rafmacalaba/datause-encoder-data huggingface dataset:
(contributing to the social security; outside of the social security; unemployed; and retired) for the cohort of 25 year old males who entered the labor market in year 1990 (see Annex 2). The distribution is presented in Figure 1. We focus only on urban areas and control for three levels of income: less than 50% of average earnings, between 50 and 75% of average earnings; and more than 75%.

<!-- Start of picture text --> Figure 4: Targeted Distribution for Cohort of 25 Year-Old Males in Urban Region<br>Income <50% of average Income 50% to 75% of average<br>100% 100%<br>Retired Unemployed Retired<br>90% Unemployed 90%<br>80% 80%<br>70% 70% Outside of the social security<br>60% 60%<br>50% 50%<br>40% Outside of the social security 40%<br>30% 30%<br>20% 20%<br>10% Contributing to the Social Security 10% Contributing to the Social Security<br>0% 0%<br>16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 70+ 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 70+<br>Age Group Age Group<br>Income > 75% of average<br>100%<br>Unemployed Retired<br>90%<br>80%<br>Outside of the social security<br>70%<br>60%<br>50%<br>40%<br>30%<br>20% Contributing to the Social Security<br>10%<br>0%<br>16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 70+<br>Age Group<br><!-- End of picture text --> _Source:_ Household surveys 1990-2006 PNAD _._ The methodology to input values for ages not observed is presented in Annex 2.

The main assumption is that the aggregate distributions that we observe are the result of millions of individuals making decisions about whether to take formal or informal sector jobs, and when to retire. Some individuals, given their individual characteristics and preferences, spend most of their active lives in the formal sector. Others are most of the time outside. Yet others move in and out with more or less frequency. These various types are determined by the vector  **.** The question is, then, what is the probability of observing a given  given the aggregate distribution? Also, we know from Bayes rule that this probability is proportional to the probability of observing the data given  **.** So we have: 14
across all columns. The conclusion is very similar in the three alternatives: when interacted with the dummy for young individuals, the higher the education level, the lower the risk. For example, in the fi fth column, the coefficient indicates that for individuals aged 18 to 29, one additional year of education decreases HIV prevalence by 0.5 percentage point.

# 4.2 HIV Incidence and Survival Analysis

So far, this paper has focused on HIV prevalence, the ”stock” of individuals infected at each round. However, HIV prevalence is affected by the incidence rate, the mortality rate, as well as by migration or attrition from the sample. As explained previously, it also re fl ects, given the 10 years period on average between HIV infection and death, sexual behavior that is on average fi ve years old.

HIV incidence is a better measure of the evolution of the epidemic. An incident case is a individual that was fi rst tested as HIV negative and later turned HIV positive. The General Population Cohort, by its design, allows to measure incidence, since the same individuals are followed over time. The GPC is the fi rst study to have reported a signi fi cant decline in adult HIV incidence in rural Africa (Mbulaiteye and others 2002a; Whitworth and others 2002). A difficulty with the study of HIV incidence is that the number of incident cases is generally very low: given the 10 year span between infection and death, incidence rates are on average ten times lower than prevalence rates. Between round 1 and round 12, 280 incident cases can be detected in the GPC. There is a substantial attrition rate in the cohort, as transition rates from one round to the next one ranged between 66.7 percent (between rounds 3 and 4) and 79.2 percent (between rounds 8 and 9). The causes for attrition might be death, migration or noncompliance. It is also worth remembering that individuals enter in the cohort as they turn 13 or if they are new to the study area. At round 11, 10 new villages were added.

Given that the education variable is only available at rounds 1, 7, 8, 9 and 11, I am restricting the survival analysis to individuals aged 18 and above so that the education level is more likely to be constant over time.<sup>17</sup>

> 17 When there was discordance across rounds in the educational level reported, I adopted the following rules: I fi rst chose the level reported after age 18, if there was still discordance, I chose the level most often reported. Most of the discordances were for years of education inside the same education category (no education, primary and secondary). I found 107 observations (1 percent of the sample) with seriously discordant levels of education reported : in that case, I assigned the fi rst level reported after age 18. I have run the analysis with and without these doubtful observations with no in the conclusion.

