rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Deviation from Montreal Protocol Targets after RTA Signature

|||_Depe_|_ndent variabl_|_e: IHS Tar_|_get Deviatio_|_n_||
|---|---|---|---|---|---|---|---|
||CFCs|Halons|CFCs (B)|CTC|TCA|HCFCs|MB|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Post Sig|0.698<sup>_∗∗∗_</sup><br>(0.154)|0.370<sup>_∗∗∗_</sup><br>(0.094)|0.022<br>(0.039)|0.139<br>(0.103)|0.021<br>(0.052)|_−_0.393<sup>_∗∗∗_</sup><br>(0.083)|_−_0.028<br>(0.106)|
|Post_×_ODS|_−_1.280<sup>_∗∗∗_</sup><br>(0.370)|_−_0.482<sup>_∗∗_</sup><br>(0.220)|0.093<br>(0.127)|_−_0.401<br>(0.274)|_−_0.319<br>(0.231)|0.322<sup>_∗∗∗_</sup><br>(0.100)|_−_0.406<br>(0.462)|
|Post_×_ODS_×_DS|_−_0.171<br>(0.501)|0.142<br>(0.255)|_−_0.191<br>(0.144)|_−_0.045<br>(0.269)|0.044<br>(0.272)|_−_0.460<sup>_∗_</sup><br>(0.248)|0.603<br>(0.500)|
|Observations|6,118|6,118|6,118|6,118|6,118|6,118|6,118|
|R<sup>2</sup>|0.527|0.551|0.390|0.484|0.602|0.643|0.528|
|Mean(ODP tons)|-1402|-261|-3|-551|-30|-116|14|

FE triple-difference regressions on a stacked country-level panel with a _±_ 3 year event window around RTA signature. All outcomes are the inverse hyperbolic sine of the difference between ODS emissions and emissions targets under the Montreal Protocol. DS is an indicator equal to 1 if the the ODS provisions are binding with dispute settlement under the RTA. All models include country-event fixed effects. Robust standard errors are (two-way) clustered at the country-event and country-year levels. We report the mean deviation from target across the entire sample in tons of ozone depletion potential (ODP). Statistical significance from two-sided _t_ tests are denoted by<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05; _∗∗∗_ p _<_ 0.01.

26
||2019|2015*|
|---|---|---|
|Average annual exposure to ambient PM2.5|83.1 μg/m<sup>3</sup>||
|No. of deaths from PM2.5 exposure per 100,000 population|130||
|Deaths from PM2.5 exposure as a share of all deaths|20.0%||
|Annual cost of health damages from PM2.5 (% of GDP)|10.2%||
|Death from PM2.5 associated with ambient air pollution (AAP)|17,948 (21,603)|11,619|
|Annual cost of health damages from AAP PM2.5 exposure|$ 4,645 (5,723) million, PPP|$ 686 million|

_Notes_ : The figure in the parenthesis is for indoor household air pollution (HAP). Source: (IHME, 2020) * The World Bank (2019) estimated the social cost of PM2.5 per mortality at US$20,900–$74,200 for 2015 in PPP.

It is estimated that brick kilns are responsible for more than one-fourth of total PM10 concentrations and about 40% of total black carbon emissions in the winter season in Kathmandu Valley, the time most of the brick kilns are in operation (Eil et al., 2020). Most deaths related to air pollution are caused by human exposure to PM2.5 (World Bank, 2021).<sup>19</sup> Based on data from the Institute for Health Metrics and Evaluation’s (IHME) Global Burden of Disease (GBD), air pollution ranks as the second highest risk factor, after malnutrition, which drives the most deaths in Nepal. The IHME GDB study also estimated the number of deaths and cost of health damages from PM2.5 exposure in Nepal (Table 6). The number of deaths from PM2.5 exposure in Nepal is highest in South Asia. Further, the average annual ambient (outdoor) concentrations of PM2.5 reached 83 μg/m<sup>3</sup> in 2019, which is way above the WHO guidelines of 10 μg/m<sup>3</sup> . Most of the deaths associated with ambient PM2.5 are from heart disease, stroke, COPD, lung cancer, and ALRI (World Bank, 2021).

