rafmacalaba/datause-encoder-data huggingface dataset:
scale, with total scores ranging from 6 to 30, and a higher score indicating worse mental health. The index is then standardized to have a mean of zero and standard deviation of one.

Respondents in the BNLA survey were also asked about whether they had experienced or witnessed any traumatic events before arriving in Australia. These include extreme living conditions (e.g., lack of food, water, shelter or medicine), war/conflict, violence, kidnapping or imprisonment, political/religious persecution, natural disaster, and/or other events. We then create an indicator of any trauma experience, interacted with the time indicator, as the instrumental variable in our analysis. We also examine the intensity of trauma experience (measured by total events experienced by refugees) as a robustness check. For the heterogeneity analysis, we obtain information on a wide range of refugee characteristics before and after resettlement such as time spent in refugee camps, social networks, and local/government benefits received. Table B1 provides summary statistics of all the variables used in our analysis.

# **References**

- Andersen, M. (2015). Heterogeneity and the effect of mental health parity mandates on the labor market. _Journal of Health Economics_ , _43_ , 74-84.

- Correa‐Velez, I., Barnett, A. G., & Gifford, S. (2015). Working for a better life: Longitudinal evidence on the predictors of employment among recently arrived refugee migrant men living in Australia. _International Migration_ , _53_ (2), 321-337.

- Edwards, B., Smart, D., De Maio, J., Silbert, M., & Jenkinson, R. (2018). Cohort Profile: Building a New Life in Australia (BNLA): the longitudinal study of humanitarian migrants. _International Journal of Epidemiology_ , _47_ (1), 20-20h.

- Flatau, P., Smith, J., Carson, G., Miller, J., Burvill, A., & Brand, R. (2015). The housing and homelessness journeys of refugees in Australia. Australian Housing and Urban Research Institute: Melbourne, Australia.

- Gong, J., Lu, Y., & Xie, H. (2020). The average and distributional effects of teenage adversity on long-term health. _Journal of Health Economics_ , _71_ , 102288.

- Kessler, R. C., Andrews, G., Colpe, L. J., ... & Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. _Psychological Medicine_ , _32_ (6), 959.

- Newman, A., Nielsen, I., Smyth, R., & Hirst, G. (2018). Mediating role of Psychological capital in the relationship between social support and wellbeing of refugees. _International Migration_ , _56_ (2), 117-132.

- Waxman, P. (2001). The Economic Adjustment of Recently Arrived Bosnian, Afghan and Iraqi Refugees in Sydney, Australia. _International Migration Review_ , _35_ (2), 472-505.

53
between 1988 and 1992, and then to investigate the subsequent impact of macroeconomic (price) stabilization which took place following the introduction of the Real Plan in 1994, and was largely completed by the end of the first Cardoso presidential term in 1998.

All members of each participating household over the age of ten are asked a series of detailed questions about their labor market activities as well as personal characteristics such as the level of education. The survey takes place during one week in September. The sample size is 69066 households in 1988, 94171 in 1992 and 112434 in 1998. In our analysis we only include individuals between the age of 18 and 65, who also report all other important information necessary such as education, earnings and so on. The total sample used therefore is 100044 (65002 males and 35042 females) for 1988, 106722 (68102 males and 38620 females) and 120176 (74378 males and 45798 females). The male-female split in the samples is thus two-thirds to one-third in favor of males.

The real hourly wage (constant in 1998 consumer prices) variable is calculated by dividing the reported monthly wage by 4.33 _H_ where _H_ is the reported weekly hours of work. Our wage regressions include the covariates to capture the following: experience, schooling, region, urban/metropolitan location, race, industry and informal status (lack of a signed labor card). The means and standard deviation of the variables are given in Table 1.

The experience variable is the conventional Mincerian approximation where experience = age - years of schooling - 6. The educational status of both sexes in Brazil has improved 10
trade dependence on India, Bhutan remains strongly dependent on India as both a supply source and export destination. For Bangladesh and Sri Lanka, regional trade accounted for approximately 12.4 percent and 8.2 percent of their total trade, respectively, in 1998 (Table 2). Both have increased their shares of intra-regional trade since unilateral reforms were initiated by Bangladesh and continued by Sri Lanka (whose reforms were initiated as far back as 1979) in the early 1990s. For Pakistan, intra-regional trade is relatively insignificant, accounting for a modest 3.6 percent of its total trade in 1998. However, Pakistan, too, has increased its share of regional trade since it implemented reforms, increasing from 2.7 percent in 1990. For India, intra-regional trade is even less significant, accounting for a low 3.2 percent of its total trade in 1998. However, India has shown a noteworthy increase in intra-regional trade since it initiated reforms in 1990 and 1991, years when intra-regional trade accounted for a meager 1.4 percent of its total trade.

