rafmacalaba/datause-encoder-data huggingface dataset:
requirements associated with the mitigation measures.

**_Table 3: Ordered logistic regression for the number of different green investments (average marginal effects)_**

|Variables|No green<br>investment|One|Two to four|Five or more|
|---|---|---|---|---|
|||_Panel A: Ov_<br>|_erall sample_<br>||
|Credit constraints|0.031***<br>(0.006)|0.004***<br>(0.001)|-0.012***<br>(0.002)|-0.023***<br>(0.004)|
|Green management|-0.196***<br>(0.004)|-0.024***<br>(0.001)|0.076***<br>(0.002)|0.144***<br>(0.002)|
||_Pa_<br>|_nel B: Allow all coeff _<br>|_icients to vary by regi_<br>|_on_<br>|
|Credit constraints ECA|0.035***<br>(0.006)|0.006***<br>(0.001)|-0.012***<br>(0.002)|-0.028***<br>(0.005)|
|Credit constraints MENA|0.009|-0.001|-0.005|-0.003|
||(0.017)|(0.002)|(0.009)|(0.006)|
|Green management ECA|-0.182***|-0.035***|0.061***|0.156***|
||(0.004)|(0.001)|(0.002)|(0.003)|
|Green management MENA|-0.263***|0.027***|0.142***|0.093***|
||(0.011)|(0.003)|(0.006)|(0.004)|
|Observations||17,|902||
|Clusters||3,0|22||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the number of green investments, based on ordered logistic regression with the categories based on the number of green investments as a dependent variable. Regression controls for the locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Locality-clustered standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

In MENA, green management quality appears to be more important for the firms’ decision to make green investments (and the number of different green investments) than credit constraints; the estimates are consistent with our hypothesis 2, but not with hypothesis 4. Estimates in Panel B indicate that compared with firms in ECA, firms in MENA appear to be less sensitive to credit constraints when deciding on the number of different green investments to make – none of the estimated average marginal effects is statistically significant. In both regions, the number of green investments firms make seems to depend more on the quality of green management practices. In the ECA region, the pattern looks similar to the one in Panel A. A unit increase in the quality of green management practices increases in magnitude with the number of different green investments. It is associated with an 18.2 and 3.5 percentage points decrease in the likelihood of making no or one green investments, respectively, and a 6.1 and 15.6 23
reporting the full range of coefficients from the Monte Carlo study (minimum to maximum) in table 3, we take each single iteration into consideration and thus account not only for random measurement error, which would simply render estimates less efficient, but also for some systematic measurement error as well. Results are fully robust. The same is true if we impose larger measurement error on stronger earthquakes and lower measurement error on smaller earthquakes (results not shown), rather than the same proportional extent of measurement error on all quakes as in table 3. This suggests that potential measurement error is unlikely to have a significant impact on our results.

# **7. Conclusion**

The politics of disaster mortality prevention exerts a large influence on actual disaster mortality rates. We have argued here that governments face very different incentives to implement and enforce an effective system of quake-proof construction regulations. One main influence on government policies comes from earthquake propensity. If earthquakes tend to be rare and – if they do occur – tend to be moderate, governments are unlikely to implement an effective earthquake mortality prevention system, because this regulation is costly in the absence of major earthquakes. In the rare event of a major earthquake, however, the lack of ex ante incentives to implement preventive measures leads to higher actual mortality.

Even if earthquakes are relatively frequent and potentially strong, governments may opt against implementation of effective earthquake mortality prevention regulation when the country is relatively poor. Under this condition, the ex ante opportunity costs of such regulation can be prohibitively high if the government can save many more expected lives at lower cost in other areas of social or health spending.

Finally, political institutions influence governments’ response to earthquake propensity. Political institutions not only exert an influence on the extent to which governments are willing to provide, and can credibly commit themselves to providing, the public good of 31
Policy Research Working Paper 10607

# **Abstract**

Violent conflict between nomadic herders and settled— mostly agricultural—communities in Nigeria occurs as both groups clash over the use of land and resources, in part, due to a changing climate. This paper uses panel data from 2010 through 2019 to study the labor responses of individuals to exposure to herder-related violence during the post-planting and post-harvest seasons. Specifically, it considers a “shadow of violence” channel, where recent exposure to a violent event alters labor-related responses to a subsequent event. Results find that in the post-planting season, exposure to a herder-related violent event leads to an increase in informal work for both men and women, a decrease in agricultural work for men, and an increase in total hours worked for women among households that have previously been exposed to herder-related violence in the preceding six months. The paper also considers two other specific forms for a “shadow of violence” channel—namely, raised tensions over open-grazing bans enacted in 2016 and 2017 within three states and a drastic peak in violence in the first half of 2018—and find similar results. Lastly, findings show how household exposure to violence can have so-called knock-on effects. Households exposed to herder-related violence in the previous post-planting season shift consumption and crop selling patterns in the post-harvest season. These findings highlight the gender-specific labor response to violence and document the role of the informal sector as a partial safety net for individuals in the presence of adverse shocks.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dfrancis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Contents (Appendix)**

