rafmacalaba/datause-encoder-data huggingface dataset:
The campaign was developed as a set of simple visual aids with two different messages: the importance of voting which focused on the relationship between the electoral process and policy, and the significance of secret balloting which explained the actual balloting process. Treated women received either the first message or the first and the second, allowing us to test whether the knowledge that ballots are cast in an environment of secrecy enhances female participation, as well as independence in candidate choice.

We find that turnout increases by about 12 percent for women in treated households compared to women in control clusters, with somewhat larger and more precise effects for women exposed to both messages. More importantly, we find comparable turnout rates for treated women and their untreated close neighbors, indicating large geographical spillovers. We then assess whether spillovers among close friends are larger than those among neighbors. The results are qualitatively similar. This is not too surprising given that close friends typically reside nearby. In addition, we use the GPS location of households to estimate spillovers beyond the (arbitrary) boundaries of the treatment clusters and find even larger effects.

We also use administrative data from all polling stations that served the study villages and find that for every 10 treated women, (roughly 4 households), female turnout increases by about 9 additional votes. Once we take this externality into account, the cost of the intervention drops from US $16.7 to about US $2.2 per additional vote. In contrast, using the same administrative data we find no effect on male turnout, suggesting either that the provision of information on electoral participation and processes is less salient for men or that there is little communication about political matters between men and women. Of course, it is also possible that men are simply not influenced by information provided by women.

We then study whether the campaign influenced candidate and party choice. We find that it did. Treated women are significantly more likely to vote for the second most voted for political party. Control women in treated clusters behaved again as if directly treated, confirming once more the importance of social interactions. Polling station data show as well that an increase of 10 percent in the share of treated women led to decrease in the share of female votes for the winning party of 6 percent. These results suggest that the campaign could have influenced the share of votes at the constituency level, had it been implemented at a larger scale.

4
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poverty line (in 2019, R$ 178, USD 42.7), and the amount depended on the number of children or pregnant women in the family. In 2019, the program paid R$ 41 (USD 9.8) per month per child or pregnant woman, with a maximum of five variable benefits per family. The variable benefit was linked to education and health conditions (see Table 1 for an overview of the rules).

Table 1: Bolsa Familia rules as of 2019

|Extreme poor families (monthly p.c. in-<br>come below 89 R$/21.4 USD)|Poor families (monthly p.c. income below<br>178 R$/42.7 USD)|
|---|---|
|• Basic beneft (89 R$/21.4 USD per<br>family)<br>• Variable beneft (41 R$/9.8 USD<br>per child or pregnant women, max.<br>5 variable benefts per family)|• Variable beneft (41 R$/9.8 USD<br>per child or pregnant women, max.<br>5 variable benefts per family)|
|• Top-up beneft after all other ben-<br>efts (top-up to reach 89 R$/21.4<br>USD p.c. income)||

**Notes:** The table describes the rules of Bolsa Familia as of 2019.

BF was not – and still is not – a constitutional right but a budgetary program, meaning that not everyone who was eligible for the program during our study period necessarily received it; the program had a waiting list. Each municipality registered vulnerable families and was allocated a budget based on poverty estimates from the Brazilian Institute of Statistics (Instituto Brasileiro de Geografia e Estat ́ıstica – IBGE). The process of including new beneficiaries from the waiting list as spots became available followed a centralized procedure led by the Ministry of Social Development (MDS).

There were several reasons why families exited the program under the 2019 regulations and many of those still apply under the new program rules. The first related to changing household demographics: once children reached adulthood, families could only receive the basic benefit, and if their income exceeded the extreme poverty threshold, they were excluded from the program altogether. Second, if families failed to meet health and education conditions without sufficient justification, they were removed from the program after being offered opportunities to comply. Third, households were required to withdraw the cash benefit within a certain period; failure to do so resulted in exclusion from the program. Finally, household income had to remain below the eligibility threshold, though with one important exception: the _Regra de Permanencia_ (permanence rule). This rule allowed beneficiaries to remain in the program for an additional two years and maintain their income above the eligibility line (up to certain limits), provided 6
**Table** 12. **Contribution to the Pension System Among Self Employed in December 1999- January** 2000 (Men of Working Age)

