rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. OLS models of socio-economic inequality in early childhood development outcomes**

||||**Exec.**||**Overall**|
|---|---|---|---|---|---|
||**Language**|**Math**|**function**|**Socio-emo.**|**readiness**|
|**Asset factor**|0.121*|0.113*|0.111|0.171***|0.151**|
||(0.053)|(0.052)|(0.057)|(0.048)|(0.052)|
|**Mother's ed. (none omit.)**||||||
|Read & write|0.379|0.545|0.411|0.088|0.490|
||(0.255)|(0.291)|(0.251)|(0.328)|(0.254)|
|Primary|0.462*|0.555*|0.636**|0.149|0.635**|
||(0.225)|(0.242)|(0.218)|(0.170)|(0.224)|
|Preparatory|0.304|0.073|0.118|-0.137|0.179|
||(0.169)|(0.209)|(0.153)|(0.153)|(0.163)|
|General secondary|0.305|0.451*|0.297|0.143|0.382|
||(0.214)|(0.211)|(0.215)|(0.210)|(0.194)|
|Vocational secondary|0.308|0.321|0.178|-0.085|0.277|
||(0.166)|(0.178)|(0.141)|(0.137)|(0.152)|
|Post-Secondary|0.303|0.508**|0.083|0.083|0.296|
||(0.194)|(0.177)|(0.183)|(0.175)|(0.170)|
|University and above|0.548**|0.540**|0.427*|0.028|0.555**|
||(0.184)|(0.188)|(0.179)|(0.158)|(0.177)|
|**Father's ed. (none omit.)**||||||
|Read & write|-0.617**|-0.657*|-0.438|-0.356|-0.655**|
||(0.235)|(0.257)|(0.259)|(0.205)|(0.247)|
|Primary|-0.446|-0.541|-0.542*|-0.012|-0.566*|
||(0.238)|(0.277)|(0.233)|(0.206)|(0.258)|
|Preparatory|-0.027|-0.090|-0.231|-0.071|-0.151|
||(0.165)|(0.191)|(0.202)|(0.186)|(0.172)|
|General secondary|-0.156|-0.353|-0.262|-0.101|-0.284|
||(0.211)|(0.279)|(0.215)|(0.294)|(0.211)|
|Vocational secondary|-0.246|-0.186|-0.210|0.138|-0.223|
||(0.172)|(0.187)|(0.153)|(0.152)|(0.161)|
|Post-Secondary|-0.127|-0.193|-0.496*|-0.087|-0.336|
||(0.201)|(0.223)|(0.216)|(0.233)|(0.184)|
|University and above|-0.371|-0.250|-0.185|0.018|-0.289|
||(0.201)|(0.203)|(0.183)|(0.169)|(0.188)|
|**Mother's occupation (manager/prof. omit.)**||||||
|Sales/service|-0.137|-0.015|-0.231|-0.103|-0.167|
||(0.198)|(0.261)|(0.216)|(0.147)|(0.192)|
|Blue collar|-0.410|-0.393|-0.201|-0.482|-0.429|
||(0.270)|(0.370)|(0.266)|(0.375)|(0.306)|
|Not working/absent|-0.049|-0.005|-0.095|-0.033|-0.065|
||(0.107)|(0.074)|(0.100)|(0.092)|(0.071)|
|**Father's occupation (manager/prof. omit.)**||||||
|Sales/service|-0.022|0.140|0.098|0.087|0.086|
||(0.128)|(0.085)|(0.119)|(0.133)|(0.109)|
|Blue collar|-0.102|-0.047|0.018|-0.112|-0.059|
||(0.105)|(0.095)|(0.107)|(0.121)|(0.104)|
|Not working/absent|-0.086|0.006|-0.046|-0.101|-0.068|
||(0.110)|(0.113)|(0.118)|(0.122)|(0.108)|
|**Child sex (female omit.)**||||||
|<br>Male|-0.103|-0.072|-0.072|-0.306***|-0.125*|

16
45 <!-- Start of picture text --> In(pm/pL)(MCN/PL) -0.0036<br>(-2.688)<br>In Q. Iln(p,/PL) -0.0010<br>(-.0114)<br>In Q. In(MCN/pL) 0.0007<br>(0.159)<br>.In K. Iln(pM/PL) 0.0082<br>(0.808)<br>In K. In(MC,/pL) 0.0098<br>(2.274)<br>Number of Observations 250<br><!-- End of picture text -->
is below this value. For phase 1 the probability that the amount of rainfall is below 35 mm is 0.53 percent, for phase 2 the probability that rainfall is below 35 mm is 0.045 percent, for phase 3<sup>5</sup> the probability that rainfall is below 220 is 7.59 percent. In other words, a 188 year event (an event that happens on average every 188 years) in phase 1, a 2200 year event in phase 2, or a 13 years event in phase 3 would trigger the respective upper trigger limits.

