rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Logit/Multinomial logit regressions: Labor market status, _returnees_ only

||(1)<br>Bangladesh|(2)|(3)<br>Ne|(4)<br>pal|(5)|(6)|(7)<br>Paki|(8)<br>stan|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Logit|Logit||Mlogit||Logit||Mlogit||
|Dependent var.|Emp.|Emp.|Out of<br>Mkt.|Un-<br>emp.|Emp.|Emp.|Out of<br>Mkt.|Un-<br>emp.|Emp.|
|Return migrant, 1-2 year|0.47***<br>(0.02)|0.18***<br>(0.03)|-0.13***<br>(0.03)|-0.04**<br>(0.02)|0.17***<br>(0.03)|0.15***<br>(0.03)|-0.08***<br>(0.02)|-0.07***<br>(0.02)|0.15***<br>(0.03)|
|Return migrant, 3+ years|0.47***|0.30***|-0.19***|-0.08***|0.27***|0.29***|-0.11***|-0.18***|0.30***|
||(0.02)|(0.04)|(0.04)|(0.03)|(0.04)|(0.03)|(0.02)|(0.02)|(0.02)|
|_Age category (age 20-29 as the_|_reference gro_|_up)_||||||||
|Age: 30-39|0.02|0.05|-0.03|-0.01|0.05|0.01|-0.02|0.01|0.01|
||(0.02)|(0.04)|(0.03)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|(0.03)|
|Age: 40-49|-0.03|0.05|-0.01|-0.04|0.05|-0.05|0.03|0.02|-0.05|
||(0.03)|(0.05)|(0.04)|(0.03)|(0.04)|(0.04)|(0.03)|(0.03)|(0.04)|
|Age: 50-59|-0.12***|-0.12|0.25***|-0.20*|-0.05|-0.16***|0.12***|0.02|-0.14***|
||(0.03)|(0.08)|(0.09)|(0.11)|(0.08)|(0.04)|(0.03)|(0.03)|(0.04)|
|_Education level (illiterate as th_|_e reference gr_|_oup)_||||||||
|Education: Primary|-0.01|0.01|0.02|-0.03|0.01|-0.01|0.02|-0.02|-0.01|
||(0.02)|(0.06)|(0.06)|(0.04)|(0.06)|(0.03)|(0.03)|(0.03)|(0.03)|
|Education: Lower secondary|0.02|-0.01|0.04|-0.03|-0.01|-0.03|0.00|0.02|-0.03|
||(0.02)|(0.06)|(0.06)|(0.04)|(0.06)|(0.03)|(0.03)|(0.02)|(0.03)|
|Education: Higher secondary|-0.01|-0.07|0.06|0.00|-0.06|-0.08***|0.05**|0.03|-0.08***|
||(0.02)|(0.06)|(0.06)|(0.04)|(0.06)|(0.03)|(0.02)|(0.02)|(0.03)|
|Education: Tertiary|0.03|0.17|0.71***|-1.50***|0.79***|-0.11***|0.02|0.08***|-0.10**|
||(0.05)|(0.10)|(0.10)|(0.10)|(0.10)|(0.04)|(0.03)|(0.03)|(0.04)|
|Rural||-0.05*|-0.06**|-0.01|-0.05*|0.06***|-0.02|-0.03**|0.06***|
|||(0.03)|(0.03)|(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|(0.02)|
|Married|0.02|0.16***|-0.08*|-0.07***|0.14***|0.07**|-0.03|-0.04*|0.07**|
||(0.03)|(0.05)|(0.04)|(0.02)|(0.04)|(0.03)|(0.03)|(0.02)|(0.03)|
|Head of household|0.04*|0.10***|-0.07**|-0.02|0.09***|0.03|0.00|-0.03|0.03|
||(0.02)|(0.04)|(0.03)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|(0.03)|
|Household size|0.01|0.01|-0.01|0.00|0.01|-0.00|0.00|-0.00|-0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|
|Number of dependents|0.00|-0.03|0.02|0.00|-0.02|0.01*|-0.01**|-0.00|0.01*|
||(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|4705|1402|1402|1402|1402|1420|1420|1420|1420|

