rafmacalaba/datause-encoder-data huggingface dataset:
problems.<sup>31</sup> Nevertheless, it should be noted that the point estimates for these two variables are very small, suggesting that they have a small explanatory power when comparing incidence across firms operating within the same country, city and detailed sector of activity.<sup>32</sup> Third, we investigate whether the positive correlation could be driven by SMEs being less profitable or having a lower access to external finance than larger firms. In our data we observe the share of reinvested profits and the access to external finance and see that SMEs have lower shares of reinvested profits and also lower access to finance.<sup>33</sup> Both firm-level and industry-level studies suggest that small firms do relatively better compared to large firms in countries with better-developed financial institutions (see e.g., Beck et al, 2008).<sup>34</sup> The point estimates in column (5) and (6) of table 7 shows that the positive correlations in the intensity to train and the firm size are robust to controlling for these differences across firms. Moreover, we always find a robust positive correlation between incidence of job training on the one hand and the share of reinvested profits and access to external finance on the other. This finding shows that improving the access to finance matters to foster the investment in human capital independently of firm size. Moreover, controlling for these differences in columns (5) and (6) leaves almost unchanged the differences cross small and large firms in the intensity to train reported in column (1).

Finally, we investigate whether SMEs are less likely to invest in job training due to the greater uncertainty on the quality of the investment in job training and thus on the 31 In theory, the effect of trade unions on job training depends on whether the effect is through the wage structure (where more unionization leads to higher wages and arguable lower investments) or through the direct negotiation of training. Booth et al(2003) and Bassanini et al (2005) discuss this in detail. The empirical work has also found mixed results. Our findings are in line with Lynch and Black, (1998), Green and Lemieux (2007) who find almost no link between unions and the provision of job training.

32 The surveys also collect information that allow us to compute labor turnover. However, the sample size falls dramatically, invalidating the comparison with a broader sample. As with the share of temporary contracts the evidence is supportive of firms facing higher turnover also investing more. 33 We measure access to external finance with a dummy variable equal to one when the firm reports financing its working capital with bank loans and overdrafts at commercial banks or with leasing arrangements.

34 With financial development, small firms grow faster since their financing constraints are relaxed to a greater extent. Furthermore, industrial sectors that naturally should have a disproportionately large number of small firms also grow faster with greater financial development, suggesting that it is the small firms that benefit the most.

27
a quicker re-opening from COVID-19 lockdowns than some other countries, we find that the overall reported increase in GDP by 1.92% is likely an overstatement of the true increase in economic activity. Figure 7 also depicted the US, seen at #34 in this list. While the 2020 statistic of -3.55% was technically an overstatement, we find a composite estimate of -3.65% was very close; in fact, the difference between reported GDP and composite is no greater than in 40% of other years over 2005-2020. Thus, we conclude reported GDP was correct in this case.

**Interpretation.** While there are nearly as many countries in the list of overstatement (39) as understatement (42), it is worth noting that percentiles for overstatement are in general larger than those reported with respect to understatement, and there are limited instances of zeros. Specifically, only 6 of the 39 overstatement countries (15%) are associated with a percentile of 0.00, whereas 19 of the 42 understatement countries (45%) are. Thus, for nearly half of the countries in the understatement list, this was the largest instance of misreporting over the entire sample of 2005-2020. Altogether, there is more convincing evidence of understatement of economic activity across countries during 2020 than overstatement. A potential explanations for this is the crisis dramatically changed the way economic activities were conducted–for example, by video conference–that may have been difficult to quantify in GDP. However, since the primary source of world electricity is fossil fuel combustion, such activities would be measured indirectly in NO2 by satellites. The same is true of changes in automobile traffic. While no signal is perfect, there is at least evidence in NO2 that many countries’ GDP in 2020 understated the extent of ongoing activities, many of them unmeasurable otherwise.

# **7 Conclusion**

In this paper, we have presented a global analysis of cyclical economic activity using NO2, and shown that this signal may be used to compute improved measures of economic activity in comparison with GDP alone. In contrast with the exceedingly short list of existing globallyavailable signals like night lights, NO2 uniquely has reliable explanatory power for off-trend business cycle fluctuations. It also has information content beyond GDP for all countries.

**Limitations.** As in any single study, there are a number of possible limitations deserving further insight. First, heterogeneity in production technologies across countries could mean that NO2 is a better signal of economic activity in some countries than others. We have found by formal statistical test that the relationship between NO2 and GDP is stable across countries, supporting our presumption of a fixed growth elasticity (Table 3). Yet, investigating dimensions of heterogeneity may be an important concern in other applications. Second, production

35
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|---|---|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Rwanda Feeder Roads Development Project|Emmanuel Taban|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Enhancingthe Climate Resilience of the West Coast Road|Rodrigo Archondo-Callao|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Ho Chi Minh CityGreen Transport Development|Shigeyuki Sakaki|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
|Cabo Verde - Transport Sector Reform Project|Vincent Vesin|
The Rural Agriculture and Fishery Census (RAFC) includes all households in rural areas, and is conducted every five years. While the agricultural census and the population census have a range of variables in common (demographics, education, dwelling unit characteristics and asset ownership), there are also some important differences.

