rafmacalaba/datause-encoder-data huggingface dataset:
received by the government. Price and exchange rate projections from the IMF’s World Economic Outlook show only limited changes in prices and exchange rates over the projection period.

Figure 2 shows that the government share of mineral revenue is expected to be large relative to the size of the Nigérien economy, reaching as high as 20 percent of GDP.<sup>19</sup> Hence, issues about absorptive capacity will be important. Furthermore, it has the typical hump shape reflecting an acceleration of production in the early stages and a rapid decline at the end as resources are expected to be depleted. Although the revenue is sizable, it is still finite. Its optimal or sustainable use is therefore another crucial issue.

**Figure 2** : **Projections of mineral revenue going to the government**

(in FCFA billions and as percent share of GDP) <!-- Start of picture text --> 1400.0 0.25<br>1200.0<br>0.2<br>1000.0<br>0.15<br>800.0<br>600.0 Mineral revenue to<br>0.1<br>government (FCFA<br>400.0 billions)<br>Ratio to GDP at 0.05<br>200.0 historical growth<br>0.0 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21<br><!-- End of picture text --> _Source_ : World Bank and IMF staff calculations .

> 19 Estimates for revenue from the petrol sector were prepared using a generic Petroleum Fiscal Analysis model that has been prepared by the IMF’s Fiscal Affairs Department for Niger and kindly shared with the Word Bank for illustration purposes.  Estimates of revenue from the uranium sector assume that 25 percent of the value of production accrues to the government as revenue, in line with historical estimates.

20
- 55 -

degree of fiscal dependency of the local communities from an upper layer of government which may be central or intermediate when a regional authority is charged with the internal re-distribution of the funds.341 4.12 Dependence on Transfers. Table 4.12a shows how central government transfers have maintained their importance as a source of income for subnational budgets, particularly at local level, in spite of the cyclical fluctuations of the economies. Expressed in constant domestic currencies, the national trends show a variable pattern with a progressive contraction in Chile, Panama, Paraguay and Uruguay, but significant upward movements in Argentina, Brazil, Colombia, Costa Rica and the Dominican Republic (Table 4.12b). However, the influence of rapid demographic growth largely offsets gains in per capita terms also in the latter countries (Table 4.12c). A detailed case study for Brazil (see Box 3) shows the differential impact of transfers on state budgets and on municipalities of various categories.

|Table<br>|4.12a:|INTERGO|VERNMEN|TAL<br>TRANS|FERS AS P|ERCENTA|GE<br>OFTO|TAL REVE|NUES||
|---|---|---|---|---|---|---|---|---|---|---|
||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|LAC SAMPLEgi|39.5|<br>38.0|38.2|33.5|38.5|40.0|41.6|42.8|37.8|37.6|
|MARKET ECONUMIES_cI_|35.1|<br>35.4|34.8|35.05|35.1|34.8|34.2|33.2|32.5|31.3|

aI Total Revenues - Own Revenues + Grants.

b/ Comprises Grants and Revenue Sharing. St Weighted averages. Comparability over time affected by the availability of data.

Source: IMF, Government Finance Statistics, various years.

- 4J/ Some alternative ways to classify intergovernmental transfers can be the degree to which the transfers are reguite (i.e., involve total or partial repayment as in the case of loans or the contribution of a concurrent, "matching share" by the recipient in form of a quantifiable benefit, product or service to the payer in return for the payment). A second criterion of classification can be the extent to which the transfer funds must be put to a specified use, and monitored by the giving authorities ("earmarked" or "categorical" transfers vs. "unmarked" or "block" transfers). A third criterion can be the extent to which the pool of resources available for transfer in a given year is "open-ended" or "limited", i.e., whether or not there is a set maximum amount that must be rationed among all eligible parties according to an established "formula" or whether "reimbursement" funds will be made available to cover all technically eligible expenditures.
bustion power plants, common in the region and more likely to be air-cooled and less affected by droughts, lead to considerable increases in excess PM2 _._ 5. Overall, our findings complement the U.S. literature by highlighting that the threat to air quality posed by hydrological droughts is more significant for developing economies.

More broadly, our paper speaks to the literature that aims to quantify the economic damage of climate change (see Auffhammer, 2018, and references therein) by providing region-wide estimates for LAC of the societal cost that hydrological droughts impose by altering electricity generation, and by projecting these losses under several climate change scenarios. We also contribute to the literature on environmental inequalities (see Banzhaf et al., 2019, and references therein) by documenting that excess PM2 _._ 5 falls disproportionately among those with lower socioeconomic status. We thereby highlight a novel channel through which droughts reinforce socioeconomic inequalities.

