rafmacalaba/datause-encoder-data huggingface dataset:
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Besides the approaches to improving country statistical capacity discussed above, a promising direction to address these challenges is to operationalize these tools using a bottom-up approach. For instance, there have been ongoing efforts at the World Bank to include SPI indicators at the project level as specific metrics for monitoring statistical capacity, especially for low-income countries in Sub-Saharan Africa. This setting is similar to the other metrics that have been employed to measure project performance in other development projects. The SPI framework is flexible enough to allow either direct application of all its indicators, some of its indicators, or even some re-weighted version of its indicators to better suit the specific country context. Another direction is to “collect more data about data”, that is, implementing institutional surveys to directly collect data from NSOs to measure progress with their statistical capacity. These surveys can offer supplementary information to enrich the current indicators and, if designed appropriately, may help shed more light on the country-specific political economy environment regarding statistical capacity.

# **Materials and Method**

# **Conceptual motivations and construction of the SPI**

# _Conceptual motivations_

The SPI is built on a framework that is forward looking, measures less mature statistical systems as well as advanced systems, covers a country’s entire NSS (rather than just the NSO as with some previous indexes), and provides countries with incentives to build a modern statistical system. In particular, by helping countries and development partners identify the strengths and weaknesses of NSSs, the SPI can support policy advice to improve or benchmark NSSs, offer advocacy for national statistics, and facilitate investment decisions for governments and (bilateral and multilateral) donors. The SPI is also built on standard _desiderata_ for a statistical index (i.e., simple, coherent, motivated, rigorous, implementable, replicable, incentive consistent), as well as clear conceptual and mathematical foundations. Importantly, the SPI is also open-data and opencode where users can freely access data and experiment with different adjustments to the index on the World Bank’s website. (We return to more discussion on data access in the next section.) It is useful to note that while measuring a country’s statistical _capacity_ is our ultimate goal, this task is difficult, if not impossible to implement at scale for all countries, given the typically unobserved inherent characteristics with an NSS. It is, however, relatively more straightforward 16
# **Table 2**

**Evaluating similarities between recent literature surveys on financial education (Pearson Correlation Table)**

|**Referen**|**ces**|**Martin 2007**<br>**Hatha**|**way and Khatiw**<br>|**ada 2008**<br>**McCormick 2009**<br>**A**|<br>**garwal et al 201**<br>**Colli**|**0**<br>**ns and O’Rourke 2010**<br>**Gale and Levine 2010**<br>**Hastings, Madrian, Skimmyhorn 2012**<br>**Xu and Zia 2012**<br>**Lusardi and Mitchell 2013**|
|---|---|---|---|---|---|---|
|**Martin 2007**|1.000||||||
||(1.000)||||||
|**Hathaway and**|0.416|1.000|||||
|**Khatiwada 2008**|(0.173)|(1.000)|||||
|**McCormick 2009**|-0.067|0.001|1.000||||
||(0.004)|(0.000)|(1.000)||||
|**Aarwal et al 2010**|0.118|0.097|-0.017|1.000|||
|**g**|(0.014)|(0.009)|(0.000)|(1.000)|||
|**Collins and**|0.161|0.104|0.026|0.425|1.000||
|**O’Rourke 2010**|(0.026)|(0.011)|(0.001)|(0.181)|(1.000)||
|**Gale and Levine**|0.171|0.190|-0.088|0.174|0.047|1.000|
|**2010**|(0.029)|(0.036)|(0.008)|(0.030)|(0.002)|(1.000)|
|**Hastings, Madrian,**|-0.057|0.009|-0.060|0.075|0.036|0.084<br>1.000|
|**Skimmyhorn 2012**|(0.003)|(0.000)|(0.004)|(0.006)|(0.001)|(0.007)<br>(1.000)|
|**X d Zi 2012**|-0.077|-0.099|-0.065|-0.088|-0.100|-0.083<br>-0.175<br>1.000|
|**u an a**|(0.006)|(0.010)|(0.004)|(0.008)|(0.010)|(0.007)<br>(0.031)<br>(1.000)|
|**Lusardi and**|-0.104|-0.038|-0.155|-0.105|-0.167|0.039<br>-0.002<br>-0.217<br>1.000|
|**Mitchell 2013**|(0.011)|(0.001)|(0.024)|(0.011)|(0.028)|(0.002)<br>(0.000)<br>(0.047)<br>(1.000)|
|**Total Number of**|||||||
|**References**|44|35|45|82|57|61<br>100<br>132<br>187|

