rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 8(a): Indonesia: Return to skill by sector over time (Senior<br>Secondary and above)<br>1<br>0.8<br>0.6 Agriculture/Mining<br>0.4 Manufacturing<br>Trade/Services<br>0.2<br>0<br>1994 1999 2003 2007<br>Year<br>Return<br><!-- End of picture text -->

**Figure 8(b): Indonesia: Proportion of skilled workers by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>Agriculture/Mining<br>40<br>Manufacturing<br>30<br>20 Trade/Services<br>10<br>0<br>1994 1999 2003 2007<br>Year<br>% skilled<br><!-- End of picture text --> 45
39 Figure 7: Average Expenditure and<sup>Serious Assaultsper Capita</sup> <!-- Start of picture text --> 0<br>0<br>0<br>0<br>Cl,<br>i 10000 0 0 0<br>10<br>0 000 18 0000 0 0<br>0 ~0 00<br>009<br>Ot a V - 0 0 0<br>0 cWVsP% 0 0<br>CD 1000<br>0.~~~~~~~~~~~~~~<br>4-. ~ ~~ 000 0p<br>> 012501000 2 00 30040<br>10 0 0 o 0 000<br>co 100 0 0 0 0 ~~~~~00020 0040<br>125 ~~~mean household expenditure per capita<br><!-- End of picture text -->

**Both** variables are<sup>in logarithms. Observationswhereseriousassaults wereequalto zerohave beenreplaced</sup><sup>**by**thesmallest valuesin thesample.</sup>
Breaking the Waves? Education, Youth Bulges, and Armed Conflict

**Table 3 Stratified models of youth bulges and education**

|**Explanatory Variables**|**Model 9**<br>Education<br>strata|**Model 10**<br>Sub-Saharan<br>Africa|**Model 11**<br>Low-middle<br>income|**Model 12**<br>High<br>agriculture|**Model 1**3<br>High resource<br>dependence|
|---|---|---|---|---|---|
|Medium β<br>secondary education Z|1.002<br>(1.41)|||||
|Low secondary education|-0.331<br>(-0.34)|||||
|Very low secondary education|-2.234<br>(-1.34)|||||
|Low and very low||-0.368|-1.447<sup>**</sup>|-0.478|-0.301|
|secondary education||(-0.28)|(-2.30)|(-0.43)|(-0.14)|
|Infant mortality rate|0.015<sup>***</sup><br>(3.58)|0.011<br>(1.34)|0.014<sup>***</sup><br>(3.56)|0.003<br>(0.41)|0.013<br>(1.09)|
|Total population (ln)|0.282<sup>***</sup><br>(3.60)|0.088<br>(0.43)|0.266<sup>***</sup><br>(3.23)|0.176<br>(1.34)|0.540<sup>**</sup><br>(2.29)|
|Regime type|0.018<br>(0.88)|0.003<br>(0.07)|0.028<br>(1.28)|0.034<br>(1.13)|0.029<br>(0.46)|
|Regime type squared|-0.013<sup>**</sup>|-0.017<sup>*</sup>|-0.013<sup>**</sup>|-0.002|-0.016|
||(-2.55)|(-1.72)|(-2.46)|(-0.29)|(-1.26)|
|Missing regime data|-0.395<br>(-0.71)||-0.151<br>(-0.27)|0.040<br>(0.05)||
|Time since conflict|-0.060<sup>***</sup><br>(-2.62)|-0.109<sup>***</sup><br>(-3.10)|-0.053<sup>**</sup><br>(-2.28)|-0.059<sup>*</sup><br>(1.73)|-0.081<br>(-1.25)|
|Youth*High secondary<br>education|-6.148<br>(-0.80)|||||
|Youth*Medium secondary<br>education|-3.423<br>(-0.81)|||||
|Youth*Low secondary<br>education|12.498<br>(1.37)|||||
|Youth*Very low secondary<br>education|32.642<sup>**</sup><br>(1.97)|||||
|Youth*High-medium||1.609|-1.965|11.536|2.952|
|secondary education||(0.11)|(-0.59)|(0.91)|(0.23)|
|Youth*Low-very low||11.670|15.639<sup>**</sup>|15.774<sup>*</sup>|-6.954|
|<br>secondary education||(1.22)|(2.10)|(1.73)|(-0.19)|
|Constant|-3.95<sup>***</sup>|-2.77<sup>**</sup>|-3.08<sup>***</sup>|-3.104<sup>***</sup>|-2.96<sup>***</sup>|
||(-4.95)|(-1.98)|(-6.84)|(-2.90)|(-2.61)|
|N|2,989|703|2,084|759|543|
|Log likelihood|-324.58|-112.59|-293.15|-140.99|-47.18|
|PseudoR<sup>2</sup><br>|0.111<br>|0.092<br>|0.073|0.047|0.123|