|**A Data Sources and Variable Defnitions**|**53**|
|---|---|
|A.A _Guerrilla territories_ . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>53|
|A.B _Geospatial variables_ . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>53|
|A.C _Population and Household Census of 2007 (PHC)_ . . . . . . . . . . . . .|. . . . . . . . . .<br>55|
|A.D _Presidential election results_ . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>57|
|**B**<br>**Descriptive Statistics**|**58**|
|**C Maps**|**59**|
|**D Robustness Tests**|**60**|
|D.A Empirical Strategy<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>60|
|D.B Main Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>68|
|D.C Public Goods Provision. . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>72|
|**E**<br>**Qualitative Study**|**95**|
|E.A Sample defnition and recruitment of participants<br>. . . . . . . . . .|. . . . . . . . . .<br>95|
|E.B<br>Instruments<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>95|
|E.C Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>96|
|E.D Main results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>96|

52
# **Appendix Table 2. ECOWAS Countries' Demographic and Social Indicators**

<u>(In units indicated)</u>

||||Population<br>Sh i|t f|A|Illitera<br>i|cy rate<br>t f dlt|
|---|---|---|---|---|---|---|---|
||||are n|ercen o|verage|n perce|n o au|
||Level(in|millions)|<br>total)||<br>annual<br>percent|<br>population|<br>)|
||1974|2004|1974|2004|change|1974|2004|
|Total|111.4|254.5|100.0|100.0|2.8|77|45|
|Benin|3.1|8.2|2.8|3.2|3.3|86|60|
|Burkina Faso|5.8|12.8|5.2|5.0|2.7|92|87|
|Cape Verde|0.3|0.5|0.2|0.2|2.0|57|24|
|Cote d'Ivoire|6.3|17.9|5.7|7.0|3.5|76|52|
|Gambia, The|0.5|1.5|0.5|0.6|3.4|n/a|n/a|
|Ghana|10.0|21.7|9.0|8.5|2.6|65|26|
|Guinea|4.2|9.2|3.7|3.6|2.7|n/a|n/a|
|Guinea-Bissau|0.6|1.5|0.6|0.6|3.0|n/a|n/a|
|Liberia|1.6|3.2|1.4|1.3|2.5|78|44|
|Mali|6.1|13.1|5.4|5.2|2.6|89|81|
|Niger|5.2|13.5|4.6|5.3|3.3|93|83|
|Nigeria|57.3|128.7|51.5|50.6|2.7|75|33|
|Senegal|5.1|11.4|4.6|4.5|2.7|83|61|
|Sierra Leone|2.9|5.3|2.6|2.1|2.1|n/a|n/a|
|Togo|2.4|6.0|2.1|2.4|3.1|74|40|
|Memorandum items:||||||||
|WAEMU countries 1/|34.6|84.4|31.1|33.2|3.0|85|68|
|Non-WAEMU countries 2/|76.8|170.1|68.9|66.8|2.7|74|32|

Sources: International Financial Statistics, World Economic Outlook, World Bank Indicators; and staff estimates.

1/ Benin, Burkina Faso, Cote d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo 2/ Cape Verde, The Gambia, Ghana, Guinea, Liberia, Nigeria, and Sierra Leone 3/ 1996.

4/ 1992.

26
of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste.

# **_4.2.1. The educational impact of the 1999 wave of violence_**

We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007.

Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance.

For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i.e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor).

20
power of parents over staffing decisions, which can be a very important factor in improving learning.

Overall, the implementation of accountability needs improvement and it is rated as _Established -_ (Table 6). The implementation indicators for the functioning of a national system of school and student assessment and for the manuals for interpreting school audits are significantly lower than the indicators of policy intent. The gaps in these indicators suggest problems with key aspects of accountability to parents, especially in the areas of parent information about the meaning of test results, and on the use of results by schools to make modifications to their pedagogical practices.