The paper is organized as follows. Section 2 describes the data. Section 3 presents the identification strategy and the results. Section 4 provides counterfactual calculations of the losses created by excess PM2 _._ 5. Section 5 presents projections of losses based on our results and climate models. Section 6 presents robustness checks. Section 7 concludes.

# **2 Description of the data**

We construct a power plant-level panel dataset to study whether hydrological droughts impact PM2 _._ 5 concentrations by shifting electricity production towards combustion power plants. This unique dataset combines market-level measures of the level of distress by hydropower plants, derived from a granular watershedlevel measure of hydrological drought, with information on PM2 _._ 5 concentrations within 50 km of each power plant. This monthly dataset covers all Latin American and Caribbean countries between 2000 and 2020.

The starting point for our panel dataset is the Global Power Plant Database detailed in Byers et al. (2018).<sup>1</sup> This database provides detailed information on the geolocation, capacity, fuel type, and commissioning year for all power plants with generators above 1 megawatt (MW). This feature of our dataset is important because little is known about the pollution burden created by plants with smaller 1https://datasets.wri.org/dataset/globalpowerplantdatabase, Dataset Last Updated June 2, 2021.

5
**<u>Table 7: Income Elasticities</u>**

|**Variable**|**Ranking**|**Income elasticity**|
|---|---|---|
|PublicBonds|1|0.20|
|Bank Deposits|2|0.35|
|Bank Assets|3|0.44|
|Pension Funds|4|0.45|
|Bank Credit|5|0.49|
|Stock Market Cap.|6|0.56|
|Insurance Assets|7|0.66|
|Inst.Inv.Assets|8|0.77|
|Mutual Funds|9|0.88|
|Private Bonds Cap.|10|1.20|
|ValueTraded|11|1.30|

- _Offshore effects_ are also very strong and consistent across the board. Offshore centers have larger (and, as a result, more efficient) banking systems (particularly as regards deposits). Their banking systems also have better reach. While their equity markets are larger in size (this is also true for bonds) and less concentrated, they are neither more liquid nor more efficient.

- Finally, _oil-based economies_ have smaller financial institutions and smaller, less liquid, and more concentrated markets. This also agrees well with what one would expect.

As regards _quality of fit_ (as determined by the adjusted R-squared), main highlights are as follows:

- All bank size indicators and (surprisingly enough) all reach indicators have very good fit. Efficiency indicators are also reasonably good, except for operating expenses.

- In the case of _institutional investors_ , the fit is quite good for insurance companies and mutual funds, but very poor for pension funds. As already noted, the latter is not that surprising given the importance of the policy component and its wide possible variance across countries.<sup>33</sup> However, it may also reflect in part poorer data quality. Interestingly, however, the fit of the sum of all three types of institutional investors is nearly as good as that of the top performer in the group (insurance companies), suggesting that pension funds can indeed be largely viewed as complements for other forms of long-term savings and collective investment vehicles.

> 33 The importance of private pension funds assets should reflect, among other things, whether there is a mandatory second pillar system (and, if so, the date at which it was introduced) and the size of contributions, which in turn depend on the existence and coverage of a first pillar, pay-as-you-go system.

24
Table 2 CLASSIFICATION OF EQUATIOU1S OF NULTINDO

|||||||IDU|STRIA|LtZE|D||Coun|tries|||||||||||RE|ST O|F T|H|E WO|RLD||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|SECTO||I<br>bL|ndust<br>ocks|ral<br> <br>|iz<br>P<br>(1)|esd<br>rotot<br>|yte||Co|Fi<br>mt|ve<br>zles<br>(2|Indu<br>**or**<br>)()45|strial<br> Resio<br>S|ns||Co|Dwl<br>tties|iop<br> <br>(|ins<br>(<br>3)|DC)||Hi<br>OL Pr|ghI<br>oduci<br>(4|nco"<br>Lng<br>)|<br>()|||<br>Wo|Rest o<br>Tld S<br>(3|f the<br>lock<br>(1<br>)|(W|)||(6).'(3|To<br>)()|tal<br>M+(|5)||||(7|)|Noda<br>M(2)(|l<br>6)||
||S|.|S|.||D.|<br>T.||B|.|E|.|0.|T.|D.||B.||D|.|T.|S.<br>|B.|0.||T|.|B.|E.|D.|T.||S.|<br>B|.||D.|T.||B.||E|.|0.|T.|
|1.<br>AgD regte||6||A||11|<br>is||S|O||5|33|9|1|||1|1|6|is|2|0||6|_||_|_|_|,||||||22|26||3|3||6|77|46|
|2. Oosrme<br>nt<br>Sector||0||1||4|<br>S|||0||5|20|25|||||||||O|2|||2|_|_|_|_|||O|1||||||0||1|20|25|
|3. meneys4nd<br>Interest<br>**Rates**||1||0||7|<br>8|||S||0|33|40---||||||-|--||||--||||||||||||||-||3||0|35|40|
|4.<br>PrLcesand<br>supply||2||2||a|<br>12||1|0|1|0|40|60|0|||1|1|0|ii|0|0||2||2|-|-|-|-|||0|1|<br>|12|13||1|0|ii||52|73|
|S.<br>International<br>Account||0||D||6|<br>6|||0||0|30|30||D||0||S|S|0|0||1||1|-|-|-|-|||0|0||6|6|||0||0|36|<br>36|
|6.<br>International---<br>Trade sad|||||||-|-|||||-|--||||||-|-|-|-||-1||||2|is|1s|||1|2||15|i6|||1||2|is|is|
|TOAofS.ctors||9||4||36|<br>49||.5||2|0|160|23|1||2||3|1|34|2|0|9|is1||||2|S|1|||4|4||3|63||49|||4|||

