rafmacalaba/data-use-mentions-extended huggingface dataset:
equity participation. A level of 4 is equivalent to little public intervention into the freedom of operation of private providers, the possibility of majority foreign ownership, and the dominance of private sector entities. Finally, a level of 5 would be equal treatment of foreign and domestic providers, a full convergence of regulation with international standards and unrestricted entry into the sector.

In adapting the template to sectors, one needs to take into account that in some sectors liberalization can proceed at different paces in different segments. In telecommunications, for example, developing countries are typically quicker to allow private (and foreign) capital into cellular services than into landlines. In segments where private entry is possible, operators tend to face relatively little public intervention in the operation of their business. As a result, one is likely to observe a coexistence of segments in which market forces can govern more freely with others that remain a public monopoly. In other sectors such as banking, there is no such natural division into segments. Instead, one might find a situation in which private (and foreign) entry has taken place into the provision of almost all banking products, but significant public interference with private decisions remains in the form of directed lending to priority sectors or interest rate restrictions. Hence the need to rephrase the index definitions for different sectors while trying to maintain the same sense of “average” openness associated to a given level of the services reform index. In what follows we present the sector-specific definitions of the index, and juxtapose these with the actual reform events that determined progress to the next level of the index. To illustrate India‟s reform progress in the services sectors we analyze in this paper, Figure 1 gives a graphical illustration of the variation contained in the services reform index.

> 26 The European Bank for Reconstruction and Development produces a similar set of indices for transition countries in their 2004 Transition Reform, and some of the definitions used in that index have inspired the construction of our index.

52
**TABLE 5: Impacts on Parenting Quality - 18-Month Follow-Up**

|||**Pare**|**nting**|**D**|**ependent**<br>**Parentin**|**variable:**<br>**g Quality**|**Subcomp**|**Par**<br>**onents:**|**enting**|
|---|---|---|---|---|---|---|---|---|---|
|||**Qualit**|**y Index**|**Stimu**<br>**In**|**lation**<br>**dex**|**Pos**<br>**Practic**|**itive**<br>**es Index**|<br>**Stres**<br>**(Inv**|<br>**s Index**<br>**erted)**|
|**Variables**||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|T2 (careiver trainin)||-0.023|-0.018|-0.043|-0.022|-0.023|-0.028|0.018|-0.004|
|g g||(0.078)|(0.079)|(0.072)
were appointed head of their respective teams to share their knowledge with newly recruited analysts on potential fraud cases and give them advice on what to look for, e.g., historical cases of fraud for some economic operators, abnormal price valuation for some imports. Moreover, they created a database of historical fraud cases and improved documentation of their risk analysis to facilitate internal knowledge transfer. Weekly meetings were organized with all senior managers in customs and head of unit/inspectors from the RMU to discuss contentious cases in terms of goods valuation and decisions were collectively taken on how to handle such cases in order to reduce discrepancies in customs procedures across ports and inspectors. At the end of the RCT, the work organization of the RMU instituted at the start of the RCT was maintained. The transformation of the RMU 28
<mark>There was also general dissatisfaction with the ineffective and untimely response of service providers to technical problems such as transformer breakdowns. All respondents were aware of the electricity company’s complaint offices, and many male respondents had visited them at least once. However, a substantial number of respondents reported having given up on their complaints after visiting the office. We also spoke to both DISCO officials, and they explained that conditions for good service and accurate billing were made difficult by resource constraints, especially short‐staffing.</mark>

# **Social Assistance**

Energy affordability assistance is arguably part of the social safety net. If subsidies are not helping households to afford basic electricity needs without resorting to harmful coping mechanisms, improved assistance should be developed in the context of the country’s broader safety net policy. BISP, Pakistan’s largest safety net scheme, is an antipoverty initiative covering over 5 million households and providing monthly cash transfers as well as other support. Despite its size, BISP is targeted only to the poorest 15‐20 percent of households in Pakistan. We specifically interviewed BISP beneficiaries and non‐beneficiaries about the program in order to explore what role it could play in any alternative energy affordability policy.

Respondents overall demonstrated a high level of awareness of BISP. Low‐income households in particular were aware of BISP’s eligibility criteria and application requirements, since most had participated in the 2010 NSER survey (from which most BISP beneficiaries were selected). BISP beneficiaries consider the cash assistance an essential element of their income that helps them afford their utility bills. Around 70 percent of BISP beneficiaries interviewed perceived the application procedure and the amount of money received as fair.

