rafmacalaba/datause-encoder-data huggingface dataset:
acceleration of food inflation. Yet, the puzzle is that agriculture has been leading the fast growth in the economy, so crop production has seen substantial growth during the period. This seems to undermine the potential role of aggregate demand in explaining Ethiopia’s recent food inflation. Changes in the structure of the economy, following a sustained rapid growth in agriculture, are viewed as a potentially better explanation for the price increases. These include behavioral changes leading to increased commercialization of crop production and reduced distress selling by peasants, which might have significant implications for aggregate demand and prices. Ahmed (2007) also lists various other domestic and external factors matter, including money supply and world commodity prices. In addition, housing shortages in urban areas and speculation have affected inflation, where a lack of regulation might have played a role in the surge in housing prices, particularly in Addis Ababa.

A macro-econometric model from the National Bank of Ethiopia (NBE), the country’s central bank, supports some of these findings; Ayalew Birru (2007) developed the model using annual data from 1970 to 2006. The chief claim is that supply shocks, inertia, and the consumer prices of major trading partners appear to be among the most important determinants of inflation. Nevertheless, the use of annual data and the need to correct for major developments in Ethiopia’s turbulent history limit the model’s applicability. In addition, it does not cover the period of rapid inflation 2007-2008.

In an unpublished policy note, the World Bank (2007) analyzes relative price shifts for major cereals at an early stage of the food crisis. It investigates several hypotheses, drawing on a number of background papers. Gray (2007) claims that the official data from the Central Statistical Agency (CSA) is relatively better than alternative estimates, though there is need for improvements of non-sampling errors. Dorosh and Subran (2007), using these official data and partial equilibrium simulations, then find that relative price changes for major cereals are broadly consistent with changes in domestic demand and supply during 2003-2007. Loening (2007) suggests expectations can explain a large fraction of inflation dynamics in Ethiopia for 2000-2006. The World Bank (2007) also suggests that activities of cooperatives may be improving the bargaining power of farmers, thus raising food prices. However, the shift from food aid to cash transfers seems to have had very 9
economic conditions which might have aected the educational investments made by the parents (see the discussion in section (4) below). To the best of our knowledge, this is the rst paper in the literature to study the eects of public school expansion on intergenerational educational mobility using a credible identication strategy and empirical specications that allow for heterogeneous relative mobility across the distribution.

There is a rich literature on the eects of school quality and school reform on various outcomes of children. In a recent paper, Card et al. (2022) provide an interesting analysis of the eects of public schools in the early 20th century United States using 1940 census data. Their focus is on the quality of schooling, and they nd that the impact of school quality varies by parental school level, supporting the emphasis on the heterogeneity in relative mobility across the distribution in our study. They provide causal evidence that salary caps reduced the quality of teachers which aected the Black children's educational attainment adversely. There are a few papers that look at the causal eects of school reform and/or school quality improvements on intergenerational income mobility using a dierence-in-dierence design; see Pekkarinen et al. (2009) in the context of a comprehensive schooling reform in Finland, and Parman (2011) for a historical study of Iowa showing that a better school quality lowered intergenerational income mobility.

# (3) Data Description and Variables Denitions

The empirical analysis of this paper is based on Indonesian census 2000 full count data. Our main estimation sample consists of the children born between 1957 to 1962 and 1968 to 1972. The school construction under the SD Inpres program of the second ve year plan began around 1973-1974. Following Duo (2001), we dene birth cohorts born between 1968 and 1972 as the exposed group as they are most likely to benet from the program, and birth cohorts born between 1957 to 1962 as the comparison group as they are least likely to benet from the program.<sup>26</sup> The intermediate birth cohorts 1963-1967 may be partially exposed to the Inpres schools, and we also provide estimates for this group. For our main analysis, we rely on father's schooling as a measure of parental education. As part of robustness checks, we also report estimates using mother's schooling as a measure of parental education. We underscore

> 26A child born in 1962 was 11-12 years old in 1973 and already completed primary schooling before the Inpres program.

