rafmacalaba/data-use-mentions-extended huggingface dataset:
.506. Still, Syria appears to have the highest ratio among oil‐based economies that have fuel exports accounting for less than 60% of exports (for which, the average is 0.397).<sup>18</sup> Taking all this into account, we use a “mid‐ point” public capital share of 0.450 for our simulations.

# _2.2.2  Central projections_

The central projected paths of key growth drivers over 2011‐2030 (panel C, Table 1) are based on:

- (i) average I/Y over 2001‐2007 of 21.5% (from WDI), and public investment share of total investment of 40%;

- (ii) average growth rates over 2001‐2010 for TFP of1.4% (according to The Conference Board 2018 and our own calculations following Kim and Loayza 2018) and human capital of 1.0% (following PWT 9);

- (iii) United Nations (2011) projections for growth rates of total population of 1.6% and of the working‐age population share of 0.6% (as discussed in Section 1.1); and

- (iv) zero growth in the LFPR (i.e. the 2010 participation rate level is maintained) given some signs of stabilization (and possible increase) from the 2013 ILO‐modelled estimations and projections.

> 14 4.25% for low‐income countries and 8.30% for middle‐income countries as of 2015.

> 15 In steady‐state/equilibrium, output grows at the same rate as capital stock, which allows us to write,

> � � � � ~~�~~ � ~~�~~ /�g� � δ � where g� is average output growth and δ the depreciation rate.

> 16 Investment‐to‐GDP and GDP growth are averages of WDI and WEO data (1981‐2007 and 1981‐2010
These estimates do not take the lower education levels of refugees into account, since this variable is missing in our main sample. This information is available in the Structural Survey, which is carried out yearly since 2010 and covers each year approximately 2.5% of the Swiss population. We match the education data from the Structural Surveys 2010–2017 with the register data in our main sample. This restricted sample contains less than one fifth of the refugees in the main sample. To see if the individuals included in this restricted sample are representative of the entire population of natives and refugees, we first run the same regression as in column (4) for the main sample. Results, which are given in column (5), are close to those of the main sample, which makes us confident that the effect of education can be reliably estimated. When education dummies are added in column (6), results give a more optimistic picture of the integration trajectory: in the long run, employment rates of refugees are 15 percentage points lower than those of natives but it takes more than ten years after arrival in Switzerland to reach this level.

To gain more insight into the role of education in the integration process, we estimate integration profiles separately by level of education (see Table A.2 in the appendix). The employment rates of refugees with an elementary level of education (Secondary I) converge more rapidly toward the long run level, which is reached within 9 to 10 years. By contrast, the minority of refugees who have higher education levels (Secondary II, Tertiary) see their employment rates start at a lower level and converge more slowly toward the long run level (it takes more than 12 years to get there). This might be explained by the difficulty of having foreign diplomas recognized in Switzerland or, especially in the case of younger refugees, the time it takes to complete further education in Switzerland. Our data does not allow us to discriminate between these two explanations.

Some of
Page 8 of books owned by the household; and an index of household socioeconomic status, were all strongly and positively correlated with student performance on final examinations.

# **_1.c.3. Findings from multivariate correlation analysis_**

26. The last section in Part II examines the robustness and relative importance of the correlations discussed above. For each measure of school performance – the drop-out rate, repetition rate, failure on exams, and student excellence in math and Albanian - four regressions were conducted, each including only one of four categories of independent variables: (1) school resources; (2) “top-down” accountability and transparency; (3) participatory “bottom up” accountability and parental involvement; and (4) family and community background. The correlation results are presented in Table 8.

27. School resources and top-down accountability structures appeared less significantly related to measures of school failure – drop-out rates and repetition - than parental involvement. There were very few correlates with drop-out, repetition and failure rates among school physical and human resources. The only significant results were a positive correlation between rural location and failure rate, and the negative relationship between teachers‟ motivation and drop-out rate. None of the top-down accountability and transparency measures appear to be correlated with drop-out, repetition and failure rates. In contrast, the number of school board meetings and whether the school director usually participates and votes in the meetings were, respectively, negatively and positively correlated with drop-out and repetition rates. Moreover, lack of parental involvement in the school, as assessed by all school stakeholders, was positively correlated with students‟ repetition rate. Among the proxies for the students‟ family background, the only significant result was a negative correlation between fathers‟ education and repetition rates.

