rafmacalaba/datause-encoder-data huggingface dataset:
productivity under RCP 2.6 and 8.5 respectively. Years between the dates in the table are estimated via linear interpolation.

Table 5: Labor productivity lost due to heat in RCP 8.5, in percent

||**Work ho**|**urs lost**|**Change as compared with 2015**|
|---|---|---|---|
|**Year**|RCP 2.6|RCP 8.5<sup>22</sup>|RCP2.6<br>RCP8.5|
|**1995**||3.73|-0.67<br>0.67|
|**2015**||4.1-4.7|0<br>0|
|**2025**||5.22|0<br>.82|
|**2055**||7.00|.92<br>2.6|
|**2085**||9.97|0.82<br>5.57|

These estimates give a good indication for the order of magnitude of heat stress on labor productivity, but do not include other factors like how heat will interact with air pollution, and changes in demographics (see below).

## _Model modifications_

Potential GDP is modified once more, but now accounting for labor productivity losses due to heat. In order to capture the loss in labor productivity, we distinguish between work hours and effective work hours , where h is the percentage loss of hours given in the second data column in Table 5. WW We implement this by modifying the equation for potential GDP to WW<sup>�</sup> = (1 −h)WW ∗ ∗ K �(1 −h � , ∗ YY αα ββ γγ where is trend TFP, is structural employment and YYtt = (1 + DD∗ωωVVGGDD)AAtt tt)CC is the share of agriculture in GDP. The first tt KKtt−1YYEE,tt part, , was discussed above in the agricultural damages section. YY tt tt VVGGDD AA CC ωω YY VVGGDD 1 + DD∗ωω

# 3.8.6 Damages to physical capital from flooding

Pakistan is already affected strongly by flooding. Serious floods caused extensive damage in almost every year between 1985 and 2018 (Figure 11), with between one thousand and 20 million people being affected annually. Further data on the World Bank’s Climate Change Knowledge Platform shows that between 1900 and 2018, a total of 93 flood events were recorded in Pakistan, which is equivalent to 45% of all natural-hazard events in Pakistan in that time interval.

> 22 Full description: “Potential annual daylight work hours lost for work at 300W, %; based on a business as usual scenario (RCP 8.5, average of HADGEM2 and GFDL models) current (1995) and up to 2085”, see UNDP (2016).

50
combine information from several different sources. Such models may be a promising avenue for monitoring changes in a region’s economic circumstances, particularly in the midst of sudden, large shocks when more traditional survey data are not (yet) widely available.

The remainder of this paper is organized as follows: section 2 introduces the data and empirical methodology, section 3 describes trends in measures of well-being, section 4 presents the results of our analysis, and section 5 concludes this study.

# **2. Data and Empirical Methodology**

The analysis uses two types of data: (1) high-frequency phone surveys measuring household well-being during the COVID-19 crisis, and (2) real-time metrics of mobility, internet search activity, pollution, and night-time lights. We use the regionally aggregated high-frequency phone survey (HFPS) data as the dependent variable for the prediction based on real-time metrics.

# 2.1 Household Well-Being Measures from the HFPS

The HFPS data cover 59 developing countries.<sup>3</sup> The process of fielding the surveys for each country was done in conjunction with national statistics offices to ensure appropriate weighting and population representation. Enumerators contacted households by phone and followed up with individuals in subsequent waves. Surveys were either carried out by recontacting respondents from a previously fielded nationally representative survey or through random digit dialing (RDD). Lower-income countries primarily opted for the former, while middle-income countries with more widespread phone coverage were more likely to use RDD. In cases where previous survey respondents were re-contacted, weights were constructed to account for the selection bias due to only interviewing households with phones who responded to the survey. However, respondents tend to be heads and the weights did not

> 3 The HFPS includes 8 countries from East Asia and Pacific (Indonesia, Cambodia, the Lao People’s Democratic Republic, Myanmar, Mongolia, the Philippines, Solomon Islands, and Viet Nam), 8 from Europe and Central Asia (Armenia, Bulgaria, Croatia, Georgia, Poland, Romania, Tajikistan, and Uzbekistan), 14 from Latin America and the Caribbean (Argentina, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Saint Lucia), 5 from the Middle East and North Africa (the Arab Republic of Egypt, Iraq, the West Bank and Gaza, Tunisia, and the Republic of Yemen), and 24 from Sub-Saharan Africa (Burkina Faso, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Djibouti, Ethiopia, Gabon, Ghana, Kenya, Madagascar, Malawi, Mali, Mauritius, Mozambique, Nigeria, São Tomé and Príncipe, Senegal, Sierra Leone, Somalia, South Sudan, Uganda, Zambia, and Zimbabwe). Among them, three countries (Djibouti, Kenya, and Mauritius) do not have a subnational region code, and thus are omitted from the analysis.