The limited overlap in terms of the literature they are reviewing helps to explain the variety of findings in these narrative literature reviews. Martin (2007), at the Federal Reserve Bank of Richmond, provides one of the earliest reviews and has a limited number of references (44). He assesses the evidence positively, writing “Generally, we can conclude from this literature review that there is a need for financial education and that many existing approaches are effective. More specific conclusions might be best described as tentative given the current scarcity of research in some areas.”<sup>7</sup> Just one year later another literature review published by the Federal Reserve Bank of Cleveland (Hathaway and Khatiwada 2008) came to a much less positive conclusion using many of the same research papers. Hathaway and Khatiwada (2008) state, “Unfortunately, we do not find conclusive evidence that, in general, financial education programs do lead to greater financial knowledge, and ultimately, to better financial behavior. However, this is not the same as saying that they do not or could not – it is just that current studies, while at times illustrating some success, leave us with an unclear feeling about whether we can grant a blanket application of these results specifically, to financial education programs more generally.”<sup>8</sup>

> 7 Martin (2007), page 22.

> 8 Hathaway and Khatiwada (2008), page 19.

9
workshop organized every year around August/September<sup>13</sup> . The selected NGOs were then assigned a given number of sites to be open in any given district, based on an estimate of the target population in the districts.<sup>14</sup> The program in each province was initially advertised to all majors of the communes included within the district. Mayors would then organize a meeting with all the communities to make them aware of the program and encourage them to participate. A community, to be eligible to open a project site, had to accessible for most part of the year (by auto/motorbike, chariot or pirogue). Accessibility in Madagascar is a major constraint for service delivery, and the phasing in of the program reflected this constraint. The NGO, and more specifically the animator who is in charge of supervising and providing support to the community nutrition worker, need to able to reach the sites regularly by motorbike. Given the logistical constraints, the NGOs, in partnership with the communes, started on average with the most accessible communities first, and then expanded to the least accessible sites over time.

# **4. The Data**

The monitoring data of the program consists of monthly detailed information at the site level. This information is originated from the registry and is kept by the community nutrition worker. Each registry contains information on the program activities, health outcomes, and the time when the site joins the program.

The monitoring data covers about 3,600 sites in the whole country (as of January 2003). The information spans from 1999 to the end of 2002, with information varying depending on the date of entry to the program. We will confine the analysis to the biggest four main provinces (Antananarivo, Fianarantsoa, Tulear and Tamatave), which represent 85% of the total target population, and for which there is substantial variation in the phase-in of the program. We will make use of the quarterly data in order to smooth out noises in the outcome indicators.

The registry for each site contains information about the monthly participation of registered mothers, the total number of children weighed in each month, and the number per age group who are classified as severely or moderately malnourished (as indicated by their position in the growth chart).<sup>15</sup> Moreover, for each community we

> 13  Eligible NGOs had to have had at least one year of experience in the district of intervention.

> 14 Each NGO could be assigned a maximum of 20 sites and had to hire a supervisor ( _animateur_ ) to be assigned to each site. 15 The registry contains also information on food supplementation (total quantity received, and the share of children receiving any food supplementation by age group), vitamin A supplementation (by age group) and on participation rates of mothers to nutritional education sessions and cooking demonstrations.

10
Although the IHS data provide extensive detail about the vehicles, unfortunately, the data are incomplete for vehicle prices and certain other attributes. We use El Motor Magazine<sup>3</sup> to impute vehicle prices. We use Carfolio.com to impute fuel economy, GHG emissions rates, and other attributes.

