rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: SECULAR CHANGES IN DEVFLOPING COUNTRIES' EXPORTS IN INDIVIDUAL COMMOITY PROCESSING CHAINS, 1965-1987

||Imports<br>developing<br>(millions|from<br>countries<br>of USS)|Share<br>ALL|of developing c<br>stages|ountries<br>Primary|in total develo<br>stage items P|ped coun<br>roce.sed|try inmorts (X)<br>stage products|<br>Share of p<br>all devetopin|rimary stage in<br>g country exports|
|---|---|---|---|---|---|---|---|---|---|---|
|Processing<br>chain|1965|1987|1965|1965-87 chg|1965|1965-87 chg|1965|1965-Z7 chg|1965|1965-87 chq|
|FOOD AND TOBACCO|||||||||||
|Meat|622|2,927|19|-5|7|2|22|-9|8|5|
|Fish eAcept shellfish|<br>82|4,057|9|19|7|19|11|20|70|3|
|Shellfish|182|6,640|46|15|53|11|23|20|90|-4|
|Coffee|2,065|9,822|92|-5|93|-2|9|27|97|-5|
|Cocoa|412|3,286|67|-20|98|-4|18|4|_70_|-35|
|Tea|412|769|95|-14|96|-14|8|16|99|-2|
|Sugar|676|1,213|54|-4|47|9|61|-17|63|-7|
|Wheat|145|43|13|-12|13|-13|3|-1|78|11|
|Rice|132|214|56|-32|36|-11|64|-41|37|-1|
|Groundnuts|'79|369|85|-28|87|-36|80|-3|66|9|
|Copra|328|487|93|-1|96|3|86|5|11|-1|
|Palm nuts and kernels|<br>115|229|87|0|96|-33|47|42|6|2|
|Soya beans|78|1,067|10|13|11|13|6|1|98|-4|
|Linseed|35|4|32|-29|4|-3|76|-61|19|67|
|Cotton seed|'1|25|53|-10|97|-32|23|-3|77|-26|
|Castor seed|42|110|91|1|90|11|92|-2|21|-2|
|Fruit|1,187|9,300|42|3|45|-1|26|20|70|1|
|Vegetables|357|4,152|24|5|24|4|28|3|73|2|
|Tobacco|370|1,77a|35|-9|40|5|2|0|96|-41|
|AGRICULTURAL MATERIALS|||||||||||
|Wood|740|8,612|22|9|51|-1|13|14|55|-21|
|Cork|10|1S|13|-10|30|-25|5|-2|72|-64|
|Pulpwood|22|1,696|1|3|4|2|0|3|29|-26|
|Rubber|656|4,220|42|-19|66|-18|1|_12_|99|-39|
|Leather|255|11,123|19|22|28|-18|12|33|61|-58|
|Silk|46|,24|30|26|61|38|12|36|76|-47|
|Jute|389|_260_|86|-2|86|-2|82|2|40|-33|
|Yoot|258|584|10|-2|13|-8|7|3|67|-50|
|Cotton|1,140|5,407|50|-8|69|-15|23|15|82|-50|
|Flax, henp and ramie|9|175|7|19|10|27|42|-31|97|-14|
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the International Roughness Index (IRI): very good, good, fair, poor, and very poor.<sup>13</sup> The data shows that the road quality substantially differs between different regions (Marzes). For example, as Figure 8 shows, Yerevan - the region of the capital city has only 4% of its roads in poor or very poor quality, the reported road quality is better in in Kotayk and Ararat Marzes - surrounding Yerevan - more than 85% of primary and feeder roads are in good or very good condition, while Lori and Shirak regions have only around one-fifth of the primary and feeder roads in good condition.

# **5 Methodology**

## **5.1 Empirical strategy**

The impacts of infrastructure are often challenging to capture. This paper studies the questions using several datasets and estimation methods.

