rafmacalaba/datause-encoder-data huggingface dataset:
including health, education or media groups, leaving us with a sample of more than 2,300 private organizations covering over 125,000 subscribers active during our 45 months of data from April 2013 to December 2016. These data include 1,350 active cell phone towers distributed across 266 of Afghanistan’s 398 districts, which collectively cover over 80% of the population.<sup>11</sup> As shown in Table 1, the average (median) firm is active for 34 (45) months and is observed to operate in 38 (24) districts. While the average firm has 52 subscribers, the median firm has 4, implying a skewed distribution of firm size with a small number of very large firms. The mobile network operator provides coarse sectoral information about firms in the form of an internally recorded “segment.” We see that 42% of firms are in either trade or manufacturing, 19% are in construction, 12% are in transportation, with less than 5% in finance, IT or security, and 24% labeled as other.

Firms with corporate phone accounts present in our data may be different from “typical” Afghan firms. While we would like to be able to characterize this selection of firms relative to all others, there is no existing census of firms covering the period of our data.<sup>12</sup> Some administrative datasets do exist for this period, but each have their own limitations for example, official business registration databases simultaneously under-count firms that do not register to evade tax obligations and over-count the registration of “ghost” firms created to pursue contracts. One reasonable benchmark is the World Bank’s Enterprise Survey conducted in May-July 2013, which includes a random stratified sample of 416 firms reweighted based on firm size, sector and location strata. In Table A1, we show that on average the firms in our CDR data appear to have twice as many subscribers as the number of employees from firms in the Enterprise Survey sample, that the CDR firms are less likely to appear in trade and manufacturing categories, and that CDR firms are more likely to have their headquarters

> 11Afghanistan’s challenging terrain, limited infrastructure and persistent insecurity limit the expansion of mobile network coverage to more remote and underpopulated districts.

> 12The Central Statistics Office completed an Integrated Business Enterprise Survey (IBES) in 2009, which included a screening survey that attempted a census of every firm with 10 or more employees in the country and used random area sampling for firms with fewer than 10 employees.

11
Product market policies, as measured by the overall summary product market indicator, are still relatively restrictive in Bulgaria compared to those of OECD countries. Simple comparisons suggest Bulgaria still scores less well than the EU 15 average, although better than many of its peers did in 2003 (Figure 3). Figure 4, which reports the summary PMR scores accounting for the uncertainty in the choice of weights used in the PMR system, confirms that Bulgaria falls into the group of relatively restrictive countries (which includes France, Greece, Italy and the Czech Republic as estimated with 2003 data).<sup>9</sup>

**Figure 3. Product Market Regulation: Country Group Comparison**

<!-- Start of picture text --> 3.0 2.8<br>2.5<br>2.1<br>2.0<br>2.0 1.6 1.7 1.8<br>1.4 1.4 1.5<br>1.5<br>1.0<br>0.5<br>0.0<br>Slovak EU15, OECD, Romania, Czech Bulgaria, Hungary, Other Poland,<br>Republic, 3002 2003 2006 Republic, 2006 2003 MICs, 2003<br>2003 2003 2003<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

**Figure 4. Product Market Regulation - Country Comparison**

<!-- Start of picture text --> Relatively Restrictive<br>Relatively<br>Liberal<br><!-- End of picture text --> _Note_ : Values refer to 2006 for Bulgaria and Romania and 2003 for all other countries.

9 See Conway, Janod, Nicolletti (2005) for details.

11
65. The above-mentioned loose monetary policy and low short- and long-term interest rates not only helped bring about the housing bubble in the U.S., but also encouraged aggressive search for higher yields on a global scale reflected in record high volumes of capital flows to developing countries, which rose from $200 billion in 2000 to $1.2 trillion in 2007 (Figure 42). Large capital flows to developing countries are also attributable to the deep structural reforms in many developing countries that were undertaken during the 1990s and contributed to record-high investment-led growth in many developing countries in the same period (Figure 43).<sup>42</sup> The accelerated growth in many countries resulted in a sharp increase in the demand for (and prices of) natural resources, as well as trade surpluses in natural resource-exporting countries. This prompted a boom in commodity prices, which in turn further accelerated growth in natural resource-exporting developing countries (Figure 44). The investment-led growth in developing countries resulted in large trade surpluses in many advanced capital-goods exporting countries, such as Germany. Since the United States is the reserve currency issuing country, the foreign reserves accumulated through trade and capital account surpluses in other countries will return back to the U.S., in the form of purchasing Treasury bills or other investments, and this led to the capital account surplus in the United States.<sup>43</sup>

