rafmacalaba/datause-encoder-data huggingface dataset:
3 Given the above mentioned concerns, this paper investigates, by way of econometric techniques, the effects of the changes in age structure on the central government budget deficits of national governments. Following conventional wisdom, I argue in this paper that increases in the population share of the elderly will tend to increase the budget deficit and estimate fixed-effects panel regressions using data for 55 countries to verify the validity of this postulation.

# **2. Population Age Structure Changes and the Budget Deficit**

## **2.1 The Effects of the Elderly Share on the Budget Deficit**

The above changes in the age structure of the population bear significant impact on the level of public social expenditure and consequently, holding other things constant, the size of the budget deficit.

An unexpected rise in the population share of the elderly due to improved longevity tends to increase the budget deficit because this will increase the amount of social security benefits that are paid out to the elderly by the government. Such social programs are largely financed by taxes and social security contributions collected from the working population. Since the size of the elderly population relative to that of the working population also increases with the elderly share, holding other things constant, the benefits that are paid out will tend to exceed the revenue collected for such programs. This consequently means that the government, in order to keep such social programs viable, must increase the level of taxes and social security contributions being collected from the working population or decrease the level of social security benefits being paid out to the elderly, or run a budget deficit by financing some of the social security benefits by bond issuance<sup>2</sup> .

Population aging also tends to lead to an increase in the government deficit by increasing the government's expenditure on health care. Surveys conducted in OECD countries show that public expenditure on health care is significantly higher for older persons than for the remainder of the population. This is predominantly due to health problems and costly medical technologies being concentrated on the old. As such, an increase in the share of the aged generally leads to an increase in government expenditure on public health care services, which consequently, with a given level of tax revenue, tends to increase the budget deficit.

2Of course, there is also the obvious choice of using any combination of the three options.
**Table B3** Treatment-Control Balance at Baseline, Balanced Panel

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Outcome**|**ELA Only**<br>**Control Mean**|**Boys-ELA**|**Supply-ELA**|**No Goal**<br>**Control Mean**|**Goal -**<br>**No Goal**|
|**A. Intimate Partner Violence**||||||
|Psychological abuse often|0.015|0.009<br>(0.008)|0.006<br>(0.009)|0.017|0.008<br>(0.006)|
|Physical abuse often|0.005|0.011*<br>(0.007)|0.004<br>(0.006)|0.007|0.006<br>(0.005)|
|Forced sex often|0.009|0.004<br>(0.006)|0.003<br>(0.007)|0.011|0.001<br>(0.005)|
|Psychological abuse in last year|0.053|0.014<br>(0.016)|0.013<br>(0.019)|0.057|0.013<br>(0.012)|
|Physical abuse in last year|0.040|0.010<br>(0.015)|0.011<br>(0.017)|0.044|0.008<br>(0.011)|
|Forced sex in last year|0.032|0.001<br>(0.011)|0.008<br>(0.014)|0.033|0.004<br>(0.009)|
|**B. Sexual Activity**||||||
|Had sex|0.247|-0.006<br>(0.038)|0.048<br>(0.037)|0.252|0.020<br>(0.021)|
|Has partner|0.204|0.013<br>(0.036)|0.043<br>(0.035)|0.218|0.006<br>(0.020)|
|Had partner past 2 years|0.258|0.009<br>(0.040)|0.040<br>(0.037)|0.266|0.019<br>(0.021)|
|Total sexual partners ever|0.295|0.029<br>(0.056)|0.065<br>(0.049)|0.316|0.020<br>(0.029)|
|Hours with boyfriend in the|0.030|0.019|0.022|0.044|0.002|
|past day||(0.014)|(0.018)||(0.013)|
|Observations|871|2|,591|1,900|2,591|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. See Table 1 for indicator definitions. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data, balanced panel.

71
confirms both the presence of a dynamic structure in the flow of private electricity projects funding and that overall economic development is a signal that is positively interpreted by private investors.

**Table 5**

# <u>SYS-GMM parameter estimates</u>

|Variable|Coefficient|Std error|
|---|---|---|
|_lag(privinvt)_|.6782351***|.0481902|
|_growth_|.9667271***|.289288|
|Obs.|436||
|Fisher test|F(2, 34) = 124.83***||
|Arellano Bond test - Order 1|-2.67***||
|Arellano Bond test - Order 2|0.78||
|Hansen-J|chi2(58) = 29.83||
|Dif-Hansen|chi2(30)= 5.69||

While the results exhibited in Table 5 are interesting by themselves as they confirm our intuition, there are at least two reasons that suggest extending the underlying model. First, the model is so parsimonious that the variable omission bias is a real threat. Second, as discussed earlier in the paper, besides uncovering the attractiveness of the economic and financial sector development of a country to potential private investors, we are also interested in the effect of the political and institutional environment of the country on private investment. Tables 6 and 7 below present results obtained when we estimate the economic growth effect while first controlling for the quality of governance in the whole economy (Table 6) and then for both that quality and for the existence of an independent regulatory authority in the energy sector (Table 7).<sup>25</sup> We see that the coefficients of these control variables have the expected sign and are significant, and that the overall economic development as measured remain highly is significant even when we adjust for the institutional environment.

