rafmacalaba/datause-encoder-data huggingface dataset:
benefit received by individuals is equal to the amount spent per capita. As the quality of school infrastructure, teachers, and health clinics and hospitals vary across the country then this is a clear limitation of the analysis.

The results show that education is progressive in relative terms, and given the amounts spent, it is also strongly equalizing.<sup>11</sup> In other words, the amount of spending on education is relatively high as a share of the incomes of those at the bottom of the distribution. However, there is variation across educational categories, with primary education being the most redistributive (Table 4). Although 50 percent of all spending on secondary is devoted to the poorest 40 percent, 52 percent of pre-primary and 61 percent of tertiary education spending is devoted to the top 60 percent (Figure 9). The overall result is a slightly progressive and equalizing education structure, although the overall concentration of spending in absolute amounts is more or less neutral.

Similarly, health spending is equally distributed across the population in absolute terms. However, in relative terms, health spending is strongly progressive, as it makes up a larger share of the incomes of the bottom of the distribution (Figure 9). Thus, the overall impact of in-kind benefits is a redistributive impact, as shown in Figures 2 and 4 above.

Figure 9. Concentration of Education and Health <u>(by market income plus pensions quintiles)</u> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Pre-primary Primary Secondary Tertiary Gross Gross health<br>education transfers<br>transfers<br>Poorest 2 3 4 Richest<br>Source: World Bank estimates based on PIS (2014).<br><!-- End of picture text -->

> 11 Following CEQ conventions, we do not assess the poverty reducing impact of health and education spending.

23
DNB detection, once the effect of the lunar cycle has been accounted for [11], therefore allows discriminating between states _S_ 2 and _S_ 3 when no infrared signals are detected (see Annex A section A.1 for more details).

# **2 Results**

## **2.1 Interpreting remote sensing data: case studies**

To provide a precise understanding of how trends over time show up in our data, Figure 3 plots the time-series of M10 detection (top row), temperature (middle row), and DNB radiance (bottom row) for three fields in Iraq and Syria. This figure highlights the fine temporal resolution of our approach at the field-level .

Ajil (top panel) is situated northeast of Tikrit near the Hamrin Mountains in Iraq. Daesh took control of the field in June 2014 and continued production without interruption until January 2015. The field was set on fire by the group to counter an attack by Iraqi forces attacks in lateMarch 2015. An engineer at the site told Reuters in July 2014 that Daesh fighters were pumping low volumes of oil from the field [20]. As shown in the M10 detection panel, the site saw normal flaring activity through mid-June 2014. At the time Daesh takes over, flaring intensity drops substantially but sporadic temperature detections continue through January 2015 and during that low-intensity period, the DNB measures are mostly greater than would be possible with electrical lighting. This activity is consistent with Daesh pumping small volumes of oil using the in-place infrastructure. From January to March 2015 there is no activity at the site; only the lunar cycle lighting is detected (bottom row). The spike in M10 detections and DNB intensity in late-March 2015 correspond to the reported burning of the field by Daesh forces. Fighting in the area continued into mid-April 2015, consistent with ongoing DNB detections which sometimes cross the threshold for flaring but could be battle-related (e.g. burning vehicles). After Tikrit is fully retaken in late-2015 and the surrounding area secured, there is continued low-level DNB 9
**<u>Table 2: Probit (Average Marginal Effects) 2007-2017</u>**

