rafmacalaba/datause-encoder-data huggingface dataset:
17 The H&S study is based on a sample of 60 ICP countries<sup>for 1980. Using</sup> this data, they estimate two share variables: SC (consumer services as a proportion of total ho.usehold consumption) and<sup>SGDP (consumer and government</sup> services as a proportion of GDP) .6 These<sup>shares are estimated at two sets of</sup> prices: nominal market prices observed in each<sup>country and a set of</sup> international (real) prices applied uniformly to<sup>all countries.</sup> The actual data and expected levels of SC and SGDP<sup>- -where expected levels</sup> are estimated on the basis of PPC-based per<sup>capita income--are presented in</sup> Table 2. Table 2A gives data for the USSR for<sup>several different years, and Table</sup> 2B gives data for Russia and the other republics in 1990.<sup>In all cases, expected</sup> levels are far above the actual levels. Indeed, the<sup>actual Soviet levels of SC</sup> and SGDP at market prices are mostly below<sup>the estimated intercept of the</sup> equations. When compared at market prices, the<sup>actual levels of SC and SGDP are</sup> estimated at 10 to 20 percent of GDP below<sup>expected levels. When compared at</sup> factor costs, actual levels are estimated at<sup>5-10 percent of GDP below expected</sup> levels. Since final services are defined to<sup>include both privately and publicly</sup> provided services like education and health and<sup>since Soviet public services--</sup> reflected in the value added and employment<sup>comparisons described earlier--are</sup> typically high, it can be concluded that<sup>the actual level of Soviet private</sup> consumer services is far below the expected<sup>level.</sup> Although the Soviet republic data in Table<sup>2B are ordered by level of GDP</sup> per **capita,** there is no clear pattern. Nevertheless,<sup>several observations can</sup> be made. Among the republics with higher than average<sup>incomes, such as the</sup> 8 H&S also define an "augmented consumer services" variable to<sup>capture</sup> trade and freight transportation services<sup>for consumer goods, estimated from</sup> input-output table coefficients. We have<sup>not tried to reproduce this variable</sup> with Soviet data.
_Nacional_ - STN).<sup>14</sup> .

# **4 Identification Strategy**

The results _−_ based financing (RBF) reform in _Ceará_ configures an opportunity to estimate if education incentives for mayors triggers student learning. Given that such a reform occurred at the state level and at clear points in time allows us to explore empirically the geographic and time characteristics in our identification strategy. That being said, we start by restricting our sample to schools located in the three immediate municipalities at the border between _Ceará_ and adjacent states ( _Piauí_ , _Paraíba_ , _Rio Grande do Norte_ and _Pernanbuco_ ). Figure 1 illustrates the municipalities included in the analysis. By limiting our sample to municipalities that are geographically close, but on different sides of the border, we intend to minimize the influence of time-invariant unobserved characteristics of schools and municipalities that affect student learning. Put simply, schools right across the border serve as a good counterfactual for the case that the RBF reform had not been implemented in _Ceará_ .

Another useful characteristic of the _Ceará_ reform is its clear timeline. We leveraged that the RBF and the technical assistance (TA) component were introduced at defined dates to compare the evolution of learning outcomes at the border at different points in time. To make this point clearer, consider the RBF and TA reform for 5<sup>_th_</sup> grades. In the first moment, the state congress enacted the RBF reform in mid-December 2007; so, from January 2008 on-wards, a fraction of Quota-Parte revenues started to be redistributed to municipalities based on learning outcomes. Then, in 2011, schools with students from 3<sup>_rd_</sup> _−_ 5<sup>_th_</sup> grade received a _non−mandatory_ package of training and pedagogical materials (the TA component). In a third and final stage, in 2012 the RBF formula was re-formulated to penalize municipalities with higher percentages of students

> 14 Public spending data can be accessed at `https://siconfi.tesouro.gov.br/siconfi/index.jsf` .

15
# _Benchmark countries_

The CPIA process is implemented in two phases. In the benchmark phase about 20 countries are assessed. The benchmark countries change somewhat from year to year, but the sample is designed to include at least one IDA country in each region, and to represent a mix of higherand lower-performing countries. The remaining 115 (approximately) countries are assessed in a second phase after ratings for the benchmark sample are finalized.

