rafmacalaba/datause-encoder-data huggingface dataset:
weaker effect on the total number of new firms in regions hosting refugees, which is likely to be driven by the new foreign firms. The results also suggest businesses benefiting in regions hosting refugees. The findings with regard to increases in gross profits and net sales reported by firms in regions hosting refugees support the hypothesis that business may benefit from refugees. In line with the finding that unskilled labor increased in regions with refugees, we also find some limited evidence showing that the increase in the number of firms is driven by sectors that are most likely to benefit from low-skill employment.

Our findings on firm entry and performance complement the previous studies on the economic impact of Syrian refugees in Turkey. Taken together, the findings are quite in line with economic theory. Both Del Carpio and Wagner (2015) and Ceritoglu et al. (2017) find negative effects on the labour market outcomes of unskilled natives who have to compete with Syrian refugees. Del Carpio and Wagner (2015) further finds that men who have completed high school benefit from occupational upgrading, which may be due to the complementarity between skilled Turks and Syrian refugees. Our study adds that businesses appear to have benefited from hosting Syrian refugees who appear to have themselves opened firms in Turkey. There is a clear increase in the entry of new foreign firms which may be driven by refugees’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their finding to lower labor costs, which may also be one of the mechanisms driving our results. Gross profits and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous effects on specific subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have benefited.

For a complete picture of the effects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term effects. More specifically for the line of research this study focused on, further analysis using micro-level firm data would be needed to understand how firms adjust their activity, 24
0.3 steps. The impact on mental health is positive and also statistically significant in both time periods. Finally, we examine the impact on ownership of household durable assets. There is a positive, but marginally significant impact in year one, and positive but insignificant impact in year three. The only evidence for a significant difference between the mentoring and training alone groups comes for the any employment outcome, which is consistent with the differential survival effect. Taken together, these results show the higher profits and sales do appear to have translated into higher overall well-being for the women given training.

# **5.3 Market-Level Impacts**

Our results show that firms assigned to treatment have grown, with no negative spillover for the untrained in the same market. This suggests that the overall markets have grown. To formally test this hypothesis, we aggregate outcomes to the market-level, and estimate the following equation at the level of the 157 markets: �������,� ��� ����������������� ∗1����� ����������������∗3������ � ���������,��� ������∑���� ��1����� ���,� (4) Where _MarketTrained_ indicates market _j_ was assigned to training, _TotalY_ is the total of outcome _Y_ (profits, sales, or customers) in market _j_ at time _t_ , and the standard errors are clustered over time at the market level.

Table 4 shows the results of estimating equation (4). By the third year, overall profits have grown 2,554 KSH per week (9.2% of the control mean, not statistically significant), overall sales in the market by 15,353 KSH per week (13.7% of the control mean, significant at the 5 percent level), and the total number of customers in the market per week by 318 (16.7% of the control mean). These results are consistent with the overall market growing, at least over the longer-term.

In rounds 2 and 4 we also conducted a market census to determine whether there were new entrants to the market since the time of training, and whether these new entrants along with existing firms that did not pass our eligibility filters had cut back on profits due to some firms being trained. Columns 3 through 6 show positive and insignificant impacts on the numbers of new entrants in these markets, the total number of firms not in our survey, and the weekly profits of these firms. The success of those trained therefore also does not come from crowding out new entrants.

# **6. Why do these impacts arise? Causal Chain and Mechanisms**

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Benes, Elisa, and Kieran Walsh. 2018c. “Measuring Working time and Time-related Underemployment in Labour Force Surveys: Main findings from the ILO LFS pilot studies.” International Labour Organization.

Buehren, Niklas, Markus Goldstein, Ezequiel Molina and Julia Vaillant. 2017. “The Impact of Strengthening Agricultural Extension Services: Evidence from Ethiopia.” World Bank Policy Research Working Paper 8169.

Buvinic, Mayra, and Elizabeth M. King. 2018. “Invisible No More? A Methodology and Policy Review of How Time Use Surveys Measure Unpaid Work.” Data2X Report.

Buvinic, Mayra, and Rebecca Furst-Nichols. 2014. “Promoting Women's Economic Empowerment: What Works?” _World Bank Research Observer_ 31(1).

Calderon, Gabriela, Jesse M. Cunha, and Giacomo De Giorgi. 2013. “Business literacy and development: Evidence from a randomized controlled trial in rural Mexico.” Banco de Mexico Working Paper, No. 201324.

Campos, Francisco, Michael Frese, Markus Goldstein, Leonardo Iacovone, and Hillary C. Johnson. 2017. “Teaching personal initiative beats traditional training in boosting small business in West Africa.” _Science_ 357: 1287-1290.

Comblon, Virginie, and Anne-Sophie Robilliard. 2017. “Are female employment statistics more sensitive than male ones to survey design? Evidence from three West African countries.” DIAL Working Paper, DT/2015-22.

