rafmacalaba/datause-encoder-data huggingface dataset:
keep the samples with different set of explanatory variables similar, we opt to impute the missing values of Trust and Individualistic Culture as the mean of the continent-income-level cell of countries.<sup>7</sup> In addition, we add their missing indicators to allow for sample selection associated with missing incidences. We do not find the missing indicators to ever be statistically significant, indicating non-selectivity of their missing incidences.

To capture preexisting vulnerabilities to the pandemic, we rely on several variables: the share of elderly in the population (Elderly) (from WDI),<sup>8</sup> the share of urban residents in total population (Urban, from WDI), obesity prevalence (Obesity, from World Health Organization), and atmospheric SO2 concentration (SO2, from European Space Agency). Obesity captures the prevalence of obesity among adults (i.e., those with BMI greater than 30), measured in 2016 and from the World Health Organization.<sup>9</sup> We use data on atmospheric sulfur dioxide (SO2) concentrations as a measure of air pollution. The SO2 data set is the Sentinel-5P near real-time high-resolution imagery, provided by the European Space Agency (ESA).<sup>10</sup> A complete list of variables and data sources is shown in Table 1. The summary statistics for the variables are in Table 2.

# **IV. Empirical specifications and results**

To explain the pandemic outcomes across countries, we estimate the following regression at the country level: + α α θ (1) Here, _i_ refers to a country. ∂∂ ∂∂ ∂∂ _Y_ is the pandemic outcomes: the number of cumulative confirmed cases ∂∂ ∂∂ ∂∂ YY = ββXX ααyy + δδ V ααVVααVVααV ααVVyy + θθα θθVVααθθyy+ η VVααV VVαα + εε of COVID-19 per 1,000 residents, and the mortality rate from confirmed COVID-19 cases. _X_ is basic country characteristics, including log GDP per capita, log total population, the tertiary dummies indicating the distance to China, and the average temperature. _Delay_ includes both _DelayI_ and _DelayD_ , capturing both international and domestic mobility restrictions. _Vulnerability_ includes the four preexisting factors that make the country more vulnerable to COVID-19: Elderly, Urban, Obesity, and SO2. _Democracy_ is a dummy variable indicating the status of being a

> 7 That is, each country is classified as “rich in this continent” and “poor in this continent,” with the threshold being the mean GDP per capita of the latest year, and if the two cultural variables are missing, they are replaced with the mean values of the continent-income-level cell.

> 8 For the share of elderly, we use year 2018 because it has fewer missing observations, and this variable changes very slowly.

> 9 Data available at https://www.who.int/data/gho/indicator-metadata-registry/imr-details/2389.

> 10 Data available at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi.

13
Harvey, C. R., Ramachandran, A., and Santoro, J. (2021). DeFi and the Future of Finance. John Wiley & Sons.

Henry, C. S., Huynh, K. P., and Nicholls, G. (2018). Bitcoin Awareness and Usage in Canada. Journal of Digital Banking, 2(4), 311-337.

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JP Morgan (2021b). Flows and Liquidity: Will BITO Bring Fresh Capital into Bitcoin? October 20.

Lischke, M., and Fabian, B. (2016). Analyzing the Bitcoin Network: The First Four Years. Future Internet, 8(1), 7.

Liu, Y., and A. Tsyvinski (2018). Risks and Returns of Cryptocurrency. National Bureau of Economic Research Working Paper 24877.

Marakov, I., and Schoar, A. (2021). Blockchain Analysis of the Bitcoin Market. National Bureau of Economic Research Working Paper 29396.

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.

Peirce, H. (2021). Paper, Plastic, Peer-to-Peer. U.S. SEC Commissioner Remarks at the British Blockchain Association’s Conference “Success Through Synergy: Next generation Leadership for Extraordinary Times”.

Poon, J. and Dryja, T. (2016). The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments. Digital Currency Initiative paper, MIT Media Lab.

Raskin, M., Saleh, F., and Yermack, D. (2021). How Do Private Digital Currencies Affect Government Policy?, NBER working paper 26219.

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Szabo, N. (2017). Money, Blockchains, and Social Scalability. Unenumerated, February 9.

