rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 8:** (Left panel) Relationship between HCI and private health expenditure per capita (USD); (Right panel) Relationship between HCI and government health expenditure per capita (USD)

<!-- Start of picture text --> Private Public<br>SGP SGP<br>JPNKOR KOR JPN<br>SVN SWE SVN SWE<br>USA CHE CHE USA<br>KAZ KAZ<br>RUS RUS<br>SYCVNMCHN VNMCHNSYC<br>GEO GEO<br>ARM ARM<br>TUV TUV<br>IDN IDN<br>KIR KIR<br>TLSZWENAM ZWETLSNAM<br>SWZZAF SWZZAF<br>MOZETHLSOAFGYEM AFGETHYEMMOZLSO<br>NGA NGA<br>LBR LBR<br>TCD TCD<br>0 2000 4000 6000 8000 0 2000 4000 6000 8000<br>Health expenditure per capita (USD)<br>High income Upper middle income Lower middle income Low income<br>1<br>.8<br>.6<br>HCI<br>.4<br>.2<br><!-- End of picture text --> The relationship between HCI and domestic government expenditures on **education** is possibly weaker than the one observed for health (Figure 9). The results may be driven by a few countries that spend a large percent of GDP on education (>9%) but have very low HCI. Similar to what we had observed for health expenditure, Kiribati (KIR) and Lesotho (LSO) stand out as countries with high levels of spending and low human capital outcomes. Even less informative is the analysis of the relationship between the HCI and education spending by different income levels. While there is a clearly positive slope for HICs and LICs, the relationship is negative for upper middle-income countries and almost flat for LMICs (Figure 10). The WDR 2018 discusses five possible reasons for why spending does not always translate into better learning (including that spending may not be used equitably, efficiently, or with learning outcomes in mind; resources may not always reach schools themselves, and public spending may crowd out private spending). The same explanations can potentially contribute to make sense of the weak relationship between spending and HCI observed here.

18
<!-- Start of picture text --> Non Liberalized Sectors Realized FDI Growth<br>=<br>oO<br>aD<br>ae a RT<br>nonS)<br>=<br>o'<br>:<br>2020q2 202093 202094 2021q1 ai keuarter 202193 202194 2022q1 2022q2 202293<br><!-- End of picture text --> <!-- Start of picture text --> 100% Liberalized Sectors Realized FD! Growth<br>©<br>x<br>oO<br>oD<br>Fa<br>Onx<br><<br>o<br>:<br>202092 202093 202094 2021q1 202192uarter202193 202194 2022q1 2022q2 202293<br><!-- End of picture text -->
It is important to note that the PD survey does not cover all countries in any region. Therefore, regional performance as overviewed in section four is simply based on countries where data is available.

# **(2) Methodology**

The results of the PD survey cannot be directly used to compare across indicators. The indicators are measured using different methodology and their targets are arbitrarily decided. For example, indicator 5a on ̳use of country public financial management (PFM) systems is measured as the share of assistance using country PFM systems out of total assistance (%), while indicator 6 on ̳strengthen capacity by avoiding parallel project implementation unit (PIUs)‘ is measured by the number of parallel PIUs. Indicator targets are set up in different ways without clear rationales. In other words, measuring performance against targets is not a sensible way to conduct comparison across indicators.

Therefore, in order to enable us to conduct such a comparison across indicators, standardized scores of the PD survey results are calculated using the following formula.

Standardized scores follow a normal distribution with a mean of _zero_ and a standard deviation of _one_ . Standardized scores show how many standard deviations the value is away from the mean. For example, when a standardized score of an indicator is 1.0, this means that the indicator is away from the mean by 1.0 standard deviation and the probability of exceeding 1.0 is 15.9 percent.

The analysis using the standard deviation has two advantages. One is that it enables us to calculate a composite global aid effectiveness score for both partner countries and development partners. The other is that the change between 2005 and 2010 can be also standardized to compare the improvement in performance between these periods.

While the standardization of partner country data is simple as data is available for all indicators in all partner countries, that of development partners needs some adjustments as not all development partners are active in all partner countries. For example, the Asian Development Bank is not operating in Africa and the African Development Bank is not operating in Asia. Assuming that development partner performance is influenced by partner country performance, the different partner country coverage in operation cannot be ignored and some adjustments are required. Therefore, standardized scores of development partners are calculated separately in all partner countries by indicators.