* p<0.10, **p<0.05, ***p<0.01. Robust z statistics in parentheses.

Note that because the education dummies are exhaustive, in other words, not all can be zero at once, no separate youth bulge parameter is required in addition to the interaction terms. One education level always applies and the interaction indicates the youth bulge effect within that education level. The discrete interaction result is strong and consistent: the lower the education level of male youth, the more strongly are large youth cohorts associated with the occurrence of conflict. The youth bulge effect in the two lowest education categories is also significant when these are combined (see Appendix E, Model 9.2). The value of stratified interaction rather than a multiplicative term of 17
**TABLE 1**

**ANNUAL GROWTH (PERCENT) IN GLOBAL GDP, POPULATION, AND CONSUMPTION FOR SELECTED COMMODITIES**

||**_1961‐72_**|**_1973‐84_**|**_1985‐96_**|**_1997‐2008_**|
|---|---|---|---|---|
|**_MACRO VARIABLES_**|||||
|Population|2.0|1.8|1.6|1.3|
|GDP, real|5.5|3.4|2.8|3.0|
|**_CONSUMPTION_**|||||
|Rice|3.3|2.7|1.9|1.2|
|Wheat|3.9|2.9|1.4|0.9|
|Maize|3.7|2.5|2.7|2.8|
|Soybeans|4.8|2.6|5.5|4.0|
|Palm Oil|8.4|10.2|7.7|8.8|
|Beef|3.2|1.8|1.1|1.0|
|Pork|3.7|4.9|2.7|2.2|
|Poultry|12.1|6.9|6.8|4.0|

**Source** : Authors calculations based on FAO, FAPRI, World Bank, and UN data.

# **TABLE 2**

**ANNUAL GROWTH** **<u>(PERCENT)</u> IN CONSUMPTION OF SELECTED COMMODITIES**

||**_‐‐‐‐‐‐ WOR_**|**_LD ‐‐‐‐‐‐‐_**|**_‐‐‐‐‐‐‐‐ CHI_**|**_NA ‐‐‐‐‐‐‐‐_**|**_‐‐‐‐‐‐‐‐‐ IND_**|**_IA ‐‐‐‐‐‐‐‐‐_**|
|---|---|---|---|---|---|---|
||**_1997‐2002_**|**_2003‐08_**|**_1997‐2002_**|**_2003‐08_**|**_1997‐2002_**|**_2003‐08_**|
|GDP|2.9|3.3|8.4|10.4|5.1|8.5|
|Wheat|1.3|2.7|‐0.7|0.2|2.7|1.8|
|Rice|1.6|1.5|0.5|‐0.6|1.0|1.9|
|Maize|1.8|3.6|2.8|3.4|1.9|5.5|
|Soybeans|5.8|3.3|16.1|8.7|‐1.4|7.1|
|Palm Oil|10.5|7.5|22.1|8.2|21.4|7.2|
|Beef|0.8|1.6|3.8|2.8|‐0.5|‐2.2|
|Pork|2.7|1.3|2.8|1.2|1.0|0.4|
|Poultry|4.7|3.8|4.9|4.6|17.6|7.7|

