rafmacalaba/data-use-mentions-extended huggingface dataset:
skills, and categorized the job titles into the 2013 Malaysia Standard Classification of Occupations (MASCO).<sup>8</sup> Burning Glass grouped individual skills into 633 clusters (groups of similar skills commonly learned together or substitutable),<sup>9</sup> which were then further grouped into 28 cluster families that are roughly aligned with sector-occupations (Burning Glass 2019).

The World Bank-Burning Glass Malaysia Job Postings data is not representative of the distribution of jobs in Malaysia for two main reasons. First, the job postings collected by Burning Glass between 2016 and 2018 are a subset of all job vacancies during this period: they represent one-fifth of the number of

> 5 Cambodia and Vietnam are lower-middle income countries and Malaysia and Thailand are upper-middle income countries. 6 For recent studies using Burning Glass data for the U.S., see Deming and Kahn (2018), Acemoglu et al. (2020), and Deming and Noray (2020). For the U.S. and five other countries (Australia, Canada, New Zealand, Singapore, and the United Kingdom), see Cammeraat and Squicciarini (2021) and Squicciarini and Nachtigall (2021).

> 7 See appendix 6 of CSC (2019, pp. 126–134) for details on the online job postings data collected by Burning Glass in Malaysia. The data we use in this paper uses one additional month of vacancies (December 2018). The preliminary version of this data set was used to identify demand for occupations in Malaysia (CSC 2019).

> 8 Only 1.8 percent of postings could not be mapped to occupation codes and are thus excluded from the sample from which we draw skills requirements for occupations.

> 9 Similar skills commonly learned together include, for example, Microsoft Word and Excel in a course on Microsoft Office.

9
<!-- Start of picture text --> Financial sanction of real estate brokerage cartel near Lake Chapala<br>17<br>Real estate brokers near Lake Chapala<br>Synthetic control<br>Financial sanction of price fixing (30M Mex$)<br>16<br>15<br>14<br>13<br>12<br>11<br>1990 1995 2000 2005 2010 2015 2020<br>Log of sales<br><!-- End of picture text -->

**Figure 3: Change in industry sales after the sanction of a real estate brokers’ cartel near lake Chapala.** The vertical black line indicates the date when the real estate brokers’ cartel near Lake Chapala was sanctioned. The thin blue line indicates the log of sales of real estate brokers near Lake Chapala, the treated industry. The thin red line indicates the synthetic control, or the weighted average of the log of national sales in control industries, with weights selected so the weighted average has a similar pretreatment trend to the treated industry. The thick blue and red lines connect average values of treatment and control before and after the sanction. The counterfactual for the treated industry, indicated by the arrow, is derived by shifting the thick red line upwards. The black arrow shows the implied synthetic difference-in-differences, which corresponds to an annual increase in sales between the pre- and post-treatment periods of 5.05 (standard error = 1.39) percent. The case id ( _expediente_ ) is DE-019-2007.
Melitz models, which means that trade responses at the sector level are not necessarily constant across the models.

# **4.1.2 Key Trade Response Elasticities.**

The key elasticities that determine the trade response are: (i) in the Melitz model, the Dixit-Stiglitz elasticities and the Pareto shape parameter; (ii) in the Krugman model, the DixitStiglitz elasticities; and (iii) in the Armington model, the elasticities of substitution of domestic for foreign goods, σ(D,M) and the elasticities of substitution of imports from different regions, σ(M,M).