|<br>Pr (Employment)||Interaction with"Year 2017"|
|---|---|---|
|Year 2017|**-0.531*****||
||**(0.000)**||
|**Individual characteristics**|||
|Age|**0.042*****|0.002|
||**(0.000)**|(0.341)|
|Age<sup>2</sup>|**-0.001*****|-0.000|
||**(0.000)**|(0.877)|
|_Own education (Ref. Illiterate or primary ed.)_|||
|Middle school|**0.083*****|**-0.031*****|
||**(0.000)**|**(0.000)**|
|High school|**0.154*****|**-0.058*****|
||**(0.000)**|**(0.000)**|
|College/University|**0.354*****|**-0.071*****|
||**(0.000)**|**(0.000)**|
|**Household characteristics**|||
|Household head|**0.092*****|-0.012|
||**(0.000)**|(0.276)|
|_Household head education (Ref. female-headed households.)_|||
|Illiterate orprimary school(maleHH head)|**-0.085*****|0.008|
||**(0.000)**|(0.434)|
|Middle school(maleHH head)|**-0.117*****|**0.018* **|
||**(0.000)**|**(0.075)**|
|High school (male HH head)|**-0.119*****|0.018|
||**(0.000)**|(0.113)|
|College/University (male HH head)|**-0.163*****|0.014|
||**(0.000)**|(0.280)|
|Urban|**0.111*****|-0.002|
||**(0.000)**|(0.895)|
|Male salaried emp.|**-0.053*****|0.010|
||**(0.000)**|(0.152)|
|Logreal income|**-0.007****|0.000|
||**(0.037)**|(0.986)|
|Children 0-4|**-0.089*****|0.002|
||**(0.000)**|(0.844)|
|Children 5-14|**-0.019*****|**-0.010* **|
||**(0.000)**|**(0.064)**|
|Elderly|**0.036*****|-0.013|
||**(0.000)**|(0.109)|
|Childcarefacilities|0.045<br>|**0.138****|
||(0.213)|**(0.013)**|
|Children0-4 *Childcarefacilities|**0.138****|0.031|
||**(0.011)**|(0.682)|
|**Labor market characteristics**|||
|_Part-time employment share_|||
|Part-time employment|**0.002*****|**-0.002****|
||**(0.000)**|**(0.019)**|
|_Unemployment rates_|||
|Female unemployment rate|**-0.007****|-0.006|
||**(0.039)**|(0.285)|
|Male unemployment rate|-0.006|0.002|
|_Average real hourly wages_|(0.147)|(0.702)|
|Female-services|0.011|**0.186*****|
||(0.769)|**(0.003)**|
|Male - industrial production|**0.078****|-0.014|
||**(0.016)**|(0.795)|
|**N**||179362|

Notes: The sample is restricted to females between 18 and 65 years old. P-values in parentheses, *** p<.01, ** p<.05, * p<.10. Source: Own calculations using data from Census, ENOE and SEDESOL.

31
<u>https://displacement.iom.int/reports/ethiopia-%E2%80%94-nationaldisplacement-report-5-june-%E2%80%94-july-2020.</u>

- Klasen, S. and Lahoti, R. (2016). ‘How Serious is the Neglect of Intra-Household Inequality in Multi-dimensional Poverty Indices?’, available online at: <u>https://www.aeaweb.org/conference/2017/preliminary/1740.</u>

- Klasen, S., Lechtenfeld, T., & Povel, F. (2015). ‘A feminization of vulnerability? Female headship, poverty, and vulnerability in Thailand and Vietnam’. _World Development_ , 71, 36–53. https://doi.org/10.1016/j.worlddev.2013.11.003.

- Mare, R.D. and Maralani, V. (2006). ‘The Intergenerational Effects of Changes in Women’s Educational Attainments’, _American Sociological Review,_ Vol. 71, No. 4: 542-64.

- Mishra, A., Ray, R., and Risse, L. (2016). ‘The multidimensional disadvantage of Australian children with a comparison between indigenous and non-indigenous children’, Discussion Paper 19, 2016, Monash University, Department of Economics.

- Notten, G. and Roelen, K. (2012). ‘A new tool for monitoring (child) poverty: Measures of cumulative D deprivation’, _Child Indicators Research_ , 5(2), pp. 335–55.

- OECD. (2021). _The State of School Education: One Year into the COVID Pandemic_ . Paris: OECD.

- Omotoso, K. and Koch, S. (2018).’Exploring child poverty and inequality in postapartheid South Africa: A multidimensional perspective’, _Journal of Poverty and Social Justice_ , 26(3), pp.417–437.

- Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). _Informing Durable Solutions by Microdata : A Skills Survey for Refugees in Ethiopia_ (English). Washington, D.C.: World Bank Group. -

- <u>http://documents.worldbank.org/curated/en/996221531249711200/Informing durable-solutions-by-micro-data-a-skills-survey-for-refugees-in-Ethiopia.</u>

32
business profits over the control group or a 0.63 standard deviation improvement. In contrast, the highly negative and statistically significant coefficient on the interaction variable shows that the effect on businesses with high exposure is significantly lower, and overall statistically insignificant (p-value of sum of interaction and main variable is 0.29). Note that firms with narrow exposure tend to benefit significantly more from the finance training as well, but the magnitudes are statistically much smaller when compared to the marketing training (p-value of difference is 0.09).

Hence, consistent with a growth focus explanation, we find that small business owners who have narrow exposure tend to do significantly better when they receive the marketing and sales training program. Participating in a training program to build marketing skills helps these firms overcome a lack of exposure by encouraging them to look beyond their existing business context and develop new perspectives on products, customers, distributors and suppliers which, in turn, assists with expanding sales and improving top line performance.

# **5.5. External Validity, Spillovers, and General Equilibrium Effects**

The results presented and discussed above are consistent with the hypotheses developed in this paper. In this subsection, we address three potential concerns related to the field research.

First is the issue of external validity or generalizability of the results to other settings. Given the sample selection methodology explained in Section 3.1, businesses in our starting sample were selected based on their interest in business training and willingness to complete a screening survey; other characteristics such as whether they operated out of a physical structure, education level of the owner, and business registration status also factored in the screening process. Hence, it is instructive to understand how our sample differed from a typical business in South Africa. In Appendix Table 7, we compare sample characteristics between our study and the 2010 FinScope Small Business Survey, which utilizes a nationally representative sample of 5,676 small business owners in South Africa.

The comparisons in Panel A show that while race and origin of business owners are not different across the two samples, other differences are present. Businesses in our sample are more likely to be owned by men (45% female owned vs. 55% female owned in FinScope), run by slightly younger 29
<!-- Start of picture text --> Q Participation in on farm activities<br>S . eee Ld<br>° et %e %e °. e<br>©) e r e ?* e, . * ° °<br>fo} bd ° oe °<br>co eve °<br>x e<br>~s<br>nw e<br>fo) °<br>l=}wowo<br>6 7 8 9<br>GDP (log)<br>* Participation Africa e Participation Non-Africa<br>——— Overall Trend ——— African trend<br>——— Africa without NGA<br><!-- End of picture text --> <!-- Start of picture text --> Q Participation in on farm activities<br>S . eee Ld<br>° et %e %e °. e e<br>©) e r e ?* e, . * ° ° ee<br>fo} bd ° oe ° e<br>co eve ° e e °<br>x e eo? eee<br>~s e toe) of e ©<br>nw e 5 ° e<br>fo) © ° e % e e e<br>e e e e<br>l=}wowo<br>6 7 8 9<br>GDP (log)<br>* Participation Africa e Participation Non-Africa<br>——— Overall Trend ——— African trend<br>——— Africa without NGA<br>-<br>° ee e ° ° ° e * é e °<br>e e e e e<br>ec @ e ee<br>iF ) = eee °. ; e e ° e ° ° e e ° ° e ° ee<br>e T e<br>e ry e<br>ee é °<br>e e e<br><!-- End of picture text -->
|**Table A9**<br>**. Proxies for ability a**|Default at<br>Maturity<br><br>**mong controls**|Changes in<br>Expenditures<br>|
|---|---|---|
||(1)|(2)|
|Female (1=yes)|0.041<br>|0.008<br>|
||(0.036)|(0.056)|
|Member has Business (1=Yes)|-0.008|-0.086**|
||(0.030)|(0.037)|
|Eligibility|-0.005|-0.001|
||(0.037)|(0.041)|
|Age|0.002|-0.003**|
||(0.002)|(0.002)|
|HH members business history (1=Yes)|0.007|-0.013|
||(0.035)|(0.040)|
|HH size|0.001|-0.007|
||(0.006)|(0.008)|
|Number of Children under 9|0.007|-0.015|
||(0.011)|(0.012)|
|Land|0.003|0.001|
||(0.003)|(0.002)|
|Member has a Bank Account (1=Yes)|0.083|-0.162***|
||(0.062)|(0.062)|
|Fraction of CO Members of same Zaat (caste)|-0.014|-0.079|
||(0.080)|(0.072)|
|Months as CO member|0.016|0.072|
||(0.058)|(0.071)|
|Holds office in CO (1=Yes)|-0.068|-0.110**|
||(0.044)|(0.052)|
|Literacy (1=Yes)|0.045|-0.056|
||(0.032)|(0.039)|
|Business knowledge index|0.001|-0.013|
||(0.012)|(0.017)|
|Risk Tolerance|0.004|0.006|
||(0.004)|(0.007)|
|Stress index|0.004|-0.015|
||(0.011)|(0.014)|
|Mean of dependent variable among controls|0.10|0.60|
|N. obs|394|1148|
|R-Sq.<br>Note: The dependent variable in column 1 takes v<br>a loan during the period, and defaulted at maturity<br>column 2 it is the change from baseline to followu<br>Standard errors are clustered at the CO level. Bra<br>The following symbols *,** and *** denote signif<br>level, respectively. Months as CO member is divi|0.09<br>alue 1 if the mem<br>on at least one o<br>p in log househol<br>nch fixed effects<br>icance at the 10,<br>ded by 100|0.06<br>ber took at least<br>f those loans. In<br>d expenditures.<br>are included.<br>5 and 1 percent|

