rafmacalaba/data-use-mentions-extended huggingface dataset:
Interviewed women and men ages 65 and above allocate 5 percent of their time to childcare, indicating that elderly dependents in Armenia can contribute to childcare within the household (or family). Women living in households with young children reduce their time allocated to childcare slightly with the presence of elderly members (from 21 to 18 percent).

Empirical results indicate that gender roles and disproportionate time allocations to social reproduction activities constrain women’s time and labor market opportunities, which conditions women’s interactions with the fiscal system. The gender gap (in labor market participation) is consistently higher for households with dependents, who most likely need care instead of helping out by providing it. The fiscal system significantly reduces gender gaps for all household types, but the gaps for households with dependents remain negative and the largest.

Aid from stay-at-home (healthy) elders can reduce gender gaps in labor earnings for households with primary school children (or older) while children and elders can aid with household chores and care for family members in need. Access to affordable and convenient care for children and elderly family members can therefore influence their labor decisions and interactions with the fiscal system, for women in particular, and potentially for every household member.

Results from the Engendered CEQ assessment with time-use data in the Armenia example demonstrates the intricacies in which a model of intrahousehold bargaining would have to consider for it to be useful within a fiscal incidence model. It appears that older dependents can contribute in a positive way to household incomes or social reproduction and care responsibilities of females within the household (or both). The type of contribution made by an older dependent, however, likely depends on the dependent’s gender and health status. Furthermore, in Armenia, the positive nature of the contribution has been neutralized at post-fiscal income stages, which indicates (among other things) that the incorporation of older-age dependents into the household is likely negotiated by the household’s adults. Both the starting positions of the negotiating members and the outcomes are likely affected too by
Prior to turning to the structural model, we first estimate a reduced form gravity regression linking cross-product variation in home bias to the characteristics of<sup>_−→_</sup> _Z_<sup>_k_</sup> . Our data/estimation strategy is somewhat unusual. Before moving to estimation we describe some of the challenges this particular setting imposes, and the way in which we manage them.

It is now conventional to estimate gravity regressions with vectors of fixed effects that sweep out important variation in the data. In a cross-sectional regression with multiple origins and destinations, origin-product fixed effects control for systematic variation in the supply of a product, while destination-product fixed effects control for variation in expenditure levels and geographic remoteness of the destination. In a time series context origin-product-year and destination-product-year fixed effects sweep out heterogeneous supplies and demands, and shocks to either.

We use data from a single destination, PR.<sup>29</sup> Including destination-product fixed effects in the regression would sweep out useful cross-product variation in the data. Instead we parameterize import demand. Since our identification strategy relies on interactions between product characteristics and _HOMEj_ , the key threat to identification is if cross-product variation in the level of import demand is correlated with the product characteristics of interest, the<sup>_−→_</sup> _Z_<sup>_k_</sup> ’s. We address this problem in a manner that is conventional in applied econometrics; we include the<sup>_−→_</sup> _Z_<sup>_k_</sup> ’s themselves in the regression, along with their interactions. In this way we control for cross-product variation in the level of demand that might bias the coefficients on the interactions. The way in which the<sup>_−→_</sup> _Z_<sup>_k_</sup> ’s affect total import demand becomes important when estimate in the sample of upstream products.

29In principle we could
21 When all effects are taken together, the net impact of higher food prices in 2008 in Brazil was substantially different in rural and urban areas – and this was driven primarily by the offsetting positive market income effect. The negative expenditure effect was in fact stronger in rural areas, where people tend to be poorer and spend a greater proportion of their incomes on food. But higher earnings from food production may have considerably alleviated those effects. Under the more optimistic assumption of 100% pass-through from food prices to agricultural wages, there may have been substantial welfare gains among the rural poor. Rural extreme (moderate) poverty incidence would have fallen by approximately six percent (two percent), and the higher-order FGT indices would also have declined. Under a less optimistic pass-through scenario (α = 0.5), poverty and inequality would have increased as a result of higher food prices, even in rural areas, but much less markedly than when the market income effect is ignored. For the country as a whole, the net effect of higher food prices on poverty is positive (i.e. poverty increases) for all three measures and for both poverty lines, even if α = 1.0. But instead of rising from 11.0% to 13.5% (the expenditure effect), extreme poverty increases to 12.3% in net terms. Moderate poverty rises from 31.3% to 33.4% when all effects are taken into account, instead of to 35.1% under the expenditure effect alone.