6
- 28 -

**<u>Table 12. Inventory Model Estimation</u>**

||OLS<br>Coef.|Std.Err.||IV<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|RAIL|1.333|(0.546)|**|9.791|(3.339)|***|
|WT|1.744|(1.601)||0.448|(2.355)||
|WT*WT|-5.372|(4.009)||-4.015|(5.422)||
|Y|0.003|(0.021)||-0.002|(0.022)||
|Y*Y|0.000|(0.000)||0.000|(0.000)||
|lnAGE|-0.171|(0.167)||-0.688|(0.284)|**|
|EDU|-0.157|(0.093)|*|-0.136|(0.119)||
|GLBL|-0.216|(0.381)||-0.955|(0.562)|*|
|FORN|-0.002|(0.005)||-0.003|(0.005)||
|Constant|1.479|(0.683)|**|2.028|(0.927)|**|
|Obs.|333|||331|||
|R squared|0.2636|||...|||
|F stat.|7.21||||||
|Wald chi||||152.96|||
|No. of dummy vari|ables:||||||
|Industry|4|||4|||
|Region|16|||16|||
|Exogeneity test:|||||||
|Chi2 (1) stat.||||10.266|***||
|Overidentifying res<br> Chi2(3) stat.|triction test:|||6.532|*||

The dependent variable is the volume of inventory, _Z_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

Second, the transport cost is regressed on rail use. The transport costs are divided by the distance to the Port of Dar es Salaam.<sup>14</sup> Freight rail is clearly cheaper than road transportation. The OLS model is considered consistent: The Hausman exogeneity test cannot be rejected in this case. The coefficient of _RAIL_ is estimated at -0.019. Hence, the benefits from rail freight can largely be attributed to low prices. Although the overall impact of rail use on firm costs is still significant (as shown in **Tables 6 and 10** ), one disadvantage may be unreliability: Unreliable transportation causes high inventory costs to firms.

> 14 To measure the transport costs, the network analyst toolbox is used. Transport costs per km are estimated given each firm’s location.
_and basic sanitation by 2015_ ”. This explains why the World Health Organization and UNICEF have jointly sponsored the Joint Monitoring Program (JMP, 2006) on water supply and sanitation which systematically tracks access to improved water sources. There are however differences between the methodology underlying JMP estimates and our work. First, JMP statistics include all African countries, whereas only a subset of countries is covered in our data set. Second, JMP statistics are based on a survey of surveys (including assessment questionnaires sent to UNICEF field representatives), whereas our results are based directly on DHS data. Third, JMP statistics apply standardized parameters in order to be able to separate protected from unprotected wells/boreholes to estimate ‘improved water’, and to determine to what extent traditional pit latrines can be considered as ‘improved sanitation’. Our analysis does not employ any parameters and reports only the statistics from the surveys.

Beyond water and sanitation, relatively little is known about the household coverage of other modern infrastructure services in Africa. Indeed, while use of household surveys as an instrument for analyzing poverty and inequality trends as well as changes in education and health indicators has been longstanding, household surveys have rarely been used to analyze trends in access to infrastructure services<sup>3</sup> . There is thus an important gap in the development literature here, especially given the fact that household surveys are the only quantitative instrument that can establish relationships between use of infrastructure services, socioeconomic variables and government subsidy policies (Lobo, Foster, and Halpern, 2000). In this paper, after providing a brief description of our data and methodology in Section 2, we provide in Section 3 both country-level and Africa-wide trends in access to electricity, water, sanitation, and landline telephones for the period 1990-2005. A brief conclusion follows.

