rafmacalaba/datause-encoder-data huggingface dataset:
# Chapter 3. Infrastructure Prioritization Framework: An Alternative Approach

The Infrastructure Prioritization Framework is a quantitative multi-criteria approach to withinsector infrastructure project comparison. The IPF condenses project-level indicators (selection criteria) into two composite indices – a financial-economic index (FEI) and a social-environmental index (SEI) – and considers these alongside the budget constraint for a particular sector. Results are displayed graphically to map the projects’ relative expected performance along these two dimensions.

While the IPF is quantitative in nature, it is also policy-responsive, since the government specifies the set of project selection criteria that reflect the sectoral and developmental policy goals. These criteria may include social, strategic, and environmental considerations alongside traditional financial and economic factors. In fact, the IPF was developed by an infrastructure team within the World Bank in response to government demand for alternative decision support approaches that could directly consider key policy goals; be feasibly applied across large projects sets within the resource means of the government; and remain systematic and evidence-based (Marcelo et al., 2016).

IPF is designed to employ quantitative measures to the greatest extent possible to systematize project comparison and limit subjectivity in selection. While the IPF is a multi-criteria approach, it can utilize the results of CBA analysis as a key decision factor. While the approach was initially envisaged for low-capacity governments, its relevance to the Chilean context is demonstrated in the results of this report. In this section, we summarize the IPF prior to presenting the results of its application to the transport and water catchment sectors in Chile.

## The IPF Process

Implementing the IPF is relatively straightforward and follows five steps: (1) selecting decision criteria; (2) gathering related project indicator data; (3) calculating social-environmental and financial-economic indices; (4) plotting projects and budget limits; and (5) comparing projects (see Figure 2). In this section, we summarize IPF application in terms of these steps.<sup>8</sup>

> 8 An extensive technical description of the IPF methodology is detailed in Marcelo et al., 2016, and Marcelo et al., 2015.

17
cointegration vector, imposes two restrictions on the first and three restrictions on the second and the third cointegration vector.

> The LR test of this model specification has a χ<sup>2</sup> distribution with 2 degrees of

> freedom. The calculated value of χ<sup>2</sup> (2) = 2.65 has a p-value of 0.27. Since the critical value is not significant,  H1 is a possible identification of the cointegration space.

Table 7 contains a summary of different hypotheses tests that represent variations of the basic restriction formulation outlined above. Hypotheses H1 to H6 retain the same restrictions on the first cointegration vector, but vary the restrictions on the second and the third. H7 to H12 exclude additionally manufacturing from the first cointegration vector.

