rafmacalaba/datause-encoder-data huggingface dataset:
# **Takle 1:**

# CAPITAL STOCKS **AND** DEBTS OF CULTIVATORS IN INDIA, **1951-1971** AS ESTIMATED BY THE ALL-INDIA DEBT AND **INVESTMENT** SURVEY a/ (rupees per cultivating household)

||1951|1961|1971|
|---|---|---|---|
|Total capital stock<br>Current prices<br>1971 prices h/|5356<br>12935|6609<br>12636|14624<br>14624|
|Total cash debt||||
|Current prices<br>1971 prices hl|316<br>763|473<br>904|605<br>605|
|Cash debt to equity ratio (percent)|5.9|7.1|4.1|
|Percentage of cultivating households<br>with cash debt|||43.0|
|Proportion of cash debt from||||
|institutional sources _/|8.7|18.4|31.6|
|From the government|||7.1|
|From cooperative societies or banks|3.0||22.0|
|From commercial banks|||2.4|
|Proportion of cash debt from<br>moneylenders d/|82.9|61.9|36.1|
|Proportion of cash debt from<br>other sources|8.4|19.7|32.3|
|From traders and commission agents||7.1|8.4|
|From relatives and friends||5.2|13.1|
|From landlords|||8.1|
|From others||_6.5_|2.7|

- A/ Data for the All-India Debt and Investment Surveys of 1981-88 appear to seriously underestimate debt of cultivators and is therefore not shown.

- _h/_ Deflator for 1951 and 1961 calculated from R.N. Lal, 1977. Capital Formation and its Financing in India. Bombay: Allied Publishers, 151-52.

- c/ Institutional sources include the government, cooperatives, commercial banks, and insurance and provident funds.

- d/ Moneylenders include professional and agricultural moneylenders.

Source: All India Debt and Investment Surveys, 1951, 1961, 1971.

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education programs. The TA was initially targeted at literacy, and in 2011 was expanded to include support for grades 3 to 5. The state government provided pedagogical and management support to the municipal secretariats of education, including elements like scripted materials for teachers, teacher training focused on classroom practice, effective use of learning assessments, and knowledge exchanges between schools. Another important milestone of the education reforms in Ceará was the reformulation of the RBF rule in 2013 to penalize municipalities with higher percentages of students below minimum thresholds of performance in mathematics and language (Portuguese).

Our empirical strategy explores this timeline and the location of schools across _Ceará_ ’s border in other states to identify RBF effects on learning at the end of primary (5<sup>_th_</sup> grade) and lower secondary (9<sup>_th_</sup> grade) education. Using data from students, teachers, and principals from the _Sistema de Avaliação do Ensino Básico_ (SAEB), we compare the average differences in education outcomes on both sides of the border before and during each implementation stage.<sup>1</sup> In total, this approach generates four parameters. To benchmark our findings and control for pre-existing disparities, we compute the baseline difference in pre-RBF periods (2005 and 2007). The second parameter refers to periods when the RBF was introduced without any complementary program in 2009. The third parameter measures the average difference in periods when the _RBF_ was combined with _TA_ (in 2011 for 5<sup>_th_</sup> grade, and in 2015 for 9<sup>_th_</sup> grade). The last difference considers the reformulation of the RBF rule in 2013 until 2017. We argue that, by comparing these parameters and controlling for student and municipal characteristics, time trends, and border fixed effects, it is possible to estimate whether financial incentives for mayors improved learning and the impacts of providing TA and reformulating the RBF rule.

> 1 It is crucial to highlight two points in this strategy. First, SAEB is not part of the RBF rule in _Ceará_ . Instead, _Ceará_ bases the redistribution criteria on its own _state_ assessment called SPAECE. SAEB is a _national_ assessment with no influence on fiscal redistribution. In this context, using SAEB is empirically important, because it is less susceptible to gaming by municipalities. The second point concerns our definition of schools at the border. Throughout the analysis, we restrict our sample to schools located in the immediate three municipalities on each side of the border between _Ceará_ and adjacent states (See Figure 1). We provide a set of robustness checks testing alternative border