_Note:_ Robust standard errors in parentheses. Sample is restricted to males aged 20-59. Marginal effects of Logit/Mlogit model are reported. Data * ** *** source: Bangladesh RMS 2018-19; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. p _>_ 0.1, p _>_ 0.05, p _>_ 0.01.
**Table 3: Mining and Assessment of Public Services: Baseline Models (2)**

||(1-LPM)|(2-LPM)|(3-LPM)|(4-LPM)|(5-OLS)|(6-LPM)|
|---|---|---|---|---|---|---|
|_Dependent Variables_|Water & Sanitation|Jobs|Health|Education|Public Services|Optimism|
|Active 50 km|-0.023***|-0.029***|-0.018***|-0.010*|-0.019***|-0.042***|
||(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|(0.007)|
|Inactive 50 km|-0.011***|-0.002|-0.006|0.001|-0.001|-0.026***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.004)|
|Interest in Public Affairs|0.035***|0.041***|0.033***|0.031***|0.041***|0.019***|
||(0.003)|(0.002)|(0.003)|(0.003)|(0.002)|(0.003)|
|Deprivation|-0.085***|-0.043***|-0.069***|-0.057***|-0.068***|-0.056***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Natural Resources Rents (ln)|-0.114***|-0.062***|-0.168***|-0.173***|-0.123***|-0.026***|
||(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|(0.007)|
|Control of Corruption|0.138***|0.189***|0.229***|0.239***|0.202***|0.293***|
||(0.011)|(0.010)|(0.011)|(0.011)|(0.012)|(0.012)|
|Constant|1.838***|1.707***|1.981***|1.768***|2.065***|1.832***|
||(0.115)|(0.107)|(0.113)|(0.112)|(0.102)|(0.120)|
|Difference in Differences|-0.012|-0.026|-0.012|-0.011|-0.019|-0.017|
|F-test: active-inactive = 0|3.9|23.7|4.3|3.5|9.4|5.7|
|P-value of F-test|0.05|0.00|0.04|0.06|0.00|0.02|
|R-squared|0.09|0.05|0.09|0.10|0.11|0.12|
|Observations|131,659|130,751|132,511|132,001|105,199|80,553|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|Yes|Yes|Yes|Yes|

# **_Notes:_**

1: Significance level: * p<0.10, ** p<0.05, ***p<0.01. Robust standard errors in parenthesis.

2: Specifications (1) to (4) refers to the respondents’ view of how the government is handling water & sanitation services to the households, job creation, health, and education. The dependent variable in specification (5) is a composite indicator constructed through polychoric correlation and which combines all the respondents’ views on various types of public services and policies, including those in specifications (1) to (4). The sample size is reduced due to inconsistent missing patterns across the different assessment variables. The dependent variable in specification (6) refers to the expected living standards of the respondent in 12 months after the survey collection. Individual-level control variables are added in all specifications: age (ln), the square of the logarithm of age, gender, education, residential area (urban), employment status.

17
1 percent increase in private credit to GDP (our main proxy for financial development) reduces the prevalence of undernourishment by 0.22-2.45 percent. Importantly, we find evidence of a specific channel: financial development increases agricultural productivity. We specifically show evidence of increases in livestock production, cereal and crop yields, which in turn lead to less undernourishment. Furthermore, we find evidence that the productivity increase channel operates through greater use of productivity enhancing equipment like tractors and fertilizers. As farmers become more productive by being able to finance more machinery or use more fertilizers, our analysis suggests that a 1 percent increase in private credit to GDP increases value added per worker by 0.14-1.7 percent. In turn, higher productivity leads to lower undernourishment. We find that a 1 percent increase in value added per worker reduces the prevalence of undernourishment by 0.41 to 0.8 percent.

Our conclusions remain with the inclusion of country controls which are known for their impact on poverty, financial development and hunger: initial levels of poverty and GDP per capita, average inflation, average trade openness, the fraction of the working population in the agricultural sector, and the fraction of the population in rural areas. The results also prevail when using panel estimation techniques. We furthermore find some support for the fact that it is not just financial sector development that matters, but also that the outreach of the banking system distribution network matters specifically for reducing undernourishment, suggesting that access to financial services is important.