The timetable for the benchmark assessments is relatively long, given that there are only about 20 countries. For example, the time allotted for the network review is usually about two weeks for the benchmark phase and about three weeks for the second phase. It is doubly important to set ratings at the appropriate level for the benchmark countries, if those ratings are often used as comparators by the regions and reviewers in the second phase. In this context, the relatively generous timetable for the benchmark phase is appropriate.

The extra time available for assessing the benchmark countries, and the added importance of getting their ratings right, should affect the write-ups and review outputs. Other things equal, we would expect write-ups for the benchmark countries to be longer and to cover more of the criteria in each question. If a longer and more thorough benchmarking process produces more accurate ratings and better write-ups, then network reviewers should be less likely to request additional information from the regions and to disagree with the proposed ratings. However, network reviewers have more time available to investigate each proposed rating in the benchmark phase. Therefore, controlling for the more thorough process (using our indicator of how many of the criteria are addressed in the write-ups), reviewers might disagree and request additional information more often in the benchmark phase.

# _Ratings increases and decreases_

Ratings changes should generally require more elaborate justifications than unchanged ratings. Country teams are perceived as having stronger incentives to increase than to decrease ratings, so they may anticipate more skeptical reactions from regional and network reviewers to proposed increases by providing lengthier and more comprehensive justifications. If it is both easier, and less important, for country teams to convince reviewers to go along with a ratings decrease than with an increase, write-ups accompanying proposed decreases may differ little from those for unchanged ratings.

# _Openness_

More information relevant to the CPIA governance questions is likely to be available in countries with more open governments that encourage or at least tolerate freedom of the press. We measure openness using the Freedom House index of press freedoms, and hypothesize that more openness will be associated with longer and higher-quality write-ups.

_Country size and income_ 9
suggest that these two targets—one for absolute poverty and one incorporating social inclusion— are reasonably consistent.

Alternatively, one can ask what reduction in the new poverty measure is implied by the expected 5% (per capita) growth rate with no change in inequality under a seemingly reasonable assumption about the elasticity. Based on the aforementioned country data for the longest available spells between surveys, and controlling for changes in inequality, the empirical elasticity of the combined poverty measure to the mean has been 0.44.<sup>37</sup> This would also get us to a poverty rate of 33% in 2022. The empirical elasticity will tend to fall over time with growth, so some extra redistributive effort or higher growth would probably be needed. Nonetheless, a target of two-thirds of the population deemed to be social included appears to be reasonably consistent with the scenario underlying the absolute target of 3% by 2022.

Reaching this ambitious target would still leave 2.2 billion socially excluded people 10 years from now—though 0.5 billion less than current projections. But remember that half of these 2.2 billion people can be accounted for by those who are absolutely poor this year. So this goal would actually entail a substantial reduction in the number of people who are currently relatively, but not absolutely, poor. That number is 1.7 billion in 2012,<sup>38</sup> but (under this target for relative poverty) it would fall to 1.1 billion by 2022.

# **Conclusions**

To set goals that can motivate extra effort, they must represent real progress but not be impossible! For absolute poverty, the proposed optimistic benchmark for the $1.25 poverty rate in 10 years time is 9%. This would clearly be a very good performance—maintaining an impressive trajectory of poverty reduction going back to the early 1980s when the corresponding poverty rate was over 50%. In the 20 years from 1990 to 2010 the developing world halved its overall poverty rate, from 43% to 21%. The optimistic trajectory would entail halving it again in a further 10 years.

> 37 This is based on a regression of the growth rate (annualized log difference) in the relative poverty measure on the growth rate in the survey means and the annualized difference in the mean log deviation, as the measure of inequality. The same data set was used as described earlier. The regression coefficients were -0.44 (with a robust standard error of 0.05) for the growth rate in the mean and 0.60 (s.e.=0.06) for the change in inequality; R<sup>2</sup> =0.85. 38 At the Bank’s current population growth projections, a relative poverty rate of 48% in 2012 implies that 2.8 billion people are poor of whom 1.1 billion are absolutely poor.

18
funds would be insufficient to cover government disaster liabilities caused by the event. Table 1 lists data sources employed for our estimation.