The first method studies the association between regional road quality and different employment outcomes. It uses the Integrated Living Conditions Survey - 10 years of repeated cross-sectional data - to answer the research questions. The survey has collected rich data on various employment outcomes, which is not usually the case in other surveys, making it possible to look at more variables of interest.<sup>14</sup> The first part of the analysis studies the association of reported road quality leading to towns and markets, and binary employment outcomes: whether a person is employed in non-agricultural sector, in seasonal employment, and whether at least one member of the household works outside the village. The second part of the analysis studies the relationship between hours worked, agricultural employment, and road quality.

The second method utilizes the opportunity of GPS location information collected by the surveys to answer the research questions. It combines the georeferenced data on quality of roads in Armenia collected by the World Bank, matching the geographic coordinates of household cluster locations collected by the DHS. The combined dataset with geographic locations provides an opportunity to calculate the proximity to good quality roads from

> 13Road quality categories: Very good (IRI range between 1.0 and 2.0), Good (2.0 - 4.0), Fair (4.0 - 6.0), Poor (6.0 - 10.0), Very poor (10.0 - 16.0).

> 14The questionnaires were slightly modified over the period of 10 years, therefore, the variables have been adjusted accordingly to ensure comparability over the years.

13
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Table 4. Parental Consanguinity and Children’s Height Z-Score, Age 5-13

|||Ordinary Le|ast Squares||IV-LIML|estimator|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Parents are Related|0.013|||-0.011|-1.35*|-1.52|
||(0.183)|||(0.182)|(0.793)|(1.20)|
|F-coefficient of Inbreeding||-0.040|||||
|||(1.53)|||||
|Parents are 1st cousins|||-0.019||||
||||(0.197)||||
|Parents are 2nd cousins|||-0.216||||
||||(0.311)||||
|Age|-0.097***|-0.097***|-0.097***|-0.091***|-0.083***|-0.077***|
||(0.018)|(0.018)|(0.018)|(0.018)|(0.021)|(0.023)|
|Male|0.245**|0.246**|0.245*|0.230*|0.260*|0.246*|
||(0.122)|(0.122)|(0.123)|(0.117)|(0.137)|(0.135)|
|Wealth Index|-0.064|-0.065|-0.070|-0.014|-0.171|-0.013|
||(0.127)|(0.128)|(0.127)|(0.118)|(0.142)|(0.114)|
|Dummy for Sindh Province|-0.295|-0.298|-0.300||-0.542||
||(0.323)|(0.326)|(0.327)||(0.441)||
|Dummy for KPK Province|-0.498|-0.502|-0.506||-0.782||
||(0.886)|(0.882)|(0.880)||(0.797)||
|Father's Years of Education|-0.013|-0.013|-0.012|-0.030|-0.005|-0.019|
||(0.024)|(0.024)|(0.024)|(0.024)|(0.025)|(0.025)|
|Father's Years of Education*Sindh|0.003|0.003|0.001|0.024|-0.015|-0.003|
||(0.033)|(0.033)|(0.033)|(0.032)|(0.040)|(0.037)|
|Father's Years of Education*KPK|0.035|0.0351|0.034|0.004|0.017|-0.028|
||(0.087)|(0.087)|(0.086)|(0.056)|(0.075)|(0.049)|
|District Fixed Effects|No|No|No|Yes|No|Yes|
|**Test statistics**|||||||
|Hansen J statistic|||||0.926|2.21|
|p-value|||||0.336|0.136|
|F-statistic, Instruments|||||8.27|5.30|
|F-probability|||||0.001|0.007|
|CLR Test|||||3.55|2.34|
|p-value|||||0.074|0.148|
|95% Confidence Interval, based on|||||[-3.33,|[-9.39,|
|<br>CLR Test|||||-0.02]|0.30]|
|Number of observations|1285|1285|1285|1285|1285|1285|

Notes: Village-level cluster robust standard errors are reported in parenthesis. Columns (4) and (6) control for factors related to village location with district fixed effects. The Hansen J statistic tests for over-identification. We report cluster-corrected F statistics to test for weak instruments and then implement the conditional likelihood ratio (CLR) test developed by Moreira (2003) to test for weak instrument bias. The CLR test is a test of the hypothesis that the instruments and the coefficient on the endogenous variable are jointly zero. We next show the 95% confidence interval suggested by the CLR test. As implemented, CLR test is generalized for clustered dependence in error terms using the minimum distance approach by Finlay and Magnusson (2009).

