rafmacalaba/datause-encoder-data huggingface dataset:
decades. Power system failures caused by insufficient power supply during extreme temperature events can be costly and can pose risks to human health and safety. In the Southeast Europe region, we have projected that the frequency and cost of power system failure events due to under-supply may be tens of millions of $US per year in some urban centers if we apply cost assumptions based on the 2021 February blackout in Texas.

Mitigating the economic damage from power outage events entails improved system planning and design (e.g., securing enough reserve capacity during months of the year when peak events are possible), improving the way in which power grids are operated during times of high demands to ensure that demands do not exceed supplies (e.g., controlled rotating blackouts), and/or decreasing the impact of the urban heat island effect by increasing the reflectivity and increasing the ability of the land surface to cool through evapotranspiration by increasing the density of trees and other vegetation within urban centers.

From an investment perspective, investing in types of energy generation that align with demand peaks is one strategic approach to mitigate the risks associated with power outages and disruptions. By investing in energy sources that have high availability during times of peak demand, such as solar during midday summer peak demands driven by cooling demand or wind power during winter peak demands driven by heating demand, investors can help to ensure a reliable and sustainable power supply. These types of energy sources are becoming increasingly costcompetitive and are substantially less carbon intensive than fossil fuel generation.

In conclusion, this analysis highlights the increasing challenges that power systems are facing with a changing climate, specifically the overburdening of grids during extreme temperature events, that can lead to power outages and disruptions to economic activity. The study illustrates the importance of timely investment in resilient and sustainable power systems that can help to mitigate these risks and ensure a reliable power supply by keeping up with the increasing demands stimulated by a warming climate.

24
divorce– fathers are more likely to obtain custody of sons compared to daughters. In a previous version, Dahl and Moretti (2004) also use Census data for Kenya and find an association between having a girl and the probability of being in a polygynous union. This paper exploits the entire women’s fertility histories (available in the NDHS but not in the Census, along with other variables such as a wife’s rank) and studies the effect of son preference on the probability that the husband becomes polygynous, that he does not co-reside with his wife, that the woman is ever divorced or separated, and that she is the head of the household.

Lastly, this paper contributes to the literature on child fostering (Isiugo-Abanhie, 1985; Bledsoe, 1990; Ainsworth, 1996; Akresh, 2009) by showing evidence that fostering patterns respond asymmetrically to a gender imbalance of daughters or sons, thereby suggesting a new motivation for household fostering decisions that is complementary to the ones already proposed in the literature.

The remainder of the paper is organized as follows. Section 2 provides some insights on the context and cultural background. Section 3 describes the data and some suggestive patterns in the variables of interest. Section 4 introduces the empirical methodology used to analyze fertility outcomes and birth spacing and further explores the identified patterns. Section 5 presents the results. Section 6 focuses on the interactions between son preference, marital outcomes, and child fostering decisions. Section 7 concludes.

# **2 Context**

As in many parts of Sub-Saharan Africa, society is organized around the extended family which still represents the most basic unit of social organization in Nigeria. Family ties are strong and play an important role in shaping individual behavior, even though there are signs that the extended family system is weakening for some ethnic groups (Wusu and Isiugo-Abanihe, 2006). There is an extraordinary ethnic diversity in Nigeria, but all ethnic groups are predominantly characterized by patrilineality and patrilocality.<sup>5</sup> Large progenies are strongly valued because they strengthen a man’s family status (Caldwell and Caldwell, 1987). The interactions between the principles of social organization, fertility preferences and family structure are documented in several demographic studies, such as Isiugo-Abanihe (1994):

> _‘Childlessness is the most dreaded tragedy for a man or a woman to experience in Nigeria’s_

> 5The three largest ethnic groups are geographically concentrated in different areas of Nigeria. They are the HausaFulani (North), Yoruba (South-West), and Igbo (South-East). They represent 28%, 18%, 16% of the population, respectively (NPC and ICF Macro, 2009).

