rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Education, Christian Religion and Mission Areas

|||W|orld Bank a|id 1995–201|4||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Mission|0.10<sup>_∗∗_</sup>|0.07<sup>_∗_</sup>|0.09<sup>_∗∗_</sup>|0.06|0.08<sup>_∗_</sup>|0.08<sup>_∗_</sup>|
||(0.04)|(0.04)|(0.04)|(0.04)|(0.05)|(0.05)|
|Average education level||0.44<sup>_∗∗∗_</sup><br>(0.05)|||||
|At least primary education (share)|||0.70<sup>_∗∗∗_</sup><br>(0.09)||||
|At least secondary (share)||||0.92<sup>_∗∗∗_</sup><br>(0.10)|||
|Christians (share)||||||0.14<sup>_∗∗_</sup><br>(0.07)|
|Ethnic dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dependent variable|0.50|0.50|0.50|0.50|0.51|0.51|
|Mean _M_||0.76|0.54|0.20||0.52|
|_R_-squared|0.43|0.46|0.46|0.46|0.45|0.45|
|Observations|2,264|2,264|2,264|2,264|2,083|2,083|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; measures of human capital and religion from the Demographic and Health Survey (DHS); and control variables from multiple sources, detailed in appendix A. _Note_ : Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by _∗ ∗∗_ barren land or more than 90 percent covered by forest are excluded from the sample. _p < ._ 1 _p < ∗∗∗ ._ 05 _p < ._ 01 39
# **Concluding Remarks**

This paper is the first to explore the emergence of gender differences in the labor market in Azerbaijan. A further novelty of the paper is the focus on early career, made possible by the ILO’s SWT survey, a unique source of information both for the size of the sample and the wealth of information on young people’s individual and demographic characteristics and labor market activity.

We address the question of whether gender wage differences appear from the beginning of the careers of young men and women or whether they emerge later in their careers. The analysis suggests that at the point of entry into the labor market, differences between men and women are marginal. However, measurable differences emerge as workers begin making choices between family formation and work. The Oaxaca and Blinder decomposition analysis shows that women have better productivity-related characteristics than their male counterparts but are paid less for the same characteristics. This points to a sizeable unexplained component of the gap. The Juhn, Murphy and Pierce decomposition confirms these results at different quantiles of the earnings distribution. In fact, quantity effects – differences by gender in productivity characteristics – are almost invariably irrelevant. Overall, both the Oaxacha and Blinder and the Juhn, Murphy and Pierce decomposition analyses suggest that differences in pay may be due to the way the labor market rewards the same characteristics depending on whether it is a man or a woman who possesses them. This case is generally associated with the phenomenon referred to as “sticky floor and glass ceiling”, namely the tendency of men and women to be paid differently especially at the bottom and at the higher end of the earnings distribution, where differences in pay are more striking. This hypothesis is strongly confirmed by Machado and Mata decomposition analysis at each percentile of the earnings distribution.

One surprising result is the selection into employment of young men and women. Contrary to expectations, it is the men, rather than the women, who experience more self-selection based on skill and motivation. The selection is based on civil status and having children. Men who start a family tend to work more and earn more than average.

The results, in addition to contributing to an important, fledgling literature, also have important policy implications for promoting gender equality. First, it is important to reduce the labor market disadvantage of women linked to maternity and try to increase their labor supply. To reduce the tendency of women to withdraw from the labor market it is important to provide more maternity services than are currently available.

In addition, the tendency of women to work in highly protected jobs, namely jobs in large sized firms and jobs with formal contracts, suggests that in other types of jobs and especially in the informal sector and in small firms, rights linked to maternity are not always granted. Implementing the current 21
small enterprises. As the size of loans get larger, banks find it harder to use automatic credit scoring methods, as mentioned in the interviews and as discussed in detail in Marquez (2008). However, banks that are actively engaged in the SME sector using more sophisticated business models and risk management systems (see below), have also developed standardized and streamlined ways of screening and rating the creditworthiness of the larger SMEs, for which automatic scoring methods do not apply well. Another way in which technology has helped banks sell services to SMEs is through online banking. Firms can now use banks’ websites to, for example, process payments, input accounting and administrative data, monitor receivables, manage their liquidity, make investments, make international money transfers, buy insurance products, and more generally hedge risk.

