rafmacalaba/datause-encoder-data huggingface dataset:
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3 recent studies have attempted to study spillovers from EMs but focusing on individual EMs mainly China (Hsieh and Ossa, 2011).<sup>4</sup> The present paper attempts to fill the gap. The main contribution of our paper is to document empirically that global spillovers are asymmetrical. Our empirical results based on a two-block set-up and covering the period 1991 to 2015 are twofold. First, we show that the size of the spillovers running from EMs to AEs is about a fifth of these running from AEs to EMs. Second, results point to spillovers from EMs to AEs having increased over the second half of the sample period. We present suggestive evidence that the (evolving) structure of interdependencies play an important role in explaining the existence of asymmetrical spillovers between these similar sized blocks.

A casual look at data on trade and capital flows between AEs and EMs suggest that global spillover may indeed be asymmetrical and changing. Figure 7 shows that the EM block depends relatively more on external demand suggesting that spillovers from AEs to EMs are expected to be large. AE’s dependence on external demand is increasing suggesting that spillovers from EMs maybe increasing (see Table 1). Figure 8 also shows that the EM block depends more on capital inflow suggesting that spillovers from AEs maybe large. AE’s dependence on capital flows is increasing suggesting spillovers from EMs are expected to increase (see Table 2). In this paper we use state of the art Vector Autogressive (VAR) techniques to systematically document the asymmetrical nature of global spillovers.

In explaining the origins of spillovers, there are three strands of theoretical literature namely the “fundamental”, “financial”, and “coordination failure” explanations (see survey by Rigobon, 2018; Arezki and Yang, 2019 and references therein). The fundamental explanation of spillovers between countries rely on real channels. The related set of papers focus on bilateral trade, trade

> 4 Also see Ashvin, and Nabar (2012) for a study of the global spillovers from China’s investment-led growth.
# Can Targeted Allocation of Teachers Improve Student Learning Outcomes? Evidence from Malawi

Salman Asim*, Ravinder Casley Gera<sup>±</sup> , Martin Moreno<sup>∞</sup> , and Kerry Wong<sup>≠</sup> JEL classification: C1; I21; I28; D73; D78 Keywords: Econometrics; Education Policy, Education Quality, Teacher, Bureaucracy; Policy implementation

*Senior Economist, World Bank; sasim@worldbank.org

±Education Specialist, World Bank ∞Economist, World Bank ≠Assistant Professor, London School of Hygiene and Tropical Medicine
worse, the data might be manipulated for political reasons. And, depending on the country’s capacity and political sensitivities, adding even a minimal set of questions on migration is often deemed impractical and undesirable, as it may have adverse consequences for the quality of the census data. Researchers must therefore judge the reliability of the data source, as would be the case in other areas, before using it.

# **_Definition of a country_**

Even if a survey or a census asks country of birth or citizenship questions of the participants, the definitions of countries change over time. Many countries gained independence (Eritrea, Timor-Leste, South Sudan and many other countries in SubSaharan Africa), dissolved into smaller states (the Soviet Union, Yugoslavia and Czechoslovakia) or unified (Yemen, Germany, Vietnam) since the Second World War. Shifting national borders creates other challenges. First, with partition, millions of migrants are artificially created without ever moving from their homes. Those born in Moscow, but residing in Kiev, would never have been classified as migrants under either of the two most commonly used definitions until August 23, 1991, but are classified as such in the following censuses. For instance, as Özden et al. (2011) show, the sudden jump in international migrants’ numbers between 1990 and 2000 is mainly due to the break-up of the Soviet Union.

Changing borders pose problems when analyzing time-series data. One option is to use the countries in existence at that point in time. Migrants from Africa who came to the USA before the 1970s were recorded under different origin countries in different 13
discussed in detail below, suggests that children who enroll in preschool have superior learning outcomes later on. The results in Table 1 are therefore an indication that there is scope in many countries in the region for expansions in preschool coverage.

<u>Evidence of deficits in early childhood outcomes and socioeconomic gradients within countries: A handful of recent papers have used micro-level data to describe deficits in early</u> childhood outcomes in Latin America and the Caribbean, and to make comparisons of households with different levels of education, wealth, or income.

Fernald et al. (2005) and Gertler and Fernald (2004) present evidence of ECD shortfalls in Mexico. Fernald et al. focus on the relationship between deficits in child nutritional status and the Mental Development Index (MDI) of the Bayley Scales of Infant Development, a test of memory, learning, problem-solving, sensory-perceptual acuities, and receptive and expressive language development. Fernald et al. find significant reductions in the Bayley MDI scales with the age of the child: At age 13-14 months, 14.4 percent of children are one standard deviation or more below the normed value for the test, and 3.0 percent are 2 standard deviations or more below. By age 21-23 months, almost half the children are 1 standard deviation below the norm, and 11.3 are more than 2 standard deviations below. These deficits in mental development are mirrored by an increasing fraction of children with low height for age—at age 13-14 months, 25.9 percent of the children are stunted, compared to 42.7 percent by age 21-23 months. However, Fernald et al. find no association between height for age and the Bayley MDI score once family and environmental variables are included in a multivariate regression framework. More surprisingly, _none_ of the parental or socioeconomic factors they control for, including income, employment, parental age, education, and whether the head of the household spoke an indigenous language, are significant predictors of performance on the Bayley MDI score.

