rafmacalaba/datause-encoder-data huggingface dataset:
who are currently partnered is very high (97%). The average respondent is about 37-38 years old and lives in a household with 5-6 members. Panel B shows education and mobile phone ownership. Average educational attainment is 5.2 years in Malawi and only 2.4 in Liberia. Sixty-six percent of women in Malawi are literate, compared to 30% in Liberia. Mobile phone ownership is similar in the two countries, ranging from 42% in Liberia to 45% in Malawi.

Panel C shows some indicators of household income and wealth, and reveals that households are better off in Liberia than in Malawi: average total monthly household expenditures are $26 in Malawi and $66 in Liberia, or about $0.17-0.39 in per capita daily expenditures. In Malawi, the average household reports about $160 worth of assets, compared to $420 in Liberia. Most of the households in the study villages are subsistence farmers, and the average monthly non-agricultural income measured in our surveys is $8-10.

Panel D shows a few proximate indicators related to female empowerment. Forty-four percent of women in Malawi have their own income source, compared to 31% in Liberia. The age difference (in years) between husband and wife is 2.9 in Malawi and 4.1 in Liberia.

Turning to Columns 2 and 4, we find two outcomes for which the differences are significant at 10% in Malawi (food security and total expenditure, which are both lower in the ACASI group), and none in Liberia. While the randomization appears to show no cause for concern, we present results separately with and without controls, and find no difference in results. In any case, we control for all variables reported in Table 1 for the main analysis.

# **3 Results**

## **3.1 Screening Questions**

We start by documenting responses to the five screening questions which were administered to all respondents via ACASI. Results, which are shown in Table 2, suggest major cause for concern. Only 95-98% report being a woman, and 91-93% report living in their county/district of residence. Even more surprisingly, only 78-86% report that they slept in the past week and 83-85% report that it rained in the past year. While we do not have a good explanation for these results, an _ex post_ explanation from some of our field staff was that some women interpreted the sleep question as “getting a good night’s sleep,” to which some women reported no. We do not have a good explanation for the 9
# Table 1: Summary Statistics

||2000||2005||2010||
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|Share of Employment in|||||||
|Self-farming|26.9%|18.2%|23.9%|17.0%|25.7%|17.5%|
|Agricultural Daily Labor|19.4%|16.5%|15.9%|14.0%|15.5%|13.2%|
|Self-Non-agriculture|19.9%|13.8%|22.9%|14.2%|20.9%|12.3%|
|Per Capita Real consumption (Taka)|812.51|349.96|889.83|317.14|1142.57|360.35|
|Agricultural Wage (taka)|66.90|49.16|56.70|24.01|158.05|68.25|
|Rice Yield (ton/acre)|0.95|0.11|1.01|0.13|1.35|0.46|
|Population in 1991|314630|148304|297031|147249|295322|144862|
|Proportion Urban|0.24|0.30|0.23|0.00|0.20|0.22|
|Proportion of household with electricity|0.32|0.32|0.40|0.31|0.51|0.31|
|Percent with irrigation|61.47|29.97|59.79|31.39|67.20|23.79|
|Proportion of workers with secondary or above education|0.14|0.08|0.14|0.07|0.15|0.07|
|Rainfall(mm)|1390.9|423.4|1635.8|382.2|1457.2|361.6|
towards completing primary school. Third, for each child we calculate the total schooling-related discretionary expenditures during the 2007-2008 school year. We focus on those schooling expenditures where parents have discretion in the amount spent and include school supplies and parent association fees. On average for all currently enrolled primary-school aged children, parents spent 845 FCFA on these discretionary items per child during the school year (about $2.04 using the June 2008 exchange rate of 415 FCFA = $1 USD).

# **4. Empirical Identification Strategy**

# _4.1 Econometric Specification_

Studies of sibling rivalry in education typically use counts of the number of siblings and the number of sisters that a child has to explain different schooling outcomes (attendance, enrollment, attainment) as follows:

# (1)

where _eih i_ in household _h_ , _Sih_ is a count of the number of is the educational outcome for child siblings the child has, _Fih_ is a count of the number of female siblings the child has, _Xih_ is a vector of individual characteristics such as age and gender that might influence parental investments, Z _h_ is a vector of household characteristics, and ε _ih_ is a random, idiosyncratic error term. The interpretation of ω0 is the change in _eih_ associated with an additional male sibling. The interpretation of ω1 is the change in _eih_ associated with the thought experiment of converting a sibling from a male to a female. ω0 + ω1 is then the change in _eih_ associated with adding an additional female sibling. This approach takes current family size and composition as given at the time the parents make the enrollment decision.