28. School resources, teachers‟ characteristics and inspections, were more robust and significantly correlated with students‟ excellence in math and language. In particular, the percentage of students excelling in Albanian was significantly higher in urban schools which suffer fewer days with no electricity
--> Figure 6 shows that green imports are as important (as a share of all imports) in developing regions as they are in high-income countries. This indicates the international transfer of green technology as embodied in green consumption goods. Further, inasmuch as some of these green goods are used as inputs, this also indicates the ̳greening‘ of the input mix, which may reflect adoption and adaptation of

> 10 The lack of good data to measure the diffusion of existing green technologies that lack sufficient global novelty for patenting, beyond product-level trade data, is also highlighted by Popp (2011). Dechezleprêtre et al. (2010) use green patent applications data to analyze the international diffusion of patented inventions (the number of patents invented in Country A and filed in Country B) as an indicator of the number of innovations transferred between these countries – an interesting empirical exploration of a small part of diffusion but a poor proxy for broader technology diffusion and adaptation across and within countries.

> 11 Details of our measurement of green trade are given in the data annex. Note that the underlying technologies embedded in these green goods and services are much broader than in the patent discussion, which look only at GHG mitigation technologies. These trade-based results are therefore not directly comparable to the patent results. Note also that an increase in a country‘s aggregate output of a green product does not necessarily reflect an innovation since it could be by firms already producing that good. Nonetheless, changes in aggregate green output are correlated with the introduction of new-to-the-firm green goods.

> 12 This is true even when looking at the absolute volume of green exports.

8
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _14_ seeks to track inflation over a long time period using a broad-as-possible food basket, a 25-item version that starts in 2017 that tracks a narrower basket of items that have better data coverage. There are minor item differences in the basket specification and data coverage, see Table 2.

Table 2—Summary of raw food price data in Papua New Guinea.

||Coverage|12-month|Items|Markets|
|---|---|---|---|---|
|2009-2022|36.96%|50.71%|25<sup>_∗_</sup>|8|
|2014-2022|29.17%|52.17%|25<sup>_∗∗_</sup>|8|
|2017-2022|39.01%|55.47%|27<sup>_∗∗∗_</sup>|7|
|FCS average|51.47%||7|27|

_Note:_ The first column reports the study period, the second column reports the data coverage across the full study period, the third column reports the data coverage in the last 12 months, the last columns reports the number of markets modeled out of the total number of markets included in the data snapshot. _∗_ Basket consists of 1 unit of each: _Aibika, Amaranthus-Aupa, Banana-Cooking, Banana-Ripe, Broccoli, Cabbage-English, Capsicum, Carrot, Cassava, Choko-Tips, Cucumber, Ginger, Lemon, Lettuce, Mandarine, Onion Bulb, Orange, Pawpaw, Peanut, Pineapple, Sweet Potato, Pumpkin-Tips, Taro True, Tomato, Watermelon. ∗∗_ Basket consists of 1 unit of each: _Aibika, Amaranthus-Aupa, Banana-Cooking, Banana-Ripe, Broccoli, Cabbage-English, Capsicum, Carrot, Cassava, Choko-Tips, Cucumber, Ginger, Lemon, Lettuce, Onion Bulb, Orange, Pakchoi, Pawpaw
# **Appendix B. Data Cleaning**

Following Bureau van Dijk (2011), Kalemli-Ozcan et al. (2015), Cusolito and Didier (2020), and Kalemli-Ozcan et al. (2023), we document the steps we apply to clean the information.

1. _Fill time-invariant data gaps_ : for a given BvD.ID-year combination, with BvD.ID standing for firm unique identifier, replace missing highly-likely time-invariant information with information available for previous years (e.g., US SIC code, NAICS, NACE, NACE main sector, company name, city, region, postal code, legal form, incorporation date, thicker, isin). To perform this step, the team first worked with auxiliary raw tables, which collect legal and sectoral information of the firm, and collapsed the time-invariant variables at the BvD.ID level.

2. _Harmonize timeframe_ : convert variable closedate from string to numeric format. Then create a new variable, name it year, and assign a year to the observation according to the following rule. If closing month corresponding to the observation is June or any other month after June, then make Year take the year reported in closedate. Otherwise, make Year the year reported in closedate minus 1.

3. _Drop duplicates_ : the raw database presents a large number of duplicates at the BvD.ID-year level. The team noticed that the information was the same, except in the SIC primary code variable. Thus, we collapsed all the SIC primary codes reported by the same BvD.ID-year in one variable, using semicolons to list all the SIC primary codes, and eliminated duplicates.

4. _Drop firms with missing relevant information_ : drop all the firms with no information for the following set of variables: US SIC code, NAICS, NACE core code, NACE main sector.

5. _Drop observations with missing information for the currency code_ : eliminate observations with missing information for the currency code.

37
10 Second, there appears extensive flooding in the Mekong River Delta region almost every year. Third, 2005 was likely the worst year for riverine and coastal flooding. Fourth, riverine flooding was further observed on and off in the other regions, in 2002 and 2005 in the northern uplands, in 2003 and 2005 in the Red River Delta region, and in all years, but scattered around the region in the Southcentral Coastal. Consistent with the events during 2001‐2006, the historical record shows that riverine floods happen most frequently in the Mekong and Red River Delta (Figure 7).