**Table 2: Officially Recorded Intra-regional Trade as a Share of Total Trade**

||**Intr**|**a-region**|**al Impor**|**ts**|**Intr**|**a-region**|**al Expor**|**ts**|**Total**|**Intra-re**|**gional Tr**|**ade**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**1981**|**1990**|**1995**|**1998**|**1981**|**1990**|**1995**|**1998**|**1981**|**1990**|**1995**|**1998**|
|India|1.3|0.4|0.6|1.1|2.9|2.7|5.1|5.6|1.8|1.4|2.7|3.2|
|Pakistan|1.9|1.6|1.5|2.4|5.5|4.0|3.2|4.9|3.1|2.7|2.2|3.6|
|Bangladesh|4.7|7.0|17.7|17.5|7.9|3.1|2.3|2.7|5.4|5.8|12.7|12.4|
|Sri Lanka|5.2|7.0|11.4|12.9|8.8|3.7|2.7|2.4|6.5|5.6|7.5|8.2|
|Nepal|N/A|13.4|17.5|31.7|63.8|7.7|9.2|36.2|47.4|11.9|15.0|32.8|
|Maldives|6|7.4|4.5|7.7|22.3|13.8|22.5|16.6|9.4|9.2|6.7|9.4|
|Bhutan|N/A|10.9|57.5|59.9|N/A|9.6|87.9|81.9|N/A|9.7|73.5|71.8|
|**South Asia**|**2.4**|**2.0**|**3.8**|**4.3**|**4.8**|**3.1**|**4.3**|**7.3**|**3.2**|**2.4**|**4.1**|**4.9**|
|MERCOSUR|N/A|14.5|18.1|N/A|8.9|8.9|20.5|N/A|10.7|14.0|21.3|23.0|
|Andean|N/A|6.4|12.6|12.0|N/A|4.1|11.8|11.9|N/A|7.9|12.3|11.4|
|Community|||||||||||||
|ASEAN|13.2|14.6|16.9|20.9|17.2|18.2|23.4|19.8|15.2|16.3|20.0|20.3|
|EU(15)|57.3|65.9|62.4|61.8|52.9|63.2|61.0|59.4|55.0|64.5|61.7|60.6|

Sources: IMF Direction of Trade Statistics.

Notes: Shares for Bhutan are based on partner data (mirror statistics). Please note the presence of discrepancies between FOB and CIF values in mirror statistics.

Intra-regional trade patterns in South Asia suggest that all members increased their relative importance in the region toward the late 1990s.<sup>9</sup> Although the relative importance of the

> 9 Pitigala and Baysan (2001) argue that the revival of intra-regional trade in the late 1990s has ostensibly occurred due to unilateral trade reforms rather than regional or bilateral agreements. However, increased

8
pole has the correct negative sign but it is not statistically signi fi cant. The estimated marginal e ff ects imply that the probability of being employed as a factory worker and as a service workers declines with an increase in distance from growth poles. The estimates in Table 5 also suggest that services are somewhat concentrated around smaller towns implying importance of local demand in the case of non-traded activities. This fi nding is similar to that reported in Foster and Rosenzweig (2004a). The results in Table 5 also con fi rm our earlier fi nding that the negative e ff ect of isolation is magni fi ed in regions with higher crop production potential.

# 5 Conclusions

Rural areas, home to most of the world’s poor, have experienced considerable employment diversi fi cation into nonfarm activities during the last decade. Recognizing its potential for stimulating growth and reducing poverty, there has been a resurgence of interest among researchers and policy makers in identifying factors that can spark growth in the rural nonfarm economy. While the roles of farm and urban linkages in promoting nonfarm activities are discussed frequently by development practitioners, empirical studies examined them in isolation of each other. Embedding the two linkages in a single speci fi cation, this paper provides empirical evidence on the relative role of farm and urban linkages in determining the probability of being employed in di ff erent types of non-farm activities.

The econometric analysis, using individual level employment data from the Household Income and Expenditure Survey, 2000 of Bangladesh, yields three main results. First, access to major urban centers matters greatly for high-return nonfarm activities. Second, agricultural potential of a village also matters, but through its interaction with access to urban centers. The results speci fi cally suggest that the likelihood of being employed in high return jobs and in self-employment increases with a decrease in distance to growth pole and the negative e ff ect 21
**1987.** Apart from the flat fee per collection, which is deducted from member contributions, all other fees are paid in addition to contributions and are deducted from a person's wages.

