although there is strong evidence of self‐selection into screening events, Table 3 shows that the drivers of this selection across screening events appear to be the same. For those who participated, we see balance across observable characteristics, which is in line with the fact that all screening events were marketed in the same way with the same characteristics. This balance of selection across events supports the possibility of comparing attendees against each other, rather than needing to rely on the intention to treat estimates.

# **_Attrition_**

The attrition rate in this study is 21.1%, which is relatively high compared to other household surveys (e.g. EFInA 2010 had an attrition rate of 6%), but within reason when compared to enterprise surveys. Intensive efforts were made to reach all respondents who were listed at the baseline, but around 12% could not be contacted again, some refused to be re‐interviewed (2.9%), and very few (0.3%) were unable to 11