rafmacalaba/datause-encoder-data huggingface dataset:
technology may change over time. Since the time sample we use of 2005-2020 is relatively short, this may not be a great concern in our context. For instance, Morris and Zhang (2018) find no technical change in the NO2-GDP relationship for China over a similar length sample. However, the issue may be pertinent for longer horizons.

**Future Work.** There are a number of other possible applications not considered here which may represent fruitful avenues for future work. First, there is the problem of subnational measurement of GDP which we have thus far not considered in a formal sense, and could lead to informative comparisons with previous night lights studies doing the same (Hodler and Raschky, 2014; Henderson et al., 2018). While the high resolution of NO2 is naturally amenable to such an application, this yields other concerns, like discontinuities near borders (Pinkovskiy, 2017).

Second, another application of the data is towards improving the time resolution of low frequency GDP data by interpolation. Especially in developing nations where GDP is only released annually, the potential to reliably estimate at higher frequency is valuable. This may also allow for synergies with previous work; high-frequency estimates of economic activity at the district level in South Asia supported a granular assessment of the impact of several major shocks, including a surge in conflict in Afghanistan, the demonetization experiment in India, and massive earthquakes in Nepal (Beyer et al., 2018; Chodorow-Reich et al., 2020). In such applications, the issue of disentangling seasonality appears to be the key challenge.

Third, with respect to GDP, the complexity of data collection gives rise to a lag between the end of the period to be evaluated and the availability of even the most preliminary of estimates. This “waiting period” may be substantial; one to two months among advanced economies and a quarter to two quarters among emerging markets. Daily NO2 data is made freely, publicly available by NASA online just a few days after measurement. Given the quick availability of this resource, there is also the potential for applications to real-time nowcasting, and the reduction in GDP revisions, with substantial benefits for policymaking.

# **References**

- **Alstadsæter, Annette, Niels Johannesen, and Gabriel Zucman** , “Tax evasion and inequality,” _American Economic Review_ , 2019, _109_ (6), 2073–2103.

- **Baragwanath, Kathryn, Ran Goldblatt, Gordon Hanson, and Amit K Khandelwal** , “Detecting urban markets with satellite imagery: An application to India,” _Journal of Urban Economics_ , 2019, p. 103173.

- **Bazzi, Samuel, Martin Fiszbein, and Mesay Gebresilasse** , ““Rugged individualism” and collective (In) action during the COVID-19 pandemic,” _Journal of Public Economics_ , 2021, _195_ , 104357.

36
# **Annex 6:  Defining Small-Scale Service Providers**

As noted in the introductory section, the phrase Small-Scale Private Service Providers (SPSPs) is one subset of a larger category of Small-scale Service Providers (SSPs), that includes both private and public owned and/or managed small-scale systems. Given the wide range of Small-scale Service Providers operating in the water supply and electricity sectors, the focus of this SPSP study was limited to Type 1 and Type 2 (private – entrepreneurs and user associations) SSPs as described below. There is some recent evidence that SPSPs are starting to play a key role in the development and management of Type 3 (public – local government) systems (Uganda and Paraguay). And in future, they could play an important role in managing Type 4 (public - community ) systems, although this may be more complex to organize until their legal status is made and their financial records are strengthened.

||**Private Sector SS**<br>**(SPSPs)**<br>|**Ps**<br>||**Public Sector SSPs**<br>|
|---|---|---|---|---|
|**KEY**<br>**FEATURES**|**TYPE 1:**<br>**Individual/**<br>**entrepreneur**<br>establishes a water<br>supply or<br>electricity service<br>which is managed<br>as a business/on a<br>commercial basis<br>(e.g. entrepreneur –<br>aguateros)|**TYPE 2:**<br>**User association**<br>**or self help**<br>**group**.<br>Established on<br>initiative of the<br>users, but not<br>always managed<br>on a commercial<br>basis. (e.g.<br>Society/self help<br>group)|**TYPE 3:**<br>**Local**<br>**Government**<br>**scheme**initiated<br>by government and<br>funded by<br>government and<br>donors. Managed<br>on a commercial<br>basis (e.g. small<br>town water<br>system).*|**TYPE 4:**<br>**Community based**<br>**scheme**initiated by<br>Government with<br>donor support. 100<br>percent financing<br>externally sourced.<br>Not managed on<br>commercial basis<br>(e.g. community<br>based gravity<br>scheme.*|
|**Water**: Piped<br>Network<br>**Electricity:**<br>Grid|Primary focus of<br>SPSP initiative|Secondary focus<br>of SPSP<br>initiative. **|||
|**Water**:<br>Point Source or<br>Kiosk<br>**Energy:**Battery<br>Recharging|Primary focus of<br>SPSP initiative.<br>Documented for<br>water supply only|Secondary focus<br>of SPSP<br>initiative. **|||
|**Water**:<br>Mobile Distributor<br>**Energy:**Home<br>based systems|Primary focus of<br>SPSP initiative|Secondary focus<br>of SPSP<br>initiative. **|||