Our results, though robust to many different specifications, do come with some provisos. As for many other studies, we have only rough measures of financial sector ⎯ development we use private credit to GDP as a proxy, and thus do not capture access to financial services for poor and undernourished households directly. We cannot establish _how_ exactly people use financial services to decrease undernourishment and in a definite way the exact channel driving the increase in productivity. This has been done on a country-specific micro basis, however, using household and firm surveys and other micro-evidence, which tends to corroborate the importance of access to financial services to reducing undernourishment. While our cross-country application complements this 4
|P079116|2004|2010|11/7/2009|18 Closed|Montenegro|
|---|---|---|---|---|---|
|P079116|2004|2010|2/20/2010|19 Closed|Montenegro|
|P079116|2004|2011|7/28/2010|20 Closed|Montenegro|
|P079116|2004|2011|12/8/2010|21 Closed|Montenegro|
|P079116|2004|2011|1/25/2011|22 Closed|Montenegro|
|P079116|2004|2011|4/10/2011|23 Closed|Montenegro|
|P079116|2004|2012|12/25/2011|24 Closed|Montenegro|
|P079116|2004|2012|6/24/2012|25 Closed|Montenegro|
|P079140|2008|2008||0 Closed|Papua New Guinea|
|P079140|2008|2009|3/1/2009|1 Closed|Papua New Guinea|
|P079140|2008|2009|5/14/2009|2 Closed|Papua New Guinea|
|P079140|2008|2010|11/12/2009|3 Closed|Papua New Guinea|
|P079140|2008|2010|6/28/2010|4 Closed|Papua New Guinea|
|P079140|2008|2011|6/22/2011|5 Closed|Papua New Guinea|
|P079140|2008|2012|1/3/2012|6 Closed|Papua New Guinea|
|P079140|2008|2013|9/28/2012|7 Closed|Papua New Guinea|
|P079140|2008|2013|5/15/2013|8 Closed|Papua New Guinea|
|P079140|2008|2014|12/30/2013|9 Closed|Papua New Guinea|
|P079156|2003|2003||0 Closed|Indonesia|
|P079156|2003|2004|6/24/2004|2 Closed|Indonesia|
|P079156|2003|2005|6/29/2005|4 Closed|Indonesia|
|P079156|2003|2006|12/27/2005|5 Closed|Indonesia|
|P079156|2003|2007|9/29/2006|6 Closed|Indonesia|
|P079156|2003|2008|3/22/2008|7 Closed|Indonesia|
|P079156|2003|2009|6/30/2009|8 Closed|Indonesia|
|P079156|2003|2010|6/30/2009|24 Closed|Indonesia|
|P079156|2003|2021|7/10/2020|9 Closed|Indonesia|
|P079161|2003|2003|6/30/2003|1 Closed|Bosnia and Herzegovin|
|P079161|2003|2004|6/28/2004<br>|5 Closed|Bosnia and Herzegovin|
|P079161|2003|2005|1/26/2005|7 Closed|Bosnia and Herzegovin|
|P079161|2003|2005|5/19/2005|8 Closed|Bosnia and Herzegovin|
|P079161|2003|2006|10/25/2005|9 Closed|Bosnia and Herzegovin|
|P079161|2003|2006|12/21/2005|10 Closed|Bosnia and Herzegovin|
|P079161|2003|2006|5/22/2006|11 Closed|Bosnia and Herzegovin|
|P079161|2003|2007|11/15/2006|12 Closed|Bosnia and Herzegovin|
|P079161|2003|2007|6/7/2007|13 Closed|Bosnia and Herzegovin|
|P079161|2003|2008|12/3/2007|14 Closed|Bosnia and Herzegovin|
|P079161|2003|2008|2/27/2008|15 Closed|Bosnia and Herzegovin|
|P079161|2003|2009|7/4/2008|16 Closed|Bosnia and Herzegovin|
|P079161|2003|2009|11/2/2008|17 Closed|Bosnia and Herzegovin|
|P079161|2003|2009|6/12/2009|18 Closed|Bosnia and Herzegovin|
|P079161|2003|2010|11/21/2009|19 Closed|Bosnia and Herzegovin|
|P079161|2003|2010|5/18/2010|20 Closed|Bosnia and Herzegovin|
|P079161|2003|2011|12/12/2010|21 Closed|Bosnia and Herzegovin|
|P079165|2007|2007|6/13/2007|1 Closed|Peru|
|P079165|2007|2008|10/12/2007|2 Closed|Peru|
|P079165|2007|2008|1/14/2008|3 Closed|Peru|
capital of their children. The main finding is that the socioeconomic status of migrant families upon arrival is on average lower than that of native families, which directly translates into fewer monetary and time investments in their children. For instance, Rangvid (2007) finds that 50% of the gap between immigrant and native students in Denmark is explained by differences in the family socioeconomic status of each comparison group. Likewise, Schnepf (2007) finds in a crosscountry analysis of OECD members that in European countries, socioeconomic background is the main determinant for the immigrant-versus-native performance gap in tests like PISA, TIMSS, and PIRLS. Furthermore, Dahl _et al._ (2020) show that in Germany, migrant parents from backgrounds with traditional cultural norms (e.g., Muslims) tend to invest less in their daughters’ schooling. Consequently, their daughters receive worse grades in school.

