rafmacalaba/datause-encoder-data huggingface dataset:
Finally, an important concern with adding financial education to the school curriculum is that it may crowd out learning in other areas. We analyze administrative data on student graduation rates and find an improvement in treatment schools, with a 1 percent higher passing rate (compared to a graduation rate of 84 percent in the control group). While this effect is small, the positive and significant coefficient allays concerns that the financial education curriculum diverted attention of teachers and students away from regular academic learning.

Overall, our study shows that financial education can be an effective tool in improving financial outcomes of students when delivered in a comprehensive manner and over a significant period of time. Also, key complementary benefits can be derived by involving the entire household, students and parents, as indicated by the trickle-up and parents’ workshop impacts. Further our results on mechanisms show that student preferences on saving intentions and financial autonomy are important pathways for improved financial behavior. Finally, the large sample size and breadth of coverage through six states in Brazil provides strong support for the external validity of our findings.

This paper proceeds as follows. Section 2 summarizes the literature on financial education for youth and details the Brazilian context. Section 3 describes the financial education curriculum, and Section 4 presents the research and sampling methodology, as well as the study timeline. Section 5 describes program take-up and implementation, and Section 6 presents summary statistics and survey participation analysis. Section 7 discusses the main results, and Section 8 analyzes mechanisms of impact. Finally, Section 9 concludes.

# **2. Literature and Context**

# **2.1. Financial Education for Youth**

There is a growing literature on financial education and its determinants for youth. A consistent finding in this literature is that financial literacy tends to peak among adults in the middle of the life cycle and is significantly lower among youth. In the U.S., for example, those in the prime age group (25-65) tend to perform about 5 percent better on financial literacy questions than those under 25 (Lusardi and Mitchell, 2011b). Strikingly, Lusardi et al. (2009b) find that less than a third of American teenagers (ages 12-17) possess basic knowledge of interest rates, inflation, and risk diversification. Mandell (2006) notes that there is even evidence that youth financial literacy has been declining in the U.S. since the late 1990s. Similar evidence comes from Australia where Beal and Delpachitra (2003) identify low levels of financial literacy among youth.

Perhaps in response to such trends and combined with the aftermath of recent global financial crises, policy makers around the world have made financial education for youth a priority, with many school based initiatives now part of education reform. Yet, the impacts of such school programs on financial knowledge and behavior are still not well understood. Existing studies 5
Panel B. Dependent variable: Lending-deposit interest rate

