rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Number of Observations for different samples

||All chil|dren|Brothers|(Men)|Sisters(W|omen)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
|Full sample|34,585|39,562|21,895|23,625|12,690|15,937|
|Rural|22,308|20,191|14,510|12,247|7,798|7,944|
|Urban|12,277|19,371|7,385|11,378|4,892|7,993|
|Less developed Areas|7,136|11,055|4,928|6,917|2,208|4,138|
|Developed Areas|27,449|28,507|16,967|16,708|10,482|11,799|

Table 2: Summary Statistics

|||**Full S**|**ample**|||**Rural**|**Sample**||
|---|---|---|---|---|---|---|---|---|
||19|92/93||2006|19|92/93||2006|
||Mean|Standard|Mean|Standard|Mean|Standard|Mean|Standard|
|||Deviation||Deviation||Deviation||Deviation|
|**Children's Schooling**|||||||||
|**Years**|||||||||
|All Children|7.36|4.51|8.72|3.92|6.28|4.44|7.71|3.94|
|Brothers|7.63|4.32|8.76|3.77|6.77|4.31|7.97|3.75|
|Sisters|6.90|4.77|8.67|4.13|5.36|4.54|7.32|4.18|
|**Parent's Schooling**|||||||||
|**Years**|||||||||
|Father|5.33|4.94|6.43|5.09|3.91|4.26|4.90|4.56|
|Mother|2.63|3.91|3.75|4.58|1.46|2.82|2.17|3.41|
|Parent's<sup>1</sup>|5.54|4.93|6.82|5.03|4.07|4.27|5.19|4.54|
|**Children's Age (years)**|||||||||
|All Children|19.55|2.98|19.22|2.96|19.52|2.98|19.08|2.95|
|Brothers|19.91|3.07|19.67|3.12|19.87|3.07|19.55|3.12|
|Sisters|18.93|2.70|18.57|2.56|18.86|2.69|18.36|2.51|
|**Caste and Religion**|||||||||
|**Composition**<br>Proportion Scheduled|||||||||
|<br>Caste|0.11|0.31|0.17|0.38|0.12|0.33|0.18|0.38|
|Proportion Scheduled|||||||||
|<br>Tribe|0.12|0.33|0.11|0.31|0.16|0.36|0.16|0.37|
|Proportion Backward|||||||||
|<br>Caste|-|-|0.33|0.47|||0.34|0.47|
|Proportion Muslim|0.17|0.37|0.16|0.36|0.19|0.39|0.12|0.33|

Note: 1: Maximum of Father and Mother's schooling years 37
by this age-specific population. Instead, we control for trends in district-specific total population.<sup>15</sup> There are three concerns that may confound the interpretation of our results. First, we may be spuriously attributing to MGNREGA the effects of other government programs aimed at influencing enrollment. The Government of India introduced two programs in the early 2000s to promote direct enrollment in schools. The first program, the Sarva Shiksha Abhiyan (SSA), was intended to provide universal access to elementary education. The second program was the Mid-day meal which provided cooked meals for children in attendance at schools. However, both programs were launched much earlier than the MGNREGA. The second concern might be that the increase in private schools driven by growth in the private school market, independent of the program, affects our results. We see significant declines in the enrollment in the private schools. Hence, an increase in number of schools cannot be causing this decline. Further, our estimates are robust to including state- specific trends, and school type by year fixed effects. Therefore, different trajectories of growth across states is not generating our results. In Appendix Figure S1 (in the supplementary online appendix, available with this article at the World Bank Economic Review website), we also show that phase wise trends in expansion of schools both for private and public schools were similar. Thus, an independent increase in demand for private schools is unlikely to be driving our results.

Finally, if MGNREGA attracts migrants into districts, then the results could be driven by changes in population. Across district migration in India is very low (Topolova, 2010). Further, if migration were responsible for the changes in enrollment, then we would expect similar sized effects for primary and upper primary grades and individual classes within these grades. In Tables 2 and 3, the size of the effect is much larger in primary school with no effect discerned in upper primary school. It seems implausible that households with children only in specific age groups would migrate into the MGNREGA

> 15We also get similar results using log specifications.

12
_Caveats about the educational production function literature._ We should not overstate our case as to the reliability of the educational production function literature in supporting _any_ hypothesis. Many of the studies find _nothing_ to be statistically significant and often find perverse patterns in the point estimates (for example negative signs on inputs like teachers or books). Moreover, even when the estimates of the individual production function coefficients are reasonable, school inputs typically have very small explanatory power. There are several additional econometric difficulties with the literature and we should at least mention four: simplistic and differing specifications'<sup>9</sup> , multi-collinearity, insufficient variation in input use, and different sensitivities among inputs.