**Source** : Authors calculations based on FAO, FAPRI, World Bank, and UN data.

**_Page 22_**
**Gender and Asset Ownership: A Guide to Collecting Individual-Level Data**

**Cheryl Doss Caren Grown Carmen Diana Deere**

JEL Codes: J2 Economics of Gender D13 - Household Production and Intrahousehold Allocation D7 - Analysis of Collective Decision-Making Q15 - Land Ownership and Tenure; Land Reform; Land Use; Irrigation Keywords: gender equality, asset ownership, poverty reduction, land, housing, non-farm business enterprises, savings The World Bank provided funding for this work which is part of the World Bank Project on Collecting Data on Individual Assets. The authors gratefully acknowledge the research assistance of María José Castillo, University of Florida and Dawn Cotton, Levy Economics Institute. Maureen Deegan, Levy Economics Institute, provided administrative assistance to the project. Thanks are due to Michael Carter, Agnes Quisumbing, and the participants in the workshop “Measuring Women’s Assets” held at the World Bank on March 9-10, 2007, for constructive comments and suggestions on an earlier draft. We are grateful to Elena Bardasi and Lucia Forte for their assistance, feedback and the coordination of this project within the World Bank.
# **Appendix I: Multi-dimensional poverty index construction**

Table A1 describes the inputs into the multi-dimensional poverty index (MPI) used in this study. Each of the indicators given in the second column are binary variables, taking the value of 0 if the household is deprived in that vector, according to the third column, and 1 otherwise. The final MPI is generated by calculating the weighted sum of the ten indicators, with weights given in the fourth column. Weights were chosen so that each of the three dimensions—education, health, and standard of living— have equal weight, and each of the indicators have equal weight within each dimension. A household is considered to be multi-dimensionally poor if its weighted sum of indicators is greater than 3. Note that the weights add up to approximately 10, the number of indicators (difference due to rounding).

**Table A1: Multi-dimensional Poverty Index**

|**Dimension**|**Indicator**|**Deprived if...**|**Relative Weight**|
|---|---|---|---|
|Education|Highest degree<br>earned|No household member has completed<br>five years of education.|1.67|
||Child School<br>Attendance|Household has a school-aged child<br>not attending school|1.67|
||Child Mortality|Household has had at least one child<br>aged 0-5 years die in the past 5 years.|1.67|
|Health|Nutrition|Household has a malnourished<br>woman aged (15-49) or child aged<br>(0-5).|1.67|
||Electricity|The household has no electricity.|0.56|
||Improved<br>Sanitation|Household does not have improved<br>sanitation.|0.56|
||Safe Drinking<br>Water|Household does not have access to<br>improved water source.|0.56|
|Standard of<br>Living|Flooring|The household has a dirt floor.|0.56|
||Cooking fuel|The household uses dirty cooking<br>fuel.|0.56|
||Asset Ownership|The household does not own more<br>than one bicycle, motorcycle, radio,<br>fridge, TV, or phone and does not<br>own a car.|0.56|

World Heath Organization (WHO) standards were followed in determining what to consider unimproved water sources, inadequate sanitation, and dirty cooking fuel.

41
<!-- Start of picture text --> 29023028 «11 «113:«17) «4 «> Chittagong)? 5 «12 20 4 O23 Mw OW<br>Ci ie a i a oh www ww ON<br>Hittg & : & :<br>peeeFag & :<br><!-- End of picture text --> <!-- Start of picture text --> Garment Textile<br>3”<br>°<br>Asia Japan EU EuroptmericasUSA Asia Japan EU EuroptmericasUSA<br><!-- End of picture text -->
these households being replacement households for refusals to participate), with 336 households assigned to each of the four labor modules. Although the sample of 1,344 is not designed to be nationally representative of Tanzania, the districts were selected to capture variation in Tanzania—both urban/rural as well as along other socioeconomic dimensions. The basic characteristics of the SHWALITA households generally match nationally representative data from the Household Budget Survey (2006/07) (results not presented here). Households were interviewed over 12 months, but because of small samples we do not explore the variations across main seasons (such as the harvest season with peak labor demand and the dry seasons with low demand).