||PROBIT|I|PROBIT|2|MEAN|
|---|---|---|---|---|---|
|CONT99|dF/dx|Std.Err.|dF/dx|Std.Err.||
|AGE99|0.008|**(0.004)****|**0.009**|**(0.004)*****|41.12|
|HEAD|0040|(0.092)|-0.009|(0.088)|0.80|
|MARRIED|**-0.115**|**(0.063)***|**-0.106**|**(0.062)***|0.69|
|SPCONT|0.017|(0.047)|0.005|(0.046)|0.24|
|YEDU|-0.005|(0.005)|-0.004|(0.005)|10.02|
|RURAL|-0.011|(0.053)|-0.010|_(0.050)_|0.22|
|FSIZE|0.001|(0.000)**|0.001|**(5E-04)****|13.77|
|WRKHRS|**0.002**|**(0.001)****|**0.002**|**(0.001)****|53.14|
|CREDCON|-0.039|(0.052)|-0.020|(0.049)|0.31|
|SHYKIDS|0.002|(0.001)|0.002|(0.001)|17.77|
|SHKIDS|0.003|(0.002)|0.002|(0.002)|6.31|
|SHOLDM|**0.008**|**(0.004)****|**0.006**|**(0.004)***|2.11|
|SHOLDW|-0.003|(0.003)|-0.004|(0.003)|3.26|
|NYFEM|0.018|(0.032)|-0.010|(0033)|1.26|
|RESFAM|0.110|(0.089)|**0.140**|**(0.086)***|0.05|
|EXKIDS|-0.005|(0.015)|-0.003|(0.014)|2.69|
|LEDKIDS|-0.006|(0.005)|-0.006|(0.005)|5.95|
|FAMEX|-0.049|(0.045)|-0.045|(0.043)|0.51|
|GIVES|0.058|(0.039)|0.036|(0.036)|0.38|
|GETS|-0.041|(0.062)|-0.018|(0.059)|0.13|
|LRENT|0.003|(0.005)|0.007|(0.006)|7.37|
|COLATT|0.010|(0.052)|0.001|(0.048)|0.63|
|FINASST|0.130|(0.106)|**0.155**|**(0.104)***|0.03|
|WRKASST|-0.071|(0.046)*|-0.053|(0.043)|0.57|
|SLIFEX|0.003|(0.002)|0.002|(0.002)|33.38|
|LJUB|-0.021|(0.009)**|-0.020|(0.009)***|1.23|
|CONT98|**0.185**|**(0.058)*****|**0.181**|**(0.062)*****|0.35|
|CONDEN|0.310|(0.101)***|0.334|(0.111)***|0.36|
|MPG|**-0.141**|**(0.073)*****|0.128|(0.136)|0.14|
|MPG*LRENT|||**-0.035**|**(0.018)*****|1.10|
|Intercept|**-0.781**|**(0.245)**|-0.782|(0.269)|1.00|
|Numberofobs|183||183|||
|Log likelihood|-53.531||-48.779|||
|<br>LRchi2(29)|85.130||94.630|||
|Prob>chi2|0.000||0.000|||
|PseudoR2|0.443||0.492|||
|obs.P|0.219||0.219|||
|pred.P|0.061|(at x-bar)|0.053|(atx-bar)||

statistically significant at 1%; "**" at 5%; and "*" at 10% 51
deviation. The average treatment effect of NSP on the perception of security in the two eastern districts is not statistically significant from those in the other eight districts.

Despite a strong positive effect of NSP on perceptions of security, the program has no significant effect on security incidents in or around villages as reported by villagers (

**<u>Table 5</u>** ). In both treatment and control villages, approximately 3 percent of respondents indicate that their village experienced an attack in the past year and that they themselves were affected by insecurity in the village or on roads around the district. In the two eastern districts, the results are similar.

Objective measures of security, which are not affected by the usual concerns with the survey data (Bertrand and Mullainathan, 2001), are provided by the ISAF dataset. Figure 2 presents estimates of the effect of NSP on these incidents.<sup>20</sup> The results indicate that, in non-eastern districts, NSP reduces the probability of security incidents both in the short-run and in the long-run, but the effect is stronger in the long-run. In the short-run the effect is significant at the 10 percent level for 4 km., 7 km., and 8 km. radii and is the strongest for the 8 km. radius, for which it corresponds to a decrease of 4 percentage points in the probability of a security incident. In the long-run, the effect is significant at 10 percent for radii between 7 km. and 11 km., with effects being significant at 5 percent for 1 km. and 8 km. radii. The effect is the strongest for the 9 km. radius, which it is significant at 1 percent level. In the eastern districts, where the initial levels of violence are high, there is no statistically significant effect on security for any of the radii either in the short run or in the long run.