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**Table. 2.** Trade and GDP comovement: All countries

||corr G|DP<sup>_HP_</sup>|corr ∆|GDP|corr G|DP<sup>_BK_</sup>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ln<br>�<br>_TPI_+_K_<br>�|**0.015**<sup>_∗∗∗_</sup>||**0.018**<sup>_∗∗∗_</sup>||**0.015**<sup>_∗∗∗_</sup>||
||(0.005)||(0.005)||(0.005)||
|ln<br>�<br>_TPinputs_<br>�||**0.014**<sup>_∗∗_</sup>||**0.015**<sup>_∗∗∗_</sup>||**0.013**<sup>_∗∗_</sup>|
|||(0.005)||(0.005)||(0.005)|
|ln<br>�<br>_TPf inal_<br>�|0.006|0.008|0.004|0.005|0.005|0.006|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|ln<br>�<br>_TPcapital_<br>�||_−_0.003||0.0001||_−_0.002|
|||(0.004)||(0.004)||(0.004)|
|_thirdIM_|0.018|0.023|0.116<sup>_∗_</sup>|0.121<sup>_∗_</sup>|0.036|0.041|
||(0.063)|(0.063)|(0.062)|(0.062)|(0.064)|(0.064)|
|_thirdEX_|**0.192**<sup>_∗∗∗_</sup>|**0.194**<sup>_∗∗∗_</sup>|**0.133**<sup>_∗∗∗_</sup>|**0.135**<sup>_∗∗∗_</sup>|**0.140**<sup>_∗∗∗_</sup>|**0.142**<sup>_∗∗∗_</sup>|
||(0.052)|(0.052)|(0.051)|(0.051)|(0.052)|(0.052)|
|CP FE|Yes|Yes|Yes|Yes|Yes|Yes|
|TW FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|15,827|15,827|15,827|15,827|15,827|15,827|
|R<sup>2</sup>|0.204|0.204|0.185|0.185|0.213|0.213|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. In parenthesis: std. deviation.

come groups, the significance of those estimate drops markedly.<sup>18,19</sup> Moreover, according to the point estimate (combining third country effect on exports and imports), this third country effect is stronger as the level of income is lower. In other words, a country among the low income group is more correlated with another country when the two countries trade with similar partners.

Notice, however, that results for the low income group becomes significant without third country indexes, with trade in intermediate and capital goods being strongly associated with GDP comovement while trade in final good is not. Table table 4 provide additional insight on such decomposition. In appendix, we also perform a country-centric exercise (as opposed to a country-pair analysis in the main text) and study the link between trade and the correlation

> 18Those results are robust to alternative trade proximity indexes, such as _TPijt_ = max � _Ti→GDPj_ <u>,</u> _t_ + _Titj→i_ <u>,</u> _t_ ,<sup>_Ti→_</sup> _GDP_<sup>_<u>j</u>_</sup><sup><u>,</u></sup><sup>_t_+</sup><sup>_T_</sup> _jt_<sup>_<u>j→i</u>_</sup><sup><u>,</u></sup><sup>_t_</sup> �.

> 19Notice that without controlling for time windows fixed effects (table 15 in appendix), estimates for trade in intermediate inputs and trade in final goods for country pairs including a high income country rise respectively to 0.087 and 0.025, both significant. This estimate for trade in inputs would imply a difference of GDP correlation of about 0.28 between the 25th and the 75th of trade in intermediate inputs, and an increase of 0.11 given the ratio of median trade in intermediate inputs.

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the bank shares are owned by foreigners. Similarly, a bank is classified as a domestic government-owned bank if 50 percent or more of its shares are owned by local or central governments. All remaining banks are classified as domestic private banks.<sup>7</sup> Table 1 presents regional level shares of banks by ownership type for each year in terms of both the number of banks and total assets. East Asia shows the lowest foreign bank presence with 12.7% of assets held by foreign banks in 2009. In contrast, Europe and Central Asia shows the highest level of foreign presence with foreign banks accounting for more than 52% of assets throughout the sample period. However, ECA FSU shows a very different composition of banks by ownership type compared to the other ECA sub-groups. The asset share of foreign banks in ECA FSU (at most 33.6%) is significantly lower than that of the ECA average (greater than 52.4%).

Among the foreign banks, we identify the parent banks and classify them using different criteria. First, we divide foreign banks into global and regional banks depending on whether the parent operates across different regions or mostly within its home region. Those parent institutions operating internationally outside of their own home region, such as CITI and HSBC, are defined as foreign global groups (FGG). Second, we categorize foreign banks by their parent’s country of origin depending on whether the parent is based in Europe, the US, or other region. Appendix Table A.1 shows the list of parent institutions in our sample, along with the information on these two classifications.