To assess the quality of the imputation, we compare imputed values with non-missing data. For vehicle attributes with continuous values, such as horsepower and price, the absolute mean difference between the observed and the imputed data was 5%, with a standard deviation of 0.8%. We obtain gasoline and electricity prices from Colombia’s Ministry of Energy.

Data for charging stations come from different sources, including OpenChargeMap,<sup>4</sup> Electromaps,<sup>5</sup> and PlugShare.<sup>6</sup> Because our registration data are at the national level, we compute the total number of public charging stations by quarter.

# 2.2 Characteristics of Colombia’s New Vehicle Market

Colombia is Latin America’s fourth largest market for both automobiles sales and production (after Brazil, Mexico, and Argentina).<sup>7</sup> There are two large light-duty vehicle manufacturers in Colombia (Colmotores/GM and Sofasa/Renault).<sup>8</sup> These two firms assemble cars from imported materials that represent around 70 percent of a fully assembled car.<sup>9</sup> These imported materials are subject to import tariffs of a similar magnitude (currently 35 percent) as imported cars. Since Colombia liberalized trade in light-duty vehicles in 1991, the incumbent advantage of the domestic assembly plants has gradually dissipated, and previously nonexistent imports of other cars have grown significantly (Tovar 2012). Currently, two-thirds of new vehicle sales are imports.<sup>10</sup>

> 3 <u>https://www.motor.com.co/precios [last accessed: April 2022].</u>

> 4 <u>https://openchargemap.org/site</u> [last accessed: April 2022].

> 5 <u>https://www.electromaps.com/en</u> [last accessed: April 2022].

> 6 <u>https://www.plugshare.com/</u> [last accessed: April 2022].

> 7 See https://www.globalfleet.com/en/manufacturers/latin-america/analysis/latam-vehicle-sales-down-2020- <u>commercial-fleet-solid</u> and https://investincolombia.com.co/en/sectors/manufacturing-industries/automotive [last accessed: April 2022].

> 8 The third large manufacturer, Compañía Colombiana Automotriz, closed its operations in Colombia in 2014.

> 9 To our knowledge, none of the cars assembled at these plants are electric or hybrid vehicles.

> 10 See https://www.industryweek.com/the-economy/competitiveness/article/22018055/growing-opportunities-in- <u>colombias-automotive-market</u> [last accessed: April 2022].

8
construct wealth by adding net foreign assets to the capital stock, constructed for all countries in the same way as we have described for China in Section 2 of the paper. We then express net foreign assets as a share of wealth, and average over all available annual observations between 1980 and 2000 to construct a single cross-section of 20year averages.

In Table 2 we first show the relationship between the net foreign asset position and per capita wealth relative to world average per capita wealth, where we define the world as the set of countries included in the regression. Not surprisingly, this relationship is strongly positive, indicating that on average capital tends to flow from rich countries to poor countries. This relationship is also quite consistent with the theoretical model of the previous section, where we have seen a positive relationship between per capita wealth and the net foreign asset position. What is also clear is that China is a very strong outlier in this relationship. We have already seen that China is a strong outlier in the relationship between capital stocks per capita and net foreign assets in Figure 5, and empirically per capita capital stocks and per capita wealth are very highly correlated across countries. The China dummy is equal to 0.19, indicating that on the basis of per capita wealth alone, we would expect China to have a net foreign asset position of -19 percent of wealth.