Assuming one-fourth of the country’s total ambient air pollution (AAP) related PM2.5 emissions are attributed to brick production, the annual cost of health damages from PM2.5 exposure from brick kilns is estimated at US$ 1,161 million in PPP and the annual number of deaths at 4,487 in 2019 (Table 6). This makes PM2.5 exposure one of the main risk factors for the number of deaths after malnutrition and before high blood pressure, dietary risks, tobacco smoking, and diabetes, among dozens of risk factors assessed by the GBD 2019 study.

The estimated social cost of PM2.5 varies by kiln technology and by province. In general, the social cost of PM2.5 from brick production is higher for less energy-efficient traditional kilns and lower for more energy-efficient modern kilns (Table 7). For example, the average social cost of brick production from FC BTK is almost double when compared to ZZK. Further, the social

> 19 Fine inhalable PM (PM2.5) comprises a portion of PM10. A recent ambient air quality monitoring study (Islam et al., 2022) shows PM2.5 to PM10 ratios ranges from 0.50 to 0.57 in Kathmandu Valley.

24
<!-- Start of picture text --> 2<br>oo= Sfa.|<br>3<br>Rs32<br>SC , i ff i<br>EI wm 4 ily 2 ? ;Ly ‘ ' Vl y 7)<br>s h 3 if " ” !<br>> I ly ig! t I vay<br>H \ i} ! \ if | " if in<br>5¢~— o I >, J 4 eee¥ vA Ly 4 L ral | LA Vy H ih o<br>< Jul'13 Jul'14— Jul '15<br>z richard-tol<br>ga A<br>& \<br>77) I is I<br>° : 1) a °<br>= ” | rt | 7 hy a<br>x i i 1) i H 1<br>s l i ! I ; 4 vay<br>an l l if<br>\y . Vv “ re LN wble<br>go o if li, Fi ipl \ \<br>< Jul'13) Jul'14.s Jul 15 Jul'13 Jul'14—sJul '15<br>Month<br>me Ave MalariaCases ~------- Avg Rainfall in cm<br><!-- End of picture text -->
Figure 18: Pass through across commodities Figure 19: Speed of adjustment across commodities <!-- Start of picture text --> 100% 80%<br>90% 70%<br>80%<br>60%<br>70%<br>50%<br>60%<br>50% 40%<br>40% 30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0% 0%<br>Rice Wheat Gram Onion PotatoMasur<br><!-- End of picture text --> Source: Author’s calculations Source: Author’s calculations Note: Speed of adjustments are plotted in absolute value Commodities with the largest pass‐through coefficient do not necessarily exhibit the fastest speeds of adjustment. In the long run, price shocks originating at the wholesale market for rice, gram, and wheat have the largest pass‐through; however, these same commodities have the slowest speeds of adjustment. For example, 96.8 percent of a price shock in wholesale rice markets is transmitted to retail rice markets in the long run, but the speed of this transmission is relatively slow compared to other commodities, at 16.3 percent. On the other hand, potatoes, which have the smallest wholesale to retail pass‐through of 52.3 percent, have one of the fastest speeds of adjustment at 30 percent (Figure 18 and Figure 19). Adjustment in retail prices to shocks in wholesale prices is slower for products in which there is substantial government intervention (rice and wheat), just as it was reported when looking at horizontal transmission in the previous section, and as reported by Sekhar (2012), also for horizontal transmission of shocks.

The speed of adjustment is highly significant for most of the state‐district‐commodity pairs, but its size varies. Much like the speed of adjustment at the commodity level, the percent of adjustment per period differs by state. For example, prices for all commodities in the Haryana retail market adjust back to equilibrium around 45 percent of the disequilibrium per period caused by a price shock in the Haryana wholesale commodity market. Similarly, price in the Madhya Pradesh retail commodity market adjust back to equilibrium around 10 percent of the disequilibrium per period caused by a price shock in the Madhya Pradesh wholesale commodity market (Figure 20).