To better understand the parental schooling investment decision, we expand on the sibling rivalry model in Equation 1 to control for previously unobserved characteristics about the 10
**Figure 28: Speed plots approach – impact of long term price change on social costs of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012)

**Figure 29: Volume to capacity ratio approach – impact of long term price change on social costs of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012) 27
# **Gendered Language**

## Pamela Jakiela and Owen Ozier<sup>_∗_</sup>

**JEL codes:** J16, Z10, Z13

**Keywords:** grammatical gender, language, gender, linguistic determinism, labor force participation, educational attainment

> _∗_ Jakiela: University of Maryland, BREAD, and IZA, email: pjakiela@umd.edu; Ozier: World Bank Development Research Group, BREAD, and IZA, email: oozier@worldbank.org. We are grateful to the Gender Innovation Lab at the World Bank for funding, to Laura Kincaide, Yujie Lin, and Kattya Quiroga Velasco for research assistance, and to Arun Advani, Sarah Baird, Michal Bauer, Lori Beaman, Premila and Satish Chand, Sameer Chand, Michael Clemens, Austin Davis, Giles Dickenson-Jones, Alice Evans, David Evans, James Fenske, Deon Filmer, Jed Friedman, Julio Garin, Garance Genicot, Jess Goldberg, Guy Grossman, Kyaw Hlaw, Guido Imbens, Clement Imbert, Anett John, Shareen Joshi, Eeshani Kandpal, Madhulika Khanna, Brent Kreider, Eliana La Ferrara, Margreet Luth-Morgan, Jeremy Magruder, Andy Marshall, Justin McCrary, David McKenzie, Ted Miguel, Hannes Mueller, Oyebola Okunogbe, Jessica Olney, A.K. Rahim, Martin Ravallion, Bob Rijkers, Jesse Rothstein, Fr ́e Schreiber, Pieter Serneels, Dominique van de Walle, Andrew Zeitlin, and seminar audiences at Georgetown University, Bocconi University, the University of Warwick, the University of Delaware, the Institute for Fiscal Studies, the European University Institute, the World Bank, UAB (Barcelona), CERGE-EI (Prague), and CSAE (Oxford) for helpful comments. All errors are our own.

The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
evolve in lockstep.<sup>20</sup> This figure reveals two striking insights: First, the relative price of non-ICT capital and output/consumption are essentially constant throughout the entire sample. Second, the price of ICT capital falls substantially, both in absolute terms and relative to all remaining types of assets, especially after the 1982 recession. Panel B of Figure 8 graphs the respective depreciation rates for each type of capital.

Based on these measures, we use the derivations in Appendix A to construct the income share for each type of capital and Figure 1 illustrates the resulting estimates. One can clearly see that the income share of non-ICT capital does not show any significant trend throughout the entire sample. During the same period, the income share of ICT capital has roughly quadrupled, from around 1% in 1968 to around 4% in 2000. This suggests that the introduction of ICT did not significantly crowd out other forms of capital, whose income share fluctuated around a trend-less long run average of around 34%; instead, it crowded out routine labor.

# 3.1. Price-Quantity Decomposition

The above results suggest a substantial increase in the income share of ICT capital relative to the income share of non-ICT capital. While we did not use the real stock of capital to compute its income share, _si,t_ , we find it instructive to decompose the payments to capital, _Ri,tKi,t_ , into a price and quantity component. To this end, we construct a chained quantity index for the stock of both ICT and non-ICT capital based on the BEA’s fixed asset accounts. Panel A of Figure 9 shows that the stock of ICT capital in 2012 is about 40 times its 1968 level. On the other hand, Panel B illustrates that the rental rate of ICT capital—measured in units of final output—fell substantially over the same period. In particular, while an additional unit of ICT capital increased final output by about 30% in 1968 it only produced less than an additional 10% in 2012. This price-quantity decomposition suggests that the increase in the income share of ICT capital is due to massive accumulation of ICT, while the rental rate of ICT capital fell during this time.

> 20See Figure D.15 in Appendix D for a decomposition of non-ICT assets into residential and non-residential assets.