_Potential driver 2: school sorting._ Another explanation for the lower performance of immigrant children compared to native peers is the potential sorting of this population into schools with unfavorable characteristics. Rangvid (2007) finds that even though immigrant students in Denmark have access to school resources comparable to native students—including class size, student-teacher ratio, language lessons per week, and physical and educational infrastructure—other factors contribute to the immigrant students’ low performance on the PISA test. These determinants include teachers’ expectations, encouragement, and pressure to achieve, as well as a scarcity of specialized teachers compared to the number available for native students of the same socioeconomic status. Also, Liu _et al._ (2010) note that immigrant families often locate in areas with lower-quality schools than those attended by native counterparts.

_Potential driver 3: stereotypes held by teachers._ Recent literature has shown how teachers’ perceptions and stereotypes can influence immigrant children’s education outcomes. Triventi (2020) finds that Italian teachers tend to assign lower grades to students with an immigrant background compared to native students, suggesting that teachers may harbor implicit biases. Alesina _et al._ (2018) confirmed that intuition, finding that Italian teachers who hold stronger stereotypes (higher Implicit Association Test scores - IAT) assign lower grades to immigrant students compared to native peers. However, teachers increase grading when they are randomly informed about the stereotypes they hold.

Similarly, Carlana _et al._ (2022) find that Italian teachers give inaccurate recommendations about 7
36 schemes; in 1990 transfers totalled 12.5 percent of the education budget (see Table 6). Although educational cash transfers are sector specific and not directly for poverty alleviation purposes, they are for all practical purposes a targeting scheme using education as an indicator.<sup>2 6</sup> Table 6

**Structure** of **Transfers by Education** Level 1983. 1990 (CFAF Million)

|||TRA|NSFER||
|---|---|---|---|---|
|SECTOR|1983<br>CFAF Million|%|1990<br>CFAF Million|%|
|Primary|4,142|12.6|3,410|13|
|Secondary|8,298|25.2|8,732|33.5|
|Higher|14,030|42.7|11,111|42.6|
|Technical|6,403|19.5|2,835|11.8|
|Total|32,875|100|26,088|100|
|% Total Education Budget||17||12.5|

Source: World Bank (1987, Vol. II) Available evidence suggests that the main beneficiaries have been urban students in higher education with award criteria that were never applied, thus creating a less than

**26** Whether education transfers have been used explicitly by the Government for alleviating poverty is an important question that is not however addressed in this paper. Besides the distributional implications of the existing transfer scheme, another question is whether education is an efficient indicator for targeting programs to the poor. For a discussion of targeting by indicators see Besley and Kanbur (1988), and Thorbecke and Berrian (1988).
This can happen if the economy is not dynamic. Or when higher education is overexpanded (Gonzalez and Uwaifo Oyelere 2011).

In scenarios 3a and 3b, the rates of return to university education remain unchanged because the crisis has a similar impact on secondary-educated and university-educated workers. The reduction in earnings among both groups of workers is such that the rate of return is the same before and during the crisis.