9
<!-- Start of picture text --> yy |<br>)<br>CJ Counts of Breach of Contract Events (1985-201 2)* ye<br>I nfed winerap loan hen rhe Ur erg » BBP. P45 e22 Jes L)37 LJo2 |<br>shown on this map do not imply, on the part of The World Bank<br>pei sms ome nylp ad *Disputes exclude outright expropriation and nationalization.<br><!-- End of picture text -->
# **Uncertainty in the surroundings of the Thai crisis 1997**

<!-- Start of picture text --> 1 3.5<br>Devaluation of the Thai bathDevaluation of the Thai baht 3<br>Biggest drop in stock Biggest drop in stock<br>market returnsmarket returns 2.5<br>2<br>1.5<br>1<br>0.5<br>0 0<br>crisis in Thai Thailand Indonesia<br>Korea Malaysia Hong Kong<br>Singapore Taiwan United Kingdom<br>uncertainty<br>incidences of crisis<br>1997:01 1997:02 1997:03 1997:04 1997:05 1997:06 1997:07 1997:08 1997:09 1997:10 1997:11 1997:12 1998:01 1998:02<br><!-- End of picture text --> Figure 1: Uncertainty around the period of the Thai crisis This paper proposes a new channel for the international transmission of crises—the “uncertainty channel of contagion”—that explains the differential pattern of contagion. Stylized facts surrounding the Thai and Argentinean crises illustrate the potential relevance of this channel. Figures 1 and 2 show the behavior of the degree of “uncertainty”, as measured by the dispersion of GDP forecasts based on surveys of private sector analysts, around the period involving the Thai crisis (a surprise crisis) and the Argentinean crisis (an anticipated crisis). The two vertical bars in Figure 1 show the two most pronounced events in the Thai crisis—the severe devaluation of the Baht in the beginning of July 1997 and the substantial drop in stock market returns one month later.<sup>4</sup> The individual lines show the measure of uncertainty regarding fundamentals for a select group of countries. Following the onset of the Thai crisis, uncertainty regarding fundamentals increased substantially for several neighboring countries. In the case of Korea, the build-up of uncertainty was subsequently followed by a currency and stock market crisis in November of 1997. In contrast, Figure 2 shows the behavior of the uncertainty measure around the Argentinean crisis in 2001. Following the announcement of default in December 2001, uncertainty regarding the fundamentals of neighboring economies barely changed. In fact, as we will see later, the level of uncertainty, conditional on other macroeconomic and financial variables, actually _decreased_ .

In line with these stylized facts, we present a theoretical model and empirical evidence illustrating the differential occurrence of contagion after surprise versus anticipated crises. The basic mechanism behind the model can be summarized as follows: Investors trade securities whose expected return is tied to the fundamentals of the economy in which the issuing

> 4The dates are chosen in accordance with Kaminsky, Reinhart, and Vegh (2003) and Goldstein (1998).

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scale services are usually much more expensive than utility tariffs, most likely because of a lack of economies of scale in operation.<sup>10</sup>

# **V. CONCLUSION**

In water-sector PPP transactions, one of the alleged concerns is that there is little market competition at the auction stage. The worldwide database indicates that the degree of competition is lowest in the water sector among infrastructure industries. In the majority of water transactions, there has been no competition. The average number of firms that participate in the competitive bidding process is merely 2.2 in Latin America.

The paper examined a tradeoff between the competition effect at the auction level and potential economies of scale in service operation. If the authorities design a large-scale PPP water transaction, it is expected to exploit operational scale economies. But the competition effect may have to be sacrificed.

The paper found that the density distribution of the operational size of the past PPP transactions is very skew in the water and sewerage sector; hence, a small number of firms are allowed to participate in the auction, if the contract involves sizable operation. The selection of the contract size could be a very restrictive condition that excludes many prospective bidders.