After the households were randomly selected and randomly assigned to one of the four assignment groups, respondents and proxies were selected according to the following rules. In households assigned to self-report, up to two individuals ages 10 and older were randomly selected to self-report. In households assigned to proxy report, one household member over 15 years was first selected to proxy report; in a second stage, up to two household members age 10 and older (after excluding the individual chosen to be a proxy respondent) were selected to be reported on by the proxy. The proxy also reported for him/herself, and was considered a self-report in this case. Random selection was conducted by first listing eligible individuals (either proxies or self-reports). Then the enumerator examined a random number table preprinted in the questionnaire that had random numbers generated and listed in columns that corresponded to the potential total number of eligible individuals that could be listed. Each of these tables was generated uniquely for each questionnaire and for each set of listing exercises (either proxies or self-reports) that were required of the enumerator.

Because eligible respondents were all those age 10 and older, the sample selected for our analysis included both adults and children. In this paper, we limit our sample to the sub-group of children. Of the total sample of 1,344 households, 494 had at least one child age 10-15 years, resulting in a sample of 566 children. We focus on the subset of households in which these children reside. Some main characteristics of these households are presented in Table 1 by survey assignment. To verify the random nature of the assignment of households to one of the four survey types, we test 11
_Table 8: Estimated losses in human and intangible capital value per capita (HR) due to earthquakes, with_ _<u>respect to number of people affected.</u>_

|No.|Country|Ave. HR<br>(2005|Affected<br>People in|Average<br>Affected|Estimated<br>Elasticity<sup>a</sup>|Losses in<br>con|HR (2005<br>stant US$)|
|---|---|---|---|---|---|---|---|
|||constant|Country_i_|People||||
|||US$)|(t+t-n)|(t+t-n)||US$|Std error|
|1|Australia*|361,092|1,667|20,964|0.013|3.66|1.379|
|2|Bangladesh****|3,497|333|20,964|0.013|0.01|0.003|
|3|Chile**|61,922|9,554|20,964|0.013|3.60|1.356|
|4|China***|5,995|2,256,960|20,964|0.013|82.35|31.005|
|5|Colombia***|31,351|260,196|20,964|0.013|49.64|18.693|
|6|India****|3,964|339,866|20,964|0.013|8.20|3.087|
|7|Indonesia***|6,401|256,522|20,964|0.013|9.99|3.763|
|8|Japan*|370,999|108,927|20,964|0.013|245.94|92.603|
|9|Mexico**|78,429|64,078|20,964|0.013|30.59|11.516|
|10|Pakistan****|5,869|3,588|20,964|0.013|0.13|0.048|
|11|Turkey**|52,195|354,595|20,964|0.013|112.64|42.411|
|12|United States*|551,193|8,408|20,964|0.013|28.21|10.620|

_Table 9: Estimated losses in human and intangible capital value per capita (HR) due to earthquakes, with_ _<u>respect to number of people affected per capita.</u>_

