rafmacalaba/datause-encoder-data huggingface dataset:
hold the incentive environment fixed and just consider how the selection of ( _τ, θ_ ) types produces test scores. Substituting for _e_<sup>_I_</sup> , we can write this second selection term as We see from this expression that there are also two selection channels. A further part of the reason that test scores are higher in schools with good people management practices is because: (i) the _τ_ -types selected in are intrinsically motivated to exert more _effort_ , and (ii) the _θ_ -types selected in are of greater _ability_ .

To see this, consider the numerical example illustrated in Figure 7. In the top panel, the grey shaded area depicts the set of ( _τ, θ_ ) types that are hired by a high management private school. The unshaded area depicts the set of ( _τ, θ_ ) types that are hired by an intermediate management public school. It is clear that the intermediate management public school experiences negative selection on both dimensions. More able teachers prefer the performance-contingent compensation schemes available either in private schools or the outside sector. And more intrinsically motivated teachers prefer private schools because they anticipate exerting higher effort (and hence higher pay).

# **3.3.2 Predictions for intermediate school outcomes and evidence from PISA**

Our theoretical framework suggests two mechanisms, teacher selection and teacher incentives, that could explain the positive correlation between people management scores and student learning outcomes apparent in the WMS, PISA and Prova Brasil data. If these mechanisms are correct, then we should see behavioural responses in intermediate school outcomes. In this section, we set out these predictions and then explore empirically whether they hold in our PISA data for Latin America.<sup>21</sup>

> 21These predictions are based on the numerical example illustrated in Figure 7 and are derived (via numerical integration) in Remarks 1 and 2 in Appendix A. We present the main results with our preferred specification, but include additional variations of the main explanatory variable in Table B.3 in Appendix B. The results are robust to alternative specifications.

19
(individuals who do not have a registered business but file taxes under a self-employment regime), and informal employees (employees without a written contract who are not in RAIS).

Table 7 shows the effect of the financial education intervention on receiving the pandemic aid and on the informal proxy. Unlike the previous tables, the analysis here is done in a cross section, not a panel. The results indicate that once all controls are included, treatment students were 1.4 percentage points more likely to access the pandemic aid transfer than control students. About 38 percent of control students received pandemic aid transfers. Treatment students were also 1.1 percentage points more likely to be informal, but this coefficient is only statistically significant at the 10 percent level. We thus find weak evidence that the financial education intervention increased the share of students in the informal sector. Unfortunately, the data do not allow us to disentangle whether this increase comes from informal business owners or employees who are not registered with the government. However, given that the program increased the share of students running a formal microenterprise (MEI) and lowered the share of students working as formal employees, we suspect that the increase is due to students running informal businesses.

# **5. Conclusion**

This paper measures the long-run effects of a high-school financial education program in Brazil on financial behavior and employment outcomes using a randomized control trial with 892 public high schools and administrative data on about 16,000 former students. Unlike previous literature that focuses on short-term effects over one to two years, we follow students for up to nine years after leaving high school.

While some literature has hypothesized that the long-run effects of high-school education would be declining or zero as students forget what they have learned, we find lasting effects. In fact, the effect on the use of expensive sources of credit goes from being positive in the short run to being negative in the long run. That is, in the long run, treatment students are less likely to use expensive sources of credit than control students, even though the opposite was true in the short run. Similarly, in the short run treatment students reported more loans with repayment delays than control students, but we find the opposite result in the long run: treatment students are less likely to have loans with repayment delays than control students.

We also observe long-run effects on labor market outcomes. Students are more likely to have formal microenterprises and less likely to work as formal employees. These effects may be attributed to the fact that the financial education program was comprehensive and included modules on entrepreneurship. Moreover, entrepreneurship can be a more desirable option than a formal job, in terms of income, independence, or flexibility (Maloney 2004).

16
# _1 Teacher provides learning activity to most students_

_How_ time is spent in school is even more important than the _amount of time_ spent in school (OECD, 2014). Classic work by Stallings (1976) summarized findings from 108 classrooms across 10 different large-scale teacher effectiveness study sites and found that classrooms where the school day is longer and there is more time for children to spend engaged in a reading activity were also related to higher reading scores in both first and third graders. Evidence from the PISA (2006) from 50 developed and developing countries demonstrated a large positive association between teachers’ instructional time on students’ test scores (Lavy, 2015). This finding was replicated in younger children: 10- to 13-year-olds in Israel (Lavy, 2010) exposed to more instruction showed higher test scores. Other studies in Germany, the United States, and Switzerland corroborated this finding, linking increased instructional time with higher student learning (Pischke, 2007; Dobbie & Fryer, 2013; Cattaneo, Oggenfuss, & Wolter, 2017).