In the next three columns of Table 2 we introduce three measures intended to capture productivity differences across countries. The first of these is simply the loglevel of total factor productivity obtained from the very simple development accounting exercise that we described in the model calibrations of the previous section. The other two are two measures of institutional quality, which we expect will be important determinants of the attractiveness of a country for both domestic and foreign investment. The first is the ICRG measure of Expropriation Risk that has been widely used as a measure of property rights protection in the literature on institutions and growth. The annual data cover the period 1984-1997, and we average this over all available years for each country. The other is a measure of Rule of Law constructed by Kaufmann, Kraay, and Mastruzzi (2004). As this variable begins only in 1996, we simply use the 1996 value of this variable. The simple correlation between our measure of TFP and the net foreign asset position is negative, while the correlations of the two institutional measures with net foreign asset positions are positive. The latter positive correlations reflect the 18
# **II. Travel Cost Modeling**

In travel cost modeling, the decision to recreate is typically modeled as a latent _*_ demand, _yi_ , representing the number of trips taken in one year as a function of travel cost (P), site quality attributes (Z) and individual demographic characteristics (X): Travel cost-modeling (TCM) can be implemented through household or on-site surveys. However, each sampling method involves a number of different statistical issues.

# _(i) Household survey_

An important modeling issue when applying TCM pertains to the treatment of non-negative integers observed in individual recreational data, as one may encounter a large proportion of zeros in a general household survey (Shaw, 1988; Grogger and Carson, 1991; Hellerstein, 1991). Observing a zero implies that the services from the site do not enter into the utility function of the individual. In the utility maximization framework, it implies that the individual is currently at some choke price where he is consuming zero trips, and that if the current “market” price were to fall below the choke price, the individual would demand a positive number of trips. However, one may also observe a zero if for some reason (such as age, health-related reasons, etc.) services from the site would never enter an individuals’ utility function (Habb and McConnell, 1996). Thus, there is an important distinction between observing zeros for those who are participants and for those who are non-participants (‘true zeros’). Standard count data models such as the Poisson or negative binomial assume that all individuals surveyed are potential users of the good in question, and that the same variables influence all potential users similarly. In the presence of a large number of zeros, and where the participation question is relevant, this assumption may not be valid and should be tested for its significance.

3
-46-

something that, in any case, it would seem easier to engineer than a fall in real wages. Indirect evidence on the efficacy of such a strategy can be gleaned from historical parallels drawn from the depression of the 1930s [Bonnell, 1981; Sachs, 19831. For four out of the five countries for which detailed data are available (Britain, Germany, Japan and Sweden, the fifth being the United States), real wage costs grew in the years 1929-32, despite the severity of the recession, suggesting, prima facie, that labour was "pricing itself out of jobs" (Table 6). Yet, the demand boosts that these countries received from 1932-33 onwards, thanks to depreciation in Britain, rearmament in Germany, and expansionary fiscal policies in Japan and Sweden, rapidly eliminated the real wage gap, as nominal wage growth remained sluggish and productivity was boosted by recovery. In all four countries employment rose and growth accelerated.

Historical parallels must be treated with caution because of obvious changes in circumstances. In particular, it is possible that the length of the recent recession may have diminished the potential cyclical boost to productivity should firms have dishoarded labour to a greater extent than in earlier downward phases of the cycle (Sachs, 1983]. Similarly, it may be more difficult in present circumstances to ensure real wage falls akin to those recorded in the 1930s, if only because of greater unionization and stronger real wage resistance. On the other hand, "growing out of the problem" need not imply falling real wages, but only rises in real wages below those of productivity, combined, ideally, with nominal wage moderation.

It is the potential difficulty of ensuring the latter that raises a - different danger the possibility that, despite very high levels of unemployment, any demand boost may raise the rate of inflation. Available
Figure 11: Evolution of Commercial Banks’ Performing Loan Portfolio (MXP Billion) /1 <!-- Start of picture text --> 900 Real Growth 2000 -<br>2005<br>800<br>- 16.1%<br>700<br>600<br>- 36.1%<br>500<br>400 + 579.0%<br>300 - 29.3%<br>200<br>100 + 8.2%<br>0<br>Commercial Housing Consumer Financial institutions Public sector<br>MXP Billion<br>Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05<br><!-- End of picture text --> Figure 12: Evolution of Commercial Banks’ Performing Loan Portfolio (% of GDP) <!-- Start of picture text --> 16.0%<br>14.0%<br>3.6%<br>12.0%<br>2.7%<br>10.0% 1.3%<br>0.5%<br>0.7%<br>8.0%<br>2.7%<br>3.5%<br>6.0% 1.8%<br>4.0%<br>2.0% 4.9% 4.4%<br>0.0%<br>Commercial Housing Consumer Financial institutions Public sector<br>% of GDP<br>Mar-00 Jul-00 Nov-00 Mar-01 Jul-01 Nov-01 Mar-02 Jul-02 Nov-02 Mar-03 Jul-03 Nov-03 Mar-04 Jul-04 Nov-04 Mar-05 Jul-05<br><!-- End of picture text --> Source: BOM. /1 Figures in constant pesos of January 2000.