19
|**Country**|**Intervention**|**Test**|**Evaluation**<br>**Method**|**Effect Size**|**EYOS_STEP**|**Reference**|
|---|---|---|---|---|---|---|
|India|The Pratham PicTalk programme: Year 2,<br>Machines only|English test|RCT|0.06|0.39|He, Linden and<br>Macleod, 2007|
|Uganda|Northern Uganda Literacy Project (NULP):<br>Gov treatment arm|EGRA test|RCT|0.08|0.52|Kerwin and<br>Thornton (2015)|
|India|PicTalk: Year 1|English|RCT|0.08|0.52|He, Linden and<br>Macleod, 2007|
|Philippines|Reading materials, teacher training to<br>incorporate reading into curriculum|Filipino test|RCT|0.09|0.59|Tan, Lane and<br>Lassibille, 1999|
|Kenya|Health and Literacy Intervention (HALI)|Swahili passage<br>reading<br>comprehension|RCT|0.11|0.72|Jukes and Dubeck,<br>2015|
|Kenya|Health and Literacy Intervention (HALI)|Swahili passage<br>reading fluency<br>(words per<br>minute)|RCT|0.13|0.85|Jukes and Dubeck,<br>2015|
|India|The Shishuvachan programme: reading<br>classes (teacher training) and a library|Normalised<br>reading level<br>(Hindi, Marathi,<br>Urdu)|RCT|0.13|0.85|He, Linden and<br>Macleod, 2009|
|Kenya|The Primary Math and Reading (PRIMR)<br>Rural Expansion Programme|Kiswahili high<br>reading fluency|Multivariate<br>analysis)|0.14|0.91|RTI International,<br>2015|
|Chile|The Collaborative Language and Literacy<br>Instruction Project (CLLIP)|Word reading|RCT|0.14|0.91|Pallante 2013|

37
Given the two distinct groups of countries in the MENA region, which are oil-exporters and oil- importers, we run two groups of regressions. For oil-importers the main dependent variable is the growth of GDP, nevertheless for oil-exporters, the main dependent variables are the overall GDP ( Table **III-1** ) and the non-oil GDP (Table III-2). For the oil importers (Table III-3) and nonMENA countries (Table III-4), the main dependent variable is the GDP.

# **Table III-3: Oil Importers and GDP Growth**

This table show the result for the regression where the main dependent variable is overall GDP growth for the oil importers and the main explanatory variable is the local peak of medium-term credit cycle growth. The definition of the local peak of credit cycle growth is when the credit cycle growth is in its maximum volume compared to past and next five years. All regressions are estimated using OLS. Standard errors are clustered on the firm level. Standard errors appear in parentheses, and ***, **, * correspond to one, five, and ten percent levels of significance, respectively.

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9<br>VARIABLES GDP Growth<br>Jordon -0.54<br>(-2.15)<br>Morocco 0.17<br>(1.05)<br>Egypt, Arab<br>2.56***<br>Rep.<br>(0.66)<br>Lebanon -2.8<br>(5.1)<br>Tunisia -0.8<br>(0.95)<br>West Bank Gaza 2.3<br>3.6<br>Yemen, Rep. 1.7<br>2.5<br>Syrian Arab<br>2.1<br>Rep.<br>1.9<br>Libya 6.2<br><!-- End of picture text --> 14
The elasticity of location choice with respect to housing rent used in the model is -0.35. Anas and Chu (1984) reported a range for housing cost elasticity between -0.26 to -0.86 from previous studies and estimated it to be -0.36 for the Chicago MSA. Indra (2014) in a study of 275 metropolitan areas, found the residential choice elasticity with respect to housing cost in US to be -0.28. We believe any value around -0.36 is very reasonable. Based on this rent elasticity, we calibrated the dispersion parameter, �, in the consumer’s choice probability.

The elasticity of location choice with respect to commute time weighted across all modes is -1.0735. The data for this mode choice elasticity is taken from the study for Beirut by Danaf et al. (2014). Since no mode choice elasticity was present for minibus, we considered the mode choice elasticity for bus and minibus to be the same. Based on their weighted value of elasticity with respect to commute time, we calibrated the travel time disutility parameter,�, in the consumer’s choice probability.

There is no value in the literature related to housing construction from vacant land for Beirut. We assumed that the probability of housing construction from vacant land is 0.0035 in both MB and GB. These probabilities are derived from the supply of newly constructed housing floor space aggregated across MB and GB.<sup>6</sup> Assuming that the probability of construction is the same for MB and GB, the probability of vacant land constructed into housing is derived. As there are no data on the construction probability of commercial floor space from vacant land, we assumed that the share is based on the existing commercial floor space relative to residential floor space, adjusting the residential construction probability with this ratio.

Based on the above, the elasticity of housing/commercial construction with respect to the value of housing/commercial floor space was set at 0.5 (MB) and 2.1 (GB). The elasticity of housing/commercial demolition with respect to the value of vacant land was set at 0.05 (MB) and 0.21 (GB), that is at one-tenth the corresponding construction elasticity. Based on these construction and demolition elasticity values, the constants in the probabilities of construction and demolition are calibrated. We also confirmed that the assumed ratio of demolished to constructed floor space of 40% seems to yield plausible comparative statics results.