# **6. Conclusions**

In this paper we combined spatially disaggregated data on changes in the consumer prices of sixteen food categories with detailed information on household consumption patterns, individual occupations and incomes in Brazil, to investigate the distribution of the welfare consequences of rising food prices in 2007-2008. These price increases were substantial: while headline inflation averaged 5.3% in the two-year period, food price inflation peaked at over 18% in mid-2008.

Because Brazil
benchmarking database**

The data used for comparing South Africa’s performance comes from the World Bank research data base that was recently developed with a specific objective to be used for this type of benchmarking exercise (Eustache and Goicoechea, 2005). The database pulls together the latest available observations from a number of well documented, specialized data sources that contain more detailed data with longitudinal information. The data base aims to measure _sector performance_ by focusing on sector _outcomes_ — **_access, affordability/pricing, and quality_** (technical or perceived). It also provides information on sector institutional reform. Data cover 207 countries (although coverage varies by sector and indicator)<sup>1</sup> with a limited number of variables<sup>2</sup> measuring sector outcomes.

For the purpose of benchmarking South Africa, we compare it with all the world geographical comparator groups--Sub-Saharan Africa, OECD, South Asia, East Asia & Pacific, Middle East & North Africa, Latin America & the Caribbean, Europe and Central Asia, and the world—and four groups of countries clustered by level of income per capita—low income, middle income, upper middle income, and OECD countries. Therefore, both region and income level are used as comparator criteria. The main benchmarking group on which much of the analysis is focused is South Africa’s per capita income group of _upper middle-income countries_ . Because of the strong association between infrastructure and income level, this is economically the most meaningful group comparator for South Africa. This is supplemented by relevant comparisons with other income groups and major regions of the world to provide a wider, geographical comparison of South Africa’s infrastructure performance. Finally, from

> 1 The percentage of available country observations from the total number of countries (207) varies by indicators from close to 90% for measures of access to roads, 85-95% for ICT access and pricing measures, 75-85% for access to water, and 60% for access
# **3.1 Data Sources**

The road data covers the stock of existing roads and their improvement between 1996 and 2016 annually, capturing RSDP phases I-IV. Three sources of data went into constructing the dataset (created and provided by the ERA): (1) federal road data from ERA, which includes dates of budget disbursements towards RSDP; (2) regional road network studies implemented at various times within selected regions that captured the inventory and conditions of roads; and (3) a nationwide road network inventory and condition survey implemented in 2014 and 2015. Speed limits were assigned to each road segment based on the pavement type and condition of the road, and road improvements are indicated by an increase in the speed limit (see Appendix S2 for a table showing ERA’s classification for road types, conditions, and speeds).

Our first outcome variable is nighttime lights, which has been shown to be a strong predictor of local economic activity (Donaldson and Storeygard, 2016; Sutton and Costanza, 2002; Doll et al., 2006; Ghosh et al., 2013), local GDP (Henderson et al., 2012), and measures of welfare such as local levels of asset wealth (Weidmann and Schutte, 2017). Nighttime lights data come from two data sources: the Defense Meteorological Program, Operational Line Scan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). Both sources are made available by the National Oceanic and Atmospheric Administration (NOAA). We rely on both sources to capture the full study period from 1996 to 2016; DMSP-OLS is available from 1992 to 2013 at a roughly 1km resolution, and VIIRS is available from 2012 to the present at a roughly 750m resolution. Two challenges must be addressed to construct a consistent time series based on the two nighttime lights data sources. First, DMSP-OLS data is captured over time using six different satellites; differences in the satellites, including the sensors and time of orbit, have resulted in significant
EU-Chile PTA of 2003).<sup>4</sup> These distinct approaches could affect the fixed costs of exporting for firms in different ways. Mutual recognition of member countries’ regulations is expected to reduce the fixed costs for firms from having to comply with a multiplicity of regulations (Baldwin, 2000). Firms only need to comply with their domestic regulation to have their products accepted in PTA partners’ markets. The harmonization of member countries’ regulations may initially increase the fixed costs of exporting for firms as they need to cope with the newly established standards. Then the use of a common standard (especially if it is an international standard) may increase the size of the market allowing firms to exploit economies of scale.<sup>5</sup> But evidence on the impacts of including SPS and TBT provisions in PTAs on firm export performance is lacking. Do the SPS and TBT regulations incorporated in PTAs benefit small firms or are benefits mitigated since large firms influence the design of PTAs as suggested by Rodrik (2018)? Do mutual recognition and harmonization of PTA member countries’ regulations have a heterogeneous impact across firms?

