rafmacalaba/data-use-mentions-extended huggingface dataset:
118–146.

- Jayachandran, S. and R. Pande (2017): “Why Are Indian Children So Short? The Role of Birth Order and Son Preference,” _American Economic Review_ , 107, 2600–2629.

- Jensen, R. (2012): “Another Mouth to Feed? The Effects of Fertility on Girls’ Malnutrition,” _CESifo Economic Studies_ , 58, 322–47.

- Jha, P., R. Kumar, P. Vasa, N. Dhingra, Neeraj, D. Thiruchelvam, and R. Moineddin (2006): “Low Male-to-female Sex Ratio of Children Born in India: National Survey of 1.1 Million Households‘,” _Lancet_ , 367, 211–218.

- Joshi, S. and T. P. Schultz (2007): “Family Planning as an Investment in Development: Evaluation of a Program’s Consequences in Matlab, Bangladesh,” _Yale University Economic Growth Center Discussion Paper_ .

- Kalsi, P. (2015): “Abortion Legalization, Sex Selection, and Female University Enrollment in Taiwan,” _Economic Development and Cultural Change_ , 64, 163–185.

- Kane, E. J. (1984): “Why journal editors should encourage the replication of applied econometric research,” _Quarterly Journal of Business and Economics_ , 23, 3–8.

- Kaur, R. (2004): “Across-Region Marriages: Poverty, Female Migration and the Sex Ratio,” _Economic and Political Weekly_ , 39, 2595–2603.

- Klasen, S. (2002): “Low Schooling for Girls, Slower Growth for All? Cross-Country Evidence on the Effect of Gender Inequality in Education on Economic Development,” _World Bank Economic Review_ , 16, 345–373.

- Kumar, D. (1983): “Male Utopias or Nightmares?” _Economic and Political Weekly_ , 18, 61–64.

- Lagerlöf, Nils-Petterf, N.-P. (2003): “Gender
Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022). This survey is specifically designed to evaluate changes in bus usage among women in Delhi following the implementation of the _Pink Slip_ scheme. The survey collected responses from 2,025 women, of which 1,294 had been using the bus before the scheme’s introduction; we refer to this group as “continuous users.” Additionally, 231 respondents who began using buses after the scheme’s introduction are termed “new users.” The remaining 500 respondents who did not use buses before or after the scheme are labeled as “non-users.” The was selected at attractions and across sample randomly major generation points 6
and or house ownership (Hentschel et al, 1998).

To complement findings from cross-country data, and to understand the importance of cross-cutting ties in reducing exclusion even in multi-racial, multiethnic industrialized countries, it is useful to examine within-country data. Sustainable economic development requires reciprocal on-going relationships and networks beyond narrow geographic, ethnic or political communities in order to ensure access to additional resources (Portes and Sensebrenner, 1993).

Verba et al (1996) in _Voice and Equality: Civic Voluntarism and American Politics_ address the issue of inequality and the decline in civic engagement in the last three decades in the United States. They demonstrate that politicians listen primarily to those with college education and those who make contributions to political campaigns, which leads to selective hearing of interests of those who are educated, wealthy and organized. Their findings are based on a 25
# **1 Introduction**

Firms’ informality is pervasive in developing countries. Often firms are not registered with business registration or tax authorities. The 2010 census of 55,000 small and medium enterprises (SMEs) in 19 major district towns in Bangladesh suggests that about 70% of firms are informal, according to either their business or tax registration status. It is widely known that informality decreases with the size and performance of firms, which suggests that formality might cause better economic performance. Clearly this might not be the case; better performing firms might simply decide to be formal. This still leads to the question of why so many firms remain informal. A 14-country survey of informal firms by the World Bank suggests that the lack of information about the registration process and the time it takes to register a business are two leading causes for firms to operate informally. A 2008 International Finance Corporation (IFC) survey of SMEs in Bangladesh suggests that while the majority of the businesses believe that it is better to register their business with the registration authority, named the Registrar of Joint Stock Companies and Firms (RJSC), and operate formally, the cost to register a business, the corruption and the complicated registration processes keep many from becoming formal. In contrast to these survey findings, however, there is a growing body of evidence suggesting that although easier, less costly, and speedier registration procedures cause a modest increase in registration, direct incentives are more effective (McKenzie and Sakho, 2010; Bruhn, 2011; Kaplan, Piedra, and Seira, 2011; de Mel, McKenzie, and Woodruff, 2012). Building on a major business registration reform in Bangladesh that substantially reduces the time, complexity, and hidden costs of registering a business, our intervention aims to encourage SME registration through a targeted information campaign. The information campaign is meant to raise awareness of the potential benefits of registration and clarify the registration procedures implemented with the recent registration reforms.

