rafmacalaba/datause-encoder-data huggingface dataset:
IMF Credit.<sup>29</sup> Direct investment contains both debt and equity flows and is split by debt and equity components in the BOP data. However, it is not disaggregated by sector in the BOP data. Nevertheless, debt flows between related enterprises are recorded as direct investment debt only when at least one counterparty is a non-financial firm. Direct investment debt flows between two financial firms (including banks) are instead classified as either portfolio investment debt or other investment debt (depending on the instrument type). If direct investment debt flows from non-financial firms to financial firms are negligible, then we can think all direct investment debt as flows either from financial firms to non-financial firms or flows from non-financial firms to non-financial firms. In either case, the borrowing sector is the non-financial sector and hence direct investment debt inflows can be assigned in full to the corporate sector. We include direct investment debt in total debt in our regression analysis of inflows. More details on the contribution of direct investment debt are given in Appendix C.1.

To complement our extensive dataset on capital inflows, we also construct a dataset of capital outflows. As with the inflows, we primarily use the BOP data. We combine the and central bank sectors into a sector. As in the case general government single (public) of inflows, we do an internal fill on missing sectors if the remaining two sectors and the total are non-missing.<sup>30</sup> The one external fill that we do is for the banking sector. We fill in portfolio debt asset flows and other investment debt asset flows using the BIS LBS by

> 29See the 6th Edition BOP Manual, paragraph A1.1.

> 30Note that combining government and central banks into a single sector makes the internal filling exercise more fruitful, as only bank and corporates needs to be non-missing in order to fill missing data for the public sector.

16
**Table 4. Estimated global demand and supply of health workers by WHO/World Bank regions and income group, 2030; disaggregated by countries with shortage and surplus (Difference: Supply – Demand).**

||**Sho**|**rtage countr**|<br>**ies**|||**Surp**|**lus countries**|||
|---|---|---|---|---|---|---|---|---|---|
||**Demand (D) Supply (S)**|**Need**|**Difference**<br>**(S‐D)**|**#**<br>**Countries**|**Demand (D)**|**Supply (S)**|**Need**|**Difference**<br>**(S‐D)**|**#**<br>**Countries**|
|WHO Region||||||||||
|Africa|643,548<br>453,757|2,423,284|‐189,791|13|1,761,259|2,612,909|6,487,188|851,649|30|
|Americas|5,294,324<br>2,177,226|2,270,537|‐3,117,097|17|9,994,286|10,565,630|3,975,926|571,344|11|
|Eastern Mediterran|5,735,711<br>3,976,303|4,248,811|‐1,759,408|12|465,804|635,105|806,814|169,301|3|
|Europe|8,813,848<br>6,485,872|2,740,579|‐2,327,976|32|9,344,924|10,317,392|3,045,689|972,468|18|
|South‐East Asia|11,420,891 9,039,083|12,091,674|‐2,381,808|3|785,895|1,129,508|2,621,313|343,613|5|
|Western Pacific|23,359,616 13,836,069|11,088,037|‐9,523,547|10|2,535,233|3,425,273|1,182,439|890,040|11|
|WB Region||||||||||
|East Asia and Pacific|23,359,616 13,836,069|11,088,037|‐9,523,547|10|3,186,411|4,414,633|3,646,462|1,228,222|13|
|Europe and Central Asia|8,499,926<br>6,323,225|2,676,878|‐2,176,701|30|9,344,924|10,317,392|3,045,689|972,468|18|
|Latin America|5,294,324<br>2,177,226|2,270,537|‐3,117,097|17|3,080,663|3,607,541|1,555,339|526,878|9|
|Middle East & North<br>Africa|4,574,811<br>3,342,787|2,615,168|‐1,232,024|13|338,608|504,161|417,742|165,553|2|
|North America|0<br>0|0|0|0|6,913,623|6,958,089|2,420,587|44,466|2|
|South Asia|13,325,263 10,153,540|14,091,101|‐3,171,723|5|134,717|140,148|157,290|5,431|3|
|Sub Saharan Africa|213,998<br>135,463|2,121,201|‐78,535|12|1,888,455|2,743,852|6,876,260|855,397|31|
|WB Income group||||||||||
|Low|1,142,167<br>787,953|4,149,362|‐354,214|14|257,907|596,623|2,899,686|338,716|15|
|Lower‐middle|19,143,815 14,797,698|16,063,131|‐4,346,117|23|2,538,766|3,161,245|5,877,124|622,479|21|
|Upper‐middle|28,403,598 15,305,990|12,465,315|‐13,097,608|23|4,888,132|6,056,042|3,469,462|1,167,910|23|
|High|6,578,358<br>5,076,669|2,185,113|‐1,501,689|27|17,202,596|18,871,907|5,873,098|1,669,311|19|
|**World**|**55,267,937 35,968,311 **|**34,862,922 **|**‐19,299,627**|**87**|**24,887,401**|**28,685,817**|**18,119,370**|**3,798,416**|**78**|

