rafmacalaba/datause-encoder-data huggingface dataset:
# **I. MMGXNG THROUGH NM YORK COTTON** FUTURES

Cotton prices have fluctuated signif icantly, especially in the latter half of the 19809. Table 2 shows the average monthly export price, its standard deviation and its coefficient for variation of each of t-he cottons examined. The coef ficient of variation, our measure of volatility, is shown to vary between 17.2's and 21.2't, although the 17.2* for Punjab SG _1505_ is for a much shorter time period. Thus, for the cotton prices examined, over the period 1985-92/93, the coefficient of variation is around 20% compared with 17%- which is the volatility of the World Bank's commodity price index over the same period.

The only market that trades in cotton futures is the New York Cotton Exchange (NYCE). The New York No. 2 cotton contract is based on grade 41, staple 34 (strict low middling 1-1/16 inch) cotton. The quality of the cottons from the countries covered here is similar (middling 1-3/32 inch) but not identical. Provided that the characteristics of the cash commodity are identical to the quality specified in the futures contract, the traditional recommendation is to hedge all of the cash commodity in the futures market. (This type of a hedge is termed a "direct hedgel"). However, in cases where the cash and futures prices are for related but not idenitcal commodities, the appropriateness of the futures contract
# **Table 10: Panel Regressions – Volume of small transactions across different crypto-assets**

<u>Dependent Variable: Log monthly on-chain bitcoin volume (transaction size < US$10K), US dollars</u>

|Variable|(1)<br>Bitcoin|(2)<br>Ether<br>**All countries**|(3)<br>Stablecoins|(4)<br>Non-stablecoins|
|---|---|---|---|---|
|Inflation (yoy)|-0.117|0.192|0.451|0.006|
|Change exchange rate (mom)|0.041|0.030|0.465|0.055|
|Change Bitcoin price (mom)|0.416***|0.814***|0.466***|0.491***|
|Change Ether price (mom)|-0.018|-0.031|-0.109***|0.021|
|US 10-year Yield (inflation adjusted)|-0.058|-0.737***|-0.996***|-0.328***|
|US Inflation Expectation Rate|0.683***|2.720***|0.696***|1.598***|
|VIX|-0.001|0.011***|-0.008***|0.004***|
|Change gold price (mom)|-0.853***|-2.505***|-1.651***|-1.496***|
|Pandemic dummy|0.512***|0.914***|0.694***|0.707***|
|N of Observations|3095|3095|3095|3095|
|N of clusters (countries)|136|136|136|136|
|Adjusted R squared|0.363|0.741|0.570|0.655|
|Country-Fixed Effects?|YES|YES|YES|YES|
|Variable|(5)|(6)|(7)|(8)|
||Bitcoin|Ether<br>**EMDEs only**|Stablecoins|Non-stablecoins|
|Inflation (yoy)|-0.141|0.206|0.272|0.012|
|Change exchange rate (mom)|0.013|0.510|0.552|0.222|
|Change Bitcoin price (mom)|0.323***|0.655***|0.331***|0.363***|
|Change Ether price (mom)|-0.035|0.010|-0.090|0.028|
|US 10-year Yield (inflation adjusted)|-0.073|-0.685***|-1.110***|-0.281***|
|US Inflation Expectation Rate|0.640***|2.663***|0.579***|1.544***|
|VIX|-0.003|0.010***|-0.013***|0.002|
|Change gold price (mom)|-1.057***|-2.503***|-1.951***|-1.555***|
|<br>Pandemic dummy|0.569***|0.955***|0.769***|0.771***|
|<br>N of Observations|1837|1837|1837|1837|
|N of clusters (countries)|85|85|85|85|
|Adjusted R squared|0.299|0.684|0.544|0.575|
|Country-Fixed Effects?|YES|YES|YES|YES|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. All independent variables are lagged by one month, except the pandemic dummy. Bitcoin, ether, and gold prices expressed in US dollars. The gold price is expressed per Troy ounce. A constant is estimated, but not reported.
The second curve in Figure 1 is the _CO_ 2 emissions in grams/km of car travel by taking the exponential of a polynomial equation that predicts log- _CO2_ as a function of the speed in miles per hours (Barth and Boriboonsomsin, 2007). The divergence between the fuel and CO2 curves occurs because the latter was calculated under cruising conditions in Southern California, whereas the curve we fit To Davis and Diegel’s data for the GeoPrizm reflects actual conditions. Since cruising speeds are not realistic for Sao Paulo, we use the U.S.E.P.A.’s formula for converting a gallon of gasoline to kilograms of CO2.<sup>7</sup> CO2 grams/gallon = 2,421 grams x 0.99 x (44/12) = 8,788 grams/gallon. (13) The above completes description of our model’s demand structure and the equations that are used to calculate fuel use and emissions. We now turn to how a congested traffic equilibrium is calculated using the above equations.

