rafmacalaba/datause-encoder-data huggingface dataset:
sector mean that public spending on health does not always translate into a larger supply of effective health services, (2) the impact of a greater supply of effective health services in the public sector on health status depends on individual demand and market supply, and (3) public monies are spent on expensive, but ineffective, curative services.

# I) Explaining cross-national variation in health status

Much of the intuitive appeal behind many proposed strategies to improve health status, such as Primary Health Care (PHC) or a "basic package" of cost-effective services, comes from the simple but powerful observation that there are countries with exceptionally good health status for their level of income (World Bank, 1 997a). The relatively good health of Sri Lanka, China, Costa Rica, and Kerala, India is frequently cited as an indication of the potential benefits from PHC.<sup>4</sup> However, it is impossible to jump from some countries' good health outcomes to the conclusion that all (or even that _any)_ of the unexplained differences in mortality are due to health policy. While it is possible that these countries' good health outcomes is due to health sector strategy, it is equally plausible that they share non-health characteristics like high levels of female education (King and Hill, 1992), better nutrition, more equal income distribution (Bidani and Ravallion, 1997) that explain their better outcomes.

To assess the _maximum_ amount of the variation in health outcomes that can be explained _by independent_ variations in health sector expenditures or policy, and so properly identify the "outliers," we estimate a multivariate regression that explains health outcomes without any ' These countries (regions) were highlighted at a conference sponsored by the Rockefeller Foundation (Halstead et al, 1985).

4
- [31] Hummels, D. and P. Klenow, 2005,”The Variety and Quality of Nation’s Exports,” American Economic Review, 95 704-723.

- [32] Imbs, J. and R., Wacziarg, 2003, ”Stages of Diversification,” The American Economic Review, 93 (1), 63-86.

- [33] Jaffe, A. and B., Hall, 2001, ”The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools,” NBER WP No. 8498.

- [34] Khandelwal, A., 2007, ”The Long and Short (of) Quality Ladders,” MPRA Paper 4496, University Library of Munich.

- [35] Lai, E., 1998, ”International Intellectual Property Rights Protection and the Rate of Product Innovation,” Journal of Development Economics 55, 115-130.

- [36] Levin, R.C., W.M. Cohen and D.C. Mowery, 1985, ”R&D Appropriability, Opportunity and Market Structure: New Evidence on Some Schumpeterian Hypotheses,” American Economic Review Paper & Proceedings, 75, 20-24.

- [37] Linder, S., 1961, _”An Essay on Trade and Transformation,”_ Stockholm: Alqvist & Wiksell.

- [38] Milgrom, P. and J. Roberts, 1995, ”Complementarities and Fit: Strategy, Structure and Organizational Change in Manufacturing, Journal of Accounting and Economics, 19, 179-208.

- [39] Motta, M., 1993, ”Endogenous Quality Choice: Price vs. Quantity Competition,” Journal of Industrial Economics, 41, 113-131.

- [40] Nickell, S., 1996, ”Competition and Corporate Performance,” Journal of Political Economy, 104, 724-746.

- [41] Porter, M. E., 1990, ”The Competitive Advantage Nations, (Macmillan Press, London, U.K.).

- [42] Rauch, J., 1999, Networks versus Markets in International Trade, Journal of International Economics, XLVIII 735.

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grams prevent retirement ages from keeping pace with changes in life expectancy, leading to an increased need for life-cycle savings. Using a cross-country panel of macroeconomic data, then, the paper finds that increased longevity raises aggregate savings rates in countries with universal pension coverage and retirement incentives. Similarly, Bloom et. al. (2003) show that aging leads to more capital accumulation even if retirement is endogenous. Echevarria (2004) reaches the same conclusion. Kalemli-Ozcan _et.al._ (2000) show that the positive effect of mortality decline on capital accumulation is made larger if education decisions are endogenous.