<!-- Start of picture text --> x 10-3<br>1 5<br>Phase 1<br>0.9 Phase 1 4.5 Phase 2<br>Phase 2 Phase 3<br>0.8 Phase 3 4<br>0.7 3.5<br>0.6 3<br>0.5 2.5<br>0.4 2<br>0.3 1.5<br>0.2 1<br>0.1 0.5<br>00 50 100 150 200 250 300 350 400 450 500 00 50 100 150 200 250 300 350 400 450 500<br><!-- End of picture text --> Fig. 6: Cumulative distribution and probability density function for each phase based on the rainfall data of the Chitedze station.

The expected payout per acre for each phase is also of interest. Instead of analytically solving the integral, Monte Carlo simulation (1 million samples) generates estimates of the expected losses and the standard deviation. The total loan is set to 4667.4 MWK. As a result the expected losses per acre in MWK are

- for phase 1 19.1 (288),

- for phase 2 1.4 (79) MKW, and

- for phase 3 68.5 (310) (standard deviation in brackets).<sup>6</sup>

# **4 Dynamic financial analysis: assessing the financial robustness of the microinsurance scheme**

We now study the accumulation of the insurance capital for a time horizon of 10 years for the PRECIS and MM5 data. A random walk model was constructed assuming independence of the annual rainfall amount. For each year, 10,000 scenarios are simulated, resulting in a total number of 100,000 scenarios for each model. Each scenario comprises 36 dekads (i.e. 360 days). If capital accumulation falls below zero in a given year, insolvency occurs. Figure 7 shows estimated extreme value distributions of the insurance pool capital over time based on the empirical data and on the MM5 future projections. Climate change in these projections clearly has negative effects in the MM5 future model as compared to the baseline case. This can be seen by the fatter tails of the

> 5 Here, the data are not capped.

> 6 As discussed above, this contract can be seen as almost two contracts, one for catastrophic events (first 2 phases) and one for more frequently less dramatic losses (third phase).

13
45

|COUNTRY|PROJECr|**LEGAL**COM PONENT|**FINANCIERS**|
|---|---|---|---|
|China (Cont.)|Economic Law Reform Project (Cr.<br>No. 2654 CHA), November II,<br>1994 (Cont.)|trade law.<br>(b) Sub-projects under the training component would provide<br>support fbr key activities which would either: (i) have a<br>broad impact on legal and other professionals teaching,<br>training or implementing economic laws; or (ii) where the<br>impact is more limited, demonstrate innovative approaches to<br>meeting the changing needs of the legal profession and legal<br>system in the market economy. Particular focus would be<br>given to the development of new forms of teaching materials<br>and training methods and to the development of legal<br>information systems. Sub-projects would be expected to assist<br>short-term needs in practical training of legal personnel as<br>well as long-term strengthening of the legal training system,<br>such as through curriculum development. Training sub-<br>projects would be expected to address primarily in-country<br>training and would not be limited to training at the national<br>level in Beijing.||
||_ __________________________|(c) The institutional support component would provide<br>targeted assistance to key legal institutions whose functions<br>will expand and grow In importance In a market economy.<br>The NPC's Commission on Legislative Affairs (CLA), which<br>reviews and revises all drafts submitted to the NPC and is<br>also responsible for the drafting of selected laws, would<br>modernize its document processing and circulation with new<br>office equipment and technology and would upgrade its<br>databases of foreign laws, for reference purposes. The State<br>Council's Bureau of Legislative Affairs (BLA) would upgrade<br>and extend its database of Chinese laws, State Council<br>regulations, ministerial circulars and provincial legislation<br>and regulations and funds would be provided for various<br>government agencies to be joined in a legal information||
methodology and a conceptually driven disaggregation by type of natural disaster and sector of economic activity.