In this paper, the calculation of the standardized scores is unweighted. In other words, all partner countries and development partners are equally treated regardless of their size and other characteristics. This is consistent with the main purposes of this paper – which identifies key aid effectiveness constraints of FCS in comparison with non-FCS.

7
seven week window.

Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level, and *** at 1 percent level.

|**Table S1.2b Effect of Delhi Metro on Air Qu**<br>**Polynomial**<br>|**ality: Seventh**<br>|**Order Tim**<br>|**e**<br><sup>**1**</sup>|
|---|---|---|---|
|**Metro Extension**|**NO2**|**CO**|**PM2.5**|
||(Percentage<br>level of poll|change in<br>utant)||
|**ITO**||||
|**Yellow Line First Extension (Jul 03, 2005)**|-7.2|-67.6***||
|**Std. Error**|(15.7)|(11.9)||
|**Observations**|1457|1497||
|**Blue Line Introduction (Dec 31, 2005)**|-28.8**|||
|**Std. Error**|(10.5)|||
|**Observations**|1639|||
|**Blue Line Second Extension (Nov 11, 2006)**|-10.8|-12.9|-21.7|
|**Std. Error**|(10.0)|(8.3)|(19.8)|
|**Observations**|1605|1605|1268|
|**Siri Fort**||||
|**Blue Line Second Extension (Nov 11, 2006)**|-27.7|-2.5||
|**Std. Error**|(23.6)|(10.1)||
|**Observations**|1601|1532||
|Source: Authors' analysis based on data describ|ed in the text.|||

59
estimators. However, as in the OLS estimation, we cannot reject the hypothesis that the impact of China in LAC manufacturing FCS is zero. The impact of India continues to be non significant.

As a robustness check, we repeated the sector analysis by using the sum of China’s (mainland) and Hong Kong’s FCS instead of China alone as the proxy for the effects from China. The results remain virtually unchanged. We continue to find that there is no substitution effect between FCS in China and India and those in LAC in the manufacturing sector.<sup>15</sup> In sum, using U.S. data we continue to find that the impact of China has been positive for LAC FCS but not necessarily for other economies. This was not the case in the manufacturing sector, where we found that China had no impact at all. In contrast, we do not find an overall impact from India with these data. The evidence in the manufacturing sector is less clear, although our preferred Negative Binomial estimator yielded a positive effect of India on LAC FCS in manufacturing activities.

# _5.2. Accounting for heterogeneity within LAC_

It is possible that China and India have had different effects across LAC countries. For example, China may be having a negative impact in those countries where assembly operations (the so-called _maquilas_ ) are important, but not in other countries. We therefore divide LAC countries into three sub regions roughly according to their production structure. These sub regions are Central America and Mexico where _maquilas_ are commonplace, Andean countries that tend to export a combination of agricultural and

> 15 These results are available from the authors upon request.

17
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Table 3: Strategies and outcomes for the 5 farms during the RPG session: initial and final values

||**Initial**|**Final**|**Difference (%)**|
|---|---|---|---|
||Irrigation Sche|me 1 (U)||
|Ha citrus old technology|<br>0|<br>0|0.0|
|<br>Ha citrus new technology|0|0|0.0|
|<br>Ha cabbage|20|20|0.0|
|Cycles cabbage|2|3|50.0|
|<br>Total Ha|20|20|0.0|
|Employment (n)|51|76.6|50.1|
|<br>Profit (R)|64,208|250,000|289.4|
||Irrigation Sche|me 2 (U)||
|Ha citrus old technology|0|0|0.0|
|<br>Ha citrus new technology|0|0|0.0|
|<br>Ha cabbage|20|10|-50.0|
|<br>Cycles cabbage|2|2|0.0|
|Total Ha|20|10|-50.0|
|Employment (n)|51|25|-51.0|
|<br>Profit (R)|64,208|250,000|289.4|
||Citrus Farm|1 (M)||
|Ha citrus old technology|30|0|-100.0|
|<br>Ha citrus new technology|0|5||
|Ha cabbage|0|30||
|<br>Cycles cabbage|0|1||
|Total Ha|30|35|16.7|
|Employment (n)|46|46|0.0|
|<br>Profit (R)|829,300|3,290,00|296.7|
||Citrus Farm|2 (M)||
|Ha citrus old technology|30|0|-100.0|
|Ha citrus new technology|0|30||
|<br>Ha cabbage|0|30||
|Cycles cabbage|0|1||
|<br>Total Ha|30|60|100.0|
|Employment (n)|46|84|82.6|
|<br>Profit (R)|829,300|740,000|-10.8|
||Citrus Farm|3 (L)||
|Ha citrus old technology|40|0|-100.0|
|<br>Ha citrus new technology|0|30||
|<br>Ha cabbage|0|0|0.0|
|<br>Cycles cabbage|0|0|0.0|
|<br>Total Ha|40|30|-25.0|
|Employment (n)|62|44|-29.0|
|<br>Profit (R)|1,105,700|2,710,00|145.1|

