rafmacalaba/datause-encoder-data huggingface dataset:
includes data from Ahmednagar; whatever process “selected” this district for the experiment may well be correlated with a large potential treatment effect.

Deaton (2012) encourages consumers of experimental evaluations to note the “experimental panel”: the set of observations eligible to be randomized into the treatment or control group. Often, this set is not chosen according to a representative sampling strategy. Unusually, we have information about our experimental panel and about a larger set of villages that was originally planned to be an experimental panel. Although we cannot know what the experiment would have found in Nanded and Nandurbar, knowing this history and that these two districts have worse sanitation coverage than Ahmednagar and, in one case, a larger Scheduled Tribe population underscores that our experimental result is not independent of a context in place and time.

The reduction in the experimental panel highlights an important fact which no randomization is required to prove: the capacity of the Indian state is limited, and there remain many places which have not yet been reached by even the most basic sanitation coverage. Despite the effects of the Total Sanitation Campaign where it has happened, half of the Indian population defecates unsafely. How well can the our findings in Ahmednagar be generalized? If reducing open defecation indeed can improve the height and health of children throughout India even approximately as well as it could in the villages experimentally studied, then this remaining half could represent an important opportunity to invest in India’s human capital.

For policy, open defecation causes negative externalities, establishing a firm theoretical claim to public resources which is complemented by the empirical findings of this paper. Economic theory would advise a resource-constrained government to address problems reflecting – – large market failures such as externalities before providing excludable and rival private goods. Because most curative care of non-communicable disease does not have such externalities,<sup>25</sup> this argument implies that sanitation should be a health policy priority, including 25Of course, the market for curative health care involves other market failures, including asymmetric 28
investors and finance the capital expenditure of projects. However, this view is contradicted by other evidence on concessions, which also involve financing risk. Figure 1 shows the number of new concessions signed each year has been constant or rising since 2000.

The data also show that leases and management contracts are growing in popularity as contract models, which can be explained by the increasing reluctance of private companies to take equity risk in developing regions. China, once again, in an exception to this pattern: there have been several large equity deals in recent years.<sup>13</sup>

# _Regional Trends_

Looking at the regional distribution, in Figure 2, Africa has seen a lot fewer deals than Asia and Latin America. Figure 1 also shows that lower-middle-income countries have had the greatest number of PSP deals, exceeding the number of contracts in upper-middle-income countries.

**Figure 2: Number of Projects by Region, Income Group and Project Type (1991-2004)**

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>LIC<br><!-- End of picture text --> <!-- Start of picture text --> Asia<br><!-- End of picture text --> <!-- Start of picture text --> BOT-type<br><!-- End of picture text --> <!-- Start of picture text --> Concession<br><!-- End of picture text --> <!-- Start of picture text --> Lease-type<br><!-- End of picture text --> Looking at the distribution of contract types across regions and income groups, we note that there have been no divestitures among low-income countries, but divestitures are the second most frequent model in the upper-middle-income group. Concessions appear to be most important in the lower-middle-income group. Across regions, concessions have been very rare in Europe and most frequent in Asia. Divestitures are concentrated in the Latin American region, although there are also some examples in Europe. These figures are consistent with the view that investors are more willing to take equity risk (in BOTs, divestitures and concessions) in higher-income countries.

# _International and Local Investors_

There is ample anecdotal evidence for the withdrawal of international private investors from the developing world. In 2005, for example, Suez (Ondeo), one of the world’s two largest water service companies, announced that it was withdrawing from two major projects, in La Paz-El Alto (Bolivia) and Buenos Aires (Argentina). Overall, the World Bank PPI database identifies 20 projects that were either cancelled or have become ‘severely distressed,’ accounting for 7 percent of projects and 37 percent of investment commitments in 1990–2004 (Izaguirre and Hunt 2005:2). Our WATSAN PSP database records 28 terminated projects or 4 per cent of the total number.