A congested traffic equilibrium is calculated by the following iterative procedure which, if iterated properly and starting from a reasonable starting point, converges robustly and uniquely. The steps of the procedure are:

1. Set the value of the aggregate car equivalent traffic, _T_ , and call it _T_<sup>ˆ</sup> .

2. Calculate the auto travel time, _G_ 3<sup>,</sup> from the congestion function (6).

3. Calculate or input any fuel tax rates and tolls, and then calculate the monetary cost of auto, _g_ 3<sup>,</sup> using (9), (10) (and (11), (12) substituted into (9) in the case of a toll on excess delay only).<sup>8</sup> 4. Calculate the choice probabilities _P_ 2, _P_ 3, from (2).

5. Calculate _T_ 2, _T_ 3, from (4) and _T_ from (5).

6. (a) If _T_ and _T_<sup>ˆ</sup> (from steps 1 and 5) are close enough, stop and declare convergence. The

> 7 <u>http://www.epa.gov/oms/climate/420f05001.htm#calculating.</u> To calculate the CO2 emissions from a gallon of fuel, the carbon emissions are multiplied by the ratio of the molecular weight of CO2 (m.w. 44) to the molecular weight of carbon (m.w.12): 44/12. In our case, a further multiplication by 0.75 is required since we assume that 25% of the fuel was alcohol.

> 8 In the case of the socially optimal toll, (11) and (12) are not used. Instead the value of _MTOLL_ is varied and a congested equilibrium is calculated for each _MTOL_ L until we find the value which maximizes social welfare (equation (15 )).

9
# **3. Data and impact evaluation design**

This study - the Business Registration Impact Evaluation (BRIE) - is a randomized controlled trial that aims at estimating the impact of business registration for informal micro and small enterprises in Malawi. In addition, it examines the added effect on top of business registration of bank information sessions. We discuss first the process of obtaining a sample of informal firms, before providing details on the randomization process and interventions.

# **3.1 Obtaining a sample of informal firms**

In this study we target the informal micro and small enterprises that are likely to be able to benefit the most from business registration, and that the government has said would be their first group of interest for a future road-show on business registration. We target firms in urban Lilongwe and Blantyre, the major commercial cities in the country. At the end of 2011, we listed over 100 business centers – that is, concentrations of firms including industrial parks, markets, streets with shops, set of workshops, etc. – and randomly sampled 46 of these business centers (23 in each city) to list all businesses operating within these areas. Through this process we listed 7,603 enterprises, 85 percent of which were not registered at the DRG. With this process, we excluded from the sample household-based enterprises. Surveys in Africa have shown that household-based enterprises tend to be the smaller on average than those operating in business centers (see for example, Bossuroy et al., 2013). Similar proportions of unregistered firms were identified in Blantyre and Lilongwe, despite the DRG being located in Blantyre. Only one quarter of the firms listed were female-owned.

We had a workshop with government officials, as well as consultations with various stakeholders including the private sector to inform the criteria for targeting firms within the informal sector for this study. There was a consensus around targeting larger firms (measured in revenues), as a proof of concept. In addition to revenues, other selection criteria that were identified by stakeholders included the number of workers and whether the firm operated from a fixed location. The firms to be identified would be those more likely to be targeted<sup>8</sup> or incentivized for

> 8 While the government was preparing accompanying regulations to a new Business Registration Bill, which indicated the type of firms that should be registered, it requested information on the distribution of revenues and formalization status using the listing data collected for this study. This aimed at preparing documentation on the type of firms that ought to be registered.

9
cheaper alternatives, and on many occasions came with clinical vouching from a health care provider.