23
Table 3: Dimensions and Components of Job Quality

|Dimension|Components|Type of|Defnition|
|---|---|---|---|
|||Threshold||
|A. Benefts and Protected by Law|Social security benefts<br>Minimum wage compliance|Yes/No<br>Yes/No|The worker is registered to the AFP (pension system).<br>A full-time worker’s wage complies with the statutory minimum<br>wage. (Part-time workers are given a value of 0).|
||||The sector’s contribution to structural child labor is below the|
||Low incidence of child labor (under 14 years old)|Normative|mean contribution to child labor (excludes the agriculture sec-<br>tor).|
||||The worker’s hourly wage is not in the bottom 30 percent of the|
|B. Wage-Job Linkage|Low incidence of relative underpayment|Cell-based<br>threshold|cell-based wage distribution in the sector-occupation-education-<br>potential labor market experience cell.<br>A full-time worker earning below the low-pay threshold does not|
||Low incidence of compensatory moonlighting|Yes/No|have a secondary job. _Low-pay threshold_ is defned as a monthly<br>wage less than 2/3 of median pay of all full-time wages.|
||||The worker has tenure, defned as working more years than those|
|C. Career advancement and growth<br>11|Tenure|Cell-based<br>threshold|of the median worker in the sector-occupation-potential labor<br>market experience cell.|
||||The worker’s level of education is not above the education level|
|D. Linkage between education and job|Low incidence of overqualifcation|Cell-based<br>threshold|of the median worker in the sector-occupation-birth cohort-<br>graduation cohort cell.|

_Notes:_ Based on data from EH 2007-2021. We define workers in micro and small enterprises (MSE) for people who are employed in a firm with less than five employees, domestic workers and unpaid workers. Average income is in 2010 prices.
<mark>default, which includes: (1) missed payments beyond the grace period; (2) material changes to the contract affecting creditors, including distressed debt exchanges that reduce the debtor’s obligations; or (3) unilateral changes imposed by the debtor resulting in diminished financial obligation.</mark><sup>13</sup> <mark>Farah Yacoub et al. (2023) strive to categorize defaults based broadly on this definition even for countries and time periods when rating agencies did not cover the debtor in question.</mark><sup>14</sup> <mark>Compared to available historical sovereign ratings, the default data set is broader both in the time and cross-country dimensions. All core results presented here remain significant and of similar magnitude when instead using a smaller data set that only considers defaults signaled by the three leading credit rating agencies merged with a study by Standard and Poor’s applying its methodology in retrospect (Standard and Poor’s, 2006).</mark> <mark>Because we strive to assess the impacts of sovereign default on social outcomes, we aim at limiting possible confounding drivers of poverty, such as wars (Bianchi and Sosa-Padilla 2022). We thus drop all those defaults, which coincide with inter-country wars, using data from Sarkees and Wayman (2010).</mark>

# **<mark>Data used to restrict the pool for synthetic controls</mark>**

<mark>In order to limit the risk of interpolation and overfitting, we restrict the pool from which the synthetic control is constructed to countries that are structurally similar to the treated unit (Abadie, 2021). To capture structural similarities, we rely on two variables: GDP per capita and Polity V. GPD per capita is measured in real terms and published by the Maddison Project, and serves as an alternative to splitting the sample by income group, as is often done in the literature. The Polity Project rates countries based on how democratic they are and chosen here as a proxy for institutional strength. The main index produced by the Polity Project score countries from -10 to +10, where the lowest score is attributed to countries that are strongly autocratic and the highest score to countries that are strongly democratic. Compared with other indicators that</mark>

