rafmacalaba/datause-encoder-data huggingface dataset:
# _MODIS_

We have also used Google Earth Engine to acquire daily measures of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometers (MODIS) onboard NASA’s Aqua and Terra satellites. AOD measurement is based on the log difference of the light arriving at the ground and the light arriving at the top of the atmosphere. The difference is attributed to particulate matter in the air, and has been extensively employed for analysis of air pollution. Since 2000, NASA’s MODIS satellites have provided AOD measures with 8- to 16-day repeat observation frequency.

Research on the relationship between AOD measures and ground-level particulate pollution is well-advanced. Recent work includes applications for the United States (Lee et al. 2016), China (Xie et al. 2015; Lv et al. 2016; Hu et al. 2019), Turkey (Özgür and Wang 2019) and India (Sathe et al. 2019). The results are somewhat mixed, but generally support the utility of AOD as a proxy for ground-based particulate measures.

# **3.3 Traffic Data**

Traffic congestion increases average vehicle transit time, which translates to higher polluting emissions per vehicle. Traffic congestion is a major source of outdoor air pollution in Dhaka.<sup>4</sup> We develop an index of traffic congestion using Google Traffic, which provides real-time traffic reports for the Dhaka metro region. Although desktop monitors do not show this, Google Traffic continuously produces a map large enough to cover the entire metro region at .001-degree resolution. However, Google provides no facility for georeferenced downloads of the map. To mobilize this huge information resource, one of the authors has developed an algorithm that maps region-scale Google Traffic displays into a panel database of traffic measures for a reference grid with arbitrary cell size.

Google Traffic provides a four-color measure of vehicle speed. We use this information to compute hourly average speeds (km/hr) in 2019 for the .01-degree cell that surrounds the CAMS-3 monitoring station. Our data are drawn from 2,241 hourly Google Traffic images for two periods in 2019: Jan. 1 - Feb. 8 and June 15 - Sept. 28. We convert these files to geoTIFF format and estimate vehicle speeds by color code using trip routings for a sample of real-time Google Traffic information. In each hour, we compute the weighted average speed for the cell in two steps. First, we calculate the share of colored pixels in each category. Then we weight the speeds in Table 1 by the color pixel shares and compute the composite weighted average speed for the hour.

> 4 For discussion of air pollution from traffic sources, see Khaliquzzaman et al. 2020, RSTP 2015.

6
The plan of the paper is as follows. The next section describes the Environmental Control System and reviews the broad deforestation trends since its introduction. Section 3 discusses methodological difficulties in assessing program impact and proposes a conceptual framework and econometric test for addressing these issues, looking at post program changes in deforestation differentials across the landscape. The following section describes the data. The ‘difference-in-difference’ predictions are then examined via simple crosstabulations in section 5 and subjected to formal multivariate statistical tests in section 6. Section 7, a digression, looks at spatial variation in land values as an important factor in understanding the pressures for deforestation. The paper concludes with a brief summary.

# **1 FEMA’s Environmental Control system**

In 2000, FEMA (the Mato Grosso state environmental agency) implemented an improved rural environmental control system (State of Mato Grosso 2001), sometimes called SLAPR (an acronym for Rural Property Environmental Licensing System). Licensing, although prominent, is only one component in an integrated system.

The goal of the system is to “enforce current environmental legislation as simply and as inexpensively as possible” with particular focus on enforcing RL and APP requirements, enforcing rules on forest and field burning, and ensuring that all land use activities are appropriately licensed.<sup>3</sup> Cost-effectiveness results from the use of remote sensing (satellite imagery) to monitor large areas for deforestation and illegal forest burning; the use of GIS (geographic information systems) to manage data on licensed properties; and a focus on properties of greater than 1000 hectares, since these are relatively few in number but control most privately owned land and are presumed responsible for the bulk of deforestation.

> 3 The following description of the system is based largely on State of Mato Grosso (2001), from which the quote was taken.

6
consumption respectively of household of individual I and working in sector _s_ . For the second group, postpandemic per capita consumption is set at the same level as the mean of the consumption<sup>9</sup> of a household whose head is unemployed (in their area of residence). It is also useful to note that we assume that public sector workers (including SOEs) and retirees will not suffer any loss of income generated by the pandemic. This methodology allows us to estimate the impact of the pandemic on household consumption levels.

