rafmacalaba/data-use-mentions-extended huggingface dataset:
To examine the activities of these ethnically-based business networks and see how network externalities confer advantages on members of the group, we focus on a subset of the RPED sample that includes entrepreneur-owned firms, as these firms constitute the SMEs in our sample. Four countries provide the backdrop for the investigation – Kenya, Tanzania, Zambia and Zimbabwe. It was in these countries that the surveys gathered data on ethnic business networks.

# **_Estimates of the Effect of Network Externalities on SME Performance_**

How might networks affect enterprise performance? In circumstances where there is a high degree of economic uncertainty, market imperfections are endemic, and formal legal institutions are weak, networks could aid entrepreneurs in entering the market. Members of a network, for example, might be expected to start larger firms, because the network provides connections and credible multilateral enforcement capabilities that facilitate access to supplier credit and other inputs, aid the flow of information on technologies and markets, or ease access to equity investments. Similarly, a network’s impact on enterprise productivity or growth could be significant where there are information barriers and credit constraints and firms have difficulty enforcing property rights and business contracts. Networks could provide positive externalities that help firms to overcome such problems, improving value added and growth prospects. Where problems of asymmetric information and enforcement are less important one would expect the value of network externalities to be smaller. But, as we know from the literature, relation-based governance via stable and cohesive networks can be found even in advanced countries with well-developed information and legal infrastructures (see for example Bernstein 1992). These issues are examined here.

Tables 1 through 3 present descriptive statistics that are used in the analysis. Table 1 describes the ethnic distribution of firms in the RPED sample. It shows that the majority of SMEs in manufacturing in Tanzania and Zambia are indigenous-African owned, while in Kenya and Zimbabwe minority ethnic groups are more important: Indian-Asians dominate in Kenya, and represent a significant share of ownership in the other three countries, and Europeans are the most
plays a role. We created institutional tiles based on the average government effectiveness index by country from the World Governance Indicator database. Government effectiveness measures the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s commitment to improving or maintaining these aspects (Kaufmann, Kraay and Mastruzzi, 2010). Our general intuition is that developing countries with low government effectiveness are not only less able to engage in BLAs but also less able to enforce the BLAs they do sign with partner countries. This likely translates into less effective BLAs and, consequently, lower migration flows.

To test this hypothesis, we created two, three, and four tiles corresponding to halves, terciles, and quartiles. Countries in the top tiles are characterized by more effective government. Results reported in table 5 strongly suggest that, although the migration response to BLAs in Africa is null, African countries belonging to the upper tiers of the government indicators do experience a positive migration response to BLAs. Conversely, African countries with poor governance indicators show no significant migration response to BLAs.

# **4.3 Persistence of the impacts**

We now turn our attention to the timing of the effect of BLAs on migration flows using an event study design. It is worth noting that one of the key advantages of the event study design is the possibility to check if the estimated effect is long lasting or if it quickly vanishes after a certain period of time. Figure 1 depicts the benchmark estimate timing of the effect of BLAs on migration flows. First, there is no pre-trend prior to the treatment. Second, it appears that the positive effect of BLAs on migration flows lasts up to 3 decades after BLAs are signed before vanishing.

Consistently, heterogeneity results show that this positive effect is driven by non-African origins (panel 2a of figure 2). An interesting result of this event study design emerges when comparing the effects on GCC and non-GCC destinations. Essentially, non-GCC destinations replicate the average timing of the
suggests a potential role for information interventions in providing accurate information on the risks and outcomes of irregular migration.

Figure 1 also shows that irregular migration from The Gambia (as recorded in the Frontex data) fell dramatically during the period in which we designed and implemented our interventions, and particularly during our follow-up period. This drop is driven by several factors. First, after the end of the Jammeh government in 2016, and the country transitioning from a dictatorship to a democratic government, European governments became much less likely to grant asylum status to Gambians on the basis of political repression, and increased deportations (along with providing more aid to projects in The Gambia designed to increase local opportunities). Then, in 2020, border from our baseline survey to show trends in survey reports over the 2012-2018 period track well those in the Frontex data.

