rafmacalaba/datause-encoder-data huggingface dataset:
24

|Country,<br>L<br>economy, or<br>territory|ast ukbl<br>P<br>Date|ished<br>censu<br>opulation<br>(1000) *|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless indicated)|Source for mortality<br>(1985-90, unless Indicated)|
|---|---|---|---|---|---|
|Thailand|Apr 80|44825 F|Official<br>est., National<br>Economic<br>and Social|Official<br>est., National<br>Economic<br>and Social|Official<br>est., National<br>Economic<br>and Social|
||||Development<br>Board|Development<br>Board|Development<br>Board|
|Togo|Nov 81|2705 F|Bank projection<br>from census|Bank assessment<br>of 1961<br>and 1971 censuses|U.N. 1988 revision<br>(prelim.)|
|Tokelau|Oct 82|2F|UNPVSR<br>4/87 (official<br>est.)|-|--|
|Tonga|Nov 76|<br>90 J|UNPVSR<br>4/87 (official<br>est.)|Bank est.|Bank est.|
|Trinidad<br>and<br>Tobago|May 80|<br>1080 F|U.N. 1988 revision<br>(prelim.)|Based on 1987 OHS|1980-85,<br>U.N. 1984<br>assessment;<br>1985-90,<br>Bank projection|
|Tunisia|Mar 84|6966 F|Bank projection from census|Bank est.|Based on U.N. 1984<br>assessment|
|Turkey|Oct 85|50664 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Turks and Caico<br>Islands|s Nay 80|7 F|UNPVSR<br>1/88 assessment|--|--|
|Tuvalu|may 79|7 F|UNP;3R 4/87 (official<br>est.)|Bank est.|Bankest.|
|Uganda|Jan 80|12637 F|Official<br>est.|U.N. 1988 revision (prelim.)|Bankest.|
|United<br>Arab<br>Emirates|Dec 80|<br>1043 F|U.N. 1988 revision<br>(prelim.)|Based on U.N. 1988 revision<br>(prelim.)|<br>Based on U.N. 1984<br>assessment|
|United Kingdom|Apr 81|55678 F|Eurostat1987|Eurostat 1987|Eurostat 1987|
|United States<br>of America|Apr 80|226546 F|UNPVSR<br>1/87 (official<br>est.)|Based on 1981-84TFR, USBOC<br>1985,<br>and 1985 CBR, UNPVSR<br>4/87|Based on UNPVSR 4/87|
|Uru%ay|Oct 85|<br>2931 F|Bank projection<br>from<br>official<br>1986 est.|1980-85,<br>Celade<br>printout<br>2/87; 1985-90,<br>Bank<br>projection|1980-85,<br>Celade<br>printout<br>2/87; 1985-90,<br>Bank<br>projection|
|U.S.S.R.|Jan 79|262436<br>F|U.N. 1984 assessment|Based on INED 10/87|Based on official<br>1984-85<br>life table|
|Vanuatu|Jan 79|<br>111 F|USSOC 1985|Bank est.|Bank est.|
|Venezuela|Oct 81|14517 F|Official<br>est.|1980-85,<br>U.N. 1988 revision<br>(prelim.);<br>1985-90,<br>Bank<br>projection|<br>1980-85,<br>U.N. 1988 revision<br>(prelim.);<br>1985-90,<br>Bank<br>projection|
|Viet Nam|Oct 79|52742 F|Bank projection<br>from census,<br>adjusted<br>for 0.96X undercount|Bank est.|Bank est.|
|Virgin Island<br>(U.S.)|s Apr 80|<br>97J|USBOC<br>1985|Based on USBOC<br>1985|Based on USBOC<br>1985|
|Wake Island|Apr 70|<br>2 F|UNPVSR<br>4/87 (official<br>est.)|Bank est.|Bank est.|
|Watlis and<br>Futuna|Mar69|<br>9 F|USBOC 1985|--|-|
|Western Sahara|Dec 70|<br>76 F|UNPVSR 4/87|--|--|
|Western<br>Samoa|Nov 81|<br>156 F|U.N. 1984 assessment|Based on USBOC 1985|USBOC 1985|
|Yemen Arab Rep|. Feb 86|<br>9274 F|Bank projection<br>from|Bank assessment<br>of World|Bank assessment<br>of World|
||||6/80 census|Fertility<br>Survey|Fertility<br>Survey|
Table 17: EARNINGS FUNCTION _(mnW,)_ - ALL EMPLOYEES