**Box 3: In‐Depth Interviews with Service Providers** According to a meter reading supervisor at PESCO, in Lower Dir district of KPK, meter readers face challenges during meter reading and bill distribution due to a shortage of field workers and difficulty reaching remote areas. Due to the limited number of staff
enterprises in each sector. About half the sample (224 firms) is located in Dar es Salaam and the balance divided rather evenly across each of the remaining regions (each with about 50 firms). Overall, small firms make up 63 percent of the sample, medium-size firms 26 percent and large firms 11 percent. However, this firm-size distribution varies considerably across sectors and regions. Small firms are mostly found in textiles and garments, fabricated metals, furniture and IT, while large firms are more prevalent in food, construction, and transportation. Mbeya and Zanzibar are dominated by small and medium firms while Dar es Salaam and Arusha have most large firms.

TESS builds on the recently completed 2013 Enterprise Survey for Tanzania, retaining selected key variables on firm characteristics, firm performance and productivity, and adding an expanded module of questions on employment and skills. It elicited qualitative and quantitative information from firms about the education and occupational composition of their workforce, their perceptions about skill gaps in education as well as different cognitive, non-cognitive and technical skills and the constraints these pose for their operations, their strategies for addressing skill gaps including the hiring of new workers, use of foreign workers and outsourcing skills, and their in-house and 12
ownership that is with foreign individuals, entities, and organizations ( _Foreign ownership_ ), and a dummy variable equal to 1 if the firm has internationally recognized quality certification and 0 otherwise ( _Quality certification_ ); access to financial services captured by a dummy variable equal to 1 if the firm has overdraft facility and 0 otherwise<sup>3</sup> ; financial transparency proxied by a dummy variable equal to 1 if the firm had its financial statements audited externally in the last fiscal year and 0 otherwise ( _Firm audited_ ), and a dummy variable equal to 1 if the firm was registered with the relevant authority when it started operations ( _Registered when started)_ ; a set of dummy variables indicating the current legal status of the firm (see table A2 for details); quality of the business environment proxied by a dummy variable equal to 1 if obtaining licenses and permits is a major or bigger obstacle for the firm and 0 if it is a lesser obstacle, losses from power outages in the last fiscal year as a ratio of total sales in the last fiscal year, and a dummy variable equal to 1 if the firm was visited or inspected by tax officials in the last year and 0 otherwise ( _Visited by tax officials_ ).

# 3.5.2 _Robustness_ c _ontrols_

We use additional controls for robustness purposes. Some of these controls involve a noticeable loss in sample due to missing data. We first check if replacing gender parity in primary education with that in tertiary education (data source is WDI, World Bank) affects our main result. Next, we add country-year level controls. The data source is WDI or as stated in brackets. The controls include the percentage of seats in the lower house of the parliament that are held by women (taken from IPU); fertility rate; percentage of women in total population; cost of starting a business as a

> 3 Results are unchanged if we use whether the firm has a checking or savings account instead of an overdraft facility. Having a
|0|0.013|0.213|0.138|3.328|
|N Tweets|2|2|2|3.324|3|44|
|||Tru<br>|th Dataset <br>|2<br>|||
|Hours Dif|0|0.161|0.597|1.715|2.144|23.417|
|KMs Dif|0|0|0.024|0.276|0.199|3.13|
|N Tweets|2|2|2|3.275|3|19|

We examine two common metrics for evaluating clustering performance: the adjusted Rand index and the Jaccard coefficient[9]. When using our algorithm to cluster crash reports, we test all combinations of 0.1, 0.5, 1, 2 and 3 kilometers and 1, 2, 4, 12 and 24 hours. For truth dataset 1, both the Rand index and Jaccard coefficient show that 12 hours and 500 m leads to best results, while truth dataset 2 shows 2 hours and 500 m (see figure ). The difference in results in the truth datasets likely results from the exercise being partially subjective, particularly when limited or no S21
comes are constructed at the teacher-level, and given their rank-based construction, normality does not seem a helpful approximation to the distribution of error terms. As stated in our pre-analysis plan, we therefore estimate equations (5) and (6) with a round-school random-effects estimator to improve efficiency. The permutations of treatments used for inferential purposes mirror those above.

Results are reported in Table 4 and, to the extent available, are based on pooled data.<sup>34</sup> Consistent with the pooled results in Table 3, we see a positive and significant impact of experienced P4P on both the summary metric and the learning sub-component. The specifications with teacher inputs as dependent variables suggest that this impact on student learning is driven, at least in part, by improvements in teacher presence and pedagogy. Teacher presence was 8 percentage points higher among recruits who experienced the P4P contract compared to recruits who experienced the FW contract; an impact that is statistically significant at the 1 percent level and sizeable in economic terms given that baseline teacher presence was already 90 percent. Recruits who experienced P4P were 0.10 points more effective in their classroom practices than recruits receiving FW, although this impact is weaker in terms of statistical significance (and it is possible that this improvement occurred only during the observation). We find no evidence of impacts on lesson planning.