Table 4: Data used in the assessment

|**Variable**|**Data source**|
|---|---|
|Disaster losses (USD)|EMDAT, Munich Re databases|
|Risk estimates|1. Cummins and Mahul (2009) based on various sources.<br>2. IIASA estimates|
|Capital stock data|Sanderson and Striessnig (2009)|
|Socioeconomic data|World Bank, 2009|

The disaster loss sample is based on information from two databases and was compiled by Okuyama (2009) with the threshold for a large event defined arbitrarily by a loss exceeding 1 percent of GDP.<sup>8</sup> One database is the open-source EMDAT disaster database (CRED, 2009) maintained by the Centre for Research on the Epidemiology of Disasters at the Université Catholique de Louvain. The other database is the proprietary Munich Re NatCat Service database, which mainly serves to inform insurance and reinsurance pricing. Probabilistic risk estimates are based on two sources: (i) a recent report by Cummins and Mahul (2009), who report such estimates from various sources for a number of countries, (ii) IIASA estimates of risk using extreme value statistics. Capital stock data were used from a new global database also generated IIASA by Sanderson and Striessnig (2009). Finally, other socioeconomic data were taken from the World Development Indicators (World Bank, 2009).

> 8 In order to define the “event set” the threshold of stock losses is set as a share (1%) of flow effects (GDP). It would have been more systematic to define an asset based threshold, yet we responded to the larger intuitive appeal of using GDP as a denominator.

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Table 3: Impact on Fishing Density and Prevalence: Using Satellite’s Boat Detection

||**Boat dete**|**ction den**|**sity (mod**|**ifed log)**|**Boat **|**detection **|**probability**|**(%)**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|treat X post|-0.239***|-0.270***|-0.134**|-0.223***|-0.0871***|-0.0768***|-0.0473**|-0.0594***|
||(0.0578)|(0.0409)|(0.0477)|(0.0341)|(0.0225)|(0.0158)|(0.0178)|(0.0144)|
|Observations|219,984|219,984|147,360|147,360|219,984|219,984|147,360|147,360|
|R-squared|0.117|0.289|0.103|0.283|0.093|0.211|0.087|0.207|
|Grid (10miSq) FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|monthXyear FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|provinceXmonth FE|No|Yes|No|Yes|No|Yes|No|Yes|
|Control Groups|All Other|Provinces|Rest|ricted|All Other|Provinces|Rest|ricted|

Standard errors in parentheses, *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Note: this table shows the impact of Formosa on fishing activity in the fishing-restricted region (i.e. 20nm near-shore of Formosa-affected provinces), using VIIRS’ monthly-aggregate boat detection data. Each observation is a grid-month. Sample includes all monthly observations between 2013 and 2016. The grid size is 10-mile-square. A grid is considered near-shore when it is located less than 20 nautical miles away from the coast line. The reported outcome variables include log-transformed monthly-aggregate boat counts in each grid (columns 1-4), and 0/1 probability that the grid was detected with at least a boat in that month (columns 5-8). The control group ”All Other Provinces” refers to all coastal provinces in the country except for the four Formosa-affected central provinces (Ha Tinh, Quang Binh, Quang Tri, and Hue). The control group ”Restricted” refers to all coastal provinces located south of Phu Yen (i.e. distant from the Formosa-affected region).
suggestions of customers (58.8%). These innovations are not significant or breakthrough in nature but rather incremental and minor. With this type of innovation, the supplier-producer-customer linkage is more important for technology learning than is the linkage with public institutions such as R&D institutes.

If the small firms tend to have innovation ideas from their own activities and customer suggestions, big firms like state corporations under key ministries seem to have a more diversified source of ideas for innovation. Public institutions such as R&D institutes and tech-info centers play a limited role in the learning activities of these firms. Instead, cooperation with domestic, foreign and FDI partners are considered important channels to address problems that firms face in the innovation process. The aforementioned innovation survey found that 71% of SMEs and 93% of non-SMEs in the mechanics sector reported having relationships with foreign/FDI firms. This means that large corporations usually have more resources for innovation and R&D and a larger window for learning than SMEs have.

Many obstacles and disincentives prevent or discourage SMEs from developing innovation capabilities, and the limited amount of innovation that does take place in SMEs is concentrated in process development and quality control. Firms do not have sufficient resources for long-term investments, including recruitment of highly skilled labor and equipment to innovate. Technical services are unavailable to small firms, which typically lack information on available technologies. Market barriers are also high, as SMEs are unaware of product requirements and distribution channels. Finally, "SMEs have a hard time obtaining credit from banks due to a lack of collateral assets and because of the complicated 8
# Figure 4. EAP: Regional actual and potential output growth