Overall, there is strong evidence that perceptions of security are better in villages receiving NSP. Although there is no effect of NSP on incidents reported by survey respondents, the number of incidents recorded by ISAF is lower around treatment villages both in the short run and in the long, with a stronger effect in the long run. Thus, the results provide support for Hypothesis 3. The positive effect of the program on security, however, is not observed in the two eastern districts, which are characterized by high levels of initial violence.

# **VI.4. Robustness of Results**

To check the robustness of results, we include baseline responses to the same (or closely related) questions from the baseline as additional controls to make sure that the results are not driven by

> 20 The corresponding regression results are presented in Table  A3 in the Online Appendix. We use a dummy variable for the occurrence of an incident as a dependent variable to limit the effect of potential outliers. The results hold if we use the number of incidents as an outcome variable (see Table A4 in the Online Appendix).

18
less satisfied than their US peers on three out of the four types of skills; notably on ― _Use of Math, Science, and Engineering knowledge_ ‖, ― _Applying Problem Solving skills_ ‖, and ― _Learn, Grow and Adapt_ ‖.

**Figure 1 Dissatisfaction Levels between Indian and US Employers**

<!-- Start of picture text --> India 21%<br>U.S. 8%<br>India 16%<br>U.S. 14%<br>India 33%<br>U.S. 20%<br>India 17%<br>U.S. 25%<br>0% 5% 10% 15% 20% 25% 30% 35%<br>% of employers being dis-satisfied<br>Use Math, l Skills<br>Sci, and Technica<br>and<br>Learn, Grow, Adapt<br>Apply Problem- Solving Skills<br>rk<br>and<br>Commun ication Teamwo<br><!-- End of picture text --> Source: Authors for India and Lattuca, Terenzini, Volkwein study for ABET 2006 for the US. Note: Year of survey for the US was 2004 and 2009 for India.

**_Soft Skills vs. Professional Skills (Satisfaction)_** _:_ As in the case of importance level, satisfaction level of both _Soft and Professional Skills_ is analyzed to see how well (or unwell) engineering graduates meet employers‘ expectation for _Soft and Professional Skills_ .

A t-test is conducted with a following null hypothesis: ―the mean of _Soft Skills_ is the same as that of _Professional Skills_ in terms of the satisfaction level‖. The mean satisfaction score of _Soft Skills_ is 3.27 (0.05), and that of _Professional Skills_ is 2.98 (0.06). The mean satisfaction score of _Soft Skills_ is statistically significantly higher than that of _Professional Skills._ Hence, the result suggests that the null hypothesis be rejected (See Table A2-11), and indicates that employers are more satisfied with _Soft Skills_ than _Professional Skills_ .

We also independently tested for the two soft skill factors ( _Core Employability Skills_ and _Communication Skills_ ) compared to _Professional Skills_ . The null hypotheses are; (i) the mean of _Core Employability Skills_ is the same as that of _Professional Skills_ , and (ii) the mean of _Communication Skills_ is the same as that of _Professional Skills_ . The mean scores of _Core Employability Skills_ and _Communication Skills_ are 3.30 (0.05) and 3.24(0.05), respectively, compared to 2.98(0.06) for _Professional Skills_ . Both mean scores are significantly higher than _Professional Skills’_ scores of 2.98, and the result of the two t- tests suggests that we reject both hypotheses (See Table A2-12 and A2-13). Hence, the results of analysis show that employers are more satisfied with both _Core Employability Skills_ and _Communication Skills_ than _Professional Skills_ .

21
**Table 2: Oil supply shock scenario: real consumption deviation from the baseline (percentages) and benchmark oil imports share of consumption and oil exports share of production.**

