rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix B: Input-Output Model

_Table B. 1. Annual spending associated with investments and households’ consumption under the Car Scrappage Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**202**<br>**0**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricultu**<br>**re**|0.00|0.02|0.02|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Forestry**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Mining**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Food**<br>**Manufac**<br>**turing**|0.00|0.06|0.06|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Textile**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Wood**<br>**and**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Paper**||||||||||||
|**Chemical**<br>**and**<br>**Plastic**<br>**Products**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Metal**<br>**Products**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Machine**<br>**ry**<br>**and**<br>**Equipme**|0.00|0.00|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|**nt**||||||||||||
|**Energy**|0.00|0.09|0.12|0.07|0.07|0.07|0.07|0.07|0.07|0.07|0.07|
|**Construc**<br>**tion**|0.00|0.35|0.35|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Trade**|0.00|0.42|0.33|-0.19|-0.19|-0.19|-0.19|-0.20|-0.20|-0.20|-0.20|
|**Accomm**<br>**odation**<br>**and Food**<br>**Services**|0.00|0.01|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.02|0.02|
|**Transpor**<br>**tation**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Banking-**<br>**Financin**<br>**g**|0.00|0.12|0.12|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Real**<br>**Estate**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Public**<br>**Administ**<br>**ration**|0.00|0.03|0.03|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

34
**Table 4.** <u>OLS Model of Experimental Public Good Contributions (CONTRIBUTION1)</u>

||Model 1|Model 2|Model 3|
|---|---|---|---|
|HILL|-13.606|-13.673|-9.921|
||(2.38)**|(2.45)**|(2.80)***|
|AGE|0.093|0.083|0.011|
||(0.71)|(0.65)|(0.09)|
|FEMALE|8.002|8.245|3.538|
||(1.94)*|(1.97)*|(1.15)|
|DALIT|8.805|9.725|7.344|
||(1.65)|(1.76)*|(1.99)*|
|FAMILY|1.481|1.278|0.218|
||(2.18)**|(1.93)*|(0.33)|
|MAXEDUC|-1.734|-1.832|-1.128|
||(2.74)**|(2.86)***|(2.22)**|
|EDUC_RESP|0.598|0.627|0.254|
||(1.13)|(1.19)|(0.57)|
|LAND_HA||2.710|0.413|
|||(1.62)|(0.26)|
|FOREST_ROP||-0.391|0.133|
|||(0.61)|(0.26)|
|OXEN||0.408|0.282|
|||(0.19)|(0.21)|
|BUFFALO||0.592|0.993|
|||(0.63)|(0.91)|
|BELIEF1|||0.249|
||||(7.11)***|
|CONSTANT|60.308|59.919|39.032|
||(4.75)***|(4.89)***|(3.45)***|
|_R_<sup>2</sup>|0.12|0.12|0.33|

N=327 in all models; * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; robust t-statistics and errors clustered at the forest user group level 31
FEI 1. Benefit‐Cost Ratio: For each water project, the benefit‐cost ratio (BCR) corresponds to the net present value of all financial and economic benefits divided by the net present value of all costs (annualized operating expenses and capital expenditures). This calculation includes the economic benefits accruing out of time savings for collection of water, monetary savings for reduction in purchase of drinking/cooking water from private suppliers, and health benefits attributable to reduced instances of waterborne diseases and chronic kidney disorder cases. It is assumed that 10 minutes per‐day, per‐family would be saved in water collection in cases where data were not readily available. Similarly, it is assumed that Rs. 20 (US$ 0.13) per family per day would be saved by not purchasing water from private vendors and that chronic kidney disorder cases could be reduced by 20% with access to higher‐quality water delivered by the new projects. The higher the benefit‐cost ratio, the more benefits accrue to the population. As such, higher BCRs contribute to higher composite FEI scores.

FEI 2. Existing Water Resource Yield: This criterion is used to check the level of implementation hazards for the projects by verifying the extent to which the new projects will be able to extract water from existing water resources. This is done by considering whether the project has Approved Water Rights, MOUs with other users, and water availability throughout the year. The higher the existing water resource yield, the higher is the contribution of this input criterion to the final composite FEI score.

