rafmacalaba/datause-encoder-data huggingface dataset:
|_Table 2 B_|_alancing ori_<br>|_ginal control a_<br>|_nd treatmen_|_tgroup at base_|_line_<br>||
|---|---|---|---|---|---|---|
||Contr|ol group|ITT|group|Difference|p‐value|
||<br>N|<br>Mean|<br>N|<br>Mean|in means||
||Individu<br>|<br>al and househ<br>|old characte<br>|<br>ristics<br>|<br>||
|Age|1,148|27.417|1,144|27.683|0.265|0.2001|
|Gender|1,148|0.602|1,144|0.611|0.009|0.6559|
|Married|1,148|0.666|1,143|0.649|‐0.016|0.4103|
|Employment status|1,148|0.612|1,144|0.624|0.012|0.5626|
|Business ownership|1,148|0.642|1,144|0.659|0.017|0.3907|
|Consumption food|1,148|5.330|1,144|5.400|0.070|0.1740|
|Consumption nonfood|1,148|2.418|1,144|2.429|0.010|0.8547|
|Formal bank account|1,148|0.373|1,144|0.369|‐0.004|0.8452|
|(Log) amount formal loans|1,139|‐0.332|1,141|‐0.367|‐0.036|0.6339|
|(Log) amount informal loans|1,134|‐1.329|1,124|‐1.225|0.104|0.4432|
|Education<br>No education|1,148|0.191|1,144|0.206|0.016|0.3517|
|level<br>Some Primary|1,148|0.315|1,144|0.330|0.015|0.4401|
|Some|1,148|0.404|1,144|0.373|‐0.031|0.1289|
|Secondary|||||||
|Some University<br>or Higher|1,148|0.090|1,144|0.090|0.000|0.9791|
|Literacy<br>No English|1,148|0.247|1,144|0.263|0.016|0.3882|
|Some English|1,148|0.273|1,144|0.295|0.022|0.2443|
|Good English|1,148|0.480|1,144|0.442|‐0.038*|0.0706|
|Numeracy<br>Low|1,148|0.238|1,144|0.247|0.010|0.5931|
|Medium|1,148|0.160|1,144|0.198|0.037**|0.0199|
|High|1,148|0.602|1,144|0.555|‐0.047**|0.0231|
|Household size|1,148|7.310|1,144|7.260|‐0.050|0.7257|
|Number of children|1,148|3.107|1,144|3.241|0.134|0.1635|
|Number of elderly|1,148|0.109|1,144|0.087|‐0.021|0.1292|
|Number of rooms|1,148|3.180|1,144|3.087|‐0.093|0.1935|
|Number of buildings|1,148|3.676|1,144|3.538|‐0.138*|0.0830|
|(Log) distance to KCB branch|1,130|2.395|1,126|2.396|0.001|0.9871|
|Conflict exposure 2011‐2014|1,148|0.000|1,144|0.084|0.084|0.3283|
|(300km buffer)|||||||
|Conflict exposure 2015‐2017<br>(300km buffer)|1,148|0.000|1,144|0.128|0.128*|0.0953|
|||State at b|aseline||||
|Central Equatoria|1,148|0.169|1,144|0.167|‐0.002|0.8966|
|Eastern Equatoria|1,148|0.160|1,144|0.152|‐0.008|0.5898|
|<br>Lakes|<br>1,148|0.158|<br>1,144|0.159|0.001|0.9256|
|Northern Bahr El Ghazal|1,148|0.170|1,144|0.176|0.006|0.7118|
|Western Bahr El Ghazal|1,148|0.172|1,144|0.171|‐0.000|0.9861|
|Western Equatoria|1,148|0.172|1,144|0.175|0.003|0.8386|

_Note: All indicators were measured at baseline. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level._ 25
- **Rosenbaum, Paul R. and Donald B. Rubin** , “The central role of the propensity score in observational studies for causal effects,” _Biometrika_ , 1983, _70_ (1), 41–55.

- **Sharma, Chandan** , “Imported Intermediate Inputs, R&D, and Productivity at Firm Level: Evidence from Indian Manufacturing Industries,” _The International Trade Journal_ , March 2014, _28_ (3), 246–263.

- **and Ritesh Kumar Mishra** , “Does export and productivity growth linkage exist? Evidence

- from the Indian manufacturing industry,” _International Review of Applied Economics_ , 2011, _25_ (6), 633–652.