10
with the survey instrument was above 98%.<sup>6</sup> In the second and third surveys, we also elicited information for a shorter reference period, i.e., last week before the survey.<sup>7</sup> As the measurement of child labor statistics could be affected by how child labor definitions are operationalized in the survey design questionnaire (e.g., Bardasi et al. 2012), we employ a relatively long rather than a short questionnaire design. Specifically, the survey contains 12 questions that aim to elicit information about the specific farm and non-farm labor activities. The most important questions refer to work at the household farm, as this activity accounts for most children work in rural Ethiopia (Guarcello and Rosati 2007). A typical labor question asks: “ _Did [name] work any time in the household farm in the last [..] days?”_ Since the keyword “work” can have a different meaning for respondents, we supplement this question with a detailed, standard explanation of the concept of work by using a set of typical farming activities which is read aloud to the respondents: planting, watering, weeding, mulching, seeding, fertilizing, handpicking cherry coffee, cattle herding. This question is followed by an ‘intensive margin’ question, “ _how many hours in the last [..] days did [name] spend working on the household farm?”_ 8 It is worth noting that household chores such as fetching water and/or firewood, house cleaning, cooking, and child and elderly care are explicitly treated and explained as activities that do not belong to farm work. This approach aims to provide children and proxy respondents a clearer description of what constitutes child work at the farm.

# **2.3 Timeline and Sample Attrition**

Fieldwork took place from July 2015 to January 2017. As the variation in work activities across the year depends on the agricultural calendar, we implemented the
Q4|-3.8%|2193.4%|-30.7%|
|Q5|-14.8%|1420.7%|-40.3%|
|Share of the initial housold income in 2019||||
|Q1|17.2%|15.6%|4.1%|
|Q2|24.5%|2.6%|1.9%|
|Q3|28.9%|0.6%|1.4%|
|Q4|19.7%|0.2%|1.1%|
|Q5|18.5%|0.0%|1.0%|
|Share of the initial housold income in 2020||||
|Q1|15.8%|38.8%|2.8%|
|Q2|18.7%|21.0%|1.2%|
|Q3|28.3%|8.7%|0.9%|
|Q4|19.0%|4.0%|0.8%|
|Q5|15.7%|0.5%|0.6%|

Source: PNAD-C 2019 and 2020.

Notes: Other transfers include poverty pension, regular pension, unemployment insurance and fishers’ insurance. Other sources of income include alimony, donations, allowance, rents, scholarships, and financial gains.

When comparing the observed changes in poverty rates between 2019 and 2020 with our projections, we find that our model correctly predicts an increase in poverty, although the size of the change is smaller than the observed values (Table 3). The poverty rate according to the 2.25 USD-a-day poverty lines increased by 4.9 percentage points and our prediction was 2.7 percentage points. For the 6.85 USD-a-day poverty line, the observed increase was 7.7 while our projection 26
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey determined in Brazil. The little evidence on this is provided by Corseuil et al. (2018), who confirm that it had an effect in boosting the demand for more skilled workers; and by Haanwinckel (2018) who finds evidence that technological changes, defined as increases in the complexity of tasks performed by firms, increase wage inequality and polarization. The effect studied by Haanwinckel (2018), however, is small, being dwarfed by other demand shocks that resulted from reduced entry cost gap between goods and convergence in productivity. He argues that these two shocks are also responsible for reductions in cross-firm wage dispersion, which is a third factor associated with decreases in inequality that has not yet been properly explained. Alvarez et al. (2018) point out that compression in productivity dispersion does not seem to be the factor driving this convergence, but rather reductions in pass-through, while Engbom and Moser (2018) argues that minimum wages are responsible for this observed reduction in pass-through. However, other possible explanations for the decline in interfirm wage premia may be a decline in monopsony power (Tucker, 2017; Card et al., 2018), a decline in gender and racial gaps (Gerard et al., 2018; Morchio and Moser, 2019), and changes in international commerce (Helpman et al., 2010; Messina and Silva, 2017). We believe this to be a promising area of research.