For our central unadjusted values of these elasticities, we have the following. We based our values of these Dixit-Stiglitz elasticities on the estimates of Broda, Greenfield and Weinstein (2006). Since the Philippines is not in the Broda, Greenfield and Weinstein dataset, we chose their estimates for Indonesia as our proxy for The Philippines.<sup>19</sup> We aggregate the estimates of the Broda, Greenfield and Weinstein dataset for three-digit industries in Indonesia to the DixitStiglitz sectors of our Krugman style model of The Philippines. For the Pareto distribution shape parameter in the Melitz model, we choose the value 4.58 from the structural estimation of the Melitz style model of Balistreri, Hillberry and Rutherford (2011). In the Armington model, we do not nest the demand structure and start with the values of the σ(M,M) elasticities in the GTAP database and scale all of them such that the export response of the Philippines is the same as in the Krugman model. These values as well as the supply elasticities are in table 4.<sup>20</sup> We take the trade response in the Krugman model as our central trade response and adjust the elasticities of substitution in the Armington model and the Pareto shape parameter in the Melitz model to be consistent with the trade response of the Krugman model. Since the selection effect magnifies the trade response in the Melitz model
gender differences in the world. Differences between boys and girls in reading proficiency have been found to exceed half a standard deviation in both the PIRLS and PISA studies (Mullis et al. 2017; OECD 2019). Previous research on TIMSS mathematics and science items has shown that items with a higher reading load (those requiring a higher volume of reading or more complex reading skills) tend to be more difficult for students to answer correctly than items with a lower reading load, and also that weaker readers tend to be disproportionately disadvantaged by a higher reading load (Mullis, Martin, and Foy 2013). For this reason, the magnitude and consistency of Saudi boys’ relative disadvantage in reading, seen across various studies, seems likely to play a role in contributing to their poorer results found here in mathematics and science achievement even after accounting for a range of contextual variables. The NALO 2018 results provide further support for this view, with boys at both grade levels being more likely than girls to report not having read a book (although it should be noted that this was the case even for a substantial minority of girls).

The proposed importance of reading skills in underpinning mathematics and scientific achievement is consistent with the pattern of residual (unexplained) variance reported in the models, which indicates a role for other factors operating largely at the student level. After accounting for a range of other student- and class/school-level factors, the majority (approximately three-quarters) of class/school-level variance was explained in the models, whereas a majority of student-level variance remained unexplained. This suggests that the residual gender differences in achievement are likely to be associated more strongly with student-level factors, such as reading skills, social and behavioral skills, or aspects of the home background, than with additional school-level factors. The finding from NALO 2018 that more boys than girls have repeated a grade at school because of poor academic performance is worth noting in this regard. Similar data on the extent of grade repetition are available from PISA 2018 (OECD
demonstrate this by employing three restricted simulations in which the 40 representative households in the analysis (differentiated by rural/urban orientation and by income level) have (i) identical consumption shares, (ii) identical factor income shares, and then (iii) identical consumption _and_ factor income shares. Since the results from simulation (i) are nearly identical to those generated when the heterogeneity of the 40 households is left intact, the authors conclude that “clearly, for the poor it is the source of income, not the pattern of expenditure that is driving the adverse impact relative to the average household” (p. 107).

The general equilibrium analysis of rice export taxation in Thailand by Warr (2001) also suggests that factor earnings effects are the driving force behind welfare and distributional effects. Although an export tax generates government revenue and lowers the price of rice for consumers, it also lowers the return to unskilled labor, which is used intensively in the Thai rice industry. Because both the rural and urban poor derive more than 40% of their income from unskilled labor (according to the Thai survey upon which the stylized households are based), the negative income effect ends up _outweighing_ the consumption benefit, such that both the rural _and_ urban poor are harmed by the export tax in Warr’s analysis.

Despite the apparent importance of factor earnings effects, until recently they have generally not been accounted for in studies that quantify the effects of external shocks on the poor in developing countries. As will be seen in the next section, this is particularly the case historically for analyses based on detailed household surveys.

# **III.  Partial Equilibrium Analysis**

The simplest, and most logical, place to begin our survey of previous work is with studies that focus on the impact of the commodity price changes flowing from trade liberalization (point (a) in Winters’ list). This takes us into the realm of partial-equilibrium/cost-of-living analysis. While this characterization is quite broad and somewhat awkward, these
_Figure 1 – Data collection using phone interviews and door-to-door survey_

# 2.2.3 Complementary data

The survey data were complemented by geospatial data at the sub-ward level collected by the Ramani Huria team. Data include information on solid waste (location and quantity of trash) collected in July 2018 and information on the drainage systems collected from July 2017 onward. The team accessed these data in September 2018 and aggregated the information at the sub-ward level. The data collection is ongoing, the data used in the current study do not cover the entire city yet. To address inconsistencies, the data were only used in regression analysis and by applying ward-level fixed effects.