||(|1)|(|2)|(|3)|(4|)|(5|)|(|6)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Intercept|9.575|(110.3)|9.679|(107.9)|9.613|(103.4)|9.585|(99.7)|9.600|(99.9)|9.852|(83.3)|
|Years of Schooling|.131|<br>(20.1)|<br>_-|||-|-||-<br>_|-|-||
|Years of Primary Schooling|-|_|.075|<br>(4.7)|<br>.079|<br>(5.0)|<br>.071|(4.4)|<br>.037|(1.5)|<br>.022|<br>(0.9)|
|Years of Secondary Schooling|_|_|.179|<br>(9.9)|<br>.183|(10.1)|<br>.158|(7.9)|<br>.127|(5.1)|<br>.113|<br>(4.6)|
|Years of Higher Schooling|-|_|.142|<br>(4.9)|<br>_.'I-_|(4.6)|<br>.157|(5.1)|<br>.106|(2.5)|<br>.097|(2.4)|
|Years of Cuerent Job Experience<br>|.098|<br>(7.4)|<br>.101|<br>(7.6)|<br>.099|<br>(7.5)|<br>.097|(7.4)|<br>.094|(7.2)|<br>.088|(6.9)|
|(Years of Current Job Experience)<br>2|-.0019|<br>(4.0)|-.0019|<br>(4.1)|-.0018|<br>(4.0)|-.0018|(3.9)|-.0017|(3.8)|-.0016|(3.6)|
|Yearsof Previous Job Experience<br>|-|-|-|-|.041|(1.7)|<br>.033|(1.4)|.032|(1.3)|.025|(1.0)|
|(Years of Previous Job Experience)<sup>2</sup>|-|-|-|-|-.0013|<br>(0.7)|<br>-.0008|(0.5)|-.0006|(0.4)|-.0001|(0.1)|
|Years of Apprenticeship|-|-|-|-|-|-|.039|(1.9)|.042|(2.1)|.027|(1.3)|
|Years of Secondary VTE|-|-|-|-|-|-|.085|(3.4)|<br>.070|(2.3)|.071|(2.5)|
|Years of Post-Secondary VTE|-|-|-|-|-|-|.163|<br>(1.8)|.023|(0.2)|<br>.027|(0.2)|
|Years of Teacher Training|-|-|-|-|-|-|.187|<br>(2.7)|.157|(1.7)|.167|(1.9)|
|CEPE|-|-|-|-|-|-|-|-|.251|(1.6)|<br>.196|(1.3)|
|BEPC|-|-|-|-|-|-|-|-|.243|(1.0)|<br>.279|(1.2)|
|SecondaryVTEDiploma|-|-|-|-|-|-|-|-|.400|(2.1)|.325|(1.7)|
|Post-Secondary VTEDiploma|-|-|-|-|-|-|-|-|.970|(2.6)|.918|(2.5)|
|Teacher Diploma|-|-|-|-|-|-|-|-|.532|(1.8)|.547|(1.9)|
|Other Diploma|-|-|-|-|-|-|-|-|.727|(2.6)|.727|(2.7)|
|Sex(1-female)|-|-|-|-|-|-|-|-|-|-|-.135|(1.5)|
|Non-Ivorian|-|-|-|-|-|-|-|-|-|-|-.264|(2.7)|
|Seasonal Job|-|-|-|-|-|-|-|-|-|-|-.622|(3.0)|
|Abidjan|-|-|-|-|-|-|-|-|-|-|.091|(1.2)|
|Rural Areas|-|-|-|-|-|-|-|-|-|-|-.295|(2.4)|
|R2|.49||.51||.52||.54||.54||.58||
|R2|.49||.51||.51||.53||.53||.56||
|F|162.5||103.1||75.2||51.2||33.9||29.5||