Finally, the new line of research based on tax records may provide useful information on the dynamics of inequality in Brazil. As for now, it seems that including such information in the estimations of inequality leads to the conclusion of its stagnation in the past decades, especially because the role of top earners in inequality seems to have increased in the period (Medeiros et al., 2014, 2015). However, an underestimation bias of inequality in usual official sample surveys such as PNAD is not necessarily true if the
> Mean squared error estimates are obtained from a parametric bootstrap procedure, as proposed by Gonzalez-Manteiga et al.(2007) and implemented in the R EMDI package (Kreutzmann et al., 2018), based on the theory developed by Butar (1997) and Butar and Lahiri (2003).

When estimating the parameters of models (1) and (2), observations are weighted using normalized inverse probability weights, using the weighting method implemented in the R nlme package (Pinheiro, et al., 2021). Because each municipality is given equal weight when evaluating the estimates, it is necessary to normalize the weights to avoid giving too much weight to more populous municipalities in the sample estimation.<sup>10</sup> We therefore normalize the weights by dividing the inverse probability of selection by the mean inverse probability of selection for the municipality, as follows: s 1 s where is the number of AGEBs contained in the sample for ∑ππ ss<sup>−1</sup> s municipality m and WW ss<sup>=</sup> NNss ss=1NNss ππ ss<sup>−1</sup> s NNss normalizes the sum of the weights in each municipality to equal the number of sample observations, ss<sup>is the probability of AGEB a being included in the sample.11 This</sup> which is one recommended method for normalizing weights when estimating conditional random ππ

> 8 The same software package was used to estimate both municipal and state level results. We experimented with omitting state-level dummies and the results were worse.

> 9 Using estimated population from WorldPop has the added benefit of being applicable to other contexts. While we have census information that would allow us to aggregate using arguably more “correct” data, this would not be possible in other contexts in which census data is missing or out of date. For this reason, we drop any AGEBs with an estimated population of zero. Only 0.6 percent of AGEBs
*|**ted**|**S**|**patially **|**defate**|**d**|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DB:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**DOU:**||||||||||
|Non-poor|Urban|73.2|22.3|0.0|4.4|94.7|4.7|0.6|0.0|
||Rural|4.3|17.6|0.0|78.1|21.1|77.4|1.1|0.5|
|Poor|Urban|0.0|0.0|81.6|18.4|3.3|0.4|85.4|11.0|
||Rural|0.0|0.0|5.2|94.8|0.3|2.0|19.3|78.4|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line. In Panel (A), welfare is deflated using official spatial deflators. In Panel (B) and (C), welfare is deflated using our updated spatial deflators.

63
Our interest in this paper is to contribute to a growing literature that examines societal norms as a barrier to female labor market outcomes (e.g. Bernhardt et al. (2018); Bursztyn and Yang (2022); Giuliano (2021); Jayachandran (2021)). While there is general acceptance that gender norms play a role in explaining gender differences in labor market outcomes, there is currently limited empirical evidence on the relationship between norms and economic outcomes (Field et al., 2021). Existing research has been concentrated in specific regions and countries, and persistent gaps in obtaining gender-disaggregated data have prevented research at scale. Studies also have relied on relatively small sample sizes and the majority of research has focused on how aggregate, country-level attitudes relate to rates of female labor supply. More recently studies have emphasized the importance of complement-

> 1 Gender norms, a subset of social norms, are defined as shared expectations about how women and men should behave in a particular social group or culture.

> 2For example, at the World Bank social norms are not an operational focus and are often simply considered the “enabling environment” under which capital- or skills-related programs operate.

2
’s work, as expressed in national surveys in Jordan. Over 80% of Jordanians believe that jobs should be reserved to men if they are scarce, and over 84% report that the children of a working mother suffer (World Value Survey, 2018). Reservations around women’s work are accompanied by restrictive norms on their movement: 80% of men and more than 60% of women believe that women should not be allowed to travel alone. Given the lack of flexible employment in Jordan, as in many developing countries, informal home-based entrepreneurship has emerged as an economic lifeline for women to balance their financial needs with familial and societal constraints on their mobility and work.

**Digital Access:** It is also worth noting that Jordan, like many other countries with young populations, has a relatively high social media penetration rate. About 61% of Jordanians report using Facebook actively and 64% report using WhatsApp (figure A.4). Conditional on access to Internet, these usage rates reach 78% for Facebook and 81% for WhatsApp. Importantly, these usage rates exhibit smaller gender gaps when compared to economic participation rates, although women are slightly less likely to use Facebook, which allows broad exposure, and more likely to use WhatsApp, which requires knowing a user’s phone number to contact her. While only suggestive, these figures imply that gender gaps in economic participation do not fully translate to differential usage of digital platforms.

