rafmacalaba/data-use-mentions-extended huggingface dataset:
human capital, investments in physical capital, technological advantages, or the reallocation of resources from the least to the most productive firms within each sector.

# **2.3 Decomposition of aggregate convergence**

The unconditional (beta) convergence hypothesis suggests that productivity catch-up growth may occur fastest where productivity differentials are the largest across countries. Following Wong (2006), this aggregate beta unconditional convergence can be decomposed into the contribution of the within-sector growth and that of sectoral reallocation.<sup>6</sup> The decomposition consists of two steps: First, regressing aggregate labor productivity growth (∆ _y/y_ ) on the logarithm of initial aggregate labor productivity ( _y_ ) gives the aggregate beta ( _βaggregate_ ) convergence.<sup>7</sup> Second, replacing the dependent variable in the first regression with decomposed components from equation (2), a regression on the logarithm of initial aggregate labor productivity is undertaken: The summation of the left-hand-side in regression (3) is equal to the left-hand-side in regression (2). Hence, rearranging the right-hand-sides in regression (2) and (3) gives:

> 6Other studies decomposing convergence employ an accounting approach. They calculate the difference of each component (1) between the frontier and all sample countries. (e.g., Bernard and Jones (1996) and Harchaoui and Üngör (2016) use the United States as the frontier and Caselli and Tenreyro (2005) use France. In contrast, Wong (2006) employs an econometric approach. Its advantage is to understand that the components are statistically significant or not.

7Following McMillan et al. (2014) , local currency value-added is converted to U.S. dollars using the PPP exchange rate obtained from the Penn World Table for initial labor productivity ( _y_ ). Van Biesebroeck (2009) builds an expenditure-based sector-specific PPP in OECD countries, using detailed price data.

5
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the net interest expense ratio as the log of one plus the bank‟s interest expenses over interest bearing liabilities net of the government T-Bill rate taken from the IMF International Financial Statistics database (IFS, 2012). As indices of the quality of lending, we consider the loan loss provision and non-performing loans variables.

The state bank variable is a dummy variable that equals one if a bank has a majority state ownership share. In our sample, 11.4% of bank-year observations concern state-owned banks. Privately owned banks can have domestic or foreign ownership. The domestic bank variable indicates majority private domestic ownership, while the foreign bank variable signals majority private foreign ownerships. Domestic and foreign banks constitute 52.8% and 35.8% of our observations.

6
_Table 8: Geographic Distribution of HLE Households_

|**District**|**Distribution of**<br>**Households**|
|---|---|
|Mosul|33%|
|Tikrit|11%|
|Ramadi|9%|
|Tel Afar|9%|
|Shirqat|7%|
|Kirkuk|5%|
|Qa’im|5%|
|Falluja|5%|
|Baiji|5%|
|Ana|4%|
|Sinjar|3%|
|Hamdaniya|2%|
|All Others|2%|
|_Data Source_:|CLCI LegacyDatabase.|

Thirty percent of the high likelihood sample is female headed, which is roughly in line with CLCI vulnerability data broadly (not shown). The average household has 8 members (slightly higher than the national and CLCI average).

The average household size and member disaggregation does not change between female- and male-headed households, and nor by district – in the most populous districts the number of women drops to 3 in Mosul and the number of children increases to 3 in Shirqat, Tel Afar, and Tikrit. The average head of household age is 40 across the entire sample, dropping to 38 among female-headed households. In terms of head-ofhousehold specific vulnerabilities, 16 percent are disabled, and 36 percent have a chronic illness, with female heads of household more often having chronic illnesses (59 percent). For 91 percent with a disability and 89 percent with a chronic illness, their condition prevents them from working. The average household has 1 disabled and 1 chronically ill member (not shown). Majority of the household heads have either no education, or have completed only primary education (Figure 8). This shifts markedly among femaleheaded households, among whom 50 percent received no education.

<!-- Start of picture text --> Figure 2: Educational achievement of household head Figure 3: Household monthly income by number of<br
Gorodets.

In short privatization has made a large contribution to the establishment of the housing market, and this could not help but increase residential mobility. Housing privatization has also made better headway in small towns, which may be one of the reasons for its success in the Baltic states, countries in which a reiatively higher proportion of the population lives in smaller population centers.

