rafmacalaba/datause-encoder-data huggingface dataset:
country did not make major progress in providing opportunities for all, and most progress came in the form of a scalar increase in coverage rates instead of an equalization of chances among different groups. Interestingly, income does not appear as a major determinant of access to opportunities; results for the effect of ethnicity, location, and household allocation of decision making suggest other dimensions of unequal circumstance groups. In such a context, public spending on education is found—unsurprisingly–– to be neither pro-poor nor progressive. Children with disadvantageous circumstances and a lower probability of attending school receive a lower share of total transfers than children in nonvulnerable groups of the population.

This evidence can help shape the ongoing policy debate in Côte d’Ivoire in several directions. In addition to the policy dialogue on the need for management and decentralization reforms and increasing public resources, the equality of opportunities in Côte d’Ivoire underscores the need to think carefully on _how_ to spend current resources more effectively. One-size-fits-all interventions will not be equally effective in improving access to opportunities that, in some cases, are very far from universal provision, while in others, there are very unequal distributions. While not exclusive in theory, budgetary constraints in practice may call for a policy decision regarding interventions that expand coverage or reduce access inequalities. HOI can be a tool to provide empirical evidence on where to use each type of intervention. Also, it identifies which groups are lagging behind in terms of opportunities and, therefore, which policies need be targeted (whether through conditional cash transfers, increasing supply, specific programs at schools, communication campaigns, or other types of interventions).

30
## **2.3 The Education Sector in Rwanda**

Rwanda uses the 6-3-3 pre-tertiary education system, which comprises 6 years of primary education; 3 years of middle school (Ordinary level); and 3 years of senior secondary school (advanced level). At the end of primary school (Grade 6), students sit for a mandatory national exam and obtain the primary leaving certificate, which allows them to move on to junior secondary education. This primary school exit examination is the focus of this study.

The exam consists of five subjects (mathematics, science, social studies, Kinyarwanda, and English). Performance in this examination is the key metric for progression into middle school (O-Level). Usually, the best-performing students in the national exams are automatically awarded places in boarding schools. Although students with low performance in the national exam are promoted to the middle school level, they usually remain in the same schools for what is commonly known as 12 years of basic education (12YBE). The average age of starting primary school education in Rwanda is 7, although most of the students complete primary school at age 14 as shown in Table 1.<sup>6</sup>

# **3 Data**

## **3.1 Test Scores**

Our primary dataset is standardized test scores of the universe of primary school students in the primary school national exams between 2012 and 2019, obtained from the National Examination and School Inspection Authority (NESA). It contains the test scores of nearly 1.5 million students who sat for the exam during the period. The dataset contains the standardized test scores in each of the five subjects (mathematics, science, social studies, Kinyarwanda, and English); grade points in each subject; age and gender of the student; and unique school ID. We match this data with a dataset on the geolocation of each school using the school IDs. Figure 3 shows the geographic footprint of primary schools and coffee mills in Rwanda.<sup>7</sup> NESA uses a nine-point grading system in order from best to worst- 1 (Excellent) to 9 (Poor). Since we are interested in students’ aggregate performance in all subjects as well as individual subjects, we use the grades in the respective subjects to generate a standardized grade point. Specifically, following Denteh et al. (2022), we reverse the scales so that a high grade means better performance. Essentially, we re-order the ranking of the grades such that 1 means Poor

> 6It is noteworthy to emphasize that Rwanda introduced 6-year compulsory primary education. However, in 2009 the country extended the program to a 12-year free and compulsory education system. 7Our data on the location of coffee mills was obtained from Rwanda Geoportal

6
Table 4: Increased Tropical Cyclone Damages from Climate Change

|Damage Model|CNRM|ECHAM|GFDL|MIROC|
|---|---|---|---|---|
|Damage|28|55|68|64|

Note: Damages are in billions of USD/yr based on A1B emission scenario and 2100 baseline.