Note: Performing restructured UDI-denominated mortgage loans that are off bank balance sheets (“Fideicomisos UDIs”) are also included in the analysis.

Commercial banks remain the primary source of financing to the non-financial private sector, although the composition of their credit portfolio has changed. As can be seen in figures 13 and 14, performing commercial bank credit accounted for around 36 percent of total domestic and foreign debt financing as of end-2005, a level that is almost unchanged to that of 2000. However, the composition of banks’ loan portfolio has shifted significantly during this period towards consumer credit at the detriment of mortgage and commercial lending.

Figure 13: Evolution of Private Sector Debt Financing by Provider (2000-2005) <!-- Start of picture text --> 100%<br>Foreign debt issues<br>80% Cross-border bank<br>credit<br>Domestic debt issues<br>60%<br>Infonavit<br>40% Development Banks<br>1st tier<br>Non-Bank FI<br>20%<br>Commercial Banks<br>0%<br>Proportion of Total Financing<br>Mar-00 Jul-00 Nov-00 Mar-01 Jul-01 Nov-01 Mar-02 Jul-02 Nov-02 Mar-03 Jul-03 Nov-03 Mar-04 Jul-04 Nov-04 Mar-05 Jul-05 Nov-05<br><!-- End of picture text --> Source: BOM.

Note: FIRA and Financiera Rural lending, certain public sector retirement programs financing mortgages (e.g. FOVISSSTE) and mortgage/asset-backed securities are excluded. Only performing loans are included, while domestic bond issuance only includes bonds issued by non-financial private sector firms. Non-Bank FIs (Financial Institutions) includes SAPS, OACs and credit unions.

13
for example interested in predicted care seeking in the dry season, when respiratory illness is more prevalent than malaria and diarrhea.

# **6 Conclusion**

The prevailing view in international health policy today is that subsidies are needed to close remaining gaps in access to care. The potential benefits are large: by some estimates, improved access to primary care could prevent 29%–40% of post-neonatal deaths in developing countries. Less than 26% of malaria cases in children are estimated to receive adequate treatment, and 411,000 children died of malaria in 2013 in Africa alone.<sup>29</sup> Many countries have begun to remove health care user fees for children, and Mali announced plans to adopt this policy in 2019. However, we have relatively sparse evidence how this affects patterns of utilization.

This study aims to open the “black box” of the extensive margin of health care demand and the effects of demand-side interventions. We estimate health care use conditional on health status to remove confounding effects of illness incidence, and benchmark care-seeking against the need for care according to WHO medical standards. By surveying all (potential) health care users we avoid selection bias due to spontaneous recovery.

Our results have very encouraging implications for the subsidy debate. First, a primary barrier to effective care seeking in the urban population we study is indeed the cost of care. Underuse is rampant: care is either sought late in the spell or, in the majority of cases, not at all. The welfare cost of subsidies could be prohibitive if they lead to substantial mistargeting and overuse of health care, risking overburdening the severely limited capacity of health care systems in poor countries and thus diverting resources away from those most in need. However, while subsidies nearly triple health care usage in our sample, the additional demand comes largely from children for whom medical care is required according to WHO guidelines. One reason is that parents have some ability to recognize differences in illness

> 29United Nations Inter-Agency and Expert Group on MDG Indicators (2015); WHO Global Malaria Programme (2015); Bhutta et al. (2008).

35