rafmacalaba/datause-encoder-data huggingface dataset:
55 1981 (9 percentXTabie 18). Therefore, there Is good reason to suppose that abour productivity on Individual ejidos has now caught up with that of small private farms (altering the pro-1970 picture when, according to the census data, ejidos showed relatively low labour productivity).

121. Evidence Is also avaliable to demonstrate that individual ejidos now have better access to irrigation than they did before 1970: according to the 1981 cens, on farms up to five hectares, 18 percent of ojido cropland--Is irrigated, compared to 13 percent In the case of private farms (Table 11). This suggests that Individual ejidos are no longer likely to have lower crop yields than small private farms. In the case of maize (the most widely-grown crop), the proportion of the sown area under Irrigation was roughly the same for each tenure sector In 1981. Also, In terms of the proportion of the sown area actually harvested (a rough guide to the extent of crop loss), ejido farms fared somewhat better than private farms In 1981 (Table 14). Other land use data from the 1981 census also suggest that Individual ejido farms are now unlikely to be less productive than comparably-sized private farms: the former show a higher proportion of land In agricultural use; and the ratio of fallow land to cropland Is the same for both tenure categories on farms up to five hectares (Table 9). This however may be due to the higher proportion of Irrigated land within the ejido sector.

# X. CONCLUSIONS AND POLICY RECOMMENDATIONS

122. In contemplating changes to Mexico's land tenure regkne It Is important to consider political as well as economic constraints: specifically, It may be unwise to seek to undo the land reform if the economic payoff from such an Initiative Is likely to be small. Since the mid-1970s, land reform has not been a significant Item
production tax credit would reduce the cost to investors, while technological innovations are expected to reduce actual costs as well. Overall, the 20% wind scenario would result in US$43 billion in incremental cost but would also result in cumulative CO2 reductions of more than 7,600 million metric tons (Mt of CO2) by 2030. Thus, by increasing reliance on wind energy for electricity production to 20%, CO2 emissions can be reduced at a cost of about $5.70 per ton of CO2 (tCO2) according the USDOE (2008). If this is realistic, then wind energy development has a promising future in the United States.

# 4. Costs of Wind Power Generation

The costs of generating wind power depend to a large extent on wind resources, in particular, mean wind speed at hub height, the cost of turbines and related equipment, the proximity to a sufficiently strong transmission grid (i.e. the cost of grid extension and grid reinforcement), and the accessibility of the site. Other factors such as the existing generating mix, system load profiles, connections to grids in other countries/regions, electricity markets, system operating procedures, and land costs also have a significant impact on the costs of wind power generation. A large number of studies compare the costs of wind power with those of other electricity generation technologies (e.g., NEA/IEA, 2005; Kammen and Pacca, 2004; Lazard, 2008; CPUC, 2008; EIA, 2008; ESMAP/WB, 2008). Since electricity generation technologies vary significantly in terms of their investment requirements and operational characteristics, costs are converted to a level or base for comparison purposes, known as the levelized (or bus bar) cost of electricity generation. The levelized cost includes mainly investment or capital costs, operation and maintenance (O&M) costs, and fuel costs. While capital and fixed O&M costs are proportional to installed capacity, variable O&M and fuel costs are functions of electricity output.

Recent studies show that wind power can be competitive with conventional sources of electricity generation. A comparison of the levelized costs of various electricity generation technologies released by the California Public Utilities Commission on May 13, 2008 shows that wind power is one of the cheapest options with levelized cost of US$89.10/MWh (CPUC, 2008). Levelized costs per MWh expressed in 2008 US$ of other technologies examined by the Commissions are as follows: Supercritical Coal – $106; Integrated Gasification Combined Cycle (IGCC) Coal – $115; IGCC Coal with Carbon Capture & Storage (CCS) – $173; 8
their covariance matrix first. The estimator for _θ_ and its covariance matrix then follows from GLS.

