rafmacalaba/datause-encoder-data huggingface dataset:
Table 5 examines the contribution of different governorates in such decline. It is clear from the results that the largest contributors were respectively Zarqa, Amman, Irbid and Mafraq. Indeed, around 75 percent of the poverty decline in the past decade came as a result of the reduction in poverty in these 4 governorates. This primarily reflects the economic development observed in these governorates, particularly the Northern ones, and the weight that these governorates constitute in the population, especially the capital Amman.

|**Poverty Line = JD813.7**<br>**Table 5: Region**|**Absolute change**<br>**al Poverty Deco**|**Percentage change**<br>**mposition**|
|---|---|---|
|Change in poverty (P0)|-17.1|100.0|
|Total Intra-sectoral effect|-17.0|99.8|
|Population-shift effect|-0.1|0.7|
|Interaction effect|0.1|-0.4|
|**Intra-sectoral effects:**|||
|Amman|-3.7|21.7|
|Balqa|-1.3|7.6|
|Zarqa|-4.6|27.1|
|Madaba|-0.2|1.5|
|Irbid|-3.2|18.7|
|Mafraq|-1.3|7.5|
|Jarash|-1.1|6.5|
|Ajlun|-0.1|0.7|
|Karak|-0.6|3.2|
|Tafiela|-0.2|1.1|
|Ma'an|-0.4|2.3|
|Aqaba<br>Source: World Bank Staff Calculat<br>Expenditure Surveys.|-0.3<br>ions using Official Hou|1.7<br>sehold Income and|

On another note, the three-fold decomposition of the Gini coefficient over the period 2002-2010 reveals that the inequality in Jordan is also driven by consumption level differences among the various regions in the Kingdom. Table 6 indicates that the within-group inequality is the major component of overall inequality that has been increasing over time. Indeed looking at the urban-rural grouping, the share of the within-group component has increased from 68.2 percent of total inequality in 2002 to 74.4 percent in 2010 and hence dominates the betweengroup component. This suggests that the reduction in inequality, observed in the past decade in Jordan, has been mainly driven by more equitable income distribution within residents of each area, rural and urban, rather than by differences in average real income.

However, when considering the governorate groupings, calculations yield different findings. In this case, Table 6 indicates a slightly more dominant role for between-group effect (33 and 27.8 percent in 2002 and 2010 respectively) along with a significant contribution of the 20
# **3.2. The Pacific Access Category**

In 2002, another channel was opened up for immigration to New Zealand through the creation of the Pacific Access Category (PAC), which allows for a quota of 250 Tongans to emigrate to New Zealand each year without going through the usual migration categories used for groups such as skilled migrants and business investors.<sup>9</sup> Specifically, any Tongan citizens aged between 18 and 45, who meet certain English, health and character requirements,<sup>10</sup> can register to migrate to New Zealand. Many more applications are received than the quota allows, so a random ballot is used by the New Zealand Department of Labour (DoL) to draw from amongst the registrations. The odds of having one’s name drawn were approximately one in ten during the period we study.

Once their ballot is selected, applicants must provide a valid job offer in New Zealand (unskilled jobs suffice) within six months in order to have their application to migrate approved. After a job offer is filed along with their residence application, it typically takes three to nine months for an applicant to receive a decision. Once receiving approval, they are then given up to one year to move. The median migrant in our sample moved within one month of receiving their residence approval.

The person who registers for the PAC is a Principal Applicant. If they are successful, their immediate family (spouse and dependent children up to age 24) can also apply to migrate as Secondary Applicants. The quota of 250 applies to the total of Primary and Secondary Applicants, and represents about 80 migrant households. Successful applicants cannot take other members of their household to New Zealand, so anyone living with parents, siblings, or other relatives will leave household members behind when they migrate.

These two features of the PAC, random selection amongst applicants and a rule specifying which family members can and cannot accompany the successful migrant, allow us to address the double-selectivity issues involved in assessing the impact of migration on the remaining household. In particular, we can compare the group of households in Tonga with a PAC emigrant to the group of unsuccessful ballots who would not be eligible to move their entire household to New Zealand had their principal applicant been chosen in the ballot.