6
|Cohort 2: 7-12 yrs in 1974|1.0610<br>(0.7374)|0.9638<br>(0.7635)|
|---|---|---|
|**_Pre-programme share of children in the population in region of b_**<br>|**_irth x birth coh_**<br>|**_ort_**<br>|
|Cohort 1: 1-6yrs in 1974|-1.7678|-1.8968|
||(2.7502)|(2.9072)|
|Cohort 2: 7-12yrs in 1974|-4.1393|-4.7471|
||(2.8350)|(2.8524)|
|**_Pre-programme enrolment rate in region of birth x birth cohort_**|||
|Cohort 1: 1-6yrs in 1974|-0.2721|0.9329|
||(1.1090)|(0.7740)|
|Cohort 2: 7-12 yrs in 1974|0.2104<br>(1.5363)|1.8596<br>(0.8217)*|
|Constant|-1.5580|-0.9567|
||(0.4792)**|(0.3435)**|
|Observations|3648|3648|
|Model Wald chi-squared<sup>(a)</sup>|874.65|618.5|
|Prob > chi-squared|0.0000|0.0000|
|**_Haussman specification test for endogeneity_**<sup>**_(b)_**</sup>|||
|chi-squared|54.|61|
|Prob > chi-squared|0.00|04|

Notes: (a) Wald statistics and p value for the test of the hypothesis that all of the slope coefficients are jointly zer0 (b) Haussman test of the null hypothesis that both the standard probit and IV probit estimators are consistent.

Robust standard errors in parentheses; * significant at 5%; ** significant at 1%

# _Skilled assistance at delivery_

Regression estimates of the probability of having skilled assistance at delivery are presented in Table 7. The coefficient for schooling is positive and statistically significant. The marginal effect of an additional year of schooling on the probability of having skilled assistance at delivery is 12.2% for a woman with average education. As in the ante-natal care model, the marginal effect is smaller in magnitude in the standard probit model.

Urban area of residence, travel to nearest midwife or health center and annual household consumption per capita have a priori expected signs: positive in the case of consumption 45
**Table 2.  Decline in Reported Son Preference, India 1992/3 to 2005/6**

||**Ideal sex ratio of childr**<br> <br>|**en***<br>|
|---|---|---|
|**Year of survey**|**India**<br>**Punjab**|**Haryana**|
|1992-93|1.42<br>1.57**|1.53**|
|1998-99|1.35<br>1.36**|1.46**|
|2005-06|1.27<br>1.22|1.32|

- This is computed using Retherford and Roy’s method (2003:33), which is to divide the reported ideal number of boys by the reported ideal number of girls. The number of reported ideal children of “either sex” is split equally between boys and girls.

- **_Source:_** Calculated from the National Family Health Survey 3 (IIPS 2007: Table 4.17.1) The observations marked ** are from Retherford and Roy (2003: Table 6.5)

23
<!-- Start of picture text --> 100% 8%<br>Ms<br>95%<br>$ 6% 2<br>4% &<br>3 90% 5% 8<br>s 85% 3% 3<br>80% 2% ©<br>1%<br>75% 0%<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Period<br>—— D(tbeugdp) ——— D(ffr) —z —— D(tbusagdp)<br>—D(bmgdp) -—D(lendint) ——— gygap —- infl<br><!-- End of picture text -->
||(1)<br>Actual gr|(2)<br>Actual gr|(3)<br>Actual gr|(4)<br>Actual gr|(5)<br>Actual gr|(6)<br>Actual gr|
|---|---|---|---|---|---|---|
|post|0.591|0.723|1.604<sup>_∗_</sup>|1.096|0.924|1.416|
||(0.680)|(0.937)|(0.829)|(0.809)|(1.236)|(0.826)|
|Fixed efects|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|CFE YFE|
|Period|Fifteen years|Fifteen years|Fifteen years|All years|All years|All years|
|Sample|All sample|Weak institutions|Strong institutions|All sample|Weak institutions|Strong institutions|
|SE|Robust|Robust|Robust|Robust|Robust|Robust|
|Obs- Country Year)|1097.00|668.00|280.00|1946.00|1187.00|472.00|
|R-sq|0.06|0.08|0.17|0.06|0.07|0.17|

inst, Standard errors clustered at country level _∗ p <_ 0 _._ 10, _∗∗ p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01 Table 12: Actual 5yr growth rates, different event samples.

# **5.4 Controlling for prior drilling and production**

We may be concerned that our result is being driven by the effects of prior oil sector activities. For example if discovery today is correlated with prior exploration drilling, we may worry that our estimated treatment effect is actually capturing some measure of the effect from earlier drilling or oil production, rather than a presource curse effect.

We therefore control for prior drilling, in terms of country well count at the time of discovery. We also control for whether the country is already an oil producer at the time of discovery.