|No.|Country|Ave. HR<br>(2005<br>constant|Affected<br>People Per<br>Capita in|Average<br>Affected<br>People Per|Estimated<br>Elasticity<sup>b</sup>|Losses i<br>co|n HR (2005<br>nstant US$)|
|---|---|---|---|---|---|---|---|
|||US$)|Country_i_|Capita||||
||||(t + t-n)|(t + t-n)||US$|Std error|
|1|Australia*|361,092|9.33E-05|4.98E-04|0.006|4.12|2.28|
|2|Bangladesh****|3,497|2.39E-06|4.98E-04|0.006|0.001|0.0006|
|3|Chile**|61,922|5.90E-04|4.98E-04|0.006|4.46|2.47|
|4|China***|5,995|1.80E-03|4.98E-04|0.006|1.32|0.73|
|5|Colombia***|31,351|6.36E-03|4.98E-04|0.006|24.35|13.50|
|6|India****|3,964|3.24E-04|4.98E-04|0.006|0.16|0.09|
|7|Indonesia***|6,401|1.26E-03|4.98E-04|0.006|0.99|0.55|
|8|Japan*|370,999|8.65E-04|4.98E-04|0.006|39.21|21.73|
|9|Mexico**|78,429|6.75E-04|4.98E-04|0.006|6.46|3.58|
|10|Pakistan****|5,869|2.36E-05|4.98E-04|0.006|0.02|0.01|
|11|Turkey**|52,195|5.38E-03|4.98E-04|0.006|34.29|19.01|
|12|United States*|551,193|2.98E-05|4.98E-04|0.006|2.01|1.11|

Notes for Tables 8 and 9: Countries considered are those with non-zero figures for affected people during periods _t_ and _t-1_ . Also, countries in the table represent four income groups: *,**,***, and **** refer to high income, upper middle income, lower middle income and low income, respectively. _Ave. HR_ refers to the average value of HR in country _i_ during three years - 1995, 2000 and 2005. “a” and “b” means that the elasticity estimates are from Table 4 and Table 5, respectively.

The results with respect to other disasters will be similar since they will also be a function of the relative elasticity of human and intangible capital, and the relative number of people affected. We can see the impact in terms of _people affected per capita_ by simply comparing the relative elasticities. This is done in Figure 1 (we have chosen the per capita specification because in 14
tions can still be consistent with a segment of internet respondents that resist choosing sensitive options in the internet survey. Rather, what the specifications are estimating is whether there is a larger share of respondents that are willing to admit sensitive opinions or viewpoints in the internet surveys than in the mobile phone surveys. Thus, similar to other techniques designed to elicit sensitive information from respondents, these estimates could still be consistent with underreporting of sensitive behaviors and viewpoints.

# **Section 6a. Differences in the Responses to Sensitive and Non-Sensitive Questions**

The empirical results are consistent with the novel and anonymous internet-based surveys used here being able to elicit more sensitive information than mobile phone surveys. Table 4 reports estimates of specification (1) for all non-demographic information that is common between the two surveys. The estimates for sensitive questions are reported in columns (1)-(4); and the estimates for non-sensitive questions are reported in columns (5)-(8).

A number of important patterns emerge from Table 4. First, for sensitive questions, respondents were significantly more likely to choose the sensitive option in the internet survey than in the mobile phone survey. The estimate of _β_ 1 in columns (1)-(4) of Table 4 are all greater than zero, large in magnitude, and are all statistically significant at the one percent level. The largest difference between the modalities is 29.4 percentage points, and for that question one can rule out a difference smaller than 24.4 percentage points at conventional significance levels<sup>21</sup> ; and the smallest difference between the two modalities in the sensitive questions is 9.9 percentage points, and for that question one can rule out a difference smaller than 6.4 percentage points.<sup>22</sup> Second, in addition to the percentage point differences being large in magnitude, the percent differences are also large. For columns (1)-(3) of Table 4, the percent increase in the share choosing the sensitive option ranges between 36.4 and 62.6 percent. However,

> 21This is the lower bound of the 95 percent confidence interval on the estimate of _β_ 1 in column (1) of Table 4.

> 22This is the lower bound of the 95 percent confidence interval on the estimate of _β_ 1 in column (3) of Table 4.

17
common and identify stylized facts about new product and market development within a dynamic industry.

Using exporter-level international transactions data, we focus on the nontraditional agricultural sector in Peru, which grew more than 700 percent from 1994 to 2007 (compared with 450 percent for traditional agriculture).<sup>3</sup> Entry of new firms and expansion into new products and markets was an important part of the story.