* Excluded from SPSP study because of public ownership and/or extent of grant financing.   SPSPs could play a  future role in developing and/or managing these systems.

37
and GPS values, both for the control and treatment group plots. We explore some of these differences in the next section.

**Figure 1: Distribution of self-reported and GPS plot size measures**

<!-- Start of picture text --> (a) Control group (b) Post-planting visit:treatment (c) Post-harvest visit:treatment<br>0 .5 1 1.5 2 0 .5 1 1.5 2 0 .5 1 1.5 2<br>GPS- Self-reported GPS Self-reported GPS Self-reported<br>.5 .5 .5<br>.4 .4 .4<br>.3 .3 .3<br>Fraction Fraction Fraction<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> Furthermore, the heaping around focal points is quite asymmetric. Figure 2 displays the distribution of GPS values across the most common self-reported focal values. The mode and median of GPS plot size measures consistently fall below the rounded, self-reported area. Farmers systematically asymmetric (upward) round at all plot sizes, although far more acutely for smaller plots, those less than 1 acre. Farmers are more likely to round upward than downward, but that likelihood decreases with true plot size. Overall, more than twice as many farmers overestimated their plot size than underestimated it (647 of 982 plots).

10
serves not only as an objective gauge of educational integration but also reflects school attachment integration within the host community, offering mental health and well-being benefits to children Total absences of Secondary school (Grades 6-13) <!-- Start of picture text --> S |<br>=<br>|<br>|<br>|<br>$ I<br>nm |<br>7 seer es “ei oer =<br>1)a I a | coset<br>pa |<br>@ > L-<br>o<br>e9 Ps<br>|<br>oO Eq----i--~<--F |<br>>7| -<br>= ——————— |<br>I<br>|<br>|<br>2<br>6 7 8 9 10 11 12 13<br>———_— Ukr pre Feb 22 =—_<_{<_—<-. Ukr post Feb 22<br>——— Migrant post Feb 22 ———_ Migrant pre Feb 22<br>—— Italian<br><!-- End of picture text -->

results in Table 3 confirm that Ukrainian refugees and recent migrant students have higher rates of absenteeism compared to other student groups. Specifically, Ukrainian refugees miss an additional 8 significant, the analysis is narrowed to a sample including only these two groups. Table 4 reports the
9 the rate of preannounced devaluation (Figure 8).

<!-- Start of picture text --> Actual and Expected DevahiLation.<br>Bassd on INEI Sr y of Expactollon.<br>A -'<br>loo 20*<br>a7.1 87.5 MI DLI Al9.1 53.3 90.1 90-3<br><!-- End of picture text --> Figure 7 <!-- Start of picture text --> Unscheduled Devaluation Expeclations:<br>Cetes-Pagafes-Pretnnounced Dsvaluotion.<br>140jiii<br>120<br>100<br>40<br>20<br>Am 198 .h, 1585 .ha log0<br>Yew<br>Figure a<br><!-- End of picture text --> A different type of risk, also reflected in high interest rates during the first phases of the Pact, was associated with the fear of explicit debt repudiation. Explicit debt repudiation refers to default through taxation or sheer debt repudiation; in contrast with implicit debt repudiation through unanticipated devaluation/inflation. The explicit debt repudiation risk premium can be measured by the interest differential between dollar-indexed domes,tic debt (Pagafes) and a risk-free dollar interest rates, we use as a prox.' the US Treasury Bill. This premium rose substantially during the first phase of the Pact and remained large until mid 1989 (Figure 9). Explicit debt repudiation fears in Mexico can be traced back to the 1982 forced conversion of dollar bank deposits at an artificially low exchange rate.
dizzving array of "openness" measures, methodologies, and sample countries, leading to results which may differ for any number of reasons. Most research has examined the relationship between economic growth and trade volumes, not policies--this is partly because measuring "policy" poses difficult questions. Second, it is sometimes difficult to know how to interpret the observed correlation between trade policies and growth (see Levine and Renelt (1990)). Policies that are not directly concerned with trade (macroeconomic policy) may have caused both superior export performance and high GDP growth.

