rafmacalaba/datause-encoder-data huggingface dataset:
**Table A5: Panel A – Nested Logit Regressions, 2005** Log Odds ratio between Renting and Owning

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**suburban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|
|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_||||||||||
|Czech Republic|0.15|-0.20|-0.75 **|0.45 ***|||-0.43||0.57 *|
|Estonia|0.41|-0.71|-0.85|-0.28|||-1.51 ***||-0.07|
|Hungary|3.18|-0.11|-5.87 *|-0.16|0.79|7.69|3.23|-4.03|-5.25 **|
|Latvia|-6.60|-4.59|-2.90|0.94||-1.41|-0.39|3.09|1.51|
|Lithuania||0.88||-0.31|||-2.19 *||-0.99|
|Poland|0.27|-4.82 ***|-0.71|0.16|0.50|-2.11|0.44|-1.61 **|-0.64|
|Slovak Republic|-0.89|-0.59|-1.19 *|-1.52 ***|-1.75 ***|0.89||-0.29||
|_EU-15 and Cyprus_||||||||||
|Austria|2.92 *|2.36|-4.25 *|1.02 ***|||-1.84 *||-1.19 ***|
|Belgium|-0.40 *|0.02|-0.96 **|0.10|-0.10|-1.96 ***|-0.85 *|-1.58 ***|-1.28 ***|
|Cyprus|-0.24|1.08|-0.46|0.70 **|0.10|-0.31|0.29|-1.02 **|-1.42 *|
|Denmark|-0.63 **|0.06|-0.71 ***|0.24 *|0.21 *|-0.84|-0.99|-0.88|-1.08|
|Finland|-0.55 **|0.17|-0.97 **|0.18|-0.13|-2.71 ***|-2.85 ***|-1.06 **|-1.00 **|
|France|-0.04|0.64 **|-0.70 ***|0.05|0.07|-2.42 ***|-2.41 ***|-1.10 ***|-1.15 ***|
|Greece|0.92|0.74|-0.19|0.17|-1.32|-2.28|-1.86|-1.13|-0.76 ***|
|Ireland|-1.24 ***|-0.55|1.54 *|-0.07|-0.11|-3.05 ***|-2.48 ***|-1.67 ***|-0.96 ***|
|Italy|0.16|0.70 ***|-0.26|0.04|-0.20|-2.07 **|-2.11 **|-0.23|0.01|
|Luxembourg|0.39|1.48 **|-0.54 *|-0.13|0.00|-1.34 ***|-0.69|-0.91 ***|-0.38|
|Netherlands|-0.57 ***|0.84 ***|-0.64 **|-0.01|||-1.29 ***||-0.93 ***|
|Portugal|0.58|-0.51|-0.14|0.84 ***|0.54 ***|-1.97 ***|-0.64|-0.52 **|-0.77 ***|
|Spain|0.54 *|0.70 ***|-0.28 *|-0.13|-0.15|-0.79 ***|-0.45|-0.50 ***|-0.72 ***|
|Sweden|-1.01 ***|0.38|0.06|-0.24 *|-0.20|-1.61 **|-1.45 **|-0.91 ***|-0.89 ***|
|United Kingdom|-1.39 ***|0.58 **|-1.92 ***|0.15 *|||-1.99 ***||-1.04 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2005 36
forces. On the other hand, if firms anticipate that they will enter export markets in the near future, they might have an incentive to begin upgrading the skills of the workforce before actually starting to export. In this case, OLS estimates on the contemporaneous effect of export participation on training would underestimate the extent of skill upgrading caused by exporting. To address these concerns, we now turn to the IV estimates on the causal effect of export participation on training. Real exchange rate movements change directly the incentives to export and are largely unpredictable. Hence they make it possible to circumvent (or at least mitigate) these challenges, and estimate the causal effect of exporting on training.

Table 6 presents IV estimates of (1) based on the sample of switchers, and using the industry-level real exchange rate instruments to generate exogenous variation in the incentives to export. The upper part of the table presents the results of the first stage, while the lower panel presents the results of the second stage. Column 1 includes only firm fixed effects and column 2 adds firm controls. As expected, the first-stage estimates indicate that a real depreciation (i.e. a rise in the industry-specific exchange rate variable) leads indeed to an increase in the probability of exporting. The instrument appears to be sufficiently correlated with the exporting variable for us to be able to identify the effect of that variable on the outcomes of interest: the individual coefficient is significant at the 5% level and the F-statistic is 10.7. Estimates from the second stage point once more to a positive causal effect of export participation on the share of workers receiving technical upgrading, but not for the other training categories. Results of the instrumented variable indicate a stronger effect in this case, suggesting that the OLS estimates are biased downwards.