|||Lendin|g-deposit in|terest rate|spread||
|---|---|---|---|---|---|---|
|VARIABLES|(14)|(15)|(16)|(17)|(18)|(19)|
|Inflation (%)|0.033|0.025|0.031|0.029|0.033|0.022|
||(1.097)|(0.866)|(1.054)|(0.999)|(1.100)|(0.753)|
|St. Deviation Inflation (%)<br>|0.040|0.023|||0.046|0.022|
|ons|(0.580)|(0.380)|||(0.651)|(0.362)|
|5-year St. Deviation Inflation (%)<br>diti|||0.047|0.029|||
|on|||(1.125)|(0.701)|||
|Public Debt (%GDP)<br>al c|0.037**|0.039***|0.038***|0.034**|0.038**|0.041***|
|isc|(2.447)|(3.094)|(2.659)|(2.504)|(2.482)|(3.057)|
|GDP percapita<br>ro-f|0.000|-0.000|0.000|0.000|0.000|-0.000|
|ac|(0.488)|(-0.533)|(0.437)|(0.221)|(0.441)|(-0.340)|
|Policy/money market rate (%)<br>M|-0.015|-0.014|-0.014|-0.013|-0.013|-0.004|
||(-0.398)|(-0.404)|(-0.391)|(-0.375)|(-0.353)|(-0.124)|
|Devaluation rate (%)|||||-0.004|-0.008|
||||||(-0.446)|(-1.020)|
|Overhead costs (%Total Assets)|0.251**|0.227**|0.230**|0.244**|0.250**|0.219**|
||(2.432)|(2.398)|(2.302)|(2.500)|(2.414)|(2.299)|
|Three bank concentration (% Total<br>bank system assets)<br>ristics|0.020|0.031**|0.017|0.022*|0.019|0.026**|
|acte|(1.528)|(2.548)|(1.291)|(1.708)|(1.460)|(2.062)|
|Non-performing loans (% Total<br>Gross Loans)<br>char|0.049|0.019|0.040|0.043|0.050|0.025|
|<br>ctor|(1.202)|(0.542)|(1.012)|(1.135)|(1.226)|(0.682)|
|Non-interest income (% Total<br>Income)<br>ing se|-0.018*|-0.010|-0.016|-0.014|-0.017|-0.008|
|ank|(-1.690)|(-0.989)|(-1.496)|(-1.401)|(-1.616)|(-0.779)|
|Private credit (% GDP)<br>B|0.001|-0.013|0.002|0.007|0.002|-0.004|
||(0.047)|(-0.872)|(0.103)|(0.431)|(0.115)|(-0.269)|
|Credit Bureau Coverage (% adults)<br>ent|-0.008||-0.009||-0.008||
|nm|(-1.230)||(-1.348)||(-1.224)||
|Time to resolve insolvency (years)<br>viro|0.270*||0.276*||0.265*||
|s en|(1.900)||(1.966)||(1.857)||
|Rule of Law (Kaufmann et al.,2010)<br>ines||-2.250***||-2.381***||-2.587***|
|Bus||(-2.685)||(-2.786)||(-3.007)|
|Constant|2.734***|3.746***|2.862***|2.476***|2.702***|3.204***|
||(2.906)|(4.848)|(3.086)|(2.717)|(2.866)|(3.910)|
|Observations|415|464|425|433|415|445|
|Number of country_code_|48|50|49|50|48|50|

_Note:_ Annual information between 2005 and 2017 for 50 EMDE countries. _Public debt_ includes debt data for general government. In case this data is not available, debt data for the central government is used. _St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y- o-y, using a one-year rolling window. _5-year St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y-o-y, using a 5-year rolling window. _Devaluation rate_ is the y-o-y change of the exchange rate vs. US dollar. Benchmark regressions in columns (14) and (15).

Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable capturing Global Financial Crisis period included.

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 51
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# **A of variables**

|_GDPi,t_|Real GDP for country I in period t|
|---|---|
|_FGDP _<sup>_t_+5</sup><br>_i,t_|Real GDP forecast for country I as per WEO published in year t<br>for period t+5|
|_ActualGrowth_<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_|=(GDP_i,t_+5_/GDPi,t_)_/_<sup>1</sup><sup>_/_5</sup> _−_1<br>The 5 year average growth rate for for country i as per WEO<br>published in year t|
|_ForecastGrowth_<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_|= (FGDP_i,t_+5_/GDPi,t_)_/_<sup>1</sup><sup>_/_5</sup> _−_1<br>The 5 year average forecast growth rate for country i as per WEO<br>published in year t|
|_GrowthDiff _<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_|= ForecastGrowth <sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_<br>_−ActualGrowth_<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_<br>The 5 year average growth diferential for country i as per WEO<br>published in year t|
|_AbsGrowthDiff _<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_|=_|ForecastGrowth_<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_<br>_−ActualGrowth_<sup>_t_+1</sup><sup>_−t_+5</sup><br>_i,t_<br>_|_<br>Absolute value of 5 year average growth diferential for country i<br>as per WEO published in year t|
|_LagDiscoi,t_|A dummy variable which equals one if one or more giant oil dis-<br>covery was made in country i in the two year prior to the WEO<br>being published in year t|
|_LagSuperDiscoi,t_|A dummy variable which equals one if one or more supergiant oil<br>discovery was made in country i in the two year prior to the WEO<br>being published in year t|
|_LagSignDiscoi,t_|A dummy variable which equals one if the estimated value of dis-<br>coveries made are larger than 1 percent GDP in country i in the<br>two year prior to the WEO being published in year t|
|_LagFirstDiscoi,t_|A dummy variable which equals one if country i made its frst giant<br>oil discovery in the two year prior to the WEO being published in<br>year t|
|_Insti_|A dummy variable which divides country into two groups based<br>on whether they scored above or below mean on the Augmented<br>Freedom House index of Political Rights in 1985, the last year we<br>have data for before our sample starts|