> 13 <mark>See Ams et al. (2018) citing Moody’s (2018).</mark>

> 14 <mark>However, in some cases our default designation differs from that of credit agencies because we may have uncovered information unavailable to these agencies at the time – for example considering a debt or dispute not captured by the agencies – or because in our judgment there are important reasons to signal or not a default. Such as creditors’ unwillingness to “accelerate” the contract because they are engaged in conversations, the presence of a legal dispute, or the technical nature of a default not capturing a sovereign’s inability to honor its commitments. See Appendix A.1.3 for an example of how the default data was compiled (Farah Yacoub et al. 2021).</mark>

10
9 In the IFLS (2007) survey data, approximately 35 percent of all labor market participants work in the formal sector. These wage-and-salary employees are potentially covered by the severance regulations. Of those individuals in covered employment who are employed by a firm falling under the auspices of the regulations, not all meet the requirements. Furthermore, of those who do meet the necessary coverage requirements, not all are eligible to receive severance pay.<sup>9</sup> Of government and private sector employees working in covered employment, only 14.8 percent state that they are entitled to severance pay as one of the set of benefits offered by their employer. Based on this low percentage, it seems that many formal sector employees lack a clear understanding of severance pay eligibility.

In both surveys, women in covered employment are less likely than men to be eligible for severance pay in the event of a job separation.<sup>10</sup> As Table 2 shows, eligibility for severance pay increases strongly with age, but there are no noticeable differences in eligibility by level of education. While there is variation in eligibility for severance pay across industries, there is no apparent pattern. There is, however, a strong systematic relationship between eligibility for severance pay and tenure: eligibility increases strongly with tenure. For firms of different sizes, the eligibility proportion steadily increases with firm size.

There are two noticeable differences, however, between the surveys. The first difference is the likelihood of eligibility for severance across firm ownership status. In the IFLS (2007), wage and salary workers employed by state-owned enterprises are more likely to be eligible for severance than employees working in a government agency. On the other hand, the likelihood of eligibility for severance is similar across firm-ownership status in Sakernas (2008). Second, neither survey shows a consistently positive association between eligibility and the level of wages across the full range of wage rates.

3.3 _Compliance with current severance pay regulations_

> 9 Question TK25A3 of the IFLS (2007) survey asks individuals to identify a set of benefits offered by the employer.  One of these benefits is severance payment eligibility.

> 10 Table 2 display details of severance eligibility and receipt of severance pay based on the Sakernas (2008) survey.  The IFLS (2007) findings are consistent.
Table 8. Supply chain linkages and their role in technology diffusion

||Dep|variable: Nor|malized share o|f technology-|related job ad|verts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|4.630***|4.468***|4.574***|4.474***|3.928***|3.893***|
||(0.398)|(0.395)|(0.396)|(0.395)|(0.774)|(0.775)|
|Pioneer_×_years since emergence|-0.202***|-0.191***|-0.199***|-0.193***|-0.186***|-0.184**|
||(0.036)|(0.036)|(0.036)|(0.036)|(0.072)|(0.072)|
|**Linkages of pioneer frms**|||||||
|<br>Buyer||1.878***||||1.490***|
|||(0.300)||||(0.319)|
|Supplier|||0.702***|||0.074|
||||(0.173)|||(0.170)|
|Innovation partner||||1.658***||1.056***|
|||||(0.275)||(0.291)|
|Affliate|||||0.791|0.564|
||||||(0.771)|(0.770)|
|**Linkages of pioneer frms over time**|||||||
|Buyer_×_years since emergence||-0.052*||||-0.053*|
|||(0.029)||||(0.030)|
|Supplier_×_years since emergence|||-0.006|||0.025|
||||(0.018)|||(0.018)|
|Innovation partner_×_years since emergence||||-0.037||-0.018|
|||||(0.026)||(0.027)|
|Affliate_×_years since emergence|||||-0.014|-0.001|
||||||(0.072)|(0.072)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|0.041|
|Observations|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|