Third, most of the literature (including the more recent efforts by Barro (1991) and others) uses cross-sectional averages or starting values for time-series data. Barro (1991), for example, examines the impact of price distortions in 1960 on post-1960 GDP growth. Applying such an approach to developing countries has two shortcomings. First, the use of cross-section data makes it impossible to control for unobserved countryspecific differences, possibly biasing the results. Second, long run averages or initi values for trade policy variables-particularly in developing countries--ignore the important changes which have occurred over time for the same country.

One exception to this approach is Bhalla and Lau (1991), which uses a panel of time series data for sixty countries to find a strong positive association between openness (proxied by the relative price of traded goods) and growth. Bhalla and Lau, however, do not control for country-specific effects, nor do they 2
Bernard, A. and B. Jensen (2004), ‘Why Some Firms Export?’, _Review of Economics and Statistics_ , 86(2), 561-569.

Bernard A., S. Redding and P. Schott (2011), ‘Multi-Product Firms and Trade Liberalization’, _Quarterly Journal of Economics_ 126(3), 1271-1318.

Cadot and Malouche (2012), _Non-Tariff Measures- A Fresh look at Trade Policy’s New Frontier_ . CEPR and The World Bank.

Cato, J., W. S. Otwell and A. Coze (2004), ‘Nicaragua’s Shrimp Subsector: Developing a Production Capacity and Export Market during Rapidly Changing Worldwide Safety and Quality Regulations’, Agriculture and Rural Development Discussion Papers (47844), The World Bank.

Cebeci, T., A. Fernandes, C. Freund and M. Pierola (2012), ‘Exporter Dynamics Database’, Policy Research Working Paper No. 6229, The World Bank.

Chaney, T. (2008), ‘Distorted Gravity: The Intensive and Extensive Margins of International Trade’, _American Economic Review_ **_,_** 98(4), 1707-1721.

Chen, X., J. Wilson and T. Otsuki (2010), ‘Standards and Export Decisions: Firm-Level Evidence from Developing Countries’, _Journal of International Trade and Economic Development_ , 17(4), 501-523.

Chen, C. and C. Findlay (2008), ‘Measuring the Effect of Food Safety Standards on China’s Agricultural Exports’, _Review of World Economics_ , 144(1), 83-106.

Czubala, W., B. Shepherd and J. S. Wilson (2009), ‘Help or Hindrance? The Impact of Harmonized Standards on African Exports’, Policy Research Working Paper No. 4400, The World Bank.

Disdier, A-C., L. Fontagné and O. Cadot (2015), ‘North-South Standards Harmonization and International Trade’, _World Bank Economic Review_ , 29(2), 327-352.

Disdier, A-C., L. Fontagné and M. Mimouni (2008), ‘The Impact of Regulations on Agricultural Trade: Evidence from the SPS and TBT Agreements’, _American Journal of Agricultural Economics_ , 90(2), 336–350.

Ferro, E., J. S. Wilson and T. Otsuki (2015), ‘The Effect of Product Standards on Agricultural Exports’, _Food Policy_ 50, 68-79.

Fontagné, L., G. Orefice, R. Piermartini and N. Rocha (2015), ‘Product Standards and Margins of Trade: Firm Level Evidence’, _Journal of International Economics_ forthcoming.

Henson S., A-M. Brouder and W. Mitullah (2000), ‘Food Safety Requirements and Food Exports from Developing Countries: The Case of Fish Exports from Kenya to the European Union’, _American Journal of Agricultural Economics,_ 82(5), 1159-1169.

Cadot, O., Iacovone, L., Pierola, M., and F. Rauch (2013), “Success and Failure of African Exporters”; Journal of Development Economics 101, 284–296.

Koenig, P., F. Mayneris and S. Poncet (2010), ‘Local Export Spillovers in France’, _European Economic Review_ , 54(4), 622-641.

Koenig, P., (2009), ‘Agglomeration and the Export Decisions of French Firms’, _Journal of Urban Economics_ , 66(3), 186–195.