# 2.3.2. Mobility Trends Versus the **Housing Problem in Moscow**

The findings of the Moscow surveys allow for a more detailed examination of the housing problem in Moscow in the years 1993-1995.20 In these three years, 11.6% of households moved, with 10.4% of them Muscovites and 1.2% persons new to Moscow.

In 1993-1994, roughly equal shares of households moved into newly constructed and into secondary market housing (Fig. 2.13), while in 1995 the place of the latter category expanded (3.2% as against 1.9%). As a result, 5.4% of the households moved into new apartments, and 6.2% moved into secondary market apartments.

The distribution by the means of obtaining housing was as follows: 4.2% exchanged their housing; 4% received free housing through the waiting list system or were moved fiom buildings slated for demolition; 3.4% bought apartments, including cooperatives. Over the three-year period, the ratio of these means remained basically unchanged. Distinctions in the purchase of primary and secondary market housing are illustrated by Fig. 2.14.

2 An analysis of the mobiLity of Muswcovites was also caied out by the Urban InstitLte (Lee,, 1996), which compared the data of four annual sveys undetaken in 1992-1996 and eveted similar trends.
(iii) unskilled labor migration from agriculture to non‐agricultural activities, and (iv) per capita consumption are passed to the household survey data and, in the microsimulation, a new hypothetical global income distribution is generated for the oil shock equilibrium.

# _The microsimulation model_

The ultimate focus of analysis is household welfare, and household real per capita income is here assumed as its indicator:

> 7 The model is solved in a comparative statics approach. This means that the model assumes no changes in the demographic structure of the population nor in its skill endowment. Likewise, the supply of capital and land is assumed fixed. The only variable factor of production is natural resources that, as mentioned in the main text, responds to changes in the international price of oil. The economy is also assumed to operate in full employment.

8
In interviews, managers of manufacturing firms in Rwanda indicate that proximity and lower transport costs is their “comparative advantage” and hence they focus on domestic trade or exports to Burundi. This might be surprising at first, because manufacturing exports are more diversified across firms and idiosyncratic demand shocks might be more important. However, many of the additional exporters in the manufacturing sectors are likely to be only marginal exporters. They are much more likely to only clear the lowest export thresholds, for the closest, most accessible markets, consistent with the generalization of the Melitz-model to multiple countries in Chaney (2008). It is also important to keep in mind that manufacturing exports started from a very low base at the start of the sample. The greater importance of richer, but more distant sub-Saharan African countries with more developed manufacturing sectors, like Kenya and Uganda, only in later years coincides with the boom in manufacturing exports.

The importance of African countries as trading partners contrasts sharply with the limited exports going to sub-Saharan African countries, as reported in Van Biesebroeck and Mensah (2019). Using sectoral input-output data from EORA, they find that in 2015 the average country exports 23% of its manufacturing exports within the region. For Rwanda specifically, the EORA data shows that only 2% of manufacturing output is exported. More than 80% of exports are recorded as going to the “rest of the world” and only 8.2% are exported within Africa. This seems highly implausible and suggests that one should be careful with the rest of the world residual category in the EORA dataset.

As has been mentioned a few times already, the results in Table 1 also suggest that a more stable situation in the DRC could really help Rwanda. From 2010 to 2014, as its neighbor was booming, manufacturing exports grew from 3.5 million USD to 45 million USD. At the same time, the share of manufacturing exports going to the DRC averaged 80%. The end of the growth spurt in the DRC in 2016
to all adjacent squares,<sup>25</sup> if that threshold is reached or exceeded. Enumerators are instructed to approach all informal businesses in the area (regardless of structure, including households and hawkers). Enumeration, in turn, develops, networks,<sup>26</sup> which can be given survey weights and considered as geographically representative (Appendix A.1). This method requires many informal businesses to be enumerated, so many that a lengthy (termed here as “long-form”) survey would be prohibitively costly and timely. As a result, a sub-set of businesses were selected randomly for a “longform” interview. In total, 3,687 businesses were enumerated in 129 networks<sup>27</sup> and a total of 515 long-form surveys were completed (see Aga _et al_ , 2017 for a full explanation of implementation).