The scoring matrix is detailed below: _Table 4: Scale for measuring existing water resources yield_

|Approved Water Ri|ghts|Water Availability Thr<br>the Year|oughout|MOU with Other<br>Users|Water|
|---|---|---|---|---|---|
|_Documents Availability_|_Score_<br>_(A)_|_Confirmation on_<br>_Availability of Water_|_Score_<br>_(B)_|_Documents_<br>_Availability_|_Score_<br>_(C)_|
|Available (or if not<br>relevant)|1|100 % Confirmed|1|Available (or if not<br>relevant)|1|
|Not Available|0|Not<br>confirmed<br>in<br>drought season etc.|0|Not Available|0|
|ExistingWater Resource Y|ield Score|=|Score A|+ Score B +Score C||

FEI 3. Non‐Revenue Water : This criterion measures the percentage of non‐revenue water (NRW) that exists in the existing water supply schemes serving the areas or DSDs where the new projects will be located. NRW is a good measure of the economic efficiency of the current water supply systems. The higher the existing NRW, the greater the need for projects that improve infrastructure and management practices. NRW values are weighted by the number of existing beneficiaries in the service area, since larger distribution networks that cover larger populations are also likely to see larger losses. Data on the NRW values were sourced from NWSDB (2015).

Table 5 below provides basic descriptive statistics on the indicators used in the IPF analysis to show the variation that exists between the projects with respect to the various indicators.

18
_Figure 4: Yield loss when electrical conductivity exceeds 100mS/m_ <!-- Start of picture text --> a. Mekong River Basin b. India c. Global<br>0% 0% 0%<br>‐2% ‐2% ‐2%<br>‐4% ‐4% ‐4%<br>‐6% ‐6% ‐6%<br>‐8% ‐8% ‐8%<br>‐10% ‐10% ‐10%<br>‐12% ‐12% ‐12%<br>‐14% ‐14% ‐14%<br>‐16% ‐16% ‐16%<br>‐18% ‐18%<br>‐18%<br>‐20% ‐20%<br>‐20%<br>30 percent 75 percent 90 percent 30 percent 75 percent 90 percent 30 percent 75 percent 90 percent<br>Cropland Threshold Cropland Threshold Cropland Threshold<br>Percentage Yield Loss Percentage Yield Loss Percentage Yield Loss<br><!-- End of picture text --> Notes: Figure shows results from estimating equation 1 in 3 different regions. Figure a (left) if in the Mekong River Basin, figure b (center) is in India, and figure c (right) is global using GEMStat data. In each region, coefficients from three different regressions are shown. The three regressions vary the restrictions on the share of cropland within a gridcell needed to be included in the regression. The left estimate includes all gridcells with at least 30 percent cropland in the year 2000. The mid and right estimate include gridcells with at least 75 percent and 90 percent cropland, respectively. The point estimate shown is the coefficient on a binary variable indicating observations where electrical conductivity in the upstream monitoring station exceeds 100mS/m. Error bars show the 95 percent confidence interval based on standard errors clustered at the gridcell level.

15
<!-- Start of picture text --> & Number of<br>Ay. X stunted chilrden<br>gs - “ei* =ou2, ed [i1) 4,001642 - 4,000- 8,000<br>& cd aeae Hl{18,00115,001- 15,000- 20,000<br>1% 4 MMM 20,001 - 56,717<br>zE Oe: : a ¥ Vee.c<br>en ae si AY<br>|, Re See<br>Fs ra rap = ee cs: al<br>f Lely, Sihulna) Barisal pit<br>he 9 hie et eer ee we NS Booms<br>SV/A dots Tees age EE ae<br>.z > Bay of Bengal \,<br>0 50 100 km *<br>oo aN<br>88°E 89°E 90°E STE 92°E<br><!-- End of picture text -->