In the analysis so far, we have examined the effects of exporting on the share of workers who completed training in that same year. In light of the theoretical mechanisms discussed in the introduction, firms might be also expected to increase the share of workers who received technical training in prior years (and are therefore already equipped with the skills required by exporting firms). While our training data do not contain information on training activity before 2009, we can use the 2009-2012 data to redefine the dependent 12
## Figure: E. 10 6

# NICARAGUA LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE TOTAL EXPORTS<br><!-- End of picture text --> <!-- Start of picture text --> 8000<br>, 6000<br>o4000<br>,2000<br>1970 -° 09 -o 19CA<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1970<br>NA (0.43%)<br>ROW (1.17%/ MFX (000%)<br>(98 40%)<br><!-- End of picture text -->

**LABOR INTENSIVE EXPORTS BY PARTNER** 1980

<!-- Start of picture text --> NA (0.25%)<br>ROW (0.07% MEX (0.17%)<br>CA (99.51%)<br><!-- End of picture text --> <!-- Start of picture text -->
|LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>NA (11.37%)<br>ROW (24.11 X (0.00%)<br>CA (64 52%)<br><!-- End of picture text -->
networked administrative databases, including unique student identifiers that allow tracking students over time, and, in the case of Guatemala, that can be directly linked to standardized test data.

In this paper, we make use of these administrative data to estimate early warning models of dropout in primary and secondary school.<sup>1</sup> Using linear regressions and basic prediction concepts, we are able to accurately predict approximately 80% of the sixth-grade students who dropped out within the next year in Guatemala and Honduras, performing at comparable levels to models used in the U.S. These early warning models, which are based on routinely collected data and relatively simple analytical techniques, are feasible to implement in a wide range of country contexts. By providing an accurate means of targeting, these models could substantially reduce the misallocation of program resources: in a simple simulation of a modest dropout prevention program, targeting students based on these models rather than targeting poor municipalities or high-dropout schools could reduce misallocation of resources by 30 to 80%.

The remainder of the paper is organized as follows. Section 2 briefly reviews the concepts and empirical evidence on early warning systems, which primarily come from the U.S. Section 3 describes the data and the current dropout situation in both Guatemala and Honduras, while Section 4 presents the results of constructing early warning models. Section 5 concludes with a brief summary of the findings and suggestions for future research.

# **2. Predicting dropout: Concepts and evidence**

The quantitative literature on dropout prediction stretches back at least 30 years in the U.S. and Canada, and includes a variety of methods ranging from using single variables (often called “dropout flags”) to applied statistical learning (or “machine learning”) based on large data sets (in terms of both number of variables and number of observations). This research has been primarily concerned with using readily available data to provide accurate predictions to school system managers on who is most likely to drop out prior to completing a full course of primary or secondary education. The variables used for prediction are based on both the conceptual

> 1 The authors thank the Ministry of Education of Guatemala ( _Ministerio de Educación_ –MINEDUC) and the Secretary of Education of Honduras ( _Secretaría de Educación_ – SEDUC) for providing access to the anonymized administrative data on which this study is based.

4
# **INTRODUCTION**

On January 1 2007, Bulgaria became a member of the European Union. This achievement was in part made possible by a substantial reform effort, which has allowed Bulgaria to make impressive progress towards long-term stability and sustained growth over the past decade.

Nonetheless, the income gap with the new member states of the EU remains large, while competition is still burdened by overly restrictive product market policies. In order to sustain growth and improve competitiveness, a second generation of reforms has been launched to help the country’s successful integration in EU and global markets. A cornerstone of this reform agenda is the implementation of less restrictive product market policies that enables firms to put resources - both capital and labor - to their most efficient use.

This paper presents a comparative analysis of Bulgaria’s performance in product market regulation (PMR). The approach used relies on a methodology developed by the OECD (Conway, Janod and Nicoletti 2005), and the associated data, which are available for all OECD members and Brazil. Bulgaria, together with Romania (and now Ukraine), is one of the few non-OECD countries to have undertaken such a benchmarking exercise. Data for Bulgaria were collected following the same methodology in early 2006. Comparators include countries at similar levels of income and development, as well as high-income EU and OECD countries.