**Estimating** _Z_<sup>ˇ</sup> = _V_ (ˇ _uO_ )

Given the residual process _u_ = _σ_ ( _I − ρW_ )<sup>_−_1</sup> _ε_ , we have: where _C_ = ( _I − ρW_ )<sup>_−_1</sup> . Recall that for _i_ = _j_ , _Wij_ = _Tij_<sup>_−τ_</sup> with _T_ a (scaled) symmetric matrix of distances between locations _i_ and _j_ , and _Wii_ = 0. Partition the variance matrix Σ according to in-sample locations ( _S_ ) and out-of-sample locations ( _O_ ) so that we can write: Also, let Σ _·S_ denote the first ’column’ of this partition: Assuming normally distributed _ε_ and survey error _eS_ , it follows that: where _DS_ is the variance matrix of _eS_ , established separately, based on the survey design, and taken as a given matrix of parameters.<sup>10</sup> The empirical counterpart is: – and is subject to model error the topic of this sub-section.

We approximate the MSE of _u_ ˇ using the Delta method. Let operator _d_ () indicate taking (infinitesimal) deviations from the true parameter values. Then: ˆ To simplify notation, write _A_ = Σ _·S_ [Σ _SS_ + _DS_ ]<sup>_−_1</sup> and _b_ = _qS − XSθ_ , with obvious derived meaning of _A,_<sup>ˇˇ</sup> _b_ . Then, denoting actual deviations from true parameter values

> 10Under the model assumptions _DS_ stems from variation in _XS_ and _σε_ 2<sup>.Alternatively,itcould</sup> therefore be argued that _DS_ is increasing in _σ_<sup>2</sup> . We ignore this complication here.

37
information regarding the number of hectares covered --as well as value of production and premiums paid-- by weather station for each crop in each municipality. There are cases in which there is more than one weather station in the municipality, and we have information at the weather station. Similarly, there are cases in which one weather station --located close to a municipality boarder-- provides information to insure crops in more than one municipality. In these cases, we have information of the number of hectares covered --as well as value of production and premiums paid-- by each weather station in each municipality. The second source of data is the National Water Commission; the data consist of daily rainfall measures in millimeters for every weather station in the country from January 2004 until December 2008. Third, we use the geographic location data of electoral sections obtained from the Department of Cartography of the Federal Electoral Institute. Finally, data on the outcomes of Presidential elections in 2000 and 2006 by electoral section are public from the Federal Electoral Institute (IFE) website. In addition to these, we use complementary information on socio-demographic characteristics of municipalities from the 2000 Population Census and the 2005 Short Census or _Conteo_ , publicly available from the National Institute of Statistics (INEGI) website.

Combining these data, we construct a dataset with the electoral section as the unit of analysis. We first identify the municipalities covered by the WII program and the weather stations used for each municipality. Municipalities in Mexico largely vary in size and population. The available data on insurance coverage does not allow us to identify those electoral sections -within each municipality- that are covered by the insurance and those that are not; therefore we use the distance from the weather station as a criterion to select electoral sections in our dataset. Using a 2006 GIS map of electoral sections, we calculate the distance from the weather station to the nearest frontier of the geographic polygon of an electoral section. For those cases in which more than two weather stations serve a single municipality we use the distance from the electoral section to the nearest weather station. Finally, we construct our dataset including only those electoral sections that are within a defined maximum distance from the weather station. Using the map of the State of Guanajuato, Figures 2.a and 2.b illustrate with an example the process of constructing the dataset.

Page **8** of **29**
- 1.73 -

**Figure** 1.C.24 LAND USE UNDER SELF-SUFFICIENCY, 2015-19

<!-- Start of picture text --> (MiI.ttos of Mr")<br><!-- End of picture text --> <!-- Start of picture text --> 14.0-<br>13.0-I<br>Ito-<br>10.0 1<br>9.0<br>3O /<br>5.0<br>4.0 -<br>2.0 T<br>120 :40 160 180 200<br>Cocoa Puce (17 C.d/KqJ<br>a Perennsal * Annual O Follow<br><!-- End of picture text -->
Policy Research Working Paper 9999

