rafmacalaba/datause-encoder-data huggingface dataset:
During these years, violence was constantly present and fluctuating, while women experienced increasing economic empowerment and consequently increased their participation in the labor market.

It is worth noting that Colombia, one of the longest-standing democracies in Latin America, has a history marked by widespread violence and human rights abuses. Colombia has consistently ranked among the most violent countries in the world.<sup>10</sup> In this context, it is observed that men are generally more prone to acts of violence compared to women. Numerous studies have consistently shown that men commit a higher number of offenses and, as a result, are more likely to experience casualties in situations of internal conflict (Knight, Ponce de Leon, and Tribin, 2021; Smith, 2014; Steffensmeier and Allan, 2005; Buzawa, Buzawa and Stark, 2017). Warfare has been known to impact women's participation in the labor market. For instance, research conducted by Doepke, Hazan, and Maoz (2015) suggests that World War II had a lasting influence on women's employment, leading to a significant increase in women entering the workforce.

From 1960 to 1989, the relationship between the male homicide rate and women's labor participation can be negative, small, and sometimes negligible (Figure 11). Between 1960 and 1969, the coefficient of -0.04pp suggests that as the male homicide rate decreased, women's labor participation exhibited a slight tendency to increase. Similarly, in 1970-1979, the coefficient of -0.03pp indicates a continued negative relationship. Moving forward to 1980-1989, the coefficient of 0.00pp suggests no significant relationship between the male homicide rate and women's labor participation. These coefficients highlight the limited impact of changes in the male homicide rate on women's labor participation from 1960 to 1989.

During the 1990s and the early 2000s, a positive relationship existed between these two variables. As the violence related to drug trafficking escalated in Colombia, resulting in the murder of many men, women began to enter the labor market at a higher pace (Iregui-Bohórquez et al., 2021). This increase in women's labor participation can be attributed to the need to offset income losses and the demand for women's skills in urban labor markets (Calderón, Gáfaro, and Ibáñez, 2011); hence, their participation showed a slight improvement compared to men. The employment rate also displayed a positive trend, reaching approximately 50% by the end of the period, indicating a modest

> 10 Data from Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2021. <u>https://ourworldindata.org/homicides.</u>

19
initial quintile, while the top and bottom quintiles are relatively more stable, in this respect.

Table 4.1. Transition Matrix (percentages)

|||P|ost-crisis|||
|---|---|---|---|---|---|
|Pre-cnsis|Quintile1|Quintile2|Quintile3|Quintile4|Quintile5|
|Quintile1|47|26|17|8|2|
|Quintile2|27|28|21|18|7|
|Quintile3|17|20|28|25|10|
|Quintile4|5|17|22|28|28|
|Quintile5|4|9|12|22|53|

There are a significant number of households experiencing consumption drops at all level of initial consumption. Overall, about 63 percent of households in Moldova experienced a consumption drop. However, table 4.1 (last column) shows that the proportion of household experiencing consumption drops increases steadily as we move to higher quintiles of initial consumption. On average, the _proportional_ change in consumption for the whole population is close to (and not statistically different than) zero. However, the proportional changes in consumption vary dramatically by initial consumption quintiles. Poor households in the lower quintiles experienced the highest proportional _increases_ in consumption. Meanwhile, non-poor households in the upper quintiles experienced the highest _drops_ in consumption.

The information in table 4.2 points to initial consumption level as a key vulnerability covariate. It also seems to suggest that the main burden of the crisis was burden mostly by households that were initially non-poor.

9
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positively associated with disruptions, while the metropolitan areas under investigation seem to be reasonably well protected against the consequences of drought.

When it comes to **transport** disruptions, mining, chemicals and manufacturing of electric and electronic devices face significantly more transport obstacles than the average firm. On the contrary, post and telecommunication, IT services, textiles and apparel production and gastronomy suffer from significantly fewer transportation problems. After controlling sector-specific effects, individual firms’ exposure towards transport obstacles increases in age, size and frequency of storms. Further, they are positively correlated with generator ownership, possibly interpreted as a proxy of remoteness. By contrast, state ownership and effective (national) governance as measured by the WGI decrease transport problems. That exporters, conditional on sector affiliation, do not disproportionately complain about transportation problems suggests that effective coping strategies exist or that such obstacles tend to arise at the immediate, local level where they are harder to circumvent.

