rafmacalaba/datause-encoder-data huggingface dataset:
returns on those efforts by taking stock of how these financial systems have developed and where they stand.

The conclusions in de la Torre and Schmukler (2008) and in other papers were based on data up to the early 2000s and suggested that outcomes did not match expectations and reform efforts. At that time, we were somewhat pessimistic about the prospects for financial sector improvement, given the difficulty of overcoming high systemic risk and volatility, the slow progress of financial development, and the large mismatches in currencies and maturities, all of which were the result of inherent deficiencies in emerging economies (see de la Torre and Schmukler 2008; de la Torre, Gozzi, and Schmukler 2007b; de la Torre and Schmukler 2004). Other economists shared our pessimism, focusing on the metaphor of “original sin” in emerging economies—that is, the inability to issue long-term debt in their own currencies—as well as on outright dollarization and “sudden stops” that would subject the economies to frequent shutdowns of foreign financing (see, for example, Hausmann and Panizza 2003; Calvo and Reinhart 2000; Eichengreen and Hausmann 1999; Hausmann et al. 1999).

More recently, however, new data from the mid- to late-2000s and several anecdotal accounts suggest some reasons for optimism. Emerging economies have improved their macroeconomic performance, lowered inflation, and reduced fiscal deficits (Gourinchas and Obstfeld 2011). These policy achievements, together with high liquidity in international markets, have allowed emerging economies to issue long-term bonds in domestic markets, as foreign investors have expected further appreciations of local currency and entered local markets in search of higher yields. In addition, these economies weathered the storms of the recent global financial crisis relatively well, indicating the strength and resilience of their financial systems (see, for example, Didier, Hevia, and Schmukler 2012; Eichengreen 2009).

Even with these reasons for optimism, the path ahead will certainly be challenging, especially for policy makers. In particular, the old model of convergence to international standards is being questioned precisely because those standards are being revised in the wake of the 2008–09 global financial crisis. One example is the housing finance model fostered by public institutions like Freddie Mac and Fannie Mae in the United States, which other countries, such as Mexico, have also followed. Another example is the definition of the limits of regulation when banks and shadow banks are interconnected and when banks pose too high a systemic risk to be allowed to fail. This situation—wherein assets are excluded from banks’ balance sheets through securitization and special-purpose vehicles—evolved in several 3
Including the factors discussed in Q3 jointly makes several inferences abundantly clear. As expected, older children score higher on the AIM-ECD than younger children. An additional year of age is associated with a 0.197 standard deviation ( ) increase in AIM-ECD Z- score. However, we do not see any significant differences in overall development by child sex. In ββ= 0.197, EE< .001 addition to age, the grade level of the class explains a huge amount of variation in AIM-ECD scores. Children in Grade 1 classes performed 0.814 standard deviations ( ) higher than those in _Katchi_ classes. However, this difference is greatly attenuated in multi-graded classes. While multi-graded _Katchi_ classes have slightly higher ( ββ= 0.814, EE< .001 ) AIM-ECD Z- scores than children in single-graded _Katchi_ classes, children in multi-graded Grade 1 classes score -.546 SD worse than those in single-graded Grade 1 classes ( ββ= 0.093, EE< .05 ) . Classes taught in Urdu and Saraiki appear to have similar levels of achievement on average, but those taught in another language had -0.156 SD lower scores on average than Urdu classes ββ= −0.546, EE< .001 ( ) . Our model finds strong evidence about the importance of ECE teaching quality as shown ββ= −0.156, EE< .05 in **Table 8** and Figure 21. A one-standard deviation difference in Overall Teaching Quality is associated with a 0.076 standard deviation difference in AIM-ECD Z- scores ( ) , even after controlling for other factors (Model 6). After controlling for classroom quality, other ββ= 0.076, EE< .001 teacher-level factors that might be hypothesized to be related to outcomes are not significantly associated with AIM-ECD. In our final model, teachers with three or more years of experience and those with higher levels of certification did not have classes with higher levels of AIM-ECD scores. One factor appears to remain salient—teacher gender. Classrooms led by female teachers have 0.060 SD higher ( ) AIM-ECD Z-scores on average, even after controlling for other factors of quality. ββ= 0.060 EE< 0.05 Using association between age and AIM-ECD scores allows us to convert differences in Z-scores to differences by age. Given that a year of age is associated with a 0.197 SD difference in AIM-ECD scores, and that a 1 SD difference in Teach ECE scores is associated with a .076 SD difference in AIM-ECD scores, we can conclude that a child in a high-quality classroom (+2SD) is predicted to have an AIM-ECD score that is similar to a child who is 0.77 years (~9 months) older in an average quality classroom.