Notes: Standard errors clustered at the firm level in parentheses.The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **4.2.3 Diffusion through supply chain linkages controlling for distance**

A key question from the results above is whether firms that are more geographically proximate to pioneer firms are more likely to have supply chain linkages. To test whether the results are driven by distance, for each firm-technology pair we also consider the distance to pioneer firms for that technology as an additional control, given the negative and significant distance coefficients in 2.3.2. Results from Table 9 show not only negative and significant coefficients on distance across all specifications, but also consistent linkage coefficients with similar significance level to those in the baseline, with buyer and innovation partner linkages remaining the most important. This suggest that firm-to-firm linkages matter above and beyond the role of distance.

25
**Table 9. Observed and Counterfactual Earnings (2006q2 anchor)**

|||**Observed**<br>|<br>|**C**<br>|**ounterfact**<br>|**ual**<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|
||**All**|**Formal**|**Informal**|**All**|**Formal**|**Informal**|||||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(6)/(3)**|**(4)/(1)**|**(2)/(3)**|**(5)/(6)**|
|**Incomplete**<br>**Primary**|||||||||||
|Mean|21.4|23.7|19.6|22.4|23.7|21.4|1.09|1.05|1.21|1.11|
|SD|9.99|11.2|8.5|10.2|11.2|9.22|.|.|.|.|
|Shares|100|43.2|56.7||||.|.|.|.|
|**Complete**|||||||||||
|**Primary**|||||||||||
|Mean|22.6|24.6|20|22.8|24.6|20.6|1.03|1.01|1.23|1.2|
|SD|11.5|12.4|9.73|11.6|12.4|10|.|.|.|.|
|Shares|100|55.5|44.5||||.|.|.|.|
|**Incomplete**<br>**Junior**|||||||||||
|**High**|||||||||||
|<br>Mean|23.1|25.2|20.7|24.6|25.2|23.9|1.16|1.06|1.22|1.05|
|SD|11.8|12.2|10.7|12.4|12.2|12.7|.|.|.|.|
|Shares|100|53.9|46.1||||.|.|.|.|
|**Complete**<br>**Junior**|||||||||||
|**High**|||||||||||
|Mean|24.1|25.9|20.4|25.3|25.9|24|1.18|1.05|1.27|1.08|
|SD|12.4|13|10.4|12.8|13|12.4|.|.|.|.|
|Shares|100|67.0|33.0||||.|.|.|.|
|**Incomplete**<br>**Senior**|||||||||||
|**High**|||||||||||
|Mean|29.1|31.6|21.2|30|31.6|25|1.18|1.03|1.49|1.26|
|SD|17.5|18.1|13|17.6|18.1|15.2|.|.|.|.|
|Shares|100|75.3|24.6||||.|.|.|.|
|**Complete**<br>**Senior**|||||||||||
|**High**|||||||||||
|<br>Mean|31.5|33.9|23.8|32.5|33.9|27.9|1.17|1.03|1.42|1.22|
|SD|20|20.4|16.6|20.4|20.4|19.7|.|.|.|.|
|Shares|100|76.4|23.5||||.|.|.|.|
|**University**|||||||||||
|Mean|51.1|55.2|34|53|55.2|43.9|1.29|1.04|1.62|1.26|
|SD|31.5|31.7|23.5|31.7|31.7|29.9|.|.|.|.|
|Shares|100|80.1|19.9||||.|.|.|.|
|**Total**|||||||||||
|Mean|32|35.9|22.8|33.1|35.9|26.6|1.17|1.04|1.58|1.35|
|SD|23|24.7|14.8|23.4|24.7|18.5|.|.|.|.|
|Shares|100|69.7|30.3||||.|.|.|.|

_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note: Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree.