Our forecast error results remain unchanged with the inclusion of these controls, suggesting such factors are not driving our main results.

||(1)<br>Dif gr|(2)<br>Dif gr|(3)<br>Dif gr|(4)<br>Dif gr|
|---|---|---|---|---|
|Lag disc dum,two years|-0.833<sup>_∗∗∗_</sup>|-0.827<sup>_∗∗∗_</sup>|-0.824<sup>_∗∗∗_</sup>|-0.819<sup>_∗∗∗_</sup>|
||(0.308)|(0.311)|(0.308)|(0.310)|
|Exploration drilling prior sum||-0.000<br>(0.000)||-0.000<br>(0.000)|
|Production dummy|||-0.157<br>(0.470)|-0.156<br>(0.470)|
|Fixed efects|Country, Year|Country, Year|Country, Year|Country, Year|
|Sample|All sample|All sample|All sample|All sample|
|Obs- Country Year)|3781|3781|3485|3485|
|R-sq|0.06|0.06|0.06|0.06|

Time trend, Standard errors clustered at country level, proddum control _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Table 13: Controlling for prior exploration well count and whether country is producing 26
# **References**

- [1] Bai, Jushan, ”Estimation of a Change Point in Multiple Regression Models”, Review of Economics and Statistics, 79 (November 1997), 551563

- [2] Banerjee, Abhijit and Andrew Newman, 1993, ”Occupational Choice and the Process of Development”, Journal of Political Economy, University of Chicago Press, vol. 101(2), pages 274-98, April.

- [3] Banerjee, Abhijit and Esther Duflo, 2007, ”The Economic Lives of the Poor”, Journal of Economic Perspectives 21: 141-68.

- [4] Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul and Munshi Sulaiman, 2012, ”Empowering Adolescent Girls: Evidence from a Randomized Control Trial in Uganda”, MIMEO.

- [5] Bandiera, Oriana, Robin Burgess, Narayan Das, Selim Gulesci, Imran Rasul and Munshi Sulaiman, 2013, ”Can Basic Entrepreneurship Transform the Lives of the Poor?”, MIMEO,

- [6] Bertrand, M., Sendhil Mullainathan and Eldar Shafir, 2006, ”Behavioral Economics and Marketing in Aid of Decision-Making among the Poor”, Journal of Public Policy and Marketing, May 2006, 25(1), pp. 8-23.

- [7] Besley, Timothy, 1995. ”Savings, credit and insurance”, Handbook of Development Economics, in: Hollis Chenery & T.N. Srinivasan (ed.), Handbook of Development Economics, edition 1, volume 3, chapter 36, pages 2123-2207 Elsevier.

- [8] Black, D. C., Galdo, J., and Smith, J., 2007, ”Evaluating the worker profiling and reemployment services system using a regression discontinuity approach”, American Economic Review, 97(2), 104-107.

- [9] Camacho, Adriana, Wendy Cunningham, Jamele Rigolini and Veronica Silva (2014) Addressing Access and Behavioral Constraints through Social Intermediation Programs: A review of Chile Solidario and Red Unidos, forthcoming, Policy Research Working Paper Series, World Bank.

- [10] Card, David, Alexandre Mas and Jesse Rothstein, 2008. ”Tipping and the Dynamics of Segregation,” The Quarterly Journal of Economics, MIT Press, vol. 123(1), pages 177-218, 02.

- [11] Cellini, Stephanie R., Fernando Ferreira and Jesse Rothstein, 2010. ”The Value of School Facility Investments: Evidence from a Dynamic Regression Discontinuity Design,” The Quarterly Journal of Economics, MIT Press, vol. 125(1), pages 215-261, February.

39
of teachers who were supposed to be in classrooms (Chaudhury et al., 2006). The survey revealed high teacher absence rates—from 11 percent to 27 percent of teachers were missing from the classroom without an official excuse. Analyses of the data indicate that teacher position and birthplace (i.e., teachers being born in the same districts as their school) are negatively associated with absence, as are school and community characteristics such as poor school infrastructure, school distance from the government education office, and parental literacy rates (Chaudhury et al., 2006). The authors conclude that weak monitoring exacerbates teacher absence, especially when excessive absenteeism has no negative consequence for offenders.

Various incentives to improve teacher effort and performance have been evaluated, but with mixed results. In a randomized study in India, Duflo, Hanna and Ryan (2012) find that the use of financial incentives and careful monitoring (through daily photos of the teacher with her students) reduce teacher absence rates by 21 percentage points relative to a control group. In another randomized study, Kremer, Miguel and Thornton (2009) find that greater parental monitoring increases teacher attendance in Kenya.<sup>7</sup> However, these results differ from those of other studies on the same countries which conclude that teacher effort does not change despite greater participation by beneficiaries (e.g. students and parents) in monitoring in India (Banerjee et al., 2010), or performance-based financial incentives in Kenya (Glewwe, Ilias and Kremer, 2010).<sup>8</sup>

> 7 Analyzing a survey on public health workers in Lao PDR, Yamada, Sawada and Luo (2013) also find that timely payment of wages and efficient monitoring of workers’ attendance can reduce absenteeism, even though absenteeism rate among these workers is low compared to other developing countries.