33
be intrinsically di¤erent from schools where the parents do not fund such a program.

# 3 Background and Program Design

In 1997, about 770,000 Kenyan children, 30 percent of the 4-6 age group, attended preschool centers.<sup>11</sup> About three quarters of the country’s approximately 25,000 preschool centers are informal non-pro...t centers that are funded by the parents but located on the compound of a governmentsubsidized primary school<sup>12</sup> . The remaining one quarter of Kenya’s preschools are run by religious organizations, companies, NGOs and private for-pro...t providers. This paper will be concerned only with informal preschools in rural areas. The Kenyan Government developed a preschool curriculum and provides teacher training, but does not otherwise subsidize preschools. Parents pay fees that fund the teacher’s salary, classroom materials and, in some cases, a feeding program. Funds for building a classroom are usually raised during a community fund-raiser.<sup>13</sup> The amount of school fees paid by the parents depends on a negotiation process between the preschool teacher and the parents. Most schools do have an o¢ cial preschool fee per term, but this o¢ cial fee has little practical signi...cance. In 2000, of all children that were found in the 50 program preschools at least once, only 3.5 percent had paid the o¢ cial amount of fees due or more. Forty-six percent of children had not paid anything. Parents can enroll their child in preschool by recording the child’s name in the teacher’s book. After enrollment, fees are collected in a staggered way. A preschool child whose parents have not paid “enough”fees but who comes to school can be sent home by the teacher and be told to tell her parents to pay fees before coming back to school. As a result, there is a strong correlation between school participation and the amount paid by the parents, and parents pay on an approximate fee-for-service basis.<sup>14</sup> Rural preschools usually have a low level of total funding, though this varies with the income level of the community and parents’ valuation of preschool education. Equipment is minimal; preschool teachers have few teaching materials and classes are often held under a tree for lack of classrooms. Being a preschool teacher is a semi-volunteer position. A 1997 survey of 100 preschools

> 11 Approximate numbers calculated from data reported in the Welfare Measurement Survey III (1997), Kenya Central Bureau of Statistics.

> 12 Total number of preschools is from Kenya Ministry of Education, Science and Technology (1999), data on organization are from Kipkorir and Njenga (1997).

> 13 For a description of how community fund-raisers work, see Miguel and Gugerty (2002)

> 14 For a given level of participation, the amount of fees collected varies substantially. Since preschool teachers are local women, they often know the families of the children attending their class. As a result, they have a pretty good idea of the economic background of the families, and it should be possible for them to price-discriminate between families. I will not discuss such price discrimination in this paper.

5
8

# **3 Rural Households in Commodity-Growing Regions in Tanzania**

To analyze the welfare effects of the different shocks, data from a household vulnerability survey conducted in rural Kilimanjaro (October-December 2003) and rural Ruvuma (February-March 2004) are used. In each region about 900 rural households were surveyed, of which two thirds were growing coffee in Kilimanjaro. In Ruvuma, about one third of the households grew coffee, one quarter cashew, and 4 percent tobacco. The remaining third did not cultivate cash crops. Features of the sampled households relevant to the empirical analysis are highlighted below (Table 1).

