rafmacalaba/data-use-mentions-extended huggingface dataset:
.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.00)|
|N|<br>1,246|<br>1,250|<br>3,172|<br>2,951|<br>1,852|<br>1,427|<br>7,854|<br>7,881|
|19<sup>th</sup>ICLS standards|0.046|0.023|0.325|0.191|0.261|0.275|0.766|0.837|
||(0.01)|(0.00)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|
|N|1,184|1,184|2,186|2,182|1,773|1,283|6,301|5,212|

_Notes:_ Based on the Nigeria GHS Panel Wave 3 and Malawi IHS-4. In the Malawi IHS-4 data, the majority of people doing wage work are in casual or ganyu labor, and do not report their sector of work. These workers were assigned to the agricultural sector. According to previous standards, 17 percent of urban workers and 23 percent of rural workers primarily engaged in casual or ganyu labor. Following the 19<sup>th</sup> ICLS standards, this increases to 19 percent of urban workers and 37 percent of rural workers.

# **6. Conclusion**

This paper explores the implications of the 19<sup>th</sup> ICLS standards on measures of employment in SubSaharan Africa obtained from multi-topic household surveys. It shows that there
|
|Downwardmobility|21.0|36.1|23.0|30.8|
|**Panel B: 2004-2015**|||||
|Upward mobility|27.5|39.1|30.6|47.3|
|Immobility|47.4|47.4|47.7|47.7|
|Downwardmobility|25.1|38.7|21.7|29.6|
|**Panel C: 1994-2015**|||||
|Upward mobility|34.4|45.7|28.4|36.3|
|Immobility|44.7|44.7|57.5|57.5|
|Downwardmobility|20.9|37.1|14.1|17.2|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with longitudinal weights, where the second survey round in each period is used as the base year. To obtain longitudinal weights we combine cross-sectional weights with the inverse dropout probabilities, which are estimated using methods suggested by Wooldridge (2002) and by Fitzgerald _et al._ (1998). Estimation sample size is 1890 and 4727 panel individuals from the 5th and 13th rounds of the RLMS, 1713 and 3555 panel individuals from the 13th and 24th rounds of the RLMS and 663 and 2371 panel individuals from the 5th and 24th round of the RLMS respectively.

52
Table 3: **Effects of ASP Social-Emotional Learning on Behavior and Academic Outcomes** _(Intention-to-Treat Estimates)_

||Behavior<br>at School<br>(1)|School<br>Dropout<br>(2)|Math<br>Grades<br>(3)|
|---|---|---|---|
|Psychology-based curricula|0.232***|-0.010|-0.017|
||(0.078)|(0.023)|(0.076)|
||[0.015]|[0.640]|[0.860]|
|Mean pure club group|0.650|0.037|0.587|
|MDE|0.068|0.037|0.099|
|Observations|887|868|855|
|R-squared|0.076|0.076|0.019|

_Notes:_ This table shows the estimated impacts of the ASP’s socialemotional learning on behavioral and academic outcomes. _Psychologybased intervention_ is a dummy equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, and 0 if assigned to _Clubs_ . _Mean pure club group_ is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. We present the estimated coefficient _μ_ ˆ2 from specification (1). The description of dependent variables is available in Section 3.2. _Behavior at school_ is a dummy that equals 1 if the student’s behavior report is above the school median. _School dropout_ is a dummy variable that equals 1 if a student dropped out of any school in our study in the 2019 academic year. _Math grades_ is a dummy that equals 1 if the student’s behavior report is above the school median. All outcomes were obtained from administrative data sources (namely, teachers
2000) — where women’s LFP is higher in poorer countries, declines initially with growth as they pursue more education and leisure, and then rises again with increases in income/expected earnings opportunities. However, employment for older age groups is less responsive to economic growth (right panel of Figure 1). This is an important policy question which countries like India have recently been grappling with (Fletcher et al, 2018). Younger women, who are less likely to have children and may have greater mobility (Including opportunities for education), could be more responsive to changes in income and market conditions — but better data are needed on economic opportunities and constraints faced by rural women overall.