> 9 The Pacific Access Category also provides quotas for 75 citizens from Kiribati, and 75 citizens from Tuvalu. A similar scheme called the Samoan Quota allows 1100 citizens of Samoa to move each year. There have been some small changes in the conditions for migration under the Pacific Access Category since the period we examine in this paper – here we describe the conditions that applied for the potential migrants studied in this paper.

> 10 Data supplied by the New Zealand Department of Labour for residence decisions made between November 2002 and October 2004 reveals that out of 98 applications only 1 was rejected for failure to meet the English requirement and only 3 others were rejected for failing other requirements of the policy.

10
INTRODUCTION Ixxxiii Table 20 (continued)

||Station|ary||||Stati|onary|||
|---|---|---|---|---|---|---|---|---|---|
||popula|tion||||populat|ion|||
||||Popula-|||||Popula-||
|Country<br>or economy|Million|Ratio<br>s to 1985|tion<br> <br>momen<br> <br>tun|Year<br>when<br>NRRc1|Country or economy|<br>Mitlion|Ratio<br>e to 1985|tion<br> <br>mome<br> <br>tumn|Year<br>n when<br>RR-1|
|Yemen Arab Rep.|84|11.0|<br>1.5|2055|Poland|47|<br>1.3|<br>1.2|1990|
|Yemen,<br>People's<br>Dem. Rep.|<br>10|4.9|<br>1.6|2035|Portugal|9|0.9|1.2|2030|
||||||Romania|29|<br>1.3|<br>1.2|1985|
|Europe and U.S.S.R.|846|1.1|<br>1.1|2030|Spain|*<br>37|1.0|<br>1.2|2030|
||||||Sweden|8|0.9|1.0|2030|
|Albania|6|2.0|<br>1.6|2005|Switzerland|5|0.8|1.0|2030|
|Austria|6|0.8|<br>1.1|2030|United Kingdom|54|<br>1.0|1.1|2030|
|Belgium|8|0.8|<br>1.1|2030|Yugoslavia|28|<br>1.2|<br>1.2|2030|
|Bulgaria|9|1.0|<br>.0|2030|Other Europe (a)|0.4|<br>1.4|1.1|1990|
|Chamnel Islands|0.1|0.8|1.0|2030|U.S.S.R.|372|1.3|1.2|1995|
|Czechoslovakia|18|1.2|<br>1.1|2030|Oceania|45|<br>i.8|<br>1.3|2040|
|Dernark|4|0.8|<br>1.0|2030||||||
|Finland|4|0.9|<br>1.1|2030|Australia|23|<br>1.5|<br>1.2|2030|
|France|58|1.0|<br>1.2|2030|Fiji|1|1.7|<br>1.6|2005|
|German Dem. Rep.|16|1.0|1.0|2030|French Polynesia|0.4|2.6|1.7|2010|
|Germany,<br>Fed. Rep. of|43|0.7|<br>1.0|2030|Guam|0.2|<br>1.8|<br>1.6|1995|
|<br> <br>Greece|9|0.9|<br>1.1|2030|Kirabati|0.2|<br>2.6|1.4|2020|
|Hungar-|10|0.9|<br>1.0|2030|New Caledonia|0.3|<br>1.8|<br>1.5|2000|
|Icelari|0.3|<br>1.4|<br>1.4|2030|New Zealand|4|1.2|<br>1.3|2030|
|Ireland|5|1.3|<br>1.4|1990|Pacific Islands|0.5|<br>3.'|1.8|2020|
|Italy|42|0.7|<br>1.1|2030|Papua New Guinea|12|<br>3.4|<br>1.4|2025|
|Luxenbourg|n 3|0.8|<br>1.0|2030|Solomon Islands|2|6.3|1.7|2040|
|Malta|0.4|<br>1.0|<br>1.2|2030|Tonga|0.2|<br>2.4|<br>1.6|2015|
|Aetherlands|13|<br>0.9|<br>t.1|2030|Vanuatu|1|<br>4.0|1.6|2030|
|Norway|4|1.0|<br>1.1|2G30|Western Samoa|0.4|<br>2.6|<br>1.7|2020|
||||||Other Micronesia<br>(a)|0.03|<br>2.1|<br>1.7|2005|
||||||Other Polynesia<br>(a)|0.2|<br>2.4|<br>1.7|2015|