Gertler and Fernald (2004) use a large number of tests to assess developmental outcomes for a sample of poor children in Mexico. These include the Woodcock-Johnson-Munoz test, a set of co-normed tests that measure general intellectual ability, specific cognitive abilities, and scholastic aptitude which has been used in Latin America (Lozoff et al. 1991; Rodriguez and Prewitt-Diaz 1990; Roselli et al. 2001); and the _Test de Vocabulario en Imágenes Peabody_ (TVIP), the Spanish version of the Peabody Picture Vocabulary Recognition Test (PPVT). The TVIP is a test of receptive language that is frequently used to evaluate Spanish-speaking preschool children (Munoz et al. 1989; Umbel et al. 1992). Data collection was carried out for an evaluation of the impact of _Oportunidades_ , the cash transfer program—further details on this are provided below. Gertler and Fernald compare the cognitive development outcomes of the _Oportunidades_ evaluation sample with the population that was used to norm a given test. On the 13
The rest of the paper is organized in the following sections: Section 2 describes the data, Section 3 reports on the international comparison of key financial ratios, Section 4 provides the analysis of financial ratios by year, Section 5 presents industry composition and trend in sales growth by industry, Section 6 presents analysis of financial ratios by firm size, Section 7 presents analysis of key ratios by leverage and size, Section 8 reports the results of regression analysis of the leverage ratios, Section 9 discusses capital market liquidity and concentration and Section 10 presents summary of results.

# **2. Data**

The data were obtained from the Capital Market Authority (CMA) and include market price and shares traded for about 1100 firms and financial information on approximately 900 firms. The data on market prices and volumes are available for 1997– 2001 and the data on financial statements cover 1995–2001. Because year 2001 is very underrepresented and biased toward the large firms, it is excluded from most of the analysis.<sup>3</sup> An important adjustment has been made to reported accounts in order to carry out the analysis. This is required because most firms continue to report fixed assets (PPE—property plant and equipment) at their gross values without deduction of any allowance for depreciation. (This is the case even though Egyptian accounting standards have been changed to comply with IAS, which requires reporting fixed assets at their net value, i.e., subtracting accumulated depreciation from gross assets). This creates a distorted picture of the true value of the assets: as fixed assets age their replacement value declines and therefore the “economic” value of the total assets should decline. By recording the PPE at gross value, the book value of the total assets might be constant (or even increasing) even if the true (economic) value were declining. To adjust for this the team estimated the net value of fixed assets by subtracting accumulated depreciation, which will also allow easy comparison with similar ratios for other countries. In calculating total assets the team used the net-ofdepreciation values for fixed assets. The adjustment gives quite a different picture: for

> 3 There are total of 140 firms available for year 2001, while there are about 500 firms for year 2000 and about 600 for 1999. The firms that have the 2001 data available are mostly large firms—for example the size of the median firm in 2001 is over twice that of the median size in 2000.

3
With the previous sections as background, Section V concludes the paper with a policy analysis of public debt management in Brazil in the near future. Our main policy advice is that the rollover of the domestic public debt should employ inflation-indexed bonds, in order to lengthen the maturity without creating time consistency problems.

## **II. Domestic Bonded Debt**<sup>**2**</sup>

# _II.1. Historical_ **_background (19_** _70-1994)_

The beginning of the existing market for domestic public debt in Brazil was the financial reforms introduced by the military government in the second half of the 1960s. Those reforms envisaged three big measures to solve the inflationary problem of the previous ten years (inflation rose from 15% to 80% a year between 1955 and 1964): the creation of marketable public securities to finance fiscal deficits; the creation of the Central Bank; and the adoption of a banking system with a clear-cut separation between commercial banks and non-bank institutions.

Figure II.1-Federal Bonds: 1970-1999-displays the evolution of the total federal government debt, separating the Central Bank holdings of government debt from the outstanding debt held by the private sector. During the high inflation years--from the early eighties to the mid-nineties--there had been a widening of the fraction of the public debt held by the Central Bank. Under high inflation, cash management activities tended to predominate in the banking sector and the Central Bank backing of such activities required

> 2 This Section draws heavily on Bevilaqua, Carneiro, Garcia, and Wemeck (1999).

