rafmacalaba/data-use-mentions-extended huggingface dataset:
7 To monitor and evaluate progress of the Eco-farming project, a baseline survey of 2,700 households was conducted during the second half of 2009. This is the first phase in a larger effort to more accurately evaluate the impact of the project.<sup>10</sup> In total, 225 villages were selected, spread equally across 3 counties in each of the 5 provinces in which the project is active. Counties were stratified by physical and economic characteristics.<sup>11</sup> Within each county, two townships were selected purposively, and within each township, two project and three non-project villages.<sup>12</sup> Non-project villages were slightly oversampled (135 non-project versus 90 project villages) to ensure a sufficient number of pure control villages that have neither the World Bank supported nor the national biogas program. In both project and control villages, the survey randomly sampled households meeting the criteria to qualify for biogas subsidies. To also permit within project village impact analysis in the future, households were slightly oversampled in project villages—fifteen households in the project villages versus 10 per nonproject village. The survey instruments comprised a household questionnaire covering all aspects of fuel and energy consumption, experiences with biogas, and a range of socio-economic conditions as well as a village questionnaire covering infrastructure and socio-economic development.

The baseline data described above forms the information base for this paper. At the commencement of the Eco-farming project at the beginning of 2009, a number of households already owned a biogas digester. In particular, the sample contains 610 households (23%) with biogas installed, of which approximately 147 were provided as part of the Eco-farming project. The remainder received it with support from various government agencies or constructed it themselves. The study exploits this feature of the data to situate the current use of biogas within the overall energy mix in this part of rural

> continues. The smallholder digesters may also be filled with manure from the larger farms, as has been reported on a couple of
Given the role of the experience of working itself, we next consider what features of the work may alter one’s psychosocial state. We first examine how working makes one feel: the self-worth imparted by working (through the sense of purpose experienced by the job) vis-`a-vis the family and the community, and the sociability facilitated by the job. We then consider features that impact how the work shapes one’s day: the structure imposed by a work schedule and the occupation of time. Third, we consider how working shapes consumption choices, as what is consumed from wages may differ from what is consumed from a cash transfer. Because we lack experimental variation along most of these margins, we cannot definitively rule in or out singular channels. Our evidence suggests that the role of employment in conferring self-worth, upon an individual within the eyes of their family, may be particularly impactful. Finally, we investigate how different workers gain from working, where we consider their gender, baseline exposure to violence, depression and extroversion.

# **5.7.1 How working makes one feel**

**Self-worth vis-`a-vis the family** We document a significant non-pecuniary impact of employment on self-worth. Our self-worth index asks the following question separately for one’s family and one’s community: “Think of the individual you most respect and who brings greatest value to your family (community). If they are ranked 10, where would you rank yourself?” We find that baseline mental health is highly correlated with how one ranks themselves within their family at baseline (Appendix Table A6). Consistent with this, the non-pecuniary impact of the employment treatment on family-rank is large, at 0.17 SDs ( _q_ = 0 _._ 01) greater than the cash arm (Column 1 of Table 6. Notably, this is a combination of a 0.11 SD increase in ranking among the work group and a 0.06 SD _decrease_ (though imprecise)
National Statistical Bureau, which has been carried out every five years since 2003. It is the main source of information on household living conditions in Bhutan, and focuses on household expenditures, within the framework of the World Bank’s Living Standard Measurement Surveys (LSMS).

Many key features of the survey have been kept the same over time for comparability purposes. The two most recent BLSS waves, 2017 and 2022, are no exception: the questionnaires are close in structure, content, and level of detail (Amendola et al. 2023). Both surveys were administered through computer-assisted personal interviewing (CAPI) 3
to build on their own military force and governance mechanisms, could eventually backfire and facilitate secession. Anderson (2007) notes that the same factors that made federalism a necessity in Iraq could also make federalism a risk to its territorial integrity.<sup>9</sup> Interesting lessons on how transfers may affect conflicts can also be learned from the Arab Spring. In countries like Tunisia and the Arab Republic of Egypt, the failure of incumbents to effectively use redistributive policies led to abrupt regime changes (see Boucekkine et al, 2016). In others such as the Gulf monarchies<sup>10</sup> and Algeria, instead, rulers were able to scale up redistributive measures at the outset of the protests, and they managed to keep their grip on power. Notice that the large increases in transfers were accompanied by a surge in repression in the Gulf States, suggesting that successful incumbents may find these policies as complementary.