> 7 We cross-checked our ownership data with the database constructed by Claessens and van Horen (2013), which provides information on whether the bank is domestic or foreign along with country of parent. While Claessens and van Horen (2013) determine the ownership based on direct ownership, we focus on ultimate ownership. This different definition results in discrepancy in ownership or parent countries in approximately 5% of observations of our data. However, our regression results replicating the Claessens and van Horen (2013) analysis produce consistent results with theirs, indicating that it does not significantly affect the results.

7
## **Table 17: Summary** **<u>of Results</u>**

||IDEB|Prova Brasil<br>Port|Prova Brasil<br>Math|Approval<br>rate|IDERio|
|---|---|---|---|---|---|
|||El<br>|ementary Schools<br>|||
|Single-Shift (CES & non-certified)|0.26***|0.13***|0.15***|0.02***|0.28***|
|Became Single-Shift in 2011|0.78*|||||
|Became Single-Shiftin 2012-13|0.22***|||||
||||Middle Schools|||
|Carioca Middle Schools|0.81***|0.76***|0.77***|0.03***|0.70***|
|Single-Shift (non-certified)|0.01|0.10|-0.09|0.00|-0.08|
|Became Single-Shift in 2011|0.57***|||||
|Became Single-Shiftin 2012-13|0.57***|||||

Notes: Results from Tables 9 through 16. DID models including fixed-effects and municipal-by-time controls. Non-restricted sample.

However, the effect of the elementary Single-Shift schools is not robust to the restricted sample of schools with low age-grade distortion. This finding suggests that most of the increase we find in Single-Shift elementary schools come from the change in the student population, which artificially decreases the age-grade distortion and, therefore, artificially increases its results. Moreover, in contrast to the Carioca middle schools, which showed very positive results independently of when they entered the program, Single-Shift middle schools that do not follow the CMS model do not seem to have any impact on IDEB and IDERio results.

These analyses suggest that the Single-Shift model is successful in influencing student performance. However, just having 7 hours for the students does not seem to have the same positive impact on student performance without the organization of what is done in these 7 hours, such as teachers with 40 hours of dedication to the school, teacher training, and curriculum. While we cannot contrast the certified versus non-certified Single-Shift elementary schools, our results make clear that for middle schools, the structured model is notably relevant.

# **6. Conclusion**

<mark>This study adds to the literature on the impact of full-time schools in developing countries. The results found in the existing literature do not indicate conclusive effects for the full-time school model; more time in school does not necessarily translate into student learning and performance. In Brazil particularly, more time in school with the federal program “More Education” is associated with negative effects in mathematics and Portuguese, as presented in Almeida et al. (2016). Aquino and Kassouf (2011) also find that 9</mark><sup>th</sup> <mark>graders in full-time schools in São Paulo showed no significant differences in proficiency and pass rates in comparison to those who attended traditional schools.</mark> In contrast, this study finds that when this extra time is organized with a structured curriculum, full-time teachers and a focused teacher training, it may have a significant impact on student achievement. The main difference between Rio’s certified Single-Shift schools and other full-time schools is the requirement that all teachers in the participating schools are fully dedicated to a single school. Other programs, such as the São Paulo full-time school model, extended the school 37
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# ChiSquare (1) - 8.1, significant at 0.01.

These results are very preliminary. There is need for further research on the performance of equity m3rkets in developing countries, and its determinants and impact.

# 5. Ccnfaa±ln

This study has used simple cross-country regressions to address the relationships between financial policies and growth. For several reasons, notably the poor proxies for financial policy (financial depth and interest rates), the diffused nature of any relationship, and reverse causality, these effects are difficult to isolate with confidence. With this caveat, the results of this study support the following propositions: Despite their supposed status as capital-short countries, interest rates in developing countries have, on average, fallen below dollar rates, especially after 1973. This suggests a serious and widespread problem of mispricing financial savings.

Real interest rates and growth are associated positively, within the historical experience of the sample; Most of this association relates to the efficiency effect (as measured by the IOCR) rather than the level of investment: the latter relates only weakly to interest rates; Much of the interest-rate/growth relationship reflects reverse causality, from growth and efficiency to higher yields on all assets to higher interest rates. But at least part reflects the causal chain from interest rate levels to growth rates via the degree of financializati **o** n f savings.

The observed interest-rate/growth relationship may partly reflect the influence of other, related, distortionary policies, but the former is still powerful when the latter are included. Financial depth does explain part of cross-country variation in growth rates but does not weaken the interest-rate growth association. There is little relationship between interest rate levels and the current account.

