rafmacalaba/datause-encoder-data huggingface dataset:
Our next step involves devising a robust model for interpreting and evaluating the data on satisfaction. Most analyses of satisfaction data begin by comparing objective indicators of quality of service delivery with satisfaction levels. Recognizing that objective quality is rarely a sufficient explanation of satisfaction (Stipak 1979), many of these models incorporate the role of expectations, pre-dispositions and perceived quality in determining satisfaction (Serra 1995; DeHoog et al 1990; Van Ryzin 2004).

A commonly-used model is the expectancy disconfirmation theory (Cardoso 1965; Deichmann and Lall 2003; Oliver 1980, 1997), where satisfaction is determined by the degree to which objective performance of service providers meets the expectations of consumers. Where positive disconfirmation occurs (performance surpassing expectations) households are satisfied; conversely, negative disconfirmation leads to dissatisfaction.

The performance model is a more dynamic variant of the disconfirmation model, where consumer expectations are constantly recalibrated based on recent experiences of service use (Boulding et al 1993; Johnson et al 1995). Satisfaction is therefore a result of (i) actual service quality and (ii) expectations, where the latter is determined by a range of factors, namely a household’s characteristics, information available to the household and its experiences with past consumption of services. Given this broad base of household specific determinants of 10
55

|**Country**|**Types of fuel andprices**|**Source**|
|---|---|---|
|Malawi|Maximum prices set by government.|meramalawi.mw|
|Malaysia|95 RON gasoline in Jan 2012 (introduced in May 2009),<br>92 RON gasoline in Jan 2009 (discontinued in May<br>2009), diesel sulfur limited to 0.3 percent in Jan 2009<br>and to 0.05 percent in Jan 2012. LPG sold in 10-, 12-,<br>and 14-kg cylinders has the same unit price.|www.caltex.com/my/|
|Mexico|87 octane index gasoline. LPG prices in Districto<br>Federal.|sie.energia.gob.mx.|
|Mongolia|92 RON gasoline.|News reports|
|Morocco|95 RON gasoline, diesel with 0.005 percent sulfur. LPG<br>sold in 3-, 6-, and 12-kg cylinders has the same unit<br>price.|News reports of government price<br>announcements.|
|Mozambique<br>Namibia|Maximum prices set by government.<br>93 RON gasoline. Prices in Walvis Bay averaged for the<br>month of Jan.|www.portaldogoverno.gov.mz.<br>www.mme.gov.na|
|Nepal|88 RON gasoline and LPG in 14.2-kg cylinders. Prices<br>in Kathmandu.|www.nepaloil.com.np|
|Nicaragua|87/88 RON gasoline, LPG in 25-pound (11.4-kg)<br>cylinders. Monthly averages of weekly price surveys<br>conducted at 74 service stations in Managua for<br>gasoline, diesel, and kerosene, and monthly averages of<br>weekly official price ceilings in Managua for LPG.|www.ine.gob.ni|
|Niger|91 RON gasoline|www.sonidep.net|
|Nigeria|90 RON gasoline. Price of subsidized kerosene as<br>supplied by the Nigerian National Petroleum<br>Corporation.|www.pppra-nigeria.org|
|Pakistan|87 RON gasoline. For figures 9, 10, and 13, Islamabad-<br>wide average retail prices notified by oil companies. For<br>pass-through calculations and figure 11, gasoline, diesel<br>and kerosene retail sale prices posted on the web site of<br>Pakistan State Oil and LPG prices in Karachi.|www.ogra.org.pk, www.psopk.com|
|Panama|91 RON gasoline, LPG in 25-pound (11.4-kg) cylinders.<br>Maximum retail price of LPG in Panama City.|www.autoridaddelconsumidor.gob.<br>pa.|
|Peru|90 RON gasoline containing ethanol, LPG in 10-kg<br>cylinders. Lima-wide, monthly average prices.|www.osinerg.gob.pe.|
|Philippines|93 RON gasoline, LPG in 11-kg cylinders. Retail prices<br>in Manila averaged over Jan and over all companies.|www.doe.gov.ph.|
|Russian<br>Federation|92 RON gasoline. Moscow prices.|www.mfa.ru.|
|Rwanda|95 RON gasoline. Monthly average prices.|www.minicom.gov.rw|
|Senegal|87 RON regular gasoline, LPG in 6-kg cylinders. Retail<br>prices averaged over the month of Jan.|www.eltonoil.com for gasoline,<br>diesel, and kerosene<br>www.total-senegal.com for LPG.|
|South Africa|93 RON gasoline, diesel with 0.05 percent sulfur. Inland<br>maximum retail prices for gasoline, diesel, and<br>kerosene, and maximum retail LPG prices in zone 9C,<br>which includes Gauteng (which is inland and includes<br>Johannesburg and Pretoria). For pass-through<br>coefficients, LPG prices are maximum refinery gate<br>prices because maximum retail prices for Jan 2009 are<br>not available.|www.energy.gov.za.|
This model is estimated by using different econometric methods to test the validity of the results obtained by Islam (1995) and Caselli _et al._ (1996), who observe that human capital accumulation contributes negatively to growth. In conformity with these studies, data are taken from Barro and Lee (1994). The sample is balanced, with six five-year periods from 1960-65 to 1985-90 and 83 countries. Per capita incomes are expressed at purchasing power parities, in intemational dollars for the year 1985. The investment rate is coherent with the measure of income, and the human capital stock represents the average schooling years in the total population over age 25 (see Appendix).