Overall, these observations suggest that transfers affect the likelihood of conflict in an ambiguous way. One may then wonder whether such an ambiguity is driven by differences in the size of transfers. To see whether this may be the case, we collected data for 86 countries for the period 1990-2011, and computed the predicted probabilities of conflict as a function of decentralization –our a proxy for transfers– from a logit regression while controlling for total public expenditures, oil rents as a share of GDP, per capita income and regional dummies.<sup>11</sup> Referring the reader to Appendix 2 for the regression table, our results are summarized in Figure 1, which portraits a non-monotonic relationship between the probability of conflict and transfers. In particular, increasing the share of transfers from 20 percent of total public expenditures to 40 percent increases the likelihood of conflict by half (from 1 to 1.5 percent). However, the likelihood of conflict starts decreasing with further decentralization, and becomes insignificant at about 50 percent. In other words while small transfers are correlated with an
Since we do not have data on migrants’ province of original residence for 2009, we do not control for these dummy variables. An alternative modeling option is to pool the 2008 and 2009 rounds of the RUMiC for analysis. While this option allows us to employ a richer econometric model by controlling for the year and city (fixed) effects, it does not offer more insights into the crisis year as discussed above. Estimation results on the pooled data (not shown) nevertheless confirm that school fees have a negative and statistically significant impacts on child migration.

> 44 The Body Mass Index (BMI), a measure of tissue mass (muscle, fat and bone) in an individual, is computed as the ratio of weight (in kilograms) to squared height (in meters). Using WHO’s guidelines, we consider that children with a BMI less than 18.5 and equal or greater than 25 are respectively underweight and overweight.

32
**Notes:** The table presents the results from the IV approach as specified in model (2). In Panel A municipalities are split into municipalities with on average positive and on average negative shifters. In Panel B municipalities are split into below- and above-average informality rates according to the Census of 2010. The regression controls for family- and time-fixed effects. Robust clustered standard errors at the municipality level are shown in parentheses.

the average. As shown in Panel B of Table 6 the effects are positive and significant for both types of municipalities. However, results show that the effects are stronger in municipalities with higher rates of informality indicating that in places, where formal dependent employment is less present, an extension of it causes a greater impact in terms of exits from the program.

**Family composition** Third, we split the sample into families that have just one adult living in the family vs. families with various adults. Panel A of Table 7 shows that the overall effect is driven by families with more than 1 adult. Those results seem plausible since single-headed households are commonly facing multiple constraints to benefit from formal employment growth

22
mentioned in the empirical strategy section, other major policy initiatives were effective in both states, such as the Electricity Act 2003 and the Special Economic Zones Act of 2005. These policies are likely to not affect our results. Another potentially confounding candidate is the overall doing business environment in a state. Taking the state-level World Bank Doing Business indices as proxies, we find that Uttar Pradesh had implemented more reforms to improve business environment than Uttarakhand between 2002 and 2015 (World Bank 2015). Criminal record rate is another proxy for overall quality of governance. Based on data reported by the National Crime Records Bureau, we find that Uttar Pradesh and Uttarakhand’s rates of cognizable crimes experienced a parallel trend between 2001 and 2012, with Uttar Pradesh seeing greater year-to-year variations (NCRB 2012).

Overall, the New Industrial Policy, as a comprehensive package of infrastructure investment and fiscal incentives, stands out as the most important policy difference between the two states over the time period. The analysis on the impact of differential tax treatments across sectors gives us further confidence that our results reveal the impact of the New Industrial Policy more than other policies.