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# **Table 1**

**Explanatory power of donor and recipient dummies for SMPD indicators (2007)**

||**Donor dummies**<br>**only**|**Recipient dummies**<br>**only**|**Donor and**<br>**recipient**<br>**dummies**|
|---|---|---|---|
|**Alignment indicators**||||
|Use of PFM systems|.19|.29|.43|
|Use ofprocurement systems|.24|.22|.41|
|Predictability|.21|.14|.34|
|Coordination of tech. coop.|.15|.18|.32|
|PIUs*|.31|.09|.39|
|**Harmonization indicators**||||
|Program-based approaches|.14|.23|.36|
|Coordinated missions|.27|.14|.40|
|Coordinated countryreports|.21|.14|.34|

Note: *log of (number of PIUs +1) Source: Authors’ calculations, based on the OECD-DAC Survey on Monitoring the Paris Declaration (2008) 22
<!-- Start of picture text --> Share of Polluting Sectors:<br>Chinese Industy, 17l-1992<br>0.8 .<br>0.75 < .<br>0.65 \<\ > .<br>0.6 V .<br>0.55. _<br>r j g 8 e i z>88|c<br>IFigure_17__Trend_in_the_Output_Share_of_th Five<br>Figure 17: Trend in the Output Share of the Five<br><!-- End of picture text -->

analyzing recent trends for the five most . .heavily-polluting industry sectors:' Chemicals, Pulp and Paper, Non-Ferrous Metals, Ferrous Metals, and Non-Metallic Minerals (principally Cement). Using production and trade data for China and its trading partners, we have developed three indices of change in pollution intensity.

The first is the proportion of China's total industrial output produced by these five sectors during the era of reform. Figure 17 provides the evidence: From 1977 to 1992, the output share ofthe five 'dirty' sectors has trended downward. Within the period, rapid decline in the early 1980's was succeeded by an increase in the late 1980's and a leveling off in the early 1990s. There is no evidence of any 1long-run shift toward a pollution-intensive industry structure.

<!-- Start of picture text --> Net Imports of Polluting Products<br>25 ----- _- -------------------- ..<br>20 r '<br>.5 /<br>10 g<br>5 .<br>1.2<br>15987 Is" 199 1990 19,91 19,92 1993 1994<br><!-- End of picture text --> Figure 18: Trend in Net Imports from PollutionIntensive Industries Our second index is the trend in net imports (imports minus exports) of products from the five heavily-polluting sectors. If China's comparative advantage is really in pollution-intensive industries, then the era of trade <!-- Start of picture text --> ConsumptlonlProducUon of Pollutng Products<br>_.04. _<br>1987 198W 1989 1990 1991 1992<br><!-- End of picture text --> liberalization should be witnessing a shift toward net exports of their products. In fact, Figure 18 shows that the opposite has occurred: Real net imports of these products have trended strongly upward Figure 19: Consumption/Production for the Output since 1987. of Pollution-Intensive Industries Finally, we have used the available data to calculate China's consumption/production ratio for the output of the five dirty sectors. If China's comparative advantage lies in 'dirty' production, then two things should be true. First, this ratio should be less than one, since China should be a net exporter of pollutionintensive goods. Secondly, the ratio should fall over time because production for export should increase faster than production for domestic consumption. The evidence is Figure 19 is mixed, but is not generally consistent with the hypothesis that China's comparative advantage is in pollution-intensive production. The series has a modest downward trend 10 See Hettige, et. al. (1995) and Mani and Wheeler (1997) for detailed information on pollution intensities by sector.

14
# **III.2. What are current and prospective investment needs in the ECA region?**

_Infrastructure needs are sizable across the ECA region._ The additional investment needed to reach the investment levels of economies at similar stages of development has been estimated at 1.3 percent of GDP per year, on average (EBRD 2015a; Figure 11).<sup>6</sup> Investment priorities vary widely across the region (Figure 12).

- _Russia_ has implemented important upgrades in certain types of infrastructure, especially railways, mobile-cellular telephone networks, and airlines. However, the overall quality of infrastructure lags many EMDEs at similar levels of development. Roads, port and air transport infrastructure, and electricity supply all need considerable upgrading. The energy extraction sector requires an estimated $1.9-3.3 trillion in investment between 2014 and 2035, while the power generation sector requires $600 billion (International Energy Agency 2014; Russian Investment Agency 2015).