Data2X. 2017. _Women’s Work & Employment Partnership: Phase One Activities & Outcomes._ Technical Summary, October 2017.

Davis, Benjamin, Stefania Di Giuseppe, and Alberto Zezza. 2017. “Are African households (not) leaving agriculture? Patterns of households’ income sources in rural Sub-Saharan Africa.” _Food Policy_ 67: 153– 174.

Daum, Thomas. 2018. “Using a Smartphone App to Collect Time Use Data in Zambia.” Presentation made at the Workshop on Improving Data Collection on Women and Youth Employment, World Bank and IFAD, November 2018, Rome.

Deijl, Claudia, Agnes Andersson Djurfeldt, and Magnus Jirström. 2017. “Agricultural policy in Sub-Saharan Africa and its Relevance for Smallholder Farmers, Women and Youth.” Agriculture for Food Security 2030 Report 2017-1.

de Janvry, Alain, Elisabeth Sadoulet, Manzoor Dar, and Kyle Emerick. 2016. “The Agricultural Technology Adoption Puzzle: What Can We Learn From Field Experiments?” Working Papers P178, FERDI.

de la O Campos, Ana Paula. 2015. _Empowering Rural Women Through Social Protection_ . Rural Transformations – Technical Papers Series #2, Social Protection Division, Food and Agricultural Organization.

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_σ_ -competition effect using the Rauch[42] classification. This classification groups sectors in three categories: goods traded in organized exchange, reference-priced products, and differentiated goods.<sup>10</sup> The second robustness check consists in employing the ratio of _R_ & _D_ expenditures in qualityinnovations to that in variety-innovations as the dependent variable. Because the survey does not provide information about the way firms splits total _R_ & _D_ expenditures across the two dimensions of the innovation process, with the exception of cases where firms innovate in one dimension, we assume that if a firm conducts quality- and variety-innovations, it spends the same amount of money in each activity. Table 6 presents the estimation results corresponding to these robustness exercises.

# [Insert Table 6 about here]

Column (2) shows estimation outputs from the first robustness check. Column (3) presents the results from the second one. The first column shows evidence of a positive _σ_ -competition effect. A change from the differentiated to the reference-priced category increases discretely the quality bias of technology in _e_<sup>0</sup><sup>_._365</sup> points. The coefficient of the _σ_ -imitation effect is negative but insignificant, while that of imitation is negative but significant at 1% level. Finally, column (3) shows that the results are robust to the use of other dependent variables. All the estimated coefficients have signs in line with the theory, and they are statistically significant at 1% level.

Now, we move to test the channel through which imitation biases the technical change. This is presented in the following section.

# **4.2.4 Do imitators copy quality- or variety-innovations?**

We have shown in previous sections, that imitation reduces firms’ incentives to innovate in both dimensions, but if 1 _._ 5 _< γ_ and/or 2 _< σ_ , it has a larger impact on quality up-grading. To test this prediction, we estimate the following probit model:

> 10The degree of product market competition decreases from the first to the third category.

21
Table 7: Operations of Cashew Nut Processing Factories

|Factory|Date of<br>Test Run|Source of<br>Finance|Est.<br>Maximum<br>Capacity||Annual|Rawnut T|hroughp|ut (Tons)||Total # of Days Operated<br>Through Sept. 1991|
|---|---|---|---|---|---|---|---|---|---|---|
||__________|___________||1985/<br>86|1986/<br>87|19871<br>88|1988/<br>89|1989/<br>90|1990/<br>91||
|Tanita|1965|GOT|10,700|0|0|218|2455|3000|1345|> 1000|
|Mtwara|1968|GOT + ES|6,800|0|0|0|0|0|0|?_I|
|Lindi|1978|WB|8,600|0|0|0|0|0|0|301|
|Mbagala|1978|ES|10,700|0|0|0|0|0|0|?|
|Mtamna|1979|WB|4,300|0|0|0|0|0|0|358|
|Likombe|1980|WB|8,600|0|0|2402|3144|6000|1473|>800|
|Kibaha|1980|WB|8,600|0|0|1383|2536|3500|1345|720|
|Nachingwea|1981|WB|4,300|0|0|0|0|0|0|95|
|Masasi|1981|WB|8,600|0|0|0|0|0|0|91|
|NewalaII|1981|WB|8,600|0|0|0|0|0|0|116|
|Tinduru|Never|ES|8,600|0|0|0|0|0|0|0|
|Newala I|Never|WB|8,600|0|0|0|0|0|0|0|

Sources: Agriconsult/Amec (1992); MDB Annual Cashewnut Report, various years.