A very preliminary analysis suggests that equity market performance in developing countries relates systematically and positively to growth rates, and that interest rate repression is not a cause of increases in stock prices.
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Only a few months later, in October 2006, a substantially better assessment appeared in a Standard & Poor’s report:<sup>6</sup> _Major Rating Factors Strengths:_

- _Macroeconomic and political stability becoming entrenched_

- _Improving economic growth prospects_

- _Weaknesses:_

- _Low level of economic development with severely limited infrastructure and vulnerability to_

- _exogenous shocks_

- _High debt compared with similarly rated peers_

- _Governance issues_

The EIU quotes convey the image of corruption and political instability, a combination normally associated with stagnation, bad public finances and a poor investment climate. The S&P quotes contain good news on stability and growth but caution that infrastructure is deficient and governance a weakness. We will not try to explain this disconnection in perceptions separated by only a few months but will instead present evidence on a perceptible change for the better in Kenya’s macroeconomic outcomes starting in 2003/04. The first step in this study was to attempt an explanation for the puzzling observation that real interest rates on Kenyan treasury bills were surprisingly low, of the order of 1-3 percent. How could a corrupt and politically unstable country (recall the EIU quotes) have such low interest rates? The possibility of manipulation was rejected because Kenya has a fully convertible currency and an open capital account. If interest rates were being kept artificially low, there would be downward pressure on both the exchange rate and foreign exchange reserves; but the opposite was true. When we asked officials or the private sector why interest rates were so low, the standard reply was “Because government’s borrowing requirements have come down.” However, a review of the government’s debt dynamics strongly suggests that borrowing requirements came down _because_ interest rates fell substantially, not the other way round.

> 6 S&P _Credit Research Report Kenya (Republic of)_ Oct. 23, 2006. A later report in February 2007, by JPMorgan titled ‘Kenya: A return to Macroeconomic Stability’ also notes that ‘the turning point for Kenya was the 2002 election of President Mwai Kibaki who was elected on an economic reform and anticorruption platform.’

3
regulatory agencies is not out of the question. In small ECOWAS countries, the domestic supply of professionals to implement this kind of regulatory system is low and inelastic, the political system is unstable, and the Rule of Law enforced by a competent independent judiciary is not in place. Thus, the pre-requisites for effective regulation of telecommunications are not likely to be satisfied, creating a significant, long-run barrier to the creation of an effective telecommunications industry.

In 1999, the telecommunications regulatory frameworks of 29 African countries were assessed in terms of their autonomy, credibility, transparency, and efficiency. Among the ECOWAS countries, the report included Benin, Cote d’Ivoire, Gambia, Ghana, Guinea, Nigeria, Senegal and Togo (EIU–Pyramid Research 1999). On a ranking of 1 (lowest) to 4 (highest), all the ECOWAS countries that were included in the report received the lowest score of 1 on autonomy; Benin, Nigeria, Gambia, and Togo also received the lowest score of 1 on regulatory credibility. Only Cote d’Ivoire had a score above 2 in any of the four dimensions of regulatory performance (3 on credibility and efficiency).<sup>41</sup> Since this assessment, considerable progress has been made in some ECOWAS nations, but many still are characterized by a very poorly performing parastatal monopoly in fixed access, long distance and international communications, combined with a weak regulatory system for supporting competition and growth in other services, such as wireless telephony and Internet services. All of the countries in the region have established some sort of regulatory agency for their telecommunications sectors, but in many cases these agencies lack independence and are mere extensions of sectoral ministries. Many governments continue to keep a tight grip on telecommunications while favoring poorly performing stateowned enterprises. Some independent agencies suffer from insufficient resources and legal authority. Annex A contains descriptions of the regulatory systems in West Africa. Here we provide a sketch of some of these agencies.

- In Benin, the regulatory authority has been La Direction de la Politique des Postes et Télécommunications (DPPT);<sup>42</sup> however, this agency is part of the Ministry of Communication and New Information and Communication Technologies

> 41 Pyramid Research. 1999. Privatizing Telecoms Markets.  Boston, Mass: The Economist Intelligence Unit.

> 42  Metozouve Dieudonné, “L’EXPERIENCE DU BENIN DANS LE DOMAINE DE REGULATION,” at www.cipaco.org/sources/convergence/communication%20MCPTN.doc.

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