Note: The dependent variable in column 1 takes value 1 if the member took at least a loan during the period, and defaulted at maturity on at least one of those loans. In column 2 it is the change from baseline to followup in log household expenditures. Standard errors are clustered at the CO level. Branch fixed effects are included. The following symbols *,** and *** denote significance at the 10, 5 and 1 percent level, respectively. Months as CO member is divided by 100
||**Authors**|**Year**|**Type**|**Quality**|**Effect**|**Country conte**|**xt Type of Job**|
|---|---|---|---|---|---|---|---|
|48|Eichler, Auxila and Pollock|2001|observational|2|positive|Developing|Craft|
|49|Eriksson and Villeval|2004|lab experiment|5|positive|OECD|Production|
|50|Fairbrother et al|1999|field experiment|5|positive|OECD|Craft|
|51|Fairbrother et al|2001|field experiment|5|positive|OECD|Craft|
|52|Fehr and Goette|2007|field experiment|5|positive|OECD|Production|
|53|Fehr and Schmidt|2004|lab experiment|5|positive|OECD|Production|
|54|Felt-Lisk et al|2007|observational|4|neutral|OECD|Craft|
|55|Figlio and Kenny|2007|observational|4|positive|OECD|Craft|
|56|Figlio and Winicki|2005|observational|4|neutral|OECD|Craft|
|57|Fryer|2011|field experiment|5|failed|OECD|Craft|
|58|Gavaghan et al|2010|<br>observational|4|failed|OECD|Craft|
|59|<br>Gerhart and Milkovich|1990|observational|4|positive|OECD|Production|
|60|Glewwe, Ilias and Kremer|2010|field experiment|5|neutral|Developing|Craft|
|61|Glickman et al|2007|observational|4|neutral|OECD|Craft|
|62|Gneezy and Rustichini|2000|lab experiment|5|positive|OECD|Production|
|63|Goldhaber and Brewer|2000|observational|4|positive|OECD|Craft|
|64|Goodman and Turner|2010|field experiment|5|failed|OECD|Craft|
|65|Grady et al|1997|field experiment|5|failed|OECD|Craft|
|66|Gratz|2009|observational|2|neutral|OECD|Craft|
|67|Grimshaw|1998|observational|1|failed|OECD|Unclassified|
|68|Hamilton, Nickerson and Ow|2003|observational|4|positive|OECD|Craft|
|69|Heckman, Heinrich and Smit|1997|observational|4|neutral|OECD|Production|
|70|Heneman and Milanowski|1999|observational|3|neutral|OECD|Craft|
|71|Hillman et al|1998|field experiment|5|failed|OECD|Craft|
|72|Hillman et al|1999|field experiment|5|failed|OECD|Craft|
|73|Hillman et al|1991|observational|3|neutral|OECD|Craft|
|74|Hochberg and Lindsey|2010|observational|4|positive|OECD|Craft|
|75|Ingraham|1993|observational|1|failed|OECD|Unclassified|
|76|Jack|2003|observational|2|positive|Developing|Craft|
|77|Jacob|2005|observational|4|positive|OECD|Craft|
|78|Jacob and Levitt|2003|observational|4|neutral|OECD|Craft|
|79|Jain and Narayan|2011|lab experiment|5|positive|Developing|Craft|
|80|Kahn, Silva and Ziliak|2001|observational|4|positive|Developing|Craft|
|81|Kelley|1999|observational|2|neutral|OECD|Craft|
|82|Kellough and Nigro|2002|observational|3|failed|OECD|Unclassified|
|83|Kellough and Selden|1997|observational|3|neutral|OECD|Unclassified|
|84|Kerr|1975|observational|1|failed|OECD|Unclassified|
|85|Ketelaar, Manning and Turk|2006|observational|3|neutral|OECD|Unclassified|
|86|Kim|2002|observational|2|neutral|OECD|Unclassified|
|87|Kingdon and Teal|2008|observational|4|positive|Developing|Craft|
|88|Kiragu and Mukandala|2003|observational|2|neutral|Developing|Unclassified|
|89|Knez and Simester|2001|observational|4|positive|OECD|Production|
|90|Koretz|2002|observational|1|failed|OECD|Craft|
|91|Kremer and Chen|2001|field experiment|5|failed|Developing|Craft|
|92|Kouides et al|1998|field experiment|5|positive|OECD|Craft|