**Table 7**

|||**Agr**|**Manu**|**Trans**|**Com**|**Fin**|**Gov**|**Oil**|**Const**|**LR-test**|**p-value**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**H1**|β1<br>β2<br>β3|0<br>0<br>1|1<br>0<br>*|*<br>1<br>0|*<br>0<br>*|*<br>*<br>*|*<br>*<br>0|0<br>*<br>0|*<br>*<br>*|2<br>χ (2) = 4.57|0.10|
|**H2**|β1<br>β2<br>β3|0<br>0<br>1|1<br>0<br>*|*<br>1<br>*|*<br>0<br>0|*<br>0<br>0|*<br>*<br>0|0<br>*<br>0|*<br>*<br>*|2<br>χ (4) =15.72|0.00|
|**H3**|β1<br>β2<br>β3|0<br>0<br>1|1<br>0<br>*|*<br>1<br>*|*<br>0<br>*|*<br>*<br>0|*<br>*<br>0|0<br>*<br>0|*<br>*<br>*|2<br>χ (2) = 2.65|0.27|
|**H4**|<br>β1<br>β2<br>β3|0<br>0<br>1|1<br>0<br>*|*<br>1<br>0|*<br>0<br>*|*<br>0<br>0|*<br>*<br>0|0<br>*<br>0|*<br>*<br>*|2<br>χ (3) = 16.32|0.00|
|**H5**|β1<br>β2<br>β3|0<br>0<br>1|1<br>0<br>*|*<br>1<br>*|*<br>0<br>*|*<br>*<br>0|*<br>*<br>0|0<br>*<br>0|*<br>*<br>0|2<br>χ (2) = 19.06|0.27|
|**H6**|β1<br>β2<br>β3|0<br>0<br>1|1<br>0<br>*|*<br>1<br>0|*<br>0<br>*|*<br>*<br>0|*<br>*<br>0|0<br>*<br>0|*<br>*<br>*|2<br>χ (4) = 15.72|0.00|
|**H7**|β1<br>β2<br>β3|0<br>0<br>1|0<br>0<br>*|1<br>0<br>*|*<br>0<br>*|*<br>1<br>0|*<br>*<br>*|0<br>*<br>0|*<br>*<br>0|2<br>χ (3) = 4.93|0.18|
|**H8**|β1<br>β2<br>β3|0<br>0<br>1|0<br>0<br>*|1<br>1<br>*|*<br>0<br>*|*<br>*<br>*|*<br>*<br>0|0<br>*<br>0|*<br>*<br>0|2<br>χ (3) = 3.69|0.30|
|**H9**|β1<br>β2<br>β3|0<br>0<br>1|0<br>0<br>*|1<br>1<br>*|*<br>0<br>0|*<br>*<br>0|*<br>*<br>0|0<br>*<br>0|*<br>*<br>*|2<br>χ (3) = 16.32|0.19|
|**H10**|β1<br>β2|0<br>0|0<br>0|1<br>1|*<br>0|*<br>*|*<br>*|0<br>*|*<br>*|2<br>χ (4) = 14.93|0.00|
||β3|1|*|0|0|0|0|0|*|||

13
———. 2011. “Economics of Tobacco Toolkit: Assessment of the Economic Costs of Smoking.” WHO, Geneva. http://apps. who.int/iris/bitstream/10665/44596/1/9789241501576_eng. pdf. ———. 2013. Global Tuberculosis Report 2013. Geneva: WHO. apps.who.int/iris/bitstream/10665/91355/1/9789241564656_eng.pdf.

———.2015a. _Global Report on Trends in Prevalence of Tobacco Smoking, 2015_ . Geneva: WHO. http://apps.who.int/iris/bitstream/10665/156262/1/9789241564922_eng.pdf.

———. 2015b. _WHO Report on the Global Tobacco Epidemic 2015: Raising Taxes on Tobacco_ . Geneva: WHO.

———. 2017. “WHO Tobacco Fact Sheet.” WHO, Geneva. http://www.who.int/mediacentre/factsheets/fs339/en/.

World Bank. 2017. “Preliminary Recommendations for Effective Tobacco Tax Design in Bangladesh.” World Bank Policy Note, World Bank, Washington, DC.

# **Annex 1 – Estimation of Quadratic AIDS Elasticities**

We consider a set of consumer demands for a set of _k_ tobacco products and ingredients for which the consumer has budgeted Tk _m_ . Specifically, the _k_ goods include cigarettes, bidis, betel leaf, betel nut, and rolled betel leaf. A few products that are consumed by fewer than 4 percent of households are not included (tobacco leaf, gul, and other smoked tobacco, khoer, lime, and other smokeless products).

The quadratic AIDS model of Banks, Blundell, and Lewbel (1997) is based on an indirect utility function that leads to a system of demands expressed in expenditure shares. Let q� denote the quantity <u>����</u> of good _i_ consumed by a household; p� the price of good _i_ ; and w� � ~~,~~ the expenditure share for � good _i_ : where ln a�p� is and b�p� is the Cobb-Douglas price aggregator: All lowercase Greek letters are parameters to be estimated, except α�, which is set to slightly less than the lowest value of ln m, in this case 1.5.