Figure 1. Rural women’s labor force participation against log GDP per capita (2016-17), by age group Younger women (aged 15-24) Women aged 25+ <u>Source: LFP data obtained from ILOSTAT (Key Indicators of the Labour Market (KILM) data), 2016 and 2017. GDP data obtained from WDI.</u> 4
2021). These sources are inconsistent due to differences in spatial and radiometric resolution, spectral responses, the spread function of the sensors, local overpass time at night, radiance range, and on-board calibration (Li et al. 2017; Sahoo et al. 2020; Chiovelli et al. 2023, 2026). Hence, we harmonize the night-light series for 1992–2021 following the process outlined by Li et al. (2020, 2017). Figure C.1 shows the spatial distribution of night-light density in 2014 (right after the oil price decline) and in our last period of analysis (2021). It illustrates a stark reduction in night-light density over a remarkably short period of time.<sup>13</sup> To measure spatial inequality, we construct a novel spatial Gini index that corresponds to the traditional Gini formula estimated for each municipality and year, using the night light of each _parroquia_ (county) within each municipality as the unit of observation. Tables B.1 and B.2 show that our measure correlates with the traditional measure of income inequality constructed using population censuses for Venezuela (1990) and Colombia (1993 and 2005).

We also use other remote-sensing variables as controls that are listed in Table C.1. These capture baseline economic characteristics and their linear trajectories. They include annual tree-cover loss from Global Forest Watch (2023) and data on urban coverage and areas covered by water from MODIS Land Cover (2023) (see Appendix B for details). All variables were recorded in 2001, the first year for which they were available.

# **3.3 Electoral Data**

**Presidential elections.** We web-scrape information to construct longitudinal data at the municipal-election-year level for the five presidential elections from 2006 to 2024. The data come from the Consejo Nacional Electoral (2023) and the Venezuela 360 (2023) project. Specifically, we analyze two outcomes: turnout (total votes divided by the electoral census) and opposition support (votes
hm = ∑ (1) cc=12 Where hm is the y-o-y inflation rate for household cc=1 hccm hccm , in month and year . Similarly, _c_ =1....12 ππ SS ∗ππ represents the 12 COICOP categories. is the share of each COICOP category for household h in ππ mm tt particular months and years. These shares are obtained from the unit-level records collected for each household through the HIES (2018-19). SS is y-o-y inflation for the same month and year . Multiplying the share of each COICOP category at the household level with y-o-y inflation ccm ππ mm provides the inflation of each COICOP category at the household level. Aggregating the tt household-level inflation rates of all COICOP categories estimates the inflation rate at the household level.

Here _d_ (=1...10) represents deciles, and N is the total number of households in each decile. This shows that decile-wise inflation is the sum of all households’ inflation divided by the total number of households in that specific decile. Using these two equations, the decile-wise monthly inflation series is obtained from November 2018 to May 2022.<sup>3</sup> The decile-specific inflation rate for each COICOP category is also estimated by employing the above formula. However, this is done separately for each COICOP category. For instance, to estimate the inflation for, say, food and non-alcoholic beverages in the first decile, the sum of household level inflation of food and non-alcoholic beverages in the first decile is divided by the total number of households in this decile. This is done for all the COICOP categories and for all deciles. Subsequently, this provides the components of inflation for each decile (as shown in Figure 5).

# **2.1. Key Assumptions**

The calculation and interpretation of decile-disaggregated inflation is based on certain underlying assumptions.

Firstly, the inequality of inflation impact exhibits through three channels: its effect on consumption through prices, the change in the asset position of a household,
, which state-owned PTT (formerly Petroleum Authority of Thailand) launched in 2002 in response to fuel smuggling into the country. The project provides tax-free diesel, dyed green, to fishing boats to reduce incentives to use smuggled fuel. The Excise Department in 2010 stated that boats using green fuel were required to be fitted with a tracking device using a global-positioning system to monitor their whereabouts (Thai News Service 2010b).