Distance from work was measured. The distance/travel time to work variable was computed from a multimodal network based on OSM for pedestrian travel and a GTFS feed produced by WhereIsMyTransport for transit. The workplace of each household’s breadwinner was geocoded in the data.<sup>7</sup> The network travel times and distances between the household dwelling and the geo-coded workplace were calculated using the GOSTnets python library.

Finally, six focus group discussions were conducted with households from different areas that had participated in the survey. The team traveled to Dar es Salaam in March 2018 for this purpose. The information gathered during these discussions is used to complement the quantitative analysis with qualitative insights directly from the families who participated in the study.

> 7 Workplace location was identified by respondents using landmarks (due to the lack of addresses in Dar es Salaam). Respondents primarily used names of bus stops, streets, markets and neighborhoods. Breadwinners were identified by the household itself and defined as the household member whose income the family relies on the most for subsistence (not necessarily the household head).

6
India. In their analysis, the authors controlled for other factors that may be associated with poverty reduction, in order to rule out the possibility that land reform activity merely serves as a proxy for other policies<sup>12</sup> . Their detailed analysis finds that while skepticism is warranted with respect to the prospects for redistributing land through land ceilings, the abolition of intermediaries and tenancy reforms (at least in some states) appear to have been more successful in reducing poverty. These findings accord reasonably well with existing, empirically based assessments of the relative success of Indian land reforms.

# A NOTE ON **DATA**

Most published accounts of the size distribution of rural land holdings (e.g. Sharma 1994) use aggregate data from the National Sample Survey (NSS). Aggregate data from agricultural censuses and surveys (e.g. NSS), however, do not present a reliable picture of land distribution, the extent of tenancy, or the terms of tenancy contracts, since landlords tend to

- lIt is also suggested that sharp caste polarization in West Bengal allowed reforms to be promulgated as 'class' reforms without becoming embroiled in the more contentious arena of caste politics (T.V.Somanathan, pers. comm.).

- 12 The specification of the land reformn variable in their analysis raises concerns, however. States are 'classified as high land reform or low land reform depending on whether they had more or less than a total of three land reforms (of any type) during the 1958-92 period'. Thus, the cumulative volume of legislation enacted is taken as the measure of actual change in practice.

12
online appendix Code (UNSPSC) classification code and detailed item descriptions for each item. We refer to the item description as the product. Since each SDC is associated with a unique Taxpayer Identification Number (TIN), the seller is identified in all transactions. If the buyer provides their unique TIN for the transaction, both the buyer and the seller are identified by the receipt.<sup>A3</sup> Receipts are timestamped with the date and time of issue.<sup>A4</sup>

**EBM location** While an SDC is associated with one TIN, a firm may have multiple EBMs. Since we do not observe the location of each SDC, we assume that the EBM is located in the same sub-District as the firm. We use firm registration data to geo-locate firms (see Appendix A.3 for a description of firm registration data). In November 2019, 95.5% of EBM II taxpayers reported having only one EBM II device. We study the product market at the sub-District level, the third administrative sub-division in Rwanda. Sub-district disaggregation allows us to leverage sufficient geographic variation, while constituting a credible definition of a market; the average sub-District has a population of 32,000 citizens and 1,000 registered firms.

Since most EBM receipts identify both a buyer and a seller, we are able to map mask supply chains from the sub-District of the seller to the sub-District of the buyer. We retain a receipt-by-firm-product dataset (aggregated to a buyer sub-District-byfirm-product-by-month dataset for our main analysis) to study mask prices and construct a balanced buyer sub-District-by-month dataset to study purchases of masks and paracetamol across sub-Districts.

> A3Since firms require receipts to claim input tax credits, we consider unidentified buyers as final consumers.