Table 2: Firm-level determinants of infrastructure disruptions

|Dependent:|Blackouts|Water disruption|Transport<br>problems|
|---|---|---|---|
|Firm age|-0.4731|0.8269|0.0033*|
|_years since official registration_|_(0.3981)_|_(0.6610)_|_(0.0019)_|
|Employment|-0.1362**|-0.0904*|0.0004*|
|_number of full time employees_|_(0.0613)_|_(0.0539)_|_(0.0002)_|
|State ownership|0.3797*|-0.3894|-0.0013*|
|_% of total assets_|_(0.2036)_|_(0.4195)_|_(0.0007)_|
|Export orientation|-0.2140|0.5570|-0.0031|
|_% of total sales_|_(3.7831)_|_(1.6675)_|_(0.0020)_|
|Generator ownership|130.1427***|21.7680|0.0829*|
|_Dummy_|_(34.1015)_|_(45.3034)_|_(0.0477)_|
|ISIC Sector Fixed Effects|YES|YES|YES|
|Output Gap|-35.7666**|1.8103|-0.0033|
|_HP residual as % of HP trend_|_(14.2614)_|_(4.2874)_|_(0.0213)_|
|GDP Growth|11.1562**|2.1227|-0.0030|
|_annual, in %_|_(5.5833)_|_(1.6454)_|_(0.0043)_|
|Electrification|-6.5491|15.1436**|0.0192|
|_% of population_|_(15.3368)_|_(6.9546)_|_(0.0116)_|
|Water access|16.9750|-2.9592|-0.0320|
|_% of population_|_(39.8634)_|_(16.1290)_|_(0.0265)_|
|Population|-4.4678|-1.2989|0.0003|
|_in millions_|_(3.0321)_|_(2.1559)_|_(0.0022)_|
|Population density|0.0000**|0.0000|0.0000|
|_in 1/km^2_|_(0.0000)_|_(0.0000)_|_(0.0000)_|
|Urbanisation|-37.1000|-80.5798***|0.0368|
|_% of population_|_(61.9500)_|_(14.6525)_|_(0.0472)_|
|WGI: Government Effectiveness|-196.1376|65.6708|-0.6385***|
|_Index, standard normal_|_(281.0073)_|_(103.7968)_|_(0.2137)_|
|Country Fixed Effects|YES|YES|YES|
|Droughts|-10.4036|3.0247|0.0053|
|_0-100 % extreme precipitation shortfall_|_(17.8841)_|_(6.9778)_|_(0.0370)_|

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Figure A4: Specification Charts for Skew of Daily Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>2 4<br>1 2<br>0<br>0<br>-1<br>* Weather Product * * Weather Product * -2<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>4 2<br>2 0<br>0 -2<br>* Weather Product TAMSATERA5* -2-4 * Weather Product TAMSATERA5* -4<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>2 6<br>1 4<br>0 2<br>* Weather Product TAMSAT* -1-2 * Weather Product TAMSAT* 0-2<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

52
Table 3 – _Continued_ _<u>from previous page</u>_

||Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|
|Facebook Built-up Area (N=1,275)|30.16|28.18|0.67|97.43|
|_Very High resolution Proprietary indicators_<br>_Road variables_|||||
|% of roads that are minor paved (4 m width)|6.78|15.50|0.00|100|
|% of roads that are main paved (5 m width)|11.32|15.07|0.00|100|
|% of roads that are paved city (4 m width)|11.74|17.64|0.00|92.76|
|_Building Density and Vegetation_|||||
|% shadow pixels covering valid area (N=1,353)|5.80|5.47|0.27|38.17|
|NDVI, mean scale 32|0.52|0.17|0.00|0.95|
|Total built-up area|69,757|68,699|0|7,08,867|
|_Roof type_|||||
|Fraction of total roofs that are clay|35.80|20.67|0.00|100|
|Fraction of total roofs that are aluminum|14.26|7.27|0.00|71.92|
|Fraction of total roofs are asbestos|7.79|11.66|0.00|71.20|
|_Cars_|||||
|log number of cars (N=1,252)<br>_Agricultural Land_|3.42|1.02|0.88|8.30|
|% of Village agriculture that is paddy|44.46|37.65|0.00|100.00|
|% of Village agriculture that is plantation|55.14|37.58|0.00|100.00|
|_Textural and spectral characteristics_|||||
|Pantex (human settlements) mean, scale 8m|0.56|0.50|0.00|3.95|
|Pantex (human settlements) mean, scale 32m|0.66|0.59|0.00|4.68|
|Gabor flter (scale 64m, features 6), mean|0.67|0.32|0.01|2.03|
|Gabor flter (scale 64m, features 14), mean|0.68|0.31|0.02|1.95|
|Histogram of Oriented Gradients (scale 16m), mean|37.91|9.66|0.00|146.18|
|Observations (N)||1,360|||

Note: GPW=Gridded Population of the World; GHSL=Global Human Settlement Layer. HIES=Household Income and Expenditure Survey; GUF= Global Urban Footprint; NDVI = Normalized Difference Vegetation Index. The NDVI scale refers to the size of the window used to calculate average NDVI. Summary statistics of LASSO-selected indicators across all models are reported.