> 6 The source of these data is from the Order of Engineers.

16
# Tables

## _Table 1: Summary Statistics of Major Data Sets_

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
||MekongRiv|er Basin||||
|Δlog(NPP)|3227|‐0.00256|0.146958|‐0.52872|0.573803|
|Electricial Conductivity|3784|127.8139|442.9442|3.2|3598.75|
|Electrical Conductivity > 100 mS/m|3784|0.075317|0.263937|0|1|
|Precipitation (m/year)|3784|1.545724|0.423054|0.4649|3.0135|
|Precipitation^2 (m/year)|3784|2.56819|1.433598|0.216132|9.081182|
|Temperature (C)|3784|27.31999|1.070222|21.225|29.70833|
|Temperature^2 (C)|3784|747.5272|57.06304|450.5006|882.5851|
|Distance to station(km)|3784|40.70026|23.44734|1.501745|99.66087|
||India|||||
|Δlog(NPP)|23,786|0.022225|0.281151|‐2.70913|3.197244|
|Electricial Conductivity|26,830|405.1288|311.6987|48.43333|2230|
|Electrical Conductivity > 100 mS/m|26,830|0.061014|0.23936|0|1|
|Precipitation (m/year)|26,830|1.156739|0.59924|0.1696|4.8393|
|Precipitation^2 (m/year)|26,830|1.697122|2.267632|0.028764|23.41882|
|Temperature (C)|26,830|26.36198|1.755244|15.13333|29.66667|
|Temperature^2 (C)|26,830|698.0349|90.26891|229.0178|880.1112|
|Distance to station(km)|26,830|54.64403|24.02906|0.783259|99.94202|
||Globa|l||||
|Δlog(NPP)|55,358|0.014986|0.276158|‐3.8473|4.766428|
|Electricial Conductivity|68,984|596.4569|1666.453|0|41914.29|
|Electrical Conductivity > 100 mS/m|68,984|0.100792|0.301055|0|1|
|Precipitation (m/year)|68,984|1.199992|0.657942|0.0005|5.1165|
|Precipitation^2 (m/year)|68,984|1.872862|2.244765|0.00|26.17857|
|Temperature (C)|68,984|21.59153|6.87379|2.333333|31.1|
|Temperature^2 (C)|68,984|513.4423|267.854|5.444445|967.21|
|Distance to station (km)|68,984|50801.36|29429.34|0|99997.37|

Notes: Table summarizes the main data sets use in each of the three models employed in this paper.

18
# The Hidden Costs of Ethnic Conflict: Decomposing Trends in Educational Outcomes of Young Kosovars"

Soumya Alva UNC-Chapel Hill Edmundo Murrugarra The World Bank ECSHD Pierella Paci The World Bank ECSHD 'This paper was prepared as part of the World Bank Poverty Assessment of Kosovo based on data from the LSMS. The Kosovo LSMS was jointly financed by the World Bank, FAO, UNDP, the UK Department for International Development and the Dutch Government. Comments from Deon Filmer and other participants at the World Bank Economists' Forum in May 2001 are appreciated.
_Figure 6: Mean change in disposable income and decomposition of change of those severely affected_ <!-- Start of picture text --> MT IE IT EL CY SK ES PT EE FR NL PL SI DE LV HU SE BG RO FI LT BE DK LU AT CZ HR<br>Market incomes Automatic stabilisers MC - compensation part MC - earnings part Other COVID policies Total<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>-70<br><!-- End of picture text --> Note: See Figure A6 in the Appendix for results by income groups. Order of countries based on Figure 4. Mean change in disposable income refers to the difference between Scenario A vs. D. The decomposition itself is based on all four scenarios. Severely refers to losses in market income of more than 11 percent (Scenario A vs. D). Source: Own calculations based on EUROMOD I4.0+.

Figure 7 shows the composition of automatic stabilizers for those severely affected (similar to Figure 5 for the total population). The composition of automatic stabilizers is similar to that of the overall population for the countries where automatic stabilizers play a more pronounced role for those affected the most. Also most other countries show very similar compositional patterns between the overall population and those most affected. The only country with notable differences is Poland where taxes play a larger role and SIC a reduced role, thus the difference is only on the contribution side but not due to differences in benefits.