_Estimation of price changes on household welfare_ Let the per capita total expenditure of household on goods at price 0 ( ). All pre-pandemic prices are normalized to 1. Let 1 ( ) the vector of post-pandemic prices. ii xx ii(= 1, ... NN) qq(1 × KK) pp 1 × KK denotes the price of good =1,...,K ) . kk,1 pp 1 × KK pp The simplest way to estimate the loss of purchasing power is to use the published CPI and define the postkk ( Such approach ignores the heterogeneity of consumption patterns across households, in particular between poor and non-poor households. For more precise estimates of the impact of price increases, it is important to estimate a price index at the household level. To do so, we rely on the approach adopted bu King (1983) and define the equivalent income. Under the budget constraint defined by 1 ) , the equivalent income is defined as the income level which, in the reference price system 1 , generates the ii,1 (pp , xx same level of utility as the level of utility achieved under 1 ) : pp 0 0 1 1 ) (pp , xxii,1 (2) where ii denotes the indirect utility function, and ii1 ii1 (. ) the equivalent income function of household . νν(pp , ΓΓ (pp , pp , xx )) = νν(pp , xx Given that all households face the same price 1 , hii(. ) can be considered as a monetary measure of νν(. ) ΓΓ ii utility. 1 ) given that h(. ) is a monotonic transformation of . Inverting the indirect utility pp ΓΓ function, the equivalent income can be derived as: ii1 νν(pp , xx ΓΓ νν(. ) 0 1 (3) Γii0 = Γii 0 1 ii0 ii0 (4) ΓΓ = ΓΓ (pp , pp , xx ) = xx where ii1 ii and ii1 denote pre and post pandemic equivalent incomes, and ii0 ii1 is the equivalent loss. The (pp , pp , xx ) = xx −EELL price effect of the pandemic on household is estimated by the . ii0 ii1 ii1 ΓΓ ΓΓ EELL Suppose that consumer preferences are represented at the Stone-Geary utility function. The ii1 ii EELL corresponding indirect utility function is given by:

> 9 Consumption surveys only provide information on consumption and not income.

12
|**Table 9: Which course characteristics are associated with better im**|**pacts?**||Differenti|al Impact on:|||
|---|---|---|---|---|---|---|
|||Control Mean||Aggregate|Ever|Currently|
||Sample|(Std. Dev.) of|Employed|Employmen|t<br>formal|formal|
|**_Treatment Interaction with:_**|Size|InteractingVariable|20+ hours|Index|byJan 12|<br>Aug13|
|_Measures of course attendance rates_|||||||
|Proportion assigned to course who attended it|5497|0.765|0.114|0.173|0.014|0.144|
|||(0.140)|(0.092)|(0.172)|(0.117)|(0.087)|
|Proportion assigned to course who completed it|5497|0.723|0.055|0.118|0.018|0.148*|
|||(0.164)|(0.083)|(0.155)|(0.095)|(0.081)|
|_Proxies for course quality_|||||||
|Course length above 320 hours|5494|0.423|-0.042*|-0.082*|-0.018|-0.024|
|||(0.494)|(0.024)|(0.044)|(0.027)|(0.023)|
|Average teacher experience greater than 12 months|4833|0.418|0.006|0.017|-0.017|0.030|
|||(0.493)|(0.026)|(0.048)|(0.029)|(0.025)|
|Percent of course teachers with tertiary education|4833|65.0|-0.000|-0.000|-0.000|-0.000|
|||(43.2)|(0.000)|(0.001)|(0.000)|(0.000)|
|Course has two or more competitors|4833|0.674|0.039|0.085*|0.042|0.004|
|||(0.468)|(0.026)|(0.049)|(0.030)|(0.028)|
|Course offered by private provider|5494|0.348|0.044*|0.117*** (a)|0.062**|0.025|
|||(0.477)|(0.023)|(0.043)|(0.028)|(0.025)|
|_Measures of what trainees thought course did_|||||||
|Proportion who thought course taught new technical skills|5497|0.796|-0.087|-0.209|-0.134|-0.049|
|||(0.127)|(0.093)|(0.185)|(0.100)|(0.108)|
|Proportion who thought course certified skills they had already|5497|0.842|-0.012|-0.096|-0.062|0.093|
|||(0.103)|(0.108)|(0.213)|(0.104)|(0.107)|
|Proportion who thought course taught new job finding strategies|5497|0.604|-0.025|-0.067|0.000|0.073|
|||(0.147)|(0.090)|(0.166)|(0.108)|(0.084)|
|Proportion who thought course made them aware of new jobs|5497|0.449|-0.052|-0.153|-0.154**|0.007|
|||(0.183)|(0.072)|(0.140)|(0.068)|(0.060)|
|_Measure of labor market demand_|||||||
|Provincial unemployment rate is above median|5497|0.463|0.013|0.033|-0.001|0.049**|
|||(0.499)|(0.023)|(0.044)|(0.026)|(0.023)|
|_Type of course_|||||||
|Accounting course|5497|0.242|0.059**|0.134**|0.046|0.034|
|||(0.428)|(0.027)|(0.053)|(0.031)|(0.028)|
|Computer course|5497|0.153|0.002|0.018|0.080**|-0.039|
|||(0.360)|(0.033)|(0.063)|(0.038)|(0.035)|