> 42 The Global Economic Prospects of  2010 notes that the fall in borrowing costs during the 2003-07 period was associated with almost 70 percent of the increase in  capital flows  into developing countries and 80 percent of the increase in domestic consumption. Also, on average, between 2000 and 2007 investment-toGDP ratios in developing countries increased by 5.2 percentage points compared with their 2000 levels. The GEP also indicates that more than half of the 1.4 percentage point increase in potential output growth rates in developing countries between 2003 and 2007 is directly attributable to the capital deepening during this period.

43 Most scholars emphasize the high demand for US treasuries from trade surplus countries allowed the U.S. government to borrow more cheaply than otherwise, a phenomenon often referred to as the “exorbitant privilege” (Eichengreen 2010). However, more important than this privilege, is that as long as the US dollar is an accepted reserve currency, the funds to support the trade deficits will recycle back to the US either by purchasing treasury bill or other forms of investments and become US’s capital account surplus to offset the current account deficits. This may appear that the investors have a preference for the US dollars (Bernanke 2005a, Bernanke et al. 2011). But in fact it is because the U.S. dollar is a reserve currency that the current account deficit financed by the reserve currency-issuing country must recycle back to that country.

49
than those in households with longer outages; the opposite is true in 2012. Women in households with more reliable electricity supply have lower labor force participation and 6.1 fewer hours a month of employment in 2005 than their counterparts. The difference in women’s labor force participation and hours of employment decreases slightly between 2005 and 2012 but the sign remains the same. In the next section we explore the relationship between hours of electricity and households’ welfare outcomes in more detail.

Anecdotal evidence suggests that many households have illegal connections especially in rural areas. IHDS reports both official and non‐standard connections. As a result, the access rate reported by IHDS is higher than that by official records. Since IHDS inquires about the mode of payment for electric connections and the amount of payment, we are able to estimate the percentage of non‐standard connections, which include those who reported having access to electricity but did not receive bills and did not make a payment or paid to neighbors. We find that about 15 percent connections were non‐ standard in 2005. The number reduces to 9 percent in 2012. There is also a strong correlation between non‐standard connection and service unavailability. Overall, households who are unofficially connected experience 12 hours of power outages per day, compared to 10 hours for those with official connections. This result suggests that illegal connections could make service availability worse, and thereby, adversely affects the effects of electrification. It should be noted that illegal connection could still be underreported by IHDS. If that is the case, then benefits of electrification based on IHDS data could also be underestimated.

# **IV. Estimation model**

A base model for estimating the impact of electrification is described by equation 1: where _Yit_ denotes the outcome variables of interest for household _i_ in year _t_ , such as kerosene consumption, income and expenditure, time spent on studies by children, and labor force participation by men and women. _Xit_ is a vector of household‐ and community‐level characteristics. Household‐level control variables include the age, gender, and education of the head of household; the number of adult males and females in the household; the amount of household agricultural land; and measures of the household’s sanitation status, such as access to running water, a flush toilet, and a separate kitchen. Community‐level control variables include dummy variables measuring the presence of paved roads, 9
<!-- Start of picture text --> Logit Model<br>April 2010<br>Caja<br>Min. Salud<br>Clear Order 4<br>Constant ——————<br>-4 -2 0 2 4<br>Estimates<br>N=623<br>Ordered Logit Model<br>April 2010<br>Salud<br>Clear Order<br>First cut point #4<br>Second cut point ——<br>ThirdcutMin.ae:a PO<br>-4 -2 0 2 4<br>Estimates<br>N=623<br><!-- End of picture text --> Figure 8: Logistic regression models: Panels display estimated coefficients with 95% confidence intervals. The top panel shows results for the binary indicator of non-compliance. The April month dummy should be negative in this model, reflecting a decrease in the probability of complete non-. The bottom panel shows results for the four category indicator of compliance. The April month dummy should be positive in this figure, reflecting an increase in the probability of a case taking on one of the codes associated with compliance
27 identified two different patterns of fiscal evolution in Russian regions: the former could be called "true decentralization", the latter may be described as a "redistribution model".