Table 4 shows the dynamics in the village managers’ decisions regarding water services and tariffs for their households. As a general trend, better water provision was introduced in all 10
**Table 11: Public input constraints, firm growth and firm characteristics**

|Country|Indicator|Size (log L)|New firm|Expanding L|Services|Foreign|Exporter|Importer|Missing importer|Small city|Obs|
|---|---|---|---|---|---|---|---|---|---|---|---|
|All|Electricity|-0.053**|0.004|0.081**|-0.164**|-0.089|0.079*|0.023|0.261**|0.043|10,913|
|All|Telecoms|0.041**|0.092**|0.060**|0.142**|-0.094|-0.055|0.146**|0.002|-0.014|9,192|
|All|Transport|0.018|0.070*|0.140**|-0.057|0.052|-0.030|0.270**|-0.072|-0.101**|10,809|
|All|Customs|0.101**|0.077*|0.112**|-0.016|0.028|0.025|0.294**|-0.265**|-0.217**|9,916|
|All|Competit|0.017|0.063|0.040|-0.111*|0.047|-0.172**|0.126**|-0.013|-0.115**|10,398|
|All|AccessLand|-0.045**|0.080*|0.049*|0.062|0.000|0.072*|0.041|0.186**|-0.048|10,590|
|All|TaxRates|0.058**|-0.094**|0.114**|-0.061|-0.184*|-0.079*|0.066|-0.079|-0.190**|10,556|
|All|TaxAdministration|0.082**|-0.059|0.170**|0.003|-0.143|-0.051|0.058|-0.322**|-0.178**|10,530|
|All|BusLicensing|0.024*|-0.031|0.078**|0.052|0.023|0.095**|0.099**|0.110*|-0.104**|10,437|
|All|PoliticalInstability|0.011|-0.008|0.040|0.120*|0.221*|0.323**|0.067|0.028|-0.226**|3,607|
|All|Corruption|0.038**|-0.088*|0.216**|-0.032|-0.050|0.038|0.163**|0.202**|-0.154**|10,588|
|All|Courts|0.049**|-0.032|0.098**|0.129*|0.116|0.174**|0.031|-0.061|-0.121**|5,747|
|All|AccessFinancing|0.000|0.081*|0.012|-0.317**|-0.308**|-0.111**|0.016|-0.257**|-0.145**|10,783|
|All|LaborReg|0.084**|-0.032|0.054*|-0.094*|-0.123|0.083**|0.023|-0.263**|-0.130**|10,321|
|All|InadEducLabor|0.045**|0.016|0.131**|0.085|-0.107|-0.010|0.029|-0.153**|-0.062*|10,815|
|All|GovPolicyUnc|0.051**|-0.079|0.114**|-0.020|0.181|-0.047|0.145**|0.272**|-0.189**|8,149|
|All|MacroInstability|0.050**|-0.046|0.109**|-0.013|0.083|0.086*|0.204**|0.405**|-0.110**|8,847|
|All|bribe|0.010*|-0.001|0.035**|-0.018|-0.056*|0.029*|0.040**|-0.157**|-0.008|9,711|
|All|mng_time|0.327**|0.446|-0.252|0.518|-0.176|1.221**|0.070|-1.034**|-0.248|11,534|
|All|generator|0.131**|-0.012|0.020*|0.114**|-0.039|0.111**|0.071**|0.010|0.093**|10,779|
|All|insufficient_water|0.011**|0.015|0.035**|0.085**|0.002|-0.008|0.038**|-0.180**|-0.036**|9,518|
|All|num_insp|2.568**|0.099|2.078**|2.322|1.496|1.977|2.848**|2.193|1.438|7,995|
|All|days_customs|0.593**|-0.492|-0.495|0.406|-2.921**|2.261**|-1.465*|-1.614|1.416*|2,926|
|All|m_power_out|-0.031**|0.000|0.052**|-0.045**|-0.045|0.015|0.027*|0.282**|0.076**|9,797|

Notes: “Pos” and green indicates a positive coefficient; “Neg” and red indicates a negative coefficient.