Note. Populations of more then 1 million have been rounded to the nearest mitlion.

a. For listings of countries in the 'other" categories, see Table 18.
**Table 6: Household Composition, Rice Prices, and Child Labor by Gender**

_limited to children 6-15 in households with 3-15 members_

||Girls<br>Participation|Hou|rs Worked|(OLS)|Boys<br>Participation|Hou|rs Worked|(OLS)|
|---|---|---|---|---|---|---|---|---|
||-1|-2|-3|-4|-5|-6|-7|-8|
|Dependent Variable:|Active|Total|Market|Household|Active|Total|Market|Household|
|Rice Price|-0.281<br>[0.161]*|-14.144<br>[5.044]**|-7.045<br> [3.624]*|-7.294<br>[3.203]**|-0.399<br>[0.167]**|-5.222<br>[4.683]|-0.121<br>[4.305]|-4.909<br>[2.568]*|
|Rice Price * # People 0-5|-0.026|-0.22|1.21|-1.235|-0.064|-1.855|0.253|-2.172|
||[0.042]|[1.307]|[0.919]|[1.009]|[0.044]|[1.164]|[1.039]|[0.657]**|
|Rice Price * # People 6-15|0.014|0.793|-0.036|0.753|0.05|0.007|-0.239|0.182|
||[0.033]|[0.915]|[0.690]|[0.604]|[0.032]|[0.775]|[0.614]|[0.446]|
|Rice Price * # People 16-24|-0.078|-2.139|-1.345|-0.779|-0.017|-1.017|-0.739|-0.281|
||[0.033]**|[0.881]**|[0.726]*|[0.580]|[0.033]|[0.943]|[0.828]|[0.490]|
|Rice Price * # People 25-64|0.009|2.696|1.103|1.616|0.037|0.875|-0.643|1.521|
||[0.063]|[2.063]|[1.507]|[1.356]|[0.068]|[1.844]|[1.753]|[0.918]*|
|Rice Price * # People 65+|0.151|2.296|-0.454|2.713|0.095|2.172|0.463|1.744|
||[0.064]**|[1.792]|[1.526]|[1.139]**|[0.070]|[1.783]|[1.556]|[1.109]|
|# People 0-5|0.075|1.357|-1.016|2.204|0.109|2.772|0.005|2.82|
||[0.044]*|[1.400]|[0.965]|[1.119]**|[0.048]**|[1.294]**|[1.134]|[0.765]**|
|# People 6-15|-0.029|-1.135|0.188|-1.242|-0.049|0.102|0.434|-0.273|
||[0.035]|[1.020]|[0.755]|[0.678]*|[0.035]|[0.876]|[0.675]|[0.516]|
|# People 16-24|0.05|1.502|1.355|0.12|-0.01|0.216|0.542|-0.34|
||[0.036]|[0.983]|[0.806]*|[0.641]|[0.036]|[1.045]|[0.906]|[0.547]|
|# People 25-64|-0.052|-4.144|-1.601|-2.558|-0.071|-1.712|0.425|-2.134|
||[0.069]|[2.165]*|[1.544]|[1.534]*|[0.073]|[1.991]|[1.908]|[1.027]**|
|# People 65+|-0.174|-2.972|0.456|-3.408|-0.121|-2.712|-0.521|-2.253|
||[0.070]**|[1.966]|[1.692]|[1.257]**|[0.080]|[2.021]|[1.772]|[1.266]*|
|Observations|4602|4597|4617|4600|4868|4860|4886|4864|
|R-squared|0.42|0.47|0.39|0.26|0.38|0.41|0.34|0.23|

Columns 1-4 are separate regressions for girls. Columns 5-8 are separate regressions for boys. See notes to table 4 for other notes about the regressions. Robust standard errors in brackets are corrected for psu-time clustering. * significant at 10%. ** Significant at 5%.
both sides. Since 2001, 60,000 people have been killed in clashes with the Fulani militias in Nigeria.

