rafmacalaba/datause-encoder-data huggingface dataset:
insurgents (Human Rights Watch 2004). Quoting figures from the Informal Sector Service Center (INSEC), UNESCO (2010) reports that between 2002 and 2006, the Maoists abducted 10,621 teachers and 21,998 students (p.8). According to additional data provided by INSEC, the total number of abductions by Maoist forces during the conflict amounted to more than 85,000. Although most abductees were seemingly returned unharmed after a few days of intensive indoctrination (Becker 2009; Macours 2011), a number of youths joined the Maoist fighters (in 2003, an estimated 30 percent of Maoist forces were aged 14–18 years). The indoctrination sessions held during abductions are likely to have played a part in their recruitment.

In this paper, I exploit differences in the intensity of violence experienced by individuals born at different times, surveyed at different times, and in different districts to shed light on the ways in which experiencing the insurgency at a young age affected educational outcomes.

Individual data from the 2001 and 2006 Demographic and Health Surveys (DHS) of Nepal are merged with detailed conflict data collected by INSEC, namely, the number of conflict fatalities, school destructions, and abductions by Maoists at the district level.

I find that overall conflict intensity, as measured by conflict casualties, was associated with an increase in female educational attainment, whereas abductions by Maoists, which often targeted school children, had the reverse effect. Male schooling also tended to increase more rapidly in areas where the fighting was more intense, but the estimates are smaller in magnitude and more sensitive to specification. Similar results are obtained across different identification strategies, and robustness checks indicate that these findings are not due to selective migration.

In the next section, I review the existing evidence on the impact of armed conflict on education outside Nepal with an emphasis on male-female differences. I then present the Nepalese conflict in section II, the data in section III, the empirical strategy in section IV, and the estimation results in section V. Section VI concludes.

4
1.6 results 27

_Fifth_ , the two biases that are corrected in the preceding step are disentangled to assess the relative dominance of each. The purpose is to find whether the failure to control for firm heterogeneity biases the coefficients more than the failure to control for sample selection bias arising from zero investment flows between numerous country pairs.

All the inferences are based on standard errors that are robust and clustered by host country under the assumption that FDI from OECD countries flowing to a common host country is influenced by the latter’s characteristics that apply in common to all source countries, in addition to pair-specific characteristics. If a shock in a host country affects potential investment from all source countries, then FDI inflows are correlated. As Moulton (1990) shows, if errors within groups are correlated, but incorrectly assumed to be independent, standard errors are likely to be substantially biased downward leading to findings of statistical significance that are spurious. In this paper, clustering of standard errors by host country yields the most conservative set of inferences on the significance of coefficients, and is the chosen approach in all the regressions.<sup>27</sup>

# 1.6 results

Columns 3 and 4 in Table 1 report the main results, obtained from NLS and OLS models, respectively. When both the biases introduced by country selection and firm selection are corrected, FDI-specific institutions significantly affect the accumulation of foreign direct investment.

The coefficient of FDI Arbitration – a variable with a close relationship with a judicial regime and enforcement – is significant at the 5 percent level, whereas it was not statistically different from zero in the benchmark estimation that does not

> 27 I also cluster standard errors by _source_ country (to account for agglomeration tendencies) and by country pairs. Clustering by _source_ country makes several coefficients appear much more significant than when clustering is by host country. Note that the magnitudes of coefficients do not change irrespective of how the standard errors are clustered.
safety, as measured by the city average of share of sales that firms spend on security staff, is negatively (though statistically insignificantly) associated with Lgrowth (with a t-statistic of 1.59).

Perhaps surprisingly, Human Capital, as captured by Skill Obstacles, proves to be unimportant for either Lgrowth or LPgrowth: the estimated coefficients on this variable are both statistically insignificant and small. This finding differs from Gennaioli et al. (2013), who provide evidence that human capital plays a prominent role in accounting for variations in sub-national regional development for a large sample of countries.<sup>16</sup> The two measures of infrastructure  Power Outage and Web Intensity  have quite distinct effects. Power Outage is not associated with firm growth. Thus, conditional on other variables, power outages within a city do not matter much for either Lgrowth or LPgrowth in our pooled sample. In contrast, our measure of modern technology, Web, proves to be much more important. Hence, a one SD increase in Web is associated with an increase in Lgrowth of 0.4 percentage points, and an increase in LP growth of 0.8 percentage points. This finding is consistent with the notion that the internet is a general-purpose technology with widespread impacts for many facets of the economy (Roller and Waveman, 2001; Basu and Fernald, 2008; Clarke et al., 2015). Electricity, the old general-purpose technology, has lost its importance in our pooled sample in this modern era.