21
ment costs - high remuneration allowed deployment in high-cost locations (with poor wind conditions) or encouraged rent-seeking behavior<sup>4</sup> - both could increase investment costs and reduce the average investment elasticities<sup>5</sup> . After controlling for the cumulative wind electricity generating capacity, we find FiT price has a small but _negative_ correlation with wind generation. Because turbine design and the selection of wind location are important determinants of generation efficiency (output per unit of installed capacity), this result suggests that high subsidies provide fewer incentives for investors to avoid high cost sites<sup>6</sup> or engage in innovation activities to enhance turbine efficiency.

In contrast, guaranteed grid access and the duration of FiT contracts are crucial policy characteristics influencing the impact of wind support. Moving from no guaranteed grid access to guaranteed grid access can almost double wind installation in one year, _ceteris paribus_ ; extending the contract length by an additional one year of an original 5-year agreement will on average increase wind investment by 6 percent annually. The results confirm that investors attach high premiums to the certainty of investment return. A predictable long-term policy commitment is likely to be more effective than excessive shortrun incentives to attract investment.

The organization of the power market also has a sizable impact on the deployment of wind power. If FiT rates are exogenous to electricity prices, higher electricity prices reduce the relative price premium of FiTs, hence the amount of wind investment. But when electricity is traded in a competitive wholesale market, higher electricity prices appear to strengthen the incentive for renewable energy development. This is because many countries allow renewable generators to either sell their electricity at the guaranteed FiTs or sell it in the open exchange. As a result, wholesale competition allows renewable developers to benefit from higher market prices especially during the peak demand.<sup>7</sup> We find that with

> 4An IEA(2011) study suggests that solar panel sellers price their systems according to the incentive in a given country and try to take a share of any excess remuneration.

> 5An incidence analysis on who benefits from high FiT rates could be an interesting topic for future research.

> 6Good wind locations are unlikely to be exhausted in most countries in the sample as existing wind generation only represents a small percentage of the estimated wind economic potential in these countries.

> 7In Turkey, average electricity price in the wholesale market was higher than FiTs before 2010. As a

4
block fixed effects for the 48<sup>31</sup> stratified ward blocks included to improve precision (Bruhn & McKenzie, 2008). For child-level outcomes we include age and month dummies as covariates. Standard errors are clustered at the ward level. We then check whether the impacts of the three treatment arms are statistically different from each other by presenting F-statistics to test whether we can reject the null hypotheses: _β1_ = _β2_ ; _β1_ = _β3_ and _β2_ = _β3_ . The p-values associated with these tests are reported in all of the regression tables.

In addition to the basic specification, we estimate the following models: (i) include control variables to reduce residual variance and account for any baseline imbalance<sup>32</sup> and (ii) estimate the local average treatment effect for receiving the program, by instrumenting random assignment on actual implementation based on program monitoring data. These additional specifications are presented in the online appendix.

# **6. Results**

For all household- and caregiver-level responses, the tables present the specification that includes block fixed effects only. For the child health indicators, the specification presented includes block fixed effects together with child age (month dummies) and gender controls. In addition we include the full set of models in the online appendix.

# **_6.1._ Program Exposure and Outputs**

Table 5 shows impacts on self-reported program exposure to TSSM and HWWS channels, with large and significant differences in the probability of being exposed to one or more messages among the three treatment groups. Three channels are included for HWWS and TSSM. Both interventions include promotional materials (which can include wall drawings, posters and comics) and participation in respective DCC events. The third channel of exposure to HWWS includes being visited by an _msabunashaji_ /front-line activator, while for TSSM it includes attending a CLTS event. 22.4% of households in the control group report being exposed to any TSSM messages, while this proportion increases by 14.1 percentage points in the TSSM-only group, by 25.1 percentage points in the handwashing-only group, and by 34.5 percentage points in the combined group. When measuring the number of events recalled, we find a nearly linear relationship with the number of DCC triggering activities in each group (1 in the TSSM group, 2 for the handwashing, and 3 in the combined group), suggesting that the frequency of program activities is strongly related to message exposure and recall.

> 31 Not all districts have ward numbers divisible by 4, in which case some blocks contain only 2 or 3 Wards.

> 32 We use the variables found to be unbalanced across intervention groups. This includes: (i) whether the household had a pit latrine or VIP at baseline; (ii) whether household members listen to the radio; (iii) household asset ownership; and (iv) whether the household has access to piped water.

18
is equivalent to a 0.12 unit increase. This impact is statically significant at 10 percent. These results imply that the direct interaction between the MNCs and domestic firms that occurs within supplier contractual arrangements is more powerful as a knowledge diffusion mechanism than the indirect effects captured by the demonstration channel.