However, what is unique to this paper is that it also predicts the distribution of damages across the world. Figure 4 displays the additional damages caused by climate change in each continent. The results from the tropical cyclone modeling (see Figure 2) suggests that the North Atlantic and the Western Pacific oceans are the primary oceans that will respond to warming, Consequently, Asia and North America are the two continents that are predicted to be consistently affected by more severe tropical cyclones. This is evident in Figure 4. North American and Asia would suffer the highest additional damages from climate change: $30 billion and $21 billion respectively. The additional damages from global warming in the rest of the world are likely to be small and inconsistent. In some regions and models, the damages from tropical storms are expected to fall with warming. Note that the predicted regional damages vary a great deal across the climate models. For example, GFDL predicts especially large impacts in the Western Hemisphere, MIROC predicts especially large damages in Asia, and ECHAM predicts large damages in Asia and North America.

Figure 5 displays the additional tropical cyclone damages caused by climate change as a fraction of GDP in 2100. The figure illustrates how burdensome the change in tropical storm damage will be to the economies in each region. The global average damage per unit of GDP was 0.01 percent. North America (0.03 percent of GDP) and Asia (0.01 percent of GDP) would have the largest average impacts per unit of GDP. The tropical cyclone damages per unit of GDP caused by climate change is expected to be low in the remaining continents.

17
# **Table A9: Ordered Probit and Probit Regressions, 2005**

Financial Vulnerability Regressions (Marginal Effects)

|||**arrears**|**2**<br>|||**arrears**|**3**<br>|||**himedc**|**ost**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|
|_New EU Countries_|||||||||||||
|Czech Republic|0.0015|0.0254 **|0.11|-2.25 **|0.0210|0.0210|0.10|0.00|0.0022|0.0042|0.06|-0.71|
|Estonia|0.0001|-0.0020|0.11|0.09|0.0149|0.0276|0.06|-0.28|-0.0104|-0.0071|0.17|-0.61|
|Hungary|0.0712 ***|0.0452 ***|0.12|1.29|0.0814 ***|0.1023 **|0.06|-0.34|0.0184 **|0.0202 ***|0.07|-0.17|
|Latvia|0.0742 **|0.0640 ***|0.11|0.17|-0.0848 **|0.0086|0.04|-1.99 **|-0.0268|0.0261 *|0.12|-1.41|
|Lithuania|0.1009 *|0.0082|0.08|1.58|0.0928 *|0.0356|0.05|0.76|0.0753 **|0.0056|0.09|1.76 *|
|Poland|0.0084|-0.0463|0.09|2.00 **|0.0425|0.0678|0.06|-1.00|0.0912 **|0.0328 *|0.06|1.45|
|Slovak Republic|-0.0176|0.0038|0.05|-1.83 *|-0.0163 *|0.0127|0.06|-3.23 ***|-0.0062|-0.0064|0.09|0.09|
|Slovenia|0.0850 ***|0.0157|0.08|2.38 **|-0.0121|0.0015|0.05|-0.35|||0.32||
|_EU-15 and Cyprus_|||||||||||||
|Austria|-0.0005|0.0071 *|0.15|-1.64|0.0836|0.0967 ***|0.22|-1.73 *|0.0029 *|0.0000|0.19|2.51 **|
|Belgium|0.0112|0.0473 ***|0.15|-3.68 ***|0.0071|0.0280 *|0.15|-1.51|0.0077 **|0.0065 **|0.19|0.35|
|Cyprus|0.0076|0.0289 **|0.11|-1.51|-0.0115|0.0251|0.09|-1.18|0.0009|0.0169 ***|0.13|-1.93 *|
|Denmark|-0.0062||0.17||-0.0355 ***||0.17||-0.0015 *||0.17||
|Finland|0.0346 ***|0.0716 ***|0.15|-4.42 ***|0.0400 ***|0.1243 ***|0.12|-5.83 ***|0.0168 **|0.0145 **|0.11|0.07|
|France|0.0004|0.0331 ***|0.14|-4.51 ***|0.0139|0.0337 **|0.10|-2.16 **|0.0047|0.0113 ***|0.16|-1.99 **|
|Greece|-0.0845 ***|0.0500 **|0.09|-4.32 ***|-0.1216 ***|0.0326|0.07|-3.59 ***|-0.0156 **|0.0128 *|0.11|-3.05 ***|
|Ireland|0.0159 *|0.0542 ***|0.21|-2.97 ***|0.0044|0.0345 ***|0.28|-3.23 ***|0.0099 **|0.0143 **|0.13|-0.50|
|Italy|0.0340 ***|0.0556 ***|0.12|-2.62 ***|0.0156|0.0942 ***|0.10|-3.65 ***|0.0308 ***|0.0246 ***|0.09|0.55|
|Luxembourg|0.0052|0.0141|0.10|-0.91|0.0106|0.0181|0.12|-0.65|0.0270 **|0.0612 **|0.41|0.41|
|Netherlands|-0.0255 ***||0.13||-0.0268||0.08||-0.0010||0.17||
|Portugal|0.0032|0.0053|0.08|-0.23|0.0078|0.0013|0.09|0.59|-0.0033|0.0183 ***|0.13|-3.63 ***|
|Spain|-0.0027|0.0217 ***|0.10|-3.87 ***|-0.0003|0.0791 ***|0.09|-3.91 ***|0.0013|0.0037 **|0.10|-1.14|
|Sweden|0.0208|0.0592 ***|0.11|-3.88 ***|0.0671 *|0.1527 ***|0.12|-4.81 ***|0.1699 ***|0.2167 ***|0.13|-1.08|
|United Kingdom|0.0003|-0.0005|0.09|1.60|0.0040|0.0400 ***|0.10|-6.75 ***|0.0003|0.0005|0.14|-0.38|