- **Smeets, Valerie and Frederic Warzynski** , “Estimating productivity with multi-product firms, pricing heterogeneity and the role of international trade,” _Journal of International Economics_ , 2013, _90_ (2), 237 – 244.

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- **Topalova, Petia and Amit Khandelwal** , “Trade Liberalization and Firm Productivity: The Case of India,” _The Review of Economics and Statistics_ , August 2011, _93_ (3), 995–1009.

31
The negative effects of population and urban population percentage on flood magnitude in the all-country sample persist when high-fatality floods are excluded (column 5 in Table 8). In addition, population now has a significant, positive effect in both fatality models, similar to its effect in the developed country sample.

The final columns of Tables 5-8 show results for models that include the Gini coefficient. Including this variable cuts the sample by more than half. Even without including the Gini (results not shown, but available upon request), models restricted to this sample show much less evidence of a significant effect of income or governance than corresponding models estimated using data from all countries or just developing or developed countries. So, the lack of significance of income and governance in the models in the final columns is due to a sample-selection effect, not the addition of the Gini. The notable result in these models is that the Gini has a positive and significant effect on fatalities (both without and with the control for magnitude) and magnitude. Increased inequality thus tends to increase fatalities both directly and indirectly.

# **6. Discussion and Conclusions**

Some of our results are consistent with ones previously reported. The robust, positive impact of flood magnitude on the number of deaths echoes findings from studies on earthquakes (Kahn 2005, Anbarci et al. 2005, Keefer et al. 2010), as does the positive impact of inequality on fatalities (Anbarci et al. 2005). Previous studies report that larger national or regional populations are positively associated with fatalities from various types of natural disasters (Kahn 2005, Anbarci et a. 2005, Kellenberg and Mobarak 2008, Keefer et al. 2010), similar to our finding that, at least in developed countries, larger populations 22
-64 -

# Arinex D Table 12. Comparison of domestic staple food prices to imported rice and wheat prices

|||As a f|raction of inte<br>rice price|rnational|As of|fraction of in<br>wheat pri|ternational<br>ce|
|---|---|---|---|---|---|---|---|
|Country|Crop|1970-72|1980-82|1985-87|1970-72|1980-82|1985-87|
|Burkina Faso|sorghum|0.58|0.79||1.31|2.08||
||maize|0.58|0.79||1.31|2.08||
|Cameroon|mai7e|0.99|1.08||2.23|2.86||
||cassava|0.36|0.33||0.82|0.88||
||plantains|0.53|0.69||1.19|1.83||
|Cote d'Ivoire|maize|1.25|1.29||2.80|3.43||
||cassava|0.93|1.25||2.09|3.32||
||yams|0.92|1.19||2.07|3.15||
|Ghana|maizz|1.02|6.88|1.92|2.28|18.26|3.52|
||cassava|0.42|3.43|1.10|0.95|9.09|2.01|
|Kenya|maize|0.56|0.70||1.27|1.85||
|Malawi|maize|0.32|0.27|0.41|0.72|0.71|0.75|
|Mali|sorghum|_0.75_|0.86|1.22|1.69|2.29|2.24|
|Niger|sorghum|0.75|1.32||1.69|3.50||
|Togo|maize|0.72|0.80||1.61|2.12||
||sorghum|0.80|0.64||1.79|1.69||
|Senegal|sorghum|1.00|0.74|1.54|2.25|1.97|2.83|
|Zambia|maize|0.60|0.60||1.35|1.60||
|Zimbabwe|maize|0.47|0.43|0.65|1.06|1.15|1.18|
- 20 -

groups (2.9 percent). The relative differences in fees charged by different types of MPFs have increased over time, reflecting the increasing weight of the asset management fee.