The importance of FDI spillovers on the performance of local high-growth firms varies both across regions and sectors. I estimate equation [5] separately for six regions of the world, following the World Bank country classification, and for manufacturing and services. The estimated coefficients and 10 percent confidence intervals are depicted in Figure 5 and Figure 6. Regression results are reported in Annex 3. The analysis delivers three key messages. First, high-growth firms in SubSaharan Africa do not internalize FDI spillovers. Since the lion’s share of FDI going to Africa is directed to natural resources, this result may indicate that the potential of this type of investment to generate positive spillovers is limited. Second, the Europe and Central Asia region is an outlier in that the demonstration channel outweighs the linkages channel. In fact, the role of the demonstration channel is the largest among the regions analyzed. Third, the linkages channel is the key engine for FDI spillovers to high-growth firms in Latin America and the Caribbean, South Asia, and the Middle East and North Africa.

**Figure 5: FDI spillovers and High-growth Firms, by Regions.**

<!-- Start of picture text --> Linkages Channel Demonstration Channel<br>3.00 3.00<br>2.50 2.25 2.50<br>2.00 2.00<br>1.50 1.50<br>1.11<br>1.00 0.69 1.00<br>0.50<br>0.50 0.10 0.50 0.02 0.06 0.08 0.08 0.38<br>0.00 0.00<br>-0.03<br>-0.50 -0.27 -0.50<br>-1.00 -1.00<br>SSA ECA LAC EAP SA MENA EAP MENA SSA SA LAC ECA<br><!-- End of picture text --> Note: These figures show the estimated coefficient of the role of the channel for FDI spillover effects on high-growth firms, by world region. Vertical lines capture 90 percent confident intervals. Regression results are presented in annex 3. The world regions are as follows: MENA: Middle East and North Africa; LAC: Latin America and the Caribbean; SSA: Sub-Saharan Africa; EAP: East Asia and the Pacific; ECA: Europe and Central Asia; and SA: South Asia.

Source: Author computation using data from the World Bank Enterprise Surveys.

High-growth firms in services capture FDI spillovers through both linkages and demonstration channels, while in manufacturing they internalize spillovers only through linkages. However, the

- 16 -
19 households and types of labor. All of the national studies include micro-simulations drawing on model results, as in the above GTAP global models.

The national results for real GDP and household consumption suggest that GDP would increase from full global trade reform, but only by 1 or 2 percent, in all nine countries. Given falling consumer prices, real household consumption would increase by considerably more in most cases. Generally these numbers are a little larger than those generated by the global LINKAGE model. They share the feature of the global models of probably underestimating the poverty-alleviating benefits of trade reform, given the broad consensus in the literature that trade liberalization increase growth, which is in turn a major contributor to poverty alleviation.

The comparative Tables 6 and 7 summarize the national results for the incidence of extreme poverty and income inequality, respectively, resulting from own-country, rest-ofworld or global full liberalization of agricultural or all goods trade. Some authors ran only six of the nine simulations shown in this table, but those that ran all nine found their results to sum up almost exactly, to one decimal place. We therefore have inferred the three missing results in the other country studies by assuming that the agriculture-only and nonagricultureonly results sum to the all-goods reform results. The inferred numbers are shown in italics in Tables 6 and 7. In each case the total effects on poverty<sup>7</sup> and inequality are subdivided into rural and urban.

One should not necessarily expect the unweighted averages of the poverty results for each region to be similar to those generated by Hertel and Keeney (2010), because only five of the national case studies were included among the 15 countries sampled by Hertel and Keeney. Nonetheless, the latter’s unweighted averages of national poverty effects for each of the key developing country regions are reported in brackets in the last 4 rows of Table 6(c), to 7 Using national or $1 a day poverty lines, except for China for which results are available only for $2 a day.
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as the dependent variable. The results show that average duration of power outages reported in IHDS are significantly and positively correlated with PSI.

**Table 3 Correlation between PSI and duration of power outages reported by**

**IHDS**

||(1)|(2)|
|---|---|---|
|outage_IHDS|0.0358**|0.0503***|
||(0.0140)|(0.0143)|
|Forest||0.00266<br>(0.00535)|
|Area||0.0000160<br>(0.0000102)|
|Population||-0.0000920**<br>(0.0000364)|
|Emp_Agriculture||0.00304<br>(0.00360)|
|N|217|206|
|adj. R-sq|0.025|0.077|

Note: The dependent variable is PSI. Standard errors clustered at the district level are reported in parentheses.

Figure 7 depicts the probability density distribution of PSI and the outage variable based on kernel density estimation. It shows that both variables generally follow a normal distribution.

16