# **4.3 Dynamic effects**

Our two-tiered experiment was designed to evaluate the impact of pay-for-performance and, in particular, to quantify the relative importance of a compositional margin at the recruitment stage versus an effort margin on the job. The hypotheses specified in our pre-analysis plan refer to selection-in and incentives among placed recruits. Since within-year teacher turnover was limited by design and within-year changes in teacher skill and motivation are likely small, the total effect of P4P in Year 1 can plausibly only be driven by a change in the type of teachers recruited and/or a change in effort resulting from the provision of extrinsic incentives.

Interpreting the total effect of P4P in
<!-- Start of picture text --> accounting, implies that total profits are equal to GDP excluding wage earn-<br>ings, we can use the same elasticities to estimate the output elasticity of the<br>corporate income tax. Again, we follow the formulae in Giorno et al (1995)<br>and Van den Noord (2000) in calculating this elasticity.<br>The output elasticities of personal income taxes, social security taxes,<br>corporate income taxes and sales/VAT taxes are then summarized in columns<br>F to I. Finally, the output elasticities of specific taxes are weighted by their<br>share of total tax revenue to give the output elasticity of tax revenues.<br>Table 2 shows our estimates of elasticities using annual data. Table 3<br>compares our estimates of the output elasticity of tax revenues—using annual<br>data-with those of the OECD, for the seven countries that appear in both<br>samples. As noted earlier, our estimates of the personal income tax elasticity<br>are virtually identical. Thus all differences in estimates of the final elasticities<br>are due to differences in estimates of macroeconomic elasticities, such as the<br>output elasticity of employment.<br>3. The effects of tax cuts: A panel estimate<br>We begin our analysis with a simple panel regression, which attempts to<br>determine the effects of tax changes on output. In doing so, we follow the<br>specification in Barro and Redlick (2009) who estimate the effects of a lagged<br>change in the tax rate on current output. They argue that tax changes at a<br>one-year lag are more likely to have an effect on output than contemporane-<br>ous changes and less likely to be affected by reverse causation.<br>These regressions are an illustration of the data available in the collected
deciles. Across regions, average share of food expenditure is highest for North West at 39 percent and Centre West at 37 percent.

**Figure 6: Share of food expenditure**

<!-- Start of picture text --> a. By decile b. By region<br>p<br>39 38 36 36 39 37<br>35 34 34 32 35 33 35 34<br>30 31<br>27<br>poorest 2 3 4 5 6 7 8 9 richest Grand Tunis Nord Est Nord Ouest Centre Est Centre Ouest Sud Est Sud Ouest<br>mean of foodshare mean of foodshare<br>40 40<br>30 30<br>20 20<br>% total expenditure pc % total expenditure pc<br>10 10<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 Several demographic or living characteristics of the Tunisian population also make the poor more vulnerable to get affected by COVID-19. First, overcrowded living conditions and low access to health insurance put the Tunisian poor at a higher susceptibility to get infected or to not be able to seek health care in the event that they contract COVID-19. Not surprisingly, poor households tend to live in denser environments, ranging from 7 to 28 percent in the three poorest deciles, in contrast to 1 percent or less in the three richest deciles.

Second, living in an inter-generational household is linked to higher susceptibility to COVID-19, as elder members are more likely to be in closer contact with the younger household members. Inter-generational households are defined as those that include at least one member below the age of 18 years and one member above the age of 64 years. Overall, 10 percent of the population lives in an inter-generational household. Nevertheless, this type of household is disproportionately represented among the poor, ranging from 14 to 17 percent in the three poorest deciles. This type of household is
) there are no further systematic disparities between formal and informal workers. Finally, equal access to public medical services may also lead to both formal and informal workers having the same basic health coverage and little systematic differences in self-assessed health across these employment types.

In sum, being employed informally is not associated with a difference in self-assessed health. Controlling for characteristics, we do not find a statistically significant difference in self-reported health outcomes. In addition, our results are not sensitive to how the binary health outcome is defined, i.e., whether we consider good or bad health.