*=significant at the 5% level; **=significant at the 1% level.  Standard errors are robust to heteroskedasticity.

47
obtain answers from, while countries in, for example, Sub-Saharan Africa were often extremely helpful in providing as extensive and precise information as possible. One explanation could be the opportunity cost; officers of IPAs in developed countries often appeared to be more busy and harder to contact. Therefore, it is not clear which way a potential sample bias would work. If anything, it could make investment promotion appear less efficient than it actually is.

In the design of the survey, special attention was given to collecting time-varying information. While this increased the effort needed to collect the data, it also allowed for the use of panel estimation techniques and made it possible to control for time-invariant country-specific unobservable factors. Attention was also given to sector-specific time-varying measures of investment promotion. The agencies were asked if they targeted specific sectors and when targeting started and ended.

The Census also included questions on more subtle characteristics of the agencies. For instance, IPAs were asked about their legal status (sub-unit of ministry, autonomous public body, semiautonomous agency reporting to a ministry, joint public-private entity, private entity), and if the status had changed, when the change happened and what the status was before the change. Additionally the Census included a question asking to whom the agency was accountable and how long they had been reporting to the overseeing entity.

Investment incentives were another aspect of investment promotion covered in the Census. Despite the attention received by investment incentives, to the best of our knowledge, a database with broad cross-country and cross-time coverage of investment incentives offered does not exist. Collection of such data in itself represents a contribution to the FDI literature.

Parallel to the survey on investment promotion, Lederman, Olarreaga and Payton (2006) collected information on export promotion agencies. Data on the date of establishment of an export promotion agency are used in our study as an instrument for the existence of an investment promotion agency in a country, since establishment of these two types of agencies have often been a part of a more active internationalization strategy of governments. At the same time, the exclusion restriction, that the existence of an export promotion agency should not be included directly in the equation explaining FDI inflows, should be fulfilled, since export promotion agencies primarily help domestic firms.

15
39

-

|Table 11: Housing Programmes by Firm Size<br>---------FLrm Size Class ------|-- .- ._-.|-_-_|
|---|---|---|
|1<br>2<br>3|4|5|
|Type of Programme:|||
|Permanent<br>2<br>5<br>8|5|1|
|Temporary<br>1<br>3<br>4|3|0|
|Housing Loans<br>0<br>6<br>4|4|0|
|No Programme<br>7<br>3<br>1|1|2|
|% of workforce|||
|;housed<br>0<br>13.3<br>26.7|75.4|2.0|
|% of non-firm|||
|tenants<br>0<br>37.7<br>36.9|39.4|45.4|
|% of tenants that|||
|have privatised<br>0<br>3.3<br>1.3|1.1|6.1|

Source: World Bank Survey
the ALSM from HDB in December 1997, after an attempted period of accommodation with the new director (Interviews with ALSM members).

# **5.1 Decision rights over finance**

Once the ALSM was formed, the manager and the ALSM members rapidly created and adopted a set of decision rights that were crucial in allowing the hospital to supplement the revenue coming from the MOH. These rights, created in the area of finance, gave HDB the option of recovering costs from its patients and the flexibility in allocating these funds toward capital and operating expenditures in rapid and flexible response to demand on the hospital. The impact on HDB's admissions rates was immediate. The average number of admissions per month jumped from 55 to 259 between 1988 and 1991. Part of this increase was due to the escalation of hostilities during the last year of the war (1990). However, the secular increase in hospital admissions after the end of the war in October 1990 was evidence of an increase in demand due to quality improvements and to the increase in HDB's (staff and capital) capacity to receive patients. The increase in revenue also allowed an expansion of HDB's service mix (and hence admissions rates) as will be shown in the section on service delivery (5.4).