# _Cyclone maps_

Tropical storms approach Vietnam from the east. They typically arrive during the Southeast and Northeast monsoon (Christiaensen et al., 2009), with 90 percent of them occurring between June and November, and almost half of them coming ashore in the northern part of the country. To generate annual cyclone maps, a GIS dataset of areas affected by hurricane force winds was developed from the UNEP/GRID‐Europe (2007a, 2007b) tropical cyclones databases. In particular, the storm track and wind speed data were used to create symmetric polygons about each path, based on the algorithm by Klotzbach and Gray (no date).<sup>21</sup> The areas exposed to hurricane force wind (of 120 km/hour) in Vietnam between 2001 and 2006 are in Figure 8, and the frequency with which they have been exposed between 1980 and 2006 is depicted in Figure 9. During the first three years, the tropical cyclones did not affect the land much, though the impact was larger in 2004 to 2006, with two separate cyclones with hurricane force winds hitting the country in 2006. The coastal areas in the Northcentral coast and the Northeastern Uplands appear most exposed to cyclones, with hurricane force winds occurring once every five years. Visual comparison of Figures 4 and 8 suggests that heavy rainfall was caused by the cyclones in some cases, though not in all. There appears less correlation between coastal
# _3.2. Changes over time_

Almost all South Asian countries have a very large number of young people reaching working age every year. Between 2005 and 2015, the number of South Asians aged 15 and above grew by 1.8 million per month, a trend that will only moderate gradually over time. In relative terms, the share of the working-age group in the total population increased by over 1 percent per year in Bhutan, and by around 0.5 percent in Bangladesh, India, and Pakistan (Figure 6). At the same time, the employment rate decreased on average more than 1.5 percent per year in Bhutan and India and by more than 0.5 percent per year in Bangladesh. Since income per capita grew considerably in all these countries from 2005 to 2015, some decline in the employment rates could be anticipated. However, the declines in employment rates are much larger than can be explained by increasing incomes per capita alone (World Bank 2018a). This contrasts with the experience of many East Asian countries like the Philippines, Vietnam, Malaysia, or Indonesia, for which the employment rate even increased between 2005 and 2015.

**Figure 6.** Changes in the working-age share and the employment rate from 2005 to 2015.

<!-- Start of picture text --> Annual change in percent<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>Working age rate Employment rate Predicted on GDP per capita growth<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

**Figure 7.** Changes in employment rates from 2005 to 2015, by gender.

<!-- Start of picture text --> Annual change in percent
could provide approximately 29 and 25 lumens of light, respectively. In comparison with traditional lighting devices used in Bangladesh, the kerosene-based open-wick lamp and the covered-wick lamp (“Hurricane” lantern) produce, on average, approximately 7.8 and 45 lumens of light, respectively (Mills and Jacobson, 2008).<sup>2</sup>

# **Sampling procedures**

We selected children who were attending primary and secondary schools as our target population for this study. More precisely, as very few variations in schooling performance or study hours were expected at the elementary level, we focused on children who were enrolled in the 4<sup>_th_</sup> to 5<sup>_th_</sup> grades of primary school and 6<sup>_th_</sup> to 8<sup>_th_</sup> grades of junior high school.

To select children (and their households) to participate in this study, we began by listing all of the primary and secondary schools located in the _Chars_ of the Gaibandha and Kurigram districts of northern Bangladesh; we then conducted quick initial inspections of the teachers and School Management Committees of these schools. To obtain internally valid estimates, without contamination effects, among all 28 schools in the eight _Chars_ listed, we restricted our sample to 17 schools in two _Chars_ , where SHS services provided limited coverage. Out of 1,665 total students belonging to the 4<sup>_th_</sup> to 8<sup>_th_</sup> grades in 17 schools, we initially selected 1,292 children as potential candidates for this study. The remaining 373 students were excluded, either because their homes (including school dormitories) had already been electrified, or because a sibling was one of the 1,292 students already chosen (i.e., for each household, we included only one child in our study sample). Potential participants were also excluded if their school attendance rate was lower than 80%
Lanka than the original sample, which was heavily skewed toward microenterprises.<sup>18</sup> While the SLLSE booster is still over-representative of smaller firm employment relative to the universe of establishments as approximated by the 2004 Census (only 12% of workers in the SLLSE sample are employed in large firms with +100 employees versus 64% of workers in the Census), there is representation of all size classes in the sample. Summary statistics are provided in Table 9. The types of innovation examined are whether the firm: (1) introduced new products or services; (2) significantly improved an existing product (this question was only available from the SLLSE); (3) introduced new or improved business processes; (4) introduced new organizational innovations; and (5) introduced new marketing innovations. The comparable 2011 WBES innovation measures and business environment indicators are systematically higher than the 2009 SLLSE variables, typically at least double in value: introduction of new goods or services (31 versus 9% of firms), introduction of a new or improved business process (44 vs 9%), use of internet (45 vs 21%), and share of sales made on credit (31 vs 18%). This is no doubt linked to the different composition of firms by size and industry: while 26% of firms in the 2011 WBES are medium and large, only 6% are of these larger sizes in the SLLSE; and the relative share of manufacturing relative to services firms is 60:40 for the WBES versus 20:80 for the SLLSE, with a significantly larger number of SLLSE firms being in the typically less innovation-intensive or less skills-intensive retail and wholesale trade, hotels and restaurants, and construction service sectors.