13
economy. Alternatively, if the economy receiving the inflow is characterized by unemployed resources then the increase in demand generated by the aid inflow may not lead to an increase in nontradable prices. Import purchases and unemployed resources can explain why there may be a muted or no response in the REER to an aid surge, but they do not explain the observed negative relationship in Tanzania. There are numerous theoretical explanations for this relationship, but a proper multivariate framework that accounts for the nonstationarity of these series and controls for other factors is necessary before this relationship can be taken seriously. The empirical section that follows will do this.

# _Trade liberalization_

Economic reforms introduced by the Tanzanian authorities from 1986 included a move away from the policies of import substitution undertaken in the 1970s. During 1980-86, nontariff barriers covered more than 50 percent of imports, and the average simple tariff rate was about 40 percent. By 1999, nontariff barriers were largely confined to restrictions on imports of petroleum products and the simple tariff rate was between 15 and 20 percent (Kanaan 2000). During the 1990s, the marketing and export of traditional export crops was decontrolled and the commercial role of the parastatal crop authorities was scaled back, resulting in the increase in the producer prices of food. The system of export and import licensing was abolished and the requirement of registration of exporting companies eliminate by 1994. The maximum tariff rate has been lowered and the number of tariffs has been reduced from six to three. Moreover, import surcharges imposed in 2001 on imports from other East African countries were gradually reduced or eliminated (IMF 2005).

Trade liberalization can have an important impact on the long-run ‘equilibrium’ REER. The lowering of **Figure 6. Trade Liberalization and Real Effective** import tariffs, for example, **Exchange rate** will decrease the domestic 300 60 price of imported goods increasing the demand for 250 50 imports and the volume for 200 40 imports. At the same time 150 30 (assuming perfect substitutability) a decrease in 100 20 demand for nontradables will 50 10 result. In equilibrium, the 0 0 price of nontradables will 1970 1975 1980 1985 1990 1995 2000 2005 decrease resulting in a Trade (% of GDP) WPI-based REER depreciation of the real <u>Source: IFS and authors’ calculations</u> exchange rate. The ratio of total trade to GDP is a commonly used measure of trade liberalization as it captures the assumption that a more liberal trade regime means higher trade volumes. Figure 6 presents this measure of trade liberalization and Tanzania’s REER. As expected, there appears to be a strong inverse relationship between the series for the entire sample, 10
property owners in control clusters and find no significant difference between the groups, suggesting no discernible spillover effects from the program using our originally specified empirical strategy for estimating spillovers (Table 6, Panel A). In Table 6, Panel B we report similar results for disconnection-ineligible property owners suggesting that the enforcement intervention had no observable spillovers on paying property owners either.

# **8.4 Heterogenous Treatment Effects on Payment Behavior**

We consider two sets of sub-group analysis. First, the calculus for resident property owners is likely to be different to that for non-resident property owners (the former would be more directly affected by service disruption in the enforcement intervention, and potentially more accessible in the engagement intervention). Second the constraints and decisions to make payment may be different when considering the intensive (getting payers to pay more) versus the extensive margin (inducing those that have never made a payment to start paying). The appendix Tables A4 and A5 present sub-group analyses for property owner residency status and the intensive vs. extensive margin of payment respectively. In both cases we find no clear evidence of strong differences across these subgroups.

# **8.5 Water Access and Use**

To measure social and economic costs of the enforcement intervention we use survey data collected nine months after the intervention, as reported in Section 7. Estimated impacts are presented in Table 7. The enforcement intervention had little effect on compound connections to water and sanitation services. The majority of the 96 disconnected compounds were reconnected after agreeing to pay a portion of their balance. We observe 27 of these compounds made a payment after being disconnected, with 20 of these payments being made within one month of the disconnection. The remaining compounds were reconnected without requiring a payment if they agreed on a plan with the utility. Compounds receiving the enforcement intervention have statistically indistinguishable piped water and sanitation connection rates at endline. This remains true when considering whether the piped water connection is currently working. We also find little evidence of illegal connections based on enumerator observation (3 cases across the sample).