Specifically, exporters that began exporting after 1994 account for nearly three-quarters of total exports in 2007. Similarly, exports of products new to the country and entry into new markets for existing products together account for almost a third of total exports at the end of the sample period.

We have three main findings about the way firms access markets abroad: (i) Firms start exporting with small trials and there is significant entry, exit, and reentry, implying that entry into exporting does not entail large initial fixed costs. (ii) Entry into new markets (for an existing export product) is more cumbersome, but the amount of trial and error suggests that entry costs are still not so large as to prohibit new markets from being discovered. And (iii) there are fewer trials in new goods, pioneers are typically relatively large exporters that are more successful than followers, and there is herding following product discovery. This suggests that finding new export products is more costly and that many new (and profitable) products may not be discovered because of high sunk costs.

Interestingly, many of the new export products are not consumed domestically— they are produced only for foreign consumption. This is a common pattern in developing

> 3 Official numbers from the Central Bank of Peru.

3
iii) If _F< F*,_ then (11) As in B(ii) above, the declining food share implies that py - _PF > 0._ The direction of change in the FGT indicator is ambiguous. Fewer individuals are considered poor but the measured depth of poverty increases.

We summarize the results from this section in Table 1.

_Table 1. Direction of Change in Measured Poverty As Consumption Becomes More Comprehensive_

||_'Traditional"_<br>_Poverty Line_|_'Austere"_<br>_Povertv Line_|
|---|---|---|
|Headcount|no change|decrease|
|FGT**_(__**_Ž_**1)**|increase|ambiguous|

Two conclusions can be drawn from this section. First, the method used to set the poverty line has a bearing on how measured poverty will change simply as a result of variation in the consumption definition. Second, different indicators of poverty do not all move in the same direction. Under the "traditional" poverty line, the headcount is unchanged as the consumption aggregate expands, but indicators of poverty in the FGT class which are sensitive to depth show an unambiguous increase as the consumption definition widens. Under the "austere" poverty line, the number in poverty falls so the headcount unambiguously declines. However, this is offset to some extent by a greater relative distance to the poverty line for some still in poverty. As a result, the movement of the FGT indicators is ambiguous as the consumption definition expands.

The exposition in this section has been in terms of just two definitions of consumption expenditures: food and a composite of both food and non-food expenditures. If we are interested in comparing two different definitions of consumption, both of which include non-food items, the foregoing analysis of the direction of biases is relevant as long as there is a behavioral regularity analogous to Engel's Law regarding the share of the more broadly defined expenditure going to items which are part of the less comprehensive definition.

7
# **I Heterogeneous Effects**

Table I.1. Heterogenous Effects on Indices by Gender

||Prosociality<br>Index<br>(1)|Attitudes<br>Index<br>(2)|Beliefs<br>Index<br>(3)|
|---|---|---|---|
|Game [=1]|0.248**|0.275**|0.202|
||(0.124)|(0.120)|(0.125)|
|Game x Female [=1]|0.034|0.100|-0.079|
||(0.162)|(0.160)|(0.163)|
|Video [=1]|0.207|0.402***|0.076|
||(0.131)|(0.123)|(0.126)|
|Video x Female [=1]|-0.011|-0.139|-0.118|
||(0.179)|(0.170)|(0.173)|
|Female [=1]|0.062|0.046|0.093|
||(0.122)|(0.121)|(0.119)|
|P-value: Game + Game x Female = 0|0.007|0.000|0.240|
|FDR q-value: Game + Game x Female = 0|0.018|0.001|0.169|
|P-value: Video + Video x Female = 0|0.107|0.024|0.725|
|FDR q-value: Video + Video x Female = 0|0.088|0.034|0.319|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|828|827|827|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the indicator variable for female, denoted as female [=1]. The dependent variables are constructed following the methodology outlined by Kling, Liebman and Katz (2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for student status. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x Female = 0 and Video + Video x Female = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x Female = 0 and Video + Video x Female = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

59