|**Table 6: Thailand: Policy Intent a**|**nd Policy I**<br>|**mplementation Indic**<br>|**ators of Schoo**|**l Accountability**|
|---|---|---|---|---|
|**Sub Indicator**|**Intent**<br>**Score**|**Implementation**<br>**Score**|**Intent Score**|**Implementation**|
|**5.1 Guidelines for using school**<br>**and student assessments for**<br>**accountability.**ESAuses the<br>guidelines for teacher assessments<br>andpersonnel management.|**2.0**|**3.1**|Emerging<br>|Established<br>|
|**5.2 National or regional systems**<br>**of educational assessments.**ESA<br>autonomy in the interpretation of<br>guidelines maybe inconsistent.|**4.0**|**3.1**|Advanced<br>|Established<br>|
|**5.3 Comparisons of school and**<br>**student performance reports.**<br>Comparisons done at National,<br>regional and municipal levels, and<br>previous years. Schools often publish<br>summaryresults online.|**3.0**|**3.0**|Established<br>|Established<br>|
|**5.4 School Council's authority to**<br>**perform financial audits.**<br>Councils tend to rely on school-level<br>negotiations, even though they can<br>ask for formal audits.|**2.0**|**2.6**|Emerging<br>|Emerging<br>|
|**5.5 Manual for the participation**<br>**of the School Councils in school**<br>**audits.**Manuals exist but personal<br>communications are preferred by<br>Councils.|**4.0**|**2.5**|Advanced<br>|Emerging<br>|
|**Overall**|**3.0**|**2.8**|Established<br>|Emerging<br>|

Source: World Bank 2013 and school survey data. The differences between the intent and implementation scores are statistically significant at the 0.001 level, except for indicator 5.3.

# **4.6 Intent and Implementation Scores by Type of School Sponsorship**

The differences between the law’s intent and what happens on the ground may be due to many factors, but one that needs to be explored first is the type of school sponsorship. High schools in Thailand can be classified by type of institutional sponsor. Most schools are administered by the Office of the Basic Education Commission (OBEC), while others are administered by the Bangkok Metropolitan Administration (BMA), the Bureau of Local Educational Development (BLED), the Commission for Higher Education (CHE) also known as SATIT schools. Private 18
depreciation rate 3.4 percentage points higher than those in English. The estimates in Chinese papers appear too high to be reasonable. For example, Bai (2006) quotes a depreciation rate of 8% for structures and 24% for equipment, based in service lives of 38 years for buildings and 12 years for machinery (Wang and Wu, 2003).

**Table 4 Depreciation rates used to calculate capital stocks**

||**Publication**||**Time**|
|---|---|---|---|
|**Authors**|**date**|**Depreciation rate**|**period**|
|**Chinese language**||||
|Meng and Wang|2000|5.0%||
|Wang and Fan<br>|2000<br>|5.0%<br>|1952-1999|
|Wang and Wu<br>|2003<br>|10.0%<br>||
|Gong and Xie|2004|10.0%|1970-1999|
|Zhang_et al._|2004|9.6%|1952-2004|
|Bai_et al._|2007|10.0%|1952-2005|
|Shan|2008|11.0%|1952-2006|
|Lei|2009|9.7%|1952-2007|
|Ye|2010|10.0%|1952-2008|
|Fan|2012|11.3%|1952-2009|
|Bai_et al._|2016|10.0%|1998-2007|
|Zhao and Zhong|2017|11.0%|1952-2006|
|**English language**||||
|Chen_et al._|1988|3.9%|1952 -1985|
|Perkins|1988|5.0%|1953-1985|
|Hu and Khan|1997|3.6%|1952-1994|
|World Bank|1997|4.0%|1978-1995|
|Woo|1998|5.0%|1979-1993|
|Hall and Jones|1999|6.0%|1988|
|Wang and Yao|2001|5.0%|1952-1999|
|Chow and Li|2002|4.0%|1978-1992|
|Wang and Yao|2003|5.0%|1952-1999|
|Chow and Li|2003|5.4%|1993-1998|
|Young|2003|6.0%|1978-1998|
|Wu|2004|7.0%|1952-2000|
|Islam_et al._|2006|3.0%|1952-1978|
|Holz|2006|3.7%|1953-1977|
|Islam_et al._|2006|4.0%|1979-1992|
|Islam_et al._|2006|5.0%|1993-2003|
|Holz|2006|5.9%|1978-2003|
|Wu|2007|4.5%|1952-2005|
|Zhang|2008|9.6%|1952-2004|
|Perkin and Rawski|2008|7.0%/9.6%|1952-2005|
|Bulman_et al._|2011|7.0%|1952-2008|
|Cheremhukin_et al._|2015|5.0%|1953-2012|
|Wu|2015|5.2%|1952-2010|
|Wang and Zhou|2016|6.5%|1952-1999|
|<br>Ma et al.|2016|7.0%|1978-2014|
|Bailliu_et al._|2016|7.6%|1978-2014|
|Wang and Zhou|2016|10.0%|2000-2015|
|<br>Hong and Li|2017|6.0%||
|Holz and Sun|2017|7.1%|1953-2005|
|Holz and Sun|2017|8.5%|2006-2015|

_Source:_ Google search.