De la Croix and Licandro (1999) and Zhang et al. (2001, 2003) argue that the effect of increasing longevity depends on its initial level. For low levels of life expectancy the effect is positive but it can turn negative for high levels. Similarly, Miyazawa (2006) also shows that the effect of an increase in longevity on economic growth has a hump-shaped pattern. This is the result of a two-effect system. First, higher longevity increases the aggregate saving rate directly by increasing precautionary saving for the prolonged retirement and indirectly by increasing the accidental bequests (bequest-wage ratio is important because the higher income group has a higher propensity to save). Second, it reduces the frequency of accidental bequests, which implies that the population share of the higher income group decreases. This leads to a reduction in aggregate savings. The relative shares of these factors change over the aging horizon. This is also true for the income inequality (first positive, then negative). Kinugasa and Mason (2006) provide empirical support to shows that an increase of wealth across countries is likely with mortality decline.

Among the studies that link the impact of aging with social security systems, Ito and Tabata (2008) find that the unfunded social security system provides a sufficient mechanism to have such a hump shaped relationship between longevity and per capita output. Tabata (2014) looks at the effect of a shift from a DB to a DC PAYG pension on growth. He shows that this shift is growth enhancing and alleviates the cost of aging. Heijdra, B. and J. Mierau (2011) also compare the relative effects of DB and DC PAYG pensions on economic growth with aging. They show that the DC formula fares better that the DB one in facilitating growth. They also show that raising the retirement age as a response to an increase in longevity dampens the growth gains. The analysis in this paper provides a comparison of several different institutional settings, i.e. different social security systems and retirement age policies, and types of aging in a unified framework. Therefore, it provides a consistent survey of the welfare effects of demographic aging under various conditions.

The rest of the paper is organized as follows. In section 2, we present the basic model and the main results for an economy that consists of identical individuals with a defined 4
here again that parental schooling (education) in our analysis is a summary measure of the socioeconomic status of the family a child is born into, and thus capture any and all correlated family and neighborhood factors that aect children's educational attainment.

Given that we have the full count census data, our main estimation sample (treatment group: 1968-1972 and comparison group: 1957-1962) gives us 2,048,164 father-child pairs where household heads are the fathers, and the children were living in the household at the time of the census. We calculate the years of schooling based on the education level a respondent has completed. A comparison shows that the children exposed to Inpres schools and their fathers have more education than comparison cohorts and their fathers (see Table 1).

The census also reports information on birth district and province of an individual. We match this birth district information with the Inpres school construction intensity data, which was graciously provided to us by Esther Duo. The school intensity data was originally reported as the number of schools per 1000 children in a district. We use a normalized measure of the Inpres intensity by dividing the number of schools by the highest number of schools received by a district.<sup>27</sup> This normalization implies that the estimated coecients can be interpreted directly as the eects for the districts with the highest treatment intensity.

We use built-up density in a district in 1975 for estimating the selection equation for father and children's coresidency. We implement inverse probability weighting to correct the biases in the estimates due to sample truncation arising from coresidency. The source of built-up data is the Global Human Settlement Layer (GHSL) (Pesaresia et al. (2015)). The built-up data are at 300 meters by 300 meters grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the total built-up area at the district level.

# (4) Empirical Strategy and Estimating Equations

Our empirical strategy follows closely the approach due to Duo (2001, 2004) that exploits both cross sectional variation across districts and over time variation across birth cohorts. The cross sectional variation comes from the dierences in the intensity of exposure depending on

> 27The district that received the highest number of schools received 8.6 schools per 1000 children. In contrast, the district that received the lowest number of schools received only 0.59 schools per 1000 children. The mean is 1.86 schools per 1000 children. After normalization, the school construction intensity value ranges from 0.0678 to 1 with a mean of 0.215.

10
quality.<sup>7</sup> Although the two studies fit gravity models to very different data sets, Table 1 shows that their core results are qualitatively similar: Exporter GDP elasticities between 1.0 and 2.0; importer elasticities around 1.0; and road distance elasticities between -1.0 and -1.7. In addition, the Coulibaly / Fontagné results suggest a road quality elasticity near 1.5.