# **II. Data**

## **A. Periods, frequency, samples (groups of countries)**

To perform our estimations, we use pooled cross-country and annual time-series data covering 87 countries over the period 1960-2007. The panel is unbalanced, with some countries having more observations than others. We refer to the data as “all countries”. Then we split the data into two groups: “rich countries” and “developing countries”. We classify 25 Arab and OECD countries into the first group and the other 62 counties into the second group. Table II.1 gives the list of countries of these groups.

## **B. Variables, definitions, sources**

The main variables used in the paper are divided in three groups. First, to study the impact of natural disasters on the economy, we define three types of growth variables. The first is the growth rate of real per capita Gross Domestic Product (GDP). The others are the growth rates of real per capita value added in the two major sectors of the economy, the agricultural sector and the non-agricultural sector. All of them are measured as the log difference of per capita output (in 2000 US dollars), where per capita output is obtained by dividing the value added of each sector by the total population.

Second, as a variable which represents the role of external conditions that may affect the growth performance across countries, we use shocks to the Terms of Trade (TOT). Terms of trade shocks are measured by the growth rate of the terms of trade (export prices relative to import prices). The idea is to capture shifts in the demand for a country’s exports. Data for all the above variables were obtained from the World Bank (WDI, 2008).

11
The results in Table 8 show that the coefficient of S&P sovereign rating is positive and statistically significant for the pre-crisis period, but much smaller in magnitude and insignificant for the post-crisis period, consistent with the baseline model. The relative rating residual retains its positive sign and significance in the post-crisis period even after the inclusion of S&P sovereign ratings. These findings suggest an independent effect of relative ratings in the post-crisis period, which is not driven by sovereign ratings.

# _6.5 Robustness to potential endogeneity of ratings_

We test whether the baseline results reported in Table 3 are robust to potential endogeneity of sovereign and relative ratings. Although the major international rating agencies attempt to maintain the independence of the rating process, it is possible that ratings may be influenced by the volume of capital inflows, in particular large debt inflows that can raise the ratio of external debt to GDP and thereby influence a country’s rating. In order to account for such potential endogeneity, an instrumental variables approach is used with the current values of sovereign ratings and relative ratings instrumented by a four-quarter (one year) lag of the Institutional Investor country credit rating (IIR). The Institutional Investor rating is based on repeated surveys of economists and risk analysts and is arguably independent of the sovereign rating process of the major international credit rating agencies. Moreover, a four-quarter lag of the instrumental variable is sufficient, in our view, to mitigate any possible feedback effects from capital flows to investors’ contemporaneous risk perceptions.<sup>14</sup> The Durbin Chi-squared and Wu-Hausman test statistics for potential endogeneity are reported for both the sub-periods. The results for the instrumental variable regression for the pre-crisis and post-crisis periods are reported in Table 9.

The coefficient of absolute rating in Table 9 is larger and statistically significant in the precrisis period but is smaller in magnitude and loses significance in the post-crisis period. The coefficient of relative rating, on the other hand, is larger in the post-crisis period compared to the pre-crisis period and statistically significant at the 5 percent level. These results for sovereign and relative ratings are similar to the baseline specification in sign and significance. Moreover, the Durbin Chi-squared and Wu-Hausman test statistics indicate potential endogeneity of only sovereign ratings for the pre-crisis period, which is mitigated by the use

> 14 The instrumental variables are statistically significant in a regression of sovereign and relative ratings on these lagged instruments. The results are available from the authors upon request.

23
2

# **1. Introduction**

Questions have been raised about the impact of government programs on the well being of the poorest of the poor. Supply-side interventions, that seek to improve the quality of publicly provided services, have been particularly vulnerable to doubts about their effectiveness.

This case study of the effect of Programa para Abatir el Rezago Educativo (PARE) on the learning achievement of disadvantaged children seeks to contribute to the discussion of this issue. PARE is an example of a supply-side school improvement program designed to improve the learning of poor, rural and indigenous children in selected low-income provinces.

As in most developing countries, there is dearth of information regarding the impact of education programs on learning achievement, let alone their effects on the poorest of the poor children. Mexico, however, has had the foresight to collect socioeconomic and education background information among control and experimental groups of schools at the beginning and the end of the project.

