rafmacalaba/data-use-mentions-extended huggingface dataset:
geographic coordinates of surveyed firms. These were provided with a random shift of up to 2 kilometers to mask firms’ true locations and allow firms to be matched with natural cities as described below.

# **2.2. Data on Natural Cities Based on Nighttime Lights Satellite Imagery**

A novel dataset of more than 1,500 “natural” cities across Asia and the Pacific developed recently (Jiang, 2021) is utilized for capturing cities. The dataset is based on nighttime lights (NTL) satellite imagery data available since 1992 from the National Oceanic and Atmospheric Administration website. Human settlements were delineated with the deblurred NTL data as contiguously illuminated areas. Those meeting certain criteria, such as covering units captured by the Global Rural Urban Mapping Project database with population greater than 100,000 in year 2000 or having a land area greater than 100 km<sup>2</sup> in 2000 are classified as urban agglomerations. These urban agglomerations are referred to as natural cities to distinguish them from administratively defined cities.

The population of natural cities is estimated using grid population data from LandScan. Other city characteristics including weather (precipitation and annual maximum and minimum temperature), distance to the nearest seaport, and citywide ruggedness are added to the dataset. Jiang (2021) provides additional details on the development of the natural city dataset.

The advantages of using natural cities are many. First, natural cities are uniformly defined and their characteristics are measured consistently across countries and over time. In contrast, official statistics on cities rely heavily on administrative designations, for example, defining cities as areas administered by municipal councils, or combining administrative designations with economic ones, for example, requiring a city to meet some country-specific thresholds on population size and density and even share of agricultural employment (ADB 2019). As may be expected, the boundaries of administratively designated cities differ substantially in their size, institutional status and governance structure across different countries. Second, natural cities offer a better representation of the urban agglomerations in which
Beyond the above issues, there is a need to understand the structures and processes needed to establish a motivating and enabling environment to ensure that highly cost-effective inputs and interventions are indeed chosen. It becomes also extremely important to think carefully about the appropriate program design and implementation strategy.

This paper presents some empirical analyses of learning outcomes based on local data and experience regarding the impact of Programa para Abatir el Rezago Educativo (PARE). The paper is divided as follows. The next section describes the PARE Program and the database. Section 3 assesses the impact of the PARE program on learning and achievement. Section 4 evaluates the cost-effectiveness of the PARE program. The final section presents concluding remarks.

# **2. PARE background**

Programa para Abatir el Rezago Educativo (PARE), 1992-1997. The objective of the program was to assist the Government of Mexico in improving the quality and efficiency of primary education, focusing on four Mexican states (Oaxaca, Guerrero, Chiapas and Hidalgo) with the highest incidence of poverty and low education indicators. These objectives, considered as being of the highest priority within the Government's Education Modernization Program, would be achieved through; (i) reducing the high repetition and dropout rates; (ii) raising the level of cognitive achievement of children, and (iii) strengthening management of the primary education system, including program design and implementation, monitoring and evaluation of the system. The program consisted of giving schools additional resources (components) like libraries, better distribution of textbooks, academic material, training aid to teachers and principals, increased in official supervision of teachers and construction and repair of schools.

From its inception its performance was monitored through statistical comparisons between the target, or _experimental,_ population (schools in the states of Chiapas, Guerrero, Hidalgo and Oaxaca) and a _control_ group formed by students in comparable schools in the state of Michoacan which falls outside the scope of the program. Special surveys were conducted yearly between 1992 and 1995. In addition, all students
# **4.2 Access to jobs and rents**

A job density proxy was measured based on spatial data of the economic activity distribution (Barzin et al., 2022) and added to the model to assess the relationship between rent and access to jobs. Better access to jobs tends to be associated with higher rents and housing costs so it is therefore assumed that _α_ ˆ2 _>_ 0 in Equation (1). Figure 5 presents the value of the marginal effects of job density on rents in percentage, based on estimations including the variable exposure to actual floods (Appendix Tables C.1 to C.4, column (7)).