Many households with piped water do not report this as their primary water source. Only 30.6% of control households report using piped water as their main source of water, which is 4.4 percentage points higher among treatment households, but non-significant (pvalue=0.243). Many households in both groups are more likely to rely on water kiosks or boreholes (40%) than piped water. Both groups report spending similar amounts on water 18
Rather than present separate tables, we plot all the estimates in specification curves, one each for years of schooling, cognitive skills, and SEM skills for men within and outside the village (Figures 3 to 5). The top panels provide coefficients; the bottom panels provide the linear combination of different restrictions. While most of the checks are self-explanatory, Appendix B discusses how we investigated the sensitivity of our specifications to sample selection using the Heckman two-step selection model and a sequential bivariate semi-nonparametric estimation following Glewwe, Song, and Zou (2022) and De Luca (2008). Even though there are specifications for which the results become imprecise, the general theme of our regressions holds: for those who remained in their original village, SEM skills are strongly correlated with earnings, while years of schooling are not; in turn, for those who left the village, years of schooling are correlated with earnings, while SEM skills are not.

A second exercise aimed to understand the extent of attenuation bias in our estimates for SEM skills. We used the test-retest reliability to estimate measurement error and rescaled the estimates using the standard formula for attenuation bias.<sup>23</sup> Doing so suggests that measurement error-corrected estimates would be $48.5 instead of the $16.5 we report in the main results. Our estimated reliability of 0.34 from the average test-retest correlations during the pilot is likely an underestimate of reliability in the final sample as Cronbach’s α is 30% higher on average, suggesting lower measurement error in the final data collection. However, even if we adjust for this and estimate reliability to be 0.44, which is now likely an upper bound, the corrected coefficient would be $37.5.

A third exercise sought to understand whether these patterns are specific to the particular sample from Pakistan. Therefore, we incorporated further data from the second study site in Cambodia. The structure of the sample and the survey is very similar, with children first surveyed in 2008 and then recontacted and resurveyed in 2016/17, allowing us to look at links between education and skills in an originally rural sample. Unfortunately, the migration status is harder to determine, and the measures of socioemotional skills are less reliable, as discussed below and in Appendix D.

To measure SEM skills, we used the same self-reported Grit and Big Five scales as in Pakistan. Using a four-item scale, we also measured growth mindset—the belief that one can get smarter through hard

> 23 For classical measurement error, the observed = <u>X</u> with <u>X</u> being the reliability, which we approximate σσ X∗ σσ X∗ by the test-retest correlation. ββ̂ σσXX∗+σσεε ββ σσXX∗+σσεε

26
FIGURE 5

# REAL TAXES AND CPI<sup>INFLATION</sup>

<!-- Start of picture text --> ARlGENTINA BRAZIL<br><!-- End of picture text --> <!-- Start of picture text --> uIYlUfl odS.121 MILA?EI 41 :.I uO?UU 7.14*9<br>ec~~~~~~~~~~~e 3<br>Sc~~~~~~~~~~~~~~~~S<br>40 ¶ <~~~~-\~ :ftt Pur<br>&"s ." :o L" :e" , .z"s :" X . *g" -M*Xno<br>eXsINWAO e_WI To 116WIR W tNO &"G"$ *U?&<br>CMIMIA dwnB<br><!-- End of picture text --> FIGURE 6 RUL PUILC IJTLI" **P117mmrn** = ass url m Js? SI _(JAM_ £S,,,el

**Pm3W &VSM M**

FIGUSRE 7 REAL PUBLIC DEBT AMN<sup>OFFICIALDEVALUATION</sup> <!-- Start of picture text --> LU .: nWn tJ , ' _: v t- l<br>REAL PUBLIC DEBT AND OFFICIAL DEVALLATIOALTIO<br>I00<br>ei US sina nagm<br>wee<br>l~~~~~~~~~~~~~~~~ *0 Un s|<br>OM" ~ &&M= _M ., anM'l ,. o :,.<br><!-- End of picture text -->
from half of the banks surveyed in those countries).<sup>31</sup> Furthermore, most banks (around 70% of the banks interviewed) do not have an assessment of how much SMEs contribute to the banks’ total credit and other risks or to the banks’ total costs. Many banks claim not to have adequate unit cost and risk accounting mechanisms in place to obtain this type of information, but are actively trying to develop them. This suggests that banks are on the steep part of the learning curve on how to deal with SMEs. Through this process, banks adapt their structures to increase profits, reduce the associated costs, and better manage the risks of the segment. The same applies to Colombia and Serbia.