34
# **6.2 Forced Displacement and Organized Crime in Weak States**

Voluntary migration typically facilitates connections between individuals in origin countries, those who leave, and those in destination countries. Previous work has shown that voluntary migration networks can promote knowledge diffusion, trade, and foreign direct investment (Javorcik et al. 2011; Parsons and V ́ezina 2018). Yet, these beneficial effects are unlikely to materialize in settings with forced displacement, which typically originates in places with weak institutions and limited rule of law. In such environments, forced displacement may accelerate connections for organized crime networks at both origin and destination locations (Kapur 2014; Sviatschi et al. 2025). This occurs, for example, when criminals exploit and recruit forced migrants by capitalizing on their economic desperation, relying on them to help fund, inform, and expand illegal activities. Autocratic regimes, in turn, benefit directly from the resulting expansion of criminal networks, which supply illicit revenue and armed enforcement capacity. These resources reduce the regime’s dependence on the broader population for fiscal support and coercive power, thereby weakening political accountability and enabling the regime’s perpetuation.

In fact, abundant qualitative evidence suggests that human trafficking is a significant criminal enterprise in these contexts, as forced migrants, especially women and children, become victims (Insight Crime, 2023d, 2021b,a). For instance, one article suggests that “migrants using irregular border crossings also represent a source of income for armed groups and predatory criminals, who extort the migrants as they cross their territory and, in some cases, also rob or kidnap them or force them into roles such as human couriers for drug trafficking. Human traffickers have also capitalized on the crisis, tailoring their recruitment of Venezuelan women and girls by offering false offers of jobs, scholarships, or even religious charity as bait for what ends up being coerced sexual exploitation.” (Insight Crime 2023c).