Firstly, the agricultural census only covers rural households such that the small area poverty and inequality estimates represent the rural population of Vietnam. Estimates based on the population census represent the entire population.

Secondly, the agricultural census includes a selection of specific variables that are particularly informative of rural livelihoods and which are not available in the population census. These include variables on rice cultivation, aquatic cultivation, household ownership of farming tools and machinery. These variables are important correlates of the household’s agricultural activities that will directly affect the household’s income.

Data on individual household members, however, is only collected for members aged 15 or older (the population census covers all household members). To ensure consistency between the variables from the census and the survey, household members aged 14 or younger were dropped from the latter. Also, the head of household is not identified in the agricultural census.

Finally, the codes that identify communes, districts and provinces did not provide a perfect match between the census and the survey. We managed to resolve this problem by using the names of both the provinces and districts to merge data from different sources.

# _II.2 Poverty line_

In the report we use two poverty lines: one for expenditure and one for income. Household members are classified as poor if their per capita expenditure (income) is below the expenditure (income) poverty line.

GSO calculated the expenditure poverty line with technical support from the World Bank in Vietnam. The expenditure poverty line is designed to measure the price of a consumption basket that meets pre-specified nutritional needs and essential non-food expenditures that include clothing and housing. For 2006, the expenditure poverty line was equal to 2,560,000 VND/person/year (in national real terms).

The income poverty line is set by MOLISA. It equals 2,400,000 VND/person/year (200,000 VND per month) for the year 2006 (in national real terms). When this poverty line is applied to household income data from the 2006 VHLSS, however, we obtain a 4
We examine whether the firm is gaining attention and traction through several measures of media attention and social media buzz. One advantage of these measures is that they are available for the full sample, with no attrition. The media intelligence specialist firm _Meltwater_ was contracted to collect online media mentions of the firms in our sample over the six-month period March 1 to August 31, 2015 (pre-intervention), and then one year and two years later (March 1 to August 31, 2016; and March 1 to August 31, 2017). Note that these time periods exclude the period of the intervention, semi-finals, and finals, so are independent of any media coverage of the program or pitch events, and correspond to an average of 6 and 18 months post-intervention. Meltwater tracks more than 250,000 global news sources in 190 countries in 25 languages (including Serbo-Croatian and Albanian). Thirteen percent of the firms in our sample had at least one media article about the firm during the six months prior to application, with a median of three articles conditional on having any media.

Column 1 of Table 5 show that 9.9 percent of the control sample was mentioned at least once in the media during the six-month intervals in 2016 and 2017, and treatment results in a 4.7 percentage point (2016) and 3.9 percentage point (2017) increase. These increases are large relative to the sample mean, but not statistically significant. Column 2 of Table 5 shows that we do see a statistically significant increase in the total number of media mentions in 2017, which are three times as high for the treatment group as the control.

Two-thirds of the firms had some form of social media presence at baseline, with Facebook (which 58% use) and twitter (42% use) being the most common. Column 3 shows a small and statistically insignificant impact of treatment on the number of Facebook followers a firm has, and Column 4 shows that treated firms have 20% more twitter followers after two years, but this is also not statistically significant. We pre-specified an overall index of _media buzz_ by taking standardized z- scores of these first four columns.<sup>12</sup> The last column of Table 5 shows that treated firms have more media buzz, with this significant at the 5 percent level in 2017.

# **5.3 Tracking Firm Performance through Follow-up Surveys**

> 12 Our pre-analysis plan also noted we would look at the impact on web-traffic, and being included on _AngelList_ , a web platform for fundraising, but that these would not be included in our overall index of media buzz. We find no significant impact on these other outcomes (Appendix 7).

22
Moreover, we have used the ICS database to assess firms’ behavior along different dimensions, which are useful as controls in the empirical analysis and as possible behavioral responses to restrictions in hiring and firing. The variables include: basic firm characteristics; changes in employment over the previous two years<sup>18</sup> ; whether the firm is upgrading its products or production line<sup>19</sup> ; whether the firms hires temporary workers<sup>20</sup> ; and finally whether the firm is providing training to its workers<sup>21</sup> (see section 4 for more details on the empirical strategy).