# 7. Conclusion

Previous studies have shown place-based policies to have mixed effects on economies. In this paper, we find that a large-scale industrial promotion scheme targeting the Indian state of Uttarakhand had a large positive impact on local employment outcomes. We apply a boundary discontinuity design and examine the impact of this policy in places close to the border between Uttar Pradesh and Uttarakhand, which were one state right before the promotion scheme. Most interestingly, we show that the place-based excise tax incentives as part of the promotion scheme had a direct positive impact on employment in the

> 13 The data are satellite-based and drawn from von Donkelaar (2016).

34
<!-- Start of picture text --> Within-country changes in unemployment and informality rates<br>ESS rounds 3 (2006-2007) vs. 2 (2004-2005); 4 (2008-2009) vs. 3(2006-200<br>LT<br>SI<br>HU<br>IE<br>RU<br>CZNL DEBE FISE FRUA CHES PT<br>CY UK GRRO NO AT<br>DK BG<br>EE<br>PL<br>SK LV<br>-6 -4 -2 0 2 4<br>Change in informality rate, % points<br>Informality rate excludes self-employed<br>Connected points correspond to the same country, with the country label at the earlier point<br>Missing ESS data of round 2 (BG, LV, RO and FR) or round 3 (CZ, LT) replaced<br>with informality rate from the Fourth European Working Condition Survey (2005/Q4)<br>Single points, for countries observed only twice, show 2007 vs. 2005 (AT),<br>2008 vs. 2006 (CY and RU), 2008 vs. 2005 (RO), 2009 vs. 2005 (GR and LT)<br>Base: Labor Force Extended to Include Discouraged Workers<br>10<br>5<br>0<br>-5<br><!-- End of picture text -->

**Figure 3 Change in unemployment and discouragement vs. change in informality rate**

# **3 Informal workers at a glance**

As noticed by Riedmann and Fischer (2008), knowledge of the characteristics of the shadow sector workers is an important prerequisite for designing appropriate political measures to deal with undeclared work. The differences between profiles of informal and formal workers might be country-specific, depending on social norms and corporate culture, on strictness or particular components of employment protection legislation, and on the sectoral composition of
# **13.4 Effectiveness of government institutions**

102. The quality of government institutions also matters for fighting undeclared work. If institutions remain weak, a tax reform aimed at reducing undeclared work may not achieve its objective.<sup>14</sup> According to the corruption perception index of Transparency International, among the NM-EU countries, Slovenia and Estonia have the lowest corruption while in Bulgaria and Romania corruption is most pervasive (Table 15). This is confirmed by the Control of Corruption index of the World Bank, which can also be taken as a proxy for the quality of government institutions among the NM-EU countries. According to this indicator, Slovenia and Estonia have the strongest and Bulgaria and Romania the weakest government intitutions in the region (Table 16).

**Table 15: Perception of Corruption**

|Country|Score|Ranking among<br>180countries|
|---|---|---|
|Slovenia|6.7|26|
|Estonia|6.6|27|
|Czech Republic|5.2|45|
|Hungary|5.1|47|
|Latvia|5.0|52|
|Slovakia|5.0|52|
|Lithuania|4.6|58|
|Poland|4.6|58|
|Croatia|4.4|62|
|Romania|3.8|70|
|Bulgaria|3.6|72|

_Source:_ Transparency International, Global Corruption Report 2009.

> 14 In the literature there is a controversal discussion about the relationship between institutional quality, corruption and the size of the shadow economy. In theory, the relationship between corruption and the size of the shadow economy is ambigous and can be substitutive or complementary, depending on the circumtances (see Dreher et al. 2005 and Dreher and Schneider 2006 and the literature mentioned in these papers).  If corruption (i.e. the use of public power by politicians and
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Simulations suggest that addressing Burkina’s infrastructure deficiencies —understood as improving the quantity and quality of infrastructure to the level of the African leader, Mauritius—could boost growth by 3.8 percentage points, with potential contributions coming equally from the ICT, road, and power sectors (figure 1b).

At the firm level, evidence from enterprise surveys suggests that infrastructure quantity and quality are responsible for about one-third (35 percent) of the productivity performance of Burkina Faso firms (figure 2a). ―Soft‖ issues related to transport—such as customs clearance and transport logistics in general—weigh most heavily on firms’ productivity; electricity is a distant second.