<!-- Start of picture text --> ww Number of severely<br>Ay. L x stunted chilrden<br>z Py5 =a it HE{1 2,001335 - 2,000- 5,000<br>“ & ae? [= 5.001<br>CPL CN oe IB 10,001- 10,0- 15, 00 0<br>~ 7 ’ HB 15,001 - 31,068<br>Psz Aei cowx c Syinetw.<br>ei ie I J<br>‘|, RESO ARE<br>z ra 1 reat ey Be Reyy<br>* 4 ee? pat gbatisal pit<br>, § Paid oS aa chee<br>SAA eh 1.2 tela eee a ie<br>*z Oe Bay of Bengal a<br>0+450 100 km 4aN<br>88°E 89°E 90°E STE 92°E<br><!-- End of picture text -->
- Autor, David H. 2015. “Why are there still so many jobs? The history and future of workplace automation.” _Journal of Economic Perspectives_ 29 (3): 3–30.

- Autor, David H, David Dorn, and Gordon H Hanson. 2015. “Untangling trade and technology: evidence from local labour markets.” _The Economic Journal_ 125 (584): 621– 646.

- Autor, David H, and Anna Salomons. 2018. “Is automation labor share-displacing? Productivity growth, employment, and the labor share.” _The Brookings Institution_ 49 (1): 1– 87.

- Autor, David, David Dorn, Lawrence F Katz, Christina Patterson, and John Van Reenen. 2020. “The fall of the labor share and the rise of superstar firms.” _The Quarterly Journal of Economics_ 135 (2): 645–709.

- Barefoot, Kevin, Dave Curtis, William Jolliff, Jessica R Nicholson, and Robert Omohundro. 2018. “Defining and measuring the digital economy.” _US Department of Commerce Bureau of Economic Analysis_ 15.

- Bloom, N, C Jones, J Van Reenen, and M Webb. 2020. “Are ideas getting harder to find?” _American Economic Review_ 110 (4): 1104–1144.

- Bloom, Nicholas, and John Van Reenen. 2007. “Measuring and explaining management practices across firms and countries.” _The Quarterly Journal of Economics_ 122 (4): 1351– 1408.

- ~~.~~ 2010. “Why do management practices differ across firms and countries?” _Journal of_

- _Economic Perspectives_ 24 (1): 203–24.

- Bond, Stephen, and Måns Söderbom. 2005. “Adjustment costs and the identification of Cobb Douglas production functions.” _Institute for Fiscal Studies_ IFS Working Papers W05/04.

33
|**_Theme_ / Layer**|**Description**|**approximate grid**<br>**size**|**Source**|
|---|---|---|---|
|irrigated<br>~~c~~rop<br>~~s~~h|Share of area with irrigated<br>crop|1km2|GFSAD1000<br>V1.0: (Telu-<br>guntla et al.<br>2014)|
|agr<br>~~o~~ppt<br>~~c~~ost|Global map of gross eco-<br>nomic rents from agricul-<br>tural lands (2000 US dol-<br>lars per hectare per year)|10km2|Naidoo and<br>Iwamura (2007)|
|_Climate_||||
|bio1|Annual Mean Temperature|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|bio4|Temperature Seasonality<br>(standard deviation *100)|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|bio12|Annual Precipitation|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|bio15|Precipitation Seasonality|1km2|Bioclim (Hi-|
||(Coefcient of Variation)||jmans et al.<br>2005)|
|bio16|Precipitation of Wettest|1km2|Bioclim (Hi-|
||Quarter||jmans et al.<br>2005)|
|bio17|Precipitation of Driest<br>Quarter|1km2|Bioclim (Hi-<br>jmans et al.<br>2005)|
|oxmap<br>~~d~~aylst|Day time land surface tem-<br>perature (Celcius)|5km2|MOD11A2 and<br>Weiss et al.<br>(2014)|
|oxmap<br>~~n~~gtlst|Night time land surface<br>temperature (Celcius)|5km2|MOD11A2 and<br>Weiss et al.<br>(2014)|

18
Friedman’s (1957) Permanent Income Hypothesis (PIH) contended that permanent income and not current income was the relevant determinant of consumption, and that permanent consumption was proportional to permanent income (the proportionality hypothesis, which implies that the permanent income elasticity is unity)<sup>24</sup> . Such a model would suggest that the marginal propensity to consume out of permanent income should be close to 1, and the marginal propensity to consume from transitory income should be close to 0. The implication of the PIH for our results is that if beneficiary households view the transfers as transitory, then they would save most of them; if they view them as permanent, then they would consume most of them.