**Table 1: Recent Gravity Model Estimates for Africa and Latin America**<sup>**a**</sup>

|Study|Exporter GDP|Importer GDP|Road<br>Distance|Road<br>Quality|
|---|---|---|---|---|
|Coulibaly and Fotagné<br>(West Africa)<sup>b</sup>|1.40<br>(22.29)|0.83<br>(6.05)|-.96<br>(4.93)|1.44<br>(4.34)|
|Carrillo and Li,|1.33**–**2.13|0.69**–**1.23|-1.13**–**-1.68||
|(Latin America)<sup>c</sup>|(15.35) (17.15)|(9.71) (17.56)|(4.95) (5.74)||

a t-statistics in parentheses b Coulibaly and Fotagné (2004), Table 5, Column 6, p. 29 c Carrillo and Li (2002), Table 5, Columns 1-5, p. 27

# **3.  Mapping the Trans-African Road Network**

To assess the potential for overland trade expansion, we analyze a road system that includes the Trans African Highway corridors proposed by the African Development Bank (ADB, 2003).<sup>8</sup> Using a computerized map and spatial network analysis software, we configure a distance-minimizing network of existing primary roads that connects all mainland capitals and cities above 500,000 population.<sup>9</sup> Figure 2 displays the network, along with the 83 connected cities.<sup>10</sup> Since the network covers all major cities, it has more connections in countries with large populations. Nigeria has 18 cities in the

> 7  Coulibaly and Fontagné use two-stage least squares to allow for the potential endogeneity of road quality. Their model is fitted to the UN Statistics Division’s COMTRADE data for the period 1996-1998. 8  The ADB’s corridors include North African countries, while our analysis is limited to Sub-Saharan Africa.

> 9  African roads are functionally categorized as primary, secondary or tertiary, depending upon whether they connect principal centers, secondary centers, or peripheral areas.  Road conditions vary widely by country, so functional categories convey little information about road quality.  Primary roads in South Africa are paved, all-weather highways, while many primary roads in the Congo D.R. are unpaved.

> 10  Appendix Table A1 lists all cities in the network.

8
Did this literature produce any insight that the cross-country literature did not? On one hand, it did not _overturn_ the qualitative result of Lederman, Olarreaga and Payton (2010) that EPAs do make a difference. On the other hand, the result is qualified; for instance, estimated effects tend to be substantially smaller at the firm level (Cadot et al., 2012 find only six dollars of additional exports for one dollar of export promotion). Second, the level of detail in the decomposition of EPA activities tends to be higher in the clinical studies than in survey-based cross-country studies, allowing for closer examination of which “treatment arms” seem to be most effective; finally, the decomposition of impacts along various margins of firm performance (extensive or intensive) is necessarily richer at the firm level. However, clinical studies have little external validity; for instance, the success of PROCHILE in fostering diversification and innovation may have to do with many features of the Chilean business and Government environment that could not be transplanted easily.

In sum, as Rodrik (2008) put it, there is an inescapable trade-off between “internal validity” (the ability to identify impact effects net of confounding influences), which improves as one goes from cross-country studies to impact evaluations, and “external validity” (the ability to draw general policy propositions from evaluation results), which may well worsen.

# 5.  CONCLUSIONS

The literature we surveyed based on traditional econometric analysis has theoretic foundations but the wide spectrum of results reveals the difficulty of drawing robust policy conclusions because of confounding influences. Impact evaluation (IE) techniques provide “internal validity” as confounding influences can be controlled better. How promising, then, is the use of IE to trade interventions?