**Table 19: Total Fees as a Share of Average Assets (%), 1998 – 2004**

|**Fund**|**Sponsor**|**1998**<br>**(%)**|**1999**<br>**(%)**|**2000**<br>**(%)**|**2001**<br>**(%)**|**2002**<br>**(%)**|**2003**<br>**(%)**|**2004**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|
|Dimenzió|EMP|12.5|5.8|3.6|2.4|1.9|2.0|2.0|
|HONVÉD|EMP|11.8|7.3|4.8|4.2|3.4|2.9|2.6|
|Postás|EMP|12.7|5.3|3.2|2.3|1.8|1.7|1.6|
|Vasustas|EMP|17.7|8.6|4.8|3.4|2.5|2.2|2.1|
|VIT|EMP|11.4|4.9|2.9|2.1|1.9|2.0|2.0|
|AEGON|FIN|15.7|8.4|4.8|4.1|3.1|3.2|3.0|
|AH|FIN|11.5|6.1|4.6|3.9|3.5|3.0|3.1|
|Budapest|FIN|9.3|5.0|3.7|3.2|3.0|3.0|2.8|
|CS|FIN|11.4|6.0|4.0|3.3|3.1|3.5|2.9|
|ERSTE|FIN|11.0|6.4|4.4|3.3|3.1|3.8|3.9|
|ING|FIN|13.6|6.9|4.4|3.7|3.2|3.1|2.9|
|MKB|FIN|12.4|5.3|4.0|2.6|2.0|2.5|2.2|
|OTP|FIN|11.1|7.0|4.9|4.2|3.2|3.3|2.9|
|Quaestor|FIN|10.2|4.5|4.2|3.1|2.5|3.5|3.5|
|UNIQA|FIN||5.9|5.4|4.8|3.8|3.3|2.9|
|Aryankor|INDEP|11.0|5.8|3.8|3.6|3.4|2.9|2.7|
|Életút|INDEP|9.0|5.7|3.7|2.4|1.8|1.9|1.8|
|Évgyűrűk|INDEP|11.3|8.7|5.1|4.3|3.3|3.1|2.6|
|Weighted Average|MKT|12.7|7.0|4.5|3.8|3.2|3.1|2.9|
|Weighted Average|EMP|12.5|6.1|3.8|3.0|2.4|2.3|2.1|
|Weighted Average|FIN|12.9|7.0|4.6|3.9|3.2|3.2|2.9|
|Weighted Average|INDEP|11.1|7.6|4.6|3.9|3.3|3.0|2.6|

Source: HFSA raw data 44. Total fees in the Hungarian second pillar still compare favorably with most other systems with the same period of implementation, especially when expressed as a share of contributions. As shown in Figure 5, the ratio of total fees to contributions has increased but is still one of the lowest, with the exception of Bolivia.<sup>14</sup> The ratio of total fees to assets is roughly equal to the average of other reforming countries, as shown in Figure 6. The difference between the two results reflects the mixed fee structure in Hungary, the low volume of assets, and the increasing dominance of the asset management fee. Fees in most other countries are defined as a share of contributions, and this ratio does not change dramatically over time. This implies a sharp decline in fees expressed as a share of assets over time. By contrast, the current mixed fee structure in Hungary implies rising fees as a share of contributions, and a ratio of fee to assets that may not decline as much as the other countries in the long-run.

> 14 The comparison between Hungary and other Latin America countries should be controlled for differences in factors like coverage, contribution rates, and structure of the labor market.
# **Using the Oaxaca-Blinder Decomposition Technique to Analyze Learning Outcomes Changes over Time:**

**An Application to Indonesia’s Results in PISA Mathematics**<sup></sup>

## **Felipe Barrera-Osorio**<sup>**1**</sup>

**Vicente Garcia-Moreno**<sup>**2**</sup>

**Harry Anthony Patrinos**<sup>**3**</sup>

**Emilio Porta**<sup>**4**</sup>

JEL codes: J15, I20 Keywords: PISA, education, test scores, Indonesia

>  Address correspondence to Harry Anthony Patrinos (hpatrinos@worldbank.org).  The comments of Dandan Chen and Menno Pradhan are gratefully appreciated.  The views expressed here are those of the authors and should not be addressed to the World Bank Group.