# **Abstract**

Researchers and policy makers face significant challenges in selecting a method to conduct remote surveys, especially when collecting sensitive information or during turbulent life stages of hard-to-reach groups. In the context of the COVID-19 lockdown, this study randomly selected about 600 adults in El Salvador to survey using two different tools: telephone interviews or a self-completion survey via WhatsApp. The findings show that phone-based surveys increase the rate of survey completion by 42 percentage points. Even larger effects are documented for women and older adults. Although the direct costs of phone-based surveys are substantially higher—doubling implementation cost—the estimates imply that when adjusted for the probability of completion, the costs of conducting phonebased surveys can be 25 percent lower.

This paper is a product of the Development Research 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 ldinartediaz@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
# Rebel with a Cause: Effects of a Gender Norms Intervention for Adolescents in Somalia<sup>∗</sup>

Rajdev Brar<sup>†</sup> Niklas Buehren<sup>‡</sup> Sreelakshmi Papineni<sup>§</sup> Munshi Sulaiman<sup>¶</sup> Impact evaluation, adolescents, gender, norms, conformity, Somalia [JEL] D63, D91, J13, J16, O12 ∗We thank Rachael Pierotti, Estelle Koussoub ́e, C ́eline Zipfel, Verena Phipps, Shubha Chakravarty, Jeannie Annan, Dave Evans, Richmond Atta-Ankomah and several seminar participants for excellent comments and suggestions. We also thank our partners at Save the Children Somalia who implemented the gender norms program called _Choices_ , in particular, Yahya Abdillahi, Hamse Koshin, Billow Hassan Abdi, Alice Shirley, Shelagh Possmayer, and Cianne Jones. This paper is an output of the World Bank Africa Gender Innovation Lab. We are grateful to the World Bank Group’s Umbrella Facility for Gender Equality (UFGE) for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. IRB approval was obtained from HML IRB Research and Ethics, reference 574TWBG18. The pre-analysis plan is registered on the AEA RCT registry at https://doi.org/10.1257/rct.2773.

> †University of California, Berkeley `rbrar@worldbank.org`

> ‡World Bank Africa Gender Innovation Lab `nbuehren@worldbank.org`

> §World Bank Africa Gender Innovation Lab `spapineni@worldbank.org (Corresponding author)`

> ¶BRAC Institute of Governance and Development `munshi.sulaiman@bracu.ac.bd`
household members (aged 15 to 64 years) who would participate in the experiment, stratifying them by age and gender. Each randomly selected member (or the member’s proxy respondent for LOA or C) provided information solicited via the three main modules: education, access to technologies, and labor. Only respondents assigned to the LOA treatment responded to that survey module. We made sure that the three relevant modules were collected at the beginning of the experiment and from all of the randomly selected self- or proxy respondents within the three treatment groups.

# **3.2 Outcomes**

We measure the effects of these alternative survey methods on employment and work as outcome variables. For this paper, the working-age population includes individuals between the ages of 15 to 64 years.

_Employment:_ We use data collected from all study participants via the standard labor module. According to the 19<sup>th</sup> International Conference of Labor Statisticians (ICLS), being employed is defined as working for pay or profit. Individuals who have engaged in work only for their own use in the past week are not considered employed. We define this variable as a dummy indicator that takes the value of 1 if the person engaged in at least one activity for pay or for profit in the last week, and 0 if the person reports either not engaging in any activity for pay or profit or engaging only in activities intended for own use during the past week (Durazo et al, 2021).

_Work:_ A person considered working must be involved in producing goods or providing services in the last week. The output of the activities could be for own use (e.g., farming produce for household consumption without the intention to sell) or for pay, sale and profits. We define this variable as a dummy indicator that takes the value of 1 if the person reported engaging in at least one activity during the previous week (e.g., farming for own consumption or sale, or working in a family- or non-family-run business, working for pay for someone else).