Table A1 in the appendix shows descriptive statistics of the displacement data that are available for the northeastern states. In January 2018, the IOM’s Displacement Tracking Matrix (DTM) registered over 1.7 million IDPs, of which 80.8 percent are female or under 17. The main reason listed for displacement is the Boko Haram insurgency (96 percent), followed by communal clashes (3.8 percent) and natural disasters (0.2 percent).<sup>6</sup> Figure 3 shows the location of IDP sites—i.e., locations where IDPs reside, including both camps and host communities. The size of the bubbles is larger the greater the number of IDPs residing in the site. The figure shows that the majority of the IDPs reside in Borno, followed by Adamawa, Yobe, Taraba, Bauchi, and Gombe.

# **3 Data and Sample**

We first describe our data sources and then present descriptive statistics.

## **3.1 Data Sources**

We combine data from different sources to identify the impact of distant violent events on economic and non-economic indicators for men and women. All databases are georeferenced.

_Household survey data._ We use data from the Nigeria Demographic and Health Survey (DHS). The DHS is conducted on a nationally representative sample of households. For the main analysis, we focus on the female roster and restrict our sample to married women between the ages of 15 and 49 who are not residing in Borno. We restrict the sample to married women because many questions related to violence and fertility are only asked of this subsample.<sup>7</sup> We have data on 25,451 married women for the 2013 wave and 27,043

> 6The main reasons listed for not returning are lack of safety (68.7 percent) and property damage (17.6 percent). Among these IDPs, 48.4 percent report having worked in activities related to agriculture, followed by 26.2 percent that worked as day laborers (construction, transportation maintenance) and 25.2 percent in petty trade (sales or trade) (IOM, 2020).

> 7The propensity of women to be married in neither significantly connected with distant Boko Haram

14
except for clearance time for clearance in panel B (but only at a 90 percent confidence level), suggesting that the inspectors did not react strongly to the beginning of the RCT.<sup>33</sup> Our evidence in Section 4 showed an important role of high-quality information, in the form of the service provider’s valuation advice, leading inspectors to catch more fraud and reduce tax evasion (albeit with some opportunism) while our evidence in Section 5.3 shows that the RCT significantly improved the quality of the comments provided. Yet, there is heterogeneity in terms of the quality of the detailed comments provided through the RCT. We assess whether inspector actions and customs outcomes respond differently to information of higher quality and more precision using the specification below: precision using the specification below: C **(6)** dd CCHH dd dd MM dd XX dd VV ff dd dd ppVV dd YYwhere = ππ CC_HHis a dummy that indicates whether a particular declaration was selected for detailed + ππ CC_CC + ππ MM + ππ XX + μμ + AA + VV + pp + ττ + VV comments with exact references, that is, those including a price, a weight, or explicitly referring dd CC_HH to a prior transaction and is a dummy for other declarations receiving less precise comments. All other variables in Eq. (6) are defined as above. dd CC_CC The OLS estimates for Eq. (6) are reported in Table 7. For the sub-sample of declarations not receiving valuation advice, only detailed comments with exact references induce inspectors to significantly increase fraud detection and make upward adjustments to import value and taxes. Those customs outcomes are not improved for declarations receiving less precise comments, relative to the control group. The estimated significant increases in the likelihood of upgrading or scanning declarations and in clearance times are of same magnitude regardless of the type of comments provided, as indicated by the p-values at the bottom of the table.<sup>34</sup> For the sub-sample of declarations receiving valuation advice, detailed comments with exact references play a significant role in increasing inspectors’ probability of upgrading the clearance channel or scanning, relative to the control group. Put simply, better information seems to yield higher returns.

> 33 This is confirmed by Appendix Figure 1 that depicts the binned averages of the outcome variables over the same period of time.

> 34 There is a potential endogeneity problem in that comments with exact references may precisely be those more likely to be fraudulent and more undervalued. In unreported results we used an IV estimator where an instrument for comments with exact references was constructed using information on which team in the RMU provided the comments. The instrument was the share of comments with exact references provided by each of the three teams in each month which differs importantly across the teams. The IV estimates are qualitatively similar to the OLS estimates shown in Table 7.

