rafmacalaba/datause-encoder-data huggingface dataset:
||**Table 2: Altern**<br>**Total credit to private**<br>**sector**|**ative measures of financia**<br>**Bank credit to bank**<br>**deposit ratio**|**l development**<br>**ATMs per 100,000**<br>**adults**|**Bank br**<br>**100,0**|**anches per**<br>**00 adults**|
|---|---|---|---|---|---|
|Dependent variable:|**(1)**<br>**(2)**|**(3)**<br>**(4)**|**(5)**<br>**(6)**|**(7)**|**(8)**|
|Woman top manager||||||
|Y:1 N:0||||||
|||**Panel A: Log odds ratios**||||
|Total credit to private|1.940***<br>2.539***|||||
|sector (ratio of GDP)|(0.350)<br>(0.679)|||||
|Bank credit to bank||0.509***<br>0.462*||||
|deposit ratio||(0.165)<br>(0.237)||||
|<br>ATMs per 100,000||<br>|0.391***<br>0.620***|||
|adults (logs)|||(0.089)<br>(0.187)|||
|Bank branches per||||0.522***|0.547**|
|<br>100,000 adults (logs)||||(0.156)|(0.245)|
|GDP per capita (logs)|-0.429|-0.120|-0.877||-0.347|
||(1.039)|(1.019)|(0.979)||(1.004)|
|Number of workers|-0.185***|-0.182***|-0.181***||-0.186***|
|(logs)|(0.062)|(0.062)|(0.063)||(0.063)|
|Age of firm (logs)|-0.098|-0.111|-0.113||-0.108|
||(0.113)|(0.114)|(0.117)||(0.114)|
|Labor productivity|-0.005|-0.004|-0.002||-0.005|
|(logs)|(0.045)|(0.045)|(0.047)||(0.045)|
|Women owners Y:1|2.645***|2.654***|2.629***||2.655***|
|N:0|(0.153)|(0.149)|(0.149)||(0.150)|
|Women workers|1.524***|1.548***|1.618***||1.548***|
|(proportion)|(0.209)|(0.208)|(0.209)||(0.209)|
|<br>Exports (direct, share|<br>-0.091|<br>-0.125|<br>-0.098||<br>-0.120|
|of sales)|(0.240)|(0.241)|(0.241)||(0.242)|
|Foreign ownership|0.314|0.317|0.261||0.314|
||(0.303)|(0.306)|(0.304)||(0.305)|
|Quality certification|<br>0.095|<br>0.120|<br>0.100||<br>0.122|
|Y:1 N:0|(0.138)|(0.137)|(0.138)||(0.138)|
|Overdraft Y:1 N:0|-0.091|-0.110|-0.095||-0.108|
||(0.129)|(0.127)|(0.127)||(0.128)|
|Multi establishment|<br>0.075|<br>0.087|<br>0.116||<br>0.079|
|firm Y:1 N:0|(0.176)|(0.176)|(0.177)||(0.176)|
|Obtaining license is a|0.100|0.070|0.088||0.069|
|<br>major obstacle Y:1 N:0|(0.187)|(0.186)|(0.188)||(0.186)|
|<br>Losses from power|<br>1.593**|<br>1.664**|<br>1.018||<br>1.631**|
|outages|(0.779)|(0.773)|(0.781)||(0.775)|
|Firm audited Y:1 N:0|-0.082|-0.085|-0.054||-0.080|
||(0.117)|(0.115)|(0.114)||(0.115)|

38
would imply that when the provision of water is controlled for, the plant destruction borne by storms can only harm agriculture.

Although the empirical analysis does not allow discerning the mechanisms through which the growth effects of natural disasters are realized as such, two channels identified in the Solow-Swan model seem especially relevant in interpreting the contrasting effects of natural disasters on agricultural growth. The first channel through which the natural disasters affect agricultural growth relates to the provision of raw materials and intermediate inputs (m): if an event decreases the availability of this resource (such as water, seedlings or unharvested plants/fruits on the fields for farming), it is likely to have a negative growth effect, and _vice versa._ The second key is related to total factor productivity (A): if an event destroys public infrastructure (say, water dams or irrigation canals) or any other productivity determinant, its growth effect is likely to be negative.

Given the critical importance of water for agriculture, the strong negative effect of droughts on agricultural growth does not come as a surprise and is consistent with the evidence from growth studies based on micro-household data (Dercon, 2004; Christiaensen and Subbarao, 2005). Similarly, storms can have devastating effects on harvests by destroying seedlings and/or unharvested products on the field as well as irrigation infrastructure. While storms cause substantial economic damage (especially infrastructure and buildings), their effect on agricultural capital (which consists largely of tools and machinery) is likely more muted, thereby leaving the capital/labor largely unaffected (or decreasing it if anything).