# **3.3 Summary Statistics**

Table 1 reports the summary statistics of the variables used in our analysis.<sup>14</sup> Panels A and B show descriptive statistics of the characteristics of the 1,008 participating households and the 2,480 selected household members, respectively. As we show in Panel A, the average household included in our sample has 3.7 members, which is consistent with the average household size (3.3

> 14 Appendix C includes definitions for each of these variables.

10
21 Table 2: Aggregate data for BIA and implied weights for different methods

|||Outpatient||Inpatient|
|---|---|---|---|---|
||CHC|Polyclinic|Hospital|Hospital|
|Govt. spending (_S_)|162,481|21,898|3,971,381|3,276,459|
|Out‐of‐pocket spending (_F_)|526,035|70,894|4,919,599|1,729,406|

Notes: The government spending (i.e. subsidies) and out‐of‐pocket payments to public facilities (i.e. fees) are in millions of VND are relate to 2005, the closest year to 2006 for which detailed NHA data are available. The data are taken from the NHA Table 2. The government spending figures reflect spending by health ministry and central level, and health departments at the provincial, and district levels, as well as spending by the social health insurance agency, Vietnam Social Security. Out‐of‐pocket payments are listed as spending by households in the NHA Table 2. The table does not break government and out‐of‐pocket spending down exactly by provider type. The assumption is that spending on ‘traditional medicine’ is all at the hospital level. Government and out‐of‐pocket spending on inpatient care are taken from the figures labeled ‘inpatient treatment’ and ‘outpatient treatment’ under the ‘traditional medicine’ heading, i.e. items HD 1.1.1 and HD 1.1.2 respectively. Spending at CHC and polyclinic level are taken from the heading ‘primary health care and school health care’ under ‘health prevention and public health’, i.e. item HD 1.2.4. This is the only item in the NHA where spending at the commune level is recorded, but it is possible that some of the polyclinic spending is recorded at a higher level. It was assumed that 75% of the ‘primary health care and school health care’ was incurred in health facilities, and the rest in schools.
# VI. CONCLUSION

that this management mode entails (Bertrand 2009). At the same time, it also provides evidence linking family management to worse performance and organizational structures (Bertrand and Schoar 2006, Bloom and Van Reenen 2005).

This paper contributes to this debate by providing novel evidence about how firm decisions to be familyor professionally-managed are influenced by the external contracting environment, and how these choices influence both firm and aggregate productivity. We thus provide evidence for a specific channel through which institutions influence TFP. Using a unique dataset we created containing the managerial mode choices for over 119,000 firms across 134 regions in 11 countries, we offer a more systematic and comprehensive view of the incidence of family management and ownership than previously possible and establish a set of new stylized facts concerning the sorting of firms across management modes according to firm, industry, regional and national characteristics. Our empirical analysis is guided by a simple model of an industry equilibrium that highlights how a weak contracting environment discourages the delegation of managerial tasks to outside professionals which in turn, due to span-of-control limitations, inhibits entrepreneurs from expanding to their optimal size. Hence, when contracting institutions are weak, allocative efficiency falls as productive firms remain suboptimally small and lower competition allows some inefficient firms to survive.

Consistent with the prediction of this model, our empirical results suggest that stronger rule of law and better contractual institutions are associated with reductions in the incidence of family firms. Within countries, we find that in regions with weaker levels of trust, firms are more likely to be family-managed and family-managed firms are likely to be relatively larger in line with the sorting patterns predicted by our model. These patterns are stronger in more complex industries that seem especially sensitive to contracting security. Further, we are able to break apart ownership from management and find that not all types of family ownership are equal: overall it appears that it is family management rather than family ownership that is detrimental to firm performance. Finally, in terms of economic significance, we show that the magnitude of these effects on productivity can be quite large, suggesting the salience of this issue to the ongoing growth debate.

22
the Ceddi Plaza, and in Cross River the Tinapa Mall that promises to provide retail and entertainment, Dubai style.