# **Informal employment and happiness**

Lastly, we examine whether informal employment affects the likelihood of being satisfied with one’s life. The specific question in the RLMS that attempts to measure this is formulated as “To what extent are you satisfied with your life in general at the present time?” with possible responses ranging from fully satisfied to not at all satisfied, on a five-point scale. Descriptive summary statistics indicate that informal workers are more likely to report to be less satisfied with life. The distribution of responses on life satisfaction in Figure 13 shows that informal workers are less likely to report to be satisfied and more likely to report to be dissatisfied. We derive two binary variables that are each equal to one and capture the bottom two and top two responses. The difference in the unconditional means of life satisfaction (or dissatisfaction) between formals and 23/67 Informal Employment and Worker’s Wellbeing in Russia
# **1. Introduction**

In recent years, researchers and policy makers have turned their attention to study the causes and consequences of inequality of opportunities among children, like access to basic services and education. Evidence suggests that universal access to opportunities come as part of the development process, for which sustained long-term growth and a good combination of policies are necessary (Ferreira et al. 2009). However, the relationship between inequality of opportunities and income inequality is not clear. Ferreira et al. (2009) hypothesize that a negative relation between the Human Opportunity Index and the Gini coefficient might be due to either a disconnection between opportunities and markets in the same period, or due to the relationship between inequality of opportunities in the past and inequality of income in the present. We attempt to shed light on the latter hypothesis by providing empirical evidence on the relationship between access to services at early stages in life and individual incomes in adulthood in Latin America.

We use approximately 70 million census records to analyze the evolution of access to services of different subgroups of the population in 12 countries in Latin America and the Caribbean (LAC), from the 1960s to the 2000s. Each subgroup is composed of children aged 0 to 15 years categorized by gender, residence in urban or rural areas, and the sub-region of the country. This classification, based on exogenous characteristics at the time of birth, provides enough granularity to identify differences in access to basic services and education across the young population. Each subgroup is followed throughout time to measure how fast the access to each opportunity grows and how the ranking among subgroups changes.

2
Building on previous studies, our paper offers three main contributions. Firstly, we explore the effect of severe drought conditions on the labor market. Using the Standardized Evapotranspiration Precipitation Index (SPEI), we identify the Moroccan provinces that have been hit by extreme drought events and frame the analysis in a staggered difference-indifferences setting in order to estimate the effects on three outcomes: the employment rate in the agri- culture sector (including the most climate-sensitive activities); the employment rate in other sectors; and the unemployment rate. This provides an overall picture of the labor market dynamics at the local level for a period of up to five years after the drought’s shock.

Secondly, we have conducted a rich analysis of the effects of heterogeneity by disentangling the impacts across age, gender, educational levels and formal/informal work participation for each of the three outcomes considered. Therefore, our analysis also speaks to the important strand of literature on climate injustice, which highlights how climatic shocks tend to have a significant distributional impact with unequal effects among the most vulnerable and most exposed population groups (Sovacool, 2013). Finally, we explore how drought events of different intensities affect the labor market; this provides a useful picture for understanding how the market responds to drought shocks of increasing severity, and how the policy response should be targeted accordingly.

Our estimates show that in Morocco severe drought events, measured by a 12-month SPEI lower than -2 s.d., caused a drop of up to 6.5 percentage points (p.p.) in the agricultural employment rate compared to provinces that were not affected by severe drought. In the same period, we also observe an non-significant increase of 4.8 p.p in the employment rate of other economic sectors, and a significant increase in the unemployment rate of nearly 2.5 p.p. Even though our data do not allow observing the same workers over time, overall, those figures provide strong evidence of a large displacement of agricultural workers and
# **3.2 Measures**

## **3.2.1 High-paying sectors and sectors dominated by men**

We asked each respondent whether they engaged in an income-generating activity within the 30 days preceding the survey. For those who answered affirmatively, we collected information on the nature of their employment (self-employed or salaried), the type of activity or sector, and the income generated. We classify a sector as male-dominated when at least 75% of its workforce is male. To establish the proportion of men in each sector in Cˆote d’Ivoire, we drew data from the ENV 2015 (Institut National de la Statistique, 2015). The ENV is a household living standards survey conducted in 2015 by the National Institute of Statistics in Cˆote d’Ivoire. The survey interviewed 47,635 individuals from 12,900 households and is also nationally representative. It is worth noting that six occupations could not be categorized, and we presumed they were not male-dominated by default. 11% of respondents engaged in unclassified income-generating activities.<sup>7</sup> The resulting classification is summarized in Table 1 for the working sectors. Mining/oil worker, electrical, refrigeration and air conditioning belong to the energy sector while computer and electronics belong to ICT. To ensure that our classification of MDSs is not highly dependent on the threshold used, we verified the robustness of our classification in Table A.1 and in Figure 9, in Appendix A.

In addition, Figure 2 provides an overview of the energy and ICT sectors compared to others in terms of earnings and proportion of men.

> 7We also use the EHCVM 2018 dataset (WAEMU Commission, 2018), and find that three out of the six unclassified sectors are indeed not male-dominated, resulting in less than 1% of respondents engaging in unclassified activities. Results are available upon request.

9