Within this perspective, the positive effect of floods on growth comes a bit as a surprise. Too much water is clearly damaging. Yet, when floods are localized, and if they are also associated with plentiful supply of water nationwide which would positively affect agriculture including through the collection of irrigation water, the latter effect may well outweigh the former, resulting in a positive overall effect of floods on agricultural growth, or no effect or a small negative one if floods are more widespread and severe.<sup>18</sup> Given much larger frequency of reported flood events (30 percent) compared to drought

> 18 Both Pooled OLS and Fixed-Effects regressions (whose results are available upon request) confirm the positive and significant association between annual rainfall (relative to the corresponding country average) and the flood intensity measure used in the paper.

21
estimated losses as a result of theft, robbery, vandalism or arson that occurred on establishment’s premises calculated as a percent of annual sales or the total annual value of the losses?” In some instances the total value of losses is reported, and in that case the share of losses due to crime over total sales is manually calculated. The main advantage of having a share of the losses over total sales as a measure of crime is that it provides a unit free measure given the range of measurement issues regarding currency exchange rates and inflation prevalent in crime measures using local currency units. We make no distinction between a firm that has experienced no crime and a firm that has experienced crime but incurred no losses. Both firms get a zero value for the dependent variable. The variable averages 0.72% in the sample with a standard deviation of 3.9%. Using country averages across all firms, Azerbaijan has the lowest amount of crime losses at 0.20% of sales, while El Salvador has the highest with 1.73%. Data source and description of the variable can be found in table A1, with summary statistics in table A2.

# _2.2 Explanatory variable_

The main variable of interest is the number of police per 100,000 of population. The variable is obtained from United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems. The variable is typically lagged by 1 year to reduce potential simultaneity bias between crime losses and police presence, however due to data constraints, for some countries the lag is not exactly one year previous, but a few years previous in certain cases. The specific details of the number of lags are available in table A3 in the appendix. The sample mean for the number of police is 288, while the standard deviation is 108. Macedonia has the highest number of police with 480 police per 100,000 of the population while Hungary has the lowest with 90 police per 100,000 of population. The country average for crime losses and number of police are presented in table A4 for each country in the appendix.

9
# **2 Data**

## **2.1 Nightlights**

We measure local economic activity using imagery from four weather satellites that are part of the United States Air Force Defense Meteorological Satellite Program. These satellites record daily cloud formation by measuring the amount of moonlight reflected by clouds at night. On nights with no cloud cover, these satellites measure the light emissions from the earth’s surface. Specifically, we use the annual composites produced by the National Oceanic and Atmospheric Administration (NOAA). These composites predominantly measure man-made lights because they only use information from cloudfree days and because NOAA’s methodology filters transient sources of light.<sup>1</sup> Nightlights provide a good proxy for economic activity because consumption of lights at night is likely to increase with income. Accordingly, nightlights have been extensively used to measure changes in economic activity (see Donaldson and Storeygard, 2016, for a literature review), including the downturns generated by hurricanes (Bertinelli and Strobl, 2013; Elliott et al., 2015; Bertinelli et al., 2016; Ishizawa et al., 2017; Del Valle et al., 2018).

Our nightlights data set is composed of 22 satellite-year composites for the period 20002013. Each composite covers Central America and contains information on 604,473 onekilometer square grid-cells. Each cell records the intensity of nightlights, on a scale that ranges from zero (no light) to 63 (maximum light). In years with overlapping satellite coverage, we aggregate nightlights by taking cell-level weighted averages across satellites, where the weights are given by the number of cloud-free days.<sup>2</sup> We restrict the sample to cells that have non-zero nightlights in at least one year during our sample period. The resulting cell-year panel is composed of 212,072 cells which we observe for 13 years. The average value of nightlights in 2000 is 5.1 with a standard deviation of 8.4.

> 1The procedure to produce composites is described in detail in Elvidge et al. (1997).

> 2This method follows Ishizawa et al. (2017). Taking unweighted averages or using information only from the newest satellite produces very similar results. These results are available upon request.

5
impact of other fishing related factors (see Table A2). Households that do not own their fishing gear, but rather “rent” the gear they use, also have a positive probability of using destructive fishing gear.<sup>15</sup> However, the impact of ownership on use of destructive fishing gear is larger than if the gear is rented.