The proportionality hypothesis and its major implications regarding growth and equity in developing countries have been tested many times, with ambiguous results. For example, Bhalla (1979) finds an estimated marginal propensity to consume out of permanent income of 0.61 and an elasticity of 0.79 (and significantly different from 1) using data from rural India. Wolpin (1982) estimates permanent income elasticities in the range of 0.91 to 1.02 for India as well and concludes that “whether or not it is unity, the permanent income elasticity of consumption may be closer to unity than suggested by previous research”. Musgrove (1979) estimates 0.881 for urban Colombia, 0.896 for urban Ecuador, and, 0.776 for urban Peru; all significantly different from 1. Finally, Paxson (1992) estimates marginal propensities to consume from 0.56 to 0.84 out of permanent income, being significantly different from 1; and from 0.17 to 0.27 out of transitory income for rice farmers in Thailand.

Our estimate of the marginal propensity to consume out of current transfers is 0.88. In view of the estimated magnitudes of marginal propensities to consume in the development literature, we are inclined to think that the rural Mexican households studied here perceive the OPORTUNIDADES transfer as a source of permanent income. Indeed, households know that once they start receiving the benefit they will continue to receive it for at least 9 years provided they

> 24 The Modigliani-Brumberg life cycle hypothesis (LCH) reaches analogous conclusions.

27
opportunity and 0 otherwise) is regressed—using a logistic regression method—on the set of circumstances of the child, for the full sample of children of the relevant age group. The estimated coefficients of the regression are used to obtain the predicted probability of access to the opportunity for each child. These data are then used to estimate the D-index for the particular opportunity, the coverage rate, and the HOI.

**Table 2. Infrastructure/Habitation and Education Opportunities**

|||Survey<br>|years<br>||
|---|---|---|---|---|
||1985 (%)|1998 (%)|2002 (%)|2008 (%)|
|Child 015 ith|_Infrastructur_|_e/habitation_|||
|ren – years w<br>access to electric light|37|45|50|52|
|Children 0–15 years with<br>access to sanitation|58|61|61|62|
|Children 0–15 years with<br>access to clean water|88|87|92|85|
||_Educa_|_tion_|||
|Attending school (6–12 years)|54|56|56|55|
|Attending school (13–15years)|47|52|47|47|
|Started primary school on time<br>(6–7years)|—|39|39|46|
|Finish sixth grade (13–15<br>years)|55|31|31|29|

_Source_ : Authors’ compilation.

13
**Figure 10: Reallocation of Exports by Value**

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10<br>Decile by value, 1992<br>Note: Data uses HS 6-digit classifications.<br>.25<br>.2<br>.15<br>.1<br>Share of total value in 2005<br>.05<br>0<br><!-- End of picture text -->

**Figure 11: Reallocation of Exports by Product Shares**

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10<br>Decile by number of products, 1992<br>Share of total value, 1992<br>Share of total value, 2005<br>Note: Data uses HS 6-digit classifications.<br>.8<br>.6<br>.4<br>Share of total value<br>.2<br>0<br><!-- End of picture text --> 25
Table 2: COVID-19 and Electricity Consumption in Rwanda