Previous analyses showed that on the whole PARE was cost effective, particularly among indigenous and rural children. No analysis, however, has been undertaken to measure and compare the learning effects of this education compensatory program on the poorest and less poor children. This study, therefore, explores the differential effects of PARE on the learning achievement between the poorest and less poor children in indigenous and rural schools.

The paper is divided as follows. The next two sections briefly describe the PARE Project and the above-mentioned database. Section 4 then discusses the main hypothesis of the study in the context of the learning achievement literature and lays the bases for the specification of the model to be tested in the next section. The final section discusses the main findings and conclusion of the study.

# **2** . **The PARE Project**

During the 90’s, the Mexican government introduced compensatory education programs as a key element in reducing inequality in the education system by channeling more resources to the poorest states. Since 1992, the programs have broadened their coverage and strengthened their institutional capacity building.

PARE was undertaken in 1992-1997. The objective of the program was to assist the Government of Mexico in improving the quality and efficiency of primary education, focusing on four Mexican states (Oaxaca, Guerrero, Chiapas and Hidalgo) with the highest incidence of poverty and low education indicators. These objective, considered as being of the highest priority with the Government’s Education Modernization Program, would be achieved through; (i) reducing the high repetition and dropout rates; (ii) raising the level of cognitive achievement of children, and (iii) strengthening management of the
5. The role of EPL of permanent contracts in shaping the impact of the credit crunch on the labor market There is a large body of literature on the impacts of EPL and temporary contracts on the labor market (see Boeri (2010) for a thorough literature review). Stringent employment protection is expected to reduce the response of employment to economic fluctuations. But this prediction becomes more nuanced when there is a “dual” labor market where firms have both “permanent workers” (who are covered under an EPL with high dismissal costs) as well as workers on fixed-term contracts with lower dismissal costs. Stricter EPL of permanent jobs gives firms a greater incentive to offer temporary jobs and preserve some ability to adjust labor in the face of shocks. The existence of this segmented labor market implies that new entrants to the labor market (which tend to disproportionately include the young, women and immigrants) are more likely to be offered temporary contracts than more experienced workers (Kahn, 2007). Empirical evidence suggests that unemployment during a recession would increase more when the EPL of permanent jobs is more stringent and the regulation of temporary jobs is less stringent (Bentolila et al., 2012).<sup>14</sup> To investigate the impacts of the credit crunch across countries with different levels of employment protection, we use the Employment Protection Index (EPLex) from the International Labour Organization (ILO).<sup>15</sup> The EPLex provides a comparative overview of regulations governing permanent

> 14 There is also evidence to this effect from India, where EPL does not cover temporary (“contractual”) workers. As EPL becomes stronger, the extent to which to which firms adjust permanent workers in response to shocks falls (Adhvaryu et al., 2013), while the extent to which to which firms adjust contractual workers in response to shocks increases (Chaurey, 2015).

> 15 We do not use the OECD’s Overall Strictness of ELP index because it covers fewer countries in our sample than that of ILO.

15
23

- involving clients more in designing, monitoring and implementing programs;

- giving public sector service providers stronger incentives to understand and reach oUc to clients;

- making more use of client-responsive service delivery systems outside the public sector; and

- carrying out more evaluation studies to determine the responsiveness of programs to clients.

The remainder of this section suggests a number of specific changes in the way the Bank does business in the PHN sectors, which, taken together, might add up to a strategy for achieving the general reorientation outlined above.

# Sector Work

Just as the kind of question asked determines the kind of answer received, the kind of sector work which the Bank undertakes strongly determines the nature of the lending which follows. As the Bank moves into the new generation of narrower and deeper subsector studies, it is recommended that the Country Departments (CDs), Technical Departments (TDs) and the central Population, Health and Nutrition Division (PHRHN) should jointly select countries where the institutional context and the level of commitment are favorable and should

- undertake at least two pieces of sector work focusing specifically on the management of PHN service delivery at the periphery, and analyzing inter alia

- a) the state of qualitative knowledge about clients and service providers,

- b) who should benefit and who actually benefits from the programs,

- c) the incentive framework which determines the priorities and activities of service providers, and

- d) the present and potential contribution of the not-for-profit, traditional and commercial private PHN sector in as great detail as the public sector.