- Infrastructure in _Turkey_ exceeds average EMDE quality, but it has come under pressure as strife in neighboring countries has brought waves of immigrants: Turkey currently accommodates about 56 percent of all registered Syrian refugees. Annual energy investments of $12 billion are required to meet the country’s development goals, to diversify the sector, and to help narrow Turkey’s current account deficit by reducing energy imports (Winrow 2015; Republic of Turkey, Ministry of Energy and Natural Resources 2014). Turkey plans to increase renewable sources of energy, including nuclear, and improve energy efficiency (EBRD 2015b). From 2014 to 2018, total infrastructure investment needs are estimated at $350 billion (EBRD 2015b).

- For landlocked _Central Asia_ , developing and upgrading infrastructure are critical for connectivity and reducing dependence on extractive industries. Investment in the energy sector will help to improve electricity access, a major concern for business (ADB 2016). Waste water systems in rural areas are also underfunded.

- In _other countries_ in the ECA region, port, road, and railway infrastructure needs improvement, and logistics infrastructure needs to be upgraded to foster trade and investment (Bosnia and Herzegovina, Bulgaria). Border bottlenecks should be addressed and customs infrastructure improved. Upgrading water supply and irrigation systems will enhance productivity in agriculture and reduce environmental degradation (Azerbaijan, Bosnia and Herzegovina, Serbia, Uzbekistan).

> 6 In addition to 24 countries in ECA region, the estimate includes the Arab Republic of Egypt, Estonia, Jordan, Latvia, Lithuania, Mongolia, Morocco, the Slovak Republic, Slovenia, and Tunisia.

22
# **Table C.5. Balance of payments in 2017 and for simulations in 2030 (% of nominal GDP)**

|Item|2017|base|ginv‐<br>tdir|ginv‐<br>dbor|ginv‐<br>fbor|pwe|remit|
|---|---|---|---|---|---|---|---|
|Current account, inflows of foreign exchange||||||||
|Exports|19.8|19.8|19.9|19.7|18.8|20.7|18.6|
|Transfers to non‐government|2.8|2.8|2.7|2.9|2.7|2.5|4.6|
|Transfers to government|0.9|0.9|0.8|0.9|0.8|0.8|0.8|
|Factor income|0.3|0.3|0.3|0.3|0.2|0.2|0.2|
|Foreign savings|4.9|4.9|4.8|5.0|6.3|4.4|4.7|
|Total|28.5|28.5|28.5|28.7|28.9|28.7|29.0|
|Current account, outflows of foreign exchange||||||||
|Imports|25.3|25.4|25.4|25.4|25.8|25.8|25.9|
|Transfers from non‐government|0.5|0.5|0.5|0.5|0.5|0.5|0.5|
|Transfers from government|0.1|0.1|0.1|0.1|0.1|0.1|0.1|
|Factor income|2.6|2.6|2.6|2.7|2.5|2.4|2.5|
|Total|28.5|28.5|28.5|28.7|28.9|28.7|29.0|
|Capital account||||||||
|Net foreign financing to non‐government|0.4|0.4|0.4|0.4|0.3|0.3|0.4|
|Net foreign financing to government|1.5|1.5|1.5|1.6|3.1|1.4|1.5|
|Foreign direct investment|2.9|2.9|2.8|2.9|2.7|2.6|2.8|
|Change in foreign reserves|0.1|0.1|0.1|0.1|0.1|0.1|0.1|
|Total|4.9|4.9|4.8|5.0|6.3|4.4|4.7|

Source: Authors’ calculations based on simulation results.

‐64‐
appropriate to focus upon measures of morbidity. It is also timely to do so since a standardised methodology is now emerging which uses a 14 day recall period for general morbidity, allows for spontanecus and probed responses and links morbidity with illness sever .ty, functional disability, utilisation of services and attitudes towards services (WHO et al 1986 and Carlson 1985). A WHS core questionnaire might contain questions on general morbidity and associated use of services (including traditional healers and private practitioners) using a 14 day recall period. Optional modules could contain further questions on attitudes towards services, costs of services and the use of drugs etc. While household surveys in the 1970s and early 1980s produced levels of general morbidity which were not comparable (see reviews by Ross and Vaughan 1984, 1986 and Kroeser 1983, 1985) the standard methodology that is emerging enables us to make comparisons of general morbidity rates across countries. Many of the African countries participating in the UNHSCP are using aspects of the methodology described above in their national health surveys. It is important that any new international initiative in health surveys complements these existing surveys.