Table I reports the econometric estimation of Equation (1) using four different econometric methods to address the problem of inconsistency in dynamic equations with individual effects first publicized by Nickell (1981). Nickell shows that estimating dynamic equations with individual effects (random or deterministic) using ordinary least squares produces asymptotically biased estimators, as long as the number of periods is finite. This bias originates in the asymptotic correlation between residuals and the lagged endogenous variable. The Hausman test of specification<sup>4</sup> , and the exhaustive nature of the sample justify the deterministic (rather than stochastic) character of the individual effects. We also verify the exogenous nature of the human capital stock.' The first column of Table 1 presents the estimation performed without correction of the bias mentioned above. Three methods are then altematively employed to correct the bias: the method of Chamberlain (1984), used by Islam (1995); the generalized method of moments (GMM) suggested by Arellano and Bond (1991) and used by Caselli _et al._ (1996); and the method of Balestra-Nerlove suggested by Sevestre and Trognon (1996).

Advantages and disadvantages of the first two methods have been largely discussed in the convergence literature. Chamberlain's method does not require the imposition of restrictive assumptions on the residuals' distribution, the initial income or the fixed effects. It bears, on the other hand, a very high cost in terms of estimation, since one needs to estimate a very large number of parameters and to resolve numerically equations of high degree, which in turn severely reduces the coefficient of human capital must also be divided by (fi + _a)_ to obtain the elasticity of GDP with respect to human capital.

> 4 This test rejects at the one percent level the null hypothesis of random individual effects.

> 5 The Hausman of specification test cannot reject the null hypothesis of exogeneity, for which the risk to be