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“Me and Otis could be married, but they all ruined that ... They don’t want to see me married! Magnolia knows that it be money getting away from her. I couldn’t spend the time with her and the kids and be giving her the money that I do now. I’d have my husband to look after ... You think a man would put up with as many times as I go over to their house ... giving half my money to her? ... That’s why they don’t want me married.” We formalize this idea by positing that the kin group as a whole can affect each member’s cost of exiting the group. We explore conditions under which, facing the possibility of members’ severing kinship ties and migrating to the modern sector of the economy, a kin group will take a collective _ex ante_ decision to raise the individual exit costs to prevent the migration of the group’s most productive members. Our striking result is that at a point in time when kin members are yet to receive job offers from the growing modern sector of the economy, a majority of the members may prefer to engage in such exit deterrence, even when such behavior is welfare-reducing in the aggregate. It is this ‘collective conservatism’ of a kin group that can generate an inefficient _status quo_ bias, and then the kin system does become a dysfunctional institution in the presence of an expanding modern economy that holds back (some of) the kin group members in a poverty trap.<sup>4</sup>

# **Kin Membership and Endogenous Market Barriers**

We consider the following simple model of an economy: The population is divided into two groups. The first group is a single kin system − each member of this group will be called a _K_ agent. The second group is a set of independent individuals who are not bound by any kinship ties − the _I_ agents. Further, the economy is divided into two sectors − the pre-modern rural sector and the modern urban sector. The _I_ agents are located predominantly in the urban sector, while all _K_ agents are initially located in the rural sector − that is where the kin system is

> 4  Kuran (1988) surveys different sources of such _status quo_ bias and collective conservatism.

9
favoritism, Nguyen et al. (2011) show nepotism of public officials in their communes in Viet Nam, mis-targeting infrastructural projects. Studying the long time span from 1960 to 2010, Jedwab and Storeygard (2017) show that cities around the leader’s place of origin in Sub-Saharan Africa were growing faster than other cities because the leaders favored their cities of origin to target road infrastructure projects. Hodler and Raschky (2014) measured regional favoritism from outer space: they found that subnational regions, where current political leaders were born, have more intense nighttime lights.

This paper uses an instrumental variable (IV) strategy to account for endogeneity.<sup>15</sup> The IV is based on a map of historical road networks of Armenia obtained from a Militarytopographic map of the Caucasus region - shown on Figure 9 - prepared under the Russian Empire in 1903. The argument of exogeneity of the historical setting of roads can be motivated by several reasons. During the beginning of the 20th century Armenia was under the rule of the Russian Empire. The southernmost state of the empire, situated on the border of Ottoman and Persian empires, Armenia was an important territory for military defense. The roads maintained by the Russian Empire were mainly used to transport armies. Even though the roads could be also used for trade and economic reasons, we can argue that the government of the Russian Empire would invest in building, rehabilitating and maintaining the roads necessary for military reasons. The second argument is that, since the historical roads were mapped before the industrialization of the region, when the large majority of people were employed in agriculture, we can argue that the roads would not have been built and maintained to promote non-agricultural employment. It is very unlikely that any decisions made in different settings far back in history, motivated mainly by non-economic reasons, could have anticipated rural employment development a century later.<sup>16</sup>

> 15The literature using similar method includes: Duranton and Turner (2012), Baum-Snow et al. (2012), Volpe Martincus et al. (2017), Garcia-L ́opez et al. (2015), Holl (2016) and M ̈oller and Zierer (2018), among others.

> 16In the section of robustness checks the paper also addresses the factor of mine locations in the region, and shows that it is highly unlikely that mines would have influenced the historical primary road placement.

15
2006).<sup>13</sup> Controlling for education attainment may be relevant if we consider our setting to be one in which schooling is exogenous and we are interested in measuring inframarginal returns to skills (Willis and Rosen 1979). Although we consider schooling to be endogenous in our case, for comparison purposes, we present both sets of results---controlling and not controlling for education---for the OLS and Logit regressions.

# **Results**

## **4.a OLS-Logit estimates**

In this section, we explore OLS and logit estimates of the relationship between six labor market outcomes (labor earnings, job formality, skill level of occupation, employment, being active or studying, and tertiary education attainment) with one measure of cognitive skills (reading proficiency) and eight socioemotional ones (extraversion, openness to experience, emotional stability, conscientiousness, grit, hostility bias, and decision making), controlling for a range of background characteristics (such as gender, age, mother’s education, and place of living). These estimates allow us to appreciate the diversity of correlations across the eight measured skills, even if they are based on likely noisy measures of socioemotional skills. Thus, one should not overinterpret these results as true differences in labor-market returns for the different traits referenced by their labels.