Table 5.1 summarizes HDB's decision rights allocation in the area of finance. "Rights created" are ones that neither HDB, nor the central administration possessed before the HDB experiment was launched. In all four areas of hospital management and finance I examined, rights that were "created" were exercised alongside existent MOH rights, i.e., they supplemented them. None of the newly created rights were meant to overrule old rights - one of the secrets to the ALSM's success. "Rights appropriated" are ones that HDB _defacto_ transferred down to its own level, despite their being _de jure_ held by central administrations of the public sector, such as the MOH, the MOF and the Civil Service Board, as discussed in Section 4.

**Table 5.1. HDB's Decision Rights Equilibrium in Finance**

|Rights Created|Solicitation of outside funds<br>Fee setting for services<br>Exemption policy<br>Fee collection<br>Allocation of fee revenue|
|---|---|
|Rights Appropriated|None|

Source: Author's construction based on results from the Decision Rights Analysis Framework (Appendix A).

The pillar of HDB's increased expenditure capacity was the creation of the cost recovery decision right. The idea was to keep HDB rates at around 1/3 of private sector rates. Fees were set in 1990, and adjusted periodically, depending on inflation and on the increase in HDB's expenditure requirements. Among the interesting comparisons Table 5.1 reveals are costs of inpatient care at HDB, when compared with private sector hospitals of the same quality range. For example, while HDB charged USD22.96 per day in the surgery ward (for the first five days) and the private sector charged USD12.50 per day, the latter figure only 19
**<u>Table 3: Desire to go back (Y/N) - Only refugees and IDPs - Probit Baseline</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|All<br>_...with refugee_<br>_dummy_|Head&Spouse|Head&Spouse<br>_...with refugee_<br>_dummy_|
|Female (d)|0.050|0.058|-0.008|-0.002|
||(0.039)|(0.039)|(0.045)|(0.044)|
|Age|0.001|0.001|0.000|-0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Higher Education (d)|-0.080|-0.060|-0.090|-0.064|
||(0.063)|(0.062)|(0.082)|(0.078)|
|Married (d)|0.048|0.051|-0.014|-0.012|
||(0.049)|(0.049)|(0.052)|(0.050)|
|Songhai ethnicity (d)|0.039|0.091<sup>**</sup>|-0.008|0.074|
||(0.040)|(0.039)|(0.053)|(0.045)|
|HH size (August 2014)|-0.008<sup>*</sup>|-0.004|-0.004|0.001|
||(0.004)|(0.005)|(0.006)|(0.005)|
|Member tribe dead in crisis (d)|0.060|0.051|0.043|0.029|
||(0.043)|(0.041)|(0.045)|(0.044)|
|HH members dead in crisis (d)|-0.057|-0.002|-0.084|-0.008|
||(0.096)|(0.070)|(0.127)|(0.082)|
|HH members behind (d)|-0.012|0.012|-0.019|0.003|
||(0.049)|(0.043)|(0.060)|(0.052)|
|HH head left behind (d)|0.043|0.035|-0.088|-0.125|
||(0.062)|(0.067)|(0.137)|(0.158)|
|Northern Mali safe (d)|-0.145<sup>*</sup>|-0.146<sup>*</sup>|-0.153|-0.163<sup>*</sup>|
||(0.085)|(0.084)|(0.097)|(0.099)|
|Safe at home (d)|0.015|0.005|-0.001|-0.008|
||(0.046)|(0.044)|(0.051)|(0.047)|
|Police issues (d)|-0.030|-0.013|-0.068|-0.048|
||(0.053)|(0.049)|(0.070)|(0.065)|
|Asset index above median (d)|-0.013|0.018|0.021|0.041|
||(0.045)|(0.042)|(0.046)|(0.039)|
|Have received assistance (d)|-0.019|-0.058|-0.041|-0.079<sup>**</sup>|
||(0.048)|(0.039)|(0.047)|(0.034)|
|Work during displacement (d)|<br>-0.097<sup>**</sup>|<br>-0.063|<br>-0.092|<br>-0.058|
||(0.049)|(0.047)|(0.060)|(0.056)|
|Refugee (d)||<br>0.186<sup>**</sup>||<br>0.259<sup>*</sup>|
|||(0.090)||(0.136)|
|Observations|243|243|180|180|
|Pseudo R^2|0.11899|0.14438|0.09456|0.13529|

Marginal effects; Standard errors in parentheses. Robust SE.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 “Asset index above median” is computed using information about asset ownership in August 2014. Source: LDPS 2014-15 (d) for discrete change of dummy variable from 0 to 1 17