Access to both formal and informal finance matters. Increasing access to formal finance (i.e., Overdraft Facility) by one SD (0.26) is associated with raising Lgrowth by 0.4 percentage points, and LPgrowth by 0.9 percentage points. Similarly, increasing access to informal finance (i.e., Trade Credit) by one SD (0.2) is associated with raising LPgrowth by 0.7 percentage points.

Turing to Refined BEs, we find that Land Access Obstacle, which we use as a proxy for entry and exit regulations more generally, does not matter at all.<sup>17</sup> Likewise, there is no significant association between Labor Regulation Obstacles and growth. Thus, labor regulation in general entails neither great benefits nor grave dangers across our global sample of, mainly, developing-

> 16 One possibility is that our measure of human capital is not as good as that which Gennaiola _et al_ use in their paper. Another possibility, at least for the firm performance part, is that we have a much larger sample of WBES data: while they only have thousands of observations, we have almost 60,000 observations for our baseline regression of Lgrowth. We also control for many more aspects of the BE than do Gennaiola _et al_ .

> 17 This is consistent with Bruhn and McKenzie’s (2014) survey of literature on the effects of entry reforms and related policy actions to promote firm formalization in developing countries. In particular, they report that there is little evidence in the literature that entry reforms impact on micro firms’formalization decisions.

14
# **Table 3**

# **Price informativeness and managerial information**

Definitiond of all variables are listed in Table 1. The dependent variable is Price Informativeness. Managers’ uncertainty is proxied by the dispersion of analysts’ earnings forecasts ( _DIS_ ) and insider trading activity 1 _− INSIDER_ . T-statistics are in parentheses. ***/**/* indicate that the coefficient estimates are significantly different from zero at the 1%/5%/10% level.

|||_DIS_|Measure of|Uncertain<br>1|ty<br>_−INSID_|_ER_|
|---|---|---|---|---|---|---|
|Dependent variable: _PI_|(1)|(2)|(3)|(4)|(5)|(6)|
|_UNCERit_|-0.10***|-0.18***|-0.06*|-0.18***|-0.08***|-0.03***|
||(17.90)|(26.30)|(1.70)|(31.11)|(13.24)|(6.39)|
|_SIZEit_|-0.41***|-0.45***|-0.05***|-0.42***|-0.54***|-0.06***|
||(55.13)|(27.87)|(4.11)|(58.29)|(33.52)|(4.42)|
|_CFit_|0.25***|0.34***|0.15***|0.22***|0.39***|0.15***|
||(32.97)|(18.91)|(10.95)|(29.76)|(21.35)|(10.95)|
|Firm fxed efects|No|Yes|Yes|No|Yes|Yes|
|Year fxed efects|No|No|Yes|No|No|Yes|
|_R_<sup>2</sup>|0.14|0.52|0.73|0.16|0.51|0.73|
|Number of Observations|24753|24753|24753|24921|24921|24921|

# **Table 4**

# **Price informativeness and managerial information: Interaction with institutional ownership**

Definitions of all variables are listed in Table 1. The dependent variable is Price Informativeness. Managers’ uncertainty is proxied by the dispersion of analysts’ earnings forecasts ( _DIS_ ) and insider trading activity 1 _− INSIDER_ . _SIZE_ and _CF_ coefficients are omitted. T-statistics are in parentheses. ***/**/* indicate that the coefficient estimates are significantly different from zero at the 1%/5%/10% level.

||Measure of Uncertainty|
|---|---|
||_DIS_<br>1_−INSIDER_|
|Dependent variable _PI_|(1)<br>(2)|
|_UNCERit_|-0.02***<br>-0.02***|
||(3.40)<br>(5.09)|
|_UNCERit × INSTit_|-0.05***<br>-0.02***<br>(11.95)<br>(4.36)|
|_INSTit_|-0.22***<br>-0.22***|
||(28.30)<br>(27.38)|
|Controls|Yes<br>Yes|
|Firm fxed efects|Yes<br>Yes|
|Year fxed efects|Yes<br>Yes|
|_R_<sup>2</sup>|0.73<br>0.74|
|Number of Observations|24753<br>24921|