**Table 6**

|SYS-GM<br>|M parameter estimates<br>||
|---|---|---|
|Variable|Coefficient|Std error|
|_lag(privinvt)_|.6880732***|.1113495|
|_growth_|.6551585***|.1988469|
|_regquality_|121.1382**|68.03486|
|Obs.|248||
|Fisher test|F(3, 33) = 21.39***||
|Arellano Bond test - Order 1|-2.20***||

statistic validates the additional SYS-GMM moment conditions. Finally, Hausman test results show that economic and financial development variables are exogenous in most of the models. In the few cases where this is not the case, the variables of interest are therefore instrumented. For a treatment of the endogeneity of institutional variables in infrastructure sectors, see Gasmi et al. (2009).

25 Note that we systematically incorporate all the control variables in the models estimated, but we only keep and present those with reasonable goodness-of-fit.

15
<!-- Start of picture text --> 100 150 200 250 300<br>2007 Portuguese Proficiency, 5th Grade<br>15<br>10<br>5<br>Difference<br>0<br>-5<br><!-- End of picture text --> Figure 2. plots the difference between the 2008 actual and Secretary of Education target performance of schools in average Portuguese proficiency of 5<sup>th</sup> grade students against some observable characteristics of schools (the number of students in 2007, the school participation rate of students in the PROEB exam in 2008 and the average Portuguese proficiency of 5<sup>th</sup> grade students in 2007). As discussed in section 3, ideally there should be no relation between whether the target is achieved and the observed characteristics. While there seems to be no (or little) relation between the difference of the 2008 actual and target levels of Portuguese proficiency and the number of students and the participation rate in the exam, it does appear as if those schools with higher levels of Portuguese proficiency in 2007 were systematically less likely to achieve the target. The same 4% improvement applied to a higher level of achievement calls for a larger absolute improvement. The graphs show there is a strong tendency for schools which had a higher absolute target to be less likely to have achieved their targets. This is more likely to due to how the targets were set in the first place, then one the ability of any particular school to achieve the target.

7
# **References**

Akresh, R., Bagby, E., Walque, D. De, & Kazianga, H. (2012). Child ability and household human capital investment decisions in Burkina Faso. _Economic Development and Cultural Change_ , _61_ (1), 157–186.

- Akresh, R., Walque, D. De, & Kazianga, H. (2013). Cash transfers and child schooling: Evidence from a randomized evaluation of the role of conditionality. _World Bank Policy Research_ , (January), 1–49.

- Baird, S., Ferreira, F. H. G., Özler, B., & Woolcock, M. (2013). Relative Effectiveness of Conditional and Unconditional Cash Transfers for Schooling Outcomes in Developing Countries: A Systematic Review. _Campbell Systematic Reviews_ , _9_ (1), 1–124.

- Baird, S., McIntosh, C., & Özler, B. (2011). Cash or condition? Evidence from a cash transfer experiment. _Quarterly Journal of Economics_ , _126_ (4), 1709–1753.

- Baird, S., McIntosh, C., & Özler, B. (2016). When the Money Runs Out: Do Cash Transfers Have Sustained Effects on Human Capital Accumulation? _7901_ (December).

- Barham, T., Macours, K., & Maluccio, J. (2013). More Schooling and More Learning? Effects of a Three-Year Conditional Cash Transfer Program after 10 Years. _IDB Working Paper_ , _432_ (July).

- <mark>Barrera-Osorio, Felipe; Cilliers, Jacobus; Cloutier, Marie-Helene; Filmer, Deon. 2022. Heterogenous Teacher Effects of Two Incentive Schemes: Evidence from a LowIncome Country. Journal of Development Economics, 156: 102820.</mark>

- Camara, B. S., Delamou, A., Diro, E., El Ayadi, M. A., Béavogui, A. H., Sidibé, S., ... Zachariah, R. (2017). Influence of the 2014–2015 Ebola outbreak on the vaccination of children in a rural district of Guinea. _Public Health Action_ , _7_ (2), 161–167.