Table 2 presents the sample sizes and basic characteristics of the firms involved in the WBES and ICS surveys and which have provided the information that is relevant to our study. Despite the different country coverage, firms’ characteristics are fairly similar across the two surveys, as are the responses of firms concerning labor regulations. Across all firms surveyed in both the WBES and the ICS, close to 70 percent of respondents in WBES (60 percent in ICS) reported that labor market regulations represented an obstacle (minor, moderate, major) to their operation and expansion. Around 16 percent report that these regulations are a major obstacle to the operation and growth of their business in ICS (14 percent in WBES).

**Table 2: Basic characteristics of the firms in the WBES and ICS databases**

|||WBES<br>|||ICS<br>||
|---|---|---|---|---|---|---|
||Mean|Number<br>of|Number of<br>firms|Mean|Number<br>of|Number<br>of firms|
|||countries|||countries||
|_Proportion of firms that:_|||||||
|Report EPL as no obstacle|33.4|81|9070|44.1|47|17461|
|Report EPL as minor obstacle|28.6|81|9070|21.7|47|17461|
|Report EPL as moderate obstacle|23.8|81|9070|18.0|47|17461|
|report EPL as major obstacle|14.1|81|9070|16.1|47|17461|
|are small (less than 20 employees)|40.7|81|9070|41.7|47|17461|
|are medium (21 to 100 employees)|41.5|81|9070|31.9|47|17461|
|are large (over 100 employees)|17.8|81|9070|26.4|47|17461|
|are owned by the government||||10.0|47|17461|
|expanded employment|54.4||4664|71.0|47|17140|
|upgraded or created a product line|-||-|66.7|39|14043|
|provide formal training|-||-|56.1|38|12170|
|hire temporary workers|-||-|41.7|38|10837|
|Average age of firms|18.6|81|9070|16.0|47|17461|

# **_4. The empirical analysis_**

_Modeling firms’ perceptions_ uncertainty, macroeconomic instability, corruption, crime, theft and disorder, anti-competitive or informal practices, and legal system/conflict resolution. 18 Firm increased employment between the previous year and the year before.

> 19 Firm developed a major new product line and/or upgraded an existing product line in the previous

> three years.

20 Dummy = 1 if firms employ temporary workers, 0 otherwise.

> 21 Firms report whether they offer formal (beyond “on the job”) training to their regular employees.

15
**_Table 1: Distribution of foreign workers by state and by sector (percent, 2018)_**

|||||Sector of e|mployment(<br>|percent)|||
|---|---|---|---|---|---|---|---|---|
|State / Federal<br>Territory|Tota<br>l(%)|Domestic<br>Helper|Construc-<br>tion|Services|Manufac-<br>turing|Plantation|Agri-<br>culture|Total|
|Selangor|29.8|9.8|21.7|17.4|39.2|5.9|6.0|100|
|Johor|17.9|3.5|15.0|8.4|55.5|7.0|10.6|100|
|FT Kuala Lumpur|14.3|11.4|36.1|21.5|9.2|14.9|6.9|100|
|Pulau Pinang|7.4|5.9|12.7|11.4|67.2|0.4|2.4|100|
|Sarawak|7.3|2.5|16.3|7.9|21.6|46.5|5.3|100|
|Sabah|7.1|6.6|4.8|5.0|8.8|50.4|24.4|100|
|Perak|4.6|5.3|7.6|8.9|49.5|20.1|8.6|100|
|Negeri Sembilan|2.9|5.0|11.9|16.9|45.1|11.6|9.5|100|
|Melaka|2.8|5.6|18.2|12.7|47.8|4.6|11.0|100|
|Pahang|2.3|5.0|9.7|10.8|13.1|36.7|24.8|100|
|Kedah|1.9|5.5|12.5|13.6|56.7|5.1|6.6|100|
|Terengganu|0.5|7.7|10.6|21.7|10.6|47.1|2.3|100|
|FT Putrajaya|0.5|6.6|6.8|12.8|0.2|67.1|6.6|100|
|Kelantan|0.4|12.3|4.1|8.5|27.9|44.9|2.3|100|
|Perlis|0.2|5.4|4.3|33.1|28.1|12.0|17.2|100|
|FT Labuan|0.1|32.8|20.3|27.3|1.0|-|18.7|100|
|Total|100|7.2|18.6|13.6|36.7|15.2|8.8|100|

_Source_ : MOHA 2018.

**Indonesians are the largest foreign worker population in these two states that share borders with Indonesia on the island of Borneo.** As shown in Figure 4, 70 percent of the foreign workers in Johor are Indonesians and Nepalese. Bangladeshi workers do not exceed 20 percent of the total foreign worker population in any state except the Federal Territories of Kuala Lumpur (the capital) and Putrajaya (the government administration city). Sarawak and Sabah used to be key host states in the past, when foreign workers were mainly concentrated in the agriculture and plantation sectors (Del Carpio et. al, 2015). Similarly, Thai workers are the majority in Perlis which has Thailand on its northern border, and Filipinos are numerous in Labuan Federal Territory, which is close to the Philippines.