39
Harbord's modified test for small-study effects: Regress 2/sqri(V) on sqrt(V) where Zis efficient score and V is score variance

|Number of studies= 31|RootMSE|= .5442||||
|---|---|---|---|---|---|
|Z/sqrt(V)|coef.|Std. Err.|t|Pat|[95%Conf.<br>Interval]|
|sqrt(V)|1.04|0.32|3.25|0.00|0.38|
|bias|0.89|0.14|6.50|0.00|0.61|
|TestofHO:nosmall-studyeffects|P=0.000|||||

|StudyCorrelation Coefficient|Coef.|Std. Err.|t|P>t|[95%Conf.|Interval]|
|---|---|---|---|---|---|---|
|precision|0.001|0.001|1.410|0.189|0.000|0.002|
|Estimator|0.195,|0.040|4.340|0.001|0.105.|0.285,|
|StandardErrorofPrecision|-0.057|0.214|-0.270|0.794|-0.533|0.419|
|<br>LogTotal InvestmentR&D Prog.|0.002|0.005|0.390)|0.704|-0.008|0.012|
|LogNumber ofwebviews|0.003|0.005|-0.550|0.596|-0.014|0.009|
|LogStudySample Size|-0.008|0.009|-0.860|0.412|-0.028|0.013|
|LogYears Implementation|-0.018|0.016|-1.130|0.283|-0.055|0.018|
|Survey Used|-0.003|0.012|-0.210|0.834|-0.029|0.024|
|Panel|-0.031|0.020|-1.500|0.164|-0.076|0.015|
|Cross-section|0.023|0.050|0.470|0.652|-0.088|0.135|
|Region ECA|0.028|0.025|1.130|0.285|-0.028|0.085|
|Region LAC|0.059|0.034|1.730|0.115|-0.017|0.135|
|Region EU|0.028|0.026|1.070|0.309|-0.031|0.087|
|Region NorthAmerica/other|0.081|0.047|1.740|0.112|-0.023|0.185|
|LateStage|-0,008|0.021|-0.370|0.721|-0.054|0.039|
|Quasiexperimentalmethod used|= -0.029|0.017|-1.730|0.115|-0.067|0.009|
|Complementarity|0.034|0.019|1.790|0.094|-0.009|0.076|
|Constant|0.054|0.105|0.490|0.634|-0.190|0.298|
## **data**

My measure of conflict intensity is based on data provided in the annual Human Rights Yearbooks published by the Informal Sector Service Center (INSEC), a Nepali non-governmental organization that monitors human rights issues in the country. INSEC reports the number of deaths due to Maoist conflict in each of the districts in the country. This dataset is identical to the one used by Do and Iyer (2010) in their study of the determinants of the conflict. I use conflict related deaths that occurred in the district between 1996 and 2001 as my measure of conflict intensity for year 2001. For year 2008, I use conflict related deaths that occurred in the district between 2001 and 2006. I set conflict intensity to zero for year 2010. I normalize the conflict deaths by dividing by the population of the district in 1991.

To convert conflict into monetary terms, I do an exercise similar to that with rainfall using three cross-sections of consumption surveys conducted between 1995 and 2010. I find that increase in conflict intensity of 1 death per 1,000 population is correlated with a consumption loss of Rs. 3,490 (10.6 percent of consumption in 2010). This correlation is much stronger for higher end of the wealth distribution, consistent with the nature of the conflict and the assumption in the theoretical framework (Appendix Table B.1 and Appendix Figure B.1)<sup>16</sup> .

# **3.4 Demand for migrant workers**

## **Demand data**

Another shock that drives migration in the theoretical framework is the increased demand for workers in the destination countries, which increases migrant income. Since most migrant workers from Nepal work in construction and manufacturing industries, I use a measure of demand that reflects growth in construction and manufacturing industries in the key destination countries. Specifically, I use the growth rate in the levels of carbon dioxide emissions attributed to construction and manufacturing industries in India, Malaysia and the Gulf countries (Qater, Saudi Arabia, and United Arab Emirates). I get this measure from the World Development Indicators database (The World Bank) which reports carbon dioxide emissions calculated by U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) using data from the United Nations Statistics Division’s (UNSTAT) World Energy Data Set and the U.S. Bureau of Mine’s Cement Manufacturing data set. The UNSTAT collects this information from several countries using questionnaires designed to collect information on emissions from various sectors. Cement manufacturing data from the U.S. is used to estimate CO2 emission from construction. Further, CDIAC continually maintains and updates this database based on supplemental information collected and upon availability of new information.