In Accra and Addis Ababa, areas with higher job density are associated with lower rents. These results are contrary to our hypothesis. In Accra, while some expensive dwellings located in the northeast of the Accra district, have relatively high job density, many households living in more expensive dwellings in the south-west have relatively low access to jobs. These households in the south-west could be driving the results (Appendix Figure D.1).

In the case of Addis Ababa, the highest job density is around the Ketema district, a populous area with low rents and improvised settlements, that means rents are lower in the economic center, and higher in the periphery. Two of the richest locations in Addis Ababa are close to the airport, and close to the African Union, respectively two and one hours travel time from the economic center (see Appendix Figure D.4).

In Dar es Salaam, the relationship between job density and rents is also negative but not statistically significant.

In Antananarivo, areas with high job density have higher rents, on average. WB (2017) also found that rents close to the city center were higher. The result is intuitive also since Antananarivo is a relatively monocentric city, where jobs are concentrated in the center (see Figure D.2) Since the variable we use to measure job density is non dimensional
Template, which compiles information on fuel subsidies for 192 nations from 2015 to 2025. To estimate the indirect effects, this analysis draws upon data from the input-output matrix, incorporating the IMF's industry subsidy ratios and the approach devised by Jellema and Inchauste (2018) for estimating these secondary impacts. For gas subsidies, our estimates incorporate the subsidy ratio based on consumer prices, as estimated by the IMF. In this calculation, the indirect effects are estimated by applying the rate above to industries, while the direct effects are calculated from the explicit subsidy applied to households.

**We assume the following on the distribution of tax burden and evasion.** Regarding tax burden, it is assumed that workers carry the economic weight of personal income taxes. At the same time, employees shoulder the burden of social security contributions, encompassing employer and employee contributions. Additionally, the weight of consumption taxes is placed on consumers. These assumptions are robust but strongly assume that labor supply exhibits perfect inelasticity and that consumers possess perfectly inelastic demand. While this may not align with real-world behaviors, these assumptions provide a practical approximation and are widely employed in the literature for analytical purposes. On tax evasion, Personal income taxes (PIT) and social security contributions (SIC) evasion are assumed to occur. Individuals earning below the minimum wage and those not contributing to SIC are categorized as informal workers and, consequently, are excluded from paying PIT and SIC, reflecting a potential form of evasion. Regarding indirect taxes, the Value Added Tax (VAT) calculation is determined using the effective tax rate. However, our analysis does not account for potential evasion of either VAT or excises due to a lack of information on the place of purchase in the 2021 HBS. Transactions in informal places, typically within the informal market, often evade VAT payments. Unfortunately, the Bulgaria HBS does not provide data on whether VAT is paid in these informal market transactions, limiting a more detailed assessment of indirect tax evasion.

**It is important
female sectoral participation (Zone I) in 2019 and the growth of employed persons between 2019 and 2021, indicating that sectors with high female participation were more affected by the contraction in labor demand than sectors with a high concentration of men (Zone II). This pattern is also evident in the job vacancies offered by the public employment services, even though formal demand for the health and financial sectors increased during the same period (Figure 10).

In 2022, DANE changed its expansion factors, making it difficult to compare information for years before 2021. However, a review of the trend of participation and unemployment for the 1st semester of 2021 and 2022 (with the 2018 factors) shows that the recovery trend is positive for both men and women (Table 1).<sup>7</sup> The participation rate has increased in both groups, especially for women, and the unemployment rate has contracted in both groups,<sup>8</sup> with the reduction being more significant in women. Therefore, it can be deduced that there was a gap reduction for both indicators. In 2022, the growth of women's labor demand has been driven by growth in the service sector, such as professional services, accommodation, food and entertainment, commerce, and industry, with more than 10% growth rates pared to 2021. Among these groups, only the industry grew more rapidly among men.