## **2.2 Online Space Setting**

Next, I document how online spaces are used by business owners in the Jordanian context. In principle, it is not clear that virtual spaces alleviate concerns on female entrepreneurs’ visibility and 6
7%|
|1 - primary complete|5.23|4.42|_-15.5%_|7.90|5.39|-31.8%|26.3%|25.4%|
|2 - secondary complete|6.55|5.83|-11.0%|7.55|6.90|-8.6%|13.3%|21.3%|
|3-preparatory complete|9.62|11.68|21.4%|10.64|12.84|<br>20.7%|7.8%|11.1%|
|4-university and above|14.93|21.96|47.1%|16.94|25.55|50.8%|4.5%|6.6%|
|_weighted average_|**5.62**|**6.88**|22.4%|7.00|7.57|8.1%|||

Source: Own calculations based on ENIGH84 and ENIGH94. All figures are weighted averages using expansion factors to reflect national representation.

Even though the average wage increased during the decade spanned by Table 1, most workers experienced a significant decline in their wages. This disparity in the evolution of real wages is most visible in Figure 2, which shows that 83 percent of the working population experienced a decline in real wages during the last decade.

<!-- Start of picture text --> Figure 2<br>MEXICO<br>Percentage Change in Real Wages by Percentile: 1984-1994<br>40.00%<br>30.00%0<br>20.00% ~~based on .6<br>a , 10.00%<br>0.00%<br>e -10.00% 10 15 20 25 30
(change in exports from Cambodia to the United States (US) weighted by sectoral employment). The dd tt,tt+nn export exposure is measured as the growth in Cambodian exports in industry tt tt xx _i_ between multiple time periods captured by the term . This change is allocated to each district in Cambodia by dividing this expression with ii ii , which is employment in district d tt+nn tt in Cambodia in the initial period 2009. This index is further weighted by share of district d WW −WWdd tt in total employment in Cambodia in industry _i_ ( <u>t</u> ). LL LLii,dd dd LLtt i,d(Wt+ni −Wti) = �<sup>Lt</sup> LdLi,cambodia dd t t tt,tt+nn xx The key coefficient of interest is 1, which measures the impact of total trade exposure on the outcome, and ′ is the vector of control variables including individual demographic background taken from Cambodia Labor Force Survey (LFS). ββ cc XX 22 | P a g e
In order to construct our instrument _ITint,j ∗ ITHHuses,t_ , we take the IT-intensity classification used in Bloom et al. (2012b) and O’Mahony and Van Ark (2003), which is based on Stiroh (2002) and interact this sectoral variable with the change in household IT-use at the state level.<sup>4</sup> The use of this second measure is broadly based on Akerman et al. (2015), who take advantage of broadband availability roll-out for Norway as a measure for IT adoption in order to overcome the endogeneity problem. To construct this variable we use two alternative sources. The first one is the 2000 and 2010 Population and Housing Censuses, while the second one is the Module on Information Technology Availability and Use in Households (MODUTIH 2010 and 2013).<sup>5</sup> An advantage of our instrument is that the sectoral IT intensity is based on U.S. data, which is by definition exogenous to the decision of Mexican firms and a better measure of “technological characteristics” at the industry level. Additionally, in our robustness tests, we consider an alternative measure of sectoral IT intensity based on the 1999 Mexico’s Economic Census in which we calculate both the share of investment in computer equipment over total investment in fixed assets, as well as the share of computer equipment assets on total assets.

# **4 Data and Descriptive Statistics**

## **4.1 Firm-level IT use**

Data regarding IT use were obtained from Mexico’s National Survey on Information Technologies 2009 and 2013. This survey was designed by the National Science and Technology Council (CONACYT) and conducted by the National Institute of Statistics and Geography (INEGI). It includes detailed information on IT use at the firm-level that, to the best of our knowledge, has not been previously used for research purposes.