There are clear legal benefits of registration, starting from name protection
violent conflict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react significantly to their adoption.

In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints.<sup>46</sup> To test these hypothesis we use data on ethnic groups’ access to executive power and night light intensity from the GROW<sup>_up_</sup> Research Front-End (RFE Release 2.0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level.<sup>47</sup> Night light data has the benefit of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data. As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0.25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013)<sup>48</sup> and they confirm that the relationship is linear and also find an elasticity of around 0.3.

Access to political power is ranked on a scale from 1 to 7 in the GROW<sup>_up_</sup> dataset.
Our results have important implications for developing countries seeking to expand exports. The recent Doha trade negotiations focused on import barriers in the United States and European Union. However, since OECD tariffs are already quite low, estimates of increased exports by developing countries from eliminating them are also relatively small—around 2-10 percent.<sup>22</sup> For the least developed countries, which already have preferential access, the benefits from additional market access are in some cases negative.<sup>23</sup> In contrast, our estimates imply that improving trade facilitation can have relatively large effects on exports. For example, in Sub-Saharan Africa it takes 48 days on average to get a container from the factory gate loaded on to a ship. Reducing export times by 10 days is likely to have a bigger impact on exports (expanding them by about 10 percent) than any feasible liberalization in Europe or North America.

> 22 Amiti and Romalis (2005) use detailed data on tariffs and trade volumes, incorporating preference schemes, and show that average tariffs on non-LDC developing country products in the U.S. and the EU are below 3 percent.  Tariffs on comparative advantage goods are also below 3 percent.  They estimate market access gains of 2.28 percent following a 40 percent reduction in tariffs in the EU and US, with no exclusions.  Anderson, Martin and van der Mensbrugghe (2005) estimate that _world_ exports would increase by about 10 percent if there was complete global trade liberalization.

> 23 Francois, Hoekman, and Manchin (2005) estimate an export expansion of 6 percent on average, ranging from -8 to 20 percent in poor countries, from full elimination of OECD tariffs.

22
WPS3739

# **Patenting and Research and Development: A Global View***

Mariano Bosch Daniel Lederman William F. Maloney _The World Bank_

**Abstract:** Using a new global data base on patents and innovation inputs, this paper employs several recently developed econometric techniques to examine the dynamic relationship between research and development and U.S. patents granted. We confirm at the country level the recurrent micro-level finding of a strong relationship between the two and estimate the OECD elasticity to be effectively unity. This conflicts with the frequent micro level finding of strong diminishing returns in knowledge generation and suggests the importance of spillover effects measurable only at the aggregate level. Developing countries, however, do show diminishing returns and implicit rates of return to innovative effort in these countries appear to be a fraction of those found in the OECD. We then seek to explain the variance in elasticities by introducing proxies for the functioning of the national innovation system, the set of institutions and agents that create and diffuse knowledge. Across the entire sample, several variables, in particular education, security of intellectual property rights, and in some specifications the quality of research institutions and their interaction with the private sector impact the elasticity of transformation of R&D into patents.

World Bank Policy Research Working Paper 3739, October 2005 _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._

* We are grateful for helpful comments from Eli Berman, Bronwyn Hall, Andrés Rodriguez Clare and
/ Summer 2020 (with permanently closed firms)_

||Model-<br>Predicted|Survey-<br>Measured|No. of<br>Observations|
|---|---|---|---|
|1. Chemicals|4.9|9.9|26|
|2. Food, drink, and tobacco|8.1|9.2|73|
|3. Garments|18.7|23.6|50|
|4. Hotel, restaurant, and||||
|transportation|17.8|17.0|118|
|5. Machinery, electronics,<br>and construction|16.3|22.6|36|
|6. Metals and non-metallic||||
|minerals|7.6|2.8|14|
|7. Wholesale, retail, and||||
|other services|19.3|19.4|147|
|8. Wood, paper, publishing,<br>and printing|16.7|15.6|21|
|Total|15.8|16.8|485|

Source: CFUWBES1.

Notes: Model-predicted estimates are based on projected jobs lost using a multi-stage model and the employment structure in LMPS. Survey-measured jobs lost are based on directly computed sector-average job losses. In both cases, to estimate the allocation of jobs lost to production and non-production workers, we use the 2013 and 2019 World Bank Enterprise Surveys for Jordan and estimate sector-specific Tobit polynomial models. The calibrated models are applied to estimate the numbers of production and This is somewhat to be expected as there may be new dynamics that emerge over time that affect the link 32
- SMSEE, 2017. Report on Brick Kiln Stack Emission Monitoring in Kathmandu Valley. SMS Environment and Engineering Pvt. Ltd. (SMSEE), Kathmandu.