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

Source: EU Statistics on Income and Living Conditions (SILC) database, 2005 46
_Tax_ is somewhat smaller in some of the specifications here than in the baseline results above, but it is still large. In fact, for the final specification, the estimated coefficient value of _Time Tax_ in the baseline specification (column 5, Table 1) and the repeated cross-section specification (column 6, Table 2) is exactly equal (coefficient value of 0.03).<sup>6</sup> Regarding the various controls, larger crime losses and worse functioning courts are associated with higher corruption, significant at the 1 percent level. However, unlike the baseline results, foreign ownership is no longer significantly correlated with corruption. Instead, we find that corruption is lower for the relatively larger firms, significant at the 10 percent level in one specification and 5 percent level in the remaining. The lower corruption for the relatively large firms may seem counter-intuitive as large firms are likely to be more profitable (higher ability to pay bribes) and more visible to corrupt public officials, and therefore, more likely to pay bribes. However, as mentioned in an earlier section, one possibility is that larger firms are better politically connected, have better outside options (if they choose not to pay bribes) and have more resources to guard against corrupt public officials. If these factors are strong enough, it could explain the lower corruption for the relatively large firms. As expected, corruption is significantly lower (at the 5 percent level) in the richer countries (in terms of GDP per capita), in countries with higher education attainment (primary education), and in the more open (in terms of trade) countries. There is also a sharp and significant decline in the level of overall corruption over time, but this becomes insignificant when we control for the various country-level factors (column 6). The implication here is that changes in macro-level factors most likely explain the downward trend. The remaining country and firm-level variables do not show any significant relationship with overall corruption.

> 6 We also experimented with including controls for country size proxied by (log of) total population and the quality of democracy as measured by the Polity2 variable from the Polity database. Adding these controls to the final specification above caused the estimated coefficient value of _Time Tax_ to decline slightly from 0.030 (column 6, Table 2) to 0.028 (not shown) and it remained significant at the 5 percent level.

19
true for employment in the financial sector. Further geographic heterogeneity is captured by interacting selected independent variables with region dummy variables, in addition to region fixed effects.

We will work with both the 2001 and the 2007 HESs data. While we allow the models to be different, by building models that best fit the data for any given year, the models we identified are found to be almost identical (in the sense that they include nearly the same set of explanatory variables).

As an experiment, a second set of models is obtained by adding a handful of variables on durable asset ownership and housing conditions that are available in the HES but not in the LFS. Because these are not LFS variables, we cannot consider these models for imputation into the LFS. We can however use them to impute consumption poverty into the HES, and subsequently assess how imputed poverty data compares to observed poverty data. The purpose of this exercise is to verify whether adding these variables to the model significantly improves the statistical precision of the imputed data. Other studies have found that durable asset ownership and housing conditions are particularly powerful predictors of poverty (see e.g. Christiaensen _et al._ , 2012). If the same holds true for Morocco, then an argument can be made for adding these variables to future rounds of the LFS.