Aside from helping identify factors that drive bank engagement with SMEs, the surveys also allow us to identify the obstacles that banks perceive as limiting their engagement with SMEs (Figure 7). As expected, the responses in this regard are heterogeneous across countries and the patterns are less clear, but several aspects are worth highlighting. SME-specific factors seem important and, among those, banks tend to highlight the informality of SMEs. High competition is perceived by some banks as another obstacle in Argentina and Serbia. Interestingly, while banks would welcome further improvements in macroeconomic stability as well as in the quality of the informational, regulatory, legal, and contractual environments, they do not see deficiencies in these areas as major impediments or binding constraints at present in their involvement with SMEs. These responses, which are not consistent with our initial expectations, may have a number of explanations. First, these countries are middle25
The third and last aspect is the registration of users’ claims. Best performing agencies register consumer claims through both paper-based and electronic mechanisms, allowing a faster resolution of the user’s case and easier access to those files by the regulatees (at both the agency and through the website). By contrast, countries at the bottom of the Social Transparency Index register users’ claims through paper-based mechanisms, preventing the modernization of the agency’s mechanisms to resolve users’ complaints.

It is interesting to observe the different behavior of Chile and Colombia in terms of transparency. Although they share a similar design of their regulatory institutions<sup>7</sup> and similar levels of autonomy, Colombia significantly surpasses Chile in its level of transparency. This factor allows us to highlight the openness of the Colombian electricity agency’s governance, despite its vulnerability to government interference.

# **B. Institutional transparency:**

The second dimension of transparency, institutional transparency, is composed of indicators related to the transparent management of the agency that are not directly linked to the involvement of the sector’s stakeholders. It includes aspects such as the nature of the agency’s decision-making (collective or individual), the existence of quarantine rules for directors, the agency’s reporting instruments (annual report and public hearing before the Congress), the publication of the agency’s institutional strategy and annual report, the publication of the agency’s audit accounts and of its career posts, the existence of norms of ethics, the record of the Board’s meetings, and the use of public exams to hire employees.

**<mark>Figure 15: Institutional Transparency Index</mark>**

<!-- Start of picture text --> Institutional Transparency Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1) .4<br>.2<br>0<br>PER T&T T1 BRA BOL MEX ESV COL JAM CHI BAR C R T2 ARG URU R D GUA NIC PAN HON ECU<br><!-- End of picture text --> Countries at both the top and the bottom of the Transparency Index keep similar positions in the Institutional Transparency Index. Peru and Trinidad and Tobago are countries with “desirable” levels of institutional transparency, obtaining the best scores. Countries under tier 3, particularly Panama, Honduras, and Ecuador do not meet the minimum levels considered in the ERGI for a transparent management of the agency’s resources and activities.

> <sup>7</sup> Each has a National Energy Commission in charge of the main aspects of regulation and some policy formulation responsibilities, and a Superintendencia as an oversight entity in charge of sanctioning and consumer complaints.

- 28 -
# **References**

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- Andreoni, James and Charles Sprenger. 2012. “Estimating Time Preferences from Convex Budgets.” _American Economic Review_ 102 (7):3333–3356.

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- Banerjee, Abhijit, Paul Niehaus, and Tavneet Suri. 2019. “Universal Basic Income in the Developing World.” _Annual Review of Economics_ 11 (1):959– 983. URL `https://doi.org/10.1146/annurev-economics-080218-030229` . ~~e~~ print: https://doi.org/10.1146/annurev-economics-080218-030229.