Households that use an outrigger or dugout canoe are significantly more likely to use destructive fishing gear. Households using canoes have a slightly higher probability of fishing reef species, whereas households using larger boats have a slightly higher probability of fishing pelagic species. This makes sense, and confirms fishermen’s statements that they lack equipment to fish further offshore, thereby putting increasing pressure on near shore fisheries resources. The higher probability of using destructive fishing gear when fishing for reef species in near shore waters is likely to exacerbate the pressure on near shore fisheries. Once we control for ownership of fishing gear, households dependent on fishing as a primary source of income are no more likely to use destructive fishing gear as other households also engaged in fishing. Household more dependent on fishing, however, are more likely to target pelagic species.

Results regarding household characteristics suggest that some aspects of poverty may contribute to environmental degradation. Households able to satisfy their normal diet most of the time during the past year are significantly less likely to use destructive fishing gear. This result could be interpreted as supporting the hypothesis that poverty leads to environmental degradation—at least extreme poverty, as measured by a household’s inability to achieve food security. Larger household and female headed households, who are generally poorer, are also more likely to use destructive fishing gear.<sup>16</sup> Higher mean male education reduces the probability a household uses destructive fishing gear, although the coefficient on this variable is rather small. Female education is not a significant determinant of choice of fishing gear. Wealthier households, in terms of value of physical assets, are less likely to use destructive fishing gear. Access to credit, on the other hand, increases the probability of using destructive fishing gear. Land ownership and the amount of land cultivated are not significant determinants of the use of destructive fishing gear.

> 15 Individuals may rent fishing gear for a fee, or, as it is more often the case, use fishing gear provided by a fish trader subject to an agreement to sell all or most of its catch to the trader at an agreed below market price. 16 From our survey questionnaire, we cannot determine which household member uses a particular type of fishing gear. Therefore the finding that female households are more likely to use destructive fishing gear does not imply that females are more likely to use destructive gear. In Tanzania, females are generally not directly involve in fishing, other than collecting seashells, sea cucumbers, and octopus for a few hours a day during the spring tides.

27
Therefore, we decided to drop the 114 wait-listed people from the sample.<sup>4</sup> In this analysis, we focus on the 1,000 applicants who were in the treatment or control group.

The study covered the assessment centers in five divisions of Bangladesh. Chittagong and Dhaka—the two most populous divisions—had two assessment centers in the survey. The distribution of the applicants in the sample across divisions and trades is shown in table 1.1. Electrical installation, graphic design, and sewing machine operation were the three trades with the most applicants in the sample, while welding had the least.

**<u>Table 1.1 Division and Trade of Applicants in the Sample</u>**

|||<br>|**Divisions**<br>||||
|---|---|---|---|---|---|---|
|**Trades**|**Chittagong**|**Dhaka**|**Khulna**|**Rajshahi**|**Rangpur**|**Total**|
|1. Electrical installation|80|40|40|40|0|200|
|2. Plumbing|80|0|0|40|0|120|
|3. Graphic design|40|40|40|40|40|200|
|4. Information technology support|80|0|40|0|40|160|
|5. Sewing machine operation|40|80|40|0|40|200|
|6. Tailor and dressmaking|0|40|0|0|40|80|
|7. Welding|0|0|40|0|0|40|
|**Total**|320|200|200|120|160|1,000|

_Source:_ Original table for this publication.

A survey firm, SRG Bangladesh, hired to collect data for this study, interviewed all 1,114 applicants faceto-face from May 7 to May 30, 2018, for the baseline data collection before grouping them into the treatment and control groups. They submitted the cleaned baseline dataset to the study team in June 2018. In January 2019, seven months after the RPL assessment, the survey firm conducted an end-line survey through faceto-face interviews. They successfully interviewed 1,097 applicants (98 percent response rate) and submitted the cleaned end-line dataset to the study team in March 2019.

Only after the end-line data collection, the project started to invite the control group participants for RPL certification. The understanding was that the project would prioritize control group applicants when the RPL Level 1 assessment resumed.

> 4 Fifty-one of the 114 applicants (45 percent) had participated in the assessments. Since some ranked lower in the waiting list have been assessed, bypassing the higher ranked applicants in some instances, we are not confident that the wait-listed applicants who were assessed are similar to those not assessed during baseline. We are worried that training providers used other criteria to select the trainees from the waiting list. As a result, we drop wait-listed observations in the analysis.

