rafmacalaba/datause-encoder-data huggingface dataset:
||OLS with <br>(Depvar: me<br>|HCSE<br>an cons)<br>|Transient <br>(Cond. quanti<br>|poverty<br>le: 0.75)<br>|Chronic <br>(Cond. quan<br>|poverty<br>tile: 0.9)<br>|
|---|---|---|---|---|---|---|
|Variable|.<br>Coefficient<br>estimate|.<br>t-ratio|.<br>Coefficient<br>estimate|.<br>t-ratio|.<br>Coefficient<br>estimate|.<br>t-ratio|
|Prop. ofkids 15-17 in hh|0.08211|2.72"|-0.62778|-1.95"|0.25405|0.52|
|Log ofhh size|» 0.21434|-17.28°|0.77778|8.06"|1.99465|12.59°|
|Mean grain yield|0.00015|3.63"|0.00233|-8.69°|-0.00492|-8.30°|
|Std. dev. ofgrain yield|-0.00006|-3.46°|0.00096|1.64TM|0.00108|2.70°|
|Mean log ofhh wealth|0.32275|39.42”|-1.23413|-19.66"|4.31922|-19.58°|
|Std. dev oflog hh wealth|0.00001|2.17"|0.00008|4.34°|0.00039|11.28"|
|Prop. offixed prod. assets inhh with (mean)|0.03698|0.97|0.26467|0.78|-2.16175|-4.53°|
|Prop. of cash in hh with (mean)|-0.23249|-4.80°|0.95507|2.38"|-2.31580|-3.59°|
|Prop. ofdeposits in hh wealth (mean)|0.03756|0.45|0.67878|-0.66|3.08756|3.07"|
|Prop. ofgrains in hh with (mean)|0.44045|7.99°|-2.08884|-4.98°|-4.92084|-7,.51°|
|Prop. ofdurables in hh with (mean)|0.82486|14.84°|-2.60226|-7,30"|-10.71815|-13.30°|
|Prop. ofgrains in hh with (std:dev.)|0.23140|-2.33"|3.27431|3.95°|0.62439|0.49|
|Prop. of fix. prod. assts. in with (std. dev.)|0.00801|0.10|1.06999|1.55|-1.83770|-1.56|
|Prop. ofcash in hh with (std. dev.)|0.38834|4.82"|-1.53518|-2.73°|2.31181|-2.47"|
|Prop. ofdeposits in hh with (std. dev.)|0.34593|3.22°|1.42115|1.30|-2.56688|-2.26"|
|Prop. ofdurables in hh with (std. dev.)|0.03437|0.36|-2.247015|-3.94°|1.10668|1.24|
|Cultivated land per capita|0.00035|5.01"|0.00184|-3.54°|-0.00304|-3.87°|
|Hilly area per capita|0.12486|0.98|1.76337|1.47|0.42448|0.21|
|Fishing area per capita|0.13585|1.52|0.145728|0.27|-3.99459|-3.70°|
|Guangdong (dummy)|-|-|0.20247|2.57|-|-|
|Guangxi (dummy)|-|-|0.19987|2.48"|-|-|
|Guizhou (dummy)|-|-|0.19831|1.77"|,<br>-|-|
|Intercept|3.73609|44.57"|-|-|-|:|
|"(1% significance), ” ($% significance), “" (10%<br>Notes:|Adjusted R<br> significance)|?: 0.7933|Pseudo|R?: 0.2286|Pseudo|R?: 0.4384|
|In addition to the above regessors, county dummy<br>Pagan-VellateststatisticsfortheTobitmodels:|variables were|also included.<br>:|||||

’ Transient poverty: Heteroscedasticity : 37.01 Skewness : 166.39 Kurtosis + 64.48 Chronic poverty: Heteroscedasticity : 25.57 Skewness : 37.75 Kurtosis : 39.34
Table A4: Robustness to using unconditional employment in mining