3 - Behavioral E * lEpirLesi O Defiknitioanal T - TOTAL
- Bertoni, Eleonora, Michele Di Maio, Vasco Molini and Roberto Nistic`o. 2019. “Education is forbidden: The effect of the Boko Haram conflict on education in North-East Nigeria.” <u>Journal</u> of Development Economics 141:102249.

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- Blumenstock, Joshua, Michael Callen and Tarek Ghani. 2018. “Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan.” American Economic Review 108(10):2868–2901.

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- Bowles, Samuel. 2009. “Did Warfare Among Ancestral Hunter-Gatherers Affect the Evolution of Human Social Behaviors?” Science 324(5932):1293–1298.

- Br ̈uck, Tilman, Michele Di Maio and Sami H Miaari. 2019. “Learning The Hard Way: The Effect of Violent Conflict on Student Academic Achievement.” <u>Journal</u> of the European Economic Association 17(5):1502–1537.

- Callen, Michael and James D. Long. 2015. “Institutional Corruption and Election Fraud: Evidence from a Field Experiment in Afghanistan.” American Economic Review 105(1):354–81.

- Callen, Michael, Mohammad Isaqzadeh, James D. Long and Charles Sprenger. 2014. “Violence and Risk Preference: Experimental Evidence from Afghanistan.” American Economic Review 104(1):123–48.

27
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33
# **3.2.3. Treatment 2: Modified 7-day recall with long list of food items**

This module (henceforth, recall long list or T2) was designed to follow the most widely used 7- day food consumption recall in countries that are part of the LSMS-ISA project. Households in this group were administered a modified version of the 7-day food consumption recall module in T1. The module captures 7-day food consumption recall of quantity consumed, and the sources of the food consumed (purchases, own-production and gifts). Households were also asked if they purchased a given food item in the last 30 days, and where applicable, the quantity and value of their most recent purchase within the last 30 days were collected. Respondents were allowed to provide quantity consumed and purchased in non-standard units (NSU), and enumerators used NSU photo albums where applicable.

Given the unavailability of conversion factors for some of the NSUs, efforts were made to only allow respondents to provide responses in units for which conversion factors were available. In addition, for those items where respondents reported in NSUs, they were asked to provide an estimate of that in standard units (kilograms, grams, liters, or centiliters as applicable) as well. A long list of food items (167) that constituted the largest share of consumption in HBS 2017/18 were included in this group. FAFH at the individual level was also implemented for this group. Food consumption aggregates were then generated using the quantity consumed and price information collected in this module, while adjusting for temporal (within survey months) and spatial price variabilities. Similar to T1, mobile teams that operate in a roving manner were used for data collection in this study arm.

10
 _Passive strategies._ These consist of cutting expenditures. They include reducing expenditure on staple foods and health expenditure, and reducing other expenditure.

The applicability of these options varies by country and according to household circumstances. We first present summary statistics on the various coping strategies used across countries, before analyzing the relationship between subjective crisis impact, coping strategies and household characteristics.

**Figure 4. Coping strategies employed by households**

<!-- Start of picture text --> percent of<br>respondents<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Active Private safety Public safety Passive Passive<br>nets nets (staple food (other<br>and health expend.)<br>expend.)<br>Affected a great deal or a fair amount Not affected by crisis<br><!-- End of picture text --> Source: LiTS II (2010).

The most common coping strategy for transition households was reducing consumption, followed by using private safety nets (see Figure 4). About 70 percent of households that were affected “a great deal” or “a fair amount” by the crisis had to reduce consumption of staple foods and health expenditure, and a similar proportion cut other types of spending. Private safety nets were used by 40 percent of affected households, followed by active strategy options used by 23 percent). Even in times of hardship, a large proportion of households seemed able to increase their labor supply (by working increased hours, taking a second job or adding another household member to the labor force). Accessing public safety nets was the least used strategy, with less than one-fifth of affected households availing themselves of the four types of state benefits that 11
29 considered here too).23 In addition, imperfect domestic financial markets (such as the lack of a long-term bond market) may contribute to the inability of the private sector to offset public actions.