Welfare is measured through the (logarithm of) total household expenditures per adult equivalent.<sup>4</sup> To avoid a downward bias in the estimated effect of health shocks on welfare, all expenditures on health and functions (baptism, funerals) are excluded.<sup>5</sup> Age of the household head (and age squared) (life cycle proxies), the dependency ratio, gender of headship and the years of formal education achieved by the household head (allowing for differential effects across primary, secondary and post secondary education) are included as explanatory variables to capture differences in household preferences. They also affect households’ permanent income and their coping capacity.

The ethnic origin of the household head is also controlled for. Cultivation of certain cash crops may traditionally be dominated by certain ethnic groups. This also helps control for people’s social capital and thus their capacity to cope with shocks ex

> 4 For details on the construction of the expenditure variable, which does not include expenditures on education, see Christiaensen and Sarris (2007).  They also provide a detailed description of the surveyed households and their communities.

> 5 Comparison of health expenditures among households with and without an illness shock shows that households who experienced an illness shock spend on average 50 to 100 percent more on health. Similarly, expenditures on functions are also larger among households who experienced a death over the past two years.  The data do not permit to distinguish between health expenditures for preventive reasons, plausibly an expression of larger household welfare, and health expenditures for curative reasons, or between expenditures on functions for funerals and baptisms.
<!-- Start of picture text --> Figure 7A: Poverty Rate, Institutions and Security<br><!-- End of picture text --> <!-- Start of picture text --> Figure 7B: Poverty Headcount, Institutions and Security<br><!-- End of picture text --> <!-- Start of picture text --> 45<br>40 240M4<br>35 220M<br>30 200M<br>22<br>e25 5<br>Fa & 180M<br>g 3<br>8 20 x<br>a<br>160M<br>15<br>140M<br>10<br>5 120M<br>0 100M<br>2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030<br>Year Year<br><!-- End of picture text --> Source: International Futures v7.04 Scenario ll Base Case ®@ Better Security BB Better Institutions i Improved Institutions and Security Source: International Futures v7.04 Scenario li Base Case ®@ Better Security BB Better Institutions i Improved Institutions and Security
With these considerations in mind, in this study we analyze the effect of weather shocks on children’s anthropometrics using the two most recent rounds of the Nigeria Demographic and Health Survey (DHS). For this purpose we interpolate climate data for each DHS cluster using daily weatherstation records from the national network of about 30 stations. Specifically, we analyze the impact of rainfall shocks on two anthropometric measures which capture two different dimensions of health status. We first look at weight-for-height, which is a good metric for acute malnutrition and is considered a proxy for short-run nutritional deficiency. In addition, we use height-for-age which better captures chronic malnutrition. In the case of short-run impacts we also analyze the effect that precipitation has on the incidence of child diarrhea. Diarrhea is a particularly important disease to study in this context because of its sensitivity to climatic conditions. As in previous papers, we focus on shocks that occur at the very beginning of life. We do not look at health status or any other outcome at adulthood since our data covers only infants and young children.

One of the main objectives of the study is to assess empirically the channels through which precipitation affects child nutritional status: the disease environment and the income effect. The relative importance these two effects is useful for the design of future policy interventions. We also analyze whether rainfall shocks have medium-run impacts on child health or whether they are shortlived. We do this by looking at the effect of shocks during the last three completed rainy seasons experienced since a child’s birth. We also test whether having easy access to health facilities helps reduce (magnify) the negative (positive) impacts that weather shocks may exert on children health outcomes. Regarding the gender bias discussion, we analyze the differential effect of shocks on girls and boys. Finally, we try to quantify the influence that increased weather variability –a predicted consequence of climate change- might have on child health.

The paper is structured as follows. In the next section we outline the conceptual framework that will guide the econometric models. In section 3 we describe the data. We then explain the reduced-form equations for weight-for-height and height-for-age in section 4, and present the results in section 5. The last section summarizes the main findings and discusses their policy implications.