Table 1. Labor Market Indicators 2021-2022

||||1er semester<br>||Chang<br>|e (p.p.)<br>|
|---|---|---|---|---|---|---|
||Indicator|2021<br>(1)|2021<br>(2)|2022<br>(3)|(3)-(1)|(3) - (2)|
|Mn|GPR|
ts between 0 and 25%, depending on their prot level. The self-employed thus have lower tax rates on average, and the self-employed below the exemption threshold can le an income tax declaration without incurring a liability. The vast majority of rms is required to le the income tax declaration by December 15 for the previous scal year (October 1 to September 30). A few large rms use the calendar year as scal year and le the annual declaration by March 15. Firms have to make three quarterly advance tax payments for the annual income tax if their previous year's liability or their average liability over the last

> 12Costa Rica's aggregate statistics do not distinguish between wage earners and un-incorporated rms, which are listed together as personas físicas. Our analysis focuses on rms.

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project and included (a) a multi-topic socioeconomic household questionnaire, and (b) a cross-country comparable individual questionnaire that was administered to the adult household members in private interviews to collect self-reported information regarding their work and employment, as well as ownership and rights over physical and financial assets, among other topics.

The paper starts out by providing precise country- and questionnaire module-specific duration estimates, as proxies for respondent burden, for an extensive range of household and individual questionnaire modules. These statistics can serve as operationally relevant inputs that can inform the decisions of survey practitioners interested in implementing similar questionnaire modules in comparable contexts. The average household interview ranges from 82 minutes in Cambodia to 120 minutes in Tanzania. Food consumption tends to be the most time-consuming household questionnaire module to complete with an average of 22 to 26 minutes, depending on the country. Besides food consumption, household modules on non-food consumption, housing, and household roster consistently rank among the highest in interview length. Likewise, the average individual interview ranges from 13 minutes in Ethiopia to 25 minutes in Cambodia, with the individual questionnaire modules on land ownership, labor, health and education consistently ranking among the top time-consuming modules.

The disaggregation of the paradata allows for a more detailed look into respondent burden. One could find the average minutes per question conditional on certain specifications (for example, 3
nicipalities, but also small and remote municipalities with no connections. These functional territories seem to be consistent with the LLM assumption that local trade and technological shocks do not spill-over to other areas through labor migration (see Table 20).

**Other data** : We use data from EUKLEMS on adoption of Information Technology (IT) and Communication Technology (CT) by sector and time for the US to estimate a measure of exposure of Mexican LLMs to such technologies in the US, for a robustness test. We use data on the degree of offshorability and routine task intensity of Mexican occupations from Mahutga et al. (2018). We use data the susceptibility of automation of occupations from Artuc et al (2018). Data on fixed assets, machinery and value added per worker by LLM come from the publicly available tabulates of the Mexican Economic Censuses for 2003 and 2013.

# **4.2 Descriptive Statistics**

Figure 1 shows the evolution of the stock of robots per thousand workers. Automation in the US, Europe and Mexico increased almost every year since they entered the sample. According to this measure, automation in Europe is almost twice the level of the US. Accordingly, Mexico’s automation is almost half that of the US. However, it increased at a fast pace since 2011 narrowing the initial gap. In contrast, automation in Brazil is much lower and did not experience significant changes since 2004.

According to Figure 2 and Figure 3, automation has been primarily driven by the automotive sector, both in Mexico and the US. Other leading sectors include computers, plastics, basic metals, pharmaceuticals and machinery. Sectors that adopted robots at a higher pace in the US and Mexico also experienced a higher increase in exports from Mexico to the US, although the correlation is rather weak (Figure 2 and Figure 4).