31
Panel A: FA3 vs. YRD, and Guerrilla vs. No Guerrilla

|County|Famine Control|Death Rate|County|Famine Control|Death Rate|
|---|---|---|---|---|---|
|FA3|0.688|19.7|No Guerrilla|0.754|14.57|
||(0.158)|(2.6)||(0.153)|(5.36)|
|Obs.|11|10||25|25|
|YRD|0.798|12.1|Yes Guerrilla|0.807|11.92|
||(0.132)|(0.6)||(0.125)|(6.08)|
|Obs.|48|48||34|33|
|YRD-FA3|0.11**|-7.6***|Yes-No|0.054|-2.65*|
||[0.046]|[1.75]||[0.038]|[1.51]|

Panel B: Interactions of FA3, YRD with Guerrilla Presence

||Famine Co|ntrol|||Death Rates||
|---|---|---|---|---|---|---|
|County|Guerrilla P|resence||Guerrilla|Presence||
||Yes|No|Yes-No|Yes|No|Yes-No|
|FA3|0.56|0.73|-0.17|24.3|17.8|6.5|
||(0.09)|(0.16)|[0.097]|(9.5)|(7.5)|[5.55]|
|Obs.|3|8||3|7||
|YRD|0.84|0.76|0.08**|10.2|13.7|-3.47***|
||(0.09)|(0.15)|[0.037]|(2.99)|(4.4)|[1.11]|
|Obs.|22|26||22|26||

Table 4: Comparisons of Famine Control and Death Rates (per 1000) Across Counties in Fujian, by Faction and Guerrilla Presence, 1959-1961.

Note: The numbers in parenthesis are standard deviations, and the number in square brackets are standard errors. *, ** and *** denote significance at 10, 5 and 1 percent, respectively.

24
samples are composed only of individuals who have obtained their degrees before coming to the US. For example, if the age-at-arrival in the US is more than 18 years old and the individual has a high-school degree I assume he has earned it before migration and exclude him from the dataset.<sup>35</sup> Second, if different countries have different return migration or death rates the conditions needed for consistency of pseudo-panel estimates do not hold. That is, the average characteristics of those observed in 1980 might differ from those observed in 1990 and 2000. Attrition due to natural causes is addressed by setting upper limits on ages of migrants included in the samples. With respect to return migration, sub-section 3.4.1 found that it does not change the main results of the paper in a significant way. Therefore I will assume that the potential of return migration to weaken the validity of the pseudo-panel estimators is minimal. Consequently, I will proceed with implementing the techniques described above in order to account for the biases related to treating repeated cross-sections as regular panels.

I focus on the 1975 arrivals, and use Education Index 1 as a measure of occupational placement. I construct cohorts (in the sense implied by the pseudo-panel theory) by country, period of arrival (1975-1979; 1985-1989), and education level (unskilled; medium skilled and highly skilled). To minimize measurement errors, I delete from the sample those cohorts which include less than 100 individuals. In the end, the pseudo-panel dataset is left with 916 cells. Next, I average the Education Index 1, as dependent variable, and age with its quadratic equivalent, as explanatory variables, by cohort for each cross section (t=1980, 1990, 2000) in order to obtain a specification similar to the one suggested by equation 11. The model is estimated using fixed effects composed of three sets of dummy variables: for period of arrival, for education level, and for country interacted with census year. Figure 6 charts the occupational placement predicted based on the pseudo-panel estimations. The findings in the sub-section 3.3 seem robust to the change in methodology.<sup>36</sup> The same conclusion is reached by using alternative measures of occupational placement and the 1985 arrival.

# **4. CONDITIONAL CONVERGENCE**

> 35 The situation is a bit complicated for highly skilled migrants, because Censuses 1980 and 1990 do not allow identification of individuals with Bachelor’s degree, Masters’ degree, Professional degree or PhD. Using the same age-at-arrival benchmark may lead to some inaccuracies in estimating the place where education was obtained.

> 36 As a robustness check, the analysis was repeated on samples that exclude origin countries identified by the previous section as having significant out-migration flows.  Results revealed no significant changes in the assimilation patterns.  They are available from the author upon request.

22