rafmacalaba/data-use-mentions-extended huggingface dataset:
the level of the single pollution measurement rather than the grid cell, by running the same equation 3 using variation across all the observed GPS coordinates _m_ , rather than aggregating at the grid-level.<sup>35</sup> Reassuringly, the results are remarkably similar.

One example to illustrate the empirical variation in our data is shown in Figures 3 and 4, which show pollution measurements and road size in one of our sample sub-counties. It is clear that pollution is higher on the larger roads.

> result of sustained exposure. Therefore, the empirical strategies adopted by related papers studying the impact of pollution on labor supply and productivity, which typically rely on high-frequency temporal variation in pollution exposure, would not be appropriate to answer the research questions we are after.

> 35In columns 3 and 4 of Table 2, 4,604 pollution observations (corresponding to 8% of the sample) are dropped because the size of their closest road was not available as they are more than 100m away from the closest road.

21
.0|118.5|
|Pharmaceutical products manufacturing (b)|2001|5,134.2|2,375|2,796.8|2,085.3|1,177.6|
|High-tech manufacturing (a+b)|2001|67,335.7|100,387|14,412.0|17,996.2|143.6|
|Total manufacturing (c)|2001|138,323.0|345,141|31,922.8|48,129.6|92.5|
||||_Percentage_||||
|Electronics manufacturing|2001|45.0|28.4|36.4|33.1||
|Pharmaceuticals manufacturing|2001|3.7|0.7|8.8|4.3||
|(a+b)|2001|48.7|29.1|45.1|37.4||

_Note_ : The high-tech manufacturing sector comprises SSIC35220 medicinal & pharmaceutical products and SSIC 384 electronics for 1990. Comprises SSIC 242 pharmaceutical products and SSIC31 electronics for 2001 _Sources_ : Economic Development Board (EDB). (1990). _Report on the Census of Industrial Production 1990_ . Singapore: EDB Economic Development Board (EDB). (2001). _Report on the Census of Manufacturing Activities 2001_ . Singapore: EDB 12
# **Table 2: Average value of 1% capital in $US million**

The values are calculated using the 2018 values of the Changing Wealth of Nations dataset. These are expressed in constant 2018 $US million.

|||T|otal capital|||Natural capital||
|---|---|---|---|---|---|---|---|
|Issuer|Hu|man capital|Natural capital|Produced capital|Non-re|newables|Renewables|
|All||182,179.8|15,203.8|88,338.3||3,378.6|11,825.2|
|A-rated||266,865.3|16,690.8|122,465.6||2,977.1|13,713.7|
|B-rated||57,974.3|13,022.9|38,285.0||3,967.6|9,055.4|
|||Renewables||Forests||Agricultu|ral land|
|Issuer|Forests|Agricultural land|Protected areas|Non-timber|Timber|Cropland|Pastureland|
|All|6,535.2|4,380.3|769.5|6,073.9|461.3|3,170.4|1,209.8|
|A-rated|8,629.0|4,261.4|656.5|8,203.5|425.5|3,202.0|1,059.4|
|B-rated|3,464.2|4,554.6|935.3|2,950.5|513.7|3,124.1|1,430.5|

variable, along with other time-invariant characteristics, by subtracting the country-average
sup> ́</sup> and Resosudarmo, 2019). Limited data availability explains why, while the hedonic approach to estimate households’ willingness to pay for a change in an environmental amenity is used extensively by researchers, it is difficult to find studies of African countries. Household survey data overcome this limitation since surveys often capture information needed to construct a hedonic pricing model, including housing characteristics, access to services and different determinants of housing costs. The surveys exploited in this study include detailed questions on actual housing costs and rents and an estimate of how much a tenant would pay in rent for the dwelling if rented, as estimated by the respondent. Since only a subset of households are renters, exploiting the “self-estimated” rental values helps increase the sample size. The analysis therefore combines reported rent values and the self-estimated rent values<sup>5</sup> to construct a rent value variable for all four cities. Observations with missing values for both items were excluded. Comparison of the rents, self-estimated rents, and the combined variable distributions are depicted in Appendix Figure B.1. Measurement errors can affect rent and self-estimated rent values. To reduce these errors, outliers were removed and replaced with estimated values.<sup>6</sup> Values are in local currency and transformed to 2017 PPP US$.

The final sample is based on observations without missing values for rents and self-estimated rents. It includes 3,132 households in four cities and four countries: Accra (996 households), Antananarivo (919 households), Dar es Salaam (466 households), and Addis Ababa (751 households). Table 2 presents a summary of the different database characteristics. More detail on the survey methodology and char-

> 3The questions related to flood perception were almost identical for all four cities although for Antananarivo the time horizon was 5 rather than 2 years.