**Figure 2. Infrastructure deficits constrain firms’ productivity**

<!-- Start of picture text --> a. Degree to which infrastructure contributes to firm’s increased b. Degree to which infrastructure is an obstacle to firm’s increased<br>productivity productivity, by subsector<br>Benin Mali<br>Senegal Senegal<br>Mali Mauritania<br>Mauritania Niger<br>Burkina Faso Burkina Faso<br>Niger Benin<br>0% 50% 100%<br>0% 20% 40% 60% 80% 100%<br>Percent of productivity deficit attributable to sectors Percent of productivity deficit attributable to sectors<br>Electricity Customs clearance Transportation ICT Water<br>Infrastructure Others<br><!-- End of picture text --> _Source:_ Escribano, Guasch, and Pena 2009.

# The state of Burkina Faso’s infrastructure

The country is characterized by low population density (figure 3a) and contrasting levels of poverty and wealth across states (figure 3b). Most of Burkina’s population and agricultural activity is concentrated along the Yatenga-Kadiogo-Boulgou corridor. Burkina’s territory is dominated by the Volta River Basin and Niger River Delta, whose potential remain largely undeveloped (figure 3c). Mineral
Models 1, 2, 8 and 9, which require predictors related to household demographics, employment attributes, housing characteristics, assets, utility expenditures, and (2) A shorter version of the TA 1 food consumption module - with an identical set-up/set of questions but with a reduced list of food items – aligned with the earlier Survey of Household Welfare and Labour in Tanzania (SHWALITA) and specifically the “short list” treatment arm in that study.<sup>4</sup> The TA 2 food consumption module is slotted immediately after the TA 2 core modules, covering 26 items out of the 71 items included in TA 1.<sup>5</sup> These selected items account for 69 percent of the monetary value of food consumption in TA 1, indicating that the reduced list of food consumption items under TA 2 misses out on a considerable share of the food expenditure compared to the full TA 1 food consumption module. As discussed later, TA 2 data on food consumption are used to estimate an additional imputation model, namely Model 3 as presented in Dang _et al._ (forthcoming), which includes household food consumption expenditures as a predictor. The TA 2 sample consists of 701 households. Table A.2 in Appendix A presents expenditures on these food categories for TA 2 and TA 3 in comparison with those from TA 1.

Finally, Treatment Arm 3 (TA 3) administered an alternative light questionnaire variant that includes:

- (1) The same TA2 core modules that allow for the estimation of Models 1, 2, 8, and 9 as presented in Dang _et al._ (forthcoming),

> 4 For more information regarding SHWALITA, please see Beegle et al. (2012) and visit <u>https://www.uantwerpen.be/en/staff/joachim-deweerdt/public-data-sets/shwalita/#introduction.</u> 5 TA 2 covers 13 individual food items and 4 item categories corresponding to 13 items on TA1
by both patients who test positive and negative for the virus — and reduced mortality among patients.

Thus, we find evidence that accountability interventions which leverage social incentives to increase the perceived quality of health care can lead to greater utilization of health systems in a low-resource setting, in ways that help build their resiliency to confront crises.

We explore two alternative explanations for why the interventions increased reporting during the Ebola crisis: by unintentionally increasing exposure to Ebola; or by enabling more top-down surveillance efforts. We do not find support for these mechanisms. Specifically, we find no evidence to indicate that the interventions contributed to transmission at treated clinics, or that they raised the infection rate among patients

> 47 The absolute numbers here are instructive: there is 1 EHC in control sections, 1 in NFA sections, and 2 in CM sections. In Figures E.4 and E.5 we drop all triplets and pairs of triplets as a robustness check to address concerns that a small number of sections could drive our results.

> 48 The locating of specialized facilities in nearby sections could depress reported cases, as patients might report directly to those facilities and, thus, not be counted within their home section. In Table E.19 we find that treated sections are not significantly further from ETUs, EHCs or CCCs in the NERC data; the distance from NFA sections to the nearest CCCs is shorter when we use the UNMEER data.