27
Table 2: Pre-CADENA effect of rainfall on yield

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||log yield|log yield|log yield|log yield|
|Minimum deviation from trigger (100s)|0.00661<sup>_∗_</sup>|0.0129<sup>_∗∗_</sup>|||
||(0.00344)|(0.00540)|||
|Minimum deviation<sup>2</sup>||-0.00144<sup>_∗∗_</sup>|||
|||(0.000645)|||
|Total precipitation (100s)|||0.00308<sup>_∗∗∗_</sup>|0.0105<sup>_∗∗∗_</sup>|
||||(0.00111)|(0.00271)|
|Total precipitation<sup>2</sup>||||-0.000237<sup>_∗∗∗_</sup>|
|||||(0.0000575)|
|N|17948|17948|17045|17045|
|Mean (s.d.) of independent var|1.16 (1.19)|1.16 (1.19)|8.92 (4.99)|8.92 (4.99)|
|Efect of 1 S.D. decrease|-0.8%|-1.7%|-1.5%|-5.8%|

Note. _∗∗_ Standard _∗∗∗_ errors are clustered at the municipality level. Asterisks indicate statistical significance:<sup>_∗_</sup> _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. The sample is restricted to insurable crops (maize, beans, sorghum barley). All regressions include crop fixed effects and are estimated using only pre-CADENA data from each municipality. Column 1 displays the results of regressions log yield on the minimum deviation from the precipitation threshold that is established once CADENA is in place, while column 2 includes a quadratic term in the explanatory variable. Column 4 displays the results of regressions log yield on the total precipitation within the growing season defined by future insurance policies, while column 2 includes a quadratic term in the explanatory variable.

26
**-** **<u>Table 5: Comparing PSG participants and non participants based on SAR & remote sensing data</u>**

||<br>**Total**|<br>**By s**|<br>**ize in hect**|<br>**ares**|<br>**By type**|<br>**of organi**|**zation**|
|---|---|---|---|---|---|---|---|
|||**< 20**|**20-50**|**> 50**|**Legal Entity**|**FOP**|**Individual**|
|**Part A. Land program successful**|**applicants**|||||||
|Agric. Land (ha)|19.92|6.64|35.76|79.75|49.46|34.29|8.78|
|Number of parcels|7.16|2.98|11.03|27.37|18.63|12.11|2.95|
|Cult. in 2019 (ha)|19.47|6.47|34.96|78.04|48.37|33.43|8.59|
|Cult. in 2020 (ha)|19.66|6.54|35.30|78.86|48.90|33.76|8.67|
|Cult. in 2021 (ha)|19.52|6.50|35.05|78.24|48.52|33.49|8.62|
|Cult. in 2022 (ha) elig.|18.62|5.97|33.32|76.07|46.55|32.22|8.09|
|Cult. in 2023 (ha)|18.99|6.24|34.03|76.68|47.36|32.68|8.33|
|Self-reported cult. area 2022 (ha)|19.49|6.51|35.19|77.74|49.58|34.09|8.16|
|Amount received (US$)|1,652|552|2,983|6,590|4,203|2,890|692|
|Applied for other programs|0.20|0.08|0.47|0.57|0.54|0.49|0.05|
|Number of farms|19,660|14,313|2,953|2,394|3,801|2,519|13,340|
|**Part B. Land program non-appli**<br>|**cants**<br>|||||||
|Agric. Land (ha)|9.04|4.85|35.08|86.85|42.55|18.18|5.77|
|Number of parcels|4.07|2.57|11.26|35.58|19.32|7.90|2.61|
|Cult. in 2019 (ha)|8.71|4.67|33.81|83.43|40.16|17.42|5.62|
|Cult. in 2020 (ha)|8.80|4.72|34.18|84.44|40.78|17.62|5.67|
|Cult. in 2021 (ha)|8.75|4.69|33.88|83.88|40.31|17.45|5.65|
|Cult. in 2022 (ha)|8.23|4.37|32.38|79.65|37.93|16.52|5.32|
|Cult. in 2023 (ha)|8.45|4.51|32.85|81.42|39.05|16.87|5.45|
|Applied for other programs||||||||
|Numberof farms|23,343|21,368|1,236|739|1,464|1,817|20,062|

_Source:_ Own computation from SAR records and cadastral data as described in the text _Note:_ ‘Cultivated area’ is area for which the farm has ownership or lease rights registered and that was, according to crop maps elaborated by KPI and JRC, covered by agricultural crops in the relevant year. Eligible area excludes area located in conflictaffected areas as described in the text.