Li, Y. and J. Beghin (2012), ‘Protectionism Indices for Non-Tariff Measures: An Application to Maximum Residue Levels’, Iowa State Working Paper No.12013.

28
17

# Table 2 (continued)

# Dependent **variable:** IR

F value of regression: 78.23 Probability given the null (all coefficients insignificant): 0.0001

|Explanatory||Standard||
|---|---|---|---|
|Variable|Coefficient|Error|T value|
|YASHP|-0.221|0.048|-4.628|
|TOTB|0.399|0.182|2.191|
|RR|-0.103|0.060|-1.712|
|LTDPC|-8.365|1.560|-5.361|
|STDPC|-3.534|2.331|-1.516|

# Dependent variable: MON

F value of regression: 82.47 Probability given the null (all coefficients insignificant): 0.0001

|Explanatory||Standard||
|---|---|---|---|
|Variable|Coefficient|Error|T value|
|YASHR|0.018|0.044|0.413|
|TOTB|-0.422|0.193|-2.184|
|RR|0.320|0.059|5.418|
|LTDPC|7.856|1.906|4.122|
|STDPC|8.017|2.790|2.874|

# Dependent variable: COV

- F value of regression: 192.09

Probability given the null (all coefficients insignificant): 0.0001

|ExpLanatory||Standard||
|---|---|---|---|
|Variable|Coefficient|<br>Error|T value|
|.<br>.............................................................|...|||
|YASHR|-0.146|0.018|-8.062|
|TOTB|-0.397|0.067|-5.914|
|RR|**0.076**|0.012|6.444|
|LTDPC|-0.677|0.406|-1.666|
|STDPC|0.814|0.712|1.144|
savings may be the inability to resist demands from the social network. Although net balances in the commitment accounts were small, the existence of the account may have provided an excuse to turn down requests for assistance from the social network by _claiming_ that their savings were inaccessible.

In columns 2, 3 and 4 of Table 6 we examine the sums of transfers made, transfers received and net transfers over the last twelve months. In order to explain the higher input use for commitment group farmers, additional availability of resources is most relevant during the preplanting period. Thus, we present results in columns 5, 6 and 7 from regressions with dependent variables similar to those in columns 2 to 4 but with the sums of transfers restricted to categories in which the biggest gift was made before or during October 2009 (see Appendix B for further details of the variable definitions).

We find no evidence of reduced net transfers for the commitment (no raffle) treatment. If anything, there is a small positive, effect on net transfers made (column 4). Results are qualitatively similar for the set of restricted transfer variables (columns 5 to 7).<sup>17</sup> It is important to note, however, that the transfer variables examined in Table 6 refer to _inter_ -household transfers, and do not capture any changes in _intra_ -household transfers. It remains possible that the commitment treatment effect operated at least in part by reducing transfers by study participants to spouses and other individuals within the same household.<sup>18</sup> Though we do not find evidence that commitment savings accounts reduced transfers to other members of the social network, the accounts may have helped farmers increase their input use by dampening another possible result of social pressure to share. Individuals who know they will be subject to demands from others in their social network can prevent others from claiming their money by spending it preemptively. Rapidly consuming income makes it unavailable to others; it is consistent with signaling a high marginal utility of consumption in a model where income is taxed and redistributed from those with low marginal utility of consumption to high marginal utility of consumption. Goldberg (2010) found support for such a model in an experiment that demonstrated that Malawian cash crop farmers who received money in public settings spent significantly more of that money immediately than farmers who received money in private settings. In this project, we do not have the high-frequency consumption data necessary

> 17 This result is reminiscent of Chandrasekhar et al. (2011), who show in a lab-in-the-field experiment among Indian villagers that savings access does not crowd out transfers to others.

> 18 We cannot shed light on this, however, because our follow-up survey did not ask questions on transfer activity within the study participant’s household. These would be important data to collect in future research.

18
effectiveness is only real on a very long-term basis (e.g., urbanism and land-use plans). Low-probability scenarios should be taken into when their potential impact is very large (e.g., possibility of large sea level rise); when impacts are more limited, it is possible to focus only on the most likely scenarios.

- Regular review mechanisms are necessary to take new scientific information into account and consequently adjust measures.  To do this, the development of a performance indicator for adaptation actions is important.

- Adaptation must take synergies and conflicts with other policy objectives into account, and particularly mitigation policies for climate change and protection of biodiversity.