The paper is organized as follows. After a brief discussion of the PMR methodology, we present the result of benchmarking Bulgaria’s product market policies to OECD countries and outline the areas where divergence from the average OECD levels or selected comparator groups is the largest. Although the PMR approach, and hence the paper, are really only diagnostic tools, we conclude by suggesting broad reform options.

Not unexpectedly, we find that overall product market policies in Bulgaria are generally more restrictive than among OECD countries, all of which are richer and with a longer tradition of reviewing their policy environment to make it more conducive to private sector development. Nevertheless, a number of interesting observations on the sources of Bulgaria’s ranking emerge from the analysis. These can be summarized as follows:

- **Bulgaria in 2006 performs better than its peers in terms of outward-oriented policies (** meaning obstacles to international trade and foreign direct investment). Its policies in this domain are more liberal than those of peers such as other middle-income countries (Brazil, Mexico, and Turkey) and the pre-accession countries of Central and Eastern Europe.<sup>1</sup> In particular, substantial progress has been achieved in ensuring the equal treatment of foreign parties and in eliminating

> 1 The data for the Czech Republic, Hungary, Poland and the Slovak republic is from 2003, so just prior to their joining the EU.

3
# **6  Conditional Cash Transfer (CCT) Programs**

In recent years, governments in many countries have adopted conditional cash transfer (CCT) programs as a strategy for alleviating poverty and stimulating investment in human capital. These programs typically provide cash grants to poor families if they send their age-eligible children to school as well as subsidies for regularly visiting health clinics for check-ups. Mexico and Brazil adopted the earliest CCT programs in the late 1990s. Subsequently, CCT programs were adopted in many Latin American countries, including Argentina, Chile, Colombia, Costa Rica, El Salvador, Ecuador, Honduras, Nicaragua, Panama, Peru and Uruguay, and similar programs are now available in countries in Asia, Africa and even in the U.S. Fiszbein, Schady, Ferreira, Grosh, and Kelleher (2009) provide a thorough overview of CCT programs around the world and an overview of the measured impacts of these kinds of programs.

Many of the CCT programs give the cash transfers to women in the household, in an attempt to improve the bargaining position of women and because it is thought that targeting transfers toward women shifts expenditures toward investments in children. Below, we summarize impact findings from three evaluation studies on how CCT programs have been found to affect women and adolescent girls, two studies of the Mexican _Oportunidades_ program and one of the Nicaraguan _Red de Proteccion Social_ (RPS) program.

## **6.1  The Mexican** **_Oportunidades_ Program: Impacts on Mothers’ Time Use**

The Mexican _Oportunidades_ program (formerly called PROGRESA) is one of the best known of the CCT programs. The program has been rigorously evaluated using both experimental and nonexperimental evaluation designs. An experiment carried out in the first two years of its implementation (1998-1999) in rural areas demonstrated statistically significant impacts of the program on reducing child labor, improving health and nutrition and increasing school enrollment and attainment.<sup>22</sup> The experiment was a place-based randomization that randomized 506 villages in or out of the program. Within the treatment villages, about half the families were deemed eligible to participate in the program. Families were visited door to door and told about their eligibility, and most families who were eligible agreed to participate. A subsequent nonexperimental evaluation of the program in urban areas also found statistically significant

> 22 See, e.g., Schultz (2000,2004), Gertler (2004), Behrman, Sengupta and Todd (2005), Parker and Skoufias (2000), Buddelmeyer and Skoufias (2003), Skoufias and McClafferty (2001), and Todd and Wolpin (2006).

47
witnesses greater availability of the tradeable goods and the lifestyles of the affluent influence those in the rest of the economy. A good example of this is the phenomenon witnessed in countries such as India with a move towards greater consumption of tastier food items that are imported and away from the domestically produced traditional food items. The ‘inequality effect’ supplements the ‘Penn effect’ in moving the PPP towards the exchange rates, i.e. increases the ‘Price Level Index’ towards unity.

This study proposes and applies an alternative methodology to that used in the ICP to present a set of PPPs that serve as a benchmark for comparing the 2011 ICP PPPs. The methodology is based on a utility theory consistent specification of consumer preferences and has quite modest data requirements. As the empirical exercise shows, the methodology can be readily implemented on easily available data sets published on the World Bank website. This is an attractive feature since it not only avoids the requirement of detailed price information, the quality of which has dogged successive ICPs, it is an easy procedure to use.