# 3. Theoretical framework: Explaining unit prices at contract award

The determinants of pharmaceutical, medical supplies, and equipment prices have been studied in a wide variety of contexts and methodologies. For example, pharmaceutical prices have been found to be determined by patent expiration and generic producers’ market entry (Morton & Kyle, 2011), production costs (Berndt, 2002), a host of regulatory instruments such as international reference pricing (Wouters & Kanavos, 2017), or quality features (Danzon & Chao, 2000). Markets for medical devices or machinery have also been studied, albeit to a much lesser degree, for example by looking at market structure or bargaining power of market participants (Grennan, 2013). However, there have been relatively few theoretical or empirical investigations of purchasing decisions by public bodies vis a vis suppliers of pharmaceuticals, medical supplies and equipment. A recent notable such study looks at pricing transparency in the public procurement of selected drugs in 2 Brazilian states, Paraiba and Sao Paulo (Kohler et al, 2015).

Our theoretical framework takes note of this large body of evidence and looks at the least studied aspect of pricing pharmaceuticals, medical supplies and equipment: public purchases. Such an analytical focus also allows us to bring in and build on a hitherto disconnected literature on the performance of public procurement systems (de Oliveira et al, 2019; Kohler & Dimancesco, 2020). This literature has generated evidence on a diversity of determinants of prices at the point of contract award, some of which overlap with the public health literature briefly enumerated above: the price impact of bidder number, market structure, or centralization of purchasing (Petersen et al, 2020). While there is a host of price predictors largely neglected by the public health literature such as procedure type choice (e.g. open versus negotiated procedure), auction design choices (e.g. length of advertising the tender), or institutional quality (Baldi & Vannoni, 2017; Coviello & Mariniello, 2014).

Studying specifically the purchasing decisions made by a wide variety of public buyers across many countries, but within a relatively short time frame (the bulk of the data comes from 20142018), implies that many of the price determinants such as production costs or patent expiry can be assumed to remain largely unchanged from the perspective of our purchase level analysis. Holding such (for us) unobserved factors constant allows the analysis to concentrate on those observed factors which take center stage in public auctions and other types of tenders for standardized health care inputs. These observed price determinants such as market structure, auction design, or bidder features have been derived from at least one of the disciplines we build on: public health, health economics and public procurement. In addition, we also briefly draw on public administration research regarding the effect of administrative quality on prices.

6
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draws from the cluster (second stage) data, and then finding the length of the interval that is symmetric around the bootstrapped mean and that contains 95.4 percent of the bootstrapped mean.<sup>12</sup>

# **_4.1 First-Stage Estimates: Income and Quality Elasticities_**

Table 2 presents the income elasticity estimates and quality elasticity estimates using the methodology illustrated in section 3.

Overall income elasticities vary from 0.386 to 0.861. As expected, product aggregates that constitute the diet of poorer households have lower income elasticities. In particular, food products perceived as necessities, such as legumes and maize, result in the lower income elasticities (0.386 and 0.422). More expensive and appealing products such as meat, dairy products, and fruit, have higher elasticities respectively with 0.850, 0.716 and 0.861. Wheat, vegetables, oils and fats and sugar are reasonably elastic with their income elasticities ranging from 0.464 to 0.630.

Table 2 also reports the estimates for quality elasticities which vary from 0.008 (sugar) to 0.262 (alcohol and tobacco). Sugar, oil and fats, have very low quality elasticities indicating homogeneity within the product aggregates and a similar variety and quality consumption across households. The remaining categories of goods have larger quality elasticity estimates suggesting a wider range of choices and quality for the consumers and a preference of richer households towards the more expensive. Because of the wide range of quality available to consumers, alcoholic beverages and tobacco products resulted in the highest quality elasticity.

> statistic its (bootstrapped) standard error can be calculated. The accuracy of the bootstrap estimator increases with the number of samples drawn. In order to construct confidence intervals, this paper follows the methodology proposed by Hall (1995) which bootstraps the distribution of the t-values rather than the estimate itself.

> 12 That is the fraction of a random variable normally distributed that lies within two standard deviations from the mean.

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Policy ReseaRch WoRking PaPeR 4913

# **Abstract**

This paper documents the prevalence of in-house generation of electric power by firms in Sub-Saharan Africa and attempts to identify the underlying causes. The analysis is based on two data sources. The UDI World Electric Power Plants Data Base (WEPP), a global inventory of electric power generating units, provides a detailed inventory of in-house generation at the country level. The World Bank’s Enterprise Survey Database captures business perceptions of the obstacles to enterprise growth for 8,483 currently operating firms in 25 African countries.