As was shown above, condition 5 (oil price volatility being the largest source of GOV revenue variability) is a realistic assumption for Venezuela. Moreover, the government has taken most of the oil price risks (looking forward, this will change somewhat as a result of the new tax formula which will pass more risk to PDVSA).

1. is difficult to argue that condition 6 or 7 holds. It has proved difficult to reject the random-walk hypothesis assumed by condition 6 for most oil prices (and many other commodities)--at least in the short-term.<sup>4</sup> Tests of the main Venezuelan oil price, Tia Juana Light (310), confirm that non-stationarity in levels cannot be rejected.' Even where the pure random-walk hypothesis is rejected, the evidence suggests that the commodity-price time series 23There is a high correlation between the oil price and the level of capital flight, R2 = 0.67. Higher oil prices allowed more capital flight and was the rational savings response of the private sector to the lack of savings by the public sector.

24See further Arrau and Claessens (1992) for copper and oil prices, Basch and Engel (1992) for copper prices, Morales (1992) for tin prices, and Hausmann et al. (1992) for Venezuelan oil revenues. The random walk hypothesis is consistent with efficient arbitrage in financial and commodity markets when rational expectations prevail.

2sAugmented Dickey-Fuller (ADF) tests with 1, 4 and 52 lags, and using a constant or a constant and a time trend applied to weekly data (4/1983-7/1991), indicate rejection of stationarity for the nominal price level (the ADF-test with 4 lags and a constant and a time trend can only be rejected at the 10% level). Over longer periods and using monthly data (e.g., 1/1970 - 6/1989), price stationarity can also be rejected (both with a constant and a time trend and with a constant only).
Panel data with information on pre- and post-migration can be most useful. However, panel data are seldom available and cross-sectional data sets must be relied on. Although the counterfactual is difficult (if not impossible) to ascertain in a cross-sectional study, one can attempt to learn about it by collecting information on pre-migration conditions, such as measures of asset holdings, that can be reconstructed using recall methods.

The endogenous relationship between migration decisions and income outcomes requires a proper identification strategy. Except where the migrants are chosen randomly, for example in the case of Pacific Islanders in New Zealand (McKenzie et al., 2006), the general approach is instrumental variables (IV) estimation. The challenge of finding valid instruments that influence migration decisions but not the outcomes of interest, especially economic outcomes such as income, is one of the most severe challenges faced by researchers. Household surveys or labor force surveys with migration indicators are the main data sources used for poverty and income distribution analysis. The instruments are also constructed with the data in these surveys. Among the most common variables used in the literature are current and historical social networks and diaspora linkages (Hanson and Woodruff, 2003; Hildebrandt and McKenzie, 2005; De, 2008). In cases where purpose-specific surveys are used or the researcher has the option to design the questionnaire, certain questions can to be inserted that will lead to construction of appropriate variables to be used as instruments. For example, McKenzie and Mistiaen (2009) use the generation of the migrant when analyzing Japanese–Brazilian migration to Japan.

Different types of migration may have differing effects on poverty and inequality (see Germenji and Swinnen, 2005). Furthermore, measurements at the household level 27
# **Table** **<u>5: Estimated average treatment effects</u>**

||**Un**<br>|**trimmed sa**<br>|**mple, si**<br>|**mple DD**<br>||
|---|---|---|---|---|---|
||**Treated**<br>|**Control**|**DD.**<br>|**(se)**|**Sig.**|
|Consumption p.c. (Rs/year)|1356|828|528|(292)|*|
|Food (Rs/year)|451|413|38|(139)||
|Non-food (Rs/year)|905|416|490|(222)|**|
|Energy intake p.c. (Kcal/day)|177|232|-55|(68)||
|Protein intake p.c. (g/day)|-0.78|0.28|-1.06|(1.51)||
|Non-financial asset p.c. (Rs)|932|472|460|(234)|**|
|No. of obs.|1880|526||||
||**Trim**<br>|**med sampl**<br>|**e, PS we**<br>|**ighted DD**<br>||
||**Treated**|**Control**|**DD.**|**(se)**|**Sig.**|
|Consumption p.c. (Rs/year)|1414|1059|355|(295)||
|Food (Rs/year)|518|451|67|(153)||
|Non-food (Rs/year)|896|608|288|(228)||
|Energy intake p.c. (Kcal/day)|236|210|26|(78)||
|Protein intake p.c. (g/day)|1.17|-0.74|1.91|(1.79)||
|Non-financial asset p.c. (Rs)|926|345|581|(273)|**|
|No. of obs.|1683|516||||

* significant at 10%; ** significant at 5%; *** significant at 1%

21