The set of expenditure share equations is estimated using the command _quaids_ in Stata (Poi 2012). As part of the model, some demographic controls are incorporated, including household size; the number 21
# **4. Modeling Health and Education Outcomes: Method and Results**

We are interested in assessing the impact of orphanhood shocks on long-run health and education outcomes for non-orphans aged 6 to 15 at baseline. The basic specification for health in this evaluation can be written as: in which _Hi1_ is health status in 2004, the vector _Xi0_ is a set of individual, household and community control variables measured at baseline, which is the first KHDS interview of the child in 1991-94, and _Hi0_ is the health status at baseline. The treatment of interest _Di_ is the losing a parent between the baseline interview and the final interview in 2004. Thus, these deaths occur sometime between period _0_ (October 1991- January 1994) and period _1_ (2004). Estimated using OLS, equation (1) is equivalent to a difference-in-differences specification with a set of controls, including initial health, and it can be straightforwardly shown, by subtracting _Hi0_ from both sides of equation (1), that the parameter γ offers the average treatment effect. Controlling for initial health status ensures that initial heterogeneity correlated to health endowments, health history and general child background as it affects changes in health status via _Hio_ , is captured. The basic specification for education is identical, using educational attainment at baseline and in 2004 instead of the health status variables.

The outcomes used in this study relate to investments which affect long-term economic prospects of children. We focus on height as the key long-term health measure, since it has been shown to affect wage-earning capacity as well as participation in the labor force for men and women (see Haddad and Bouis, 1991, and Thomas and Strauss, 1997). We model height as influenced by several factors, including the characteristics of the child (sex and age, via a full set of age dummies), genetic background of the family (using height at baseline for both the child and the mother), as well as the socio-economic environment in the household at baseline which is assumed to influence human development investment (including whether the child was living with the mother, whether the child was living with the father, years of education, sex and age of the household head and two indicators of wealth: cemented floor in dwelling and log per capita household consumption).

Educational attainment can be used as a proxy for human capital levels. We focus on the educational attainment in terms of the years of education completed, counting each grade completed as a year. We use a similar specification to the one above, whereby years of education in 2004 are determined by several factors, including child characteristics, years of education completed, and the same household characteristics as above. By controlling for years of education completed and whether enrolled in school at baseline, we further isolate the effect of orphanhood on education, net of its correlation with other unobserved background characteristics of these families influencing education. Finally, all our results 8
-6-

calculation using household income surveys from earlier years indicates that household incomes have probably fallen by roughly the same amount, although comparing household incomes since the onset of the transition is extremely difficult because of the dramatic changes in the structure of remuneration and taxation in the past few years. In any case, this analysis suggests that most households have suffered significant income losses and much greater uncertainty over the future--under the old system employment was guaranteed until retirement when one was eligible for a comfortable pension.

The Level of Income. Using the 1992 household survey, average household income per capita is estimated at Leva 16,803, about US $ 710 (see Table 1). The level of income in the urban and rural sectors can be compared to the national level. The survey includes 2202 households: 1386 households (or 63 percent) are urban households; and, 816 households (or 37 percent) are rural.<sup>4</sup> This geographical distribution is roughly the same as the 1985 Census figures of 65 percent for urban households and 35 percent for rural ones. The average household income per capita in the rural sector is Leva 19,722 (about US $ 832) and it is 30 percent higher than the urban average. Furthermore, for each income decile rural household income is higher than urban (see Table 1). The difference in income level between the two sectors is statistically significant.

4/ The definition of urban/rural is based on the type of settlement (town verss village). This distinction is a state decision which is not entirely based on the size of the populaion (the well-known 5000 people cut off line). In other words, some villages have more population than some towns.
18 developing countries' :omparative advantage falls.

Tiible 3 indicates that several variables are not affecting developing countries' revealed _.omparative advantage, with the market size variable failing to achieve significance for each of the three groups of exporters.'6 Relatively weak results are also achieved for the scale variable and first trade data -- - these terms are not significant for either of the two Asian country subgroups - while the product differentiation variable does not appear to influence the RCA profiles of the Asian exporters of manufactures. These three variables are, however, significant for the runs for all developing countries.

To what extent can the variables listed in Table 3 jointly account for differences in developing countries' revealed comparative advantage? While a high degree of inter-correlation between some of the independent variables made estimation difficult, a series of linear multiple regressions were run to provide an approximate answer.'7 Representative results for these regressions are summarized in Table 4.