# **2. Data and methodology**

In order to document trends in access to modern infrastructure services, we constructed a cross-country database relying on Demographic and Health Surveys (DHS hereafter; the list of surveys is provided in Annex). DHS are nationally representative surveys conducted by MACRO International that collect comparable information across countries on health, HIV-

> 3 Among previous work in this area, one should mention global infrastructure study by Komives, Whittington, and Wu (2003) which relied in surveys for 15 countries to present access trends and to evaluate the relationship between access to infrastructure and household income. For Africa, the reader is also referred to Estache and Wodon (2007) who present evidence on infrastructure and access and affordability trends for most countries in Africa.

4
# **Figure 8A: Unskilled Worker Migration Corridors in EAP that Expanded the Most between 2007 and 2050 (Excluding Hong Kong SAR, China).**<sup>**a**</sup>

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Malaysia / Indonesia South Korea / Rest of East Asia<br>Thailand / Rest of SE Asia Rest of East Asia / China<br>Hong Kong / Rest of East Asia<br>Millions of People<br>2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> Source: Authors’ results Note a. Number of unskilled migrant workers by location/home region

# **Figure 8B: Skilled Worker Migration Corridors in East Asia and the Pacific that Expanded the Most between 2007 and 2050.**<sup>**a**</sup>

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Malaysia / Indonesia South Korea / Rest of East Asia<br>Thailand / Rest of SE Asia Rest of East Asia / China<br>Hong Kong / Rest of East Asia<br>Millions of People<br>2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> Source: Authors’ results Note: a. Number of unskilled migrant workers by location/home region 27
in influencing overall investment expenditures as well as its composition between equipment and structures.

For instance, Bischoff (1971) compared the alternative models of investment for the 1953-1968 period using quarterly data for the U.S. economy; separate regressions were estimated for equipment and structures. In the case of both equipment and structures investment, the modified neoclassical model was found to be the best, followed by the accelerator model.

Clark (1979) also undertook an investigation of the alternative investment models for the 1954-1973 period using quarterly data for the U.S. economy, with separate regressions for equipment and structures. Like the results obtained by Bischoff (1971), Clark found modified neoclassical model followed by the accelerator model to be the best for structures as well as equipment. Clark therefore concluded that output was the primary determinant of non-residential fixed investment in the economy; variables like the rental price of capital services, interest rates, and tax rates proved to be not very helpful.

Likewise, Bernanke et al. (1988) carried out non-nested specification tests of time-series investment models at the level of the economy; separate equations were estimated for equipment and structures using quarterly U.S. data for the 1955-1983 period.<sup>7</sup> The conventional goodnessof-fit statistics indicated that no one model of investment uniformly outperformed all other models. Of the four models, the accelerator and the modified neoclassical model were found to be the best models for equipment investment. In the case of structures investment, Q model was found to be the best. However, when non-nested tests that takes into account serial correlation 7 One difference between the specifications in Bernanke et al. (1988) and those of Bischoff (1971) and Clarke (1979) was that Bernanke et al. (1988) tested only four models: (i) accelerator; (ii) neoclassical; (iii) modified neoclassical; and (iv) Q. Somewhat surprisingly, they did not test the cash flow model.

10
- Dray, A., Perez, P., Jones, N., Le Page, C., D’Aquino, P. and Auatabu, T. (2006) The AtollGame experience: from knowledge engineering to a computer-assisted role playing game, _Journal of Artificial Societies and Social Simulation,_ 9 (1), <http://jasss.soc.surrey.ac.uk/9/1/6.html>.

- DWAF (2001) _Fish to Tsitsikamma Water Management Area, Water Resources Situation Assessment_ , WMA 15 Report No. P WMA 150000/00/0203.

- Farolfi, S. and Abrams, M. (2005) ‘Water uses and their socio-economic impact in the Kat River catchment, A report based on primary data’, Unpublished Water Research Commission Report, Project no K5/1496, Institute for Water Research, Rhodes University, Grahamstown.

- Farolfi, S. and Bonté, B. (2005) ‘KatAWARE: A negotiation-support tool for participatory water resource management in the Kat River Valley (Prototype model)’, Unpublished Water Research Commission Report, Project no K5/1496, Institute for Water Research, Rhodes University, Grahamstown.

- Farolfi, S. and Rowntree, K. ‘Accompanying Local Stakeholders in Negotiation Processes Related to Water Allocation Through Simulation Models and Role-Playing Games: an Experience from South Africa’, paper presented at the Conference ‘End-user Ownership and Involvement in Integrated Water Resource Management’, Cairo, Egypt, November 1317, 2005. Available: <u>www.empowers.info/page/704</u>

- Farrington, J. and Boyd, C. (1997) ‘Scaling up the Participatory Management of Common Pool Resources’, _Development Policy Review,_ 15(4): 371-391.