# **References**

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- Agrawala S. (Ed.). 2007. _Climate Change in the European Alps: Adapting Winter Tourism and Natural Hazards Management_ . Paris: OECD.

- Agrawala, S., and S. Fankhauser (Eds.). 2008. _Economic Aspects of Adaptation to Climate Change_ . Paris: OECD.

- Ambrosi, P., J.-C. Hourcade, S. Hallegatte, F. Lecocq, P. Dumas, and M. Ha-Duong. 2003. “Optimal control models and elicitation of attitudes towards climate damages.” _Environmental Modeling and Assessment_ . **8** :133–147.

- Arrow, K. J., and A. C. Fischer. 1974. “Environmental preservation, uncertainty and irreversibility.” _Quarterly Journal of Economics_ **88** (2):312–319.

- Athukorala, P., and B.P. Resosudarmo. 2005. “The Indian Ocean Tsunami: Economic Impact, Disaster Management, and Lessons.” _Asian Economic Papers_ **4** (1):1–39.

- Bigano, A., F. Bosello, R. Roson, and R.S.J. Tol. . 2008. “Economy-wide impacts of climate change: A joint analysis for sea-level and tourism.” _Mitigation and Adaptation Strategies for Global Change_ **13** (8):765–791.

- Cochran, I. 2009. “Climate Change Vulnerabilities and Adaptation Possibilities for Transport Infrastructure in France.” _Climate Report 18_ (september). Paris: Caisse des Dépôts.

- De Perthuis, C. 2009. _Et pour quelques degrés de plus, Nos choix économiques face au risque climatique_ . Paris: Pearson.

- De Perthuis, C., and A. Delbosc. 2009. _Idées reçues sur le changement climatique_ . Paris: Cavalier bleu.

- Hallegatte, S. 2008. “An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina.” _Risk Analysis_ **28** (3):779–799.

- Hallegatte, S. 2009. “Strategies to adapt to an uncertain climate change.” _Global Environmental Change_ **19** :240–247.

- Hallegatte, S. 2010. “Challenges ahead: Risk management and cost-benefit analysis in a climate change context.” In D. Guha-Sapir et I. Santos (Eds.) _The Economic Impact of Natural Disaster_ . London: EarthScan. [in press]

- Hallegatte, S., F. Henriet, and J. Corfee-Morlot. 2011. The Economics of Climate Change Impacts and Policy Benefits at City Scale: A Conceptual Framework.”, Climatic

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14 variation in the extent of welfare reductions in policy over the period shown. In Western Europe, most countries have seen a decrease in their WRI in recent decades. For some countries this comes after a peak in the 1980s – such as in France, Ireland and Italy – whereas for other countries there has been more of a continual decline, as for example in the United Kingdom, Germany, Netherlands and Sweden. Norway, Switzerland and Iceland stand out among Western Europe countries for their exceptionally high WRIs, although these countries have experienced the steepest declines in recent decades. Canada’s WRI series is notable for its large increase above the NRA aggregate in the 1980s (when there was a large increase in the dispersion of its NRAs around the weighted mean).

The country-level WRI measures, which are derived using an overall measure of the distortion to producer and consumer prices in individual sectors of the 34 focus countries, masks the contribution of different policy instruments to welfare losses in each country. Figure 3 reports the decomposition of the overall country WRI by policy instrument for the 6 key regional groups. The decomposition is found by estimating WRI series for individual policy measures, and then apportioning the shares of these series to the overall WRI. In our 34 focus countries as a whole, border measures – which distort both producer and consumer prices – are by far the most significant of the distorting policy instruments. They account for upwards of 90 percent of the welfare losses in all 6 sub-regions over time, with the proportion being above 97 percent in most instances.

The decomposition of border measures in Figure 3(a) shows that import tariffs are the dominant measure of distortion in terms of market price support in most regions. In the European Union and Northeast Asia, in particular, import taxes dominate border supports. In EFTA countries, import tariffs also dominate but these countries together also have significant export subsidies. Oceania has significant export subsidies in 1980–84, but they are reduced over time along with other reductions in policy distortions in those countries. Data are available only from 1992 for Eastern Europe’s transition economies. In 2000–04, this sub-region has a range of distortionary policy instruments in use: import taxes dominate, but export taxes and subsidies are also present.

The final perspective from which to consider the trade- and welfare-reducing effects of policies in our 34 focus countries is at the commodity market level, for individual commodities. Figure 4(a) shows that rice is the most distorted commodity