Figure 20: Speed of adjustment across states <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Note: Speed of adjustments are plotted in absolute value<br>Source: Author’s calculations<br>Speed of Adjustment<br><!-- End of picture text --> 24
- 10 -

Various techniques exist to predict future precipitation, _rain_<sup>_e_</sup> . One practical and computationally easy way is to assume that this year’s precipitation would be the same as the last year’s. Given the original work schedule, expected precipitation can be calculated by adding up monthly precipitations during the same period last year.<sup>6</sup> This is a simple backward-looking expectation but considered as a good prediction in our case, because the Nepal’s precipitation data exhibit similar seasonality every year (Figure 9). In addition, the project duration of our sample projects is relatively short, up to one year and half. Therefore, at least in the short run, the simple backward-looking expectation is a good forecast method.

Another way of forecasting future precipitation relies on statistical inference. Given timeseries precipitation data, the following simple autoregressive (AR) process is considered with deterministic periodicity taken into account: _yt_ is the amount of precipitation at period _t_ . As shown above, the data clearly exhibit seasonality. This is adjusted by dummy variables for each month, _D_ { _month_ }. With the seasonality adjusted, the data seem to be stationary. According to the Dickey-Fuller unit root test with 13 lags in the first differences, the null hypothesis of a unit root cannot be rejected in most cases. Therefore, the autoregressive model with 13 lags is applied for precipitation data in each district.<sup>7</sup> Then, the estimated AR models are used to compute one-step-ahead predictions for each project location. The predicted precipitation looks to much better fit the actual precipitation than the above simple backward-looking expectation. Many observations are mapped near the 45 degree line, meaning that the ratio of _Rain_<sup>_a_</sup> to _Rain_<sup>_eAR_</sup> is close to one (Figure 10).

> 6 The precipitation data the year before last are also used when the work schedule exceeds one year. 7 The Dickey-Fuller unit root test results are shown in Appendix.
# **Table S8:** Program impacts on empowerment (beneficiaries: **secondary** outcomes) **<u>Panel A:  Baseline Dropouts</u>**

||Index of Self-<br>Efficacy<br>(Standardized)|Index of<br>Preferences for<br>Child Education<br>(Standardized)<br>Empowe|Index of Social<br>Participation<br>(Standardized)<br>rment|Aspirations|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|=1 if Conditional Schoolgirl|-0.041|-0.020|-0.052|-0.221|
||(0.076)|(0.079)|(0.068)|(0.225)|
|Mean in Control Group|0.000|0.000|0.000|3.267|
|Sample Size|744|744|744|744|
|**Panel B: Baseline Schoolgirls**|||||
|=1 if Conditional Schoolgirl|0.059|-0.004|-0.026|0.235|
||(0.079)|(0.076)|(0.068)|(0.228)|
|=1 if Unconditional Schoolgirl|-0.149|-0.106|-0.095|0.004|
||(0.100)|(0.087)|(0.069)|(0.207)|
|p-value UCT vs. CCT|0.061|0.343|0.424|0.379|
|p-value Treatment|0.170|0.477|0.393|0.566|
|Mean in Control Group|0.000|0.000|0.000|3.352|
|Sample Size|2,049|2,049|2,049|2,049|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Detail on the construction of the indices can be found in Appendix A and Appendix B. Aspirations asks the respondent where she sees herself on a 10-step ladder comparing today to five years from now, where zero represents the worst possible life she could have and 10 represents the best possible life she could have. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
per month, which is to say 70% of the average consumption level, this problem affects a significant percentage of consumers.

The whole efficacy of IBT structures as income redistribution mechanisms hinges on the hypothesis that water consumption is well correlated with household income. However, this hypothesis has rarely been put to an empirical test. Figure 10 summarizes data on average water consumption by income quintile for eight Latin American utilities. In twothirds of these cases, the variation between the water consumption of the top and bottom quintiles of the income distribution is very weak, no more than 20% in all, and in some instances the top quintile actually consumes less water than the bottom quintile (Figure 11). However, in other cases such as Paraguay and Colombia, the top income quintile consumes around twice as much as the bottom quintile.