- Colavita, F., Biava, M., Castilletti, C., Quartu, S., Vairo, F., Caglioti, C., ... Di Caro, A. (2017). Measles cases during ebola outbreak, West Africa, 2013–2106. _Emerging Infectious Diseases_ , _23_ (6), 1035–1037.

- de Walque, D., & Valente, C. (2022). "Incentivizing School Attendance in the Presence of ParentChild Information Frictions." _American Economic Journal: Economic Policy_ , Forthcoming 2022.

- Fiszbein, Ariel, and Norbert R. Schady (2009). Conditional cash transfers: reducing present and future poverty. World Bank Publications,

- Ganimian, A. J., & Murnane, R. J. (2016). Improving education in developing countries: Lessons from rigorous impact evaluations. _Review of Educational Research_ , _86_ (3), 719-755.

Glewwe, P., & Muralidharan, K. (2015). Improving School Education Outcomes in Developing Countries : Evidence , Knowledge Gaps , and Policy Implications Paul Glewwe and Karthik Muralidharan. _Research on Improving Systems of Education_ , (October), 1–112.

17
# **Figure 6. Global financial conditions and activity**

_Global financing conditions have been exceptionally benign from 2010 until late 2016, with policy rates in EMDEs and AEs at historic lows. Since 2010, output growth has slowed sharply in EMDEs and has been mediocre in AEs._

## **A. Monetary policy rates**

## **B. Output growth**

<!-- Start of picture text --> Percent EMDE commodity exporters Percent 1990-2008 average<br>EMDE commodity importers 8<br>40 2003-08 average<br>Advanced economies<br>6<br>30<br>4<br>20<br>2<br>10 0<br>0<br>1980 1986 1992 1998 2004 2010 2016 EMDEs AEs World<br>2010 2012 2014 2016 2010 2012 2014 2016 2010 2012 2014 2016<br><!-- End of picture text --> Sources: Haver Analytics, World Bank.

A. Medians for available data for 69 EMDEs and 26 AEs. Last observation is for November 2016.

B. Weighted averages.

# **Figure 7. Terms of trade and investment growth**

_The terms of trade of commodity exporters have deteriorated since 2011, reflecting the 30‐60 percent declines in global energy, metals, and agricultural commodity prices from their early‐2011 peaks. EMDEs with larger declines in their terms of trade experienced lower investment growth over 2010‐15._

## **A. Cumulative change in commodity prices**

<!-- Start of picture text --> Percent 2011Q1-2014Q2<br>20<br>2014Q2-2016Q3<br>0<br>-20<br>-40<br>-60<br>-80<br>Energy Agriculture Metals &<br>minerals<br><!-- End of picture text -->

**B. Investment growth in EMDEs with high and low changes in terms of trade, 2010‐15**

<!-- Start of picture text --> Percent<br>8<br>6<br>4<br>2<br>0<br>Low High<br><!-- End of picture text -->

### Source: World Bank.

A. Energy index includes crude oil (85 percent weight), coal, and natural gas. Agriculture index includes 21 agricultural commodities. Metals and minerals index includes 6 metals traded on the Land & Metal Exchange, plus iron ore. B. “Low” and “High” indicates annual terms of trade growth in the bottom and top one‐thirds of the distribution, respectively. Difference in medians between “high” and “low” subsamples is significant at the five percent level. Group medians for 108 EMDEs during 2010‐15.

31
Haines, Martha Mahan, _Disclosure in the Municipal Market: Fundamental Concepts for Issuers,_ <u>www.sec.gov, Washington DC, September, 2000</u> IMF and World Bank, _Guidelines for Public Debt Management,_ 2001 International Organization of Securities Commissions, _Objectives and Principles of Securities Regulation,_ Annex III, www.iosco.org, May, 2003 International Organization of Securities Commission _, Transparency and Market Fragmentation,_ Technical Committee, November 2001 Landis, James M., _The Legislative History of the Securities Act of 1933,_ 28 Geo. Wash. L. Rev. 29, 37 (1959) quoted in Joel Seligman, _Gotterdammerung for the Securities Act?,_ 75 Wash. U. L. Q. 887, fn 16 (Summer 1997) Lehmann, Richard, _Mutual Funds can be Dangerous,_ Forbes.com, April 2002 Levitt, Arthur, _The Importance of Transparency in America’s Debt Market,_ Speech by SEC Chairman at the Media Studies Center, New York City, September 1998.