These findings have also been replicated in studies of developing countries. Using data from seven Latin America countries, Bruns & Luque (2014) showed that teachers from schools ranked in the top 25<sup>th</sup> percentile of student learning averaged 80 percent of their time on task, compared to teachers in the bottom 75<sup>th</sup> percentile, who were only on task 30 percent of the time.

# _2 Students are on task_

Student engagement is one important predictor of whether learning is taking place (Christenson et al., 2012; Castillo, 2017). Literature has characterized engagement as malleable, and factors like teacher support, classroom structure, and task characteristics influence how engaged students are (Fredricks, Blumenfeld & Paris, 2004). Bruns & Luque (2014) found that when students are on task and engaged (e.g., reading, writing, listening, or talking about a relevant topic versus staring out the window, engaging in idle chatter, or fiddling with the contents of one’s desk), they learn significantly more than when they are not. A subsequent study of 51 secondary school math teachers in Chile found that teachers who keep students on task tend to have better classroom organizational skills overall and better behavioral management skills, and spend more time on classroom instruction (Bruns, De Gregorio, & Taut, 2016). When students are off task they not only limit their own learning opportunity but also disrupt their peers. This impacts peer learning in the short term (Hanushek, Kain, Markman, & Rivkin, 2003; Feld & Zolitz, 2017) and income in the long term (Carrell, Hoekstra, & Kuka, 2018).

9
# **4. Conclusion and policy implications**

The paper builds a new _de facto_ index of public investment quality based on World Bank data on investment project evaluations for the period 2000-21. The index allows to assess public investment quality for a large number of EMDEs and to track the evolution of public investment quality over time. We show that low-income countries and commodity-exporting EMDEs have much lower public investment quality in comparison with other EMDEs and that this gap has widened over time. This is especially alarming in light of the importance of public investment quality for growth-inducing effects of public investment.

The paper also shows that public investment quality has important implications for sovereign risk. Specifically, we show that scaling up of public investment leads to lower sovereign risk for countries with high public investment quality and increases sovereign risk for countries with low public investment quality. The results indicate that this effect is particularly important for countries with low levels of creditworthiness. We also show that in countries with high public investment quality, scaling up of public investment pays for itself. Debt-to-GDP ratios do not increase after a positive public investment shock. The opposite is true for low-public-investment-quality countries, where scaling up of public investment leads to an increase in public debt in the following years.

The results of this paper help to inform policy debates by providing empirical evidence on the macroeconomic implications of public investment and its quality. Many EMDEs face debt sustainability issues, particularly in the aftermath of the COVID-19 pandemic and the associated global economic recession. We show that the quality of public investment has non-trivial consequences for the cost of capital and debt sustainability. In this regard, scaling up of public investment when public spending is not efficient may be detrimental to macroeconomic stability.

These issues are especially critical for LICs that face significant infrastructure gaps but have limited capacity to address them given high sovereign spreads, limited fiscal space, and intermittent access to the international capital market. For these countries it is vital to step up technical and financial support from the global community with a multipronged strategy integrating a public investment management framework—including careful assessment of investment projects at all stages of development and implementation (Adarov 2024; Kim et al. 2020; IMF 2022)—with improved public expenditure and debt sustainability frameworks. Given the massive scale of the reform effort needed for effective operationalization of these frameworks, this also requires coordinated efforts among creditors and development partners to provide development assistance on concessional terms.

32
( _Ki_ ( _j_ ) _t_ ) are correlated with the bilateral random effects ( μ _ij_ ), we correct for this endogeneity using the Hausman-Taylor (1981) instrumental variables method.<sup>37</sup> Results are reported in table 2. Note first that the FE and RE estimates in cols. 1 and 2 are very close indicating that endogeneity issues have been handled adequately (this is the case for all the variants). Signs and magnitude of coefficients are plausible. As suggested by the theory, the elasticity of trade with respect to income is significant and close to unity. The negative coefficients for the population variables confirm that larger countries are more self-sufficient. It could also reflect that for large countries, the costs of trading with themselves rather than with others is relatively less. The multilateral resistance variables also have the expected positive sign. Thus given the absolute distance between i and j, the further country i is far from its trade partners, the more country i will trade with j. As expected, an improvement in the quality of infrastructure increases significantly the volume of trade. One could also interpret these positive coefficients in the broader sense of proxies for the quality of social infrastructure (physical infrastructure is largely a public good that will be underprovided in countries with poor social infrastructure). The distance coefficient in the HT specification is close to unity when taking into account the zero trade flows (ln(1+Mt)).