Notes: robust standard errors in parentheses, clustered at the course level. *, **, and *** indicate significance at the 10, 5 and 1 percent levels respectively. (a) indicates significance after applying the Benjamini-Hochberg procedure to maintain the false discovery rate over the 12 different interactions at 10 percent.

Each row shows the treatment interaction from a regression which includes controls for course*age*gender lotteries, and for the level of the variable being interacted.

41
order for them to: (a) endorse the approach, (b) work with local farmers, including local urban farmers, to introduce or improve on-farm practices to minimize the health risks, and (c) to communicate the safety of wastewater-irrigated foods to those who consume them. In addition local environmental health officials need to be trained in the basics of HACCP so that they can establish locally appropriate HACCP procedures to ensure the safety of wastewater-irrigated foods.

This will enable QMRA to be implemented and used correctly so that the most appropriate costeffective and health-protective practices for the agricultural use of wastewater can be selected, implemented, and monitored.

# **Box  4.4 QMRA investigations in urban Ghana**

In urban Ghana many people eat lettuce as part of street-vended fast food (sandwiches, etc.) typically at a rate of around 10−12 g on four occasions a week. Most of the lettuce is produced by urban farmers who use wastewater, graywater, drainage water or other contaminated waters for irrigation. Almost all the fast-food/street-food vendors say that they wash the lettuce with tap water and use disinfectants such as lemon, household bleach and vinegar during the preparation of lettuce salads, although it was believed that this washing/disinfection process was less than perfect. QMRA-MC investigations were done to determine the associated health risks as there was concern that the consumption of wastewaterirrigated lettuce and other salad crops was having an adverse effect on consumer health.

QMRA-MC risk simulations found that the annual infection risk associated with the consumption of lettuce irrigated under the current wastewater-irrigation and post-harvest practices common across the country showed a high median viral infection risk of ~0.1 pppy, whereas bacterial and protozoan infection risks were only ~10<sup>−5</sup> pppy. These infection risks resulted in about 477,000 mainly selflimiting cases of diarrheal disease, representing 0.68 episode per consumer per year; however, about 14 % and 0.1 % of these diarrheal-disease cases were severe and fatal, respectively. The resulting burden of disease was an annual DALY loss of 12,000, equivalent to a 0.017 DALY loss per consumer per year, with this figure representing nearly 10 % of the WHO-reported DALY losses occurring in Ghana due to various types of water-, sanitation- and hygiene-related diarrhea (Prüss-Üstün _et al_ . 2008). The study also used QMRA-MC risk simulations to investigate _Ascaris_ infection risks to both the urban farmers and consumers. It was found that the risk was ~1 pppy and that, for the farmers, this was due to the involuntary ingestion of wastewater-contaminated soil.

The study identified that poor on-farm and at-market practices were responsible for much of the contamination of the lettuce and hence the infection risks, and that simple on-farm and at-market measures, of the types identified in Table 4.2 and discussed in sections 4.3.1 and 4.3.2, could reduce crop contamination and infection risks to more acceptable levels.

Source: Seidu _et al_ ., 2008.