4
|vif|129.8|129.9|129.8|130.4|130.3|130.1|130.9|130.4|130.4|130.4|130.3|130.5|130.9|130.9|130.6|131.0<br>ise. All<br>. *|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|z-stat<br>Gini WIDER|(3.57)**|(3.41)**|(3.73)**|(3.39)**|(3.48)**|(3.93)**|(3.19)**|(3.50)**|(3.36)**|(3.36)**|(3.57)**|(3.56)**|(3.18)**|(3.13)**|(3.76)**|(3.23)**<br>th `0' otherw<br>e satisfaction<br>ar points.|
|coeff.<br>ple 2**|-0.049|-0.052|-0.05|-0.057|-0.046|-0.048|-0.052|-0.058|-0.048|-0.064|-0.047|-0.054|-0.052|-0.057|-0.045|-0.047<br>ded and wi<br>ep. Var.=Lif<br>country/ye|
|vif<br>Sam|11.5|11.7|11.5|12.2|11.6|12.3|12.2|12.2|11.8|12.3|11.6|12.9|12.2|12.4|12.4|12.9<br>`1' if inclu<br>viduals. D<br>ations, 56|
|z-stat<br>Gini WVS|(7.65)**|(8.42)**|(7.51)**|(7.81)**|(6.79)**|(6.97)**|(7.05)**|(7.60)**|(7.49)**|(8.69)**|(6.77)**|(6.88)**|(6.94)**|(7.70)**|(5.93)**|(6.04)**<br>ndicated with<br>s. Units=Indi<br>6,630 observ|
|coeff.|-0.041|-0.051|-0.041|-0.042|-0.038|-0.036|-0.039|-0.042|-0.046|-0.052|-0.038|-0.037|-0.04|-0.047|-0.031|-0.034<br>ssors are i<br>ixed effect<br>ample 2: 6|
|vif|78.2|78.2|78.2|78.7|78.7|78.4|79.2|78.8|78.7|78.7|78.9|78.9|79.5|79.3|79.1|79.6<br>The regre<br>and year f<br>nts; (**) S|
|z-stat<br>Gini WIDER|(3.38)**|(3.36)**|(2.80)**|(3.09)**|(4.11)**|(2.70)**|(3.79)**|(2.53)*|(3.88)**|(3.10)**|(3.25)**|(2.43)*|(2.94)**|(3.58)**|(3.13)**|(2.81)**<br>regressors.<br>ers, country<br>ntry/year poi|
|coeff.<br> <br>ple 1*|-0.045|-0.048|-0.042|-0.046|-0.052|-0.041|-0.052|-0.043|-0.053|-0.05|-0.048|-0.041|-0.048|-0.054|-0.046|-0.045<br>n and set of<br>gional clust<br>ons, 77 cou|
|vif<br>Sam|16.8|16.9|16.8|16.8|16.8|17.3|16.9|16.8|17.0|17.0|16.8|17.3|16.9|17.0|17.4|17.4<br>nt equatio<br>errors, re<br>observati|
|z-stat<br>Gini WVS|(4.54)**|(4.60)**|(4.53)**|(6.34)**|(4.95)**|(4.36)**|(6.54)**|(6.34)**|(4.95)**|(6.86)**|(4.95)**|(5.98)**|(6.52)**|(6.87)**|(4.65)**|(5.91)**<br>with a differe<br>ust standard<br>ple 1: 95,612|
|coeff.|-0.028|-0.034|-0.028|-0.031|-0.03|-0.027|-0.032|-0.031|-0.034|-0.039|-0.029|-0.029|-0.031|-0.037|-0.027|-0.029<br>stimated<br>d logit, rob<br>. (*) Sam|
|**C**<br>**Contr**<br>**ols**|0|0|0|0|1|0|1|0|1|0|1|0|1|1|1|1<br>able is e<br>ordere<br>nt at 1%|
|**W**<br>**GDP/**<br>**cap**|0|0|0|1|0|0|1|1|0|1|0|1|1|1|0|1<br>n the t<br>ed with<br>gnifica|
|**R**<br>**Rel.In**<br>**c.**|0|0|1|0|0|1|0|1|0|0|1|1|1|0|1|1<br>fficient i<br>estimat<br>5%; ** si|
|**X**<br>**Inc.**|0|1|0|0|0|1|0|0|1|1|0|1|0|1|1|1<br>Gini coe<br>ons are<br>ant at|
|**Eq.**|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16<br>Each<br>equati<br>signific|

22
# **3.3 Infrastructure and income distribution**

We turn to the regressions exploring the empirical relationship between infrastructure development and income inequality. Our dependent variable is the Gini coefficient, for which the main source is the database constructed by Deininger and Squire (1996) complemented by the WIDER-UNU database on income inequality and poverty. The selection of explanatory variables follows the existing empirical literature on the determinants of income inequality (Li, Squire and Zou, 1998; Milanovic, 2000; Lundberg and Squire, 2003). Among the regressors we include the (log) level of GDP per capita and its square, to allow for nonlinear effects in the spirit of the conventional Kuznets curve.<sup>16</sup> In addition, we continue to include our education proxy, the measure of financial depth, macroeconomic instability (proxied by the CPI inflation rate), and trade openness. As before, infrastructure quantity and quality are measured by the synthetic indices **IK1** and **IQ1** derived from principal components analysis described earlier.

As a preliminary step, Table 2 shows that across countries the Gini coefficient of income inequality is strongly negatively correlated with the synthetic indices of infrastructure quantity and quality: their correlations with inequality range from -.47 to - .56.<sup>17</sup> The literature on the linkages between infrastructure and income distribution argues that infrastructure development can reduce inequality if it enhances the access of the poor to telecommunication services, electricity, roads and railways, safe water and sanitation.<sup>18</sup> The first column in the bottom half of Table 2 confirms this view: across countries,<sup>19</sup> the percentage of population with access to the services of each of these infrastructure sectors is negatively associated with the degree of income inequality, as measured by the Gini coefficient of income distribution. For our purposes, one key question is to what extent the synthetic indices of infrastructure quantity and quality

> 16 The same specification is used by Milanovic (2000) and Gradstein, Milanovic and Ying (2001). 17 This negative association also holds across individual infrastructure sectors. The Gini coefficient is negatively correlated with the quantity of infrastructure in telecommunications (-0.44), power (-0.47), and roads (-0.53). Regarding the quality of infrastructure services, the Gini coefficient is also negatively related to quality in telecommunications (-0.34), power (-0.37) and transportation (-0.62). 18 The appendix offers a cross-regional comparative perspective on standard indicators of access to electricity, roads, water and sanitation.