**Figure 10: Average monthly water consumption by quintile**

<!-- Start of picture text --> 50<br>45<br>40 Paraguay<br>35 Merida<br>Sonsonato<br>30<br>Santa Ana<br>25<br>San Miguel<br>20<br>Panama City<br>15 Colombia<br>10 Uruguay<br>5<br>0<br>1 2 3 4 5<br>Income quintiles<br>Average monthly water consumption (m3)<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data 25
<!-- Start of picture text --> Capital adequacy<br>7%<br>2%<br>are<br>Ws<br>*<br>m<br>o<br>AN<br>— Se<br>cae..anhkre_ = eee<br>% - T T<br>ae 200 620M OU OCA OA OO AT<br>agg egale (bank) —aypegade(guup) — lowesi (group)<br>lowest (rank) —rpilioy een<br><!-- End of picture text --> <!-- Start of picture text --> Annual growth of real sector domestr debt<br>Fils4<br>ore5<br>a<br>ae<br>cs<br>Are|<br>Th<br>4<br>-1%<br>2a~<br>2 AM? ANG 6OANM OA OAK OAR O27 A A OI<br>—=—ldsedo —ndcepdehas = —<ndhosinglban<br><!-- End of picture text -->
# **Robustness checks**

We check the robustness of our results in several ways. First, we run placebo tests by re-estimating the main regression model with a dummy variable capturing the departure from linear trends for every PISA cycle. These placebo effects should be insignificantly different from zero if our assumption of stable trends over time is valid. Figure 3 shows that although estimates for different years vary, representing small variation in results of different PISA rounds, only the departure in 2022 is substantial. This confirms two things. First, PISA results in mathematics were relatively stable before the pandemic. Second, the results in 2022 are distinct as they do not follow the average results from previous rounds.

**Figure 3. Departures from the time trend separately for each year of PISA assessment**

**(95% confidence intervals)**

<!-- Start of picture text --> departure from the time trend assuming (placebo) treatment in various years<br>2003 2006 2009 2012 2015 2018 2022<br>5<br>0<br>-5<br>mathematics<br>-10<br>-15<br><!-- End of picture text --> To further test the robustness of our results, we use different regression specifications, controlling or not for individual and country-average effects. The results were nearly identical, 18
The monthly sales volume of a crop in a _mandi_ measures the total quantity that was sold in a month. This volume consists of two key components. The first component is the total amount of the crop sold inside the _mandi_ yard, which is recorded and calculated as described above. The second component is the quantity sold by farmers directly to private buyers outside the _mandi_ yard but for which _mandi_ tax has been paid to the concerned _mandi_ .<sup>32</sup> Thus, the quantity of soybean sold at the ITC hubs is included in the _mandi_ sales volume data. Since I observe monthly procurement of soybean by ITC at its hubs, I deduct this amount from the relevant quantity sold in the _mandi_ where it paid tax to obtain my preferred measure of the total sales volume of a _mandi_ , which excludes the amount sold at the ITC hubs.

To my knowledge, this is the first attempt to collect and compile detailed data on price and volume of crops sold in government regulated wholesale agricultural markets to examine the impact of a change in the procurement strategy of a private buyer on the functioning of rural markets in India.

Table 3 provides summary statistics. Throughout the empirical analysis, I use data from 23 high soy producing districts that received the intervention. Out of a potential 66 months of data for each crop in each of the 144 _mandis_ in 23 districts of the state, any observation where the outcome is recorded as zero or missing is excluded. From a panel of potential 9504 _mandi_ *month (144*66) observations for soybean, I am left with 8276 (8639) positive observed values of mode price (sales).<sup>33</sup>

[Insert Table 3 about here]

# **IV. Empirical Strategy**

The goal of the empirical analysis is to examine the impact of a change in the procurement strategy undertaken by ITC on the functioning of government regulated wholesale agricultural markets over time. To do this, I utilize panel data on monthly crop prices

> 32A buyer operating outside the _mandi_ yard is required by the law to provide complete documentation (recording the quantity bought) to the _mandi_ officials; this amount is added to the sales volume of a particular mandi.

> 33The 9504 potential number of observations contain 12 (43) instances where the mode price (sales) is recorded as zero and there are 1216 (822) missing observations. This leaves a total of 8276 (8639) positive observed values of mode price (sales) for soybean.