6
<!-- Start of picture text --> All Children<br>a) b) c)<br>Aggregate Educational Deprivation Rural - Urban Educational North - South Educational<br>- GHS Deprivation - GHS and MICS Deprivation - GHS and MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_R Wave 2_R Wave 1_N Wave 2_N<br>Wave 3_R MICS 2016/2017_R Wave 3_N MICS 2016/2017_N<br>Wave 1 Wave 2 Wave 4_R Wave 1_U Wave 4_N Wave 1_S<br>Wave 3 MICS 2016/2017 Wave 2_U Wave 3_U Wave 2_S Wave 3_S<br>Wave 4 MICS 2016/2017_U Wave 4_U MICS 2016/2017_S Wave 4_S<br>Girls only<br>d) e) f)<br>Aggregate Educational Deprivation Rural - Urban Educational North - South Educational<br>- GHS Deprivation - GHS and MICS Deprivation - GHS and MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30%20%10%0% child 5-9 child 10-14 child 15-17 Total 30%20%10%0% child 5-9Wave 1_Rchild 10-14 child 15-17Wave 2_R Total 30%20%10%0% child 5-9Wave 1_N child 10-14 child 15-17Wave 2_N Total<br>Wave 3_R MICS 2016/2017_R Wave 3_N MICS 2016/2017_N<br>Wave 1 Wave 2 Wave 4_R Wave 1_U Wave 4_N Wave 1_S<br>Wave 3 MICS 2016/2017 Wave 2_U Wave 3_U Wave 2_S Wave 3_S<br>Wave 4 MICS 2016/2017_U Wave 4_U MICS 2016/2017_S Wave 4_S<br>Boys only<br>g) h) i)<br>Aggregate Educational Deprivation Rural - Urban Educational North - South Educational<br>- GHS Deprivation - GHS and MICS Deprivation - GHS and MICS<br>80%70%60% 80%70%60% 80%70%60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10%0% 10%0% 10%0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_R Wave 2_R Wave 1_N Wave 2_N<br>Wave 3_R MICS 2016/2017_R Wave 3_N MICS 2016/2017_N<br>Wave 1 Wave 2 Wave 4_R Wave 1_U Wave 4_N Wave 1_S<br>Wave 3 MICS 2016/2017 Wave 2_U Wave 3_U Wave 2_S Wave 3_S<br>Wave 4 MICS 2016/2017_U Wave 4_U MICS 2016/2017_S Wave 4_S<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Waves 1-4 and MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

20
<!-- Start of picture text --> Affected by compulsory law*<br>=5 S+c 82<br>o 10 ec 3;<br>88;<br>§oe KR, °<br>o ,\% Tis<br>= 4 ° -TM<br>Oo 6 + y * ° ¥ 4<br>g 7 wi /MMA NL<br>® 4 ® Lj ¥<br>7 4 " - Ps 4 ~ rer ba es a<br>ior) + K 18 p * \. + do<br>> Pi A ~ Ke + AAS oo Y V . oe<br>¢ 2 * + * 2 LA ¥ J 6 YW f * ~* ate* vo an Va ¥ a, AS ° aad ! ~~ -4\he<br>wn Hel+/ \y ° A ov of 00 ad oe O_o¢9° o omy © es y ©><br>o? ©o °. J o . * Py<br>oe DOV GOHGE” F<br>0 OV aca g cee t ere reer tO eee or °<br>1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012<br>Birth year<br>°~ Pooled *— Female ~— Male<br>*The shaded area represents those who began 7 grade (secondary education) in 2004 and were thus affected by the policy change.<br><!-- End of picture text -->
**Table 1**