Table 17 presents data on the average ratio between federal transfers to regional governments and total regional expenditures in different administrative types of regions.

**Table 17: Fiscal Subordination of Regional Governments by Administrative Type of Region, %**

|**Measure of Fiscal Subordination / Year**|**1994**|**1995**|**1996**|
|---|---|---|---|
|**1.**Ratio between federal transfers to<br>regional governments and total||||
|regional expenditures in:||||
|1.1 Republics||||
|-Mean|51.19|44.77|47.55|
||(7.49)|(7.42)|(6.18)|
|-Minimum|1.61|0|0.04|
|-Maximum|96.06|98.62|88.95|
|- Valid number of observations|17|18|19|
|1.2 Oblasts and Krais||||
|-Mean|37.30|27.95|40.70|
||(1.93)|(2.25)|(2.62)|
|-Minimum|10.34|1.85|4.37|
|-Maximum|75.13|69.72|82.19|
|-Valid number of observations|_55_|_55_|53|
|1.3 Autonomous Okrugs||||
|-Mean|56.45|59.19|43.11|
||(9.05)|(11.14)|(9.13)|
|-Minimum|8.86|3.18|4.44|
|-Maximum|78.40|97.23|87.68|
|- Valid number of observations|9|9|10|

Standard error of the mean in parentheses As seen from the table, in 1994 and 1995 oblasts and krais received much less federal assistance than both republics and autonomous okrugs<sup>21</sup> . In 1995, the mean of the ratio of federal transfers to total regional expenditures in autonomous okrugs was more than twice the average in oblasts and krais. In 1996, the federal government finally adopted a more equitable allocation scheme of federal assistance among the different types of regions. A plausible explanation of the huge discrepancies between the amount of grants received by republics and autonomous okrugs, and oblasts and krais until 1996 is that the federal government tried to use the transfers as the "carrot" in its policy to discourage the attempts of non-Russian regions to become independent states (Treisman, 1996).

21 The differences in the average ratios between federal transfers to regions and total regional expenditures in republics and oblasts and krais, and in autonomous okrugs and oblasts and krais are statistically significant in both years at the 95% level of confidence.
approximately twice as big with a reduction of the odds of dying by 15.8%. The results for improved water infrastructure are similar, with a reduction of the odds of dying by 4.6% for improved wells or springs and 15.8% for piped water into the household. The estimated effects for the included control variables appear consistent with our prior assumptions both regarding the expected sign and magnitudes of coefficients. In terms of its protective effect, the single most important factor appears to be mother’s education, with odds ratios ranging between 0.89 for primary and 0.41 for tertiary education.

Even though the data set used in this paper is substantially more restricted in terms of temporal and spatial coverage than a previous paper of us (Günther and Fink, 2010),<sup>16</sup> the estimates reported in Table 5 look overall very similar to our prior estimates. With the more restrictive coding rules applied in this paper, the marginal effects of piped water and flush toilets are slightly larger; the difference in the estimated coefficients is, however, not significant.

# **4.2 Aggregate Mortality Effects and Contribution to MDG 4**

Having estimated an empirical relation between water and sanitation infrastructure and child mortality, we can compute predicted mortality under the counterfactual assumption that all children in our sample are provided with access to improved water and sanitation. Given that we distinguish between three levels of water and sanitation, we consider two alternative scenarios: In the first scenario, we assume that all children are provided with access to basic improved water and sanitation. In the second scenario, we assume that all children are provided with access to the highest quality water and sanitation infrastructure, i.e. water pipes and flush toilets in their households.

To see how these assumptions translate into changes in mortality, we first predict mortality under the true (observed) distribution of water and sanitation access, which, by construction, yields current mortality rates. In a second step, we recode all water and sanitation variables to reflect the counterfactual assumptions, and predict child mortality under the assumption that all other child, household and survey characteristics remain unchanged. We apply the DHS country-specific survey weights for these mortality calculations.

Table 6 shows the results of these counterfactual computations. In column 1 of Table 6, we show current under-5 child mortality rates (deaths per 1000 live births) as measured in the

> odds of dying by 7.9%: (1-0.921)*100. Given the “small” probability of child death within a given year in general (0.021, Table 4), odds ratios are very similar to probability ratios in this case. 16 See Section 2 for a more detailed discussion on sample selection.

18
[38], while a subsequent report indicated that prices depend on the field of origin, and that some fields charged $40 to $45 dollars per barrel [17]. At such levels, the annual revenue from oil would be far below many published estimates.