The interest and participation in the SME segment is not a small bank or niche bank phenomenon, as the relationship lending hypothesis would predict (Figure 2). Banks were asked to provide their views regarding the main players in the SME market.<sup>32</sup> Universal large banks (including foreign ones) were the most frequently mentioned banks. Niche banks seem to play a role only in Argentina and Chile, but a less important role than public banks. Available quantitative data for Argentina and Chile show that, on average, private domestic banks are the most exposed to the segment, with a level of exposure of 56% in Argentina and 16% in Chile. In Argentina, private domestic banks are followed by public banks (31%) and foreign banks (27%), while in Chile they are followed by foreign banks (12%) and public banks (6%).<sup>33</sup> The important participation of foreign banks is a significant finding, given the priors in some discussions that these banks are “cherry pickers,” interested mainly in high net worth individuals and large

> 31 The banks that provided this information and that are considered in this average account for 23% of total private sector loans in Argentina and 41% in Chile.

> 32 Since each bank can mention more than 1 type of bank as being the leader in the segment, the numbers across bank types do not have to sum to 100%.

> 33 These ratios are calculated as the sum of SME loans over the sum of private sector loans considering the banks belonging to each category of bank type (public, private domestic, and foreign).

19
implications.

# **1.** Motivation for the Analysis

Bulgaria is a lower middle-income country with a per capita income of approximately USD 1,330 in 1993, according to the World Bank Atlas (1994). Between 1946 and 1989, it was a centrally planned economy in which almost all activities were directly or indirectly controlled by the state. This is particularly true for large social sectors such as education. The emphasis that the former system gave to building extensive education facilities across the country resulted in the considerable stock of szhool and classrooms in Bulgaria today, and correspondingly, a large number of teachers. On the surface, Bulgarian indicators for education appear favorable with a literacy rate of over 95% and the primary pupil-teacher ratio (15) much lower than in other countries in its income group **(25).1** Even when compared with some Eastem European countries with the similar legacy of socialism, the Bulgarian numbers appear better. In Poland and the Slovak Republic, for example, primary pupilteacher ratios are 17 and 19, respectively. However, these data probably hide considerable slacks and inefficiencies arising from the extensive facilities and the number of teaching and non-teaching staff.

The transition to a market economy necessarily hardens the budget constraint on all sectors and economic agents: the state and state enterprises, and households. Education is no exception. In Bulgaria, total government expenditures have dropped from over 60% of GDP in 1990 to 47% in 1993 and expenditure on education has fallen proportionately. Increasing efficiency in education is thus imperative if this sector, particularly primary education, is to meet the twin objectives of _learning_ and _access_ under increasingly tighter budgete. Thus identifying and reducing technical slacks becomes particularly important.

- _1/ World Development Report 1994,_ Annex Table 28.

21 World Bank (1990).
Joern Kleinert and Julia Spies. Endogenous Transport Costs in International Trade. IAW Discussion Papers 74, Institut für Angewandte Wirtschaftsforschung (IAW), July 2011. URL https://ideas.repec.org/p/ iaw/iawdip/74.html.

Jane Korinek. Clarifying trade cost in maritime transport, 2011. OECD.

- Paul R. Krugman. Scale economies, product differentiation, and the pattern of trade. _American Economic Review_ , 70(5):950–59, December 1980.

- Ahmad Lashkaripour. Weight-based quality specialization. _Journal of International Economics_ , 127, 2020.

- Marc Levinson. _The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger_ . Princeton University Press, 2008.

- Nuno Limao and Anthony J. Venables. Infrastructure, Geographical Disadvantage, Transport Costs, and Trade. _The World Bank Economic Review_ , 15(3):451–479, October 2001.