# **3. Data and Stylized Facts from Past Crises**

We use data on economic growth rates from the World Development Indicators <u>(http://datatopics.worldbank.org/world-development-indicators/) and estimates of the</u> returns to university education obtained from the following sources:

- Indonesia: 1990, 1992, 1994, 1996, 1998, 2000, 2002: Psacharopoulos and Patrinos (2018); 1999, 2003, 2004, 2006, 2008, 2009, 2010: Montenegro and Patrinos (2014).

- Pakistan: 1991 and 1995: Psacharopoulos and Patrinos (2018); 1992, 1999, 2004, 2005, 2006, 2007, 2008, 2010: Montenegro and Patrinos (2014); 2012, 2014, and 2018: authors’ calculations.<sup>2</sup>

- South Africa: 1993: Psacharopoulos and Patrinos (2018); 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, and 2011: Montenegro and Patrinos (2014).

Figures 1, 2, and 3 present economic growth rates and the returns to university education in Indonesia, Pakistan, and South Africa, respectively. Despite the different social and political circumstances, we find two patterns across the three countries. First, the returns to university education steadily increased in all three countries. With globalization and economic development, including the expansion of the service sector, automation (reduces the earnings of secondary-educated workers) and technological revolution (increases the earnings of university-educated workers), those with higher education experienced higher earnings growth than those with only secondary education for most of the years.

> 2 <u>https://datacatalog.worldbank.org/dataset/data-depot-pakistan.</u>

4
of imported intermediate inputs variety.<sup>29</sup> The sum of the number of local input suppliers and the number of imported input variety gives me N: Given that the number of local input suppliers and the number of imported input variety probably measure N<sup>D</sup> and N<sup>I</sup> with errors, the least square estimate could have a downward bias.

Columns (1) and (2) of Table 10 present the least squares results. Column (1) ignores the number of imported input variety, N<sup>I</sup> ; and only focuses on the relationship between productivity and local input variety, while Column (2) includes both local and imported input variety in the regression. Firm ...xed e¤ects are used to control for ln �i; and given that N<sup>D</sup> and N are common across all ...rms within a year, the standard errors are clustered by year. Both columns show that there is a positive and signi...cant relationship between the productivity of domestic ...rm and the number of input varieties. However, these results can be downward biased given that N<sup>D</sup> and N are measured with errors.

To show, empirically, that an increase in the number of FDI ...rms in the ...nal good industry may lead to an increase in the number of local input variety and, thus the TFP of domestic ...rm in the ...nal sector, I instrument N<sup>D</sup> ; using the number of FDI ...rms in a ...rst stage regression. In addition, I use the international price of cotton fabrics as an instrument for N<sup>I</sup> :<sup>30</sup> Here, the exclusion restriction is that the total number of FDI ...rms in the Bangladeshi garment sector is exogenous and has no direct impact on the productivity of individual domestic ...rms other than through local intermediate inputs. This is justi...able given that during the sample period, foreign investment in the garment sector was highly restrictive under the Bangladesh Investment Policy (1999). While existing FDI ...rms may invest and expand their capacity, new FDI ...rm entry was tightly regulated by the government, which makes the total number of FDI ...rms de facto exogenous during the sample period. From 1999 to 2003, only 6 new FDI ...rms were established, all were green ...eld investments, and half were new additions to the existing FDI conglomerates in Bangladesh from South Korea. Comparatively, 54 percent of the domestic ...rms in the data were set up during the sample period, suggesting that the number of FDI ...rms were, in fact, tightly controlled by the

> 29 In the survey, ...rms report the HS 6 digit codes for the inputs they used for production. For each of these HS 6 digit inputs, I consider imports from di¤erent countries as di¤erent varieties. I match these HS codes with Bangladesh bilateral import data from Comtrade to construct the number of unique imported input variety for each year, from 1999 to 2003, respectively.

> 30 The international price of cotton fabrics is constructed using the unit value of Indian’s export of cotton fabrics to the world, according to data from Comtrade.