One reason why our estimates differ substantially from many publicly-available ones is likely sampling bias. To our knowledge, prior estimates all relied on what is effectively a _survey_ of Daesh’s oil assets. Information was obtained from a few selected sites and at specific dates on the basis of key documents or interviewee self-reports, which were then extrapolated. In spite of being supplemented with expert opinions, generalizations to the universe of Daeshcontrolled oil facilities are intrinsically imprecise in that the underlying data have observations that are few and might not be representative, therefore leading to imprecise and potentially biased inferences. Updating these estimates over time faces similar methodological challenges.

The approach proposed here instead conducts a _real-time census_ of Daesh oil production facilities with daily temporal resolution. The estimates coming from our analysis are not inferences made from observations on a few selected sites and at a few selected dates but from all sites and in real time. Thus, they have the substantial advantage of enabling less bias than previous estimates of the impact of various kinds of events (e.g. attacks, leadership conflicts, territorial losses, etc.) on oil production.

Our study aims to help bridge the knowledge gap on economic activity in Daesh-controlled territories. The results here can be built on to inform planning for short-term humanitarian assistance and long-term reconstruction. We also provide a methodological contribution in that reliable external measures of oil output can enable better approaches to a broad range of policy challenges. Across the world’s poorly-governed states, few report reliable oil production numbers. Yet assessing production is critical for making sound economic policy and can enable governments and international organizations to identify illegal or untaxed production, as well as for assessing the impact of policy changes. We show that combining reliable records for 16
(DRUS). The adjacency dummy DADJ takes the value one if country i is adjacent to country j and zero otherwise. Geographical distance between capital cities i and j is denoted DiSTii. b0 is the intercept, D2003 is a dummy for year t (t = 2003). This dummy is included in the model to control for time-specific shocks. bi is a country-specific effect when a country is an exporter. Parameters b’s are the coefficients. ɛ ij(t) is the error term that is assumed to be normally distributed with mean zero.

In the literature, it has been found that poorly-performing ports can strongly reduce trade volumes and may have a greater dampening effect on trade for small, less-developed countries than many other trade frictions (Wilson et al, 2003a, 2003b, 2004; Clark et al, 2004). Thus we may expect that improvement of port infrastructure affects positively the trade flows.

Customs is a mandatory element in the movement of goods across borders and the procedures apply to these goods significantly influence the role of national industry in international trade and their contribution to the national economy. Effective and efficient clearance of goods increases the participation of national industry in the world marketplace and contributes to economic competitiveness of nations, encourages investment and development of industry, and increases the small and medium enterprises in international trade (World Customs Organization).

Well developed institutions are likely to decrease the transaction costs for market participants and thus increase the efficiency of markets. They can do this through three channels (World Bank, 2002). (i) They decrease information asymmetries as they channel information about market conditions, participants and goods; (ii) They reduce risk as they define and enforce property rights and contracts, determining who gets what and when, and (iii) They increase competition in markets or decrease it. The improvements of the regulatory environment will affect trade positively.

Services infrastructure sector (SI) is designed to measure the extent to which an economy has the necessary domestic infrastructure (e.g., telecommunications, financial intermediaries, and logistic firms) and is using networked information to improve efficiency and to transform activities to enhance economic activity. For example, the Internet can improve the
|Public Sector Management Loan Project||
|---|---|
|Public Sector Management Loan Project||
|Public Sector Management Loan Project||
|Public Sector Management Loan Project||
|Public Sector Management Loan Project||
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Santa Catarina Small Towns Improvement Project|MaryC. Sheehan|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|Northeast Basic Health Services Project(02)|Jean J. De St Antoine|
|ElectricityTransmission and Conservation Project|Andrew C. Waldrop|
|ElectricityTransmission and Conservation Project|Andrew C. Waldrop|
|ElectricityTransmission and Conservation Project|Andrew C. Waldrop|
|ElectricityTransmission and Conservation Project|Andrew C. Waldrop|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|Water Sector Project in the State of Sao Paulo|Franz Kriegler|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|State Highways Management Project - Sao Paulo|Gerard L. Liautaud|
|Sao Paulo Natural Gas Distribution Project|Luis M. Vaca-Soto|
|Sao Paulo Natural Gas Distribution Project|Luis M. Vaca-Soto|