8
<u>Table 11. Indonesian Exports, ICT use, and Log of Skills Measures. Female workers. 2005-2010</u>

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||An|lytical|||Interp|ersonal|||M|anual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000371||0.000339|0.000187|0.000339||0.000306|0.000252|-0.000437||-0.000401|-0.000354|
||(0.000528)||(0.000533)|(0.000702)|(0.000450)||(0.000456)|(0.000590)|(0.000394)||(0.000399)|(0.000530)|
|Share of hhlds with computer||0.210|0.234|0.252||0.200|0.225|0.247||-0.198|-0.226|-0.250|
|||(0.106)**|(0.103)**|(0.106)**||(0.0991)**|(0.0949)**|(0.0984)**||(0.101)**|(0.0962)**|(0.101)**|
|IDN exports per worker *||||0.000713||||0.0000783||||-6.71E-06|
|Share of hhlds with computer||||(0.00219)||||(0.00182)||||(0.00164)|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.678|0.679|0.679|0.679|0.405|0.405|0.406|0.405|0.730|0.730|0.730|0.730|
|||||Routine ski|ll measures||||||||
|||M|anual|||Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.0019||-0.00186|-0.00128|-0.00044||-0.000413|-0.000332|||||
||(0.000576)***||(0.000577)**|* (0.000802)|(0.000342)||(0.000346)|(0.000464)|||||
|Share of hhlds with computer||-0.182|-0.202|-0.215||-0.155|-0.176|-0.193|||||
|||(0.133)|(0.135)|(0.140)||(0.0869)*|(0.0820)**|(0.0834)**|||||
|IDN exports per worker *||||-0.00338||||-0.0003|||||
|Share of hhlds with computer||||(0.00253)||||(0.00146)|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.919|0.919|0.919|0.919|0.782|0.782|0.782|0.782|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

32
**Table 3**

# **Identification of structural break, aggregate yield index**

|**Identification of structural break, aggregateyield index**<br>**Wald test statistics**<br> y<br>ll (y)|
|---|
|<br>_Supremum_<br>18.85<sup>***</sup><br>162.39<sup>***</sup>|
|<br>_Average_<br>8.38<sup>***</sup><br>92.07|
|_Exponential_<br>6.94<sup>***</sup><br>78.49<br>|
|_Breakyear_<br>1993<br>1978<br>|
|**Note:**Null hypothesis is that no structural break exists in yield (y) and logarithmic yield (log (y)) index.|

**Table 4**

# **Box-Cox transformation**

||**Null hypothe**<br>|**sis**<br>|
|---|---|---|
|**Regressors**|λ<br>λ**= 0**|λ**= 1**|
|_Trend_|0.93 [0.77, 1.11]<br>68.29<sup>***</sup>|0.49|
|_Trend, trend_<sup>_2_</sup>|0.95 [0.39, 1.51]<br>12.60<sup>***</sup>|0.03|
|_Trend_before_1993, trend_after_1993_|1.07 [0.65, 1.48]<br>25.35<sup>***</sup>|0.10|
|_Trend_before_1978, trend_after_1978_<br>|0.33[-0.20, 0.68]<br>3.18<sup>*</sup><br>|13.73<sup>***</sup><br>|
|**Notes:**All regressions include a consta<br>vals of the optimal transformation para<br>test statistics of the null hypothesis of th<br>denote significance at 10 (<sup>*</sup>), 5 (<sup>**</sup>), and 1|nt term. Square brackets (second column) denote c<br>meter,λ, at a 95% significance level. The last three<br>e logarithmic (λ= 0) and linear (λ= 1) transform<br>(<sup>***</sup>) percent levels.|onfidence inter-<br>columns report<br>ations. Asterisks|

_— 21 —_
- 19 -

# **IV.** TUE COSTS OF CONTIGENCY T:IRE DBBS POROIJO

We shall now develop the model by allowing the lender to have a further choice: either investing in the project undertaken by the borrower or in a market portfolio. A fixed sum is to be invested but there is a choice between an investment in the comodity-related project or in the market portfolio. There is now a cost to contingency to the extent that the commodity project return is positively correlated with the return on the market portfolio. Thus, contingency may or may not be preferred. A sufficient condition for net benefits to contingency is that the restricted covariance between the returns to the project and the returns to the market are negative, i.e., there is a negative beta in a restricted capital asset pricing model.

The expression for the expected utility of borrowers remains unchanged from equation (1), however, the expected utility of the lender now becomes: Jves __v=_ + (1 - )w)f(",)dOdw _+_ fJ'fV(or * - a) + (1 - 8)w)f(8,w)drdw (9) where w = random market return f(O,w) = joint distribution function for 0 and w B refers to the proportion of the bank's assets invested in the project where we have normalized the bank's assets at unity.