Livingston, Miles and Zhou, Lei, _The Impact of Rule 144A Debt Offerings upon Bond Yields and Underwriter Fees,_ Bowne Securities Connect, Abstract from Winter 2002 Financial Management, pp. 5 –27, <u>www.bowne.com, March 2003</u> Lucier, Jennifer A., _Clearance & Settlement Workshop,_ SEC International Institute for Securities Market Development, Washington DC, 2003 Maco, Paul S., _Building a Strong Subnational Debt Market: A Regulator’s Perspective,_ Richmond Journal of Global Law & Business, Vol 2:1, 2001 Maco, Paul S., _Securities Laws and the Municipal Market: Points Every Issuer Should Keep in Mind,_ Remarks before the NY State Government Finance Officers Association, West Point, New York, October 1999.

Malvey, P., _Uniform Price Auctions: Evaluation of the Treasury Experience,_ Office of Market Finance, US Department of Treasury, Washington DC, 1995 Mathieson, Donald J. and Schinasi, Garry J., _The Changing Structure of the Major Government Securities Markets: Implications for Private Financial Markets and Key Policy Issues,_ Chapter 4, Development, Prospects & Key Policy Issues, IMF International Capital Markets, August 2001 Miller, George, _Debt Markets in the United States,_ The Bond Market Association, SEC International Institute for Securities Market Development, Washington DC, 2003 51
_Services PTAs_ a strong positive effect on services trade, with no evidence of trade diversion from nonmembers (Park and Park, 2011). Guillin (2013) confirms that _only_ services PTAs matter for services trade, and goes one step further analysing the heterogeneous impact of the agreements. Guillin introduces a ‘depth’ measure which clusters services PTA in three groups of low, medium and high liberalization depending on common features, and finds that deeper agreements tend to result in greater services trade.

This paper contributes primarily to the strand of literature on the heterogeneous impact of PTAs characterized by different depth. Studies on PTAs’ depth have multiplied in recent years (e.g. Baier et al. (2014); Orefice and Rocha (2014); Kohl and Trojanowska (2015); Kohl et al. (2016); Mattoo et al. (2017); Mulabdic et al. (2017); Dhingra et al. (2018); Baier et al. (2018); Dhingra et al. (2021)), thanks to various PTA classifications and depth measures constructed on the basis of data on the content of PTAs produced by Horn et al. (2010), Hofmann et al. (2017), and the DESTA project (D ̈ur et al., 2014). The common finding of this literature is that deeper agreements generate more trade, due to the growing importance of PTAs that go beyond reducing tariffs on goods, but also concern aspects relating to foreign investment, dispute settlement, competition between domestic and foreign firms, intellectual property rights, and market access for services providers. Despite the wealth of studies examining the depth of trade agreements, works that have explored its impact specifically on trade in services are few, and are those more directly related to our work. Mulabdic et al. (2017) and Dhingra et al. (2021) exploit the World Bank Deep Trade Agreements 1.0 ( _horizontal depth_ ) Database<sup>5</sup> (Hofmann et al., 2017) to measure the depth of PTAs, while Dhingra et al. (2018) exploit data from DESTA. All three works extract data on goods, services and value-added trade from the

> 5This database is a precursor to that used in this paper, which is the World Bank Deep Trade Agreement 2.0 ( _vertical depth_ ), by Mattoo et al. (2020)

10
Table 1: National Evaluation of Learning Achievements for Sixth Grade: Sampling of Schools

|Year of Conversion<br>to FTS|N<br>Total|umber of Public <br>Tested in 1996|Primary Schools <br>Tested in 1999|(excluding special<br>Tested in 2002|schools)<br>Never Tested|
|---|---|---|---|---|---|
|1996 or before|52|49|39|52|0|
|1997|2|2|2|2|0|
|1998|2|2|2|2|0|
|1999|10|8|8|10|0|
|2000|7|3|1|7|0|
|2001|15|12|2|15|0|
|2002|3|1|0|2|1|
|2003|5|4|0|1|1|
|2004|8|7|0|2|1|
|Never a FTS|2382|1196|189|205|1162|
|Total|2486|1284|243|298|1165|

from ANEP. Table 1 shows the sample of schools tested in each year broken down by year of conversion to the FTS program (including those not converted). It was ANEP’s policy to apply the achievement tests in all full-time schools. However, schools that converted to full time status after the year of the test were only tested if the random selection of a nationally representative sample included those schools. For example, there were 24 schools that converted between 1999 and 2002, but only 2 of them were included in the sample of the 1999 achievement tests. For this reason, it is not possible to construct a panel of schools for 1996, 1999 and 2002. Instead, we use the 1996 test data as baseline data, and use the 2002 data as follow-up testing. Since very few schools converted between 1996 and 1999, the 1999 data are of little use for evaluating the impact of the full-time schools program.