Overall, so-called own generation by firms—which has been on the rise in recent years—accounts for about 6 percent of installed generation capacity in Sub-Saharan Africa (equivalent to at least 4,000 MW of installed capacity). However, this share doubles to around 12 percent in the low-income countries, the post-conflict countries, and more generally on the Western side of the continent. In a handful of countries own generation represents more than 20 percent of capacity.

Rigorous empirical analysis shows that unreliable public power supplies is far from being the only or even the largest factor driving generator ownership. Firm characteristics have a major influence—in particular, the probability of owning a generator doubles in large firms relative to small ones. Our model predicts that the prevalence of own generation would remain high (at around 20 percent) even if power supplies were perfectly reliable, suggesting that other factors, such as emergency back-up and export regulations, play a critical role in the decision to own a generator.

This paper—a product of the African Sustainable Development Front Office, Africa Region—is part of a larger effort in the region to gauge the status of public expenditure, investment needs, financing sources, and sector performance in the main infrastructure sectors for 24 African focus countries, including energy, information and communication technologies, irrigation, transport, and water and sanitation. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at jirving@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
were considered in the LiTS II. The greater use of private coping strategies (relative to accessing public safety nets) in transition countries is in contrast to that in the western European comparator countries (see Figure 4). The south Caucasus, where the use of public safety nets was lowest (7 percent of affected households), also had the highest use (58 percent) of the private safety option. In Western Europe, public safety nets are as prevalent as private ones (both used by about 20 percent of affected households), and reducing essential expenditure is much less frequent (at 40 percent). The greater reliance on passive strategies in transition countries, and particularly on cutting staple food and health expenditure, could be explained by less efficient safety nets, lower stocks of household savings and food price inflation.

**Figure 5. Proportion of households using various coping strategies (as a percentage of households affected by the crisis), by subregion**

<!-- Start of picture text --> Western Europe New EU member States* South-eastern Europe<br>Belarus-Moldova-Ukraine South Caucasus Turkey<br>Russia Central Asia Transition average<br>Active Private safety nets<br>Public safety nets Reduced staple food and health exp.<br>Reduced other exp.<br>* Includes the countries of Central Europe and the Baltics, Bulgaria, and Romania<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br><!-- End of picture text --> Source: LiTS II (2010).

# **_Public safety nets_**

In response to the crisis, governments have deployed an array of instruments, including adjustments to social assistance programs, unemployment insurance or pensions, or the provision 12
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37 that developing countries tend to have somewhat lower labor force participation rates and also a higher labor share in national income than advanced countries<sup>40</sup> .

**Table 6: Gross Domestic Product Per Capita Based On Purchasing‐Power‐Parity (PPP), US Dollars, 2016**

|Afghanistan|1,919|
|---|---|
|Albania|11,840|
|Central African Republic|652|
|Democratic Republic of the Congo|773|
|Eritrea|1,410|
|France|42,314|
|Germany|48,111|
|Iraq|17,944|
|Italy|36,833|
|Kosovo||
|Myanmar|5,832|
|Serbia|14,493|
|South Sudan|1,657|
|Sudan|4,447|
|Sweden|49,836|
|Syria|n/a|
|United States|57,436|

Source: IMF WEO, World Bank World Development Indicators 40 Reflecting an accounting identity, average wages differ from average per capita incomes reflecting labor force participation rates and the share of labor in national income: other things equal, the higher the labor force participation rate, the lower is wage relative to per capita income, and, other things equal, the higher the share of labor in national income, the higher the wage relative to per capita income. The reference data for labor force participation is taken from the World Bank (WDI, 2016), which shows 59% from lower middle‐income countries (which in our view is the most representative of today’s situation in countries that are the largest source of refugees), and 61% in high‐income countries. In a comprehensive survey by Guerriero (2012) she provides a preferred estimate of the labor share which accounts for self‐employment income and which yields 75% for lower middle‐income countries and 69% for high‐income countries. These estimates, taken together, imply that wages in lower‐middle income countries – including self‐employment income ‐ are 27% higher than average per capita incomes on account of the fact that the labor share in income is higher than the labor force participation share. Similarly, in high‐income countries the average real wage is 13% higher than per capita income. Thus, the difference in real wages between developing and advanced countries would be expected to be some 15% smaller than that of per capita incomes.