> 8 It is interesting to see these mixed results for the same countries. While there are perhaps a large number of randomization studies for these countries, Banerjee et al. (2010) also observe that the implementers of these incentives matter: incentives implemented by nongovernmental organizations (NGO) were effective while those implemented by government bureaucrats were not. See also Glewwe, Holla and Kremer (2009) for a review of teacher incentives, and Kremer, Brannen and Glennerster (2013) for an overview of education challenges in developing countries.

6
respect to the mean of the non-RAMV group (see Table 1). The findings show that eligible applicants were much better off: their family members were 15.6 p.p less likely to skip a meal during the previous month, the number of days in which they ate proteins was 15% higher, and they were 10.5% less likely to be evicted (see Table A.4 in the Appendix).

What drove the stronger resilience of PEP eligible applicants? According to Ib ́a ̃nez et al. (2022), PEP holders were 26 p.p. more likely to be employed and their labor income was 24 p.p higher. In addition, they were 22 p.p and 44 p.p. more likely to receive monetary transfers from the government and to have bank accounts, respectively. PEP eligible applicants seem to be more self-reliant, and better able to cope with the negative shock brought by the pandemic.

# **VI Conclusions**

Regularization programs are a promising pathway to improve the lives of forced migrants. Beneficiaries of the PEP program were more self-reliant and thus resilient to negative health and economic shocks, relative to the ineligible migrants. Programs for forced migrants have mostly concentrated on providing humanitarian assistance, while restricting their access to labor markets in the destination country and to regular social services. The evidence of this offers a paper complementary pathway. Regularization programs, by promoting the rapid integration of migrants into destination countries and their self-reliance, may not only improve the lives of migrants but may also reduce the short-term impacts on the local populations.

# **References**

- Baez, Javier E (2011) “Civil wars beyond their borders: The human capital and health consequences of hosting refugees,” _Journal of Development Economics_ , 96, 391–408.

- ECDC (2021) “The Economics of the COVID-19 Pandemic in Poor Countries,”Technical report, European Centre for Disease Prevention and Control - ECDC.

- Ib ́a ̃nez, Ana Maria, Andres Moya, Mar ́ıa Adelaida Ortega, Sandra V. Rozo, and Maria Jos ́e Urbina (2022) “Life Out of the Shadows: Impacts of Amnesties in the Lives of Migrants,” IZA Discussion Papers 15049, Institute of Labor Economics (IZA).

- Ib ́a ̃nez, Ana Mar ́ıa, Sandra V. Rozo, and Mar ́ıa J. Urbina (2021) “Forced Migration and the Spread of Infectious Diseases,” _Journal of Health Economics_ , 79, 102491.

- Kling, Jeffrey R, Jeffrey B Liebman, and Lawrence F Katz (2007) “Experimental Analysis of Neighborhood Effects,” _Econometrica_ , 75, 83–119.

- Kluge, Hans H, Zsuzsanna Jakab, Jozef Bartovic, Veronika D’Anna, and Santino Severoni (2020) “Refugee and migrant health in the COVID-19 response,” _The Lancet_ .

- Montalvo, Jose G. and Marta Reynal-Querol (2007) “Fighting against Malaria: Prevent Wars while Waiting for the ”Miraculous” Vaccine,” _The Review of Economics and Statistics_ , 89 (1), 165–177.

- Zambrano-Barrag ́an, Patricio, Sebasti ́an Ram ́ırez Hern ́andez, Luisa Feline Freier, Marta