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**Figure 2. Inflation rates between months in EICV3 and January 2014**

<!-- Start of picture text -->
1.25<br>1.20<br>1.15<br>1.10<br>1.05<br>1.00<br>0.95<br>0.90<br>1011 1012 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110<br>NISR 2016 CPI (National)<br><!-- End of picture text -->

The third major difference is the overall weight given to the food index and the non-food index used for the CPI and COLI calculations. Food CPI gets a weight of 44 percent in total national CPI, 53 percent in rural CPI, and 70 percent in COLI.<sup>13</sup> Ceteris paribus, when food inflation is higher than non-food inflation (as was the case in Rwanda), then, giving a higher weight to the food index (as in COLI) should lead to higher inflation. This however, was not the case in Rwanda; the overall CPI presents a higher inflation rate as compared to the inflation rate based on COLI. This primarily owes to the item selection for COLI. The 42 food items and 84 non-food items in COLI registered lower price increases than the full-CPI food and nonfood basket, and these differences were enough to offset the effect of a higher weight on food in the estimation of COLI.

It is also important to note that COLI uses a subset of items from the CPI (126 out of 1,022 in the full CPI), which are more relevant for the poor. Thus, both the overall food and non-food inflation, as well as the item level budget shares ‘for the same items’ in the COLI and CPI basket can prove to be very different. This represents one the key differences between the national CPI for Rwanda and the COLI used in poverty estimation.

Lastly, it is not clear as to whether the calculation/aggregation methods used in COLI and CPI are similar.<sup>14</sup>

# **VI. Revisiting the debate on the poverty trend**

Results from Rwanda’s fourth Integrated Household Living Conditions Survey (the 2013/14 EICV4) were published in September 2015. It showed a continued reduction in poverty and extreme poverty between 2011 and 2014. Soon after the launch, a debate on the comparability of poverty estimates from EICV3 and EICV4 to the previous series ensued. For instance, Reyntjens (2015) questioned the comparability of poverty rates between EICV3 and EICV4 in NISR (2015) as mentioned below.

> .<sup>13</sup> The corresponding shares are 47 and 55 percent for national and rural CPIs if the category of ‘restaurants and hotels’ is included.

> 14 NISR states that the official CPI uses a Modified Laspeyres formula (NISR, CPI 2016).

19
when taken into account simultaneously, can be reconciled with the empirical evidence. Let us suppose, for example, that the lawyers who disproportionately drop high-stakes cases and disproportionately do not drop low-stakes cases have better information. The fact that these lawyers disproportionately settle lowstakes cases is perfectly consistent with having better information. The question then becomes how we can reconcile the fact that these lawyers also disproportionately do not settle high-stakes cases? This result could only be reconciled with our theory if the better-informed lawyers also had lower costs of collecting awards.

It therefore seems that the best explanation of what we observe in the data is that the lawyers with more accurate information about the quality of their clients’ cases also have lower collection costs. Perhaps having more accurate information and lower collection costs are in fact, simply reflections of being a well-informed lawyer. A well-informed lawyer should understand the law better and therefore should have a more accurate signal of the quality of the case. A well-informed lawyer should also know how to handle the evasive techniques employed by some firms, and therefore should have lower costs of collecting judgments awarded by the judge.

# 8 Conclusions

Government regulations, combined with the mechanisms through which regulations are enforced, have a crucial impact on a country’s business climate. In this paper, we analyzed the interaction between an extremely rigid labor law and a court system that is inefficient at enforcing the law. In particular, we used data from a labor tribunal in Mexico to show that 56% of awards "won" by workers were not collected. This never occurred in cases in which the worker had more than seven years of tenure with the firm.

Although we could not analyze worker heterogeneity in lawsuit outcomes directly, we could analyze heterogeneity across the lawyers representing them. We showed empirically that those lawyers who drop a lot of cases tend not to leave trial awards uncollected. One interpretation for this result is that betterinformed lawyers anticipate cases in which they would be unlikely to collect the amount awarded at trial and drop these cases at earlier stages. Another interpretation is that lawyers with high costs of collecting awards drop all lowstakes cases and only go to trial with high-stakes cases.