**<u>Cross-Sectional Dependence Tests</u>**

||**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|<br>**‐‐‐‐‐‐‐‐‐‐ Pesaran**<br>|<br>**Method ‐‐‐‐‐‐‐‐‐‐**<br>|<br>**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐**<br>|<br>**‐‐‐‐‐ Breusch**<br>|**‐Pagan Method ‐‐‐‐‐**<br>|
|---|---|---|---|---|---|---|
||**‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ T**|**oT ‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐ Nomin**|**al Prices ‐‐‐‐‐‐**|**ToT**|**Nominal Prices**|
||**Fixed**|**Random**|**Fixed**|**Random**|||
|**GDP measured at**<br>|**Market Prices,**<br>|**constant US$ 2**<br>|**010, total**<br>||||
|World|14.08***|13.95***|11.45***|11.35***|224.5***|179.3***|
|High Income|13.48***|13.35***|11.46***|11.36***|205.8***|175.9***|
|Middle Income|15.02***|14.87***|11.36***|11.27***|254.6***|183.3***|
|Low Income|15.68***|15.52***|11.38***|11.29***|275.6***|182.9***|
|**GDP measured at**<br>|**Purchasing Po**<br>|**wer Parity (PPP**<br>|**), total**<br>||||
|World|14.46***|14.32***|11.44***|11.34***|236.5***|180.8***|
|High Income|13.58***|13.45***|11.46***|11.36***|208.2***|176.8***|
|Middle Income|15.04***|14.89***|11.36***|11.27***|255.3***|182.8***|
|Low Income|15.63***|15.47***|11.39***|11.3***|273.8***|182.5***|
|**GDP measured at**|**Market Prices,**|**constant US$ 2**|**010, per capita**||||
|World|14.46***|14.32***|11.44***|11.35***|233.8***|181.8***|
|High Income|13.36***|13.23***|11.46***|11.36***|201.6***|174.8***|
|Middle Income|15.71***|15.55***|11.28***|11.19***|278.1***|185.5***|
|Low Income|16.78***|16.62***|11.35***|11.25***|315.6***|186.9***|
|**GDP measured at**|**Purchasing Po**|**wer Parity (PPP**|**), per capita**||||
|World|15.02***|14.87***|11.43***|11.34***|253.8***|183.3***|
|High Income|13.50***|13.37***|11.45***|11.35***|204.9***|176.0***|
|Middle Income|15.68***|15.52***|11.29***|11.20***|276.9***|184.8***|
|Low Income|16.71***|16.56***|11.36***|11.26***|313.4***|186.5***|

**Notes** : The null hypothesis is cross‐sectional independence. The test statistics are based on Pesaran (2004) and Breusch‐Pagan cross‐sectional dependence tests under either a standard fixed effects or random effects model. One (*), two (**), and three (***) asterisks indicate significance at 10%, 5%, and 1% levels, respec‐ tively.

_16_
<!-- Start of picture text --> infections Modeled incidence from Spectrum ART measured incidence/<br>ages 15+ 2000-2012 | 2004-2012 | 2004-2012 2012 ALPHA network studies<br>Faso<br>Incidence rates higher in<br>specific populations/<br>Uganda 122,806 0.75 0.77 0.02 <0.01 3% 64% demographcis, but<br>otherwise similar in general<br>population<br><!-- End of picture text -->

*Lack of studies reporting this data
Both real estate market and securities markets have been very volatile in East Asia. Real estate price fluctuations during the 1 990s were the highest in Philippines and Malaysia, with ratio of highest to lowest prices since inflows started of 3 and 2, respectively. Still, in both countries, vacancy rates in 1996 were relatively low at around 2% (and the banking sector exposure to real estate appeared to be low in the Philippines). In Thailand and Irndonesia, the variability of real estate prices was lower, with high to low ratios of 1.25 and 1.32, respectively. In both countries, vacancy rates in 1996 were relatively high at around 14% and increasing. The space under construction in Southeast Asia at the end of 1996 already suggested a significant oversupply of real estate during 1997-99, especially in Thailand (Figure III.13).

**Figure 111.13 Real Estate Office Supply and Vacancy Rates**

<!-- Start of picture text --> Thailand Indonesia<br>0xwhawWcm iinm.erta<br>450,0 .. ........... 25 aD D 14<br>o00, '0.so 14<br>§50,0( 20 12<br>Sq§00'0C15 4M<br>ua 200,0C % 3MD<br>9ooE|5% 0mmSA1 6<br>%000 02<br>19 19 19 19 19 19 19 19 19 19 1919 , X °a N X e * e f D . a,<br>S8 89 90 91 92 93 94 95 96 97 98 99 : oa 6<br>_____________[ :_-t -- J<br>Malaysia The Philippines<br>Cotrucbni bom m Kib LU, Costrucion bom i N M<br>1825007<br>5W.00D ~~~~~~~~~~~~~~16<br>14 200<br>5<br>400,00 12 000<br>2 2~~~~~~~~~~~~~~~~1<br>83<br>01~~~~~~~~~~~~~~~~~~~~~. jh 1W m 0<br>0 JO 0<br>Wb 7- 98 .d :-.nl_<br>_ &I ydwespO +Vac&y fre SI q 0d spm +Vawayr*e<br><!-- End of picture text -->

- **Increased Foreign Exchange Exposure.** Especially iin Thailand, Malaysia and the Philippines there was a significant increase in foreign exchange exposures of banks since the late 1980s (Figure 111.14). Also, for Korea, Thailand and the Philippines, there was a very rapid increase in the stock of foreign liabilities of NBFIs. In Indonesia, the increase of foreign exchange exposure of banks was significant up to 1994, and was followed by a small decrease,

40