This paper addresses these questions by investigating the impact of PTAs regulating SPS and TBT on firms along the size spectrum combining two main novel sources of data. The first source of data we exploit is the World Bank’s Deep Trade Agreements database of Mattoo et al. (2020) that contains all provisions related to the policy areas of SPS and TBT (as well as many other policy areas) included in PTAs signed over the last 40 years. We combine this data with longitudinal administrative firm-level databases from customs in Chile, Colombia, and Peru covering the universe of exporters between 1996 and 2015 part of the Exporter Dynamics Database described in Fernandes et al. (2016). These three countries share many similarities in their economic and trade structures and constitute an excellent laboratory for studying the impacts of PTAs as they were signatories to close to 40 new PTAs with a variety of
watch Carbon, a short and engaging edutainment movie on climate change. The movie was delivered in four episodes, for a total length of 25 minutes.<sup>14</sup> Treatment 1 and control video clips were in Hindi with English subtitles, while Treatment 2 was mainly in English.<sup>15</sup> For each arm, episodes were released in a staggered way. In particular, each new episode was delivered two hours after the individual self-confirmed to having watched the previous episode. Participants received reminders to encourage viewership and were free to choose when to watch the episodes. While this approach increased the – duration of the total intervention which took an average of 7-to-10 days for completion, depending on the arm – it mimicked the way competing content is commonly consumed and shared in social media platforms (e.g., Instagram reels), making the study more generalizable.

# **3.3 Outcomes and measures**

We measure program impacts on three categories of outcomes: (1) creating awareness on gender norms and VAW in India, (2) changing attitudes regarding gender norms and condoning VAW, and (3) online information-seeking and posting behaviors related to gender issues. The first two sets of outcomes are self-reported and measured through the survey instrument. Specifically, awareness and knowledge questions cover issues explicitly discussed by the series. Attitudinal items aimed to measure gender norms and attitudes were derived from the India’s National Family Health Survey.

In addition to self-reported data, we independently measured two online outcomes. First, we measured clicks and visit durations to gender- and environment-related website links provided in both short- and medium-term follow-up surveys. We hypothesized that the treatment group would be more likely to click and spend a longer time browsing gender websites as opposed to other topics.<sup>16</sup> Second, study participants were provided with the option to publicly display

> 14Specifically, the material is the following: Treatment 1: Sex Ki Adalat (E1
activities. This survey focuses on a subset of this chain: _the cost of overland transportation_ in a single segment via a single mode, i.e., using either a railroad or a motorized road vehicle.

An overland shipment may entail the crossing of an international border between adjacent countries. When there are border checks and controls, delays and inspections can add to the total cost of shipping. Similarly, to the extent that overland freight transportation regulations differ across administrative units within a country, internal borders could contribute to shipping costs. Although trade-reducing effect of domestic borders are well-documented—see Coughlin and Novy (2021) for a recent estimation using US data and a summary of the related literature—actual evidence that ties these estimates to transport related frictions is scarce. In a rare example, Carballo et al.

2
# Appendix E. Alternative data sources

IOM’s CBNA collects information on arrival IDPs twice a year based on key informant interviews at the level of settlements, which are aggregated into districts and regions. In 2020, new displacements were reported across 1,069 settlements in 7 districts of the Badghis province. In CBNA, respondents are asked to indicate the percentages of IDPs that arrived at a settlement due to conflict and natural disasters; in the dataset, displacements are grouped into four categories, such as induced by conflict, conflict-natural disasters, natural disasters, and natural disasters-conflict.

OCHA’s dataset (HDX 2021) includes information on conflict-induced displacements at the level of districts, collected through the alert system; additionally, OCHA reports the dates of displacements. In 2020, there were 66 incidents (dates) of displacement due to conflict, and those IDPs arrived in 2 districts of the Badghis province.

Comparisons of IOM and OCHA data on new displacements were performed for the districts reported by both organizations. IOM reported more conflict-induced displacements than OCHA in the Ab Kamari district but no settlements with only conflict-induced arrival IDPs in the Qala-e-Naw district. At the same time, the IOM’s numbers of IDPs due to conflict and conflict-natural disasters exceed the OCHA’s numbers of conflict-induced IDPs in both districts. Effective comparisons of IOM and OCHA data on displacements may not be conducted due to differences in their data collection methodologies.

## Period of comparison

The IOM’s CBNA contains data on new displacements in Afghanistan during 2018-2020; the dataset that we currently have includes 4 periods: January-December 2018, January-June 2019, January-June 2020, and January-December 2020. The OCHA data on conflict-induced displacements are available for 2016-2020.