5
In what follows, we explore an alternative methodology in obtaining additional information about the relative sizes of the non-cognitive vs. cognitive components of the return to schooling. Following Ishikawa and Ryan (2002) and Pasche (2007) (useful insight can also be found in Tyler (2004)), consider the following theoretical model: _Wi_ = _a0_ + _a1Si_ + _a2SCSi_ + _a3NSCSi_ + _a4NSNCSi_ + _a5Fi_ + _a6Ai_ + _a7Xi_ + _ε_ (7) In this model, the coefficient _a1_ is a measure of the pure return to schooling non-cognitive skills. Note that measures of innate ability and non-schooling non-cognitive skills are not usually available in datasets, including the IALS. If a measure of schooling cognitive skills (SCS) was available, the following equation could have been estimated: where _W_ stands for earnings, _S_ for years of schooling, _SCS_ for schooling cognitive skills, _NSCS_ for non-schooling cognitive skills, _NSNCS_ for non-schooling non-cognitive skills, _F_ for family background variables, _A_ for innate ability, while _X_ is a vector of other controls (in our case years of potential labor market experience and its square). The coefficient _β1_ would be a true measure of the non-cognitive return to schooling. The full measure of cognitive skills (the IALS score in our case) is regressed on quantity ( _S_ ) and quality of schooling ( _SQ_ ), that is years of schooling and type of schooling (that is, high school, university, etc.), as well as family background information: 21
# **C Extraction Methods**

Extending section 4.1, in this section, we present further evidence on Hypothesis 1 ( _H_ 0<sup>1-different</sup> obfuscation procedures implemented to preserve privacy of farms or households have no impact on estimates of agricultural productivity). The following figures (Figures C1 through C6) pool the results from 77,760 regressions and then divide the pool into ten bins, one for each extraction method for 7,776 regression results. Extending the results presented in the main text, these figures examine the differences in coefficients ( _β_ 1) and their relative significance by extraction method.

Reviewing the coefficients and confidence intervals for rainfall and temperature measures, disaggregated by extraction methods, as presented in Figures C1 through C6 overwhelmingly supports the conclusions of section 4.1, specifically that we cannot reject the null for Hypothesis 1.

## **C.1 Selecting a Single Extraction Method**

Prior to the unblinding of the Data Analysis Group, all analysis was conducted using a randomly selected extraction method. The following text is what was contained in section 4.1.3 “Selecting a Single Extraction Method” in version 2 of the paper: Concluding that different extraction method do not produce substantially different results, we narrow the set of results used from this point forward. As that the Data Analysis Group is still blinded to the identity of each extraction method, we cannot simply select the “true” coordinates and proceed. Instead, we us a random number generator in Stata to select one extraction method to continue the analysis with. Once the Data Analysis Group is unblinded, the analysis will be updated using the true coordinates. If our conclusion that extraction method does not introduce substantial mismeasurement, then the results and analysis using a randomly selected extraction method should be qualitatively the same as results and analysis using the true GPS coordinates.

The code for the random selection process is: ```
*chooseoneextractionmethodatrandom
preserve ``` ``` clearall setobs1 setseed3317230 gendoubleu=(10 -1)*runiform ()+1 geni=round(u) sumui ``` ``` restore ``` Following the results of this random number generation, the findings presented from this point on only rely on extraction method. Time-stamped code for this process is available in the Github repository.

108
WPS 3232

# **Business Environment and Firm Entry: Evidence from International Data**

## Leora Klapper, Luc Laeven, and Raghuram Rajan*

**Abstract:** Using a comprehensive database of firms in Western and Eastern Europe, we study how the business environment in a country drives the creation of new firms. Our focus is on regulations governing entry. We find entry regulations hamper entry, especially in industries that naturally should have high entry. Also, value added per employee in naturally “high entry” industries grows more slowly in countries with onerous regulations on entry. Interestingly, regulatory entry barriers have no adverse effect on entry in corrupt countries, only in less corrupt ones. Taken together, the evidence suggests bureaucratic entry regulations are neither benign nor welfare improving. However, not all regulations inhibit entry. In particular, regulations that enhance the enforcement of intellectual property rights or those that lead to a better developed financial sector do lead to greater entry in industries that do more R&D or industries that need more external finance.

World Bank Policy Research Working Paper 3232, March 2004 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Klapper is at the World Bank, Laeven is at the World Bank and CEPR, and Rajan is at the IMF and NBER. We thank Simeon Djankov, Robert Hauswald, Simon Johnson, Vojislav Maksimovic, Atif Mian, Michel Robe, and seminar participants at the Fifth International Conference on Financial Market Development in Emerging and Transition Economies in Hyderabad, American University, and University of Maryland for valuable comments, Ying Lin and Victor Sulla for outstanding research assistance, Sebastian Roels at Bureau Van Dijk for help with the Amadeus data, and Brian Williams and Ryan Paul at Dun & Bradstreet for help with the Dun & Bradstreet data.  Rajan thanks the National Science Foundation, the Center for the Study of the State and the Economy at the Graduate School of Business, University of Chicago for research support. We also thank the World Bank for financial support.