Cultural changes also accompany these sharp economic transformations, although not all of them appear positive. Sule et al. (2011) argue that a majority of the new Nigerian middle class are more focused on the consumption of material goods and not as much on increasing the production base in the country. Furthermore, the authors report that some of the consumption is also being financed through loans thereby pushing the households into debt. Several authors also warn against an overly optimistic identification of the emerging middle class with an entrepreneurial bourgeoisie. Echoing the sixties debate in Latin America about the ‘borguesia compradora’, they argue that the Nigerian middle class should move from a consuming middle class to a producing middle class.

In summary, while disregarded by the academic economic literature, the discussion on an emerging African - and Nigerian - middle class is buoyant. This overview has, however, emphasized the need for a more systematic and quantitative approach to the problem; notably the definition of an interval/threshold in the income distribution where we can identify middle class households. Using a methodology already experimented in Latin America, in the following sections we measure the Nigeria middle class and examine the changes that have taken place in the last decade.

# **III. Data and Methodology**

# _Data_

The General Household Survey Panel (GHS-Panel), one of the datasets used in this paper, is a randomly selected sub-sample of 5,000 households from a larger cross section survey of 22,000 households. The GHS-Panel is a Living Standard Measurement Study (LSMS) survey representative at the national and zonal (geopolitical) levels. The GHS-Panel waves 1 and 2 were implemented across 2010-2011 and 2012-2013 respectively. The same households were interviewed in the post planting period (between August and October) and in the postharvest period (between February and April) in both waves and administered almost the same questionnaires. It is a multi-topic household level survey that covers a variety of topics including consumption and income generating activities. Consumption data in the GHSPanel are collected using a 7 day recall period. The second dataset used in this analysis is the Harmonized Nigeria Living Standard Survey (HNLSS). Unlike the GHS-Panel, the 9
# **Poverty and Inequality Maps for Rural Vietnam: An Application of Small Area Estimation**

Nguyen Viet Cuong Tran Ngoc Truong Roy van der Weide<sup>_1_</sup> JEL classification: I31, I32, O15 Keywords: Poverty measurement, poverty mapping, agricultural census, household survey, Vietnam.

> 1 Nguyen Viet Cuong is a lecturer at the National Economic University; Tran Ngoc Truong is a researcher at the Institute of Labor Science and Social Affairs (ILSSA), Roy van der Weide is affiliated with the Poverty and Inequality Research group at the World Bank. This report documents the main findings of the poverty mapping project initiated by Dr. Nguyen Thi Lan Huong from ILSSA, MOLISA and funded by the World Bank in Vietnam. We would like to thank Gian Thanh Cong from ILSSA, Le Trung Hieu and Lo Thi Duc from GSO for their help and comments. The logistic and financial supports have been provided by the World Bank in Vietnam and the Institute of Labor Science and Social Affairs, Ministry of Labors, Invalid and Social Affairs.
24 <u>Table 5: Median values of the Q90/Q50 dispersion measure of risk of out-of-pocket expenditures on health, latest year</u>

||Low-<br>income|Lower<br>middle-<br>income|Upper<br>middle-<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|East Asia & Pacific|34|9|10|4|9|
|Europe & Central Asia||6|7|5|5|
|Latin America & Caribbean|45|10|13|13|13|
|Middle East & North Africa||7|7|7|7|
|North America||||6|6|
|South Asia|5|7|||6|
|Sub-Saharan Africa|9|8|12||10|
|Total|9|9|11|5|9|

<u>Table 6: Median budget share for out-of-pocket expenditures on health, latest year</u>

||Low-<br>income|Lower<br>middle-<br>income|Upper<br>middle-<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|East Asia & Pacific|4.0|1.7|3.7|3.1|2.6|
|Europe & Central Asia|3.7|3.6|3.8|2.6|3.2|
|Latin America & Caribbean|3.0|4.1|3.6|3.5|3.9|
|Middle East & North Africa||3.5|3.7|4.7|3.5|
|North America||||2.6|2.6|
|South Asia|6.4|3.6|||4.7|
|Sub-Saharan Africa|2.7|2.7|2.0||2.6|
|Total|2.8|3.1|3.4|2.7|2.9|