One point on the latter two concerns. Suppose that there were a number of potential non-teaching inputs with high, but steeply declining marginal products (like algebra books). A perfectly random allocation of resources among these non-teaching inputs would produce a pattern of some very large and some small estimated marginal products with the high productivity of some of these factors due to the very low utilization. Different inputs might have different amounts of variation in use<sup>20</sup> . This pattern suggests that there are inputs for '9 The specifications in this literature use quite simple assumptions about the production function, usually that output is linear or log-linear in inputs. In particular this rules out interactive effects between inputs which are potentially important. Also, the analyses reported above may suffer from the fact that the specifications are not identical across the various results reported. Even so, World Bank (1996a) reports results from estimating the exact same functional form of an educational production function in low literacy districts in eight Indian states, for two different educational outcomes. The results clearly show that the significance (and sign) of the effects of the inputs vary widely across the different states even for exactly the same specification.

20 Using the results of World Bank (1996a) we perform a crude regression of the significance of the parameter estimate on the mean and standard deviation of the input. The 26
5,555 to 7,438. The number of students in secondary schools increased from under 6.4 million in 1997 to 8.3 million in 2009 (INEP 2003; INEP 2011).<sup>10</sup>

# **3. Data**

The primary data used in this study comes from two sources: the Brazilian school census ( _Censo Escolar_ ) and Brazilian vital statistics data ( _SINASC_ ) from the Ministry of Health. In addition, we use auxiliary data, including population estimates for Brazilian municipalities from the Brazilian Census Bureau, and municipal expenditure data from a variety of sources. Descriptive statistics for the key variables in our analysis are reported in Table S1.1 in the supplementary appendix. As municipal boundaries changed over the period of interest, we base our analysis on 4,884 minimal comparable areas (see Appendix S2 for details). For simplicity, we continue to refer to the unit of observation as municipality.

# _Schooling data_

We use 13 waves of the Brazilian school census, collected annually for the Ministry of Education by the _Anisio Teixeira Institute of Research on Education_ (INEP) which provides administrative data on the universe of schools in Brazil (Glewwe and Kassouf 2012). The school census includes detailed information on the universe of public and private schools in Brazil, such as enrolment by grade, age and sex, information on the number of classes, the physical characteristics of the schools, as well as information on teachers.<sup>11</sup> The school census is available from 1995, but because of inadequate quality of the data, we discard the first two census years. We use the census data to create a dataset on the number of secondary schools, the number of classrooms and the number of students between 1997 and 2009—when the vast majority of the school expansion has

> 10 This increase is mirrored by a steep increase in education expenditure; between 2000 and 2009, Brazil reported the largest increase in education spending as a percentage of total public expenditure for 33 countries for which data is available (OECD 2012).

> 11 These data can be downloaded at the website of INEP (http://portal.inep.gov.br/microdados).

9
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Equation (1) indicates that changes in inflation are positively related to the cyclical component of output, with a lag of one period. Equation (2) relates the output gap positively to its value in the previous period and negatively with the _ex post_ real interest rate, again with a one-period lag. In this model, policy actions (changes in the nominal interest rate) affect output with a one-period lag and, as implied by (1), inflation with a twoperiod lag.<sup>3</sup> The lag between a change in the policy instrument and inflation will be referred to in what follows as the _control lag_ or _control horizon._ The assumption that the central bank controls directly the interest rate that affects aggregate demand warrants some discussion. In principle, what affects private consumption and investment decisions is the cost of borrowing, that is, given the characteristics of the financial structure that prevails in many developing countries, the bank lending rate. In general, bank lending rates depend on banks' funding costs, a key component of which is either the money market rate or (ultimately) the cost of short-term financing from the central bank.<sup>4</sup> Thus, to the extent that bank lending rates (and money market rates) respond quickly and in a stable manner to changes in policy rates, the assumption that the central bank controls directly the cost of borrowing faced by private agents can be viewed simply as a convenient shortcut.<sup>5</sup> What, then, is the evidence? Figure 1 reports impulse response functions of a one-standard deviation increase in the central bank's discount rate (taken to be the policy rate) in a group of six developing countries for which data were readily available. These responses are obtained from a bivariate vector autoregression (VAR) model that includes the policy rate and the money market rate (see Appendix B for details). The figure shows 3Note that introducing a forward-looking element in equation (1) would imply that monetary policy has some effect on contemporaneous inflation; this would make the solution of the model more complicated but would not affect some of the key results discussed below. See Appendix A, which dwells on Clarida, Galf and Gertler (1999), for a discussion. Nevertheless, it should be kept in mind that the assumption of model-consistent expectations also has drawbacks; in particular, it downplays the role of model uncertainty-which, as discussed later, may be very important in practice.