However, these well publicized views of a few high profile international investors do not tell the whole story. As Figures 3 and 4 demonstrate, private water investment is booming in some parts of the world,

> 13 Chinese municipalities show little or no interest in management contracts and generally require upfront capital investment by the private sector, especially foreign companies (Blanc-Brude and Jensen 2004).

6
- 22

Table 10: Percent between-school variance in secondary achievement explained by school type, school organization and quality, and school composition, Brazil 1988

||Mathe|matics|P|ortuguese|
|---|---|---|---|---|
|Model|tau|Percent<br>variance<br>explained|<br>tau|Percent<br>variance<br>explained|
|Unconditional|34.28|<br>-|9.87|<br>-|
|School type|10.40|<br>69.7%|3.46|<br>64.9%|
|School organization<br>and quality|7.45|<br>78.3%|2.73|<br>72.3%|
|School composition|7.12|<br>79.2%|2.43|<br>75.4%|
|Final reduced|7.13|<br>79.2%|2.54|<br>74.3%|

# 4. Conclusior

33. We found that a significant portion of the variance in achievement in secondary schools in Brazil could be attributed to the types of school attended. Students in federal technical schools outperformed students in general secondary, SENAI and teacher training schools in both mathematics and Portuguese, after holding constant sex, age, family size and the number of hours the student spent working. In mathematics, students in private schools outperformed those in public schools. We also found between-school differences in the SES-achievement gap for mathematics.

34. In seeking to explain the federal technical and private school effects on average achievement, we explored variations in school organization, quality and social composition. Two school quality factors were significantly related to average mathematics achievement: class size (achievement was higher in larger classes) and the number of hours mathematics was taught (the more
to a benchmark country (e.g., the largest trading partner, most often China and second most often i W where W are the total exports by country _i_ of product _k_ .<sup>1</sup> A practical advantage of this kk measure is that it requires only the exports of country ii,W WW _i_ and the benchmark country, and so can be xx calculated without data on total exports of product _k_ by the world, as required by Balassarevealed comparative advantage. Large economies like the United States or China are good benchmarks because they export most products, and report product-level export data almost immediately, whereas data on total exports of product _k_ by the world are only available with a two- or three-year lag in the export series from COMTRADE, CEPII BACI, and the Growth Lab at Harvard University.<sup>2</sup> The relevant benchmark country may vary depending on the question one has. If a country is negotiating a bilateral trade agreement that lowers trade costs, indexing comparative advantage to the other country in that agreement identifies which industries are expected to grow in the context of that agreement. A country may also index to a country viewed as a local competitor, or aspirational peer. Vietnam might benchmark to higher-income Malaysia, for instance. One could conceivably pick a different benchmark depending on the product. If a country has a comparative advantage in a product relative to the leading exporter of that product, it is likely it could succeed in exporting more of that product.

Another approach argues measures of comparative advantage should control for market potential as determined by trade costs and foreign demand, which the Balassa-revealed comparative advantage measure does not. Modern trade models are designed to reflect the empirical regularity that exports follow a gravity pattern: countries export more both to markets that are closer geographically, culturally, or linked by trade agreements and to larger markets.

> 1 One may alternatively define W as net exports, equal to total exports minus total imports by country _i_ of product _k_ , to ensure that revealed comparative advantage does not identify products with minimal domestic value kk ii,W WW added. This is a concern especially in entrepôt countries, like Djibouti and Singapore. There are other approaches to xx identifying export value added that rely on inter-country input-output matrices, though these are available only at a high level of product aggregation.

> 2 The latter two databases are most popular because they impute frequently missing export values for developing countries in COMTRADE with so-called mirror data on imports reported by other countries.

10
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5 and Orefice (2023) that in response to energy price increases during 1996-2019, French manufacturing firms reduced their energy demand and improved their energy efficiency. In addition, our findings support the hypothesis of Zhunussova (2022) that higher energy prices from the crisis could have positive implications on the clean energy transition.