> 4In most studies, flood models or risk area identifications (Beltr ́an et al., 2018) are large surfaces.
18

# **5. Conclusion**

New anti-poverty programs implemented by governments across the world often have similar predecessor programs. Taking evidence from India’s public works programs, this article highlights the need to be cautious while using the initial roll-out of “new” programs as quasi-experimental research designs. This article shows how ignoring an older public works program, SGRY, can substantially underestimate the general equilibrium effect of the new employment policy, NREGA, on a variety of outcomes. The underestimation of program impact can be attributed to the actual gap in employment provision between the treatment and control districts, which is lower than what is initially assumed. Using a novel dataset of program expenditure at the district level for SGRY, I find that ignoring the older program underestimates the general equilibrium impact of public workfares on labor market outcomes for adults by 30-40% in most instances. This paper, therefore, shows how ignoring the continued existence of a similar older program can have first-order effects on measuring the true impact of the policy. A key area for future research is identifying the exact mechanisms through which public workfares affect the private labor market.

# **References**

Adukia, A. 2020. “Spillover Impacts on Education from Employment Guarantees.” _Education Finance and Policy_ . Afridi, F., A. Mukhopadhyay, and S. Sahoo. 2016. “Female labor force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme.” _IZA Journal of Labor and Development_ 5 (7): 164–178. Altonji, J. G., T. E. Elder, and C. R. Taber. 2005. “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ 113 (1): 151–184.

Angrist, J. D., and J.-S. Pischke. 2014. _Mastering ’Metrics: The Path from Cause to Effect_ . Princeton university press. Atkin, D. 2016, August.
15 results. We next add measures of potentially confounding national policy changes.<sup>18</sup> In Column (3) we control for fossil fuel subsidies (as % of GDP), since governments in more exposed countries may have been more likely to respond to the crisis by subsidizing more affected firms. Including this control slightly increases the magnitude of our coefficient estimates, consistent with the targeting of subsidies to dampen the energy price impact lowering our results. In Column (4) we control for national industrial policy support for solar and wind energy and electric vehicles, using policies appearing in the Global Trade Alert database targeting HS codes for these technologies as outlined in Rosenow and Mealy (2024). We find that while the number of these industrial policies is correlated with green technology-related job postings, controlling for the former does not substantially weaken the estimated impact of pre-crisis exposure in differential establishment adoption of green technologies. Finally, to address concerns with the use of the IHS transformation, notably around coefficient interpretation, following Chen and Roth (2023), the specification in Column (5) repeats the baseline analysis but with a dependent variable that equals one if an establishment had any green technology-related job postings in year-month _t_ and zero otherwise. The statistically significant result at the 1 percent level indicates that the impact of the energy shock on demand for green technologies, as evidenced by related job openings, is also strong at the extensive margin. The results at this margin have a more straightforward interpretation:

# **4.2 Event study results: Timing and magnitude of effects**

We next examine how the increase in green technology-related hiring evolved throughout the 2022 energy crisis. Event study results presented in Figure 3 plot estimates of the coefficient on the interaction between the country- and firm-level exposure measures and a year-month indicator, illustrating the effect on establishments’ demand for green technology-related hiring in each month (relative to the 2021Q2 omitted period).

Figure 3 shows that more energy-intensive establishments in countries with higher levels of
ratio, would also be drastically affected by such adjustments.

Measuring inequality globally using the top-adjusted distributions also results in a much higher level of global inequality, with the top income adjustment increasing the global 32 For example, in a country where poverty is estimated to be 30 percent in an unadjusted survey, and we expand the distribution by adding 10 percent of the population to adjust for missing people the top, the poverty rate would fall to 27.2 percent. Since the method only adds a rather small share of people to the distribution, the effect on aggregate poverty measures is small.

33 We apply the Chandy and Seidel (2017b) method by adding a top segment to the Lorenz curve from the survey distribution and thank the authors for providing the replication code to implement this.

25
draws on a diverse set of household consumption or expenditure surveys from 91 countries as of 2010, covering a range of topics including transport by region, country, and area.

The data standardization methodology of this database is of great significance to the harmonization of transport expenditure data from LSMS and other household surveys after 2010. The first step is annualizing each household’s expenditure data for each commodity, according to the “recall period”, on which the household is asked to report by recalling their expenditures. For example, if the transport expenditure data is collected for the past week, then it would be multiplied by 52, while monthly data would be multiplied by 12. The second step is mapping the transport products and services in each survey to a standard classification of products according to International Comparison Program (ICP) classification, equivalent to the Individual Consumption According to Purpose (COICOP) as shown in Annex 8. Finally, all expenditure data would be converted to a common reference year, 2010, taking account of inflation and real growth in household consumption, then converted from local currencies into U.S. dollars based on the 2005 round of ICP.