Note: Absolute t - values In parentheses.
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Figure 4 Catfish Income Shares in 2002 <!-- Start of picture text --> 0 .1 .2 .3 .4 .5<br>catfish share in 2002<br>10<br>8<br>6<br>density<br>4<br>2<br>0<br><!-- End of picture text --> Note: non-parametric estimates of the density of catfish income shares in 2002 using a Gaussian Kernel and the standard optimal bandwidth. The sample is M4, the Mekong provinces of An Giang, Can Tho, Dong Tha, and Tra Vinh. The vertical lines represent the median catfish share (the leftmost line), the mean share (the centerline) and the median share, conditional on producing more than the mean (the rightmost line).

Kernel methods, for sample M4 (the landlocked Mekong provinces of An Giang, Can Tho, Dong Tha, and Tra Vinh). The distribution of catfish shares is clearly unimodal and right-skewed. The mode is close to 0.025, while mean and median are respectively 5.5 and 11.2 percent.

# **4.2 The Impact on Household Income**

We first estimate the impact of the AD shock on household income, using model (6).<sup>17</sup> We present separate results for total and per capita household income—measured including earnings from all economic activities, including home production and transfers—and for net income, which is calculated subtracting from total income the cost of inputs in farm activities

> 17As a reminder, expenditure-based indicators cannot be used as outcomes because the expenditure modules were responded by only a small sample of less than 50 observations in our panel of aquaculture households in the Mekong.

21
# **<u>Table A.16. The placebo test of the program effect on exogenous variables of individuals</u>**

||||Depende<br>|nt variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Age of<br>individuals|Gender of<br>individuals<br>(male=1,<br>female=0)|Ethnic<br>minorities<br>(yes=1,<br>Kinh=0)|The completed<br>education<br>level (aged<br>from 30)|Individual<br>completed<br>high school<br>and above<br>(aged from 30)|Individual<br>completed<br>college and<br>above (aged<br>from 30)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|-1.0608|0.0024|-0.0278|-0.0161|0.0007|-0.0090|
||(1.0445)|(0.0131)|(0.0689)|(0.1153)|(0.0270)|(0.0075)|
|(Poverty rate – 50) * Post-program period|0.0610|-0.0013|0.0008|-0.0008|-0.0008|0.0005|
||(0.1287)|(0.0014)|(0.0073)|(0.0118)|(0.0033)|(0.0006)|
|Program * (Poverty rate – 50) * Post-|-0.1254|0.0023|-0.0007|0.0028|0.0011|-0.0005|
|program period|(0.1341)|(0.0014)|(0.0076)|(0.0128)|(0.0034)|(0.0007)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|26.4921***|0.4950***|0.7302***|1.9141***|0.0964***|0.0093***|
||(0.1128)|(0.0013)|(0.0078)|(0.0135)|(0.0032)|(0.0009)|
|Observations|187,734|187,737|187,734|76,879|76,882|76,882|
|R-squared|0.028|0.002|0.559|0.197|0.051|0.022|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level characteristics. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

66
Table A.7: Effects of mining on main outcomes for women with sample restricted to those within 200 kilometers.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|active (20 km)|0.025**|0.020***|-0.002|-0.008|
||(0.010)|(0.005)|(0.008)|(0.013)|
|inactive (20 km)|0.067***|0.002|-0.015|0.051**|
||(0.020)|(0.005)|(0.015)|(0.025)|
|Observations|264,905|264,905|264,905|264,905|
|R-squared|0.220|0.095|0.157|0.395|
|F test: active-inactive=0|3.635|5.902|0.631|4.496|
|p value|0.0566|0.0151|0.427|0.0340|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. Please see Table 3 for more information about coefficients of interest. *** p<0.01, ** p<0.05, * p<0.1 the effects of mine openings are similar, but that the sample size is significantly reduced when we restrict the sample to these countries and control for being within 50 kilometers of a road. This analysis cannot ensure that road construction is not driving the results, but it shows that there is no difference in the effects of mines in areas with roads as compared to the effect in areas without large roads. We think it unlikely that time-varying road infrastructure explains the main findings in this paper.