4
processes such as steaming to remove the harshness of taste, and have become more flexible in switching between coffee types, and c) a small but viable segment of the market has emerged that focuses on quality and product differentiation (e.g., specialty and gourmet coffees, and “issue coffees” such as Fair Trade and environmentally friendly coffees).

**Figure II.1:  Coffee Price from 1967 to 2002  (prices in nominal US$ per qq)**

<!-- Start of picture text --> 360<br>310<br>260<br>210<br>160<br>110<br>60<br>10<br>Feb-67 Feb-70 Feb-73 Feb-76 Feb-79 Feb-82 Feb-85 Feb-88 Feb-91 Feb-94 Feb-97 Feb-00<br><!-- End of picture text --> At the same time that supply of coffee has expanded there are growing consumer markets for gourmet and other specialty coffees (gourmet, fair-trade, organic, eco-friendly, etc.) that command a significant price premium (Fleischer, 2002). Central American and Caribbean countries are important players in these markets, and these niche markets are growing at a faster rate than more traditional markets. However, in the aggregate, these special coffee markets only represent a small share of global exports. Competition for these markets is increasing over time, as improving coffee quality and increasing access to these markets has become a major strategic direction of coffee programs for Central American and Caribbean countries (e.g., the new USAID “Coffee Initiative” that provides funding for all of Central American countries and the Dominican Republic). For more details on the global coffee situation, with a focus on Central America, refer to the report by Varangis, Siegel, Giovannuci, Lewin (2003), and recent coffee sector studies in Mexico (World Bank, 2002b) and Columbia (World Bank, 2002c).

# _The Dominican Republic’s Position in World Coffee Markets_

The Dominican Republic is considered a “minor player” in world coffee markets, in terms of its share of total world production and exports, which ranged between 0.4% to 1% from 1997 to 2002. The Dominican Republic also has a small share of global production relative to Central American countries (e.g, Costa Rica, Guatemala, Honduras) and is comparable to Nicaragua.

25
Table 2a: State-level distribution of female activity in unorganized manufacturing

||Estimat<br>owne|ed count of<br>d establish|female-<br>ments|Share of e<br>that a|stablishme<br>re female-o|nts in state<br>wned|Estima|ted count of<br>employees|female|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|A & N Islands|156|276|1,077|0.058|0.124|0.365|582|735|1,489|
|Andhra Pradesh|462,841|781,483|738,543|0.293|0.523|0.448|1,211,931|1,343,064|1,280,655|
|Assam|50,482|86,028|31,967|0.182|0.256|0.149|108,839|148,888|61,584|
|Bihar|100,153|308,913|83,989|0.080|0.230|0.106|585,070|738,829|250,850|
|Chandigarh|1,517|385|2,590|0.280|0.307|0.394|1,778|507|2,971|
|Dadra & Nagar Haveli|63|336|373|0.052|0.378|0.219|169|414|664|
|Daman & Diu|257|1,235|1,004|0.274|0.541|0.456|297|821|745|
|Delhi|14,651|10,421|16,895|0.069|0.117|0.080|29,660|16,303|36,748|
|Goa|5,557|2,119|2,156|0.236|0.229|0.245|16,218|3,942|2,962|
|Gujarat|64,193|140,227|731,896|0.124|0.227|0.518|180,765|250,451|984,459|
|Haryana|24,979|43,600|31,900|0.137|0.207|0.147|35,973|53,724|66,222|
|Himachal Pradesh|16,670|28,594|15,801|0.176|0.271|0.172|27,974|38,642|25,034|
|Jammu & Kashmir|20,739|37,002|70,198|0.101|0.221|0.317|127,045|64,533|77,184|
|Karnataka|527,936|554,116|441,334|0.513|0.583|0.516|831,698|916,185|613,393|
|Kerala|231,825|322,860|250,942|0.493|0.540|0.510|390,135|482,499|378,332|
|Madhya Pradesh|187,745|203,577|321,501|0.193|0.197|0.299|577,256|629,079|560,929|
|Maharashtra|267,695|350,540|495,671|0.222|0.321|0.351|523,060|481,317|750,058|
|Manipur|43,663|40,241|30,574|0.831|0.826|0.728|54,834|51,321|38,622|
|Meghalaya|5,549|12,078|4,164|0.266|0.351|0.272|9,968|19,076|8,295|
|Nagaland|2,150|4,345|3,009|0.377|0.454|0.436|2,812|5,199|3,055|
|Orissa|214,223|246,641|128,810|0.220|0.268|0.212|631,835|736,597|333,903|
|Pondicherry|3,368|3,364|7,594|0.296|0.275|0.463|10,776|9,279|13,609|
|Punjab|105,863|113,022|122,894|0.327|0.400|0.302|131,763|142,785|134,767|
|Rajasthan|139,662|172,954|168,201|0.231|0.277|0.262|281,378|243,984|237,139|
|Tamil Nadu|634,130|764,995|775,129|0.428|0.533|0.472|1,326,412|1,465,538|1,388,878|
|Tripura|5,304|12,437|14,318|0.150|0.279|0.202|26,660|25,507|28,143|
|Uttar Pradesh|444,719|626,239|724,302|0.190|0.271|0.295|1,257,009|1,207,882|1,437,150|
|West Bengal|841,320|1,306,482|1,430,641|0.307|0.487|0.535|1,929,985|2,101,350|1,997,934|
|Nationwide|4,417,408|6,174,508|6,647,471|0.266|0.375|0.386|10,311,885|11,178,449|10,715,774|