Based on the most up-to-date harmonized consumption data provided by the Data Analytics and Tools team in the Development Data Group, covering 105 countries between 2000 and 2018 (in which 73 countries have data after 2010), we can analyze the share of household transport expenditure by rural or urban setting and expenditure quintile<sup>7</sup> at the global level and different income levels. The details of the transport subcategories are recorded in the COICOP classification. The country coverage of each subcategory varies as listed in Table 8.

**Table 8. Country coverage of the transport subcategories**

(105 countries are included in the standardized consumption database)

|**Category**|**Number of countries**|
|---|---|
|Motor cars|94|
|Motorcycles|89|
|Bicycles|97|
do not enter directly into the computation of the HDI, and neither does the latter explicitly adjust for dissimilarity across types in any way. Conversely, life expectancy at birth, GDP per capita and literacy do not enter the HOI explicitly.

A correlation of 0.94 between these two indices, albeit calculated only over a non-representative sample of 39 countries in two of the world’s regions, suggests two things. First, it suggests that the average coverage rates of services like access to water, electricity, etc. are highly correlated with the constituent elements of the HDI. Second, it suggests that the HOI is determined, to a very large extent, by the first term in the product π�<sup>j</sup> �1-D<sup>j</sup> � . In fact, the correlations between average coverage and the component-specific HOI in this sample are extremely high: they are greater than 0.99 for school attendance; access to water; access to electricity; and having finished primary school on time. It is 0.987 for access to sanitation. This implies, of course, that the penalty for inequality of opportunity, �1-D<sup>j</sup> � , accounts for a much smaller share of the variance in the HOI than mean coverage.

A final international comparison issue our data can shed light on is the association between the dissimilarity index (the measure of inequality of opportunity contained within the HOI) and the index of inequality of economic opportunity (IEO-R). The dissimilarity index can be interpreted as the proportion

> 17 A possible caveat with viewing the dissimilarity index within the HOI as a measure of inequality of opportunity is that the index is typically calculated “for children”. This justifies the use of certain variables - like geographic location or education of the adults in the household - as circumstances, which are clearly in the realm of choices for the adults. The argument is that the index applies to children, and these are
– provision of the road in itself will alter the villagers’ disease environment unless, for example, the excavation work leaves behind fine breeding grounds for mosquitoes, or the easier access to towns and markets increases their exposure to communicable diseases. It remains the case, however, that any attempt to measure the effect of providing an all-weather road on morbidity and mortality must address the question of whether villages with all-weather roads have the same disease environment as those without. This is particularly important when there are no ‘before’ and ‘after’ data, so that comparisons are perforce cross-sectional ones. If, for example, villages that are infested with malaria are specially favored with the provision of all-weather roads by way of compensation, or, on the contrary, neglected as hopeless cases, then comparing villages with and without such roads will be hazardous where drawing inferences is concerned. As in Section 4, we begin with the data from the household survey, which cover individuals’ life histories (births and deaths), but their ailments only in the _kharif_ season of 2009.

# **5.1 The household survey: Morbidity**

In the absence of ‘before’ and ‘after’ data, the only available measure of the disease environment where morbidity is concerned is the incidence of disease among the 1,292 villagers comprising the sample, 455 of whom were residents of the 11 villages with an all-weather road. The simplest index of morbidity is the fraction of the population reporting not a single episode of sickness during _kharif_ 2009: in villages with and without such a road, respectively, this fraction was 55.4 and 56.4 percent. A small refinement is to break down the category of the sick into those suffering one, two and three or more episodes, respectively; but this alters the finding not at all ( _χ_<sup>2</sup> (3) = 2 _._ 30). A further alternative is to break down episodes of sickness by ailment: viral
expansion as the pandemic is brought under control (Nayyar and Davies 2023; Coulibaly and Foda 2020; World Bank 2021c; Zhan et al. 2020).

The four decades prior to the global financial crisis saw a steady increase in global economic integration through trade, assisted partly by falling tariffs (figure 4). Since the global financial crisis, however, trade integration has stalled, with the COVID-19 pandemic and Russia’s invasion of Ukraine having added further obstacles. With Russia’s share of global oil production having increased considerably in recent decades, there is now a material risk that the disruptions caused by Russia’s invasion of Ukraine could lead to a major reconfiguration of global trade and investment networks, as countries look for alternative sources of energy. While this may boost trade in some parts of the global economy, it is likely to disrupt trade elsewhere. Since such a reconfiguration would be motivated by political and security considerations rather than economic considerations, it is likely to reduce global economic welfare as well as trade in the long term (Ruta 2022).