21
analyzed the impact on household earnings due to access to services, three years after securing the access and the numbers are quite large as seen in Figure 11. Having access to water and electricity increases earnings by 13 percent; having access to water, electricity, and sanitation increases earnings by 23 percent; having access to water electricity, sanitation, and telecom increases earnings by 36 percent; and having market access through rehabilitated roads increases earnings by 35 percent.

**FIGURE 11: INCREASE IN HOUSEHOLD EARNINGS FOR ACCESS TO INFRASTRUCTURE PUBLIC SERVICES, THREE YEARS AFTER SECURING ACCESS TO SERVICES (AS A PERCENTAGE OF EARNINGS)**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Water Water + Electricity Water+ Electricity Water + Electricity +<br>+ Sanitation Sanitation +<br>Telecom<br>Percentage<br><!-- End of picture text -->

**_<u>Source: Escobal and Torero (2004)</u>_**

33
<!-- Start of picture text --> S1: Highly Suitable<br>S2: Moderately Suitable<br>Maize Soil Property Y<br>S3: Marginally Suitable<br>N: Not Suitable<br><!-- End of picture text --> _Figure 1._ Underlying crop-soil property suitability class structure, following FAO (1976). The maize suitability analysis completed here includes pH, cation exchange capacity (CEC), organic carbon, salinity (soil electrical conductivity), and plot slope (percent).

The following two-step empirical approach is employed: (1) estimate crop suitability measures at the plot-level; and (2) estimate stochastic frontier models to estimate production frontiers for each class of maize suitability. The contribution of this paper comes from the ability to execute each of these steps on the same sample and from being able to do so with objectively-measured soil properties and crop production. In addition to executing the aforementioned analytical steps using the MAPS plot-level soil data, the steps are replicated using the geospatially-derived soil property data from AFSIS.

# **4.1.Assigning Maize-Specific Soil Suitability Measures**

Estimating aggregate crop suitability measures requires comparing a vector of optimal soil properties against the levels of said properties observed on each plot. Crop suitability cannot be reduced to a single soil property, as several properties affect plant growth simultaneously, and soil property requirements vary by crop. The crop suitability framework set forth by FAO (1976), and illustrated in Figure 1, will serve as the foundation for the suitability classifications at the cropsoil property level. The maize suitability analysis completed here includes pH, cation exchange capacity (CEC), organic carbon, salinity (soil electrical conductivity), and plot slope (percent).<sup>11</sup> After identifying the suitability class of each soil property individually, based on the propertyspecific critical values borrowed from Naidu (2006) and further reviewed and modified with input from the World Agroforestry Centre, we utilize a fuzzy membership method to construct a membership grade for each suitability class, allowing for identification of the suitability class that best approximates the soil sample overall. The fuzzy membership method is commonly employed in land suitability analysis with GIS data (Ahamed et al., 2000; Ceballos-Silva & López-Blanco,

> 11 Multiple variations of the soil suitability framework were created, each containing a different combination of key soil properties. The selected framework that was chosen based on its superior predictive power in bivariate regression on yields measured via crop-cutting.

10
<!-- Start of picture text --> 250<br>ih<br>200 | WA<br>150 A |1 \<br>\ |<br>100 /| \\\|<br>50 q LAIN 17<br>0 \--77 \_/ /<br>va<br>1980 1990 2000 2010 2020<br>—— PPG<br>——- PNG<br><!-- End of picture text -->

<!-- Start of picture text --> !i\<br>° A ~~ / Loa\ / \<br>‘\ i\ Ni Yi 7 \/<br>7 \yey/Ali SY y\ |I\\7NY /<br>an lt<br>2 \)<br>V<br>1980 1990 2000 2010 2020<br>— PPG<br>——- PNG<br><!-- End of picture text -->

<!-- Start of picture text --> 30<br>7<br>r<br>10 \~. //\Vsi\!IN<br>of \V A/S< “A _/- \—KF Vl<br>-10<br>1980 1990 2000 2010 2020<br>—— PPG<br>——- PNG<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>4<br>2 PAWA h /\‘ l-\<br>of ST YNIVIN A AWN<br>uy / viv<br>2<br>1980 1990 2000 2010 2020<br>—— PPG<br>——- PNG<br><!-- End of picture text -->
- Choi, S., Furceri, D., Loungani, M. P., Mishra, M. S., and Poplawski-Ribeiro, M. (2017). _Oil prices and inflation dynamics: evidence from advanced and developing economies_ . International Monetary Fund.