> 19 Unfortunately, there is too little time-series data on access to infrastructure to examine the correlation in the time dimension. Instead, we use cross-section data for the period 2001-5.

22
more informative measure of governance than any individual indicator. A further significant benefit of aggregation, however, is that it allows for the construction of explicit margins of error for both the aggregate indicator itself as well as its component individual indicators.

The Worldwide Governance Indicators (WGI) that we have developed over the past decade illustrate how these margins of error can be calculated (refer also to Box 1). In particular, the statistical methodology underpinning the Worldwide Governance Indicators, the unobserved components model, explicitly assumes that the true level of governance is unobservable, and that the observed empirical indicators of governance provide noisy or imperfect signals of the fundamentally unobservable concept of governance. This formalizes what we have been discussing throughout this survey -- that all available indicators are imperfect proxies for governance. The estimates of governance that come out of this model are simply the conditional expectation of governance in each country, conditioning on the observed data for each country. Moreover, the unobserved components model allows us to summarize our uncertainty about these estimates for each country with the standard deviation of unobserved governance, again conditional on the observed data. These can be used to construct confidence intervals for governance estimates, which we often refer to informally as "margins of error". Intuitively, these margins of error for our estimates of governance are smaller the more data sources are available for a given country. We can also estimate the variance of the error term in each individual underlying governance indicator using this methodology, following a calculation that generalizes the simple one we discussed above.

From the standpoint of users, these margins of error associated with estimates of governance are non-trivial, as illustrated in Figure 2. The graph reports selected countries' scores on the WGI Control of Corruption indicator, for 2006. The height of the bars denotes the estimates of corruption for each country, and the thin vertical lines on each bar denote 90 percent confidence intervals. For many pairs of countries with similar scores, these confidence intervals overlap, indicating that the small differences between them are unlikely to be statistically, or practically significant. However, we do also note that there are many possible pair-wise comparisons between countries that do result in significant differences. Roughly two-thirds of the possible pair-wise 27
<!-- Start of picture text --> 6<br>eIRL<br>5<br>*HRV<br>POL<br>oD 4 ° ect *LTU<br>& *BIH<br>o *CYP<br>Q *SRB<br>re) *AUT *SVN<br>2<br>= 3 “RUS<br>< *ROU<br>2 *NORsczeE = . “FRA *GEO<br>wn *BGR *BEL °*MDA<br>2 *CHE *NLD *UKR<br>*FIN .HEN “ESR PRT ita<br>1 *LVA *DNK *TUR°*GBR . GRC<br>*SWE<br>0<br>40 50 60 70 80 90 100<br>Stringency at the start of reopening<br><!-- End of picture text -->
# _Overall results_

Survey data confirm the importance of investor protections and other measures to minimize political risk in attracting and retaining FDI. To explore the effects of political risk on investments, the GIC Survey 2019 asked respondents how they would respond to hypothetical political risk scenarios. Two in three existing investors would consider withdrawing investments or cancelling planned investment in the face of political risk exposure in host countries (Figure 3). These findings are consistent with the broader literature on political risk which consistently identifies political risk and regulatory uncertainty as major concerns for foreign businesses. They are also in line with other surveys on the topic.<sup>6</sup> Risks of expropriation and government breach of contract are likely to elicit particularly negative investment reactions, in line with findings by Kusek and Silva (2018). About 50 percent and 40 percent, respectively, of investors would consider withdrawing existing or cancelling planned investments when faced with these risks (Figure 3). Sudden legal changes, currency restrictions, and delays in obtaining permits and approvals elicit somewhat less severe reactions. Such risks are more likely to cause investors to delay investments rather than cancel or withdraw investments completely.

**Figure 3. Reactions to political risks**

<!-- Start of picture text --> Question: How would the risk of the following events affect your investments in this<br>country?<br>At least one of the below 39% 27% 16% 11% 4%2%<br>Expropriation 24% 23% 13% 23% 12% 5%<br>Gov. breach of contract 17% 22% 16% 28% 13% 6%<br>Sudden, adverse change in laws 12% 16% 25% 36% 7% 4%<br>Currency restrictions 9% 13% 29% 33% 12% 5%<br>Delay in permits and approvals 6% 9% 38% 34% 9% 4%<br>0% 20% 40% 60% 80% 100%<br>Withdraw existing investment Cancel planned investment Delay planned investment<br>Consider delay/cancellation None Don't know<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

Affiliates with more years of experience in a given host country are likely to be less sensitive to political risk in general. For each respondent, we recorded the most negative reaction across all hypothetical 6 For example, political risk ranks second among nine categories of possible impediments to FDI according to the MIGA-EIU Political Risk Survey 2013; political risk and uncertainty is ranked fifteenth among 69 organizational risks according to the Aon Global Risk Management Survey 2019; and political risks and regulatory uncertainty is ranked fourth among 12 risks according to the Association for Financial Professionals and Oliver Wyman Risk Survey 2019.