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27
16 Table 3: Competition

|_Countries_|_Concentration_<br>_Ratio_<br>_(Share of 3 largest_<br>_banks in banking_|_Minimum Capital_<br>_Requirement_|_Economic Needs Test_<br>_Jor New License_<br>_(Convenience and needs_<br>_of community test)_|_Openness to_<br>_Foreign_<br>_Competition_<br>_(WT0)1_<br>_9_|_Openness to_<br>_Foreign_<br>_Competition in_<br>_Practice20_|_Share of number qf_<br>_Foreign Banks to_<br>_total Banking_<br>_System (end 1997)_|_Share of Foreign_<br>_Banks'assets_<br>_as_<br>_percentage_<br>_of total_<br>_Banking System_<br>_assets (end 1997)_|
|---|---|---|---|---|---|---|---|
|G 10 Countries||||||||
|Germany|35.2%|Universal banks:<br>US $ 5.6 million<br>(Euro 5 million)|No|0.6|NA|30.3%|17.5%|
|Japan|16.0%|US$ 8.7 million<br>(Yen I billion)|Yes|0.8|NA|1.8%|0.4%|
|United Kingdom|34.9%|US $ 5.6 million<br>(Euro 5 million)|No|0.6|NA|35.1%|13.0%|
|United States<br>Latin America|19.7%|<br>NA|Yes|0.6|NA|9.0%|3.1%|
|Argentina|30.9%|US $ 15 million|NA|0.8|NA|25.3%|19.4%|
|<br>Chile|27.8%|US_$_23.5 million|NA|0.2|NA|35.3%|12.3%|
|Mexico|37.8%|US $ 16 million<br>(Ps 160 million)|NA|0.4|NA|21.2%|6.9%|
|Asia & Pacific||||||||
|Hong Kong|55.2%|US $ 19.5 million<br>(HK $ 150 million)|NA|0.6|4.58|29.5%|48.1%|
|India|38.0%|Universal banks:<br>US $ 23.5 million|No|0|2.18|17.2%|3.5%|
|||(Rp I billion)||||||
|Indonesia|35.2%||NA|0.6|3.10|42.9%|23.3%|
|Korea|16.3%|US $ 85 million<br>(Won 100 billion)|Yes|0.4|1.90|10.4%|11.7%|
|Malaysia|27.6%||NA|0.6|2.45|18.0%|23.0%|
|<br>Philippines|33.4%|Universal banks:<br>US$ 115 million<br>(P 4.5 billion)<sup>21</sup>|NA|0.2|2.88|17.5%|36.8%|
|Thailand|46.8%|US $ 270 million<br>(Baht I billion)|NA|0.2|2.43|22.2%|10.6%|

Source: World Bank data, unless otherwise indicated.

_'9_ Source: Qian, Ying (1999). Scale: 0 to I with I most open.

20 Scale: I most closed, 5 most open. Index is average of banking and securities indices of degree of openness, in practice. 21 Under discussion.
<!-- Start of picture text --> 1<br>Re]<br>oO<br>5-7<br>a4<br><!-- End of picture text --> <!-- Start of picture text --> 3 5<br>ro<br>55 = 4<br>Sd<br>2<br>a 2<br><!-- End of picture text --> <!-- Start of picture text --> "och ch<br>0 - - _ -<br>Selection Performance Detection Attrition Reporting Sample size<br>Bias type<br>{[) Early [EG Later<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>9<br>2a<br>67<br><!-- End of picture text --> <!-- Start of picture text --> gs<br>w<br>5 4<br>=<br>R68<br>2<br>a 2<br><!-- End of picture text --> <!-- Start of picture text --> aB|<br>———_—__—_— a a a<br>Selection Performance Detection Attrition Reporting Sample size<br>Bias type<br>[Early [GG Later<br><!-- End of picture text -->
**Figure 3.2: Extreme rural poverty (P0) in Mexico by region,** **<u>selected years 1992-2002</u>**

<!-- Start of picture text --> 80.0<br>1992<br>70.0 1996<br>2002<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Mexico Norte Capital Golfo Pacifico Sur Centro- Centro<br>Norte<br>Source: Calculations based on ENIGH 1992, 1996 and 2002. Rural: areas population with <15,000.<br><!-- End of picture text --> The distinction between regions seems to matter more for rural poverty than does that of rural and urban. As Figure 3.2 clearly shows, the Capital, Center, and CenterNorth regions experienced a considerable reduction in the headcount poverty rate over the past decade. The share of poor people in the total population fell by more than 10 percentage points in each region of these regions. This compares to the Gulf region where poverty increased by more than 10 percentage points during 1992-2002.