6
21 have been the 2006 World Development Report, _Equity and Development,_ and a monograph, _Measuring inequality of opportunities in Latin America and the Caribbean_ (Paes de Barros et al., 2009). The more recent publication contains a wealth of information on the effects of social circumstances on various measures of achievement and output.

Paes de Barros et al. (2009) propose a measure of equality of opportunity.

Consider a particular kind of opportunity, such as ‘attaining the sixth grade in elementary school.’ Let the total sixth-grade attendance in a country be _H_ , and the total number of children of sixth-grade age be _N_ , and define to be the _access_ on average of children to the opportunity of a sixth-grade education. measures the level of this opportunity in the country, but not the extent to which access is unequal to different children, based upon their social circumstances. Now using a logit model, estimate the probability that each child, _j_ , in the country has of attending the sixth grade, where that probability is a function of a vector of circumstances; denote this estimated probability by . Define . _D_ measures the variation in access to the opportunity in question across children in the country. The normalization guarantees that . Now define the _human opportunity index_ as ; note that .

The human opportunity index is a non-consequentialist measure of development, because the probabilities can only be computed knowing the circumstances of the children. The measure combines a concern with the level of provision of opportunities
(4) to (7) – one for each socioeconomic group of the recipient student. The baseline category is the proportion of peers from the same the socioeconomic group. All the estimated coefficients should be interpreted in reference to this category in each column.<sup>15</sup> Table 6: Heterogeneous effects of class compositions

||D|ependent vle: Tes|t score in Spani|sh|
|---|---|---|---|---|
||Lower<br>(1)|Lower Middle<br>(2)|Upper middle<br>(3)|Upper<br>(4)|
|Prop peers in L||-.060<br>(.007)<sup>_∗∗∗_</sup>|-.121<br>(.014)<sup>_∗∗∗_</sup>|-.115<br>(.016)<sup>_∗∗∗_</sup>|
|Prop peers in LM|.049<br>(.008)<sup>_∗∗∗_</sup>||-.064<br>(.012)<sup>_∗∗∗_</sup>|-.052<br>(.013)<sup>_∗∗∗_</sup>|
|Prop peers in UM|.079<br>(.013)<sup>_∗∗∗_</sup>|.025<br>(.009)<sup>_∗∗∗_</sup>||.017<br>(.014)|
|Prop peers in U|.024<br>(.014)<sup>_∗_</sup>|.008<br>(.009)|-.049<br>(.014)<sup>_∗∗∗_</sup>||
|Obs.|125,204|152,756|59,056|52,485|
|e(N-g)|7,225|7,414|6,973|6,735|
|_R_<sup>2</sup>|.048|.061|.068|.063|
|Test LM-UM (p-value)|.018||||

Note: Standard errors are robust and clustered at the school level. Other variables included in the regressions are the student’s number of books at home, gender, repeat, class size and age. Source: ONE, 2000. Argentina From these tables three features emerge: (1) from the point of view of the two lowest groups, there is little or no benefit from mixing with the wealthiest students; (2) as previously noted, the upper-middle group has the strongest effects on others, including themselves; (3) the upper group is statistically indifferent between being with peers of their own group or the upper-middle one. The implication of these results is that mixing children from different socioeconomic groups, especially lower and upper-middle categories, will help narrow the gaps in performance observed while the effect on the average test score is unclear. These results are reminiscent of the social distance model discussed earlier. Students at the bottom of the distribution perform better when surrounded by others from higher social groups, while those above perform worse, hence approaching one another.

> 15I also include in the appendix a single regression (Table 17) using interaction terms, which will be used in the simulations in the next section.

23
37

# Table 9: Brazil - Distributdon of Households According to Per Capita Adult Income and Per Capita Household Income, Sao Paulo Metropolitan Area, 1984

|Adult Income<br>Percentile|Lowest 5%|Tota<br>5-10%|l Income Perc<br>10-25%|entile<br>25-50%|Above_50%_|
|---|---|---|---|---|---|
|Lowest 5%<br>5-10%<br>10-25%|4.4<br>0.6<br>--|0.4<br>3.9<br>0.8|0.2<br>0.5<br>13.2|0.0<br>0.1<br>1.0|--<br>--<br>--|
|25-50%|--|--|1.1|23.2|0.7|
|Above 50%|--|--|--|0.7|49.3|
|Percent Reclassified|10.0|24.0|12.0|7.0|1.0|

Source: author's tabulations otes: Adult income refers to the sum of the income from all sources of all adult members (age 18 and over) of the household. Total income refers to the sum of the income from all sources of all members of the household.