45
**Table 8. Parent Matchmaking and Number of Children**

|||**The number of child**<br>|**ren**<br>|**Dummy =**<br>|**1 if three amatnd mo**<br>|**re children**<br>|
|---|---|---|---|---|---|---|
||**OLS**|**OLS**|**2SLS**|**OLS**|**OLS**|**2SLS**|
|rural|0.750**|0.856**|-0.497|-0.012|0.032|-0.535***|
||(0.347)|(0.345)|(0.556)|(0.103)|(0.089)|(0.175)|
|parent matched|0.009|-0.001|2.373|-0.009|-0.013|-1.323*|
||(0.047)|(0.045)|(2.154)|(0.013)|(0.013)|(0.770)|
|parent matched * rural|0.188***|0.184***|3.150|0.080***|0.080***|2.655***|
||(0.065)|(0.062)|(2.164)|(0.022)|(0.021)|(0.785)|
|Other controls as in column (1)<br>of Table 5|Yes|Yes|Yes|Yes|Yes|Yes|
|Religion and ethnic dummies|No|Yes|Yes|No|Yes|Yes|
|Number of observations|6,334|6,334|6,334|6,334|6,334|6,334|
|Adjusted R2|0.299|0.305|-3.797|0.120|0.127|-2.200|
|Cragg-Donald Wald F stat|||29.8|||29.8|
|Kleibergen-PaapWalk rk F stat|||16.9|||16.9|

Note. *, **, and ***: statistically significant at the 10, 5, and 1 percent level. White-corrected standard errors clustered at the township level in parentheses.

43
Policy Research Working Paper 9044

# **Abstract**

This paper summarizes the latest update of the World Bank Bank Regulation and Supervision Survey. The paper explores and summarizes the evolution in bank capital regulations, capitalization of banks, market discipline, and supervisory power since the global financial crisis. It shows that regulatory capital increased, but some elements of capital regulations became laxer. Market discipline may have deteriorated as the financial safety nets became more generous after the crisis. Bank supervision became stricter and more complex compared with the pre–global financial crisis period. However, supervisory capacity did not increase in proportion to the extent and complexity of new bank regulations. The paper documents the importance of defining bank regulatory capital narrowly, as the quality of capital matters in reducing bank risk. This is particularly true for large banks, because they have more discretion in the computation of risk weights and are better able to issue a variety of capital instruments.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at danginer@sfu.ca, ata.bertay@sabanciuniv.edu, rcull@worldbank.org, ademirguckunt@worldbank. org, and dmare@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
created is relatively high, at R90,758 per job created for the low elasticity case, because the wage subsidy is provided to all low-skilled formal and medium-skilled formal labor hired, not just to the additional workers. The cost per job created declines dramatically as the economy becomes more flexible.

**Table 11 Government revenue (billion rand) and fiscal cost of wage subsidy** (10% wage subsidy to low-skilled and medium-skilled formal labor)

||Base|Low|Medium|High|
|---|---|---|---|---|
|||10%|10%|10%|
|Direct tax|169.0|172.7|173.5|174.9|
|Indirect tax|||||
|Tariffs|8.3|8.3|8.4|8.5|
|Domestic|62.5|63.1|63.5|64.3|
|NetVAT|69.6|70.2|70.7|71.7|
|Totaltax revenue|309.4|314.3|316.1|319.3|
|Additional tax revenue<br>(revenue effectfromexisting taxes due toincreased employment and GDP)||4.9|6.8|10.0|
|Wage subsidy cost|0.0|-19.7|-20.5|-21.9|
|Net wage subsidy cost (implied lump sum tax)||-14.7|-13.7|-11.9|
|Effectivewage subsidyrate (percent ofthe costnot covered by therevenue effect)||74.9|67.0|54.5|
|Wage subsidy cost perjob created (Rperjob)||90758.4|47406.5|27039.8|

Source: CGE model simulations.

The employment and GDP effects increase as the subsidy rate increases. Here we report the changes for the base case. As seen in Figure 5, total employment growth ranges from 1.8 percent for a 5 percent wage subsidy to 11 percent for a 25 percent wage subsidy. GDP growth ranges from 0.6 to 3.3 percent.

**Figure 5: Employment and GDP changes in response to wage subsidies, medium elasticity case**

<!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>0.05 0.1 0.15 0.2 0.25<br>Wage subsidy<br>Low -skilled Formal Labor Medium-skilled Formal Labor<br>Total Labor Force GDP at market prices<br>Percent change<br><!-- End of picture text --> Source: CGE model simulations.