The estimated trend from column (2) is reported in figure 6. It is slightly higher than the trend estimated with the ET tobit or the PPML (+11% vs. 7.6% and 6.3% respectively). Using the same approach as in (13), we also estimate the trend for the richest and poorest terciles and report the results in figure 7. Again, the results are quite close to those obtained by the repeated cross-section estimations. While it could be argued that not accounting for the censorship of zeroes in the panel estimates might make a difference, given the close values across estimators in the cross-section results in table 1, it is unlikely that not accounting for truncation would have significantly changed the results here.

> 37 We carry out a pretest estimator based on Hausman (1978) (see  Baltagi, Bresson and Pirotte (2003) to choose between the fixed effects, random effects, and HT estimators. The test statistics lead us to choose the HT estimator. See Brun et al. (2005) appendix B for the construction of instruments and the choice of estimator.

28
Rashad, Inas, Michael Grossman, and Shin-Yi Chou. 2005. "The Super Size of America: An Economic Estimation of Body Mass Index and Obesity in Adults." NBER Working Paper 11584. Cambridge, MA: National Bureau of Economic Research. Ravallion, Martin and Shaohua Chen. 2006. "China's (Uneven) Progress Against Poverty." _Journal of Development Economics_ 0(0): 0.

Rivera, Berta and Luis Currais. 2005. "Individual Returns to Health in Brazil: A Quantile Regression Analysis," in Lopez-Casasnovas, Guillem, Berta Rivera, and Luis Currais (eds.), _Health and Economic Growth_ . Cambridge, MA: MIT University Press.

Romer, Paul. 1986. "Increasing Returns and Long-Run Growth." _Journal of Political Economy_ 94(5): 1002-1037.

Sachs, Jeffrey. 2001. _Macroeconomics and Health: Investing in Health for Economic Development_ Sala-i-Martin, Xavier. 2005. "On the Health-Poverty Trap," in Lopez-Casasnovas, Guillem, Berta Rivera, and Luis Currais (eds.), _Health and Economic Growth_ . Cambridge, MA: MIT University Press.

Schieber, George, Cristian C. Baeza, Daniel Kress, and Margaret Maier. 2006.

"Financing Health Systems in the 21st Century," in Jamison, Dean T., Joel G. Breman, Anthony R. Measham, George Alleyne, Mariam Claeson, David B. Evans, Prabhat Jha, Anne Mills, and Philip Musgrove (eds.), _Disease Control Priorities in Developing Countries_ . New York: Oxford University Press, 225-242. Schlosser, Eric and Charles Wilson 2006. _Chew on This: Everything You Don't Want to Know About Fast Food_ . London: Puffin Books.

Schnell-Anzola, Beatrice, Meredith L. Rowe, and Robert A. LeVine. January 2005.

"Literacy As a Pathway Between Schooling and Health-Related Communication Skills: a Study of Venezuelan Mothers." _International Journal of Educational Development_ 25(1): 19-37.

Schultz, T. Paul. 2005. "Productive Benefits of Health: Evidence From Low-Income Countries," in Lopez-Casasnovas, Guillem, Berta Rivera, and Luis Currais (eds.), _Health and Economic Growth_ . Cambridge, MA: MIT University Press.

54
2 DASGUPTA AND GROVER

# **1. Introduction**

Geography plays a significant role in determining the distribution of economic activity across space. In particular, the suitability of a region to engage in domestic and international trade is critical in influencing spatial concentration of people and economic activity (Henderson et al., 2018). Recent trade liberalization episodes in some countries have enhanced spatial concentration within countries. In China, for instance, the decline in international trade costs enabled export-oriented industries to emerge close to ports at the expense of other regions (Cos ̧ar and Fajgelbaum, 2016) and pushed remote regions to specialize in agricultural products (BaumSnow et al., 2017).<sup>1</sup> This has inevitably led many researchers to conclude that greater openness to trade may lead to skewed regional development (Kanbur and Venables, 2003).

Using establishment-level data from the Indian organized manufacturing sector covering the period 1989-2009, we examine whether a decline in _external_ trade costs, triggered by the trade liberalization episode of the 1990s, affected the spatial distribution of economic activity, and the role that _internal_ trade costs played in shaping such spatial dynamics. We then explore whether the subsequent decline in internal trade costs brought about by the large scale construction and upgrade of highways during 2000s, the Golden Quadrilateral (GQ) project, may have affected spatial development.

To analyze how internal geography interacts with international trade to affect regional growth, we group Indian districts into three regions, based on their distance from the nearest major port (air or sea): the primary region (districts within 200 kilometers of the nearest port), the secondary region (districts between 200 to 400 kilometers from the nearest port) and the interior region (districts located farther than 400 kilometers from the nearest port). At the outset, we note three key stylized facts on spatial development in India.