Notes: See Table 1.
**TABLE 4: UGANDA’S ORGANIC COFFEE STATISTICS**

||**_1999/2000_**|**_2000/01_**|**_2001/02_**|**_2002/03_**|**_2003/04_**|
|---|---|---|---|---|---|
|**_ROBUSTA_**||||||
|Exports, total (60‐kg bags)|2,390,682|2,617,777|2,716,955|2,220,440|2,039,482|
|Exports, organic (60‐kg bags)|2,540|5,020|1,440|2,500|1,828|
|Share of organic exports|0.11%|0.19%|0.05%|0.11%|0.09%|
|Value, total (US$)|121,864,609|79,847,671|64,496,820|81,813,846|82,608,487|
|Value, organic (US$)|136,173|232,137|48,937|120,723|77,337|
|Share of organic value|0.11%|0.29%|0.08%|0.15%|0.09%|
|Unit price, total ($/kg)|0.85|0.51|0.40|0.61|0.68|
|Unit price, organic ($/kg)|0.89|0.77|0.57|0.80|0.71|
|Organic premium|5%|52%|43%|31%|4%|
|ICO robusta indicator ($/kg)|1.08|0.66|0.59|0.82|0.80|
|**_ARABICA_**||||||
|Exports, total (60‐kg bags)|526,575|456,996|430,426|442,448|483,610|
|Exports, organic (60‐kg bags)|—|620|4,180|4,380|1,110|
|Share of organic exports|—|0.14%|0.97%|0.99%|0.23%|
|Value, total (US$)|42,899,183|24,928,755|19,440,132|22,973,248|33,093,283|
|Value, organic (US$)|—|36,375|223,428|339,947|83,283|
|Share of organic value|—|0.15%|1.15%|1.48%|0.25%|
|Unit price, total ($/kg)|1.36|0.91|0.75|0.87|1.14|
|Unit price, organic ($/kg)|—|0.98|1.06|1.29|1.25|
|Organic premium|—|8%|18%|49%|10%|
|ICO arabica indicator($/kg)|2.14|1.45|1.31|1.43|1.62|

**Notes:** Years reflect October‐September averages. Unit prices have been calculated as value (US$) over exports (bags*60).

**Source:** Author’s calculations based on data from the UCDA _Annual Reports_ .

**_— 21 —_**
# **Appendix 3: Balance of baseline characteristics**

_Balance of baseline characteristics and variable definitions, used in the Philippine data_ This Appendix describes the individual, household, and community characteristics we include as controls in the regressions presented in Appendix Tables 3, 4, and 5. We constructed these characteristics using the baseline Proxy Means Test survey, unless noted otherwise. We briefly describe why these characteristics are appropriate potential covariates and present balance tests to assess the validity of the village-level randomized assignment. In each balance test, we regress the vector of covariates on the treatment indicator and cluster the standard errors at the village level. There are no statistically significant differences between the treatment and control groups across the range of outcomes considered.

Appendix Table 11a shows the balance along child (aged 10-14) level controls: age, gender, and an indicator variable taking the value 1 if neither the child's mother nor his/her father lives in the child's household. We consider the latter an important covariate because a large literature shows that parental absence (mostly death) is an important predictor and determinant of schooling outcomes (e.g. Evans and Miguel, 2007) and cash transfers can help compensate for parents’ absence (Fitzsimons and Mesnard, 2014). All of these child-level controls are constructed using follow-up data as no childlevel information can be derived from the data collected for the proxy-means test. In the absence of differential attrition, these variables are unlikely to be affected by the program (and the lack of significance in the balance tests indeed implies there was no differential attrition unless the characteristics were originally unbalanced at baseline and then experienced a degree of differential attrition that would result in balance at end line, a situation not supported by any supplementary quantitative or qualitative evidence).<sup>27</sup> 27 In the impact estimates, we do not include the age variable as a linear control, but rather indicator variables for age equal to 10, 11, 12, and 13 interacted with the indicator variable for being male.

42
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56
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