- Stockwell, C.E., Christian, T.J., Goetz, J.D., Jayarathne, T., Bhave, P.V., Praveen, P.S., Adhikari, S., Maharjan, R., DeCarlo, P.F., Stone, E.A., Saikawa, E., Blake, D.R., Simpson, I.J., Yokelson, R.J., Panday, A.K., 2016. Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of trace gases and light-absorbing carbon from wood and dung cooking fires, garbage and crop residue burning, brick kilns, and other sources. Atmos. Chem. Phys. 16, 11043-11081.

- Subedi, S.K., 2020. Study of Characteristics of Bricks Produced in Kathmandu, Nepal. American Journal of Civil Engineering 8, 64-76.

- Tandon, S., Gupta, S., Singh, S., Kumar, A., 2017. Respiratory Abnormalities among Occupationally Exposed, Non-Smoking Brick Kiln Workers from Punjab, India. Int. J. Occup. Environ. Med. 8, 166-173.

- TERI, 2017. Resource Efficient Bricks (REB): Benefits of Producing Clay-fired REBs. The Energy and Resources Institute (TERI), New Delhi.

- Thygerson, S.M., Sanjel, S., Johnson, S., 2016. Occupational and environmental health hazards in the brick manufacturing industry in Kathmandu valley, Nepal. Occupational Medicine & Health Affairs 4, 248.

- Tibrewal, K., Venkataraman, C., Phuleria, H., Joshi, V., Maithel, S., Damle, A., Gupta, A., Lokhande, P., Rabha, S., Saikia, B
likelihood of having been affected by the violence. There may also be unobserved household and individual characteristics which are correlated both with the conflict and the outcome variables.<sup>15</sup> This may potentially lead to biases in the analysis, which are typically corrected through instrumental variable estimation techniques.

The economics literature on violent conflict usually employs geographical correlates of violence at a suitable level of analysis (village or district) as instruments for self-reported conflict information. Unfortunately, the codes for sampled villages and districts in Timor Leste were not disclosed in the 2001 TLSS, and we are unable to employ this strategy in the analysis. We only have suitable information at the area level. As Timor Leste is divided into only five areas, the IV estimator is not reliable at that level. However, we are able to make use of the DID estimation

> 14 Employment characteristics are collected only on individuals of 10 years or older.

> 15 One of the most common omitted variables in empirical research on the impact of violent conflict is the level of support of households for armed groups (see Wood 2003, Petersen 2001). Another common omitted variable is the level of control of different armed factions (Kalyvas 2006). In the case of Timor Leste, the level of control of FRETILIN and the Indonesian troops is likely to vary with the geographical characteristics of each area, as well as their proximity to West Timor.

18
and their **|**interaction)**|
||(16)|(17)|(18)|(19)|(20)|
|Excess interaction share|-0.639***|0.007|0.004|0.002|0.129***|
||(0.113)|(0.019)|(0.005)|(0.006)|(0.027)|
|Withheld from randomization (WFR)|-1.060*|-0.075|-0.014|-0.012|-0.097|
||(0.522)|(0.078)|(0.021)|(0.023)|(0.101)|
|WFR*Excess interaction share|-2.542**|-0.370**|-0.128**|-0.083*|0.956**|
||(0.899)|(0.124)|(0.043)|(0.046)|(0.303)|
|Observations|16,453|17,167|17,145|15,186|17,157|
|R-squared|0.241|0.390|0.272|0.250|0.192|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. WFR stands for withheld from randomization. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated
# **Introduction**

As efforts to decarbonize progress, it is vital to ensure the reliability of power systems within the context of the challenges of phasing out fossil fuels, aging transmission and distribution infrastructure, and increasing weather-induced fluctuations on energy supply (i.e., wind, solar, and hydro-power) and demand (i.e., heating and cooling demand). Even without these increasing challenges, the economic costs of unreliable critical infrastructure are already costly, particularly in less developed countries. A 2019 World Bank report estimated that the cost of unreliable infrastructure in 137 low- and middle-income countries (a cohort of countries that represents about 80 percent of the GDP of all low- and middle-income countries) was nearly $300 billion a year (about 1.5% of GDP), with over half of that cost associated with electricity outages and the subsequent losses to sales and the costs of self-generated electricity (Rentschler et al. 2019 _)_ . A 2019 survey found that power outages in a typical month in Kosovo, North Macedonia, Albania, and Serbia were estimated to occur about 8, 5, 4, and 2 times each month, respectively (Dalloshi et al. n.d.).