- Cole, M. A., Elliott, R. J., Occhiali, G., and Strobl, E. (2018). Power outages and firm performance in sub-saharan africa. _Journal of Development Economics_ , 134:150–159.

- Elder, J. and Serletis, A. (2009). Oil price uncertainty in canada. _Energy Economics_ , 31(6):852–856.

- Elliott, R. J., Nguyen-Tien, V., and Strobl, E. A. (2021). Power outages and firm performance: A hydro-iv approach for a single electricity grid. _Energy economics_ , 103:105571.

- Ertefaie, A., Small, D., Flory, J., and Hennessy, S. (2016). Selection bias when using instrumental variable methods to compare two treatments but more than two treatments are available. _The international journal of biostatistics_ , 12(1):219–232.

- Fisher-Vanden, K., Mansur, E. T., and Wang, Q. J. (2015). Electricity shortages and firm productivity: evidence from china’s industrial firms. _Journal of Development Economics_ , 114:172–188.

- Foster, L., Haltiwanger, J., and Syverson, C. (2008). Reallocation, firm turnover, and efficiency: Selection on productivity or profitability? _American Economic Review_ , 98(1):394–425.

- Grainger, C. A. and Zhang, F. (2017). The impact of electricity shortages on firm productivity: Evidence from pakistan. _World Bank Policy Research Working Paper_ , (8130).

- Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. _Political analysis_ , 20(1):25–46.

- Halvorsen, R., Palmquist, R., et al. (1980). The interpretation of dummy variables in semilogarithmic equations. _American economic review_ , 70(3):474–475.

- Hardy, M. and McCasland, J. (2021). Lights off, lights on: The effects of electricity shortages on small firms. _The World Bank Economic Review_ , 35(1):19–33.

- Hsieh, C.-T. and Klenow, P. J. (2009). Misallocation and manufacturing tfp in china and india. _The Quarterly journal of economics_ , 124(4):1403–1448.

- Jones, C. I. (2011). Misallocation, economic growth, and input-output economics. Technical report, National bureau of economic research.

- Lee, C.-C., Wang, C.-W., and Ho, S.-J. (2020). Financial inclusion, financial innovation, and firms’ sales growth. _International Review of Economics & Finance_ , 66:189–205.

- Leung, D., Meh, C., and Terajima, Y. (2008). Firm size and productivity. Technical report, Bank of Canada.

- Levinsohn, J. and Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. _The review of economic studies_ , 70(2):317–341.

- Lin, S. and Ye, H. (2009). Does inflation targeting make a difference in developing countries? _Journal of Development economics_ , 89(1):118–123.

29
**Figure 4.21. Reliability of water for richest and poorest quintiles in the capital cities**

<!-- Start of picture text --> Lowest Highest<br>100<br>80<br>60<br>40<br>20<br>0<br>Albania Bulgaria Bosnia* Armenia Georgia Kazakhstan Turkmenistan*<br>Balkans/EE Caucasus Central Asia<br>%<br><!-- End of picture text -->

- For Bosnia, urban households

- **There were no Q1 households in the capital of Turkmenistan.

Source: Table 2.1

**Figure 4.22. Reliability of water for richest and poorest quintiles in the secondary cities**

<!-- Start of picture text --> Lowest Highest<br>100<br>80<br>60<br>40<br>20<br>0<br>Albania Bosnia* Bulgaria Armenia Georgia Kazakhstan Turkmenistan<br>Balkans/EE Caucasus Central Asia<br>%<br><!-- End of picture text -->

- For Bosnia, households in mixed areas.

Source: Table 2.1

**Poor households are somewhat less likely to pay for services than rich households.** As we have seen, the poor are less likely to have access to infrastructure and energy services, and there is some evidence that those who do have access receive poorer service.  One would expect service availability and quality to be related to payment.  Figure 4.23 and 4.24 show the payment rate (or share of households reporting making any payment for a given service) for different services in capital cities and other urban areas. Higher income households have a slightly higher payment rate than lower income households in capital cities, but the difference is a bit larger when payment rates in secondary cities are taken into account.  In both capital cities and other urban areas, the most striking pattern is the large number of people who do not pay at all.

39