VI. Econometric Specifications & Descriptive Statistics

# _<u>Main Econometric Specification</u>_

The basic econometric specification uses a probit estimate of bribery, which follows from Eq. 3: Y<sup>*</sup> is a latent variable for the binary outcome of interest, Y. The main outcome measure is BRIBE, which records if an informal business reports having to pay a bribe to remain informal.<sup>28</sup> This measure uses a ‘vague present’ time reference and is not linked to the “last completed month” as are several other variables. Such a direct measure of bribery may be unreliable if respondents are reticent to admit having to pay a bribe (e.g. Kraay and Murrell, 2016), as a result a “yes” value to BRIBE is likely a lower bound.

The log of the last month’s sales (R�) is the main measure of having resources to pay. The data include a monthly measure of revenues, as without formal accounting, businesses’ recall of the last month’s sales
visualize a future that most parents had not considered before for their children, we decided to use videos instead of other media formats. The entertainment media used in this trial were produced by Impact(Ed), formerly Discovery Learning Alliance, a US-based non-profit media firm that works with ministries of education in developing countries to produce television and mass media initiatives. The study videos are an excerpt from the 10-hour video series “My Better World”, an entertaining animation program that also includes real people sharing their experiences in inspiring mini documentaries, often set against a pan-African village life. These everyday role models reflect the animated story’s highlighted themes with their personal stories of struggle, courage, and perseverance.

The community screenings of the aspirational videos were implemented in the following way. Community mobilizers invited eligible households, living within a radius of two kilometers of a randomly selected local school, to attend community events that would discuss “relevant issues for parents”. When households were visited to deliver such invitations, no mention was made about the main themes of the videos and screening discussions to avoid any potential self-selection of participants against or in favor of education for children. Using data from the listing exercise, the target child (6-9 years old) and both parents were invited to two sessions that would take place in the local school over two subsequent weekends. The implementing NGO was responsible for organizing and staffing the community screenings. NGO female facilitators were trained by the authors about which messages to highlight during the post-screening discussions based on the aspirational video content. To host a maximum of 75 households in the local schools for the screenings and discussions, two parallel screenings (in two different classrooms) took place in most treated communities. The day of the screenings, a registration desk was set at the entrance to ensure that invited were admitted to the up only participants venue. Snacks and a gift bag with a few early-grade literacy booklets were handed to each household head as they completed registration. The community leader welcomed attendees, briefly highlighting the
the only effect relevant for families that would have sent their daughters to middle school with or without the program. At the same time, for families with daughters out of school, the program’s condition on school attendance also induces a substitution effect since it reduces the price of education.

Both effects point toward an increase in the demand for education, particularly among credit-constrained households in environments with imperfect capital markets. On the one hand, if this demand is sustained over time, the higher utilization of education services can lead to higher school progression and completion. On the other hand, there might be perverse incentives or constraints that prevent program participants from accumulating more human capital. For example, families may try to keep their daughters in those grades that make them eligible for the transfers, in effect preventing them from progressing and graduating. Moreover, assuming no changes in supply, schools in program areas may become overcrowded due to the additional demand, which can hinder progression and completion. We seek to shed some light on the net effects of these conflicting forces in the context of the FSSP.

The analysis based on school-level data provides systematic evidence that the program helps girls progress through middle school. The point estimates from DD and RDD models indicate that the program raises enrollment not only in grade 6 but also in grades 7 and 8 for the same cohorts (Table 5).<sup>23</sup> For instance, cohort C sees higher percentage changes in enrollment in stipend than in non-stipend districts in both grade 7 in 2005-06 and grade 8 in 2006-07 (the second and third year of exposure to the program). The increases relative to enrollment in stipend versus non-stipend are 18 and 32 percent, respectively. Although the relevant cohorts (cohorts B to G) participate in the program at different times and for different periods, estimates of program effects on enrollment yield consistent results for all of these cohorts and grades over time (positive and significant impacts). The insignificant impacts in 2004-05 are expected since

> 23
columns 3 and 4 include all students that were successfully surveyed, and column 5 includes all Yoruba students in the cohort. Columns 1 and 3 use baseline student characteristics from both administrative and survey data while columns 2, 4 and 5 use administrative data only. In all, the results indicate that participants’ postings based on these different state characteristics are not systematically correlated with participant characteristics.