30
2

# **I. Introduction**

The last few years have seen a growing commitment around the world to universal health coverage (UHC) with many countries embarking on UHC-inspired health reforms, and commentators urging others to do so (see e.g. Horton and Das 2014). Yet there are still considerable differences of view about what UHC actually means. Unsurprisingly, as a result, there is a lack of clarity on how to measure progress towards UHC; this in turn has led some (e.g. Fan et al. 2012a) to question whether UHC could ever be the umbrella goal for health in the post-2015 international development goals.

In this paper we propose a way of measuring progress towards UHC. Our index takes as its starting point the definition of UHC proposed by the World Bank and the World Health Organization (Boerma et al. 2014b; Boerma et al. 2014c); we explain below why we adopt this definition. We go on to discuss how our UHC index can be operationalized with existing data in such a way that countries’ performances vis-à-vis UHC can be compared and tracked over time. Finally, we use our index to compare UHC performance across 24 countries that have implemented UHC-inspired reforms (which we define as reforms that have sought to increase the fraction of the population covered, the range of services covered, and/or the financial protection associated with use of health services) and for which we have detailed information on how they have done so. While the term “universal health coverage” was popularized by the 2010 World Health Report (World Health 2
negative due to the high increase in the domestic interest rate in an effort to stabilize the nominal exchange rate. The responses then faze out and converge to the steady-state values for output and inflation. In general, Macedonia is able to contain the effect of the external shock better than Slovakia, but at the cost of a large spike in the domestic interest rate and significantly negative responses of the output gap and inflation.

While allowing for larger volatility in the exchange rate and focusing on inflation targeting Slovakia is able to secure lower volatility of output and inflation in the long run (see Table 11). This is because if the external relative price is not allowed to adjust to absorb negative external and domestic shocks the adjustment has to happen in the domestic economy through prices and production volumes. The latter two are much less flexible than the exchange rate, and should exchange rate targeting be the long-term choice for monetary policy the Macedonian economy has to become more flexible to ensure that macroeconomic stabilization is not in the way of long-run growth. This can be achieved through improved labor markets functioning, financial intermediation, capital stock adjustments, and more competitive production.

**Table 11: Asymptotic standard deviation of economic variables for Macedonia and** **<u>Slovakia based on the estimated models’ simulations</u>**

|Volatility of|Output gap|Inflation|Interest rate|Real exchange rate|
|---|---|---|---|---|
|Macedonia|5.9931|4.6698|8.2882|5.0862|
|Slovakia|4.3198|3.0333|3.7332|5.2433|

# **7.  Conclusion**

In this paper, an attempt was made to fit a New Keynesian Policy model to the data on Macedonia and Slovakia, while providing some justification for the differences in estimated model parameters and structural shocks using a gap analysis of relevant microeconomic data. Under the current monetary policy regime of exchange rate targeting, Macedonia needs to improve the transmission mechanism of the monetary policy by increasing competition in and efficiency of financial intermediation, promoting financial deepening, increasing access to finance, improving the response of domestic 31
28 Third, the "long-run" correlations of trade regimes and size distributions are quite different from the short-run year-to-year correlations. Not only are the effects of trade exposure stronger in the long run, but the correlations of export shares change sign. The short run correlations show exports associated with relatively large plants while the long run correlations show exports associated with relatively small plants. We trust the long run figures more, because we limited our short-run analysis to simultaneous correlations, and have not attempted to model the dynamics of adjustment. Nonetheless, the short run findings suggest caution in extracting policy recommendations from our figures.