16
have been pushed to lower levels of government. This requires a comprehensive alignment of fiscal responsibility and accountability, but also coordination in terms of project selection. Third, regional integration could significantly help in reducing infrastructure costs and improving access to regional and global markets. Benefits from exploiting large economies of scale in ports, airports, or power generation and transmission could be reaped through enhanced regional cooperation (World Bank 2009). In Africa, for example, regional power trading could reduce energy costs by US$2 billion and carbon emissions by 70 million tons annually (Foster and Briceño-Garmendia 2010). Fourth, the long-term growth impact of infrastructure investments may be mitigated if they lead to significant environmental damages. Fossil fuel energy generation, for example, can create emissions that contribute to acid rain and global warming. Irrigation works can lead to overuse of water, land degradation, and water pollution. In total, environmental costs associated with infrastructure investments have been estimated to reach 4 to 8 percent of GDP for some developing countries (World Bank 2007). Supporting environmentally-sustainable infrastructure investment could significantly reduce these costs.

46. Not surprisingly, identifying the _right_ projects often requires significant resources aimed at project selection and preparation. Developing a project ideally requires an array of institutional, legal, social, environment, financial, regulatory, and engineering studies. These studies tend to be costly, particularly for complex projects (see World Bank 2011c). For example, project preparation costs for the Nam Theun 2 hydropower project in Lao PDR, with total investments of US$1.4 billion, amounted to US$124 million, or 9 percent of investment costs. By one estimate, bringing Africa’s key transformational projects to a stage where they can actually attract investors (public or private) would require some US$500 million (Foster and Briceño-Garmendia 2010). Generally, both governments and the private sector are reluctant to allocate substantial resources upfront to support project preparation activities, however. The international community could help developing countries by providing targeted financial resources and technical assistance.

47. Some new initiatives are underway to address these shortcomings. In November 2010, the World Bank Group launched the Infrastructure Finance Center of Excellence, which aims at leveraging Singapore’s expertise in urban development and financing and the World Bank’s global development knowledge and operational experience to attract more private capital for 21
benefit amount (PRs 24 a year).<sup>21</sup> These values remained the same over the study’s observation period. Thus, the total annual per-beneficiary cost was PRs 2,624. This perbeneficiary cost almost perfectly matches the value from official government accounting records of actual annual outlays and beneficiaries for the program.

Note that the PRs 2,624 value reflects recurrent costs and does not capture fixed costs or costs incurred in designing and establishing the program. Moreover, for recurrent costs, the value does not capture the cost of time and other resources spent by provincial and district education department officials or schoolteachers in carrying out their program responsibilities. Nor does it capture any costs borne by households in ensuring that they were present when and where needed to receive the benefit transfer or in pursuing any grievances with the authorities relating to problems with receiving the benefit. While such omissions from cost calculations are not unusual in impact evaluation studies for developing countries (Dhaliwal et al. 2013), the PRs 2,624 value should be considered an underestimate of the actual annual per-beneficiary cost of the program.

In 2015, there were 28,559 girls in secondary grades in government schools in the RD program district. Comparing enrollment numbers from the school census rounds and program beneficiary numbers from government administrative data, we find that about 90 percent of enrolled girls receive program benefits.<sup>22</sup> Using these numbers, we estimate that the government spent PRs 67,496,341 on the program in the RD program district in 2015.

For the calculation of cost-effectiveness, we arbitrarily choose the program effect for the subperiod 2003–15 (that is, the full observation period), an average gain of 41 girls per school in the RD program district. This effect, which lies roughly in the middle of the range

> 21 This maximum administrative cost of PRs 224 per beneficiary per year is about 9 percent of the maximum benefit transfer per beneficiary per year. This percentage is on par with the modal ratio of per-beneficiary annual administrative cost to benefit transfer in a sample of CCT programs in developing countries that have been evaluated (Garcia and Saavedra 2017).

> 22 That the number of beneficiary girls is lower than girls’ enrollment is presumably due to the application of the regular attendance condition for program benefit eligibility.

20