Although the use of general morbidity questions is becoming more common, the problems associated with them must not be forgotten. Extensive pre-testing is required to make questions culturallv appropriate; validation methods need to be developed; and, in order to present results in terms of standard epidemiological measures of frequencv, it is desirable to record and analyse the duration of completed and uncompleted episodes of illness separately. Ideally proxy-reporting should not be permitted, except for children under a defined age, when the mother should respond on their behalf. If any proxy-responding is allowed, results from proxy-interviews should be analysed separately from subject-interviews to allow comparisons to be made (Ross and Vaughan 1984).

Validation against facility-based or home-based health records is not very fruitful (Kroeger 1983, Ross and Vaughan 1986). A method of validation which has been little used but is probably the best of all, is the study of attenders at the various potential sources of health care. During the pre-testing stage a sample of attenders at a clinic, 27
lowest within the group. The total travel time (adjusted for number of visits to and from the application center) was almost six hours for a driver’s license. In terms of monetary costs, it equaled an individual’s day wage to procure a driving license. Estimating the cost of obtaining a driver’s license in terms of time (or the wage equivalent of that time), by contrast, is seldom done.

**Table 8: Percentage of Applications Outside of Government Office**

Percent of adults that “Needed and Applied” for a goods or service in the past five years

|Voter ID(%)|11|Public Utility (%)|10|
|---|---|---|---|
|Aadhaar Number(%)|20|Public School(%)|12|
|DrivingLicense(%)|12|Health care(%)|8|
|Register Land /Property (%)|7|||

# **_<u>5.5 Inter-State Variation in Quality</u>_**

Table 10 below constructs a relative ranking of states on their quality of service delivery. For each state it calculates the average time required to process an application (in months), the average number of documents needed to make an application, the average time spent travelling to and from the application center, the cost of filing an application, and the overall respondent rating about the ease of the application process. Processing Time is the average number of months needed to process an application. Cost of Application is coded as 0 if the respondent reported application cost as ‘Expensive’, 0.5 if the respondent reported application cost as ‘Reasonably Priced’, and 0 if the respondent reported application cost as ‘No Cost’. Total Travel Time is the time spent, in minutes, in travelling to and from the government office to submit an application. Ease of application (discussed in Section 5.5) is from Column 5 of Table 9.

38
quality.

However, PPP programs can be structured such that they lose the market-based accountability mechanisms at play in the private-school market. Schools under PPP programs may be less effective than nonprogram private schools in such cases. For example, some PPP programs require that schools offer free education, as is the case with the PPRS program. This condition cancels out the role that prices play in informing market participants of the relative efficiencies of producers and the preferences of consumers (Hayek, 1945).<sup>10</sup> The effect of PPP programs on learning then is theoretically ambiguous. Notwithstanding, the available evidence from developing countries, including from Pakistan, generally indicates that PPP programs have positive effects on school enrollment and test scores (Kim, Alderman, and Orazem, 1999; Alderman, Orazem, and Paterno, 2001; Alderman, Kim, and Orazem, 2003; Barrera-Osorio and Raju, 2015; Barrera-Osorio et al., 2016).

# **4 Data**

SEF administered a vetting survey to determine whether proposed villages qualified for the program. This survey, which we refer to as the baseline survey, was conducted in February 2009. Following the baseline survey, 263 villages that qualified for the program were randomly assigned to the two subsidy treatments, or to the control group. However, after random assignment, SEF scaled down the original evaluation sample of 263 villages to 199 villages to correct for errors in determining whether a village qualified for the program. The decisions taken by SEF were orthogonal to the assigned program status of the village. The effective evaluation sample consisted of 82 villages under the gender-uniform subsidy treatment, 79 under the genderdifferentiated one, and 38 as controls.

Schools were established in summer 2009. Because the new school year normally commences in the spring, program-school students had an abbreviated first school year. A follow-up survey was conducted in April/May 2011, after the conclusion of the second school year under the program.

The baseline survey consisted of a village survey answered by village leaders, a school survey of all schools in the general vicinity of the village, and a household survey of 12 households randomly selected from the list submitted by the entrepreneur of 75 households that had agreed to send their children to the proposed program school. The household survey collected information on the household, the household head, and on each child aged 5–9. In each village, the baseline survey also consisted of a survey of the entrepreneur and proposed teachers, as well as physical checks by the survey interviewers of the proposed school site

> 10Prices may, however, obscure broader problems of limited information in the education market (MacLeod and Urquiola, 2012).