||**Real cons**|**umption de**<br>**baseline**|**viation fro**<br>**(%)**|**m the**|**Oil imports**<br>**share of**<br>**consumption**<br>**(%)**|**Oil exports**<br>**share of**<br>**production**<br>**(%)**|
|---|---|---|---|---|---|---|
||2013|2015|2020|2025|2004|2004|
|**Cameroon**|0.7|0.5|0.6|0.8|13|46|
|**Côte d’Ivoire**|0.6|0.5|0.4|0.4|26|30|
|**Ghana**|-1.0|-0.5|0.1|0.1|100|0|
|**Nigeria**|2.5|1.1|0.8|1.0|7|88|
|**Senegal**|-0.8|-0.3|0.0|0.1|70|4|
|**Ethiopia**|-0.9|-0.5|0.0|0.0|100|0|
|**Kenya**|-1.8|-1.0|-0.2|-0.1|100|0|
|**Madagascar**|1.6|0.9|0.4|0.4|23|70|
|**Malawi**|0.6|-0.2|-0.2|-1.0|25|66|
|**Mauritius**|0.6|0.2|0.1|0.1|40|54|
|**Mozambique**|-0.6|-0.4|-0.1|-0.1|100|0|
|**Tanzania**|-0.8|-0.5|0.0|0.0|100|0|
|**Uganda**|1.3|0.6|0.5|0.7|17|70|
|**Zambia**|-0.5|-0.4|0.1|0.3|100|0|
|**Zimbabwe**|-1.8|-1.4|-0.7|-0.6|100|0|
|**Botswana**|-1.0|-0.5|-0.1|-0.1|100|0|
|**Namibia**|-1.1|-0.5|-0.2|-0.2|100|0|
|**South Africa**|-0.9|-0.6|-0.2|-0.2|43|11|
|**Rest of SSA**|4.9|3.0|2.6|3.0|5|81|

Source: Authors’ simulations and calculations.

# Drought

In Africa, droughts are recurrent events with tragic repercussions to the ecosystem, population and governments (Sheffield and Wood, 2011). In a region where the majority of people depend on farming or raising animals, droughts inevitably have very negative consequences. In fact, no region in SSA has been spared over the past three decades (Table 3). The most severe drought occurred in the early 1980s with thousands of deaths in Ethiopia and Mozambique and substantial economic damage. The Sahel region of West Africa, which covers parts Senegal, Mauritania, Mali, Algeria, Niger, Chad, Sudan and Eritrea, has also experienced many droughts. One of the worst occurred in 1914 but more recent famines between 1968 and 1974 and in 2010 affected large proportions of the population. As recently as 2012, more than 18 million people were hit by food shortages and over 1 million children faced the risk of acute malnutrition<sup>5</sup> . Even though several factors, including international food price movements, have contributed to the crises, low rainfall in the region led to a poor harvest in 2011 and 2012. Grain production in many areas was 36 percent lower than in 2011 and 20 percent lower than the average of the past five years. For example, Senegal’s production of ground nuts, one of its main crops, was 59 percent lower than in the previous year.

> 5 Metro News, **metro** .co.uk/.../ **sahel** - **drought-in-west** - **africa** - **leading-to-crisis-as-millions-of-lives-at-risk** -521534/

12
# Organizing Knowledge to Compete

Impacts of capacity building programs on firm organization<sup>_∗_</sup> Marcio Cruz<sup>_†_</sup> Maurizio Bussolo<sup>_‡_</sup> Leonardo Iacovone<sup>_§_</sup> JEL Classification: D22; L23; F14.

Keywords: Firm organization; Managers; Impact Evaluation; Brazil.

> _∗_ We thank Jean-Louis Arcand, Nicolas Berman, Marcelo Olarreaga, Christian Volpe Martincus, Juan Carlos Hallak, Luiz A. Esteves, participants of Development Therapy in Geneva (November, 2011), participants of the 4th Trade, Integration, and Growth Network annual conference organized by the IDB and LACEA in Buenos Aires (April, 2013), participants of the 20th Annual LACEA Meeting 2015, in Santa Cruz de La Sierra, and participants of the Annual AEA Meeting 2016, in San Francisco, for their comments. We also thank all the team of DISET-IPEA in Bras ́ılia, particularly Fernanda De Negri, Jo ̃ao De Negri, Ricardo Cavalcante and Bruno Oliveira, for granting access to data from the Brazilian Ministry of Labor and Lucas Rocha for his assistance. We also thank Marcos L ́elis, Thiago Terra, Rˆomulo Clezar and all the team of the Intelligence Unit of Apex-Brasil for granting access to Apex’s data and for their help in understanding this program. The data was used under confidentiality agreement. The usual disclaimer applies. The views expressed in this paper are the authors’ only and do not necessarily represent the views of any of the institutions that the authors are affiliated with.