Table 4 shows that labor intensity, by itself, accounts for about 24 per cent of the total variation in developing countries revealed comparative advantage with the relationship improving when nonlinear regression forms were

> 16 While results for the resource based production variable are shown in Table 3 the basis for running rank correlations on this term are weak. This dummy variable establishes a dichotomy between industries, by taking value of zero or one, and therefore establishes only a very weak ranking. More appropriate regression tests are given in Table 4 and these show that natural resource requirements have an important influence on developing countries comparative advantage.

> 17 The author will provide interested readers with full Pearson and Spearman correlation coefficients between the RCA indices and explanatory variables listed in Table 3 upon request.
_established democracies_ (hereafter “democracies” or DEM) if they have an average Polity IV score >7.5 (Marshall et al. 2016), and as _autocracies or transition countries_ (hereafter “autocracies” or AUT) otherwise. The purpose of the high polity cutoff is to exclude countries that transition in and out of democracy, since countries are categorized once as one regime or the other.<sup>30</sup> Combined, there are around 125 countries, 7,000 observations, and 1,100 leaders for which we have growth, leader, and polity data. Of these, 23% are established democracies. (See the Online Appendix for further details and descriptive statistics.) For our results involving individual leaders’ growth estimates, we restricted the sample to leaders with tenure ≥ 3 years, and growth data available for their whole tenure, leaving a sample of approximately 650 leaders and 5,400 observations.

There are six regions (for region-by-year fixed effects), which are based on the World Bank classification, with three modifications: (i) European offshoots (the United States, Canada, Australia, and New Zealand) are added to Europe, as is common in the literature; (ii) South Asia and East Asia & Pacific are combined, due to the small number of countries in the former; and (iii) Communist transition countries geographically close to the former USSR form their own region, as they experienced a distinct set of common shocks, especially in the early 1990s.<sup>31</sup>

**Outliers.** Per capita GDP growth rates are often very volatile—for example, around wars— and a small number of extreme observations can have a large effect on estimated results. Accordingly, we drop outliers, which we define as | ⋆|>40%. This is quite conservative and only tt gg

30 AUT includes autocracies as well as democratic-transition countries that spent much of the sample as autocracies or anocracies, even if they are now democracies. Several small countries without a polity score have been dropped. 31 The six regions are: Sub-Saharan Africa (SSA); Middle East and North Africa (MENA); Latin America and the Caribbean (LAC); Asia (a combination of World Bank regions of East Asia & Pacific, and South Asia); Europe and European offshoots; and Communist Bloc countries close to the USSR (Albania, Bulgaria, Hungary, Mongolia, Poland, and Romania). Countries with fewer than 30 years of growth data are dropped, which removes other newly created post-Communist countries. We also exclude Kuwait in 1990-91 as it was occupied and not a separate country.

20
26 87. The state sales tax in India preents an especially difficult problem, because proper enforcement requires interstate cooperation. Some firms falsely declare their sales as interstate to avoid state sales tax - an easy evasion technique, since officials have no physical proof that goods did not pass out of the state. An attempt at control requires the importer to hold a license, issued by his home state, if makdng an interstate purchase. However, verification of a licensed importer requires the cooperation of the importing state, which stands neither to gain nor to lose revenue since liability is with the seller. The result is rampant evasion. A 1981 evaluation of the Bihar sales tax system by the National Institute of Public Finance and Policy found that in most years revenue collection fom motor parts was less than half of the estimated tax potential (as reported by Acharya, 1985). A more recent study of the sales tax collection rate in all of India shows a lower rate of revenue realization for state govenment tax than for central governmen **t** ax (Government of India, 1989, Chapter IV). The tax recovery rates from this study, shown below, define leakages from full taxation as the sum of exemptions and evasion: T|BMy RW& Goem t State Q ntmen -entral