32
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32
**Table 1: Summary Statistics of Parent Characteristics**

|Parent Characteristics|All|Coresident|Non-coresident|P-values|
|---|---|---|---|---|
|Age|68.44|68.34|68.54|0.726|
|Father (%)|42.59|43.52|41.58|0.582|
|_Marital Status (%)_|||||
|Married with spouse/living with partner|59.44|53.79|65.53|0.001|
|Married but not living with spouse temporarily|1.39|0.49|2.37|0.028|
|Separated|1.14|0.98|1.32|0.657|
|Divorced|0.51|0.00|1.05|0.045|
|Widowed|37.01|43.77|29.74|<0.001|
|Never married|0.51|0.98|0.00|0.045|
|# of children|3.40|3.54|3.26|0.016|
|Zhejiang (%)|52.85|44.01|62.37|<0.001|
|Urban (%)|41.95|34.23|50.26|<0.001|
|Living with adult children (%)|51.84|100.00|0.00||
|House owner (%)|89.48|94.38|84.21|<0.001|
|_Education (%)_|||||
|Illiterate|54.88|57.21|52.37|0.172|
|Less than primary education|21.80|22.00|21.58|0.885|
|Primary school|13.43|12.71|14.21|0.539|
|Middle school|5.83|4.65|7.11|0.144|
|High school and above|4.06|3.42|4.74|0.353|
|Pre-transfer income per capita (PTI, 000s)|5.32|5.08|5.58|0.501|
|Household Pre-transfer income (HPTI, 000s)|16.52|21.68|10.98|<0.001|
|Total net amount of transfer (TT, 000s)|2.50|2.10|2.93|0.111|
|Transfer-income ratio (HPTI/TT, %)|15.15|9.68|26.71||
|_Self-reported Health (%)_|||||
|Excellent|1.39|1.47|1.32|0.856|
|Very good|8.75|7.82|9.74|0.344|
|Good|15.34|15.16|15.53|0.886|
|Fair|35.23|32.03|38.68|0.051|
|Poor|26.24|27.87|24.47|0.278|
|_Cognition (%)_|||||
|Score=11|10.27|10.02|10.53|0.817|
|Score in [8, 11)|32.32|25.18|40.00|<0.001|
|Score in [0, 8)|23.95|22.98|25.00|0.508|
|_Disease (%)_|||||
|Minor illness|46.51|47.68|45.26|0.498|
|Major illness|30.42|31.30|29.47|0.579|
|CES-D score|8.52|8.81|8.21|0.090|
|ADL or IADL disability (%)|51.08|58.92|42.63|0.090|
|Observations|789|409|380||

Note: 1) Sample are main respondent parents with children no younger than 25, and older than 60 or spouse older than 60. 2) P-values are from t-test of the coresident and non-coresident groups.

25
sweeping the industrialized West in the 1980s, most visibly in Thatcher's England and Reagan's United States.

35. In addition to the avowed overall, and in part ideologically motivated objective, there were several more concrete objectives behind the privatization strategy. Privatization was seen as a means to reduce the burden of SEEs on the economy and the budget. Thereby a more efficient allocation of resources would be promoted, including the distribution of credit, and a lowering of the budget deficit facilitated. Inflation abatement would become less difficult and inputs to export-oriented private firms more competitively priced.

36. By means of privatization, it was thought that productive efficiency would be improved in the short-term through better use of existing resources by private owners/managers, while over time allocative efficiency would also be improved by rendering the privatized plants more responsive to shifts in demand and relative prices. By removing, through privatization, the numerous privileges, explicit and implicit, that SEEs had benefitted from, a more competitive and less distortionary economic environment would be created. Although Turkish state capitalism differs in many ways from the socialism until recently practiced in Eastern Europe, Turkish SEEs have also benefitted extensively from the "soft budget constraint", that is the ability to stretch the budget constraint through adjustments in prices, subsidies, taxes plus credit availability and cost. _j/_ _J/_ For a summary presentation of the various aspects of the "soft budget constraint", see The Economist, April 28, 1990, A Survey of Perestroika, page 14.

21
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