The variation in this dataset comes from the fact that the destination countries are in different cycles of construction and manufacturing. Further, the great recession of 2008 affected these destination countries quite differently, creating a useful variation in the data on the growth of migrant demand.

> 16See Appendix B for details of this estimation.

13
specifically developed to analyze these issues originally follows from Johansson _et al_ (2000). Johansson (2000) surveys the literature until 1998. Then Johansson (2005) updates the former by including the work in the area published up to 2000. In this paper, we attempt to contribute to the previous surveys in two ways. Firstly the time dimension is updated by covering work in the literature between 2000 and 2007. Secondly the computable general equilibrium models are covered in a more detailed and systematic manner.

The paper is organized in two parts. The second part aims to give a brief discussion of the issues in the economics of water allocation. In this section pricing methods and institutional frameworks are analyzed. The third section focuses on the analytical models. Partial and general equilibrium models are surveyed in detail with their structure and main findings. The last section is reserved for concluding remarks.

# **2 Allocation Mechanisms**

Increasing scarcity of water makes the issue of efficient allocation more important. On the other hand, since water is crucial for human life, equity is also a central concern in allocation. Economic efficiency is concerned with the amount of wealth that can be generated by a given resource base. Equity on the other hand is concerned with the distribution of the total wealth among the sectors and members of society. Equity objectives are particularly concerned with fairness of allocation across economically disparate groups. Hence these two criteria may or may not be consistent (Dinar _et_ . _al_ ., 1997).

Allocation mechanisms can be defined as sets of institutions and predefined rules that determine the quantity (and sometimes the quality) of water that individual (sometimes groups of) users are entitled to use. There are three frequently used institutional settings: Markets, public administration and user based administration. Any institutional setting allocates the water through some pricing mechanism.

The allocation of water as an economic good is more complicated than the allocation of other economic goods as will be discussed in the paper, because water possesses unique characteristics that make its allocation complicated. Howe _et. al._ (1986) and Winpeeny (1994) give the criteria that should be considered for any allocation mechanism that leads to an optimal solution. These criteria are flexibility in the allocation of supplies, security of tenure for established users, real opportunity cost of providing the resource as paid by the users, predictability of the outcome of the allocation process, equity of the allocation process, political and public acceptability, efficacy so that the form of allocation changes existing 4
- (3) The two parties must stand to loose something if they go through official settlement.

- 5.2 The question of representation and conflict resolution

As Figure 5 shows, the normal pattern in Indonesia has been one where strikes are of very short duration --around 10 hours on average--. The incidence of strikes declined from 1981 to 1989, and has risen in the 1990s. According to employers' declarations, in a number of these cases, public protest is the first step in the negotiation process between "unorganized" workers and managers. A significant proportion of these strikes take place outside of any process of negotiation, and among unorganized workers, that is, workers who are not affiliated to the official union or to workers' councils.

<!-- Start of picture text --> ____ ____ ____Figure 5<br>Incidence and Persistence ofStrikes<br>90 300<br>C 60 - ..... .200 . .<br>80~~~~~~~~~~~<br>e3 50 :000 l) 00-000-; l000000 l: l ;i;002 llg000000 l000- 50 - _ lost hours/w orker<br>o 40 . ......... L.0-0-- -*-S trikes<br>,, 0w:::Sii:t\ ::X::iSS00E: ;: ;0;:df:i/00iSS: ;::Si:7-: 100 n<br>20 . 250 :<br>o 10-' O<br>a) a) 0 0) 0 0) 0) 0)<br><!-- End of picture text --> Source: Ministry of Manpower This changing trend can be explained at least in part by workers' attempts to win better working conditions at their place of employment. This more aggressive attitude is encouraged, to a significant degree, by favorable market conditions; and to a significant degree, by the more active government involvement in setting wages. To the extent that these claims take place outside of negotiations between elected workers' representatives and managers, they cannot be resolved before the representation question is settled. It can be argued, therefore, that another motivation for the strikes may be to get an opportunity to elect a union.