Using a large PPP transaction database, the cost function of the water-sector operation is estimated. The estimated overall scale effect is found significant and positive. But economies of scale tend to diminish quickly as production increases. When the amount of water sold exceeds about 40 million m<sup>3</sup> , the statistical significance of economies of scale would

> 10 WSP (2007) shows that private small-scale water and sanitation services in Peru are 15 times as expensive as utility services. At the same time, however, 6.7 million or 24 percent of total population of Peru do not have access to water supply services and are dependent on some small-scale service provision.
covering the entire period to the end of August 2019. A total of 18,224 unique panel observations are available for analysis, excluding the baseline data collected before the phone-based panel began.

Attrition is one potential concern using panel data of this type. To ensure that non-take-up in the first round (and attrition in subsequent rounds) did not seriously affect the required sample size for survey representativeness, households that refused to participate were replaced with other households drawn from the same sample cluster. However, any systematic difference in the household characteristics due to refusal to participate could lead to bias if the replacement households were different on average (with respect to observable characteristics) from the household that refused. Among the random sample of 1,500 households originally drawn from the baseline, about 25% refused to participate in the first round (i.e. initial take-up in the first phone round totaled 1,122 randomly sampled households, and 381 randomly selected replacement households to make up for those that refused or could not be contacted). Attrition rates (or nonresponse rates) have tended to be low and stable across rounds of the L2CU panel survey, ranging from 1 to 6 percent, and 72 percent of households that completed the first round completed all 12 used in the analysis reported here,<sup>6</sup> comprising 85 percent of the total interviews completed. These results are particularly encouraging if compared to similar high-frequency surveys. Other examples World Bank high-frequency surveys in Africa have resulted in similar rates of attrition, or higher (Demombynes et al. 2013; Croke et al. 2012. A similar study in Tajikistan (Listening to Tajikistan) that began in 2015 met with similarly high rates of compliance.

To take non-take-up and attrition into account, the participating sample is reweighted by developing a model using observable and relatively time-invariant characteristics from the baseline to predict the probability of dropping out for each household. Responses are then weighted to account not only for the sampling design but are also reweighted with respect to baseline characteristics in each round to partially account for any bias introduced due to households dropping out (if it is unaccounted for by sampling from the same PSUs). Appendix (F) provides additional detail on attrition and reweighting.

# III – Domestic Migration

> 6 Note that it is possible for a replacement household to themselves be replaced.

10
Issues in Socialist Economy Reform <!-- Start of picture text --> by<br>Stanley Fischer<br>and<br>Alan Gelb<br><!-- End of picture text --> Table of Contents

|1.|Introduction|1|
|---|---|---|
|Ii.|Initial Conditions for Reform|3|
|IlI.|Elements 3f Reform|12|
|IV.|The Sequencing of Reforms|29|
|V.|The Role of Foreigners|35|
|VI.|Conclusion|37|
|Refer|ences|40|
Existing research consistently finds that parental out-migration has an unambiguously negative impact on children’s mental health, behavioral issues, and delinquency. Children raised by grandparents have a higher prevalence of low self-esteem, mental health problems, school bullying, and specific psychological symptoms compared to those living with at least one parent (Tang et al., 2018). Left-behind children are at greater risk for developing depression, especially when experiencing negative events or when left behind by both parents (Guang et al., 2017; He et al., 2012; Liang, 2016). A meta-analysis of 39 academic articles confirms the prevalence of depressive symptoms in left-behind children (Y.-Y. Wang et al., 2019). Left behind children are also more likely to engage in unhealthy behaviors like smoking and drinking (Wen and Lin, 2012; Yang et al., 2016) and are at higher risk for psychotic experiences and traumatic events (Sun et al., 2017). These negative associations suggests that the detrimental effects of reduced time with parents may outweigh the potential benefits of increased income when measuring children’s welfare by their mental health status. Parental migration leads to reduced supervision, protection, bonding and communication, which cannot be compensated for by increased economic resources. Moreover, grandparents and other extended family members serving as primary caretakers are imperfect substitutes for parents.