_Figure 7: Composition of automatic stabilizers of those severely affected_ <!-- Start of picture text --> LU DK SE EE CZ BG RO NL FI BE LT ES LV CY PT PL SI HR FR HU DE SK EL MT AT IT IE<br>Unemployment Social assistance Other Taxes SIC<br>20<br>15<br>10<br>5<br>0<br>-5<br><!-- End of picture text --> Note: See Figure A7 in the Appendix for results by income groups. Order of countries based on Figure 5. Other benefits include housing benefits, family and education benefits, health benefits as well as old-age and survivor benefits. Severely refers to losses in market income of more than 11 percent (Scenario A vs. D). Source: Own calculations based on EUROMOD I4.0+.

Similar to the overall population, Figure A6 in the appendix shows decomposition results by income groups for those severely affected only and Figure A7 the composition of automatic 17
# **2.2.2 Elasticity parameters**

Most of the elasticity parameters are taken from the literature. Since the results are sensitive to some elasticities, special attention was paid while choosing the values for elasticity of substitution between biofuels and competing fossil fuels. Birur et al. (2008) collect historical data allowing them to determine a default value of 2.0 for this elasticity parameter. Yet, they point out the lack of data availability for conducting econometric analysis and they acknowledge that the value could vary significantly across countries (especially if a country is already equipped with flex-fuel vehicles or not). After several sensitivity analyses varying this parameter, we decided to nearly triple its value overtime, between 2004 and 2020, from 1.2 to 3.0 for all countries. We think it is realistic with future expansion of flex-fuel vehicles and we prefer not to consider higher values as they would tend to accelerate biofuel penetration too rapidly.

In our CGE model, we split total land into 18 Agro-Ecological Zones (AEZs) depending on climate type and humidity levels of the land (Hertel et al. 2009). We use a CES functional form with a high value of 20 for substitution elasticity between AEZs. For a given AEZ, the land supply is constrained across the different land-specific uses with nested constant elasticity of transformation (CET) functional forms. Following Hertel et al. (2008) we select -0.2 for the first level of the structure but consider the same values as Choi (2004) with respectively -0.5 and -1 for the next second levels. We apply the same elasticities for all countries.

Values for other elasticities (as shown in Figures 1a-c), are taken from Burniaux and Chateau, OECD (2010); van der Werf (2008); Timilsina and Shrestha (2006); Ma et al. (2010); Jarrett and Torres (1987) and Narayanan and Walmsley (2008).

# **3. Key results from model simulations**

We considered three simulations for this analysis: increase in oil prices by 25%, 50% and 100% from their corresponding baseline values starting from 2012. While alternative scenarios, for example, different growth rates for oil prices as compared to that in the baseline, could be developed, the results would be the same for the terminal year of the study horizon although impacts for earlier years would be lower in the latter case as compared to that in former case.

12
# **4.2 Calibration**

The model is calibrated to reproduce facts about the historical dynamics of U.S. imports from China. We initialize the model in 1970 with zero exporters (that is, all firms have _ξ_ = _∞_ ) in all goods to capture the transition from autarky, and then feed in the realized path of tariffs (NNTR rates during 1971–1979 and MFN rates from 1980 onward). We then choose the model’s parameters so that its transition dynamics match the facts documented in section 3 and other salient facts about export participation dynamics reported elsewhere in the literature. The calibrated parameter values are summarized in Table 7.

**Assigned parameters.** Several of the model’s parameters are assigned externally. A model period is one year. The wage is normalized to one and the interest rate used to discount future profits is four percent. We take the time series for MFN and NNTR tariffs, _τg_ 1 and _τg_ 2, directly from the data. The parameters that govern the firm’s productivity and survival processes are taken from Alessandria et al. (2021a). To eliminate the role of the elasticity of substitution in the size distribution of firms, we assume producer productivity <u>1</u> is _z_ = _θ−_ 1<sup>log</sup><sup>_a_.Afirm’sproductivityfollows</sup>

which we convert to a Markov-chain approximation for computation. Firms are subject to exogenous death shocks that depend on the firm’s productivity; the probability of death is 1 _− δ_ ( _a_ ) = max �0 _,_ min � _e_<sup>_−δ_0</sup><sup>_a_</sup> + _δ_ 1 _,_ 1��.

**Parameters calibrated to facts about exporter dynamics.** The parameters that govern fixed and variable exporting costs are calibrated so that the model’s terminal steady state—in which tariffs have remained at MFN rates for many years—matches statistics on export participation dynamics documented by Alessandria et al. (2021a). The sunk entry cost, _f_ 0, is chosen so that 22.3 percent of firms export. The continuation cost, _f_ 1, is chosen so that 17.0 percent of exporters exit. The low variable exporting cost, _ξL_ , is normalized to one and the high variable exporting cost, _ξH_ , is chosen so that the average export entrant is half as large as the average incumbent exporter (as measured by sales).

22