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**Table 4:  Supervisory Stance (SOARS)**

||**Pr**<br>|**obability ra**<br>|**ting (PAIRS)**<br>|||
|---|---|---|---|---|---|
||**Low**|**Med**|**ium**|**High**|**Extreme**|
|||**Low**|**High**|||
|**Extreme**<br>**Impact**|Normal|Oversight|Mandated<br>Improvement|Restructure|Restructure|
|**High**|Normal|Oversight|Oversight|Mandated<br>Improvement|Restructure|
|**Medium**|Normal|Normal|Oversight|Mandated<br>Improvement|Restructure|
|**Low**|Normal|Normal|Oversight|Mandated<br>Improvement|Restructure|

_Source: APRA_ There are four generic categories in SOARS, as shown in the table:

# • **Normal**

In the normal supervisory mode APRA collects and analyses the standard statistical and other information and conducts routine on-site reviews, with a minimum interval of two years.

# • **Oversight**

This category involves more intense monitoring with more frequent meetings with trustees, meetings with auditors and collection of additional information - and a readiness to step up supervisory or enforcement action quickly if there is any further deterioration.

# • **Mandated Improvement**

When a fund is rated in this category, APRA will direct trustees to develop and implement plans to correct the weaknesses it has identified. These may originate in management, asset composition, operational controls or the trustee board itself. APRA may accept legally enforceable undertakings from trustees in regard to their remediation plans. It may also issue a ‘show cause’ notice requiring trustees to explain why the regulator should not take more severe action.

# • **Restructure**

At this point, the fund has encountered serious financial difficulty or is at risk of this, or members’ investments are considered to be in jeopardy from improper use by trustees. There may be significant breaches of the gearing prohibition or the ‘in-house’ investment restrictions.

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30 The fourth and fifth columns of Table 4 provide further support for the argument that unobserved village characteristics do not drive the results in the first three columns . Column four reports estimates on a subsample that excludes the 25 percent of villages (47) located further than 32 kilometers from an urban center. Results are unchanged; the effects of community radio increase, such that additional stations increase the share of literate children by six percentage points. Column 5 excludes the 25 percent of the sample villages in which more than 53 percent of households reported income of less than 30,000 CFA Francs. Effects of community radio access are unchanged.

The sixth column separately examines the effects of within-commune variation in village access to within- and out-of-commune community radio stations. The coefficient for out-ofcommune stations is similar to the aggregate coefficient in the earlier estimates: one additional outof-commune community radio station is associated with a 4.8 percentage point increase in the literacy share. The coefficient for within-commune stations is larger, suggesting that own-commune stations are likely to have a larger influence on household behaviors and education outcomes. The seventh column applies an atheoretical robustness test, using robust regressions to arbitrarily reduce the weight given to influential observations. The weight given to those observations is the Stata default (tune = 7). The effects of community radio remain significant, though the coefficient value drops to .026.

# **7. Channels of influence of village radio access on education outcomes**

The data allow us to test, for the first time, the mechanisms through which radio access exerts effects on public policy – in this case, literacy. One basic issue in examining mechanisms is whether community radio access is actually associated with increased village exposure to educationrelated information. Like other studies, this analysis measures media access with the number of radio station signals that households can receive. However, in contrast to previous work, the data
# Assumptions and Limitations

## Assumptions

Several simplifying assumptions underpin the final Cost Time model. Some, e.g. the absence of snowfall and landslides, are unavoidable over‐simplifications given currently available data. These can be modeled in a separate simulation environment but not on a nationally representative average. Accounting for other factors, such as disabilities or encumbered travel speeds, is possible but would introduce greater errors elsewhere in the model. We may address such factors in separate future analysis, as with monsoon and walking travel.

### **Data accuracy**

- All roads are completely accounted for

- All road surface and condition data are accurate and up‐to‐date

- All service facilities are completely accounted for

- All service facilities in a given category are the same in terms of services and care

- WorldPop population density data for 2015 are accurate

### **Travel modalities**

- Walking individuals travel unencumbered, i.e. without carrying significant loads

- Walking individuals have no disabilities or injuries that affect travel speed

- Vehicles can only move on roads. Some very small roads are only accessible to motorcycles.

- There are no traffic jams.

- Planes and boats are never used for travel

- Individuals _immediately_ take the fastest possible means of land transportation in a given cell. (E.g. not only do people always drive on roads, they spend no time waiting for transport to arrive.)