The average level of commissions, which also include the cost of buying insurance for disability and survivorship pensions, increased from 3.40% of pensionable salary in **1981** to 4.83%<sup>in</sup> **1983.** It has since declined steadily and reached **3.22%** in **1990.** These calculations refer to contributors with a constant pension:ble salary equivalent to 12 **U.F.** The cost for contributors with lower incomes was much higher because of the impact of the flat fee per collection.

|||Commissions|||
|---|---|---|---|---|
||Gross<br>aofwages|_Net_<br>%**of**wages|Net<br>acontrib.|Wet<br>%assets|
|**1982**|**3.56**|**1.77**|**17.7**|14.3|
|**1983**|4.83|**2.06**|**20.6**|**7.3**|
|1984|4.63|**2.32**|**23.2**|**5.6**|
|**1985**|_4.42_|**2.17**|**21.7**|4.1|
|**1986**|4.21|**2.05**|**20.5**|3.4|
|**1987**|4.06|**2.06**|**20.6**|**2.9**|
|**1988**|**3.93**|1.43|14.3|2.9|
|**1989**|3.48|**1.51**|**15.1**|2.8|
|**1990**|**3.22**|1.54|15.4|**2.3**|

Note: Gross commissions include premiums for disability and survivorship insurance Sourcet Habitat **(1991),** Iglesias **(1990)** One of the most persistent criticisms of the new system relates to the level of commissions charged **by** AFPs, especially on low income affiliates. It is estimated that, after deducting the premium for insuring disability and survivorship pensions, commissions amounted<sup>onaverageto</sup> **2.3%** of peasionable income or **_23%_** of contributions in the early i980s,<sup>but fellto</sup><sup>**_1.5%_**of wagesor</sup> **_15%_** of contributions in more recent years. Net commissions as a ratio of average<sup>pensionfLnd</sup> assets fell sharply from 14.3% in **1982** to 3.4% in **1986** and 2.3% in 1990Y.

The cost for low income workers was, however, much higher, especially in the early years of operation of the system. But, as already noted above, the flat fee<sup>per collectionhasfallenin real</sup> terms over the 1980s, while in recent years several AFPs<sup>abolishedsuchfeesaltogether.For</sup> instance, four AFPs -Cuprum, Futuro, Proteccion and the ill-fated Bannuestra-<sup>didnotchargeaflat</sup> fee in **1990,** while Habitat eliminated this fee in **1991,** after<sup>significantlyreducingits realvalue</sup>

**12** Expense ratios equal to **_15_ %** of contributions or **2%** of asset- are rather high for mutual funds but compare _very_ favorably with those of most life insurance companies. Since the services offered **by** AFPs are a hybrid between those of mutual funds and life insurance companies, their expense ratios, which<sup>have declined substantially</sup> over time, may not be unreasonable. However, in the light of the very<sup>high realrates of returnon equity achieved</sup> **by** AFPs **in** rectnt years (see **Table** 14 below), a further lowering of commission<sup>charges and expense ratios ought</sup> to be expected in the f'ure.

**23**
Source: Ontario Fair Tax Commission, Fair Taxation in a Changing World: Report of the Ontario Fair Tax Commission, 1993.

The largest conditional transfer program is the provincial subsidies to school boards for elementaxy and secondary eduction.30 The funding mechanism is embodied in a set of legal documents known as the General Legislative Grants (GLG) regulations. Through a combination of operating and capital assistance programs, the GLG regulation attempts to mitigate inequalities in financial resources among school boards across the province. These assistance programs can be referred to as "equalization payments" since they attempt to equalize the financial resources among school boards by taking into account the size of the local tax base (i.e., resources available) and the resources required by a school board to provide the base level of education service.

The General Legislative Grants are comprised of four components. The first and the most important component, called "Basic Per Pupil Grants," is an equalization payment made by the province to a school board. This provincial grant equals the difference between the amount considered necessary by the province for a school board to provide the base level of education and the amount raised from local property taxes.

The calculation of the basic per pupil grant is based on two key variables: (1) Average Daily Enrollment (ADE), which is the measure of the number of pupils enrolled in each school board. The ADE multiplied by the provincially established basic per pupil amount equals the recognized ordinary expenditure of the school board. In 1995, the basic per pupil amounts were $4,184 for each elementary pupil and $5,116 for each secondary pupil.