29
<!-- Start of picture text --> Cons. PG 7<br>Cons. PR RIoO<br>% '<br>a 1<br>& 1<br>fo} 1<br>aS} '<br>2 1<br>fe) 1<br>Cons. Gini (pc) ——<br>Cons. Gini -+—e—_<br>-0.077 -0.027 0.023 0.073 0.123 0.173 0.223<br>Estimate<br>—® Cons. Gini -® Cons. Gini (pc) —®- Cons. PR Cons. PG<br><!-- End of picture text -->
setting, improvements in capacity are very unlikely to lead to improvements in performance. At first glance it seems that knowledge will not have an important impact on performance because there is a very weak link between knowledge and performance. However, when we use the model to take a closer look, we can see that knowledge might interact with a measure of motivation — the degree to which a health worker feels valued by their facility. As knowledge (and therefore capacity) increases, health workers who feel valued by their facility significantly improve their performance.

By deliberately examining performance through other models that are less nuanced than the Three-Gap model, we can show that this potentially important interpretation of the impact of policy could have been missed. In fact, if we look at performance in isolation, it looks as if feeling valued by one’s health facility significantly decreases performance on average. This demonstrates one of the important advantages of a more nuanced model.

This paper examines baseline data for a pay-for-quality intervention in hospitals in Liberia. The project is providing payments to facilities that improve performance as measured by quality (not quantity) and is paired with a plan to improve both training and equipment and facilities in hospitals. This baseline analysis suggests that the improvements in equipment are unlikely to lead to improvements in quality, but that the improvements in training might improve performance specifically because they are paired with an increase in motivation. If the project succeeds in motivating hospital administrators to pay attention to (value) the health workers within their facilities, then our analysis suggests that improvements in training will lead to improvements in performance.

26
there was a gradual trend-conversion in the monthly earnings of the affected fishes; by the end of 2016, the average monthly income of these individuals seemed to have recovered to the lower-bound level of their pre- _Formosa_ earnings. Also consistent with the descriptive statistics from Table A1, we observe a rather flat earning profile for the control group (i.e. the unaffected fishers) across the entire analysis period.

# **3.1.2 Satellite data on boat detection**

To measure the impact of _Formosa_ on fishing activity, we use the Visible Infrared Imaging Radiometer Suite (“VIIRS”), which documents high-resolution night-time light luminosity at the globe’s surface. VIIRS is administered by the National Oceanic and Atmospheric Administration (NOAA). Specifically, we utilize a special Boat Detection Module of VIIRS (“VBD”), which detects the ocean’s night-time light source emitted from fishing boats.<sup>11</sup> VBD is jointly sponsored by the U.S. Agency for International Development, NOAA, and the World Bank. The project collects and processes remote-sensing images from the Suomi National Polar-orbiting Partnership (Sunomi-NPP) satellite. Joint Polar Satellite System (JPSS) is the new generation polar-orbiting operational environmental satellite system in the U.S. The VIIRS itself is the primary imager on Sunomi-NPP.<sup>12</sup> The use of night-time light brightness as a measure of economic activity has become increasingly popular in economic research. Especially in developing countries where subnational accounting data are often missing or unreliable, luminosity at night has been shown to provide an unbiased proxy for growth outcomes. It is noted that the majority of existing studies adopting night-light measure have relied on the Meteorological Satellite ProgramOperational Line Scan package (DMSP-OLS). DMSP-OLS final output provides composite annual light density measures at a coarse footprint of 5km _×_ 5km pixel resolution (see Donaldson and Storeygard (2016) for a comparative analysis on this literature). Compared to the imaging sensors suite in DMSP-OLS, VIIRS provides higher quality remote sensing imagery in terms of spatial resolution, coverage interval, and the ability to detect weak light sources, which makes it ideal for our analysis. The VIIRS Day/Night Band sensor unit has a 742m _×_ 742m footprint, and thus can detect boat activities within as fine an area as 0.2 mile squares. Couple with the implementation of an automatic boat detection identification system that converts light intensity to actual boat counts, VBD has greatly advanced the usefulness of

> 11Night-time fishing often requires the emission of high-luminous light to attract catch.

> 12 We collect the raw-, raster-formatted VBD’s light intensity and boat detection data at https://ngdc.noaa.gov/eog/viirs/download ~~b~~ oat.html [Accessed July 26, 2018].

8