> _†_ The World Bank and UFPR. Email: marciocruz@worldbank.org

> _‡_ The World Bank. Email: mbussolo@worldbank.org

> _§_ The World Bank. Email: liacovone@worldbank.org

i
More importantly, despite Latvia’s rapidly rising trade **<u>deficit</u>** <u>, in recent years the forestry</u> sector generated a rapidly increasing trade **<u>surplus</u>** <u>. Indeed, without the forest sector</u> surplus, Latvia’s overall trade deficit would be nearly 50% greater.

**Figure 13 Trade Balance of Latvia**

<!-- Start of picture text --> Trade Balance of Latvia<br>1200 2400<br>1000 2000<br>800 1600<br>600 1200<br>400 800<br>200 400<br>0 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Export of Forest Sector Import of Forest Sector<br>Export of Latvia Import of Latvia<br>mln LVL mln LVL<br>Foreign trade of Latvia<br>Foreign trade of forest sector<br><!-- End of picture text --> _Source_ : Swedish National Board of Forestry, 2003 However, the structure of Latvia’s forestry exports and forestry trade surplus cast serious doubt on the long term sustainability or desirability of these trends. Approximately 70% of Latvia’s total forestry sector exports consist of sawnwood, roundwood, firewood, and plywood – products that require minimal processing and which generate limited value added in Latvia. Thus, although Latvia has significant potential to generate more value added in the forestry and wood products sectors, it is only latent potential. Latvia is not yet have the capacity to exploit this potential.<sup>20</sup>

> 20  According to representatives of the Latvian forestry and wood sectors, building this capacity will require action on several fronts including: (i) Training architects and engineers to work with wood and wood products.  These skills and this know-how is currently absent in Latvia.  (ii) Reviving, restoring and renewing the capacity of teaching and research institutes in the forestry and wood sectors. According to many businessmen, most research institutes are working with obsolete equipment and their scientific cadre is not fully conversant with cutting edge knowledge.  Foreign and domestic firms operating in the Latvian market will not enter into joint R&D programs with these institutes until these deficiencies are remedied. (iii) Helping Latvian enterprises learn how to be more innovative. According to industry representatives, managers of most enterprises simply do not know how to market and produce new, niche products  or how to connect their firms with large global players in the wood and forestry industry.

23
Additionally, it is important to consider that the current PIT allowance is tied to the gross minimum wage,<sup>29</sup> necessitating careful consideration in any assessment.

# Who is more likely to be affected?

**We provide additional characterization of the employees potentially impacted by the wage increase in scenario 1 (baseline).**

**Our results show that the impact of the minimum wage increase differs across groups of employees and sectors of the economy** .

**The minimum wage increase affects more male than female employees in all age groups; youngest and oldest employees experience higher wage increases or job losses.** Table 9 provides information on the proportion of employees affected by the minimum wage hike, categorized by gender and age brackets. The increase in minimum wage has a more significant impact on men than women, with 13.64% of male employees and 12.51% of female employees affected. Among women, the youngest age group is most affected, with 14.5% of female employees aged up to 29 impacted, compared to 11.6% to 12.8% across other age groups. For men, the highest percentages of impacted employees are in the youngest (15.0%) and oldest (14.4%) age categories. Across all age segments, the proportion of affected male employees exceeds that of female employees. As presented above, both wage increases and job losses are higher for the youngest and oldest employees.

**Table 9. Share of employees potentially impacted by the minimum wage increase by Sex and Age (Scenario 1),**

|Group|Men|Women|
|---|---|---|
|All|13.64%|12.51%|
|Age: upto 29|14.96%|14.52%|
|Age: 30 – 39|13.66%|12.73%|
|Age: 40 – 49|13.99%|12.37%|
|Age: 50 – 59|12.31%|11.65%|
|Age: 60+|14.42%|11.56%|

_Source: Own estimates based on administrative tax data. This is estimated as the share of men or women of each cohort earning between the old and the new (simulated) minimum wage._

**The minimum wage increase can significantly impact the accommodation and food services sector, affecting almost half of employees. In contrast, fewer than 1% of employees in the electricity, gas, steam, and air conditioning supply sectors are potentially affected.** Figure 9 illustrates the potential longrun impacts of the minimum wage increase across various economic sectors. Significant impacts are observed in the accommodation and food services sector, wherein almost half (50%) of the employees experience the

> 29 Workers in the construction sector have been exempt from social health insurance since 2019.

42