|||||logof|Consumpti|on|||
|---|---|---|---|---|---|---|---|---|
||A|ll|Resid|ential|Non-Res|idential|Small & Me|dium Industries|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|DID (Month> March=0)<br>DID (Month> March=1)|0.017***<br>(0.001)<br>-0.017***<br>(0.001)|0.019***<br>(0.001)<br>-0.017***<br>(0.001)|0.032***<br>(0.001)<br>0.022***<br>(0.001)|0.036***<br>(0.001)<br>0.022***<br>(0.001)|-0.074***<br>(0.002)<br>-0.279***<br>(0.003)|-0.072***<br>(0.002)<br>-0.279***<br>(0.003)|-0.130***<br>(0.038)<br>-0.240***<br>(0.058)|-0.146***<br>(0.041)<br>-0.239***<br>(0.058)|
|DID (Month> March=2)|-0.004***|0.001|0.029***|0.033***|-0.177***|-0.172***|-0.052|-0.043|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.003)|(0.003)|(0.042)|(0.044)|
|DID (Month> March=3)|0.003***<br>(0.001)|0.002**<br>(0.001)|0.027***<br>(0.001)|0.026***<br>(0.001)|-0.130***<br>(0.003)|-0.131***<br>(0.003)|0.029<br>(0.042)|0.033<br>(0.042)|
|DID (Month> March=4)|0.010***<br>(0.001)|0.007***<br>(0.001)|0.031***<br>(0.001)|0.027***<br>(0.001)|-0.112***<br>(0.003)|-0.114***<br>(0.003)|0.031<br>(0.043)|0.04<br>(0.043)|
|DID (Month> March=5)|0.020***<br>(0.001)|0.030***<br>(0.001)|0.039***<br>(0.001)|0.049***<br>(0.001)|-0.101***<br>(0.003)|-0.091***<br>(0.003)|0.03<br>(0.043)|0.041<br>(0.048)|
|DID (Month> March=6)|0.044***<br>(0.001)|0.041***<br>(0.001)|0.063***<br>(0.001)|0.061***<br>(0.001)|-0.084***<br>(0.003)|-0.085***<br>(0.003)|0.066<br>(0.044)|0.071*<br>(0.043)|
|DID (Month> March=7)|0.054***<br>(0.001)|0.052***<br>(0.001)|0.071***<br>(0.001)|0.068***<br>(0.001)|-0.056***<br>(0.003)|-0.057***<br>(0.003)|0.038<br>(0.044)|0.049<br>(0.045)|
|DID (Month> March=8)|0.037***|0.027***|0.048***|0.038***|-0.049***|-0.060***|0.02|0.019|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.003)|(0.003)|(0.046)|(0.048)|
|DID (Month> March=9)|0.024|0.022|0.031|0.028|-0.062|-0.062|-0.022|0|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.003)|(0.003)|(0.047)|(0.048)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. Var|27.552|27.552|20.426|20.426|67.526|67.526|256.741|256.741|
|Obs|21352142|21352142|17097530|17097530|3212773|3212773|13345|13345|

Notes: Dependent variable is the log of monthly electricity consumption. DID (Month> April= _ω_ ) represents the DID estimates for the _ω_ months since the start of COVID lockdown (April, 2020). The DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly average</sup> temperature and total precipitation . Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 28
7 figure I INDUSTRIAL COUNTRIES: IMPCRTS AO aODP 4. The member countries **(if** the EC are the world's most open industrial economies. This is partly the result of trade within the Corrmmon Market itself, as members' imports from each other amounted to almost 14 percent of their combined GDP" in 1990. Total merchandise imports of EC member countries are equivalent to almost a quarter of their GDP, roughly comparable to the import/GDP ratios of much smaller economies like New Zealand or Finland. The Netherlands' 1990 GDP was abuut the same as Australia's, while its mer- I chandise imports weret three <!-- Start of picture text --> CHART i<br>0. MP ce MO .<br>0_g<br>b kartu h<br>a<br>.4 +_<br>t Am<br>N Z . I.awnwf<br>02 a *oIlnend G ;_ _ __<br>+ +<br>O. +. __ Y<br>+<br>4.5 S 5.4 5.8 6.2 9.9<br>MP_Clogmm<br>a aI iffnwtoP * umnuf. IK1oCmP<br><!-- End of picture text --> times higher. Estimates of trade in commercial services are rather more imprecise than those of merchandise trade, and must be treated with caution. As national data rarely indicate origins and destination, adjustments for intra-EC trade and estimates of EC imports are unavailable. Mostly for that reason, this Report concentrates on nmerchandise trade; but the wisps of information available on trade in services do not contradict its findings.