> 1 World Bank

> 2 World Bank

> 3 Teachers College, Columbia University

> 4 World Bank
_Figure 6.9: Changes in labor supply/demand for rural uneducated_ <!-- Start of picture text --> Change in labor supply/demand (as % change from base case)- for<br>(Uneducated)-Rural<br>2018 2022 2026 2030<br>(5.0 USD/ton) (13.3 USD/ton) (21.7 USD/ton) (30.0 USD/ton)<br>0.1%<br>0.0%<br>-0.1%<br>-0.1%<br>-0.2%<br>-0.2%<br>-0.3%<br>-0.3%<br>-0.4%<br>-0.4%<br>-0.5%<br>DTAX STAX SAVING CORPORATE TRANS<br>0.0% 0.0% 0.0% 0.0%<br>0.0% 0.0% 0.0%<br>0.0% 0.0%<br>-0.1%<br>-0.1% -0.1% -0.1% -0.1%<br>% change from base -0.2% -0.2% -0.2%<br>-0.4% -0.4% -0.4%<br><!-- End of picture text --> The tax has differing impacts on the consumption of the lowest quintile households in urban and rural areas. In urban areas (Fig. 6.10), the main source of income for poor households is employment income (wages). As the sectors that poor households work in (services and industry) are affected more by the carbon tax, their income decreases due to either increased unemployment or a decrease in wage. The implication for economic welfare depends on the scenario: revenue recycling under STAX and CORPOR leads to smaller decline in consumption than others because there is less impact on the economy. TRANS, on the other hand, limits the decline in consumption by transferring resources to poorer households.

In rural areas, the ‘almost non-existent’ effect of the carbon tax on agriculture means that employment is not really affected. In addition, the tax slightly increases the return to land owned by rural households. The combination of these two effects implies that the impact of the carbon tax on the consumption of poor rural households is minimal.<sup>7</sup>

> 7 Ex ante one would expect that the poor will fare better under the transfer scenario. However, the result shows the opposite. There are a couple of reasons for this. First, the  transfer scenario does not restrict transfers to poor households. Since a large share of transfers are made to urban /richer households, it limits the extent to which the aggregate transfer can reduce poverty and inequality. Second, the slowdown in economic activity under the transfer scenario introduces unemployment/ wage reduction that affects welfare directly. Another way to understand the result under the transfer scenario is that one needs to restrict the transfer to poor households in order to achieve a meaningful reduction in poverty and inequality.

21
<!-- Start of picture text --> So<br>So<br>o<br>o<br>vr<br>ae<br>7” - “ °<br>- -<br>=] = a<br>£ Poa on 7_<br>= —" - i_ a—"<br>nD S ra - a ~ _ ~<br>5 fo a ow<br>S _TM ee<br>2 “ “a . — ~<br>=} _ a<br>So a<br>S Po<br>-3 -2 -1 0 1. 2 3 - = 6 7 8<br>time since program<br>------- pre-treatment trend, boys © — — - pre-treatment trend, girls<br>——+—_ enrollment, boys — — — enrollment, girls<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>w+ — as<br>=a<br>s5 oer<br>a a7woe /<br>+ - “" . °<br>ueere<br>~<br>- cal-<br>“ ~<br>- y<br>w<br>-<br>-3 -2 -1 0 1 2 3 - 5 6 7 8<br>time since program<br>------- pre-treatment trend ©—+— female %<br><!-- End of picture text -->
# C O N T E N T S

|Table o|f Contents|ii|
|---|---|---|
|List of|Figures|iv|
|List of|Tables|iv|
|Acrony|ms|v|
|1<br>inv|esting across borders: do fdi-specific regulations mat-||
|ter|?|1|
|1.1|Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|1|
|1.2|Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|4|
|1.3|Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|8|
|1.4|Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|16|
||1.4.1<br>Investing Across Sectors<br>. . . . . . . . . . . . . . . . . . . . .|19|
||1.4.2<br>Arbitrating Commercial Disputes<br>. . . . . . . . . . . . . . . .|20|
||1.4.3<br>Starting a Foreign Business . . . . . . . . . . . . . . . . . . . .|22|
||1.4.4<br>Bilateral Investment Treaty<br>. . . . . . . . . . . . . . . . . . . .|23|
||1.4.5<br>Gravity and Other Variables<br>. . . . . . . . . . . . . . . . . . .|23|
|1.5|Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|24|
|1.6|Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|27|
||1.6.1<br>Country Selection Bias<br>. . . . . . . . . . . . . . . . . . . . . .|34|
||1.6.2<br>Firm Selection Bias<br>. . . . . . . . . . . . . . . . . . . . . . . .|38|
||1.6.3<br>Endogeneity Bias . . . . . . . . . . . . . . . . . . . . . . . . . .|39|
|1.7|Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|41|
|1.8|Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|43|
|App|endix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|45|
|1.A|Deriving the Gravity Equation for FDI with Heterogeneous Firms<br>.|45|
|1.B|Firm Productivity, Exports, and Horizontal FDI<br>. . . . . . . . . . . .|51|
|1.C|Additional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|58|