The empirical contribution of this paper includes the feature that this is the first attempt at benchmarking the 2011 ICP, indeed any ICP, not by reconciling the PPPs with those from a previous ICP, but with PPPs calculated on a similar data from the same year. Another distinguishing feature is the moving of the ‘base country’ from the USA to India. The latter country shares many of the features of a developing country including high poverty rates, but at the same time provides a market and an economy size that places it in the top tier of nations. The significant result here is the finding that, while there is remarkable closeness between the two PPPs for many countries, the overall picture is one of understatement by the ICP PPPs leading to an overstatement of the PPP deflated real GDP. The study does find, however, that there are large regional variations in the sign and size of the deviation of the two PPPs from one another. Another feature of this study is the use of expenditure percentile disaggregated data sets to show that the PPPs vary significantly between the expenditure percentiles. This has the important policy message that the aggregate data based PPPs may not be appropriate for use in poverty calculations.

This study takes place at a time when the ICP exercise is at a cross road with questions over its methodology, the quality of the price information used and accuracy of its PPP estimates. The results of this study point to the need to experiment with alternative methodologies that allow proper robustness check on the PPP estimates. The results also raise questions over the wisdom 31
10

# **Are current policies still harming farmers in developing countries?**

What do the available protection database and model from Purdue University’s Global Trade Analysis Project (GTAP) say about recent distortions? The database suggests that as of 2001, and consistent with table 1, the import-weighted average nominal ad valorem tariff and subsidy interventions were greater in agriculture than other primary sectors and manufacturing even in developing countries, although less so than in high-income countries. Using the GTAP model, Anderson and Valenzuela (2007) report the effects on agricultural value added (real net farm income) of full global liberalization of those import tariffs and agricultural subsidies. The results, reported in table 3, suggest that real net farm income would rise in all key developing countries and regions with a move to free trade. Moreover, that rise is greater than the increase in value added in nonfarm sectors except in Sub-Saharan Africa (where the two rise by a similar amount). That is, according to the GTAP database and model, developing country farmers as a group are still being discriminated against by the structure of distortions as of 2001.

That estimated positive impact of such reform on farmers in Sub-Saharan Africa is consistent with results using another global economy-wide model known as Linkage. That model has been used by Anderson, Martin and van der Mensbrugghe (2006c) to explicitly address a question raised by Panagariya (2005), who suggests that such a region would be likely to lose from liberalization of at least OECD agricultural protection. The basis of Panagariya’s suggestion has two elements. One is that net foodimporting countries of the region would lose because OECD reform would raise
Table 2: Targeting simulation results

|**Targeting Method**|(1)<br>**AUC**|(2)<br>**Accuracy**|(3)<br>**Precision**|(4)<br>**Recall**|
|---|---|---|---|---|
|_Panel A: Matched Sample (N=535) -_|_for whom w_|_e have survey an_|_d CDR data_||
|Random|0.50 (0.028)|0.60 (0.025)|0.27 (0.038)|0.27 (0.038)|
|Asset Index|0.73 (0.024)|0.72 (0.020)|0.49 (0.041)|0.49 (0.041)|
|Consumption|0.71 (0.026)|0.69 (0.023)|0.45 (0.038)|0.45 (0.038)|
|CDR|0.68 (0.027)|0.69 (0.021)|0.42 (0.042)|0.42 (0.042)|
|Combined|0.78 (0.022)|0.75 (0.020)|0.55 (0.039)|0.55 (0.039)|
|_Panel B: Balanced Sample (N=1,007_|_) - as above, _|_plus households _|_without phone_|_s_|
|Random|0.50 (0.017)|0.90 (0.006)|0.05 (0.010)|0.05 (0.010)|
|Asset Index|0.72 (0.026)|0.90 (0.006)|0.10 (0.013)|0.10 (0.013)|
|Consumption|0.70 (0.028)|0.90 (0.006)|0.15 (0.025)|0.15 (0.025)|
|CDR (Target Phoneless First)|0.68 (0.030)|0.90 (0.006)|0.11 (0.035)|0.11 (0.035)|
|CDR (Target Phoneless Last)|0.51 (0.028)|0.90 (0.006)|0.12 (0.033)|0.12 (0.033)|
|Combined (Target Phoneless First)|0.74 (0.026)|0.90 (0.006)|0.11 (0.046)|0.11 (0.046)|
|Combined (Target Phoneless Last)|0.57 (0.022)|0.90 (0.006)|0.18 (0.007)|0.18 (0.007)|
|_Panel C: Full Sample (N=2,814) - a_|_s above, plus _|_households with _|_phones on oth_|_er networks_|
|Random|0.50 (0.009)|0.89 (0.005)|0.06 (0.007)|0.06 (0.007)|
|Asset Index|0.65 (0.017)|0.89 (0.005)|0.07 (0.014)|0.07 (0.014)|
|Consumption|0.69 (0.015)|0.89 (0.006)|0.08 (0.031)|0.08 (0.031)|
|CDR (Target Phoneless First)|0.52 (0.008)|0.89 (0.005)|0.06 (0.008)|0.06 (0.008)|
|CDR (Target Phoneless Last)|0.48 (0.008)|0.89 (0.005)|0.08 (0.010)|0.08 (0.010)|
|Combined (Target Phoneless First)|0.52 (0.008)|0.89 (0.005)|0.06 (0.008)|0.06 (0.008)|
|Combined (Target Phoneless Last)|0.49 (0.008)|0.89 (0.005)|0.09 (0.009)|0.09 (0.009)|