During 1992-2002, the pattern of overall poverty changes has closely followed the macroeconomic cycle and the associated rhythm of the labor market. The overall Mexican poverty was not stable, nor was rural poverty stable over the last decade. In rural areas P0 fell from 36 to 32 during 1992-94 and increased during the crises in 199596 where it hit an all-time high of 52 percent poverty rate. More populated rural areas (with between 2,500-15,000 inhabitants) have been more affected by the economic instability than dispersed rural areas (localities with less than 2,500 people). Poverty increased by 51 percent in the former and 27 percent in the latter areas during 1992-96. The crisis of 1994-95 constituted to a large setback for well-being in Mexico, and income poverty in 2002 is still close to levels prevailing a decade earlier, before the crisis. One result of the crises has been: (1) low labor returns in self-employment and wages independently of sector of engagement, and (2) increased under-employment for the poor.

Since 1996, the P0 embarked on a downward path and reaching in 2002 a level lower than at any point during the previous decade. While long run progress has been feeble, the measured trends in the 2000-02 period are encouraging, with a significant decline in extreme poverty, despite stagnation in average incomes driven by a 11
in the full sample, power outages cease to have an effect on decisions to invest in generators (our _δ0_ measure in the model), when they exceed 12 per year.

In columns 3 and 4, we introduce credit constraints, using as proxy a dummy variable indicating if firms quote access to finance as a major or severe constraint. While the findings of columns 1 and 2 are robust to this inclusion, we also find that both credit constraints alone and their interaction with power outages are significant and decrease firms’ probability of having invested in complementary capital. In terms of marginal effects, an increase of 1% in the share of financially constrained firms in implies a 0.05% reduction in the probability to have a generator, while facing 10 additional outages per year increases this marginal effect by 6% (since 0.029/100*10/0.048 = 0.060).

Note finally that, in columns 2 and 4, the coefficients of the age, capital city, and particularly export and foreign ownership dummies are large, positive and significant, while their inclusion actually reinforces the effects of power outages and credit constraints.

**<u>Table 8: Complementary Capital Decision</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Probit|Generator|Generator|Generator|Generator|
|Number of Power Outage (/100)|0.120|0.172|0.146|0.206|
||(3.39)***|(5.10)***|(4.16)***|(6.70)***|
|Number of Power Outage (/100) Square|-0.002<br>(2.74)***|-0.006<br>(3.62)***|-0.002<br>(3.31)***|-0.007<br>(4.61)***|
|Access to Credit is Major/Severe Constraint (dummy)|||-0.193<br>(6.46)***|-0.136<br>(4.16)***|
|Access to Credit is Major/Severe Constraint (dummy) *|||-0.064|-0.082|
|Number of Power Outage (/100)|||(3.13)***|(2.90)***|
|Age||0.005||0.005|
|||(3.84)***||(3.67)***|
|Capital City dummy||0.106<br>(2.06)**||0.116<br>(2.24)**|
|Export dummy||0.372||0.368|
|||(7.86)***||(7.70)***|
|Foreign dummy||0.216<br>(4.92)***||0.212<br>(4.77)***|
|Constant|-0.313|-0.717|-0.336|-0.155|
||(1.41)|(2.63)***|(1.54)|(0.56)|
|Firm Size dummies||Yes||Yes|
|Country dummies|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|
|Observations|24 943|18 786|23 953|18 017|
|Threshold value δ= -α1/2α2|22.00|15.25|26.50|12.17|

Absolute value of z statistics in parentheses, standard errors are clustered at the country-industry level. * significant at 10%; ** significant at 5%; *** significant at 1%
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