There is substantial variation across regions in terms of exposure to US and local automation (see Figure 6). As expected, when weighting by exports, exposure
to jobs**

In an efficient housing market, property prices rise with increased access to jobs and amenities (Ahlfeldt, 2013), and in choosing where to live people make a trade-off between low housing value and proximity to jobs. To understand rents, we need to understand access to economic opportunities and how it influences housing markets. However, there are two significant data constraints, common to developing country cities which make it difficult to capture access to economic opportunities, particularly for SSA cities: 1) lack of information on job locations, and 2) lack of information on transit routes and travel times (Peralta-Quiros et al., 2019). This study captures access to economic opportunities using the spatial density of employment developed in Barzin et al. (2022). This measure is used to proxy for job density. It was created using a machine learning based algorithm, and data from OpenStreetMap (OSM) and Google Earth Engine (GEE). OSM data provide information on the location of different types of public and private services and institutions (hospitals, pharmacies, banks, schools, etc.), amenities (rivers, parks, etc.), and infrastructure (roads, airports, etc.). GEE data were used to add information on population density, intensity of night lights, and land cover. This is a novel way to estimate job density. The main advantage of this proxy is that it can be calculated for any city which has accurate and available OSM and GEE data and where data on economic activity are scarce or missing. It allows comparison of economic activity across cities, based on a unique measure. The job density proxy measures relative job density where a low value denotes lower job density and a high value higher job density. The values themselves cannot be interpreted directly. The maps of the four cities analyzed here, and with the spatial distribution of jobs are provided in Appendix Figures D.1 to D.4.

# **3.3 Descriptive statistics**
# _3.2.2 Institutions_

As mentioned above, Pakistan’s performance on access is clouded in uncertain statistics. However, we do know that there is no specific rural electrification agency or fund with a mandate to improve access rates. Not surprisingly the country has a low score on the energy access regulation indicators for new connections, solar home systems, and for mini-grids (Table 10). There are no incentives or requirements for utilities to expand access, and off-grid solar is not yet regulated. Grid connected roof-top solar has just been provided with a tariff and consumers of all DISCOs can now use net metering.

**Table 10: Regulatory framework for electricity access in Pakistan and comparators, 2015**<sup>**35**</sup>

||India,<br>AP|India,<br>Odisha|India,<br>Raj|Pakistan|<sup>International</sup><br>benchmark|
|---|---|---|---|---|---|
|**Energy Access Regulation**|81%|78%|81%|16%|56%|
|_Regulation of New Connections_|75%|66%|77%|48%|65%|
|_Regulation of solar home systems_|100%|100%|100%|0%|66%|
|_Regulation of mini-grids_|67%|67%|67%|0%|44%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform

# **_3.3 Efficiency and financial viability_**

The low efficiency of the electricity sector in Pakistan has been a feature of the country for many years. The creation of the independent regulatory body (NEPRA) and the unbundling of WAPDA led to regular collection and
Within the aggregate sector in the input-output table, the share of gross domestic product by disaggregate sector is used to decompose the aggregate sector. It is furthermore assumed that the input output structure for all the disaggregate sectors is identical to the input output structure of the corresponding aggregate sector.

For “Financial intermediation”, comparable national accounts data is not available. The two major sub-sectors are banking and insurance services. To obtain data on the share of the two sub-sectors, we collected data for the year 2007 on assets in the sub-sectors from the Central Bank of Armenia website. Table 8 shows the data. The share data is used to disaggregate the aggregate sector “Financial intermediation” as explained above.

# **References**

Light, Miles (2010), “Armenia CGE Framework - Documentation for a Pilot CGE Model and Data System”, project report presented to the Government of Armenia.

Badri Narayanan G. and Terrie L. Walmsley, Editors (2008). Global Trade, Assistance, and Production: The GTAP 7 Data Base, Center for Global Trade Analysis, Purdue University.

# **Data**

National Statistical Office of the Republic of Armenia, “Supply-Use Table, 2006”, unpublished data.

National Statistical Office of the Republic of Armenia, “Generation of Income Account by Economic Activities, 2007”. Available at http://www.armstat.am/file/article/hah_09_3.pdf.

National Statistical Office of the Republic of Armenia, “Use of Gross Domestic Product, 2007”. Available at http://www.armstat.am/file/article/hah_09_4.pdf.