_Notes_ : Four different measures of performance (columns) reported for different targeting methods (rows), using different samples of survey respondents (panels). Standard deviations, calculated using 1,000 bootstrap samples, in parentheses. Panel A: The 535-household subsample that is matched to CDR. Panel B: The 535-household matched sample, plus the 472 households that do not have a phone; this is meant to approximate targeting performance if CDR from all mobile networks were available. Sample weights are applied as described in Section 2.5. Panel C: All 2,814 observations from the TUP survey, including households matched to CDR, households that own phones not matched to CDR, and households without phones, with sample weights applied. For Panels B and C, we simulate two types of CDR-based targeting: targeting households without phones first and targeting households without phones last.

25
# Table IV(a)

|Effect of Elections on Com<br>[t-statistics in parent|modity T<br>hesis]|axesa||
|---|---|---|---|
|[1]|[2]|[3]|[4]|
|Independent Variables:||||
|Scheduled election year<br>-3.42|-3.94|||
|[-1.37]|[-2.27]|||
|Midterm Election Year||4.97|2.20|
|||[1.73]|[0.88]|
|State domestic product [SDP]|0.05||0.05|
||[8.84]||[8.76]|
|Share of agriculture in SDP|-57.18||-55.16|
||[-2.89]||[-2.73]|
|Proportion of rural population|-960.2||-962.8|
||[-10.5]||[-10.4]|
|Number of Observations<br>456|394|456|394|
|R-sq<br>0.87|0.94|0.87|0.94|

a. All taxes and SDP are in 1973 rupees per capita. Regressions include year and state effects, and are reported with robust standard errors.

40
that raising the proportion of wealthier classmates (while reducing that of the lower group), increases the test score of the average pupil. Contrary to what could be expected, the largest effect comes from increasing the presence of children from the upper-middle group, and not the upper group. This points to a non-linear relationship between peers’ family background and student’s performance at school. The different between the coefficients on the two upper groups (.090 versus .067) is statistically different at an 0.6 percent level. See table 15 in the appendix.

Table 5: Class composition effect

||Dependent vle:|Test scores in Spanish|
|---|---|---|
||(1)|(2)|
|Prop peers in LM|.052<br>(.006)<sup>_∗∗∗_</sup>|.044<br>(.007)<sup>_∗∗∗_</sup>|
|Prop peers in UM|.091<br>(.008)<sup>_∗∗∗_</sup>|.082<br>(.009)<sup>_∗∗∗_</sup>|
|Prop peers in U|.068<br>(.008)<sup>_∗∗∗_</sup>|.047<br>(.009)<sup>_∗∗∗_</sup>|
|school fxed efects||✓|
|Obs.|389,501|333,267|
|e(N-g)|7,444|6,463|
|_R_<sup>2</sup>|.069|.067|

Note: Standard errors are robust and clustered at the school level. Other variables included in the regressions are the student’s number of books at home, gender, repeat, class size and age. Source: ONE, 2000. Argentina At worst, the results indicate that there is no gain from having peers from the wealthiest group. At best, they suggest that it is preferable to integrate students from lower socioeconomic background with others from upper-middle class children than with those from the top of the distribution. In both cases, the conclusion of non-monotonicity of peer-group effects remain. In other words, the specific conformation of the class in terms of the socioeconomic background of the students matters for the total effect on test scores. These coefficients estimate effect, however, for the average child. It might very well be that class composition have different effects depending on the parental background of the recipient student. I do this in the next table.

Tables 6 and 7 present the estimation of four regressions – equations 22