16
At the same time as the initial stove disbursement in 2013, 4 control households were randomly selected in each village, for a total sample size of 504 households. Baseline demographic, socioeconomic and other data were collected from both treatment and control households in JuneJuly 2013 and confirmed balance across the treatment and control groups. No baseline HAP measurements were collected and therefore we utilize a cross-sectional randomized study design to compare air pollution levels across households who randomly received a Mirt and those that did not.

In November-December 2016, 6 of the 14 households under study in each village were randomly selected for HAP monitoring. Three of the households in each village were treatment and three control, for a total sample size of 216. In the field research design, if a randomly selected household was given a Mirt stove that was subsequently abandoned, another household from the same village was randomly selected; only households currently with Mirt stoves in place (though perhaps not actively used) were compared to households that never received a Mirt stove.<sup>4</sup> This sample size was chosen based on standard conventional power calculations in the HAP measurement literature (see Edwards et al., 2007). These standard conventions include achieving a statistical power of 0.80, a p value of 5% in two-tailed tests and detecting a 30% HAP reduction. A reliable, Ethiopia-based, estimate of the coefficient of variation in HAP reduction was not available prior to our study to compute the minimum sample size. We therefore used a conservative COV estimate of 0.7 (Edwards et al., 2007), which, given our cross-sectional study design, gave a minimum sample size of 86 households in each arm (total 172). Allowing for possible monitoring equipment failure, we utilized a sample size of 216 (108 in each arm). We collected valid data from over 95 percent of the target households, with missing data due to HAP monitoring equipment failures and a small number of households where all members were in the field for coffee harvest on the day of data collection. We note, though, that these failures were random across the treatment and control households and such issues were not unexpected. The sample size remains sufficient to achieve the standard level of power with a modest HAP reduction detection level.

Maximum, mean and median PM2.5 levels were measured in the 204 households using Particle and Temperature Sensor Plus (PATS+) light-scattering particle sensors developed by Berkeley Air Monitoring Group of Berkeley, California. The PATS+ devices were in place in

> 4In the appendix we show that based on 23 observables the sample is balanced across those who were subject to HAP monitoring and those who were not monitored.  The subsample of households who experienced HAP monitoring is therefore quite representative of our overall sample.

7
from the Climate Research Unit (CRU). Second, this work takes advantage of the panel structure of this data set, which includes about half of the households in at least two of the three survey rounds. Regression techniques based on the panel data set can estimate the weather impact on income changes over time while reducing omitted variable bias. Despite the strength of these data and methods, a quantification of future impacts subject to uncertain climatic, environmental and socioeconomic changes is beyond reach.

The remainder of this paper is structured as follows: Section 2 explains the data and methods applied for the analyses. Section 3 shows that rural households remain highly reliant on agriculture and other ecosystem‐based activities. Section 4 demonstrates the extent of weather inter‐ and intra‐annual variation communes are exposed to. Section 5 presents the estimated income effects of weather variation from the various regression analyses. Section 6 concludes that in the face of climate change greater attention needs to be paid to make rural livelihoods more resilient to weather variation.

# **2. Data and methods**

Based on data from the Vietnam Household Living Standard Survey (VHLSS) collected in 2010, 2012, and 2014 with gridded weather data from the Climate Research Unit (CRU), this study fits a number of regression models to explain income differences.

## **2.1 Household and commune data**

Information on incomes and socioeconomic conditions is derived from the household and commune data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally representative and contain detailed information on individuals, households and communes. In total ca 9,400 households nationwide are included in each round with about half of the households in each round also being surveyed in the previous round so that the data set includes a short‐term panel.

These analyses focus on rural households and communes leaving a data set of about 20,000 household observations from ca. 2,250 communes. Each survey round covers about 6,600 and 6,700 rural households. About 1,400 households were interviewed in all three rounds, 1,600 in 2010 and 2012, and 1,400 in 2012 and 2014.

The household surveys include a wide array of socioeconomic data. At the individual level these data cover demographics, education, employment, health, and migration. At the household level the data comprise information on income and expenditures, employment and self‐production, durables, assets, and participation in government programs. Consumption estimates are based on per‐capita expenditure as calculated by the World Bank and GOS to determine the national poverty line. Incomes are calculated based on the raw data in line with classifications from the GSO (Section 3). All consumption and income values are expressed in 2010 prices using data on the Consumer Price Index from the World Development 4