Intuitively, living near several active mines should affect labor market opportunities more than living close to only one mine. To investigate whether agglomeration of mines creates stronger effects, we include a measure of mining intensity. The intensity score, which equals the number of active mines within 100 kilometers, is significantly associated with our main occupational outcomes. As shown in Table A.9, an extra mine within 100 km is correlated with an increased likelihood of women working and working in services and agriculture, but decreases the likelihood of women working in sales. The magnitudes of the effects increase if we consider treatment intensity. For example, having two extra mines within 100 kilometers would increase the probability of working in services by 3.3 percentage points (1.5+0.6*3+0), and this increase is statistically significant at the one percent level.

All our regressions include region fixed effects, regional time trends, and year fixed effects in addition to our individual level control variables. We also cluster the standard errors at the DHS cluster level. In Table A.10, we show that our main findings are robust to the inclusion of fixed effects for the closest mine and mineral fixed effects. In Panel B, we also show that the results hold for clustering at the regional level and at the closest mine level, as well as multi-way clustering on both the closest mine and the DHS cluster.

# **A.4 Additional heterogeneity and outcomes.**

This section presents results discussed in the paper. In particular, we present results for different sub-samples, and results on related outcomes of interest.

35
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30
# **4.2.2 Non-cognitive skills**

Table 5 Panel C presents average effect sizes for outcomes measuring non-cognitive skills: executive functioning, social-emotional skills, and behavior. When pooled across income levels (column 1) and across all outcomes, estimated effects suggest that preprimary education interventions lead to significant gains of 0.121 sd in non-cognitive skills. Again, we cannot statistically distinguish impacts estimated from studies in high-income countries from those estimated in studies from low- and middle-income countries (see Appendix Table 7 for the estimated difference between the two effects and its precision). In this case, however, estimated impacts from studies evaluating modifications to existing programs (0.159 sd in Column 5) largely drive the pooled average effect size, whereas the average impact is smaller and insignificant (0.048 sd) among the smaller sub-sample of studies evaluating preprimary expansions that also measured non-cognitive skills.

When we examine specific domains, we see that executive functions improve by an average of 0.095 sd (p<0.10). This average effect is driven largely by interventions evaluated in high-income countries, most of which directly aimed to improve these types of skills, and which increased these skills by an average of 0.169 sd (p<0.01). We lack sufficient sample size to estimate a separate average effect size for executive functions among studies from low- and middleincome countries.

Preprimary education interventions also significantly improved social-emotional skills by an average of 0.115 sd when we pool across both income groups, with high-income countries and low- and middle-income countries demonstrating similar significant effects of 0.094 and 0.130 sd, respectively (columns 2 and 3). The average effect size for outcomes related to child behavior in studies from high-income countries was a statistically insignificant 0.07 sd. Studies conducted in low- and middle-income countries did not collect this outcome.<sup>2526</sup>

> 25 In high-income contexts, the largest effect sizes, both positive and negative, come from a US study reporting a statistically significant improvement in behavior of 1.06 sd to -0.77 for externalizing behavior following a teacher intervention to reduce children’s behavioral problems (Raver et al., 2009). In low- and middle-income contexts, standardized effect sizes range from a significant 0.45 sd on a social composite index following exposure to a preprimary curriculum that promoted social and emotional abilities through play in India (Dillon et al., 2017), to an insignificant -0.08 sd in prosocial scores following a preprimary school expansion in Cambodia (Bouguen et al., 2018).

> 26 Appendix Figures 7–10 graph study-specific effect sizes and visually demonstrate the overall positive but still mixed results for non-cognitive skills.

27
surveys in Australia, Fiji, and the United States were used to survey the top students from Tonga and PNG.

The most difficult part of the fieldwork was tracking down the current location of these top students. A wide variety of methods were used to do so. For the New Zealand top students, the initial points of contact included contact information provided by the organizers of the Mathematical and Chemistry Olympiad teams, the mailing address at the time of graduating high school for top bursary scholars provided under a research agreement with the New Zealand Ministry of Education, and information from the secondary schools used for the sample of Duxes. This was followed up by an intensive internet search, using Google, social networking sites such as Facebook and LinkedIn, alumni networking websites, and other web searches. Finally, this was complemented by social networking among the top scholars, and by telephone book searches in the New Zealand telephone directory.