Extreme demand for cooling is among the many reasons why power systems can fail to maintain reliable supply. High temperatures heat the land surface and infrastructure, trigger the usage of air conditioning where available, and cause some of the highest electricity demand spikes that systems experience (Franco et al. 2007, Waite et al. 2017). As such, periods of elevated electricity demand driven by hot temperatures are critical times for electrical grids and can lead to soaring energy prices and, most critically, power outages or rolling blackouts (Hatvani-Kovacs et al. 2016). Examples include the blackouts triggered in California in July 2018 when spikes in electricity demand during a heat wave could not be met by the power system (Khan 2018). The July heat wave of 1995 was an even more
Policy Research Working Paper 9818

# **Abstract**

Little is known about how gender inequality influences poverty rates of forcibly displaced people. This paper uses a nationally representative survey to analyze poverty among internally displaced people and non-displaced people in Somalia. More than half of internally displaced people’s households and 47 percent of non-displaced people’s households are female headed. Although poverty rates are higher among internally displaced people than non-displaced people (77 versus 66 percent), male-headed households are poorer than female-headed ones among both groups. Extending the analysis beyond headship to demographic characteristics and by the gender and number of earners provides a more nuanced picture. Demographic characteristics are strongly associated with poverty rates for internally displaced people but not for non-displaced people. Having more income earners reduces poverty risk for all households. For internally displaced people’s households, the largest decrease in poverty risk is associated with having more female earners, while having more male earners is associated with the lowest poverty for nondisplaced people’s households. The analysis highlights that poverty reduction policies and programs must cover all households and lift barriers to women’s economic opportunities. Programs that respond to women’s care responsibilities and address barriers to women’s economic opportunities are especially important for internally displaced people.

This paper is a product of the Gender Global Theme. 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 lhanmer@worldbank.org (corresponding author), erubiano@worldbank.org, and juliethsa@iadb.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
�<br>_I_<sup>_F P E_</sup><br>_z_<br>�||(0.046)<br>(0.021)||
|_y_<br>||[0.021]<br>[0.007]||
|�<br>Years of Schoolingiz|||-0.529|
|y|||(0.165)<br>[0.001]|
|First Stage F-Statistic||5.93|5.93|
|Number of Clusters|30|30<br>30|30|
|N|13,922|13,922<br>13,922|13,922|

_Source_ : Author’s analysis in panel A is based on data from the Ethiopian census of 2007 and from the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016; each data source is used separately in panels B and C.

_Note_ : The dependent variable is years of schooling in column 2 and is number of births in the other three columns. Years� of Schooling _izy_ is the reported number of years of schooling from the data; Years of Schooling _izy_ is the predicted number of years of schooling, instrumented with the free primary education (FPE) intensity measure, _Izy_<sup>FPE</sup> . All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age when multiple survey waves are included. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

26
concepts like “joint” ownership may not be well understood (Jacobs and Kes, 2015). Using plotlevel data from Ethiopia and Malawi, Kang et al. (2020) also find that joint ownership may not necessarily translate into actual decision-making roles, with men often continuing to have soledecision making on planting on jointly-owned plots.<sup>7</sup> Other subjective issues arise aside from overall ownership — Doss et. al (2018), for example, find using nationally representative data from Ghana and Ecuador and for the state of Karnataka, India, that across all three samples, the distribution of monetary values of dwellings reported by women tends to be more narrowly clustered around the middle of the distribution, as compared to men, potentially affecting calculations of wealth inequality depending on the share of men and women in the survey sample.

In 2016, the Malawi National Statistical Office concurrently implemented (i) the Fourth Integrated Household Survey 2016/17 (IHS4) - a cross-sectional, nationally-representative survey of 12,480 households, and (ii) the Integrated Household Panel Survey 2016 (IHPS) - a longitudinal survey of 2,508 households that have been followed since 2010. The IHPS was the first nationallyrepresentative multi-topic household survey attempting to operationalize the aforementioned UN Guidelines and to interview each adult household member in private regarding their personal ownership of and rights to selected physical and financial assets. The individual interviews were attempted to be conducted simultaneously in each household and with a gender match between the enumerators and the respondents. On dwelling (inclusive of the residential plot) and agricultural land, the IHPS administered adapted versions of the MEXA questionnaire modules, inquiring directly regarding the respondents’ personal — exclusive as well as joint — ownership of and rights. In contrast, the IHS4 followed the traditional (i.e. business-as-usual) approach of interviewing the most knowledgeable household member(s) to provide information on household members’ ownership of and rights to the same set of assets.

The parallel implementation of the IHPS and the