# A. GDP growth

<!-- Start of picture text --> Percent<br>EAP Median<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>2000-21 2000-10 2011-21 2022-23<br><!-- End of picture text --> C. Contribution of potential growth and business cycle to actual growth <!-- Start of picture text --> Percent<br>12 Growth Potential Cycle<br>9<br>6<br>3<br>0<br>China EAP excl. China<br>E. Regional potential growth by different<br>estimates<br>Percent<br>2000-10 2011-21<br>10<br>8<br>6<br>4<br>2<br>0<br>PF MVF UVF Exp.<br>2000-10 Contribution 2011-21 2000-10 Contribution 2011-21<br><!-- End of picture text --> E. Regional potential growth by different estimates <!-- Start of picture text --> B. Potential output growth<br>Percent EAP EAP excl. China 2000-21<br>8<br>6<br>4<br>2<br>0<br>2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text -->

# D. Potential growth estimates

<!-- Start of picture text --> Percent<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2000-10 2011-21 2000-10 2011-21<br>China EAP excl. China<br><!-- End of picture text --> F. China’s potential growth by different estimates <!-- Start of picture text --> Percent<br>2000-10 2011-21<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>PF MVF UVF Exp.<br><!-- End of picture text --> Sources: International Monetary Fund; Penn World Tables; UN Population Prospects; World Bank, World Development Indicators database; Note: EAP = East Asia and Pacific. Shaded areas indicate forecast. GDP weighted averages (using average real U.S. dollar GDP at average 2010-19 prices and market exchange rates). Period averages. A. Markers show median GDP-weighted averages of the six EMDE regions; orange whiskers show minimummaximum range.

B.C. Potential growth estimates based on production function approach. Sample includes six EAP economies (China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand).

C. Blue bars denote average actual growth over each ten-year period. Red bars denote contribution of potential growth to change in actual growth between the two five-year periods; orange bars denote contribution of cyclical growth.

D. Orange whiskers show min-max range of potential growth estimates in the four sources listed above. “EAP ex. China” includes Indonesia, Mongolia, Philippines, and Thailand.
# **Table 5: Gender differentiated impact of RCBP**

|**Panel A: Household**|**Income, Assets, a**<br>|**nd Consumption**<br>|||
|---|---|---|---|---|
||Round 1 x<br>RCBP|Round 1 x RCBP x<br>Femalehead|Round 2 x<br>RCBP|Round 2 x RCBP x<br>Femalehead|
|Food consumption from market purchases (birr)|.059|19.5*|-20.1*|23.3|
||(9.29)|(10.5)|(11.2)|(15.5)|
|Total value of assets (birr)|243|-403**|341|-27.4|
||(162)|(199)|(275)|(234)|
|Non-food consumption (birr)|150|62.0|97.1|628|
||(292)|(306)|(531)|(593)|
|Nb of household members who contribute to income|.006|-.085|.381*|-.088|
||(.152)|(.167)|(.192)|(.191)|
|Nb of household members who work on household farm|-.393***|.026|.123|-.076|
||(.133)|(.172)|(.103)|(.142)|
|Total plot area (ha)|-.102|-.124|.360**|.095|
||(.359)|(.227)|(.173)|(.255)|
|Livestock ownership (=1)|-.010|-.020|-.023|.008|
||(.012)|(.045)|(.021)|(.048)|
|Total value of livestock owned (birr)|1,410|-1,929|5,482|-5,860|
||(1,332)|(1,485)|(6,205)|(6,483)|
|Total agricultural sales (birr)|1,011|-522|954|-52.9|
||(692)|(773)|(684)|(791)|
|**Panel B: Agricultura**|**l outcomes and hi**|**gh-value farming**|||
||Round 1 x<br>RCBP|Round 1 x RCBP x<br>Femalehead|Round 2 x<br>RCBP|Round 2 x RCBP x<br>Femalehead|
|Productivity (harvest value/ha)|253|-249|1,710|-1,850|
||(820)|(903)|(1,570)|(1,684)|
|Tried new agricultural activities in the past year (=1, head)|.069|-.046|-.021|.053|
||(.044)|(.064)|(.042)|(.066)|
|Intercropping (=1)|-.110***|.094*|-.044|-.067|
||(.038)|(.049)|(.035)|(.044)|
|Irrigation (=1)|<br>.038|<br>-.032|<br>.060**|<br>-.066*|
||(.043)|(.038)|(.027)|(.035)|
|Crops with improved seeds (number of crops)|.146|-.276*|-.091|.091|
||(.123)|(.164)|(.104)|(.156)|