For the purpose of the analysis, we restrict the sample of schools in the following way: we exclude those schools that were not tested in 1996 (i.e. the schools for which we do not have a baseline), the schools that converted to the full-time model by 1996, and the schools that did not participate in either of the post-baseline measurement in 2002. These restrictions lead to the sample presented in Table 2. There are 28 schools that converted between 1997 and 2002, and which were tested both in 1996 and 2002. In addition, there are 190 schools that were also tested in 2002, but that either did not convert to full time status or converted after 2002.

The achievement tests were complemented with questionnaires addressed to each child, the head of household of each child, the class teacher and the school director. While the additional questionnaires are slightly di¤erent in the three years, it is possible to extract a common set of questions, which cover: (1) in the pupil questionnaire: classroom activities and motivation for 5
in each municipality. The regression results for both consumption and life expectancy are reported in Table 5.

**Table 5: Estimated short-term relations between climate and income/life expectancy in Bolivia**

|**Explanatory variables**|**(1)**<br>(log percapitaincome)|**(2)**<br>(life expectancy)|
|---|---|---|
||5.2046|50.2433|
|Constant|(65.21)|(43.57)|
||0.0421|0.1352|
|Temperature|(4.53)|(1.01)|
||-0.0007|0.0057|
|Temperature<sup>2</sup>|(-2.54)|(1.34)|
||-0.0291|-0.2318|
|Precipitation|(-0.35)|(-0.19)|
||-0.0231|-0.5805|
|Precipitation<sup>2</sup>|(-0.88)|(-1.52)|
||0.1940|1.3051|
|Education level|(19.51)|(9.08)|
||<br>0.4429|<br>7.8189|
|Urbanization rate|(3.56)|(4.35)|
||-0.3320|-7.8159|
|Urbanization rate<sup>2</sup>|(-2.83)|(-4.61)|
|Number of obs.|311|311|
|R<sup>2</sup>|0.9194|0.7407|

_Source:_ Authors’ estimation based on assumptions explained in the text. Note: Numbers in parenthesis are t-values. When t-values are numerically larger than 2, we will consider the coefficient to be statistically significant, corresponding to a confidence level of 95%.

The results at the bottom of the table show that just these four explanatory variables (temperature, precipitation, education and urbanization rates) explain 92 percent of the variation in consumption levels between the municipalities in Bolivia. This is an extremely good fit, which suggests that we have included the most important explanatory variables, and that including additional variables would make little difference. The same four variables only explain about 74 percent of the variation in life expectancy, which is less impressive, but still very good for a cross-section model.

Education is by far the most important variable, explaining about 88 percent of the variation in consumption levels and about 54 percent of the variation in life expectancy. Urbanization rates are also significant in both regressions, in a non-linear manner that suggests that the optimal urbanization rate is around 70 percent for consumption and about 50 percent for life expectancy.

The temperature variables are highly significant in explaining consumption, but not life expectancy. Precipitation is not significant in any of the regressions. As it is difficult to assess the non-linear effects of temperature directly by looking at the estimated coefficients, we have plotted the estimated relationship in Figure 1. The axes are scaled to 7
18 are the same used to solve both scenarios’ least-cost diets. Further, no single item is so optimal in nutrient density for all individuals and households that its missing price would likely be binding in all cases.

# **4. Methodological considerations**

# <u>Individual Nutrient Requirements</u>

Biological nutrient requirements for individuals by age, sex, maternity status, and physical activity level have been defined by the Institute of Medicine in the US and are known as the _Dietary Reference Intakes_ (DRIs) (Institute of Medicine of the National Academies 2006). These requirements dictate lower and upper bounds for all the essential macronutrients, vitamins, and minerals. Essential nutrients are those that must be consumed through food because the body cannot make them at all or cannot do so in large enough amounts for all the functions they are needed to perform (e.g., metabolism, growth, immunity, etc.). The nutrients we include are energy, macronutrients (carbohydrates, protein, fat), and all the micronutrients (vitamins and minerals) where there is sufficient scientific evidence to set an average minimum requirement at the population level (vitamins A, C, E, B6, and B12, thiamin, riboflavin, niacin, folate, calcium, copper, iron, magnesium, phosphorus, selenium, zinc and sodium). Twelve nutrients have an upper and lower bound, seven have only a lower bound and no upper limit, and retinol has only an upper bound (National Academies of Sciences Engineering and Medicine 2019; Institute of Medicine of the National Academies 2006; Institute of Medicine of the National Academies 2011).