4Bank lending rates also depend on the perceived probability of default of potential borrowers and, in an open economy, the cost of funding on world capital markets. See Agenor and Aizenman (1998) for a model that captures these features of bank behavior.

5Note that if aggregate demand depends on longer-term interest rates, a similar effect would arise. This is because longer-term rates are driven in part by expected future movements in short-term interest rates, which are, in turn, influenced by current and expected future policy decisions of the central bank.

6
Second, the pricing strategy adopted by a plant for its new products can affect the innovationsurvival link.<sup>38</sup> If a plant charges for its new products prices that are higher than those charged by competitors, it risks lower sales unless consumers are willing to pay extra to have the new product instead of an old more cost-effective product. New products priced much higher than the common practice in the industry may not see their sales take-off and may have to be withdrawn from the market soon after being introduced. Columns (4)-(6) of Table 8 present the results from estimating our main specifications separating new products into those priced (in their introduction year) above versus below the median across all plants selling the product. The estimates show a clear benefit for plant survival of introducing new products priced below the median but no effect for new products priced above the median.

Third, we assess the role of market risk for the innovation-survival link by examining whether a recession period hurts innovators by reducing demand for their products relatively more than for noninnovators. New products are likely to be among the first products that consumers decide to postpone consuming. Recession periods tend to increase exit rates - with the evidence suggesting that even superior performers such as innovators might be affected by higher exit risk (Salvanes and Tveteras, 2004) - and innovation rates are often lower (OECD, 2009; forthcoming; Paunov, 2012).<sup>39</sup> We estimate our main specifications interacting the product innovation variables with a dummy for 1999 which was a recession year for the Chilean economy. The results in columns (7)-(9) of Table 8 show that the impact of innovation on plant survival is insignificant during the recession period but is significant during growth periods. The differences are however not significant as indicated by the t- tests. Hence, we cannot establish that such a general shock to sales affects the innovation-survival link but this could be simply due to the very short duration of this recession.

Fourth, we consider the possibility that innovation is more risky for survival in industries with higher sales volatility, where plants are less certain of how their innovations will perform in the market. Columns (10)-(12) of Table 8 present the results from estimating our main specifications allowing the effects of product innovation to differ across industries with higher versus lower sales volatility over the sample period. The results show that innovation improves Chilean plants‟ survival odds significantly _only_ in industries with lower sales volatility.

> 38 Note that the pricing strategy will also be related to production costs and thus to technical aspects.

> 39 The studies show these patterns for the financial crisis of 2008-2009 in OECD economies (OECD, 2009, forthcoming) and in eight Latin American economies (Paunov, 2012).

20
- 20-

# FRANCE

France is the largest general development donor in Sub-Saharan<sup>Africa and the second largest</sup> on the continent. France's ODA rose from FF24.8 billion in 1985<sup>toFFZ7 billion in 1987 and is</sup> expected to increase funher in 1988 and 1989.

Aid to the health sector has climbed from approximately $232 million<sup>in 1985 to S295 million in</sup> 1988.' Maternal health funding in the form of maternal and child health<sup>programs, health facilities</sup> development, disease control, nutrition and UlN contributions is estimated<sup>at S70 million (1985),</sup> **S60** miliion (1986), and S90 million each in 1987 and 1988.6 Ministry officials endorse the importance of protecting the health of<sup>mothers. Projects for thik</sup> purpose are largely integrated with programs for health and preventive<sup>medicine, making estimates of</sup> SM content extremely difficult.

France supports a broad approach to health, which includes<sup>both preventive and hospital-based</sup> maternity care. Approximately 90 percent of these programs are<sup>concentrated in 36 African and</sup> Caribbean countries. Programs emphasize prevention and disease<sup>control in populations that have</sup> limited access to medical facilities. An attempt is made to integrate<sup>health in general development</sup> programs, including nutrition, the role of women, and regional programs<sup>to control major endemic</sup> diseases.

To accwmplish this program France employed 850 technical field<sup>staff in 1988. The Cooperation</sup> and Development program emphasizes the importance of education<sup>and training for health. Medical</sup> research receives continued support through Africa-based French institutions<sup>such as ORSTOM,the</sup> French overseas scientific and research organization.

While the financial prospects for maternal health are favorable, there<sup>is no immediate prospect</sup> of a special emphasis on SM in view of the inclusion of principal componenis<sup>in the general health</sup> program.

Ministere de la Cooperation et du Developpement 20, rue Monsieur 75700 Paris ' Based on official reports to DAC/OECD, with supplementary data on contributions<sup>to WHO,</sup> UNFPA and UNICEF.