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.:Growth inper capita i|ncome(1996-20|11)|||
|Homeland (=1)|1.428***|1.610***|0.971**|0.300|
||[0.530]|[0.530]|[0.467]|[0.966]|
|Share (Uncond.) Employed|-0.761*|-0.431|-1.241***|-0.499|
|mining1996|[0.445]|[0.495]|[0.471]|[0.456]|
|Homeland * Share (Uncond.)|2.298***|2.291***|2.274***|1.808***|
|Employed mining1996|[0.565]|[0.582]|[0.558]|[0.543]|
|Observations|9,619|9,619|9,619|9,593|
|R-squared|0.592|0.6|0.634|0.711|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 20111996. The share of unconditional employment in mining for 1996 is defined as the number of individuals employed in mining over total population. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
- Brathwaite, R., Rocha, T. B.-M., Kieling, C., Gautam, K., Koirala, S., Mondelli, V., Kohrt, B., and Fisher, H. L. (2021). Predicting the risk of depression among adolescents in Nepal using a model developed in Brazil: the IDEA Project. _European Child & Adolescent Psychiatry_ , 30:213–223.

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**Figure 4: Exports, imports and migration**

<!-- Start of picture text --> Exports Imports<br>Number of immigrants from other Mexican Number of immigrants from other Mexican 158.3<br>160 142.1 states 160 states<br>140 140<br>120 120<br>100 100<br>80 63.3 High exports 80 High imports<br>60 60 47.1<br>Low exports Low imports<br>40 40<br>20 20<br>0 0<br>Number of immigrants from other countries Number of immigrants from other countries<br>160 160<br>140 140<br>120 120<br>100 100<br>High exports High imports<br>80 80<br>60 42.4 Low exports 60 43.3 Low imports<br>40 24.7 40 23.8<br>20 20<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median.

**Figure 5: Exports, imports and informality rate**

<!-- Start of picture text --> Exports Imports<br>Informality rate Informality rate<br>100 100<br>87.9<br>85.0<br>80 72.0 80 69.1<br>60 60<br>High exports High imports<br>40 Low exports 40 Low imports<br>20 20<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median. (3) Labor informality rate is measured as the percentage of employees without pension or retirement benefits at work.

In addition, this study also sheds light on the role of geography, sectors and types of trade which can all explain differences in local labor market outcomes. As for trade, we specifically delve into the role of final versus intermediate imports and GVC spillovers. Drawing on Economic Census data, the analysis 7
participated; 217 experts in total. The experts were identified from membership lists of regional associations of environmental and resource economists, and contact lists of the current authors (all experts on the lists are environmental valuation practitioners).

**Table 1: Experts including WTP statements by country and region other than Europe**

|**Country group**<br>**(number of**<br>**experts)**|**Country (number**<br>**of experts)**|**Stated Mean WTP for**<br>**Plan A, Round 2,**<br>**US$/household/year **|**GDP (PPP-**<br>**adjusted),**<br>**US$/capita/year **|
|---|---|---|---|
||U.S. (71)|67.7|50,000 (50,000)|
|OECD countries|Canada(11)|90.2|52,200(42,500)|
|(98)|Australia(9)|44.9|67,000 (44,600)|
||NewZealand (7)|24.7|37,700 (32,200)|
|Low-income Asian|Bangladesh(1)|10.0|800 (1900)|
|countries (6)|Cambodia(1)|4.0|1000 (2500)|
||Nepal(4)|4.2|700(1500)|
||India(10)|23.8|1,500 (3,900)|
|Lower-middle-|Indonesia(6)|6.2|3,600(5,000)|
|income Asian|Pakistan(3)|11.0|1,300 (2,900)|
|countries (40)|Philippines (11)|6.5|2,600 (4,400)|
||Sri Lanka(2)|36.5|2,900(6,200)|
||Vietnam(8)|5.8|1,600 (3,600)|
|Upper-middle-|China(6)|23.4|6,200(9,200)|
|income Asian|Malaysia(11)|31.5|10,400 (17,100)|
|countries(24)|Thailand(7)|18.9|5,500(9,800)|

In Tables 1-2, the Asian experts are grouped by main country income characteristics,<sup>8</sup> and the European experts by region. The tables also indicate GDP per capita (standard and PPP adjusted), on average by region in Europe, and otherwise by country. All experts except 11 have PhDs (among the rest, 10 have Master’s degrees). Women comprise 39 out of 147 experts (26.5%) from regions apart from Asia, and 30 out of 70 experts (42.9%) from Asia.