_5._ Considering only trade with non-member countries, the EC had an import/GDP ratio of over 9.9 percent in 1990, as against 9.6 percent for the United States<sup>9</sup> . The ratio was 8 percent for Japan, a much smaller economy with a poorer natural resource base. The EC is a major producer both of energy (German coal, British petroleum and Dutch natural gas) and of agricultural commodities. Yet its imports of primary commodities (including energy) were higher, in proportion to GDP, than those of the US (as was to be expected, given the large area and vast natural wealth of the United States) and not much lower than Japan's (3.7 of GDP as against 4.6 percent).

**Import** data **rom** GATr except when **otherwise** indicated. **National Account** _GDP_ come **generally from the** Wodd Bank, Wodd Tables (WT), **nd are** convened by taking the **Word** Tables conversion facton, **which are esentally** annual average **exchane rates. In a** few **cases, manufactuing** production **data** have been obbtined from **the World ak's World** Development Indicators **(WDl).** Manufacturing **production** for **the** BC u **a whole had** to be **esmated, becauae** of **a** gap **ia** count, -At".

s Except If **otherwise** spe4ifled, **all** references to EC concem **the** twelve pruent **member counties. For** the **ke** of **simplicity,** import into the EC from the outside **will** be **refrred** to **a 'imootsu. Trade** among **BC meben will** be **called ints-EC** trmde, or **'inta-trde'.**
Table A3.2: Unit Value Simulation: Numbers of actions triggered, by importing country and Numbers of tariff lines on which the hypothetical trigger would impose an import restriction on <u>imports from at least one country</u>

||Total # of<br>restrictions<br>against ICs|Total # of<br>restrictions<br>against DCs|Total # of<br>restrictions<br>against DCs +<br>ICs|Number of HS<br>lines with at least<br>one restriction|Total nu<br>of HS li<br>with imp|
|---|---|---|---|---|---|
|China|15|24|39|15||
|India|10|18|28|13||
|Indonesia|30|49|79|17||
|Malaysia|12|34|46|20||
|Brazil|5|9|14|9||
|Mexico|11|9|20|13||
|Ofthese:||||||
|Asiancountries|67|125|192|65||
|Latin Americancountries|16|18|34|22||
|Allsixcountries|83|143|226|87||

69
Table 6: Impacts on Fishing Intensity and Prevalence by Quarters

||**Boat detect**|**ion density (log)**|**Boat detection **|**probability (%)**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|treat X [Q2-2016]|-0.333***|-0.364***|-0.0931***|-0.0919***|
||(0.0941)|(0.0928)|(0.0231)|(0.0238)|
|treat X [Q3-2016]|-0.310***|-0.226***|-0.0857***|-0.0640**|
||(0.0654)|(0.0667)|(0.0205)|(0.0236)|
|treat X [Q4-2016]|-0.128**|-0.0532|-0.0481*|-0.0215|
||(0.0480)|(0.0501)|(0.0257)|(0.0264)|
|Observations|219,984|147,360|219,984|147,360|
|R-squared|0.289|0.284|0.211|0.208|
|Light pixel FE|Yes|Yes|Yes|Yes|
|monthXyear FE|Yes|Yes|Yes|Yes|
|provinceXmonth FE|Yes|Yes|Yes|Yes|
|Control Groups|All Other|Restricted|All Other|Restricted|

Standard errors in parentheses, *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Note: this table shows the quarterly impacts of Formosa for the rest of 2016, on fishing activity in the fishingrestricted region (i.e. 20nm near-shore of Formosa-affected provinces), using VIIRS’ monthly-aggregate boat detection data. Each observation is a grid-month. Sample includes all monthly observations between 2013 and 2016. The grid size is 10-mile-square. A grid is considered near-shore when it is located less than 20 nautical miles away from the coast line. The reported outcome variables include log-transformed monthlyaggregate boat counts in each grid (columns 1-2), and 0/1 probability that the grid was detected with at least a boat in that month (columns 3-4). The control group ”All Other” refers to all coastal provinces in the country except for the four Formosa-affected central provinces (Ha Tinh, Quang Binh, Quang Tri, and Hue). The control group ”Restricted” refers to all coastal provinces located south of Phu Yen (i.e. distant from the Formosa-affected region).

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