> A4While transmission of the receipt to RRA can be delayed, either if software is malfunctioning or if the internet connection is unstable, the RRA estimates 98% of total receipts due to be declared are transmitted within two weeks.

A2
prosperity. The impact of these choices on the shared property indicator is an empirical question. This issue becomes even more important when the monitoring of shared prosperity is done at the global level. For example, the World Bank constructs corporate numbers on shared prosperity based on welfare indicators used for estimating poverty headcount rates measured at the $1.90 per day per capita in 2011 PPPs poverty line. This decision was made to ensure consistency for the two indicators of the WBG measuring extreme poverty and shared prosperity. As a result, the estimation of the shared prosperity indicator follows special conventions of adjustments adopted for estimating extreme poverty. Some of these adjustments are not necessarily consistent with the recommendations of the World Bank’s poverty measurement manuals (Deaton and Zaidi 2002; Ravallion 1992, 1996), which have been adopted by many developing countries when estimating national poverty and inequality statistics. It is, therefore, crucial to understand how different choices in the estimation process affect the shared prosperity indicator.

Given that shared prosperity is a relatively new indicator, there is a lack of studies that systematically test the robustness of the indicator to different parameters. Most of the existing literature focuses on global performance in shared prosperity and its relationship with overall growth and inequality (Narayan et al., 2013; Cruz et al., 2015; World Bank, 2015ab). To the best of our knowledge, there were only a couple of illustrative tests in the World Bank (2015a) thus far, demonstrating how the choices of data and time intervals affect the indicator of shared prosperity using the example of Peru. Results of systematic robustness tests will be useful to the World Bank for a better measurement of its corporate goal and to countries which may choose to deviate from the methodology used by the World Bank and to introduce changes to the construction of the indicator of shared prosperity.

2
_Services PTAs_ PTAs, followed by South Korea, which has concluded 15 such agreements. The EU is party to 14 agreements, as is Japan; the US, China and Peru each have 13, and Australia and New Zealand have signed 10 services PTAs.

Figure 1: Number of Agreements per Economy in 2016 <!-- Start of picture text --> NEW AGREEMENTS ENTERED<br>2000 - 2016<br>13 - 20<br>11 - 12<br>6 - 10<br>3 - 5<br>2<br>1<br>0 (NO PTA NOTIFIED)<br>IBRD 45683 |<br>MARCH 2021<br><!-- End of picture text -->

Source: Authors’ elaboration using the World Bank DTA 2.0 Database. Note: Zero (‘0’) means that an economy has no agreements notified to the WTO. This could be because there is in fact no services PTA (the vast majority of cases), or because an economy is not a member of the WTO, or even if it is, an agreement may not been notified to the WTO until well after it has taken effect.

As a result of this spread of services PTAs over the past 20 years, the majority of services trade now happens under preferential terms for most countries (Figure 2). The graph depicts the log average value of services exports with partner for which it has a services PTA (red circle), if there is one, and the log average value of services exports with non-PTA partners (black diamond). For most countries PTA trade exceeds services under most favoured nation (MFN) terms. Two notable exceptions are the US and Japan, which trade services extensively with EU member states but as of 2016 neither had a services PTA in place with the EU.

The proliferation of services PTAs has been accelerating since 2000. That said, participation of economies has been highly unequal; for instance
# **BOX 1: Electronic Sales Registration Machines in Ethiopia (Ali et al., 2021; Mascagni, Mengistu and Woldeyes, 2021)**

**Problem:** Despite its in-built self-enforcing mechanism that creates opposite incentives for buyers and sellers, the collection of VAT is thought to be largely underperforming in low and middle - income countries (Keen and Simone, 2004; Gordon and Li, 2009; Besley and Persson, 2013; Pomeranz, 2015). One of the main reasons, apart from the administrative complexity, is widespread evasion as the lack of information on sales severely undermines the enforcement capacity of revenue authorities.