Table A1. New displacements in Badghis province reported by IOM and OCHA.

|Organization|2016|2017|2018|20|19|20|20|
|---|---|---|---|---|---|---|--
ports or port systems. Tongzon (2001) for instance compares the Australian ports to a set of best practice international ports for 1996 using a DEA. Estache et al. (2002) for instance shows how efficiency measures can be used to promote yardstick competition in the sector in a very pragmatic way. The analysis is done for 14 Mexican port authorities during the 1996-1999 period. The paper includes a discussion of how the use of efficiency measures can be used to shift the burden of proof on the operators in the context of scheduled or unscheduled tariff revisions. Container terminals located in hub ports tend to be more technically efficient than those in feeder ports as documented by Notteboom, Coeck and Van den Broeck (2000) .

# _3.2.2. Railways_

The railways sector also suffers from a low coverage by the academic and regulatory literature offering quantitative assessment of economic efficiency in developing and transition countries as seen in Table 5. Technically, these papers are not very different from those on developed countries in terms of the choices of methodologies. Indeed, a few of them pool data from OECD and non-OECD countries to generate performance rankings in the sector.

Most of these papers deal with the two main characteristics of the railways sector which have long attracted the interest of researchers interested in measuring economic efficiency: multi-output production and natural monopoly. Railway companies have traditionally operated passenger and freight trains simultaneously on their own networks, either at the national or at the regional level. As seen in the Argentina and Brazil papers however, this was changed by the reforms of the 1990s in many countries. Indeed, in these two countries, long distance passenger service has essentially be reduced to a minimum. Intercity infrastructure is for freight and local suburban traffic is mostly 16
long run) by higher tiers of government.” Sorribas-Navarro (2011) uses regional fiscal data in Spain during 1986-2006 to document that regions with larger debt burden tend to receive more federal transfers. The potential endogeneity problem is addressed by (1) adding cyclical economic indicators as control variables, such as unemployment, GDP, and population net flows; and (2) using the share of debt at a variable interest rate interacted with changes in interbank interest rates as an instrumental variable.<sup>47</sup> The second approach follows Rodden (2005a) to see if the financial market distinguishes regional governments based on their debt service abilities. If regions’ debt service abilities do not affect their debt ratings, it implies that the financial market believes that the central government will eventually help indebted regional governments with their debt burden, either through emergency bailout or periodic transfers in normal times. Rodden (2005a) compares the dispersion of regional debt ratings in countries where regional governments face “harder” budget constraints (Canada and the U.S.) and “softer” constraints (Germany and Spain) in the late 1990s. He finds that in Canada and the U.S., regional debt ratings are more dispersed, and the cross-region rating difference can be largely explained by the heterogeneity in regional debt burden (proxied by regional debt-to-own revenue ratio). To the contrary, the debt ratings of German Landers and Spanish regions are much less dispersed and closely follow their sovereign ratings, and debt-to-own revenue ratio is a weak factor

> 46There are two reasons why the FFPP and FLA can provide cheap loans to regions. The first is that the FFPP and FLA can borrow from banks under the central government’s guarantee, which lowered the funding cost of the FFPP and FLA. The second is that the central government also made direct budgetary contributions to the two funds. For example, the initial budgetary contribution to the FFPP announced in 2012 was €6 billion (Minguez et al. 2012).

> 47The
Despite a high degree of potential labor mobility, regulations covering the conditions of employment are not uniform. For example, the maximum duration of fixed term contracts in Germany is 96 months, in Spain it is 36 months while in the UK and in Belgium there is no maximum duration. There is no mandatory minimum wage in Germany or Italy, but there is one in both the UK and in Belgium. The rules governing severance pay also fluctuate among the countries of the EU. In Portugal, 20 months of full wages are payable as severance pay after covered employment of twenty years. The corresponding number of months for Belgium, Spain, Germany and the UK are 0, 12, 0 and 7.5.<sup>5</sup> In the UK and Belgium, it is not considered unfair to terminate the employment contract without cause. In both Italy and Germany, it is.

Notwithstanding borders that are unrestricted in terms of capital flows, banking regulations and capital market institutions vary significantly within the EU. Several research papers have been written analyzing bank-based versus market- based banking systems and the impacts of particular institutional structures on growth and development yet none have indicated that one or the other system will penalize economic outcomes in open economies. Recent research indicates that there is no particular institutional design that is critical to capital accumulation and growth but rather what is important is the overall level of development of the financial sector and legal system and the efficiency with which it allocates financial resources (Beck and Levine, 2002).

With respect to financial reporting, the EU member states have significant differences in national practices, yet follow a mutual recognition policy. They have acknowledged that financial statements from the other states must be accepted in all other member states without any need for restatement or reconciliation (Hegarty, 1997). This

> 5 Source:  Doing Business Database, The World Bank 2004.

16