We constructed a panel including firms that appeared in both surveys. Due to the sample design of this survey
|Primary identity - religion|-0.11|(0.07)|[0.38]|0.84|(0.36)|1,097|
|Primary identity - not national|-0.07|(0.06)|[0.50]|0.87|(0.33)|1,097|
|Days of media consumption|-1.08***|(0.40)|[0.08]|4.78|(2.78)|1,102|
|Refugees a top challenge|0.04|(0.04)|[0.76]|0.05|(0.22)|1,102|
|Perception of Jordanian economy|0.38**|(0.17)|[0.25]|-0.01|(0.95)|1,092|
|Share of Syrians receiving aid|-0.31|(3.87)|[1.00]|62.12|(32.13)|1,075|
|Avg aid amount (PPP)|-347.17***|(109.69)|[0.04]|630.84|(657.41)|685|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- Each of the following outcomes are standardized so that an increase corresponds with a more “pro-refugee” attitude. Refugee relocation is the response to “All refugees in Jordan should be relocated to live in the camps.” Refugee work corresponds to “ Refugees who live in Jordan right now should be allowed to continue to work outside the camps.” Refugee citizenship corresponds to “Refugees should be allowed to become full citizens if they have lived in
chapters, though typically with less rigorous standards in certain areas. It is also noteworthy that the majority of these IP-related PTAs are legally enforceable, as designated in the World Bank data. Specifically, the database ranks the legal enforceability (LE) of PTAs on a scale of 0, 1, or 2, with 0 essentially indicating no enforcement language exists, 1 indicating weak enforcement language (e.g., “should” or “may”), and 2 identifying those with commitment language (e.g., “shall” or “must”). We use this designation (LE = 2) in selecting our treatment IPAs in the econometric analysis.

Figure 1 illustrates the substantial growth since 1990 in the number of IPAs in the World Bank data that feature strong enforceability. The red lines indicate the number of agreements and the blue line shows the number of countries in one or more of them.<sup>4</sup> As shown in panel (a), the number of IPAs rose from less than 20 in 2000 to 100 in 2015, while the number of countries involve now exceeds 100. Thus, currently around 64 percent of WTO contracting parties are members of at least one IPA, as defined here. Panel (b) shows more modest growth in IP-related PTAs involving the United States as a partner. The establishment of the CAFTA-DR agreement in 2005 markedly raised the number of countries, currently around 20, in such agreements. Panel (c) demonstrates the faster growth in EU-related and EFTA-related IPAs, with discrete jumps in country coverage in 1994 with the EEA and in 2008 with the establishment of the EUCARIFORUM agreement. Together, the EU and EFTA are party to more than 40 enforceable IPAs. Because of the high degree of IPRs harmonization within them, the EU and EFTA themselves are counted as IP-related trade agreements in our sample. Finally, panel (d) indicates that there are some 40 IPAs, involving around 60 countries, that do not involve the United
7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|Medium-bound|-0.97|-0.70|-0.60|-0.51|-0.43|-0.38|-0.28|-0.23|-0.21|-0.07|
|Upper-bound|-1.17|-0.90|-0.80|-0.71|-0.63|-0.58|-0.48|-0.43|-0.41|-0.27|
|Lower-bound|-0.77|-0.50|-0.40|-0.31|-0.23|-0.18|-0.08|-0.03|-0.01|0.00|

_Source_ : Authors’ estimation based on data from the IHS (2012-2016) and HIES (2017). _Notes:_ Deciles are created based on household per capita consumption, based on the ECAPOV harmonized aggregate by the World Bank. The upper- and lower-bound scenarios were simulated with 0.2 distance from the medium-bound estimation. The lower-bound simulations were capped to avoid positive price elasticities.

> 12 Simulating a lower-bound elasticity for decile 10 resulted in a positive value of 0.08. Hence, the upper-bound for this case was intentionally corrected to zero.

11
%)|5.74|11.30|5.60|9.34|15.20|
|.. if yes Birr/ha|9,394|10,790|7,961|9,654|10,376|
|Provides local benefits|67.18|79.50|59.45|73.76|88.25|
|Number of obs.|3484|822|1659|194|323|

_Source:_ Own computation from 2013/4 CSA large farm and smallholder farm surveys. _Note:_ Although data are from a census of all farms > 50 ha, weights are applied to adjust for non-response.

17