Our results also contribute to the literature on green jobs. For the US, Curtis and Marinescu (2022) use the same online job postings dataset, but focus only on wind and solar energy jobs from 2010 to 2019. Another closely related paper is that of Saussay et al. (2022), who also use job postings data in the USA from 2010-2019 but with a more expansive definition of ‘green jobs’ based on the skills listed in postings. Our research adds to this literature in three important ways. First, our definition of green jobs is based on a carefully selected set of technologies mentioned in the EPO Y02 class, “the international standard for clean innovation studies” (Calel, 2020). Second, by using the full text of the job postings, we may be able to more comprehensively identify green technology-related jobs. Third, by considering a wide range of countries and extending our analysis to the end of 2022, this paper is able to study global patterns of diffusion and explore the impact of the energy crisis.

# **2. Data and methods**

## **2.1 Measuring green technology diffusion using job openings**

Our main data source is 580,441,273 scraped online job postings from Lightcast for 16 advanced economies during 2014-2022. This group of countries represented 46 percent of global GDP in 2019.<sup>4</sup> More details about this data and its representativeness can be found in Bastos et al. (2024).

To measure green technology-related postings we extract a list of keywords from the titles and descriptions of the European Patent Office’s Y02 classification of patents related to ‘climate change mitigation technologies’. This is a continuously updated and comprehensive list of green technologies used for classifying patents.<sup>5</sup> It covers green technologies related to buildings

> 4These countries are the US, Canada, UK, Australia, New Zealand, Singapore, Denmark, Norway, Italy, Austria, Belgium, Switzerland, Germany, France, Luxembourg, Netherlands.

> 5We found alternative lists of green technologies, including the International Energy Agency (2021)’s Clean Energy Technology Guide or the classifications of Dussaux, Dechezleprˆetre and Glachant (2022), to be more limited
cyclical income elasticity of import demand (a2) does not appear in the nonstochastic part of equation 3 and cannot therefore be estimated there. However, as already mentioned, the coefficient a2 can be estimated, together with a consistent estimate of its standard error, from the twostep procedure previously outlined. Applying separate estimation procedures for a, and a2 does not prejudge the possibility of testing whether cyclical and secular income elasticities are equal. A closer look at equation 3 reveals that a test of a1 . a2, under the maintained - assumption that COV ([(t), e(t)] 0, is equivalent to a test of independence between Y(t) and the error term. A standard Hausman-Wu procedure can be used for this purpose, which is the approach taken here.<sup>3</sup> The determinants X of Yc still need to be specified. Secular or trend GDP has been assumed to follow a segmented trend, with structural breaks occurring in 1974 and 1981.4 Hisspecification of this trend equation will result in inconsistent estimates of the coefficient a2, but will not affect the two-stage least-squares estimation of equation 3, except for an efficiency loss stemming from the choice of instrument.

- 3/ Our approach while drawing on Khan and Ross (1975) allows the recovery of a consistent estimate of the coefficient variancecovariance matrix. A different set-up such as that in Haynes and Stone (1983) would rely on spectral analysis, but the relatively small size of our sample precludes the use of this technique.

- 4/ A simple logarithmic function was used to generate the trend income variables. However, to allow for the impact of the external shocks that hit most developing countries during the estimation period, three subperiods were distinguished, each of them corresponding tentatively to a relatively constant growth rate. The first oil shock (1974) was taken as marking the beginning of the second period and the year following the 2-1/2 fold increase in oil prices (1981) as marking the beginning of the third period. The structural breaks in 1981 and 1974 were then found to be statistically significant for 90Z and 50Z, respectively, of our sample countries.
**_Data_ .** Using annual data, the sample includes 155 EMDEs for 1970-2018. This includes 36 EMDEs that are energy exporting (oil, gas, or coal), defined as in Appendix 1, and 120 other EMDEs. Data on output, investment, consumption, and productivity are drawn from the World Bank _World Development Indicators_ . The exchange rate classification follows the IMF’s _Annual Report on Exchange Arrangements and Restrictions_ . High (low) public debt is above (below) 70 percent of GDP for high-income EMDEs and 30 percent of GDP for upper-middle-income, lowermiddle-income, and low-income EMDEs.