**STYLIZED FACT 1:** _During 1989-2009, organized manufacturing activity was highly concentrated in a few districts, mainly in the primary region._

**STYLIZED FACT 2:** _Between 1994 and 2000, there was a strong reallocation of manufacturing activity away from the primary to the secondary region; however, this temporary dispersion in activity reversed post-2000._

**STYLIZED FACT 3:** _The interior region lagged behind in all aspects of manufacturing activity irrespective of the time period._

> 1Historically too, regional development could have been shaped by international commerce. Globally, the distribution of employment is skewed toward the coasts: according to the United Nations, over half of the global population lived within 200 kilometers of a coastline in 2001 (http://www.oceansatlas.org/subtopic/en/c/114/).
database. The resulting dataset comprise an unbalanced panel with 16 sectors for 39 countries over the period 1991-2014. Tables S1.3 and S1.4 provide the list of countries and sectors.

# 3. **RESULTS**

# A.   Aggregate Results

A concern in the background is that tariff changes may themselves be a function of macroeconomic variables (reverse causality), which would contribute to biased coefficient estimates. A first pass at the relevance of such concern in our data can be gleaned by regressing tariff changes on key macroeconomic and structural variables (one variable at a time), controlling for time and country fixed effects: Table 1 reports the results. It is reassuring that, with the exception of M2 growth and financial crises, the other variables (e.g. the unemployment rate, inequality, fiscal deficit, political regime) are not correlated with tariff changes in the sense that the coefficients are not statistically significant; we address the issue of M2 growth and crises directly below by explicitly controlling for these variables in the regression. Moreover, to check whether tariff changes can be determined by external factors like foreign demand, we regress tariff changes on the real GDP growth of top-5 trading partners and again find no statistical significance.

# <u>Baseline</u>

Our benchmark aggregate results are presented in Figure 1. Each of the six panels presents the estimated dynamic response for a variable of interest (output, productivity, and so forth) to a one-standard deviation rise in the tariff rate. This is a moderate increase in the 11
As a benchmark, I evaluate the rate of return with the same mortality schedule for both categories (Q<sup>P</sup> t<sup>=QS</sup> t<sup>).Ihaveestimatedtheyearlyincomeperworkerbyeducationlevelformales</sup> in rural Uganda, using the Uganda National Household Survey 1999/2000.<sup>24</sup> The costs of going to secondary school per pupil, including tuition, room and board and other supplies, have been estimated from the same survey. The age speci fi c mortality rates, identical for both education levels, are based on estimates published in the reports of the DHS 1995 and 2000 (Republic of Uganda 1995 and 2000) for Uganda. The calculation yields a benchmark rate of return to education of 10.23 percent as reported in column A of table A1.

The next step is to introduce mortality differentials due to the fact that more educated individuals are less at risk of being infected by HIV/AIDS. Since I am looking at the prospect of a young individual in 1999/2000, I use as benchmark for the difference in HIV exposure the ratio of the HIV prevalence at ages 18—29 for individuals with primary and secondary education at round 11 (see fi gure 1). To estimate the timing of the death due to AIDS, I take the age-speci fi c incidence rate estimated from the data from the GPC and add 10 years, the median survival between HIV infection and death (Morgan and others 2002). The difference in the mortality schedule for individuals with primary and secondary education obtained using this procedure is illustrated in fi gure A1 that depicts the survival curves from age 13 for both categories. Figure A1 shows that HIV/AIDS is a disease that affects individuals in their prime ages, since most of the effect of the education differential is concentrated between ages 30 and 60. The fi rst line of column 2 of table A1 indicates that taking into account the mortality differential (Q<sup>p</sup> < Q<sup>s</sup> ) across the two education groups due to their different HIV exposures increases the returns to education from 10.23 percent to 10.71 percent, an addition of 0.48 percentage point.

Those estimates, however, only account for the lost income stream due to HIV/AIDS mortality. Starting with Rosen (1988), the economic literature has stressed that the value of a statistical life exceeds the present discounted value of future income streams. The value of life should account for the utility derived from full income and full consumption, including the value of leisure. Different studies have estimated the value of a statistical life. I am using, as a starting point in the following calculations, two ranges of estimates for the United States: the value between 1 and 2 million dollars

> 24 In rural Uganda, most individuals do not earn wages as employees but work on the household farm. In such cases of household enterprises, I have used the family enterprise revenue divided by the number of full-time workers from the family in that enterprise as the measure of income per worker.

31