(2) The value of the equalized assessment of all property in each community served by the school board determines the amount of money that can be raised from local property taxes.

The second component of the General Legislative Grants is called "Board-Specific Grants." The provincial government recognizes that the cost (teacher wages, rental cost, etc.) of providing the base level of education varies with geographic, demographic, and social-economic conditions across the province. The "Board-Specific Grants" are therefore design to assist localities with additional costs so that they can provide the base level of educational services without placing additional financial burden on local taxpayers.

The third component, called "Program-Specific Grants", is provided to school boards to encourage them to extend education programs and services into areas that respond to local needs, and to meet provincial priorities. These grants are grouped into four subcategories which include language grants (e.g., French & English as second languages), initiative grants (e.g., class size reduction in grades I and 2), special grants (e.g., student transportation, education programs in care and treatment facilities), and other grants (e.g., isolated boards).

30 Local Government Finances in the Greater Toronto Area, "Background Report 3: Subsidy and Service Levels", 1996.

70
Table A1.13 - Basic Data and Principal Results for Grain Mill Products. Brazil and MNxico

|---------------.-|---------------------------.---------------------|__---------__-----<br>Brazil|--<br>Mexico|
|---|---|---|---|
|Part|I- Basic data used in Calculations|||
|I.1|Total Gross Value of Output. 1975 in 1975<br>million national currency units|7,428.1|9,123.1|
|I.2|Matched Gross Value of Output. 1975<br>a) in 1975 million Cruzeiros<br>b) in 1975 million Pesos|6.087.0<br>11,319.4|3,047.3<br>5,960.1|
|I.3|Coverage Ratio Matched Output to Total Gross<br>Value of Output. (%)|81.95|65.33|
|1.4|Value Added. 1975 in 1975 million national units:<br>- present national accounts concept<br>-<br>former national accounts concept|1,630.8<br>1,585.1|1,981.3<br>' 680.1|
|I.5|1975 Ratio of Value Added to Gross Value of Output:<br>- present national accounts concept<br>-<br>former national accounts concept|21.95<br>21.34|21.72<br>18.42|
|1.6|Employment in 1975|19.082|12,502|
|1.7|1975 Exchange Rate (pesos/cruzeiro)|1.5375|1.5375|
|:=_________<br>Part|____________________________________________________________________________<br>II- Principal Results. 1975 in all cases|||
|11.1|Purchasing Power Parity for (Matchad-Total)<br>Gross Value of Output (pesos/cruzeiro)<br>a) Brazil quantity weights<br>b) Mexico quantity weights|1.8596<br>---|-__<br>1.9559|
|I1.2|Total Gross Value of Output<br>a) Brazil unit value weights, fmill. Cr.)<br>b) Mexico unit value weights. (mill.**Ps.)**|7,428.1<br>13,813.3|4,664.4<br>9,123.1|
|I1.3|Value Added (present national accounts concept)<br>a) Brazil unit value weights. (mill. Cr.)<br>b) Mexico unit value weights. (mill.**Ps.)**|1630.8<br>3,032.5|1,013.0<br>1.981.3|
|11.4|Value Added (former national accounts concept)<br>a) Brazil unit value weights, (mill. Cr.)<br>b) Mexico unit value weights. (mill. Ps.)|1,585.1<br>2,947.6|859.0<br>1,680.1|
|I1.5|Value Added (present national accounts concept)<br>per Employee<br>a) Brazil unit value weights. (Cr.)<br>b) Mexico unit value weights. (Pa.)|85,460<br>1586921|81.025<br>158.476|
|II.6|Value Added (former national accounts concept)<br>per Employee<br>a) Brazil unit value weights. (Cr.)<br>b) Mexico unit value weights. (Pa.)|83.068<br>154.472|68.711<br>134.390|
|....<br>...|...<br>.........-............-.....|-............||
|Part|III- Pro Memoria|||
|111.1<br>----------------|Alternate Purchasing Power Parity for Gross<br>Value of Output (pesos/cruzeiro)<br>a) Brazil quantity weights<br>b) Mexico quantity weights<br>--------------------------------------------------|2.3326<br>---|---<br>2.4534|

__---------__------Note: lines I.1, and I.4 to 1.6 derived from table A1.l; lines 1.2 and 1.3 from tables A1.7; The other figures are all derived from the basic data in part I.
cohorts of the gender that is currently dominant in the labor force. However, the deviation from the baseline with respect to the 51-60 aged population is driven by the increase in female labor force participation as the deviation of male labor force participation from the baseline is much smaller.<sup>17</sup>