In PNG and Tonga the first point of contact was their school and local community, with family members and former classmates often providing information on which country migrants were in, and some contact information. The interviews in Tonga were carried out by a former school teacher, with good contacts in schools and churches in Tonga, while those in PNG were carried out by a local survey firm. Migrants were tracked through community networks abroad and through searches of telephone directories in New Zealand, Australia and the United States. Newspaper and radio advertisements in both source and destination countries were also used to try and reach target subjects. Finally internet searches in Google and in ex-students reunion websites helped to track down a few more.

A number of methods were used in order to try and maximize response rates. First, for each of the three countries we used natives of these countries to contact participants. The survey was marketed to participants as a survey of their country’s “top students”, and the potential policy uses of the survey explained. Secondly, the use of web-based surveying allowed busy respondents to fill out the survey at a time of their convenience, while in-person surveys of Tongans and Papua New Guineans allowed those without easy access to the internet to participate. Participants received token compensation in the form of either a small monetary payment, a draw for Amazon.com

- 11 -
21 _Table 8: Results for log total hours worked by head of household_

|VARIABLE|_log t_|_otal hours w_|_orked by h_|_ousehold he_|_ad_|
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|**-0.056**||**-0.075**|**-0.065**|**-0.064**|
||**-2.790**||**-2.640**|**-2.760**|**-2.750**|
|SAH_POS_CH_LGE||-0.015||||
|||-0.130||||
|SAH_POS_CH_SML||0.015||||
|||0.280||||
|SAH_NEG_CH_SML||**-0.100**||||
|||**-2.000**||||
|SAH_NEG_CH_LGE||-0.196||||
|||-1.860||||
|POOR X SAH_CHANGE|||0.041|||
||||0.930|||
|HI X SAH_CHANGE||||0.037||
|||||0.720||
|URB_HI X SAH_CHANGE|||||0.058|
||||||0.980|
|RUR_HI X SAH_CHANGE|||||-0.009|
||||||-0.110|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|-0.096|||||
||-1.364|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||-0.045||||
|||-0.303||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||0.005||||
|||0.038||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||-0.033|||
||||-1.049|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||-0.028||
|||||-0.638||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||-0.006|
||||||-0.110|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||-0.073|
||||||-0.949|
|N|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t- statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (twosided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_
likelihood of being poor. Improved access to credit could increase the ability of households to diversify by engaging in other activities within agriculture and outside of agriculture. In our analysis, we find a significant positive relationship between access to credit and lower likelihood of being poor.

Without a doubt, agriculture is an important activity for rural households in Nigeria and the performance of the agricultural sector has implications for rural poverty. Nigerian agriculture appears to be characterized by smallholder farmers, employing very basic technology in their production activities. Land and labor (particularly male labor), with very little specialization, are the key determinants of production. We find positive, albeit low, returns to modern inputs like fertilizer and herbicides, and this could indicate inadequate use of inputs and/or low quality of available inputs. Lack of information on proper use of these inputs could also be a factor. Increased access to inputs, credit, and information about proper farming practices for poorer households could increase agricultural productivity and in effect, rural incomes. Although we cannot ascertain causality between agriculture and rural poverty in this analysis, we are able to show a strong link between increased agricultural productivity and lower likelihood of poverty in rural Nigeria. We also show that diversification is important for rural activities. Thus, policies favorable to increasing agricultural productivity and encouraging diversification of income generating activities both within and outside agriculture could reduce rural poverty.

The analysis in this study is limited to a single cross section of Nigerian farmers in 2010/2011. As such, we are unable to examine how the determinants of productivity and the link between productivity and poverty change over time. In the future, we plan to incorporate data from two subsequent waves of the GHS into the analysis. This will allow us to use more rigorous panel methods to assess these two relationships. Additionally, we will be able to roughly gauge any potential impact of more recent agricultural initiatives in Nigeria on agricultural productivity and poverty. The analysis in this paper will provide a baseline analysis for this future work.

12