# **4.2 Empirical Specification**

Since compliance with NYSC state assignment is not perfect, I use the ethnic composition of the assigned state as an instrumental variable to estimate the effect of the ethnic composition of the state in which the individual serves. This yields the local average treatment effect (LATE) for those for whom the NYSC assignment determines their state of service. Since there are essentially no defiers (only one individual who is untreated takes up treatment), the LATE can be interpreted as the “treatment on the treated.” With 87 percent compliance with treated assignment, there is a strong first stage with F-statistic of 1176. The primary estimation equation is where _Yiosp_ are the outcomes of interest (migration decisions, knowledge, attitudes towards the nation, towards own ethnic group, and attitudes towards and relationships with other ethnic groups) for individual _i_ from state of origin _o_ posted to state _p_ and served in state _s_ . _ServeNonY oruba_ is an indicator variable for whether the participant served in a different ethnic region (instrumented by a dummy variable for whether the participant was posted to a state in a different ethnic region).<sup>19</sup> _Xiosp_ is a vector of controls (age, female,

> 19The explanatory variable could also be a continuous measure–the share of non-Yoruba in the state. However, as Figure 2 shows, most of the variation in share of Yorubas occurs within the Yoruba region as there are very few Yorubas in any other state. For this reason, as well as to have a reference “treatment”

17
new firms before and after the 2005 reform across districts based on the level of distrust in regional courts (as measured in the 2006 Life in Transition Survey). That is, we test whether the regions where a large fraction of individuals report distrust in courts saw a larger increase in new firms after the reform than districts with less distrust in courts. To verify whether this is a valid identification strategy, we show that the level of distrust in courts is not correlated with pre-reform changes in the number of new firms over time.

Our results suggest that the reform increased the number of new firms by up to 34%. We also look at employment, survival and growth rates of these new firms, but find no significant effect of the reform on the average number of employees or employment growth rates of new firms. We do, however, find that the reform increased the one- and two-year survival rates of new 3
our results suggest strong adaptation effects. While the first lockdown came as a surprise and few were prepared for its implications, measures to keep economic activity ongoing were already in place during the later ones.

Other potential applications for the use of the electricity data are numerous. For example, preliminary findings suggest that deviations of daily electricity consumption from the model prediction can be used to track the economic impact of natural disasters. However, more research is needed to convert changes in electricity consumption into changes in economic activity. We used electricity consumption to compare the impacts of the different lockdowns but abstained from quantifying the economic impact. A properly estimated elasticity between electricity consumption and monthly economic activity would be needed for that. In the United States, there seems to be a one-to-one relationship between electricity consumption and output (Cicala 2020), whereas in Europe, output seems to decline between 1.3 to 1.9 percent for each percentage point decline in electricity consumption (Chen et al. 2020). Our regression results indicate that the elasticity may be smaller in Bangladesh, but we leave the estimation of a proper elasticity for emerging markets and developing economies for future research.

17
identified clerical and professional job categories as being more exposed to the risks of automation with GenAI (Cazzaniga et al., 2024; Gmyrek et al., 2023; Ozdeneron, Hakki, 2023; WEF, 2023), with pre-GenAI regional assessments also classifying female-held jobs in LAC as being at a higher risk of automation from digital technologies (Egana-delSol et al., 2022).

In accordance with the technical documentation of ISCO-08, such differences in the occupational structures also correspond to varying levels of skills and educational attainment, as clerical support workers, technicians and professionals are typically classified in the mid- to high-skill level brackets (ILO, 2023b). Given that educational attainment and earnings gaps across skills groups have been important drivers of income inequality in LAC (Azevedo et al. 2013), the impact of GenAI that follows the existing labor market structures would likely also have an effect on the overall income inequality. In the best-case scenario, GenAI would boost the productivity of lowerskilled workers in the exposed occupations, allowing them to access higher incomes and therefore leading to a more broad-based income distribution. In the worst-case scenario, the technological transition could result in the automation of largely female-held jobs in the clerical, technical and professional occupations, while the opportunities for new GenAI-augmented jobs could be limited, given the high concentration of current employment in elementary occupations and in the informal sector, where technology adoption and private sector investment are low. To better understand how the first-order effects of GenAI may affect inequality, this study provides a detailed profile of the socio-economic groups most exposed to this technology.

Finally, we acknowledge that the final outcomes of the technological transition process will also be largely dependent on the existing and future policy frameworks in the region. While the analysis of country-level polices and legal frameworks is beyond the scope of this regional study, the detailed country-level statistics that we make publicly available alongside this publication can serve as useful inputs to the discussions underpinning such policy responses.<sup>10</sup>

> 10 Access to