# Lendinz

Identification/preparation. It is recommended that managers in the CDs should, for all future PHN projects with a service delivery/IEC orientation, ensure that
14. Migrant workers arrive in Kuala-Lumpur and are received by the employers in the presence of a representative from the Bangladesh High Mission in Kuala-Lumpur.

# Appendix A.2: Survey finding rates

## **Survey finding rates**

With the field protocol described in Section 3, we were able to find and interview a higher share of T1 group compared to T2 and the control group. As **Appendix Figure 1** shows, the overall interview rates were 94 percent for T1, 69 percent for T2, and 68 percent for the control group. The large follow-up rate for T1 is seen in both the phone-based tracking as well as field-based tracking. While 47 percent of the control group were found through phone calls, conditional on having a phone, or getting phone numbers from fellow applicants, 55 percent of T1 were found and 89 percent of T2 were found. The reason for this discrepancy is that the phone records we got from BMET, albeit incomplete, were more up-to-date, as they kept interacting with the winners for further recruitment processes.<sup>1</sup> Among respondents who we tracked on-field (all those not found by phone), the finding rate for the control group was about 40 percent whereas the finding rates for the treated groups were significantly higher at 89 percent and 64 percent for T1 and T2 respectively. Enumerators found it much easier to track the treated individuals in the villages because their information was more up to date with the local authorities. The winners had to interact with local authorities to submit the necessary information for their recruitment processing. Additionally, the treated applicants also became more well known in the local community as a result of winning the lottery.

## **Impact of differential finding rates**

Common non-parametric bounding approaches, such as the Lee (2009) bounds are uninformative for many of our outcomes due to our particularly high finding rate in the treatment group (94 percent) relative to the control group (68 percent). This means that the Lee bounds approach drops the highest and lowest 27 percent of the outcome variables in the treatment group to construct the bounds. This extreme assumption naturally leads to wide and uninformative bounds. However, even under the extreme assumption of Lee bounds, migration and monthly income measure have bounds that are significantly different from zero (columns 2 and 3, **Appendix Table 9** ).

However, for other measures where the impact of the lottery is not very high, traditional Lee bounds estimate wide confidence intervals. This is partly because most of the outcomes are intermediated 47
**Table 5 (continued):**

|**Variable**|**All farms**|**Dryland**|**Irrigated**|
|---|---|---|---|
|Household access to<br>electricity (1/0)|117.4**|95.47**|297.8**|
|Soil (geCU)|-692.4**|-393.3**|-1265.7**|
|Soil (ilqHS)|-454.4**|-228.1**|-1038.0**|
|Soil (loMH)|-2322.0**|-1999.8**||
|Soil (vcFU)|-1065.1**|-894.3**|-1585.5**|
|Soil (lcMFU)|-261.2**|-250.2**||
|Soil (qc)|1642.8**|1709.0**||
|Soil (ql)|-539.9**|-269.6**||
|Soil (lcMS)|-2267.6||-5812.3**|
|Soil (nd)|370.7||7343.7**|
|Soil (lg)|-179.0**|-125.2**||
|Soil (frFHS)|992.4*||3540.0|
|Soil (ykCMUH)|1279.6**|-636.3**||
|Constant|141.8|702.4|-243.3|
|N|8459|7238|1221|
|R2|0.351|0.171|0.29|
|F|68.59|33.81|52.45|

_Notes:_ * significant at 5% level ** significant at 1% level _Soil definitions_ : Rhodic ferralsols with fine texture in hilly to steep areas (frFHS), eutric gleysols with coarse texture in undulating areas (geCU), lithosols hilly to steep slope (ilqHS), chromic luvisols with medium to fine texture in undulating areas (lcMFU), chromic luvisols in moderate to steep areas (lcMS), gleyic luvisols (lg), orthic luvisols in moderate to hilly areas (loMH), dystric nitrosols (nd), cambic arenosols (qc), luvic arenosols (ql), chromic vertisols with fine texture in undulating areas (vcFU), calcic yermosols with coarse to moderate texture and in undulating to hilly areas (ykCMUH), lithosols in hilly and steep areas (ilqHS), luvic arenosols (ql), dystric nitosols (nd), gleyic luvisols (lg).

30