The current model specification assumes that the wage subsidy only affects the number employed without affecting the market wages. Alternatively, the presence of labor unions means that some of the wage subsidy is collected by union workers in the form of higher wages. To

- 19 -
should be driven by informal households. Tables 4 and 5 explore whether this is the case by separately focusing on the sample of households that are informal or formal in both periods. According to table 4, compared to informal households from non-Azteca municipalities, informal households in Azteca municipalities were 76% more likely to know they can borrow from banks (�3 = 0:1009) after the opening of Azteca. Their likelihood of obtaining loans from banks more than doubled (�3 = 0:0139)<sup>9</sup> . Moreover, after the entrance of Azteca in their municipalities, the probability that informal households obtained pawnshop loans decreased in 45% (�3 = �0:01). In addition, the fraction of informal households saving substantially decreased in 13% (�3 = �0:043). While the average savings of households below the 40th income percentile declined by 46% (Mx $2,264), savings held by households below the 60th income percentile did not change substantially, suggesting that it was poorer households those reducing their savings.

Consistent with the clients that Banco Azteca targets, table 5 suggests that formal households from municipalities with Azteca branches did not experience significant changes on their saving patterns after the opening of this bank. Altogether, this exercise suggests that informal households from municipalities where Azteca opened concentrate the changes in saving patterns observed from the data.

# **3.3 Consumption patterns**

In the MxFLS questionnaire, each household that obtained a loan is asked about its stated reasons for borrowing. The two most reported reasons were: i) to finance consumption expenditures during a bad economic situation such as unemployment, sickness of family member, etc. (50% of responses); and ii) to purchase/repair durable goods (35% of responses). Only 8.5% of the borrowers stated that they obtained their loan to start a new business or to invest in one.

Table 6 examines whether consumption patterns of households in municipalities where Azteca opened a branch experienced any change. In particular, since borrowers report using loans towards durable goods purchases/repairs, this exercise first explores if households from Azteca municipalities own more durable goods and of higher value. Panel A of the table presents the means over time and DID estimates on: the proportion of households owning electronic appliances (radio, TV set, VCR, computer, etc.), furniture and large appliances (washing and dryer machine, stove, refrigerator), and other appliances (blender, iron, microwave, etc.), and the value of these goods. The

> 9Conditional on borrowing from banks, the average loan size also declined: while in 2002 the median bank loan size obtained by informal households in Azteca municipalities was 23,141 Mexican pesos, by 2005 it was 19,494.

11
> <sup>29</sup>

French for financial sector development, approximately the same as British and that German is potentially several times more conducive than British legal origin to financial sector development.

**Table 4:  Legal origin and financial sector development**

|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Growth<br>75 – 00<br>(_OLS_)|Level 2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|
|---|---|---|---|---|---|---|
|British legal|.008|.23|.24|.02|.21|.26|
|origin|(_.43_)|(_.12_)|(_.03_)|(_.06_)|(_.02_)|(_.003_)|
|German legal|.017|.64|.90|.03|.62|.93|
|origin|(_.113_)|(_.001_)|(_0.00_)|(_.02_)|(_0.00_)|(_0.00_)|
|French legal|-.01|.04|.002||||
|origin|(_.31_)|(_.76_)|(._98_)||||
|Scandinavian||||.01|.10|.48|
|legal origin||||(_.31_)|(_.35_)|(_0.0_)|
|_R_<sup>_2_</sup>|.09|.46|.26|.09|.47|.31|
|_N_|78|94|117|78|94|117|

N.B. Specifications are as in the first three columns of Table 1a, substituting the legal origin for the political variables. _p_ -values are reported in parentheses, based on robust standard errors. Constants and other conditioning variables not reported.

Tables 5 and 6 replicate the regressions in Tables 1a, 1b, 2a and 2b, with the addition of the legal origin controls. Following these earlier tables, Tables 5a and 6a present the results of ordinary least squares regressions and Tables 5b and 6b the instrumental variable estimates, using colonial heritage variables as instruments.

From Tables 5a and 6a, it is clear that controls for legal origin reduce somewhat the estimated effects of some political variables. However, in contrast to the results found elsewhere in the literature using different political variables, the political hypotheses considered here are not rejected in ten of 12 specifications even when controlling for legal origin and even recognizing that legal origin may itself capture political effects. The