- Landslides, floods, snowfall, road maintenance and other movement‐blocking events never occur.

### **Roadways**

- Every travel route above 2400m is an unpaved walking path, _unless_ the data set explicitly says otherwise (e.g. in Mustang).

- Every DRCN marked Hill or Mountain in provinces 6 and 7 is an unpaved walking path, _unless_ the data set explicitly says otherwise.

- Every SRN marked Hill or Mountain in provinces 6 and 7 is an unpaved road, _unless_ the data set explicitly says otherwise.

## Limitations

We must note several limitations to this model and the resulting analysis. Localized inaccuracies in our model are inevitable given the contrast between the high resolution of the data set and local shortcomings in input data quality and completeness. Errors in coverage or geolocation from the service data sets, sourced from relevant ministries, would naturally lead to inaccuracies in the model outputs. For instance, we were unable to source a complete, high‐ quality bridges data set that aligned with spatial data on rivers. Therefore, roadways crossing rivers were the only representation of bridge crossings, which are important chokepoints for transportation in Nepal’s hills. We assumed walkers used these vehicle roads for river crossings, although in reality walkers in rural Nepal commonly use trail bridges and cable‐pull tuins. Thus, the model may exaggerate travel times in some areas where walking is the principal modality.
5. Visualization of Value-Added Network of B&R Economies FIGURE 29: COMPARISON OF COUNTRIES WITH THE LARGEST VS1*, 2010 <!-- Start of picture text --> 5.3<br>2.0<br>1.6 1.6<br>1.2 1.3<br>1.1<br>0.8 0.8<br>0.5<br>IND RUS IDN CAN FRA GBR JPN CHN DEU USA<br>5<br>4<br>3<br>2<br>1<br>Domestic Value Added Returned Home (Forward, VS1*) 0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the TiVA database.

# **5. Visualization of Value-Added Network of B&R Economies**

In this section, we take the same value-added data by origin and destination used in the previous section to build network graphs.<sup>11</sup> Network analysis allows us to visualize the most important providers and users of value-added. The value-added that we are considering is the amount of foreign value added in exports and the domestic value added used by third countries for re-exports. Formally, the technique defines countries as “nodes” and the “trade flows” between them as edges. Network visualization algorithms map these graphs of nodes and edges to identify specific patterns.

We start by plotting the most important sources of value-added (Figure 30) for the world economy. The sources of value-added can be thought of as the origin of intermediate tasks that are necessary for countries to produce their own exports. For example, the automotive industry in Germany needs ignition mechanisms from Turkey. In our example, Turkey would be an important source of value-added for Germany. In Figure 30, we compare the top 3 most important sources of value-added in 1995 and 2010. The size of the arrows denotes the amount of value-added traded and the size of the node reflects the centrality (number of partners) of the economy in the network. We observe that in 1995 (Panel A) the two dominant sources of value-added were the United States and Germany. The links between Japan, the United States, Canada, Mexico and the Republic of Korea are obvious. Germany, together with some European countries (Italy, France, Great Britain, Belgium and the Netherlands) formed a strong network too. In 2010 (panel B), China emerged as the third important actor with much more activity at the global level. The Germany and United States networks are still present. To better grasp the role of China we plot, in Figure 31, only the nodes that are active in its network. We see that China is a truly global actor and we can distinguish a network with the Americas (United States, Brazil, Chile, Mexico, Canada and Colombia) and another one with mostly B&R Economies.<sup>12</sup> FIGURE 30: ORIGIN OF VALUE-ADDED IN GROSS EXPORTS, 1995 AND 2010, WORLD ECONOMY PANEL A: ORIGIN OF VALUE-ADDED IN EXPORTS, 1995 PANEL B: ORIGIN OF VALUE-ADDED IN EXPORTS, 2010

> 11 Taglioni and Winkler (2016) provide a detailed discussion on the use of network analysis for studying international shared production.

> 12 In the appendix (A4), we consider the same type of network using the EORA multi-regional input-output tables.

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