# IV. Patterns in trade costs

The fading momentum of global trade growth is diminishing its role as an engine of output and productivity growth. Countries therefore need to find new ways to reap the benefits from trade. One possibility is to cut trade costs to boost exports and encourage imports in a manner that is growth-enhancing. A number of studies have documented the negative impact of trade costs on trade growth (Anderson and van Wincoop 2003) and the boost to productivity that can result from lowering trade costs (Bernard, Jensen, and Schott 2006). Trade costs have also been recognized as an important factor in firms’ decisions to choose out-sourcing over in-sourcing (Hartman et al. 2017).

## IV.1 Definition

The analysis in this paper relies on a comprehensive UNESCAP-World Bank dataset of bilateral trade costs. Following Novy (2013), Arvis et al. (2013) derive measures of annual trade costs for the period 1995-2018. For any
Analyses of this case have highlighted political, economic and personal factors (De Gramont 2015, Gaspar et al 2016, Cheeseman and De Gramont 2017, Bodea and LeBas, 2016). The ruling party in Lagos was an opposition party in stiff competition with the central government, and, in the nascent democracy, was keen to show that it could provide a better model of government and public services. Providing a steady source of domestic revenues was thus important not only for meeting the severe needs of road infrastructure, waste management, and security in a congested city, but also in securing funding to consolidate and expand political influence. Although the drive to increase taxation was already underway, the need for tax revenues was underscored in 2004 when the central government withdrew government transfers to Lagos local governments following a dispute over the creation of new local governments. The social conditions in Lagos, with a strong middle class and active civil society, pushed the local government to deliver public goods to win elections rather than the commonplace strategy of relying on patronage politics. Because Lagos is one of the most populous states and the commercial center of the country, the robust formal sector could support an expansion of personal income taxation through employer withholding. There were also widespread allegations that the leadership of Lagos state held a personal stake in the consulting company that was contracted to process the electronic tax payments, including a lawsuit by the company founder against the former governor of Lagos (Olawoyin 2020). Thus, personal benefit and the ability to solidify political power through access to finances may have provided an added incentive.

**Figure 4: Tax Revenues in Lagos State**

Data Source: Lagos State Government Digest of Statistics 2006, 2010, 2013.

18
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- Koehler, D. and J. Spengler. 2007. The toxic release inventory: Fact or fiction? A case study of the primary aluminum industry. _Journal of Environmental Management_ , 85, 2, 296-307.

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- Li, Wanxin, 2006, Commentary, _Opening Up the Floor: Environmental Performance Information Disclosure Pilot Programs in Zhenjiang and Hohhot_ , 8 CHINA ENV’T SERIES 125, 125 (2006).

Lopez, J. G., T. Sterner and S. Afsah. 2004. Public disclosure of industrial pollution: The PROPER approach for Indonesia? _Resources for the Future_ . Discussion paper 04-34. Washington, D.C.

- Magat, W.A., and W.K. Viscusi. 1992. _Informational Approaches to Regulation_ . The MIT Press, Cambridge, MA. USA.

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Paquin, M. and C. Sbert. 2004. Towards effective environmental compliance and enforcement in Latin America and the Caribbean. Unisfera International Centre.

- Powers, N., Blackman, A., Lyon, T.P., and U. Narain
use of these deduction options, the taxpayer can make a refund request through form 402. This form requires detailled information on the withholding agent, amount of tax withheld and timing of withholding, and asks the taxpayer to certify that she has no outstanding liabilities in any other tax.

# 3.4 Data

Our analysis combines anonymized tax return data and third-party and withholding declarations from the General Directory for Taxation in Costa Rica. The tax return data contains the universe of income tax declarations (D101) for 2006-2014 and sales tax declarations (D104) for 2008-2014, as well as the corresponding payment returns (D110) for the income and sales tax. Since 2006, all tax returns have been digitzed, and electronic ling has gradually been introduced for the dierent declarations, ensuring that the data have nearly complete coverage and a high degree of acurracy. The ling software EDDI-7 conducts automatic validation checks to ensure the internal consistency of led returns. The data contain all line items of the tax return, including rm type and sector, income sources, cost items, deductions, gross and net liability and payment.The nal data set contains 112,000 to 250,000 self-employed per year, 90,000 to 150,000 corporations and 58,000 to 70,000 sales tax lers per month.<sup>21</sup> We merge the tax records with the informative declarations D150, D151, D153 and D158, also for the period 2006-2014. These data have been led eletronically through the DECLAR@7 system, which conducts similar validation checks as EDDI-7. Table III provides an overview of the number of records and their coverage for each of the informative declarations.

Declaration D151 registers both the largest number of observations, and the widest coverage, being available for approximately half of all rms. The coverage is similar for the self-employed and corporations. The ling of informative declarations is more concentrated than the coverage, meaning that an even small share of rms act as informants (results available upon request)