- Lankford, B. and Watson, D. (2006) ‘Exploring metaphor in natural resource gaming: insights from the River Basin Game’, in Barreteau, O., Etienne, M., Le Page, C., and Perez, P. (eds.). _Symposium issue of Simulation & Gaming_ , SAGE, USA.

- McMaster, A. (2002) ‘GIS in participatory catchment management : a case study in the Kat River Valley, Eastern Cape, South Africa’, Unpublished MSc Thesis, Rhodes University

- Munro, G. (1979)  ‘The Optimal Management of Transboundary Renewable Resources’, _Canadian Journal of Economics_ , 12(8): 355-376.

- Ostrom, E. (1992) _Crafting Institutions for Self-Governing Irrigation Systems_ , Institute for Contemporary Studies, San Francisco, CA.

- Ostrom, E., Gardnner, R. and Walker, J. (1994) _Rules, Games, & Common-Pool Resources_ , The University of Michigan Press.

Owen, G, (1995) _Game Theory_ , Academic Press, San Diego.

- Parrachino, I., Dinar, A. and Patrone, F. (2006) _Cooperative Game Theory and its Application to Natural, Environmental and Water Resource Issues_ . _3. Application to Natural andEnvironmental Resources_ .  Policy Research Working Paper 4074, World Bank, Washington DC.

- Goodhue, R.E. and McCarthy, N. (2007) _Traditional Grazing Rights in Sub-Saharan Africa and the Role of Policy_ , in: Dinar, A., J. Albiac and J. Sanchez-Soriano (Eds.) _Playing Games With Nature: Game Theory, Natural Resources and the Environment_ .  London: Routledge.

22
The underlying road database is based on the Digital Chart of the World (DCW), which was expanded using attributes from Michelin 2004 regional maps to distinguish among primary, secondary, and tertiary roads.<sup>4</sup> In addition, further refinements to these maps were made through a Uganda national roads dataset in order to allow for more precise analysis. This database was then layered with various GPS data in order to extract travel time and distance from major cities.

<u>Power supply: A dummy variable showing the presence of an electric grid in</u> district is used to proxy for access to power supply. This is a crude measure, but we could not obtain data on actual usage or reliability of power supply.

<u>Localization: There are several ways of measuring localization. These include</u> own-industry employment in the region, own-industry establishments in the region, or an index of concentration, which reflects disproportionately high concentration of the industry in the region in comparison to the nation. We use own-industry establishments as the measure of choice as we want to test if there are gain from locating in areas that are already specialized in the firm’s chosen line of business. Also, when location “shopping” entrepreneurs are more likely to observe the density of establishments in an area, compared to the number of people employed in them. Using the number of firms in the same sector has also been commonly used in empirical work, with the underlying premise that localization economies come from the absolute volume of similar activities in the neighborhood.

For the localization measure, we consider the number of establishments in the same 2 digit industry sector who are within 20 kilometers of each firm under consideration.5 As discussed earlier, we only use establishments that have been in business for more than 5 years for this calculation. For the location modeling, we then average these numbers at the district level.

<u>Economic diversity (urbanization economies): We use a region’s economic</u> diversity to reflect potential gains from urbanization economies. Typically, larger cities have a greater diversity of firms (Deichmann et. al 2008). This allows greater

> 4 Thomas (2007) assigns travel along primary roads to be 60km/hr, secondary roads 40km/hr, and tertiary roads 20km/hr.

> 5 We also tried localization measures with 5Km and 60Km, and in general found that agglomeration economies are higher at closer distances.