Another way of lowering prices is to reduce taxes and fees levied. Tax cuts tend to be ad hoc, except in Chile which has formalized rules for adjusting the specific fuel tax to smooth enduser prices for small and medium-size consumers. Brazil, China, the Dominican Republic, El Salvador, Kenya, the Philippines, Rwanda, Senegal, Thailand, and Vietnam have all reduced taxes to lower end-user prices.

Several reasons are cited for providing fuel price subsidies, and helping the poor is always among them. In practice, universal price subsidies—even if they are available only to households—have been shown time and again to benefit the rich disproportionately. For example, studies of petroleum product subsidies in 13 countries consisting of eight in SubSaharan Africa, three in Latin America, Bangladesh, and Jordan show that, on average, the top two expenditure quintiles captured more than 60 percent of universal price subsidies (Coady et al. 2010). When diversion of subsidized fuels set aside for households—such as kerosene and LPG—is taken into account, petroleum product price subsidies become even more regressive, although when confined to household consumption only, kerosene subsidies have been found to be least regressive (Bacon and Kojima 2010a, 75–79; Kojima 2011, 28–30).

# **_Subsidies borne by fuel suppliers_**

In most countries with fuel subsidies, fuel suppliers bear some costs because reimbursements are seldom delivered immediately, or even at all, and fuel price increases are
more efficient judicial systems have a higher share of private credit to their GDP. To assess the economic significance of these results, consider the state of Sergipe in North East Brazil, which had the lowest level of judicial efficiency and a share of credit to GDP of 11.15% in 1996. An increase to the average judicial efficiency in the North East would triple the credit activity in Sergipe to 31.11%. **'"** Additional anecdotal evidence suggests that banks try to avoid certain states with weak judicial systems in their credit activity.'<sup>2</sup> The quality and amount of information creditors can obtain about potential borrowers is also an important determinant of financial development. To assess this impact quantitatively, we regress private credit on two dummies that indicate whether a country has a negative (black) information system only or a positive (black and white) information system.13 The results in Table Al indicate that the existence of a black and white information system increases private credit by about 21 percentage points of GDP, whereas the existence of a black information system only does not have any significant impact on financial development. If Brazil moved from its current system of mostly negative information to a system with both positive and negative information, its level of private credit over the period 1980-95 would have been 45% of GDP instead of 25%.

> 11 The indicator ofjudicial efficiency is constructed form two surveys conducted in 1996 and 1997 among Brazilian businessmen. The surveyed were asked to evaluate the state judiciary along the dimensions cost, speed and fairness. **12** See Pinheiro (1998).

> **13** This regression uses data collected by Japelli and Pagano (1999). Although these data do not correspond to the same time period as the data on Private Credit, we continue using data over 1980-95 to make our results comparable to column 1 in Table Al.

6
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33
defines its own regulatory code to grant operating licenses, issue permits, define sector-specific technical standards, and define regulatory fees and charges.

Second, Peru recently implemented a worldwide unique ex post control mechanism governed by the competition authority to enforce the elimination of subnational barriers to market entry that are inconsistent with national law. Before 2013, the competition authority’s legal mandate to contest regulatory barriers imposed by subnational governments that are inconsistent with national law had been very limited. But in 2013, it became much easier to eliminate similar regulatory barriers to market entry across the country once they had been confirmed as inconsistent with national law in one municipality. Therefore, the complaint of a single firm in a random municipality, once confirmed, triggered the elimination of similar regulatory barriers across the country. The dependence on such precedents led to a ‘roll-out’ of eliminated, often sector-specific regulatory barriers across municipalities which can be regarded as exogenous to other contemporaneous municipal policy changes.

The legislative changes produced a database of eliminated regulatory barriers to market entry and competition across municipalities in 2013 and 2014 which we match with establishment census data from 2008-15. We follow the integrated approach of De Loecker and Warzynski (2012) to derive establishments’ (revenue) productivity and markups and estimate how they are impacted by the subnational regulatory changes in a difference-in-differences specification. The integrated approach is critical for our purpose as it allows to control for unobserved establishmentlevel productivity shocks, permits technologies to differ across establishments within the same industry, and is valid under imperfect competition.