- Volodymyr Lugovskyy and Alexandre Skiba. How geography affects quality. _Journal of Development Economics_ , 115(C):156–180, 2015.

- Volodymyr Lugovskyy and David Terner. Shipping towards greener horizons: How market structure affects firm-level response to environmental regulation. _Indiana University, Mimeo_ , 2021.

- Inmaculada Martínez-Zarzoso and Felicitas D. Nowak-Lehmann. Is distance a good proxy for transport costs? the case of competing transport modes. _The Journal of International Trade & Economic Development_ , 16(3): 411–434, 2007. doi: 10.1080/09638190701527186.

- Marc J. Melitz. The impact of trade on intra-industry reallocations and aggregate industry productivity. _Econometrica_ , 71(6):1695–1725, November 2003.

- Olaf Merk, Lucie Kirstein, and Filip Salamitov. The Impact of Alliances in Container Shipping. Technical report, OECD, International Transport Forum, 2018.

- Saif I Shah Mohammed and Jeffrey G Williamson. Freight rates and productivity gains in british tramp shipping 1869–1950. _Explorations in Economic History_ , 41(2):172–203, 2004.

- Theo Notteboom. Trade and transport modes: the search for global connectivity through transport networks. In _Handbook of International Trade and Transportation_ , pages 541–566. Edward Elgar Publishing Limited, 2018.

- Theo Notteboom and Jean-Paul Rodrigue. _Re-assessing port-hinterland relationships in the context of global commodity chains_ . Routledge, 2007.

- Theo E. Notteboom and Bert Vernimmen. The effect of high fuel costs on liner service configuration in container shipping. _Journal of Transport Geography_ , 17(5):325–337, 2009.

- OECD. Competition Issues in Liner Shipping. Note for item iv of the 59th meeting of the working party no. 2, OECD Secretariat, 2015.

- Luigi Pascali. The wind of change: Maritime technology, trade, and economic development. _American Economic Review_ , 107(9):2821–54, 2017.

45
# Promoting Parental Involvement in Schools: Evidence from Two Randomized Experiments<sup>_∗_</sup>

Felipe Barrera-Osorio, Paul Gertler, Nozomi Nakajima, and Harry Anthony Patrinos<sup>_†_</sup>

**_Keywords—_** parents, schools, experiments, Mexico, indigenous, trust **_JEL Codes—_** I20

> _∗_ We thank seminar participants at 3ie, AEFP, APPAM, Columbia University, IZA Economics of Education Workshop, University of Massachusetts Boston, Vanderbilt University and the World Bank for comments and feedback on the paper. The research was supported by grants from the Spanish Impact Evaluation Fund, the World Bank Research Committee (RF-P1123327-RESE-BBRSB), and the Bank-Netherlands Partnership Program. We dedicate this paper to Eduardo Rodriguez-Oreggia who worked on the original thinking about the study and passed away in 2014. We are thankful for excellent research assistance from Angelica Rivera-Olvera, Stefan Metzger, Diego Cardozo-Medeiros and Pedro Pablo Parra-Diaz. Manuel Felix supported the project since its inception. We are grateful to SEP for access to data. Thanks for all the support to CONAFE staff at the time, namely: Arturo Saenz Ferral, Lucero Nava Bolanos, Jose Carlos Rocha Silva, Alfonso Gonzalez Ramirez, Dolores Ramirez Vargas, Georgina Quintanilla Cerda, Rafaela Merecias Sanchez, Maria Angelica Santiago Antonio and Teresa Nateras Valdez. The authors have no financial or material interests in the results of this paper. The registration number of the trial at the AER RCT Registry is AEARCTR-0006424. The opinions expressed herein are those of the authors and not necessarily of the institutions they represent. All errors remain our own.

> _†_ Barrera-Osorio: Vanderbilt University (felipe.barrera.-.osorio@vanderbilt.edu); Gertler: University of California, Berkeley and NBER (gertler@berkeley.edu); Nakajima: Harvard University (nnakajima@g.harvard.edu); Patrinos: World Bank (hpatrinos@worldbank.org)