Abstracting from their level of schooling, adults with higher measures of cognitive skills are much more likely to also have better jobs (formal, higher-skilled, and with higher earnings) than those with higher measures of socioemotional skills. The correlation between reading proficiency, the single raw measure of cognitive skills, and log hourly labor earnings from the main job —which include wages for salaried workers and net profits for self-employed— is double than that from any measure of socioemotional skills. While a one standard deviation increase in the measure of reading proficiency is related to a 16-percent increase in hourly labor earnings from one’s main job, an equally sized increase in the measure of openness to experience (reflecting creativity, curiosity, and appreciation for beauty), the only socioemotional measure significantly correlated with earnings, is associated with an 8-percent increase in hourly earnings (see figure 1 and table 3). Consistently, adults with one more standard deviation of reading proficiency also are 6 and 14 percent more likely to have a formal job and a job in a high-skilled occupation.<sup>14</sup> For socioemotional skills measures, only having less hostile-attribution bias—the perception that other’s intents are hostile, which leads in return to more hostility—is significantly correlated with 3 percent more

> 13 The latter includes aspects of schooling influencing outcomes not necessarily linked to education increasing skills, but skills affecting education attainment. For example, education attainment being a signal of productivity or the creation of professional networks that help navigate the labor market.

> 14 A worker is defined here as formal if he or she benefits from social security through his or her job.

13
share increases with age (Figure 3.2).

**Figure 3.2: Proportion of children who can recognize 10 letters of the alphabet and the numbers 1 to 10, by age in months and country**

<!-- Start of picture text --> 35 40 45 50 55 60<br>Age in months<br>Central African Rep. Costa Rica Dem. Rep. Congo<br>Kazakhstan Vietnam<br>.6<br>Proportion .2.4<br>0<br><!-- End of picture text --> Source: Authors’ analysis of Multiple Indicator Cluster Surveys (Round 4). http://mics.unicef.org Accounting for the fact that years of learning may differ from years of schooling requires a modification to the _LAYS_ calculation. The easiest way to show this is graphically. A key feature of the illustration in Figure 2 is that the ratio of test scores between any two points that are vertically aligned (vertical ratio of highest to lowest value of variable on the vertical axis) is equal to the ratio of years of schooling of any two points that are horizontally aligned (horizontal ratio of highest to lowest value of the variable on the horizontal axis). That is why the ratio of test scores at Grade 8 (400/600 = **2/3** ) is the same ratio that is used to adjust the average years of schooling (9 × **2/3** = 6).

Figure 3.3 illustrates how this calculation needs to be modified if we assume that learning starts either “at birth,” which we assume is 6 years prior to Grade 0 (we call this Grade “-6” for convenience), or that learning starts 3 years prior to Grade 0 (Grade “-3”). In this case the vertical ratio between points A and B is no longer the same as the horizontal ratio between the 12
# _4.2. Data_

The data—World Bank Fortaleza Survey—were collected in three neighborhoods in Fortaleza: Autran Nunes, Edson Queiroz, and Pirambú in 2003.<sup>9</sup> Each has a population of around 20,000 people and around 4,500 houses. Approximately 500 questionnaires were applied in each neighborhood and, as a result, the sample contains about every ninth household in each of the three neighborhoods.

Two sets of teams were involved in the data gathering process: the research team, composed of statisticians from the local university, psychologists, NGOs, Human Rights spokespersons, youth and other people living in the selected neighborhoods; the interview team, composed of one general coordinator, three neighborhood coordinators, and the interviewers, recruited in each of the neighborhoods. The Institute of Organized Research of Ceará<sup>10</sup> (IPOC), a local NGO operating in the poor neighborhoods, led the interview process. Both the pilot and final interviews were carried out by youth from the neighborhoods. Some of the interviewers were part of the MH2OCE (Organized Hip-Hop Movement of Ceará<sup>11</sup> ), an organization that works on violence prevention in poor neighborhoods in Fortaleza. MH2OCE works with local gangs and their members, an arrangement that facilitated the team's access to the most violent areas of the neighborhoods, as it could negotiate access to areas under the control of violent groups, especially rival gangs. The interviews were conducted June 19-29, 2003, by five interviewers in each of the three neighborhoods. Each interviewer completed around 10 questionnaires daily, under close supervision of the team coordinator.

The survey instrument covered five major areas: i) socio-economic background; ii) education; iii) health and sexuality; iv) social capital and violence; and v) employment and economic activity. More details on the data collection procedures can be found in Verner and Alda (2004).

# _4.3. Sample selection_

Different age brackets have been chosen by different studies of school performance and school drop-out, with the lower bound ranging from 5 to 18 years old, and the upper one from 13 to 21, in the literature surveyed. The choice invariably involves some degree of arbitrariness and is often constrained by data availability. In the current study, the analysis will focus on teenagers aged 12 to 18 years (the lower bound being dictated by data availability, since very few

> 10 Instituto de Pesquisa Organizada do Ceará.

9 See Verner and Alda (2004).

11 Movimento Hip-Hop Organizado do Ceará.

6