Table 1 shows selected descriptive statistics for the four regression models (urban versus rural plus 2001 versus 2007). A number of characteristics are apparent: (a) the models provide good in-sample fits of the data judging by the high adjusted R-squared, (b) the urban models fit the data better than the rural models, which is typical for these type of regression models, and (c) as expected, adding the five durable assets and housing variables significantly improves the in-sample fit. Whether this also translates into better out-of-sample fits is examined in the next section.

**Table 1 – Summary Statistics, Urban and Rural Models 2001 and 2007**

||**20**|**01**|**20**|**07**|
|---|---|---|---|---|
|Statistic|Urban|Rural|Urban|Rural|
|R2|0.59|0.43|0.58|0.42|
|R2 (assets)|0.64|0.48|0.63|0.48|
|# vars|52 (57)|45 (50)|58 (63)|51 (56)|
|Obs|7888|6355|4266|2796|

Source: HESs (2001, 2007); the numbers in between brackets denote the number of independent variables in the models where additional “asset” variables have been included

# **5.1 Urban model**

Table 2 presents the urban models for Morocco (for both years; with and without additional variables). The _IcuX* variables denote interactions between selected independent variables and regional dummy variables.

13
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel E: Formation/operation of non regulated microcredit institutions (0 4)</u>

||V|<br>ersion 1(Ful sampl|<br>Depvar: Portfolio at<br>es)|Risk(>30days)<br>Version 2|<br> (DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Formation/operation of non-regulated microcredit institutions (0-4)|0.0063|0.0015|0.0008|0.0059|0.001|-0.0009|
||[0.353]|[0.688]|[0.880]|[0.350]|[0.770]|[0.846]|
|NBFI dummy * Formation/operation of non-regulated microcredit institutions (0-4)|-0.0057|0.0033|0.0069|-0.0037|0.0034|0.0082|
||[0.504]|[0.487]|[0.268]|[0.662]|[0.445]|[0.148]|
|Combined group dummy * Formation/operation of non-regulated microcredit institutions (0-4)|-0.011|-0.0061|-0.0003|-0.0049|-0.0042|0.0033|
||[0.254]|[0.237]|[0.970]|[0.518]|[0.394]|[0.589]|
|NBFI dummy|0.0231|-0.0063|-0.0128|0.018|-0.0079|-0.0187|
||[0.323]|[0.568]|[0.403]|[0.457]|[0.448]|[0.180]|
|Combined group dummy|0.0462*|0.0211*|0.0127|0.0287|0.0093|-0.0017|
||[0.077]|[0.074]|[0.424]|[0.261]|[0.432]|[0.905]|
|Observations|627|627|598|572|572|546|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.071|0.206|0.159|0.069|0.242|0.164|
|Adjusted R-squared|0.0416|0.182|0.131|0.0373|0.215|0.134|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.000575|0.00477|0.00767*|0.00226|0.0044|0.00735*|
||[0.926]|[0.156]|[0.072]|[0.709]|[0.162]|[0.073]|
|Combined group dummy|-0.0047|-0.00469|0.000503|0.00101|-0.00325|0.00236|
||[0.461]|[0.221]|[0.913]|[0.874]|[0.379]|[0.589]|

53
# Should Price Reform Proceed Gradually or in a "Big Bang?"

by Sweder van Wijnbergen*

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Intertemporal Speculation and the Supply Response to<br>Gradual Price Decontrol|3|
||2.1 The Basic Model<br>2.2 Aggregata Supply Response, Hoarding, and<br>Gradualism|3<br>9|
|3.|Shortages and the Probability of Reform Failure|15|
|4.|Gradualism, Intertemporal Speculation, and the<br>Political Economy of Price Reform|21|
|5.|Conclusion|26|
|Refe|rences|27|
|Appe|ndix: Consumer Welfare and Price Decontrol|29|

> * I am indebted to Max Corden, Alex Cukierman, Antonio Estache, Arye Hillman, Santiago Levy, Dani Rodrik, Lars Svensson and seminar participants at the World Bank, the IMF, and Princeton and Pennsylvania State Universities for helpful comments.
results need to be interpreted with caution not least because 16 percent of surveyed population provided “Don’t know” as their response to this question.