# **4.3 Empirical results from the local projections model**

**_Demand-driven versus supply-driven oil price plunges._** EMDE output evolved differently in demand-driven and supply-driven oil price plunges. In the first year of both supply- and demanddriven oil price plunges, EMDE output fell by about 0.5 and 0.3 percent, respectively (Figure 5). The recovery, however, differed: output recovered after demand-driven oil price plunges and, three years later, had returned to the baseline; after supply-driven oil price plunges, EMDE output did not recover and remained below the baseline three years later.<sup>12</sup>

**_Demand-driven plunges._** The impact on energy exporters and importers were similar for demanddriven plunges. Previous demand-driven oil price plunges were associated with global recessions or slowdowns, which tended to be associated with an initial output decline in EMDEs (0.3 percent) in the year of the plunge that was recouped within three years. Output, investment, and consumption in energy exporters and other EMDEs recovered together with oil prices.

**_Supply-driven plunges._** The impact from supply-driven oil price plunges left a lasting impact on EMDE energy exporters. Supply-driven oil price plunges were associated with initial output losses in energy exporters of somewhat larger magnitude than those associated with demand-driven plunges (0.5 percent in the first year). Almost three quarters of these output losses persisted into the third year. Three years after the shock, investment and consumption in energy exporters were still 1.4 and 0.6 percent, respectively, below baseline levels. These lasting losses may have

> 12 Based on vector autoregression models, existing studies find wide ranges of impacts. A demand-driven 30 percent oil price decline reduces output by 0-5 percent over a year or two, an oil-specific demand decline reduces output by 0.3-4 percent over a year or two, and a supply-driven oil price decline reduces output by 0-15 percent over a year or two. These studies include Aastveit, Bjørland, and Thorsrud (2015); Baumeister and Hamilton (2019); Baumeister and Peersman (2013); Cashin, Mohaddes, and Raissi (2014); Killian (2009); Kilian and Murphy (2014); Mohaddes and Raissi (2019); and Peersman and Robays (2012).

13
**Figure 4.4: Simulation of inclusion and exclusion errors**

<!-- Start of picture text --> 1<br>0.9<br>0.8 Errors of exclusion<br>0.7 (connected poor)<br>Errors of exclusion<br>0.6<br>(all poor)<br>0.5<br>Errors of inclusion<br>0.4<br>0.3<br>Leakage rates<br>0.2<br>0.1<br>0<br>Consumption threshold (kWh/mo.)<br>Figure 4.5: Simulation of subsidy cost<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>50 100 150 200 250 300 350 400 450<br>Subsidy threshold (kWh/mo.)<br>Percentage error<br>50 100 150 200 250 300 350 400 450 500<br>Annual cost (US$m)<br><!-- End of picture text --> _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala Notwithstanding the considerable policy attention that has gone into subsidies for electricity consumers, the empirical evidence suggests that households that lack access to electricity are in a much worse position in terms of their ability to afford basic energy requirements. The reason is that traditional substitutes for electricity, such as candles and kerosene lamps are extremely inefficient at delivering usable energy (Table 4.2). In particular, electric light bulbs give out 50 times more luminosity per kWh of raw energy used than do candles, and 100 times more than primitive kerosene wick lamps.

These differences in efficiency need to be taken into account when comparing the prices of these different sources of energy. In the table below, the gross price reports the standard unadjusted market price, while the net price corrects for differences between the efficiency of electricity and alternative energy sources. The results indicate that households without electricity pay 75 to 150 times more per kWh of light, and 5 to 30 times more per kWh to power home appliances using dry cell or car batteries.

32
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