Moreover, estimating the reform impact on establishments’ productivity and markups makes it possible to infer their impact on physical productivity. That is, physical capital can increase because of increases in revenue productivity or increases in establishment-specific prices from higher markups.<sup>1</sup> Markups thus drive a wedge between the observed revenue and unobserved physical productivity, potentially biasing the measured reform impact. Specifically, the elimination of regulatory barriers to access domestic markets may increase establishments’ markups since it raises the demand for
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29
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- d’Ivoire and ICF.

- Malhotra, A. and S. Elnakib (2021): “20 years of the evidence base on what works to prevent child marriage: A systematic review,” <u>Journal</u> of Adolescent Health, 68, 847–862.

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31
. Moreover, due to data limitations, the classification is applicable to French data only starting from round 3, and to Romanian data - from round 4.

# **Table 2 Prevalence of informal employment in Europe, by region. 2008-2009**

_Per cent_

## **_A. Estimates based on current (during the survey week) status of respondents_**

||Empl<br>as a<br>Non-res<br>about c<br>exclu<br>a|oyees with<br>share of al<br>ponse<br>ontract<br>ded<br>b|out a cont<br>l employe<br>Non-res<br>treate<br>„No con<br>a|ract<br>es<br>ponse<br>d as<br>tract‖<br>b|T<br>Tot<br>employ<br>a|otal infor<br>al<br>ment<br>b|mal emplo<br>Extende<br>forc<br>a|yment as<br>d labor<br>e<br>b|a share of<br>Populat<br>aged 1<br>a|ion<br>5+<br>b|
|---|---|---|---|---|---|---|---|---|---|---|
|_South_|9.5|20.2|10.1|21.1|28.2|35.6|24.5|31.3|14.0|18.5|
|_West_|5.1|6.3|5.5|7.1|15.8|17.8|14.5|16.4|
value of preschool education and guide them through the enrollment of their children at CPS. An additional component was to provide information about returns to education. Such information has been shown to effectively increase attendance and change the social composition of students in other lower-income contexts (Nguyen, 2008; Jensen, 2010). The local village head and the field staff responsible for the study’s data collection performed the D2D activities.

At baseline, caregivers were informed about the new preschools and key details, such as that a preschool has been constructed and that there was no school fee. In addition, caregivers received a printed leaflet that had more information about the newly established CPS (see Figure A2). It noted that the teacher had been trained and it suggested how preschool in general could help children improve primary school readiness and, potentially, their overall educational attainment. The leaflet also provided information about average incomes in Cambodia by educational background, visualized in a graph using data from the Cambodian Socioeconomic Survey 2009. Caregivers received another informational leaflet about one year later, at midline.<sup>10</sup> Importantly, since survey field staff were responsible for the leaflet distribution (after the baseline and midline survey), we can ascertain that all eligible respondents received at least the information contained in the leaflet.

The home-based program (HBP) formerly operated in Cambodia as an independent early childhood education service to support parents of children aged zero to five years. However, it was redesigned as supplementary to CPS, aiming to enhance the effect of CPS enrollment. The program was implemented by local “core mothers” who received initial and ongoing training from MoEYS. The 35 day long training covered a wide range of subjects such as child rights, pre- and postnatal care of mothers, hygiene, nutrition, disease prevention, developmentally appropriate activities for children, school readiness, disabilities, health services, and child protection. Similarly to CPS teachers, core mothers participated in a written examination before and after the training. They were
-0.1611***|-0.1064***|0.0129|-0.0926|-0.1209*|-0.1359***|-0.1941**|-0.1214***|
|**Constant**|5.5152***|6.1937***|5.3133***|5.4711***|6.1275***|4.9538***|5.0654**|5.5964***|4.9288***|5.4960***|6.7281***|5.0239***|
|**N**|1896|316|703|877|658|1238|129|187|240|463|289|588|

_Note: * p<.1; ** p<.05; *** p<.01. 1 For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 27