**Figure 7:   Additional Share of the 51-60 Age Group in Total Human Capital over the Baseline**

<!-- Start of picture text -->
1.6%<br>1.4%<br>female  LFPRs  remain at  age<br>1.2% 50 level  for  ages  51-60 &<br>time invariant male LFPRs<br>1.0%<br>0.8%<br>0.6%<br>female  LFPRs  remain at  age  50<br>level  for  ages  51-60<br>0.4%<br>0.2%<br>time  invariant  male  LFPRs<br>0.0%<br>2006 2011 2016 2021 2026 2031 2036 2041<br>Source:  ILO Authors’ calculation.<br><!-- End of picture text -->

Figure 8 summarizes the ultimate results of this quantitative exercise. Recall that the major determinants of the evolution of human capital, i.e. population growth, skills development through education and the baseline labor force participation path determine a baseline increasing path of human capital. In the case of Morocco the annual rate of human capital growth stands at 3.56% in 2006 and this rate is expected to decrease to 1.1% by 2040.

The human capital growth rate would be higher than the baseline if corrected early retirement incentives convince a significantly higher ratio of those who are close to the standard retirement age to remain in the labor force.

> 17 Note that the human capital calculations underlying the results shown in this paper assume that the productivity of a man and a woman with identical age and educational attainment are identical. This seems to contradict the fact that the wage regression equation identifies gender as significant explanatory variable suggesting higher ceteris paribus human capital for men. Two possible explanations for the significance of the gender dummy is wage discrimination or that men are truly more productive than women after controlling for education and age. Our calculations assume that the wage differential is due to wage discrimination and consequently we associate identical human capital values for women and men with identical age and education.

16
where �ct0 and hct0 are endowments of physical and human capital of country c, ^ and �kt0 and h<sup>^</sup> kt0 are the corresponding revealed factor intensities of product k, all in log terms.

We di¤er from Cadot et al. (2011) in using the normalized di¤erences between the product factor intensities and the country factor endowments, with mean 0 and standard deviation 1. This assures equal weights of physical and human capital in the overall distance, as � and h are measured in di¤erent units.

The data on national factor endowments are from Cadot et al. (2009). The stock of physical capital per capita (�ct0) is constructed according to the perpetual inventory method. Human capital per worker (hct0) is calculated from the average years of schooling in a country, using attainment data.

The product revealed factor intensities of product k are from Cadot et al. (2009). They are calculated as weighted averages of the factor endowments of the countries exporting that product, following the methodology introduced by Hausmann et al. (2007). For instance, the revealed physical capital intensity of product k is calculated as: where �ct0 is country c’s endowment of physical capital, and the weights are given by !ckt0 = ~~P~~ Xc ckt<sup>Xckt</sup> 0 =X0<sup>=X</sup> ct0<sup>ct</sup> 0<sup>,withXdenotingexports.Theseweightscorrespondto</sup> the revealed comparative advantage of country c in product k. The numerator, Xckt0=X ct0, measures the importance of product k in the overall exports of country c (<sup>P</sup> k<sup>Xckt</sup> 0<sup>=Xct</sup> 0<sup>).Thedenominator,P</sup> c<sup>Xckt</sup> 0<sup>=Xct</sup> 0<sup>,aggregatestheexport</sup> shares of product k across all countries. Weighting countries’factor endowments by revealed comparative advantage instead of simple export shares prevents distortions due to di¤erent size of countries (Hausmann et al. 2007 and Cadot et al. 2009 provide numerical examples).<sup>8</sup> The revealed human capital intensity of product k is calculated in a similar way, with hct0 being the endowment of country

> 8The formulation of !ckt0 used by Hausmann et al. (2007) and Cadot et al. (2009) slightly differs from the original index of revealed comparative advantage by Balassa (1965). This modi...ed formulation ensures that weights add up to one:<sup>P</sup> c<sup>!ckt</sup> 0<sup>= P</sup> c ~~P~~ Xc ckt<sup>Xckt</sup> 0 =X0<sup>=X</sup> ct0<sup>ct</sup> 0<sup>=</sup> <u>P</u> ~~P~~ <u>cc</u><sup>XXcktckt</sup> <u>00</u><sup><u>==XXctct</u></sup> <u>00</u><sup>=</sup> 1

11