8
continued to fight and therefore experienced post-settlement armed conflict already the first time of observation, that is, the year following the agreement in 1991.

In both datasets, the post-settlement armed conflict is measured as of the year following the peace agreement to ascertain that the violence has taken place _after_ the settlement was signed. While it would have been preferable to measure the dependent variable directly after the signing of a settlement, the fact that it is the yearly accumulation of at least 25 battle-related deaths which is studied makes such an approach unfeasible.<sup>14</sup>

# **_Independent Variables_**

A key aspect is the rebel groups’ inclusion or exclusion in relation to a particular peace agreement. The first variable _Exclusive-Multiple,_ which is used at the settlement level, is coded 1 if one or more rebel groups are standing on the outside of the deal, and is coded 0 otherwise. In order to account for the fact that two-party conflicts are all-inclusive by definition, this variable will be used together with the variable _Two-Party Conflict,_ coded 1 if the conflict involves only two parties, and is coded 0 if the conflict involves three or more actors. By using these variables in the same model it becomes possible to assess the effect of partial agreements versus allinclusive agreements in a multiparty context.

In the analysis at the dyadic level, the variable _Excluded_ refers to a particular rebel group’s inclusion or exclusion in relation to a settlement. The variable is coded 1 if the rebel group, in a given year, had not signed a peace agreement, and is coded 0 if the rebel group had signed a peace agreement, or previously signed a peace agreement and then refrained from using violence.<sup>15</sup> In a sub-sample of the dyadic dataset covering all the signatories two other variables concerning exclusion are used. The first variable _Exclusive-Peace Agreement_ is coded 1 if the dyad was a signatory to a deal that excluded some actor, and is otherwise coded 0. The second variable

> 14 The lowest peace duration is therefore one year, although a dyad may engage in conflict immediately after a settlement.

> 15 This means that a group that has signed an agreement and then resorted to violence is considered to be a potential signatory to any subsequent peace agreement signed in the incompatibility. Thus, if another peace agreement is signed in the incompatibility and they remain on the outside of the deal, such a group is seen as excluded. Conversely, if a rebel group has signed an agreement and then refrained from using violence during the observation period, it is coded as included in any subsequent agreement.

12
# 5.4.2 RDD Results

In this section, we examine the RDD estimates of the impact of the INDH on economic outcomes, allowing for a more flexible relationship than in the regressions above. Estimates are presented for a number of different bandwidths in Table 5. Figure 4 and Figure 5 show the RDD graphs for the 50 percent bandwidth (those for larger bandwidths are essentially more smoothed versions of the 50 percent estimates). Impact estimates are generally positive, but they are not consistently statistically significant and are very sensitive to the bandwidth used. Higher estimated impacts typically occur for smaller bandwidths. Although it is plausible that the relationship between the poverty rate (the forcing variable) is nonlinear, in a range of 27–32 percent, it seems unlikely that there is a radically different relationship right at the 30 percent mark. For this reason, estimates should be interpreted with caution, particularly for smaller bandwidths. As Figure 4 and Figure 5 show, substantial fluctuations by poverty rate are estimated at the 50 percent bandwidth that do not have any apparent explanation. Those fluctuations at the discontinuity do not appear to be substantially different from those fluctuations elsewhere.

In 2008, for the 50 percent and 100 percent bandwidths, there are significant differences in income, log income, and log consumption. Consumption itself is also significantly higher in 2008 with the 50 percent bandwidth. However, only the log income result persists at the 200 percent bandwidth, where log incomes in treatment areas are predicted to be 22.9 percent higher. Significant differences in log income do not persist in 2013; by 2013, values for the difference in levels have also dropped substantially. The differences in log income fall in 2011 and rise in 2013 but are only statistically significant at the 50 percent bandwidth, where they are a rather improbable 56.1 percent higher. Differences in wealth and log wealth remain insignificant. Although there are significant consumption and log-consumption differences in 2011, these fall with increasing bandwidth to a 23.9 percent higher consumption rate, significant only at the 10 percent level for the 200 percent bandwidth in 2011. In 2013, impacts of Phase I have dissipated to near zero differences in consumption and log consumption for the 100 percent and 200 percent bandwidths, although there are significant differences in consumption at the 50 percent bandwidth. Overall, although the RDD results suggest that there may have been positive effects, the results are not consistently statistically significant. In addition, differences have largely faded by 2013, although this may be in part due to the rollout of Phase II. The results are also very sensitive to the bandwidth used.

19