This paper is a first step in the direction of micro-based examinations of the rationalization hypothesis. Though suggestive, much remains to be done. Aside from modeling the dynamics of adjustment, we hope to study the relationship between average costs and size, and the degree to which plants adjust costs endogenously with changes in the trade regime.
period of 2004-05 to 2011-12. It is also interesing to note that between 1994 and 2005, there was a significant 5 to 7 percentage point increase in urban areas that was ultimately undone during 2005-12. In contrast, very little change occurred in rural areas between 1994-2005.

**<u>Table 12: Change in FLFP Rate</u>**

||C<br>|hange in Rur<br>|al FLFP Rate<br>|<br>||Change in Urb|an FLFP Rate||
|---|---|---|---|---|---|---|---|---|
||ase ear|-|-|-|ase ear|||-|
|Consumption<br>Deciles|<br>FLFP rate<br>(1993-94)|<br>to 2004-<br>05|<br>to 2011-<br>12|<br>to 2011-<br>12|<br>FLFP rate<br>(1993-94)|1993-94 to<br>2004-05|2004-05 to<br>2011-12|<br>to 2011-<br>12|
|1|52.6|-1.1|-14.5|-15.6|36.8|-4.9|-8.4|-13.3|
|2|49.4|0.1|-13.9|-13.8|30.6|1.3|-7.5|-6.2|
|3|48.7|0.6|-13.8|-13.2|25.2|7.1|-7.5|-0.4|
|4|47.3|1.0|-13.1|-12.1|26.4|2.2|-5.0|-2.8|
|5|45.7|1.1|-10.3|-9.2|23.2|6.5|-8.0|-1.5|
|6|45.2|1.8|-12.5|-10.7|21.4|6.5|-6.2|0.3|
|7|44.4|1.5|-11.5|-10.0|19.2|7.0|-6.4|0.6|
|8|43.2|1.7|-10.7|-9.1|19.2|5.1|-5.3|-0.2|
|9|43.0|-1.1|-9.3|-10.4|19.6|1.2|-0.8|0.4|
|10|40.7|-2.0|-5.6|-7.5|21.8|1.2|-2.2|-1.0|

Source: Authors’ own estimate based on NSSO data.

In summary, we observe two broad changes: first, a significant drop in FLFP is observed between 2004-05 and 2011-12, which wiped out any increase between 1993-94 and 2004-05. This phenomenon is distinctly visible across all age groups, socio-economic groups and across groups with different levels of educational attainment. Second, the phenomenon is more pronounced in rural than in urban areas. In other words, the drop is more pronounced across all of these socioeconomic groups in rural areas than across their respective counterparts in urban areas.

When we delve deeper within each group, we find that this rural-urban difference in the FLFP rate varies over time across subgroups. For example, rural-urban differences in the FLFP rate are more pronounced at the lower strata of consumption deciles and gradually decline with increases in per capita consumption (Figure 1). The rural-urban difference in the FLFP rate over time is also more 23
damage across all flooded areas. To construct an inundation map of the affected areas, a map based on MODIS satellite pictures from Anderson _et al._ (2004) is used with spatial algorithms to detect the difference in color between inundated and non-inundated areas. Once the map is constructed, it is weighted and aggregated just like the other indices.

As for the indices themselves, the actual coefficients for floods and volcanic eruptions are simple intensity measures that do not convey anything on their own apart from an intensity weighted by nightlights. For earthquakes and the tsunami, the numbers show the overall damage to buildings in the district.

Table 1 shows the descriptives of the damage indices, with floods striking districts 2,417 times over the 8 year time period, meaning that approximately 300 districts are affected by floods annually. The earthquakes struck 435 times, while the volcanic eruptions and the tsunami affected a limited number of districts, due to the limited number of big events. The strongest earthquake damaged almost 5 percent of the buildings in a district, while the district that was worst hit by the tsunami experienced damage to 23 percent of the building mass.