_Improving the Economic Impact of Unions_ Unions generally become the vehicle of worker' s voice in large industrial establishments where many workers perform similar tasks and face similar working 22
|<br>|**Table 2: Module-Sp**<br>_Source of_<br>|**ecific In**|**cidence o**|**f Proxy**<br>**Educ**|**Respon**<br>**ation**|**dent Use**|**for Data**|**Collect**|**ion on In**|**dividual**<br>**He**|**s 18 Yea**<br>**alth**|**rs & O**|**lder in Se**|**lected L**|**SMS-IS**|**La**<br>**A Count**|**bor**<br>**ries, B**|**y Gender**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Country_|_Individual-Level_|**Ov**|**erall**|**M**|**ale**|**Fe**|**male**|**Ov**|**erall**||**Male**|**Fe**|**male**|**Ov**|**erall**|**M**|**ale**|**Fe**|**male**|
||_Information_|_Obs_|_Percent_|_Obs_|_Percent_|_Obs_|_Percent_|_Obs_|_Percent_|_Obs_|_Percent_|_Obs_|_Percent_|_Obs_|_Percen_|_t_<br>_Obs_|_Percen_|_t_<br>_Obs_|_Percent_|
|Mli|Proxy|3,412|47|1,907|55|1,505|41|3,392|47|1,905|55|1,487|40|3,354|47|1,892|54|1,462|39|
|aaw|Self|3,797|53|1,589|45|2,208|59|3,813|53|1,588|45|2,225|60|3,852|53|1,600|46|2,252|61|
|Mli|Proxy|13,062|75|5,002|60|8,060|89|13,151|75|5,056|61|8,095|89|12,961|74|4965|59|7996|88|
|a|Self|4,382|25|3,344|40|1,038|11|4,293|25|3,290|39|1,003|11|4,483|26|3381|41|1102|12|
|Ni|Proxy|5,447|50|2,280|44|3,167|55|5,548|51|2,331|45|3,217|56|5,294|49|2201|43|3093|54|
|ger|Self|5,440|50|2,865|56|2,575|45|5,339|49|2,814|55|2,525|44|5,593|51|2944|57|2649|46|
|Nii|Proxy|8,526|52|3,482|45|5,045|58|N/A|N/A|N/A|N/A|N/A|N/A|8,616|53|3569|46|5048|58|
|gera|Self|7,834|48|4,238|55|3,596|42|N/A|N/A|N/A|N/A|N/A|N/A|7,744|47|4151|54|3593|42|
|Ti|Proxy<br>|N/A|N/A|N/A|N/A|N/A|N/A|2,865|22|1,721|28|1,144|17|2,835|22|1,707|27|1,128|16|
|anzana|Self<br>|N/A|N/A|N/A|N/A|N/A|N/A|10,249|78|4,530|72|5,719|83|10,275|78|4,543|73|5,732|84|
|Ud|Proxy|3,481|49|1,951|58|1,530|41|3,610|50|2,008|59|1,602|42|3,489|49|1,938|58|1,551|42|
|gana<br>**Note:**Th|Self<br>e most recent round|3,654<br>for surv|51<br>ey data t|1,409<br>hat is pu|42<br>blically|2,245<br>availabl|59<br>e is used|3,628<br>for each|50<br>LSMS-I|1,408<br>SA cou|41<br>ntry.|2,220|58|3,561|51|1,387|42|2,174|58|

35
for nonstationarity in household endowments (i.e., in νc).<sup>8</sup> Since, in using the household basis, it is likely that the same plot will appear twice in a given household group, we correct the standard errors in this case for clustering on household.<sup>9</sup> Table 1 breaks down the number of plots available, by survey year. With respect to the household-year basis, the yield sample consists of 1,718 plots belonging to households with multiple plots. Of these, 403 belong to owner-cum-sharecropper households,<sup>10</sup> and the rest to pure sharecroppers or owner-cultivator households. Including this latter group of households increases the precision of the estimates when we control for plot characteristics. Using the household basis boosts the multiple-plot household sample to 1,993 and the number of plots among these cultivated by owner-cum-sharecroppers to 771.

Our regressions control for the most important plot characteristics: area, value, location, irrigation, and soil type. We also control for the crop composition on a plot — i.e., the fraction of area planted to each of the fi ve major crops — and allow these composition effects to vary by survey year so as to capture any changes in relative crop prices. Table A.1 in the Appendix describes these variables.

While, in theory, crop choice may be speci fi ed in the tenancy contract, and hence endogenous, in Pakistan, share-tenants generally have autonomy over crop choice and grow basically the same mix of crops as owner-cultivators. Indeed, for all fi ve of the major crops in our yield measure, there is no signi fi cant difference in the proportion of area cultivated between sharecropped and other plots, once we control for tehsil (there are 23 tehsils or subdistricts in our sample).