Journalistic reports also mention that forced displacement outflows go hand in hand with the expansion of drug-trafficking groups from Colombia to Venezuela. Oftentimes, this expansion 37
# **B.2 Hybrid technology structures**

Figure 3 illustrates various possible structures among the technologies in a business function. Different types of technology structures capture substitutability between technologies within each business function. There are four different types of technology structures defined: (1) quality ladder, (2) horizontal relationship, (3) hybrid I (i.e., two quality ladders), and (4) hybrid II (i.e., tree). In the main text, we described the structure of quality ladder (Panel A of Figure 3) as well as horizontal relationship (Panel B of Figure 3). In this section, we describe hybrid structures that combine quality ladder and horizontal relationship.

Panel C of Figure 3 represents a hybrid structure where the technologies in the function are organized in two quality ladders. In this case, the technological level of each ladder is defined by the most sophisticated technology used in the ladder, but we need to consider both ladders to define the technology level of the function. One example of this structure is the technologies used in molding of plastic injection in the automotive sector. The molding is used for both interior and exterior components of a car. The molding process can be done by operators or automated robotics. Because the molding of interior or exterior component means different process rather than different sophistication, each process has one ladder. But combined with operators or robotics, each ladder provides different sophistication of technologies.

Panel D of Figure 3 illustrates another common, hybrid structure that we refer to as a tree. Trees are characterized by a quality ladder formed by the technologies with lower sophistication and, at the top of the ladder, there is a horizontal relationship across the more sophisticated technologies that can be used in the function. An example of this structure are the technologies used in fabrication processes. Those at the bottom of the tree (manual processes and machines controlled by operators) have a clear vertical relationship. The rest of technologies in the function (machines controlled by computers, robots, additive manufacturing, and other advanced manufacturing processes), also have a vertical relationship with respect to the first two (as once present, they make irrelevant manual processes and machines controlled by operators). But, for the fours technologies with higher sophistication, it is possible to find tasks for which each of them most effective. Hence, the horizontal relationship between them. These four structures characterize all the relationships between the technologies in the functions covered in the FAT survey.<sup>57</sup> In Table B.1, we provide the technology structure of each business function. We first show whether each business function is under general vs. sector specific business functions

> 57Another possible structure that we do not observe in the business functions in FAT is an inverted tree structure where at the bottom we have a horizontal relationship, and then a ladder of technologies that are technologically more sophisticated than those in the horizontal portion.

64
> Figure 7. Net Trans-Tasman Migration: Total Amrvals Minus Total Departures

<!-- Start of picture text --> 5000<br>°- -<br>-5000l<br>-10000l<br>-15000I<br>0<br>o-20000\<br>-25000\<br>-30000<br>-35000<br>-40000 194 1950 1955 1980 a 1Q85 1970 1973 a-t---- 1180 15<br>Mormh Year<br><!-- End of picture text --> Source: Brosnan and Poot, 1986: Chart 1.
**Figure 2. Immigration structure in the OECD area**

# **_A. Destination of skilled im m igrants in percent of the OECD_**

<!-- Start of picture text --> 60 %<br>50 %<br>40 %<br>30 %<br>20 %<br>10 %<br>0%<br>Ye ar 1990 Y ear 200 0<br>USA<br>E U-15 Can.<br>Au str.<br>Othe rs<br><!-- End of picture text -->

# **_B. Skilled imm igrants in percen t of the im m igration stock_**

<!-- Start of picture text --> 60 %<br>50 %<br>40 %<br>30 %<br>20 %<br>10%<br>0%<br>V ariatio n 2000-1990 Ye ar 1990 Y ear 2000<br>USA<br>EU-15 Can.<br>Austr. OECD<br><!-- End of picture text -->

# **_C. Percentage of skilled immigrants from low income countries_**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>V ariation 2000-1990 Y ear 199 0 Y ear 2000<br>USA<br>EU-1 5 Can.<br>Austr. OECD<br><!-- End of picture text --> 8