In sum, parental migration enables rural households to invest more in children’s education and nutrition, but it also reduces parental care and supervision, which cannot be perfectly substituted by nonparent family members. Consequently, while the literature documents some improvement in early child nutrition and school performance among left-behind children, it also reveals a concerning prevalence of mental health and behavior issues.

Although many health studies do not establish causality, the consistently reported negative associations between parental migration and children’s mental health and behavioral problems are of great policy concern. In 2014, the central government issued policy guidance urging local governments to provide and improve and improve various services (e.g. counselling, supervision, extracurricular activities) designed for left-behind children (State Council, 2014b). However, the implementation and effectiveness of these government programs have not been evaluated.

**Left-Behind Women** . Migration rates in rural China differ by gender and age, with men and younger individuals migrating at higher rates (Figure 2). Although the literatures on left-behind women and elderly are less extensive relative to those on left-behind children, some consistent themes have emerged.

Migrant women are more likely to return to their village, either temporarily or permanently, due to their role in providing child and elder care (Connelly et al., 2012; Ye et al., 2016). When husbands migrate 23
# **Size, age and education**

Nonfarm enterprises in rural Tanzanian are very small. The majority is operated by one person (the owner) during most parts of the year (figure 2). Self-employment is thus a crucial element in rural Tanzania given the scarcity of available jobs. However, during the peak season, enterprises often employ part-time or casual labor. About 77% of rural enterprises are owned by men in contrast to other Sub-Saharan countries where a larger share are owned and operated by women.<sup>6</sup> Most enterprises are young and new firms emerge rapidly. The median firm age is 5 years; figure 3 shows the distribution of firm age.<sup>7</sup>

**Figure 2: Number of Workers per Enterprise, 2005**

**Figure 3: Distribution of Enterprises by Age, 2005**

<!-- Start of picture text --> Four P erson E nterpris e<br>Five or M o re P erson E nterprise<br>Thre e P erso n Enterprise<br>Two Person E nterprise<br>One Perso n E nterprise<br>0 20 40 60 0 20 40 60<br>(% ) Enterprise age<br>S ou rce: 2005 Tanzania R IC S Source: 2005 Tanzania RICS<br>25<br>20<br>15<br>10<br>Percentage distribution<br>5<br>0<br><!-- End of picture text --> There is little regional variation in enterprise size and age. The only exceptions are Kilimanjaro, where businesses tend to be bigger, and Tabora, where enterprises are more experienced. Formal schooling appears to be an important prerequisite for entrepreneurial activities. Some 75% of rural entrepreneurs have primary education. About 11% have completed primary

> 6 The low female participation is surprising given Tanzania’s reputation of female entrepreneurship. In small survey in the Morongo and Ruvuma regions, Tovo (1991) finds more than 90% of women have at least one income generating nonfarm activity, and almost two thirds have two. Brewing and beer selling top the list of women’s business ventures: this popularity is because it does not require regular labor. This is followed by cooking and selling food, and by selling agricultural or fishing surplus products. We conclude that these activities may be significant, but at the same time economically too small to be fully captured through the 2005 RICS.

> 7 The median instead of the arithmetic mean is used throughout this paper when the distribution of a variable is skewed.

9
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system of production controls it has sent a message to the rest of the world that it is not prepared to cater to that market and supply more sugar at world prices. Countries needing more sugar have taken it into their own hands to supply that sugar adopting whatever polices are veeded to help and encourage their growers, who invariably are less efficien **t** han Brazilian growers.

The Brazilian government may wish to bolster such an initiative by devoting more resources toward publicizing and demonstrating the competitive strengths of its sugarcane based sector. The industry could sponsor and promote studies on sugar and ethanol trade which.reveal the cost to foreign consumers and domestic producers of interntionists policies. By standing ready to supply the world market with low-cost sugar, BruiL will raise the cost of these policies to interventionist counties. Brazil, as a member of the Cairns group, plays an active role in GATIT and has made itself visible through the Uruguay Round. It may need to make itself more visible on sugar and ethanol trade issues.