20
**_Table 6: The relationship between maternity leave entitlement and the hiring of married women_**

|**Y = Married Woman Hired Over Last Two Years (Y/N)**|**(1)**|**(2)**|
|---|---|---|
|Female permanent employees entitled maternity leave (Y/N)|0.435**|0.372**|
||(0.178)|(0.185)|
|Log of total female employment||0.231*|
|||(0.134)|
|Female top manager (Y/N)|0.045|-0.012|
||(0.143)|(0.138)|
|Log of age of firm|0.042|0.066|
||(0.124)|(0.122)|
|Log of size|0.076|-0.061|
||(0.064)|(0.091)|
|Part of a larger firm (Y/N)|-0.396***|-0.352**|
||(0.142)|(0.147)|
|Offers formal training (Y/N)|0.055|0.064|
||(0.161)|(0.160)|
|Top manager experience in sector (years)|-0.001|-0.005|
||(0.009)|(0.009)|
|Exports 10% or more of sales (Y/N)|-0.330**|-0.310**|
||(0.154)|(0.151)|
|Foreign ownership (Y/N)|0.012|0.046|
||(0.319)|(0.303)|
|Has checking or savings account (Y/N)|0.138|0.176|
||(0.189)|(0.186)|
|Has a line of credit or loan (Y/N)|-0.087|-0.129|
||(0.252)|(0.180)|
|Experienced losses due to crime (Y/N)|0.252**|0.210*|
||(0.118)|(0.123)|
|Constant|-0.130|0.082|
||(0.593)|(0.572)|
|Number of observations|154|153|
|R2<br>Notes: Only firms that engaged in hiring activity in the past fiscal<br>sector (ISIC 2 digit) fixed effects as well as location (within countr<br>parentheses;***p<0.01,**p<0.05,*p<0.1. Sampling weights ar|0.433<br>year are included in the sa<br>y) fixed effects. Standard<br>e used throughout.|0.459<br>mple. Both models include<br>errors are presented in|

The results presented above provided strong suggestive evidence that discriminatory behavior by firms in the DRC prevent women from achieving their full economic potential. This may be an important part of the reason behind low participation of women in formal employment in the DRC. The results suggest that the impact of task-specific restrictions and restrictions on night-time work are particularly strong for firms operating in the manufacturing and construction sectors, which may help to explain the low participation of women in these activities -- in the DRC at the time of the survey less than 4 percent of female employment 19
# **4.4 DECOMPOSING CHANGES IN UNFAIRNESS OVER TIME**

One of the broad takeaways from the regression results is that both the aggregate inequality trends and the individual’s characteristics are associated with unfairness perceptions. A natural follow-up question is to ask what factors explain to a greater extent the reduction in the unfairness beliefs over the last decade: the observable characteristics of the individuals or the aggregate inequality trends. To analyze this point, we perform a basic OaxacaBlinder decomposition, taking 2002 and 2013 as the two ‘groups’ to be compared (see Appendix C for further detail on the Oaxaca-Blinder decomposition). The covariables included in the decomposition are analog to those of Column (4) in Table 2. The results are summarized in Figure 8.

_Figure 8. Oaxaca-Blinder decomposition of unfairness perceptions in LA, 2002-2013_ <!-- Start of picture text --> 90<br>86.9 4.5<br>Change in Unexplained<br>unfairness +9.5 Gini Coefficient<br>80 9.3<br>perceptions<br>2002-13 Explained -0.1 Composition<br>Effects<br>73.0 73.0<br>70<br>60<br>50<br>2002 2013 Change 2002-13<br>% Unfair or Very Unfair<br><!-- End of picture text --> Note: This figure presents the estimates of the Oaxaca-Blinder decomposition. The dependent variable is a dummy that indicates whether the individual believes income distribution if unfair or not, and the regressors include the Gini coefficient, age, age squared, and dummy variables for: civil status, gender, literacy, maximum educational attainment, labor force participation and unemployment status. Results were calculated pooling data for 18 LA countries. The ‘explained’ part of the results refers to the endowment effects (changes in the value of the covariables), while the ‘unexplained’ refers to changes in the coefficients and the interaction terms.

During the 2002-13 period, the share of the population perceiving the distribution as unfair decreased 13.9 percentage points, from 86.9% to 73.0%.<sup>20</sup> The decomposition results suggest that a third of such change (4.5 percentage points) cannot be explained by changes

> 20 These figures are slightly different from those presented in the previous section due to some observations having missing values in the covariables relevant for the decomposition.

21