iii
|**Table 2: Interaction**|**with Rule of L**|**aw(log odds r**|**atios)**||
|---|---|---|---|---|
|Dependent variable: Quality Certificate|**(1)**|**(2)**|**(3)**|**(4)**|
|Y:1 N:0|||||
|Informal Competition (CY avg.) *Rule of Law|-4.737***|-4.888***|-5.828***|-5.133***|
||(0.673)|(0.812)|(0.903)|(1.166)|
|Informal Competition (CY avg.)|-0.063|0.668|0.255|7.055|
||(0.438)|(0.700)|(0.559)|(13.491)|
|Rule of Law|3.751***|4.002***|3.409***|3.166***|
||(0.616)|(0.940)|(0.944)|(0.998)|
|GDP per capita (logs)||-1.125|-0.335|0.313|
|||(1.495)|(1.364)|(1.902)|
|Labor Productivity (logs)||0.474***|0.407***|0.400***|
|||(0.118)|(0.133)|(0.127)|
|Number of workers (logs)||1.185***|0.998***|0.251|
|||(0.188)|(0.186)|(0.924)|
|Age of firm (logs)||0.290**|0.356**|0.346**|
|||(0.140)|(0.140)|(0.136)|
|Manager experience (logs)||-0.301*|-0.260|-0.256|
|||(0.176)|(0.183)|(0.185)|
|Herfindahl index|||-0.287|-0.323|
||||(1.029)|(1.084)|
|Exports (proportion of sales)|||0.610|0.649|
||||(0.400)|(0.407)|
|Foreign ownership (%)|||0.014***|0.013***|
||||(0.004)|(0.004)|
|Women top manager Y:1 N:0|||-0.267|-0.248|
||||(0.306)|(0.306)|
|Annual employment growth (%)|||-0.007|-0.007|
||||(0.006)|(0.006)|
|New Product Y:1 N:0|||0.054|0.052|
||||(0.204)|(0.203)|
|Line of Credit Y:1 N:0|||0.001|0.001|
||||(0.002)|(0.002)|
|Number of power outages (per month)|||-0.019|-0.018|
||||(0.018)|(0.018)|
|Foreign Technology Y:1 N:0|||0.008***|0.008***|
||||(0.002)|(0.002)|
|Website Y:1 N:0|||0.006***|0.006***|
||||(0.002)|(0.002)|
|Total population (logs)|||12.372***|10.620**|
||||(4.342)|(4.958)|

41
# **4 Mechanisms**

So what are the potential mechanisms via which the threshold scheme did not yield higher survival rates than the linear group payment scheme? In view of the theory presented in section 2.1 and in Appendix A, we hypothesize two. First, survival rates were measured at endline, and the survey questions aimed at gauging treatment impacts on the intermediate measures of cooperation analyzed above asked for an endline evaluation of the process of cooperation. The reported outcomes may, however, hide important differences in the dynamics of cooperation over time – consistent with our theory’s prediction that the higher thresholds are less likely to be equilibria. Second, coordination in the threshold group payment schemes becomes more difficult when there is uncertainty regarding the probability of accidentally crossing the target threshold. In the following two subsections we aim to test the relevance of each of the two.

## **4.1 The dynamics of cooperation**

At endline, the intermediate measures of cooperation were higher in the threshold group payment treatment, while the ultimate measures of cooperation were lower. These opposing outcomes may be reconciled if cooperation improved over time, but that this improvement materialized too late in the process. To probe this possible mechanism, we asked participants in the endline survey how they evaluated the (dynamics of the) amount of cooperation in their maintenance group; answers are summarized in Table 3. Overall, we do not find that the distributions in answers differ significantly between the two treatments ( _p_ = 0.275 according to a _χ_<sup>2</sup> test). About 69% of the subjects in the linear group payment scheme reported that the cooperation was intensive from the start and that it remained very good over the entire nine-month period; about 2% of them stated that there was hardly any cooperation. In the threshold group payment scheme these percentages were, respectively, 65% and 1.5%. However, we also find that, if anything, cooperation was more likely to increase in the threshold group payment scheme. Focusing on those who reported a change in cooperation over time, we find, however, that the share of respondents stating that their group’s cooperation improved over time is significantly larger in the threshold group payment treatment than in the linear group payment treatment ( _p_ = 0.080 using 19
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46
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