In order to help us sort through these two interpretations, we developed a simple theoretical model to help interpret the effects of having a cost of collecting awards after a trial. The model generated distinct testable hypotheses of how workers (and the lawyers representing them) would act differently depending on differences in the accuracy of their information and on differences in their costs of collecting awards. We find evidence that lawyers are different both in terms of the accuracy of their information and in terms of their collection costs.

We therefore see that the distinction between de facto and de jure labor regulation is a complex one. We show that differences in the information available 22
WPS3784

# **Table 7. Distributional and Regional Impact of a 50 percent Fall in Transport Costs -** **<u>among Coffee Farmers (assuming 100 pass through)</u>**

|Share|ofcoffeeintotal income|Changein real income|
|---|---|---|
|**Expenditure decile**|||
|Poorest decile|6.70%|1.34%|
|2nd|8.23%|1.65%|
|3rd|7.16%|1.43%|
|4th|14.42%|2.88%|
|5th|4.33%|0.87%|
|6th|5.06%|1.01%|
|7th|5.04%|1.01%|
|8th|3.09%|0.62%|
|9th|4.16%|0.83%|
|Richest|1.79%|0.36%|
|**Province**|||
|Butare|3.72%|0.74%|
|Byumba|2.51%|0.50%|
|Cyangugu|13.89%|2.78%|
|Gikongoro|5.50%|1.10%|
|Gisenyi|6.02%|1.20%|
|Gitarama|2.68%|0.54%|
|Kibungo|2.59%|0.52%|
|Kibuye|9.03%|1.81%|
|Kigali Ngali|9.22%|1.84%|
|Ville de Kigali|1.15%|0.23%|
|Ruhengeri|2.42%|0.48%|
|Umutara|0.00%|0.00%|

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liquidity shocks (Huang 2003). Second, even if most of the overtrading by individual investors could be attributed to overconfidence, there is no reason for such investors to not be paying attention to transaction costs. In this paper, we directly test whether investors pay attention to transaction costs by examining the relationship between transaction costs and the holding periods of individual investors. Rather than focusing on trading performance alone, we analyze if individual investors understand the trade-offs between selling an illiquid asset more quickly than a liquid one. This amounts to testing the first proposition in Amihud and Mendelson (1986) that investors will hold stocks with higher transaction costs longer compared to stocks with lower transaction costs.

In order to test Amihud and Mendelson’s (1986) first proposition, we model investors’ sell versus hold decisions with survival analyses using 799,469 transactions made by 66,000 US households. In particular, we model investors’ holding periods using Cox hazard regressions. Our findings are three-fold. First, we find that transaction costs are an important determinant of investors’ holding periods after controlling for various household and stock characteristics, confirming Amihud and Mendelson’s (1986) predictions. Specifically, we find that a stock in the highest transaction cost decile is 40% less likely to be sold compared to a more liquid stock with similar firm and investor characteristics conditional on the stock not having been sold up to that point in time.

We confirm our results by analyzing transactions data from Finland, which serves as an “outof-sample” verification of the US findings. Almost identical to the US results, we find that a stock in the highest transaction cost decile in Finland is 40% less likely to be sold compared to a stock that has lower transaction costs with similar firm and investor characteristics. Since the data set from Finland includes the complete transactions of all Finnish households between 1995 and 2003, 4
**Household Savings in Transition Economies**

Cevdet Denizer World Bank Cdenizer@worldbank.org Holger Wolf Center for German and European Studies Georgetown University and NBER Holger.wolf@mailexcite.com Yvonne Ying Oxford University and World Bank Yvonne Ying@hotmail.com Keywords: Household Savings, Transition, Comparative Economics JEL Codes: D12, D31, D91, 016, P36