Tires 90 80 Maintenance and parts 50 40 Vehicle purchase 100 85 88. Vehicle purchase tax in Argentina is redatively simple and claims a low rate of evasion. The main leakage on the new vehicle tax is in the area of concessionary sales by manufacturers. Otherwise, the tax is collected with relative ease from a small number of manufacturers/dealers. There is some evasion in the sale of spare parts because more establishments are involved. The total revenue loss due to evasion of the purchase tax on motor vehicles and parts is estimated at about 10 percent of tax collection.
<!-- Start of picture text --> 3,000 4,000<br>2,500<br>2,000 3,000<br>1,500<br>1,000 2,000<br>500<br>0 1,000<br>-500<br>-1,000 0<br>-1,500<br>-2,000<br>-1,000<br>1 2 3 4 5 6 7 8 9 10<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of per capita household PRE-FISC income<br>Deciles of per capita household PRE-FISC income<br>In-Kind Health Transfer Health User Fee Net health transfer<br>In-Kind Edu.Transfer Edu. User Fee Net Edu Transfer<br>Yuan RMB Yuan RMB<br><!-- End of picture text -->

# C. Average Spending on Private Education by Decile

<!-- Start of picture text --> 250<br>200<br>194<br>156<br>150 135 134 131<br>116 114 115 116<br>106<br>100<br>50<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of per capita household PRE-FISC income<br>Yuan RMB<br><!-- End of picture text -->

**Third, cash benefits are low but are essential for those at the bottom of the distribution** . For the poorest households, 20 percent of their final income comes from Residents’ pension benefits (11 percent) and cash transfers (9 percent). The average amount received by the first decile is also higher than for the rest of the deciles, in absolute terms. Despite the relatively modest cash amounts of both residents’ pension and cash transfers, they do help lower overall inequality as will be shown later. Still, these amounts are low, particularly in poorer provinces and rural areas, where the benefit is a small fraction of average wages. Compared to the average pension received by those that contributed to the Employee System, the average pension for those in the resident’s pension system is  10 times lower on average (World Bank, _China Economic Update, December 2023_ ). Considering cash transfers, overall coverage is low (14 percent of households) and significantly lower if we consider only the flagship cash transfer program Dibao. This program covers less than 4 percent of the population, compared with an average of 17.6 percent of the population among upper-middle-income countries (World Bank, ASPIRE database). Most households in the program are those whose members are unable to work due to age or disabilities. As such, the program has limited reach for households that may experience unexpected fall in income due to loss of employment or business.

**Fourth, and as expected, richer households contribute more to the fiscal system than poorer ones in relative terms, contributing to the progressiveness of the system.** Households pay into the fiscal system in the form of PIT, VAT, and consumption taxes on their purchases and through their contributions into the employee or resident

17
explanatory factors to see if our causal estimates persist. Our approach can be modeled as follows: _Yi_ , _t_ is the outcome of interest from our international adjusted test score database, _X i_ , _t_ is the vector of explanatory variables, _Zi_ , _t_ is the vector of covariates, and _ui_ , _t_ is the error term. Our estimator, β," provides an estimate of the effects of the different systemic elements of school systems, explanatory variables, and covariates respectively, on the adjusted test score. In addition, we include covariates to control for several other potentially confounding factors between our variables and outcomes of interest. In our case, these potentially confounding factors include several macroeconomic and demographic factors at the country level, such as GDP per capita. In a pure cross sectional approach, several studies have already applied this estimation strategy for sub-samples of countries using TIMSS or PISA data (Woessmann 2003). While results from these types of studies are the starting point of our extended approach here, they have many drawbacks which also apply to possible results from equations (7) and (8). First, they likely suffer from tremendous omitted variables biases, because country-specific institutional variables could be associated with many other unobserved factors that simultaneously affect test scores. Thus, it is hard to draw causal conclusions, which policy makers are after. Nonetheless, we will present results from our estimations using equations (7) and (8) as a baseline for our other estimations.

We further include country and time fixed effects. We focus on the variation of our variables of interest over time _within_ a single country as well as variation of characteristics _across_ countries. This allows us to omit any potential bias to our association between systemic elements of educational systems that could stem from time-invariant factors at the country level as well as time varying factors. The equations for these fixed effects estimates can be expressed as follows: 26"