Table 1: Descriptives Damage Indices - 2005 - 2012

|Disaster|N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Flood|2417|27.94|22.25|0.01|175.08|
|Earthquake|435|2.54|4.94|0.01|45.07|
|Volcanic Eruption|61|32.25|42.58|0.03|184.67|
|Tsunami|6|0.14|0.08|0.06|0.23|

Note: Earthquake mean, SD, min and max multiplied by 1,000 7
# **Tables and figures**

**Table 1: Sample Descriptive Statistics, N=8973**

|**(a): Districts**|**Percent of**<br>**Sample**|**(b): Caste**|**Percent of**<br>**Sample**|**(c): Jati (sub-caste)**|**Percent of**<br>**Sample**|
|---|---|---|---|---|---|
|Gaya|3.38|SC|69.93|SC: Chamar|20.44|
|Madhepura|31.06|ST|1.13|SC: Dobha/Dobh|2.51|
|Madhubani|5.07|OBC|16.9|SC: Dom|0.67|
|Muzzafarpur|18.51|EBC|4.65|SC: Dushad|16.72|
|Nalanda|5.58|Muslim|3.82|SC: Musahar|25.93|
|Saharsa|19.06|FC|3.58|SC: Pasi|0.89|
|Supaul|17.34|||SC: Sardar|2.08|
|||||Other SCs|0.69|
|||||ST: Adivasi|0.86|
|||||Other STs|0.27|
|||||OBC: Dhanuk|1.19|
|||||OBC: Koeri|0.91|
|||||OBC: Kurmi|1.27|
|||||OBC: Shershabadia|0.78|
|||||OBC: Yadav|6.56|
|||||Other OBCs|6.17|
|||||EBC: Keuta|0.53|
|||||EBC: Mallah|0.76|
|||||EBC: Nat|0.99|
|||||Other EBCs|2.36|
|||||Muslim: Ansari|0.68|
|||||Other Muslims|3.14|
|||||FC: Brahmin|1.34|
|||||FC: Rajput|1.46|
|||||Other FCs|0.78|

_Source:_ Authors' calculations based on data collected by Social Observatory, World Bank and Government of Bihar, Odisha and Tamil Nadu, respectively.

13
In the absence of consumption data, wealth indexes can be constructed that can offer a measure of household welfare. We discuss in this section the construction and some main properties of the wealth index (Section III.1) and the application of the wealth index to tracking household welfare over time (Section III.2).

# **III.1. Measuring Welfare with Wealth Indexes Definition**

Wealth indexes were used in earlier studies to measure household consumption and poverty (see, e.g., Montgomery _et al._ (2000)), but perhaps the study by Filmer and Pritchett (2001) helps significantly popularize its usage. Non-consumption surveys such as the DHS now automatically offer some version of wealth indexes in all their new data releases. Perhaps the main reason behind its popularity is—compared to the typical survey module that consists of hundreds of consumption items required to construct the consumption aggregate—data on a list of assets are both cheaper and easier to collect.

The idea behind a wealth index is, in fact, rather straightforward: it is a single-variable measure for household wealth that can be used to rank household welfare. In practice, it is essentially some combination of the various components of household wealth such as household assets (e.g., whether the household has a television, a car, and a telephone), the living area of the house, and the physical materials out of which the walls (or roof) of the house are constructed (e.g., whether more durable materials such as cement or bricks or less durable materials such as mud or grass are used). The types of a house’s facilities such as the toilet are also commonly used since a better type of toilet such as a flush toilet is often observed to proxy for more wealth than other flimsier ones such as a pot toilet or no toilet at all (i.e., open defecation).

To be more precise, consider a variant of Equation (1) where the left-hand side variable, household consumption<sup>_y_</sup> _ij_<sup>, is now missing, but we have data on household assets�</sup> ��<sup>—a subset</sup> 13