# _urthter, environmental poEicy should recognize ethanol's attibutes_

As part of its broader environmental policy, Brazil needs to attempt to quantify the health and environmental benefits of ethanol and develop a separate and explicit policy to ensure it makes the most of those benefits.

# _BRut should policy reforms be phased?_

Two main arguments have been advanced for why Brazil should- move slowly on reforms.

- **S** There are so many ethanol cars that letting the market determine ethanol prices will greatly force up the price of fuel to consumers, reduce the demand for ethanol and leave existing ethanol powered cars underutilized.

- The impact on the world sugar price wvould be too great if Brazil moved immediately to reform.

If the same taxes were levied on ethanol as on gasoline and the market was left to determine ethanol prices, the ehanol price is licely to rise in line with its costs of production and with the price of sugar on the world market. Demand, however, is not likely to change greatly. In the short term, consumers are locked into buying ethanol as a fuel and registered ethanol powered cars are likely to be discounted on the second hand market to reflect the premium of the ethanol price over gasoline. The demand for new ethanol cars may diminish. However, if cheap methanol is freely imported and blended with ehanol, this may well moderate the price rise in ethanol. What is more, it appears that the current price discount of ethanol to gasoline to reflect its lesser efficiency as a fuel maybe overestimated. Many consumers may well continue to have a preference for ethanol powered
|**Table A31: Vanderbilt University Americas Barometer (VAB)**|
|---|

|**Table A**<br>**Data Provider**|**1: Vanderbilt Univers**<br>Vanderbilt University|**ity Am**|**erica**|**s Bar**|**omet**|**r (V**|**B)**|||
|---|---|---|---|---|---|---|---|---|---|
|**Description**|United States university|||||||||
|**Website**|www.lapopsurveys.com|||||||||
|**Data Source**|Americas' Barometer|||||||||
|**Type**|Survey|||||||||
|**Respondents**|Households|||||||||
|**Frequency**|Bi-annual since 2004|||||||||
|**Coverage**|Sample of Latin American|countrie|s|||||||
|**Public Access**|Some country-level aggre<br>available via subscription|gates ar|e free|ly av|ailable|, the|rest of the|dataset is||
|**Description**|The Americas Barometer<br>and behaviors in the Ame<br>Latin America with que<br>Institutions, Social Policie|is an eff<br>ricas us<br>stions o<br>s, Civic|ort by<br>ing co<br>n are<br>Cultur|LAP<br>mmo<br>as su<br>e, and|OP to<br>n ques<br>ch as<br>other|meas<br>tionna<br>Dem<br>Curre|ure democr<br>ires to hou<br>ocracy, Po<br>nt Issues.|atic values<br>seholds in<br>litics and||
|**Voice and Accountability**||2008|2007|2006|2005|2004|2003<br>2002|2000<br>1998|1996|
|Trust in Parliament||X|X|X|X|X|..<br>..|..<br>..|..|
|Satisfaction with democracy||X|X|X|X|X|..<br>..|..<br>..|..|
|**Political Stability and Absen**|**ce of Violence**|||||||||
|NA||..|..|..|..|..|..<br>..|..<br>..|..|
|**Government Effectiveness**||||||||||
|NA||..|..|..|..|..|..<br>..|..<br>..|..|
|**Regulatory Quality**||||||||||
|NA||..|..|..|..|..|..<br>..|..<br>..|..|
|**Rule of Law**||||||||||
|Trust in Supreme Court||X|X|X|X|X||||
|Trust in Justice system||X|X|X|X|X||||
|<br>Trust in Police||X|X|X|X|X||||
|Have you been a victim of cri|me?|X|X|X|X|X||||
|**Control of Corruption**||||||||||
|Frequency of corruption amo|ng government officials|X|X|X|X|X||||
|**Country coverage**||**23**|**22**|**20**|**11**|**11**|**..**<br>**..**|**..**<br>**..**|**..**|
|**Year of Publication**||**2008**|**2006**|**2006**|**2004**|**2004**|**..**<br>**..**|**..**<br>**..**|**..**|

70