# **2.2 Survey Instrument**

We conducted the Delphi exercise by email. Each expert was provided with a description, including key visual aids, of two Amazon protection scenarios that might be presented to a population sample in the expert’s home country. The survey instrument provided to the U.S. experts is reproduced in the appendix. The instruments used in the other regions were similar,

> 8 The European survey was conducted in the spring and summer of 2012. The other surveys were conducted over the period May – September 2013. Altogether 299 experts were contacted. The overall response rate was 70.2%, highest in Europe (82.7%), and lowest in Oceania (41%), where less effort was directed toward non-respondents.

6
Poverty impacts of trade policy are also increasingly arising in the national policy debate. Concerns about urban poverty during the Asian financial crisis caused the government of Thailand to Consider introducing an export tax on rice (Warr, 2001), while recent concern for the rural poor in Indonesia precipitated the introduction of an import tariff on rice in that country in 2000, followed by temporary rice import bans in 2004. China’s recent debate over WTO accession – largely a unilateral trade policy reform – was also permeated by a concern for the potential adverse impacts on rural poverty in a country exhibiting historic levels of rural-urban inequality (Bhattasali, Li and Martin, 2004). More generally, the World Bank is now mandated to conduct trade diagnostic studies for all least developed countries, and these studies have an explicit goal of assessing the poverty impacts of prospective trade reforms.

Without an analytical framework it is hard to evaluate the impacts of trade policies on the poor. For example, in the case of the proposed Thai export tax, Warr (2001) finds that the intended beneficiary, namely the urban poor, would have been hurt by this policy proposal, due to the adverse effect on unskilled wages – an unanticipated by-product of the proposed policy. To place future debates on a firmer economic foundation, new policy modeling frameworks are needed to evaluate the claims of special-interest groups, and identify policy packages that promote poverty reduction (Harrison, Rutherford and Tarr, 2003).

With this high level of policy interest, it is hardly surprising that the issue of trade and developing-country poverty has become a focus of much research activity over the last several years. This paper offers a survey of recent studies that analyze how trade policies – both in developed and developing countries – affect the incidence of poverty in the latter. The objective is to summarize the approaches and insights offered by the diverse strands of research that are currently being conducted. While a number of the most important findings are reviewed, it must be said at the outset that the field is undergoing rapid evolution and has yet to reach a consensus on many key issues.

Not surprisingly, a variety of methodologies have been proposed to analyze the various trade/poverty linkages, which suggests that the range of findings will also be diverse. The most obvious methodological gulf is between researchers who come at these issues from a tradition of measuring poverty using detailed household expenditure data, and those who are of a trade background and more accustomed to dealing with economy-wide, national accounts data. One might 2
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31
detailing their scores on each component of the assessment, their relative rank on this component, and a summary paragraph describing what the score meant.

# _3.2.2 Matching Job Candidates with Firms_

The second step involved using this information collected on candidates to match them with firms that may potentially want to hire them. The matching process proceeded as follows:

- i) BDC staff called the firms in our sample and explained the concept of psychometric testing and its use for job matching, giving examples of how the tested concepts could help them better find a good fit for different types of jobs. For example, extroversion is likely to be important for sales positions, and organizational personality types for administrative positions, while some positions may require analytic ability, and others depend more on English ability. They then asked the firm manager or HR manager what their current needs for workers were, whether they currently had or would soon have any open positions, and what kind of traits they were looking for in each position.

- ii) BDC staff would then filter our job candidate database for the top three to five candidates that would be considered a good fit for the position. Marwan Al-Zoubi prepared an instruction manual in Arabic which helped in this process, giving examples of the types of personality and ability characteristics that would be ideal for different positions.