I These totals exclude bilatera! assistance through other channels, for instance the Ministry of Foreign Affairs, the Central Bank for Economic Cooperation, and the Ministry<sup>of Finance.</sup>
**Table8: Evidence of convergence in productivity at rayon level by** **<u>quintiles</u>**

||**10**<sup>**th**</sup>|**25**<sup>**th**</sup>|**Percentile**<br>**50**<sup>**th**</sup>|**75**<sup>**th**</sup>|**90**<sup>**th**</sup>|
|---|---|---|---|---|---|
|Init. share > 3000 ha|-0.481***|-0.543***|-0.581***|-0.557***|-0.598***|
||(-3.951)|(-7.910)|(-8.190)|(-7.758)|(-6.619)|
|Initial productivity|-0.522***|-0.649***|-0.659***|-0.747***|-0.830***|
||(-4.792)|(-7.151)|(-9.103)|(-5.715)|(-8.913)|
|Init. share > 3000 ha|-0.283|-0.233|0.111|-0.056|-0.108|
|* productivity|(-0.437)|(-0.933)|(0.269)|(-0.152)|(-0.340)|
|Rayon fixed effect|1.372***|1.214***|0.983***|0.698***|0.876***|
||(4.812)|(5.353)|(7.974)|(2.806)|(5.389)|
|Init. share > 5000 ha|-0.363***|-0.432***|-0.405***|-0.402***|-0.492***|
||(-2.771)|(-4.550)|(-2.816)|(-3.530)|(-4.733)|
|Initial productivity|-0.414***|-0.530***|-0.569***|-0.553***|-0.785***|
||(-2.629)|(-9.506)|(-7.695)|(-5.970)|(-14.095)|
|Init. share > 5000 ha|0.448|0.149|0.685|0.156|0.117|
|* productivity|(0.857)|(0.292)|(1.054)|(0.277)|(0.234)|
|Rayon fixed effect|0.977***|0.847***|0.628***|0.314**|0.671***|
||(4.076)|(4.983)|(4.767)|(2.463)|(4.770)|

_Note:_ t-statistics in parentheses based on bootstrapped standard errors: *** p<0.01, ** p<0.05, * p<0.1 25
Table 7: Maximum Share of Variance Explained By U.S. MP Shocks (in %)

||For|eign interest ra|tes||Exchange rates||
|---|---|---|---|---|---|---|
||Low 20%|40-60%|Top 20%|Low 20%|40-60%|Top 20%|
|NS|2.20|2.08|2.02|16.60|9.30|4.36|
||[0.40, 12.45]|[0.25, 10.14]|[0.41, 8.16]|[3.35, 37.89]|[0.90, 27.55]|[1.13, 14.95]|
|BRW|5.56|8.02|4.96|3.32|5.05|8.73|
||[1.00, 18.09]|[0.86, 21.49]|[0.87, 15.18]|[0.45, 16.25]|[0.54, 18.72]|[0.75, 28.90]|
|SS-FG|2.06|1.49|3.84|11.53|7.50|4.95|
||[0.51, 7.72]|[0.25, 7.33]|[0.82, 11.61]|[1.40, 25.18]|[0.47, 20.22]|[0.71, 19.17]|

Notes: (1) The table shows the maximum share of U.S. monetary policy shocks in a forecast error variance decomposition over 30-month horizons. (2) The lower (upper) bound of 68% confidence bands are shown in the brackets.

# **7 Conclusion**

Emerging markets and developing economies (EMDE) exhibit significantly greater volatility in asset returns and output growth than advanced economies. Across countries, there are significant commonalities in these risky asset returns and flows, as documented by the influential literature on the Global Financial Cycle (GFC). The effects on EMDEs are particularly strong and occur independently of the exchange rate regime (Rey 2015, Miranda-Agrippino & Rey 2020, KalemliÖzcan 2019, Miranda-Agrippino & Rey forthcoming). If these effects are furthermore independent of countries’ financial openness, the classic Trilemma becomes Obstfeld (2022)’s “Lemma”: countries can do nothing to decouple from the GFC. Under the view that U.S. monetary policy is the key driver of the GFC, countries may inherit U.S. monetary policy no matter what they do on exchange rates or capital controls policy.

Empirical results in this paper suggest that countries with more stringent capital controls exhibit smaller responses of interest rates and exchange rates to U.S. monetary policy shocks and that this result holds more firmly for EMDEs than advanced economies. In contrast, we find only weak evidence that the degree of exchange rate regime flexibility affects U.S. spillovers to foreign interest rates and exchange rates. Our findings indicate that (i) the global financial system lies between the classic Trilemma and Obstfeld’s “Lemma”, and (ii) policy tools like capital controls are still powerful under this backdrop, for EMDEs in particular.

27
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