Figure 2: Percentage of population who agree, disagree or are ambivalent about how their household is faring relative to 4 years ago <!-- Start of picture text --> 70 66<br>60 57<br>50<br>44<br>40<br>30 27 25<br>18<br>20 15 14 16<br>10<br>0<br>Armenia Azerbaijan Georgia<br>Disagree Neutral Agree<br><!-- End of picture text -->

# _Source_ : LiTS (2016)

_Note_ : The bars for each country may not add up to 100 percent because responses that were “Don’t know” and “Not applicable” are not plotted here. Strong agreement and agreement are grouped into a single agreement category and likewise strong disagreement and disagreement are grouped into a single disagreement category.

LiTS also provides another way of looking at economic mobility. The survey asks each respondent to situate themselves in a 10-step Cantrill ladder representing various levels of wealth. A person placing himself at ten in this ladder would be positioning himself in the top decile of the wealth distribution within the country and a person placing himself at one would be placing himself at the bottom decile. In addition to asking about the current position, the survey also asks about where the households would place themselves in the same ladder four years ago. Tracking the changes in the position in the ladder chosen by the household at these two points in time, one can make inferences about perceived directional income/wealth movements. A household is deemed to have moved up if the position in the ladder today is higher than what it was four years ago and moved down if it is the opposite.

The results presented in Figure 3 broadly corroborate the developing narrative with some differences. Across all three countries, there is a larger proportion of households that report themselves to be at the same step in the ladder in comparison to those who had neutral assessment of how their household had fared relative to four years ago in Figure 2. For 8
# **4.4 Distributional and Poverty Results of the Trade Scenarios**

Two fundamental results emerge from analyzing the micro impacts of the trade scenarios. Firstly, our initial hypothesis that trade liberalization, by working against the “natural” forces of structural change, might weaken long-term trends of poverty reduction has been discarded. Although less people migrate towards higher paid non-agricultural jobs, mainly through increased agricultural incomes, poverty is further reduced in the trade liberalization scenarios. However, and this is the second fundamental result, trade reform as envisaged in the current Doha scenarios – but even in the hypothetical full liberalization one – is not of great help in the fight against poverty and its complete eradication needs additional more targeted and possibly more costly interventions.

These two sets of results are clearly illustrated by Table 12, which shows the poverty and distributional outcomes as percentage point differences between the trade scenarios and the BaU scenario for the final (2015) year: the full liberalization scenario leads to a further reduction in the headcount poverty index of 0.5 percentage points, whereas for deep Doha scenario the effects are almost negligible.<sup>35</sup> Similarly the Gini index is reduced by 0.2 and 0.1 percentage points in the full liberalization and deep Doha scenarios.

## _<u>Table 12: Poverty and Distributional Impact of Trade, all households</u>_

|||% point|% point|
|---|---|---|---|
||BaU 2015|diff. Doha|diff. Full|
|Gini|58.5|-0.1|-0.2|
|P0|18.0|-0.2|-0.5|
|P1|6.6|-0.1|-0.2|
|P2|3.5|0.0|-0.1|

Source: Authors’ calculations.

As for the BaU scenario, a thorough assessment of the trade scenarios needs to go beyond these aggregate indicators and should rely on more disaggregate poverty and distributional analyses. In search of trade-induced poverty effects, the remaining part of this section considers an array of indicators, from growth incidence curves to poverty statistics estimated on specific sub samples. In particular poverty and distributional impacts are separately measured for the agricultural and non-agricultural groups, the movers and stayers, the rural and urban populations, the regional samples, and the

> 35 Given that the weak Doha scenario does not produce any visible results, in this section we just report results for the full liberalization and the deep Doha scenarios.

42
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