- iii) These staff would then call these candidates and explain the job description and likely salary range, as well as location of the job, and check the candidate was interested in being considered for this position. If they were not interested, a replacement match would be drawn from the next best fit in the database.

- iv) The firm would then be sent a list of the three to five job candidates matched to the position, along with the resumes of these candidates, and a description of why these candidates would be a good fit for the job. Steps ii) through iv) normally took 2 to 3 business days from the initial discussion with the firm in step i).

- v) Firms would then contact the job candidates they were interested in to arrange an interview, and following this interview, potentially make a job offer. BDC staff followed up with the job candidates and firms on a weekly basis to record the

12
paise per quintal. This was increased to Re 1 per quintal in 1971, to Rs 6 in 1975, and to Rs 12 per quintal in 1988, at which level it still remained in 1995. This compares with free market prices which were reported to be of the order of Rs 100 to Rs 150 per quintal in the late 1980s and early 1990s, and international prices estimated to be over Rs 1 00/quintal during the same period. The controlled domestic prices and estimated fob export prices all expressed in constant 1981 Rupees are compared in Fig. 15. In real terms, there was a big increase in the controlled domestic price in 1975, but it was still less than one fifth of the fob price. After 1975 it declined in real terms and in 1994 was lower than it had been in 1975.

Figurel5: **Controlled domestic prices and estimated fob prices of Indian molasses in 1981 Rupees, 1965-94** <!-- Start of picture text --> 80 +FOB PRICE AT INDIAN PORT<br>c 70 -u-CONTROLLED PRICE<br>'2<br><!-- End of picture text -->

**Source: Annex 10.**

Since molasses exports have consistently been suppressed both by direct controls and indirectly by the allocation of supplies at low prices to distilleries and industrial alcohol producers, we have treated it as an exportable in estimating its nominal protection. Both domestic and international transport costs are high relative to international prices, and the assumptions made about the exporting areas in India and the international markets that would be supplied have a considerable influence on the estimates. As regards world prices, we found thatexcept in years with very low volumes- Indian unit export values have fairly closely followed changes in US tank car prices at New Orleans, which are the industry standard indicators of world prices (see Annex 9). Indian fob export unit values are also close to export unit values in Pakistan, which consistently exports much larger quantities than India. As only about two grades of molasses have been exported, we have used the export unit values to represent average fob 3 9
# **<<B>>Consumption linkages**

An expanding urban area will generate an increase in the demand for rural goods. This channel is likely to operate via an _income_ effect as well as a _substitution_ effect. The income effect is related to increased demand for agricultural goods due to higher (nominal) incomes in urban areas relative to rural areas. This phenomenon was documented as early as the Industrial Revolution; Allen (2009) describes how trade and proto-industrialization in British cities increased the urban consumption of goods produced in the countryside. This higher income is explained by urbanized economies: urban areas have denser markets for products and factors, which increase labor productivity and wage levels over the levels of rural areas (Duranton and Puga, 2004). The substitution effect is related to the increased share of higher value-added products in total agricultural demand that is typical of more sophisticated urban consumers. Empirical evidence from India and Vietnam confirms this composition effect (Parthasarathy Rao et al. 2004; Thanh et al. 2008). As noted below, we expect these effects to grow stronger closer to urban areas as a result of the weakly integrated agricultural markets within India (Jha et al. 2005).

# **<<B>>Rural nonfarm employment**

Expanding urban areas can also favor the diversification of economic activity away from farming, which typically has a positive effect on income (e.g., Berdegue et al. 2001; Lanjouw and Shariff 2002; Jacoby and Minten 2009). This effect is particularly important in the rural areas surrounding the cities. Three concomitant effects can explain this increased diversification. First, proximity to cities may allow some of the peripheral urban workforce to commute to the city to work. Commuting, in turn, generates suburban nonfarm jobs in services, such as consumer services and retail trade, which are needed by the growing commuter population. Second, because 6