**Technology intervention:** The Ethiopian Revenue and Customs Authority (ERCA) started the rollout of electronic sales register machines (ESRMs) in 2008 with the goal of moving from paper-based receipts, which make monitoring business transactions on a daily basis extremely expensive and unfeasible, to a digital solution. ESRMs, on top of printing out receipts, automatically report transactions via a network to an ERCA server – with the agency being able to monitor sales and eventually use that information to enforce compliance.

**Research design:** The studies of Ali et al. (2021) and Mascagni, Mengistu and Woldeyes (2021) heavily rely on administrative tax data provided by the revenue authority. The authors have access to tax returns and payments data over a large span of years, as well as to the date of machine adoption. Both studies employ a difference-in-difference design. In Ali et al. (2021), the design is reinforced by a matching technique making adopters and non-adopters more comparable. In Mascagni, Mengistu and Woldeyes (2021), several restrictions are imposed on the sample (for instance, never adopters are excluded), and a triple difference-in-difference is performed to reduce imbalance between treatment and control groups.

**Impact:** Impacts from ESRM adoption are largely positive. On the one hand, Ali et al. (2021) find that the adoption
|
|Constant|<br>-0.612|
||(0.37)|
|Observations|<br>197|
|R-squared|0.21|

**Note:** Regression based on observations at village level and not on the entire sample of children’s observations. Absolute value of t statistics in parentheses. * indicates significance at 10% level; ** at 5% and *** significant at 1% level of confidence.

26
policy. A 100-point increase in AQI increases spending on all masks by 54.5 percent and antiPM2 _._ 5 masks by 70.6 percent (Zhang and Mu, 2018).

We make three contributions to the literature. First, studies on air pollution and its impacts primarily use wind directions to evaluate unequal exposure or instrument pollution. However, actual pollution disperses as a mass with changing directions along its pathways. We combine the HYSPLIT model and Gaussian dispersion to better simulate trajectories. Second, existing papers focus on one region or conduct program evaluations. Our paper analyzes all operating coal power plants worldwide and projects pollution on income maps. This practice provides a global picture of pollution and its coexistence with poverty. Third, while earlier evidence shows that pollution worsens labor productivity, there is no causal evidence that pollution affects aggregate economic outputs. We adopt econometric methods to test changes in GDP per capita, GDP, and HDI due to plant operations.

# **3 Data and methods**

## **3.1 Data**

In this study, we analyze air pollution originating from coal-fired power plants. Basic characteristics of coal plants are derived from the Global Coal Plant Tracker developed by CoalSwarm 5
Banks with more exposure to the oil shock seem to benefit less from interest rate actions, as evidenced in both the full and restricted sample. The result highlights the key difference between liquidity and solvency risks. Banks that are heavily exposed to the oil sector are more susceptible to losses due to the likelihood of defaults in this industry. To the extent that markets internalize that the profitability of these banks will be lower, interest rate cuts would have small effects. On the other hand, banks with short-term liquidity constraints, but with less exposed portfolios, stand to benefit more from the ease in funding conditions.

Announcement concerning asset purchases were also followed by increases in the abnormal returns of illiquid banks, albeit with marginal statistical significance in the restricted sample. We interpret this finding as evidence that unconventional monetary policy also played a role in reducing the liquidity risk premium.

Since announcements should have a greater impact if they are unexpected than if they are expected, we further restrict our sample to days with monetary policy surprises. That is, we restrict our sample to days when the monetary authority made policy rate announcements after emergency meetings, or when the domestic policy rate cut was larger than the expected by market analysts.<sup>21</sup> We further confirm earlier findings with the sample of unexpected monetary policy announcements. Although surprises of interest rate cuts were not associated with significant increases in bank stock prices, such announcements did appear to have differential effect across banks. For instance, monetary policy surprises were associated with declines in the liquidity risk

> 21 According to the Reuters forecast based on the one-week analyst survey of benchmark rates. Some examples of monetary policy surprises include Thailand’s 25bps cut in the benchmark rate (February 5), Canada’s repo rate cut of 50bps (March 4), and the United Kingdom 50bps repo rate cut during the emergency meeting of March 11. The only case in which the rate cut was smaller than expected was in Thailand on March 20. We drop this date from