National Statistical Office of the Republic of Armenia, “Production Account by Economic Activities, 2007”. Available at http://www.armstat.am/file/article/hah_09_2.pdf.

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04 (-0.08, 0.077, School attendance (6 y.0.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.12, 0.047, School attendance (7 y.o.) (Watis et al 2013) USA (H5)}-TT & Coaching & Mental Health Counselling 0.19 (-0.01, 0.40)1, Self-reported GPA (10-11 y.0.) Low & Middle Income - Basic (Spier at al 2020) BED Subsidized Preschool (Additional year) (0.14 (-0.31, 0.58)7, Primary school enrollment (6 y.0.) (Brinkman et al. 2017) IDN Preschool Construction & TT & Community Outreach > (0.68 (0.60, 0.78) T, Enrolled in playgroup (8-0 yo.) (Brinkman et al. 2017) IDN Preschool Construction & TT & Community Outreach > 0.86 (0.74, 0.97) T, Enrollment duration in playgroup (months) (2-9 y.o. (Martinez etal. 2017) Moz Preschool Consiruction & TT & Parental Engagement > 0.21 (0.09, 0.32) T. Enrolled at primary school (5-8 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement ~> 0.19 (0.07, 0.30) T, Ever goneto primary school (5-9 yo.) (Martinez etal. 2017) Moz Preschool Construction & TT & Parental Engagement > 0.12 (0.04, 0.21) T, Appropriate grade for age (5-9 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement 0.41 (-0.17_ 0.38)T, Dropout from school* (5-8 yo.) (Martinez et
exchanged between civil society leaders and the district administrator significantly decreased when civil society leaders were included in the sessions. Thus, civil society leaders may have been _less_ able to activate sanctions from above as a result of the intervention.<sup>25</sup> In the bottom panel of table 14, we consider whether the civil society leader variant of the intervention increased the ability of civil society leaders to mobilize villagers to pressure the village head. If this were the case, we would expect civil society leaders to play a bigger role in organizing community members as a result of the intervention, and we might also expect villagers to have more information about the regulations guiding village governance. Inconsistent with this sanctioning channel, the variant of the intervention that included civil society leaders is associated with a substantively large, even if statistically insignificant, reduction in organizational activity on the part of civil society leaders in the village. We also not find any evidence that the civil society leader variant of the intervention increased households’ knowledge of village governance regulations.

Thus, the evidence indicates the ability of civil society leaders to identify transgressions by the village head increased as a result of the intervention, which by itself would be sufficient to motivate reformed behavior if civil society leaders were already in a position to trigger sanctions on village heads, whether directly or indirectly. From the analysis above, it appears most likely that civil society leaders applied direct social pressure to change behavior; these leaders typically already had high social standing in their communities, even if we do not detect a measurable increase in status as a result of the intervention, and their interactions with the village head increased as a result of the intervention. In contrast, the likelihood of civil society leaders triggering pressure from above or below appears to have lessened as a result of the intervention.

# **7.2 Qualitative Research**

We also collected qualitative data from 10 villages for the purposes of better understanding mechanisms of change. Because our main goal for this research was mechanism
<br>fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age fixed effects|No|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|4428|4480|3741|3789|21382|21654|12239|12261|5107|5107|
|Number of countries|163|165|151|154|158|160|145|145|168|168|
|Adjusted R-square|0.997|0.997|0.999|0.999|0.997|0.999|0.986|0.993|0.998|0.999|

Source : Barro and Lee 2013; Key Indicators of the Labor Market (KILM), International Labour Organization; Labour Force Statistics, Organisation for Economic Co-operation and Development (OECD); UN Population Prospects; World Development Indicators, World Bank; and World Bank staff estimations.

Note : Business cycles defined as deviation of real GDP from linear-quadratic trend. Sample includes unbalanced panel of 35 advanced economies and 133 EMDEs for 1987-2020. p-statistics are shown in parentheses.

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