# **Methodology and empirical model**

Motivated by the model of innovation proposed by de Mel et al. (2009), we test the correlation of both firm/owner-specific and business environment indicators with various measures of firmlevel innovation. Our basic empirical
4 each percentile of Brazil‟s income distribution. The net effect is computed as the sum of three components: an “expenditure effect”; a “market income effect”; and a “transfer income effect”.

The expenditure effect is estimated as each household‟s compensating variation, in the standard way: the difference between the expenditure functions at the new and old price vectors. Estimation of the market income effect takes advantage of the detailed information available in the Brazilian household survey on the specific agricultural activity (by crop) where each person works, and relies on different scenarios for the pass-through from food prices to the wages of workers engaged in the production of specific agricultural commodities. Finally, the transfer income effect accounts for the fact that the Brazilian government increased the transfer amounts for two important social assistance benefits (at least in part) in response to the „food crisis‟: Bolsa Família and the Benefício de Prestação Continuada (BPC). Using pre-crisis data on transfer recipients, we impute the expected effect of the rising transfers.

Our main findings are that, despite living in one of the world‟s largest food exporters, most – but not all – of Brazil‟s population experienced a decline in welfare as a result of the price shock in 2007-2008. The expenditure effect is large and regressive, with an average compensating variation of 7% of baseline expenditure, and a range from 11% at the lowest percentiles to just under 2% at the top of the distribution. On the other hand, both market and transfer income effects are progressive, with larger gains at the bottom of the distribution. These effects are small in large urban areas, but can be substantial in rural areas: If agricultural wages rose in the same proportion as food prices, this would lead to falling poverty in Brazil‟s rural areas. For the country as a whole, the net effect is U- shaped, with small welfare gains for the bottom ventile of the population, and the largest losses
using household consumption data and refers to those households that belong to the highest monthly per-capita consumption expenditure group<sup>11</sup> .

# **_Institutional Variables_**

I also control for the quality of institutions within the location. I include a dummy variable which is set equal to one for states with labor laws rated as pro-business by Besley and Burgess (2004). While labor regulations are mainly legislated and enforced by state governments, they also have an important effect on the cost of contracts at the district level. I also include a district-level variable on the frequency of riots and social unrest per capita across different years as a proxy for social institutions. This information is drawn from Marshall and Marshall (2008).

In summary, the firm characteristics and economic geography variables are supplemented with controls for infrastructure (transport, education, electricity and telephone), input costs (wages) and institutional variables (flexibility of labor regulations and social unrest).

> 11 The actual consumption category differs depending on the year of the survey, the type of district (rural or urban) and the population of the district.

11
**Figure 5. Level of GDP per capita and share of urban informal employment**

<!-- Start of picture text --> 100<br>BEN<br>90<br>80 NERTCDGNBTGOBFAMLIGIN SEN CIV<br>BDI UGA<br>70 MWILBR CMRSTP GHA<br>SLE<br>60 ZMB AGO<br>ZWEKEN GAB<br>50<br>NGA<br>40<br>SWZ<br>30<br>20 ZAF<br>10<br>0<br>0 2000 4000 6000 8000 10000 12000 14000 16000<br>GDP per capita, PPP (constant 2017 USD)<br>Informal urban employment (%)<br><!-- End of picture text --> Source: Own elaboration based on GMD data and World Development Indicators (World Bank, 2021). Notes: Informal employment includes unpaid workers, LE-SE and informal wage employees.

The GMD data allow us to observe short-term informality dynamics in five countries. Using comparable data from Cameroon, Ghana, Liberia, Uganda and Zambia that was collected at least two years prior to the most recent survey, we calculate the distribution of urban informal workers by employment types for previous years. Since the time between observations differs by country, we generate the average annualized change in the share of workers in each employment type.

Non-parametric estimates find that informality trends are not uniformly increasing or decreasing across this subset of African countries over the short term, suggesting that the urban labor force does not uniformly move toward formality. As indicated in Table 1, the share of urban informality decreased in three of the five countries with annual rates of change ranging from –0.5 percent in Cameroon to –5.0 percent in Liberia. In contrast, informality increased in Uganda by an annualized 4.1 percent and in Zambia by an annualized 0.9 percent. The composition of the informal sector changed in different ways across countries.<sup