36
# **Annex 3. Robustness: Enlarging the Sample**

## **Table A3.1. Use of Labor, Extended Model**

Dependent variable: employment ratio (ln)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|
|**Pillar 1**|-0.001|-0.001|0.003|0.004|-0.001|0.004|0.005|0.003|
||(-0.04)|(-0.06)|(0.87)|(0.92)|(-0.07)|(0.98)|(1.02)|(0.60)|
|**Pillar 2 and 3**<br>Infra index<sup>a</sup>||||||||-0.001<br>|
|||||||||(-0.24)|
|Infra index (WEF)|-0.001|-0.001|-0.002|-0.003|-0.002|-0.002|-0.003||
||(-0.11)|(-0.11)|(-1.09)|(-1.17)|(-
and private), industries and skill categories cannot be accounted for by observed characteristics.

Focusing on the changes between 2004 and 2015, the reductions in the inequality within sectors, industries, and skill levels were the main driver of the reductions in the gender wage gap observed between 2004 and 2015. However, changes in industrial and occupational segregation benefitting women also contributed to the reduction in the gap in all three cases.

Previous work has demonstrated large scope for policy interventions in reducing the gender wage gap in Georgia. For example, Khitarishvili (2016) finds that, in part due to the presence of high degree of industrial and occupational segregation, public sector reforms have enabled swift reductions in the gender wage gap in Georgia. Our analysis further underscores the important role that policy interventions can play. In particular, the findings of the high unexplained share of the intra- and inter- components emphasize that unobserved barriers that limit their labor market earnings remain sizable for women in Georgia. Thus, our policy recommendations include the implementation of measures that will directly contribute to equal wages, such as the laws that mandate equal pay for equal work and ensure stronger representation of women on company boards (World Bank 2016).

Despite its relatively modest role in explaining the gender wage gap in Georgia, the high degree of industrial and occupational segregation is nevertheless problematic to the extent that it reflects strong gender specialization patterns by subject in tertiary education that can widen gender gaps in the long run, especially as Georgia attempts to strengthen its STEM-trained workforce. According to the UNESCO data, the percentage of tertiary-level female graduates in fields such as arts and humanities, education, health and welfare, is twice the percentage among men. Yet for fields such as engineering, manufacturing and construction, information and communication technologies, and services, the percentage of female graduates is less than half that of male graduates. Hence introducing programs that encourage women to pursue these presently male-dominated fields in tertiary education and ultimately employment should be a policy priority. To complement these efforts,
# Figure VI: Enrollment in Vocational Training across Countries

<!-- Start of picture text --> 80<br>CZE AUT<br>SVK SVN<br>60 CHE<br>POL ITA BEL NLD LUX<br>TUR<br>FIN<br>HUN NOR<br>40 MEX LVA PRT ISR FRAGBR<br>CRI DEUSWE<br>COL CHL EST<br>GRCRUS LTU JPNESP<br>20 KOR DNKAUS<br>NZL ISL IRL<br>BRA<br>0 ZAF CAN SAU<br>9.5 10 10.5 11 11.5<br>Log GDP per worker in PPP<br>Enrollment Share in Vocational Programs<br><!-- End of picture text --> _Notes_ : The figure plots the share of upper secondary students aged 15-19 enrollment in vocational programs in 2019, against the country’s log GDP per worker (in PPP terms) in 2019. Enrollment by program orientation is taken from OECD (2022), while GDP per worker is taken from version 10.0 of the Penn World Tables (Feenstra et al., 2015).

more general theme that the appropriateness of general and vocational education is contextspecific, with faster technological process and higher labor market uncertainty favoring the accumulation of general skills, and increases in the division of labor favoring specialization (Alon, 2018; Alon and Fershtman, 2023).

# **2.4.3 On the Job Accumulation**

**The accumulation of human capital on the job is an important determinant of wage growth over the life cycle** . Such accumulation is facilitated by both formal training programs provided by firms (Acemoglu and Pischke, 1998), as well as more informal learning opportunities associated with work experience (Bagger et al., 2014). Within countries, firms are heterogeneous in the quality of the learning environment they offer; for example, firms with more knowledgeable co-workers, (Jarosch et al