The fi rst three speci fi cations in Table 2 use the less restrictive household-year basis for the fi xed effects. Without any covariates, we fi nd that yields are about 3% lower on sharecropped plots. Sequentially adding controls for plot characteristics and crop composition in columns (2) and (3), respectively, lowers this estimate of γ only slightly, along with the standard error. For the sake of brevity, we do not report the individual coefficients on the plot characteristics and crop composition variables. However, they are highly jointly signi fi cant in all speci fi cations as indicated in Table 2. Results are virtually identical when we use the household basis for the fi xed effects and there is no evident gain

> 8 For example, in the eight years between the two rounds of the survey, a household’s farm assets or fi nancial position may have changed.

> 9 Because only households were followed and not individual plots, one cannot say for sure how many of the same plots were captured in PRHS-01. However, it is likely that most of the owner-cultivated plots and many of the leased plots belonging to panel households are in fact the same across survey rounds.

> 10 Owner-cum-sharecropper households are over-represented in the IFPRI survey by geographical accident; these households tend to be concentrated in central Punjab.

7
Estimating the Economic and Distributional Impacts of the RCEP Table 3. Top expanding and contracting sectors by country, productivity kick scenario. In percentage variation compared to business-as-usual, 2035

|**Country**|**Top expanding sectors**|**Less expanding/top contracting sectors**|
|---|---|---|
||Meat products (5.4%)|Wearing apparel and leather (-11.5%)|
|Australia and<br>New Zealand|Fossil fuels (2.1%)|Textiles (-5.8%)|
||Animal products (1.8%)|Electrical equipment and machinery (-5.2%)|
||Wearing apparel and leather (2.9%)|Basic pharmaceuticals (1.8%)|
|China|Other transport equipment (2.8%)|Public administration (1.6%)|
||Textiles (2.7%)|Air transport (1.2%)|
||Electrical equipment and machinery (4.1%)|Petroleum and coal products (1.4%)|
|Indonesia|Wood and paper products (3.9%)|Wearing apparel and leather (0.9%)|
||Other manufactures (3.1%)|Fossil fuels (0.9%)|
||Textiles (4.9%)|Animal products (-3.9%)|
|Japan|Chemicals, rubber and plastics (4.8%)|Meat products (-7.2%)|
||Petroleum and coal (1.6%)|Wearing apparel and leather (-9%)|
||Other transport equipment (17.6%)|Animal products (0.9%)|
|Cambodia|Chemicals, rubber and plastics (14.3%)|Petroleum and coal products (0.1%)|
||Wood and paper products (13.8%)|Food and beverages (-1.8%)|
||Textiles (6%)|Wearing apparel and leather (-0.8%)|
|Korea, Rep.|Chemicals, rubber and plastics (3.1%)|Fossil fuels (-1%)|
||Petroleum and coal (3%)|Other transport equipment (-1.5%)|
||Chemicals, rubber and plastics (35.1%)|Meat products (-3.9%)|
|Lao PDR|Fossil fuels (20.2%)|Textiles (-12.3%)|
||Wood and paper products (10.4%)|Wearing apparel and leather (-16.3%)|
||Wood and paper products (5.2%)|Public administration (1.8%)|
|Malaysia|Air transport (5%)|Basic pharmaceuticals (1.3%)|
||Tourist services (4.8%)|Wearing apparel and leather (-0.8%)|
||Electrical equipment and machinery (6.3%)|Non-metallic minerals (-0.9%)|
|Philippines|Tourist services (2.9%)|Textiles (-2.3%)|
||Air transport (2.7%)|Motor vehicles and parts (-3.5%)|
||Motor vehicles and parts (5.4%)|Basic pharmaceuticals (1.2%)|
|Thailand|Meat products (4.5%)|Fossil fuels (-1.4%)|
||Animal products (3.2%)|Textiles (-1.6%)|
||Wearing apparel and leather (14.7%)|Fossil fuels (0.31%)|
|Vietnam|Electrical equipment and machinery (12.1%)|Wood and paper products (-0.5%)|
||Textiles (9%)|Basic pharmaceuticals (-1.6%)|

Source: World Bank Staff calculations using ENVISAGE model.