13 -
Table A7: Impacts of Extended Provider Training and Patient Information on Other Aspects of Care

||(1)|(2)<br>Clinic Survey|(3)|(4)<br>Home|(5)<br> Survey|
|---|---|---|---|---|---|
||Number of<br>Medications<br>Prescribed|Number of<br>Medications<br>Prescribed<br>Excluding<br>Antimalarial|Prescribed<br>Antibiotic|Time Waiting<br>at Clinic|Time<br>Consulting with<br>Provider|
|Extended Training|-0.0698|-0.0853|-0.0200|-0.707|-1.081|
||(0.165)|(0.123)|(0.0474)|(1.471)|(0.822)|
|Patient Infomation|0.101|0.106|-0.0410<sup>_∗_</sup>|0.479|-0.363|
||(0.0728)|(0.0688)|(0.0209)|(0.983)|(0.627)|
|Mean (Basic Training, No Pat. Info.)|3.78|3.00|0.64|18.25|13.42|
|N|1971|1971|1971|1427|1427|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the potential covariate set and set of pairwise interactions. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.
else (or almost nowhere else) in the data set. This is because Bitcoin prices are volatile and the unit of account for most crypto-currency trades is the underlying fiat currency (e.g., where someone sends dollars from the United States to relatives elsewhere). The third section presents the main results, which we interpret as compelling evidence that Bitcoin are being used both for domestic payments and for international transmission of funds (including the large global pool of international remittances). The next section discusses how our methodology can, in principle, be applied (or adapted) to other cryptocurrencies and exchanges, including many classes of stablecoins. This analysis can perhaps guide regulators in identifying the selection of off-chain transactions to focus on when requesting IP addresses for audit. It also highlights how individuals or institutions, who may not have access to any private information, might still be able to draw inferences on the transaction use of cryptocurrencies and cross-border transfers, including capital flight. The final section concludes.

# **I:  Local Bitcoins, and off-chain exchange**

The core data set, described in detail in the Data Appendix (Appendix A.7), makes use of data from LocalBitcoins.com, the world’s largest peer-to-peer (P2P) Bitcoin exchange, which allows Bitcoin to be traded in 135 different fiat currencies. The data encompass more than 45 million trades over the period March 15, 2017 – July 23, 2021.<sup>4</sup> To contextualize, the universe of trades that occurred on-chain over the same period, encompasses 452 million transactions.<sup>5</sup> LocalBitcoins.com is able to operate in such a large number of countries because it only offers deposit services for members’ Bitcoins, but does not involve national fiat currencies, thereby side-stepping national banking regulations. Although other P2P exchanges

> 4 LocalBitcoins has existed since 2012. However, we limit our analysis to the period since March 2017, when the website revamped the exchange’s back-end, guaranteeing consistency in the format of the data.

> 5 One transaction recorded on the Blockchain can include the transfer from one sender to many recipients, thus, when instead counting these as individual transfers, the cumulative number of on-chain transactions from the inception of Bitcoin in 2009 through May 2021 amounts to 862 million (Source: Blockchain.com API, Authors’ Calculations) As noted previously, the universe of off-chain transactions is easily an order of magnitude larger than on-chain transactions.

3
# **<u>Table A3:</u> World diffusion and diffusion by income category including GDP growth outliers**

||Low and Lower|Middle Income|Upper Middle|and High Income|A|ll|
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Log real GDP per capita|1.634*|-0.146|0.629|0.607|1.761***|0.115|
||[0.946]|[0.667]|[0.619]|[0.475]|[0.508]|[0.456]|
|Log real cost|-0.086|0.044|-0.468***|-0.032|-0.151*|0.021|
||[0.111]|[0.051]|[0.120]|[0.076]|[0.079]|[0.047]|
|Log lines per capita|2.647***|0.087|1.412***|-0.264|2.125***|-0.082|
||[0.355]|[0.276]|[0.357]|[0.302]|[0.266]|[0.223]|
|Log computers per capita|2.019***|0.226|2.823***|-0.114|2.333***|0.138|
||[0.173]|[0.193]|[0.140]|[0.246]|[0.121]|[0.162]|
|Lag Internet users||0.584***||0.810***||0.673***|
|||[0.054]||[0.085]||[0.046]|
|Constant|-0.146|0.154**|-4.398|0.055|-7.892*|0.106***|
||[7.614]|[0.062]|[6.363]|[0.039]|[4.735]|[0.037]|
|Method of estimation|GLS|IV|GLS|IV|GLS|IV|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number Observations<br>|503|388|510|419|1013|807|
|R<sup>2</sup>|0.86|-|0.91|-|0.91|-|

Notes: The dependent variable is the log of Internet users per capita. The robust standard errors are in brackets.

* , ** and *** indicate significance at the 10 percent, 5 percent and 1 percent levels respectively. The regression controls for country fixed effects.

30