rafmacalaba/datause-encoder-data huggingface dataset:
between 1995 and 2012 the Gini coefficient for earnings fell by 18% and other measures, such as the Theil index and the p90-p10 ratio, by between 30% and 40%.

The dominant narrative in the literature attributes this decline primarily to educational dynamics: a substantial increase in years of schooling for working-age adults has translated into a rising supply of skills, followed by a decline in the returns to those skills in the labor market (revealing, presumably, that demand for skills has failed to keep pace with supply). Our analysis draws on RIF regression-based decompositions to investigate the relative roles of a broader set of potential determinants including—beside human capital— changes in minimum wages and formal employment; in demographic characteristics of the labor force (chiefly race and gender); and in the sectoral and spatial distribution of employment.

We find that the decline in earnings inequality between 1995 and 2012 was driven primarily by changes in the structure of remuneration in the Brazilian labor market, rather than directly by changes in the distribution of worker characteristics. The main exception to this was the inequality-augmenting effect of the increase in years of schooling across the population. This is the so-called “paradox of progress” effect, whereby a rightward movement in the distribution of years of schooling shifts population density to steeper segments of the earnings-education profile, leading to wider earnings gaps. Thus, contrary to the conventional view, the educational upgrade was roughly neutral for inequality dynamics. The reduction of the schooling premium was strong and did have important equalizing effects, particularly during 2003-2012, but it was largely offset by the inequality-augmenting compositional changes.

The most important driver of the observed inequality dynamics was the reduction in the experience premium, in particular during 2003-2012. Almost two-thirds (63 percent) of the reduction in the Gini during this period can be attributed to reductions in the returns to potential experience. If one considers the p90-p10 earnings ratio instead, the reduction in the experience premium accounts for even more than the observed changes in inequality, and was partly offset by other, inequality-augmenting, factors. Since the supply of potential experience was largely unchanged over the period—because population ageing was offset by longer educational spells—one can be relatively confident that this large change in returns was driven by a decline in the demand for more experience workers. The deeper causes of this reduction in the demand for experience—which is consistent with evidence on “agebiased technical change” from elsewhere—are beyond the scope of this paper, but invite further research.

25
# Figure3 World Bank Commodities Price Forecast - Silver (Constant US dollars; Unit: $/toz)

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Firm registry data can provide estimates of jobs created in the formal sector but lack general information on job characteristics, and even more on workers’ profiles. An attempt at measuring skill needs is made in some modules of the Skills Towards Employment and Productivity Surveys (“STEP surveys”) directed to employers and implemented in several countries, including in Kosovo in 2015-2016. It identifies the existence of skills constraints across sectors and types of occupations. However, the data on demand for skills are not granular enough for specific feedback to policy makers on skills needs and curricula alignments. Detailed information on both technical and soft skills demanded by employers for specific types of occupations is not available, thereby limiting its usefulness for students and parents seeking to take career decisions. We show in Appendix D the differences in granularity level between job portal data and STEP data, to further highlight how job portal analysis can substantially contribute to the policy dialogue on skills development.

This paper is thus an example of how the analysis of job descriptions from online vacancies can generate useful and detailed complementary data on current demand for skills in a labor market. Our paper contributes to existing work on skills by (i) using data that directly reflect employers’ skills demand rather than the skills stock, and (ii) presenting demand for skills at the broad and granular levels.

# **IV. Methodology**

First, we define a _job posting_ as a unique job advertisement on a given job portal during a specific time interval in the year 2018.<sup>17</sup> This means that in the presence of duplicated postings at the same time across job portals, only a single job posting is taken into account.<sup>18</sup> Furthermore, a _vacancy_ is defined as a unique work position that is associated with a job posting. By definition, a job posting can therefore be posted as advertisement for several vacancies. For instance, when a firm is searching for 10 workers for a construction site by advertising only one post, we consider the number of vacancies to be 10 and the number of job postings to be 1. For the analysis of job creation, we are interested in the number of _vacancies_ . However, because the numbers of postings and vacancies typically coincide (as shown below), we take a conservative approach by conducting our analysis at the posting level. By doing so, we also address the data provider’s concerns that some of the postings advertising several vacancies may overstate the true number of vacancies. Note that we present robustness checks in Appendix C showing that key results are valid regardless of the approach.

While the comprehensive methodology is described in detail in Appendix A, we highlight here two main methodological aspects: (a) the deduplication of job postings across portals, and (b) the text analysis of job descriptions.

> 17 The time dimension of the postings is defined according to exact start and end dates of each posting. 18 This task is performed using a deduplication algorithm.

- 10 -
and Petrongolo 2017, Del Rey et al. 2021). Since our results show a positive impact of maternity leave on female employment (Hypothesis 1), the positive feedback effect implies that our regression results may overstate the true impact of maternity leave on female employment (upward bias). Del Rey et al. (2021) argue that this source of upward bias can be significantly mitigated by the inclusion of women in political office and social norms as controls in the regressions. The rationale for this is that female employment affects maternity leave policies, at least partly, through the impact of female employment on social norms and women in political office. In his seminal study, Ruhm (1998) argues that if there is reverse causality from female employment to maternity leave, then lead values (t+1 values) of maternity leave should impact female employment currently (at time t). They test for this using panel macro-level data for 9 European countries and confirm no significant reverse causality. Micro-level studies that focus on a single country seek to compare outcomes between observationally similar women in the tretament and control groups (see Dahl et al. 2016, Baker and Milligan 2008; Rossin-Slater 2017 provides a review of the related literature).

Our first defense against the revese causality problem is that we use lagged values of maternity leave, and as suggested by Del Rey et al. (2021), control for women in the public sector (women in parliament) and social norms proxied by the level of the country’s urbanization and its religious composition. Furthermore, we argue that while higher female employment may create demand and political pressure for maternity leave, this is unlikely to be so in individual industries that account for a small share of all formal sector female workers in the country. Simply put, some industries are too small to exert any meaningful effect on maternity leave laws that are determined at the macro-level. Thus, finding a large, postiive, and statistically significant relationship between maternity leave and female employment in an industry that employs a small share of female 12
<mark>.</mark>

# **<mark>1.    Introduction</mark>**

The idea that the financial sector has the potential to affect patterns of innovation and growth is an old one going back at least to Schumpeter (1912). In the recent literature, the question of whether economic growth is enhanced in the presence of a strong and efficient financial mechanism has been examined in many contexts and the conclusion has usually been that it is. A prominent line of empirical research - started by King and Levine (1993) and extended by Levine and Zervos (1998), Levine, Loayza and Beck (2000), and others - has studied cross-country data and reached a consensus that financial development is an important factor in national growth.<sup>3</sup> This line of research has demonstrated that the size and depth of an economy’s financial system (usually measured as the value of a financial aggregate, such as credit, to GDP) is positively correlated with its future growth in per capita, real income.<sup>4</sup> The evidence from cross-country regressions, however, is plagued by omitted variables problems and must be viewed with some skepticism because the data have been used so intensively by so many researchers (see e.g. Levine and Renelt (1992) who demonstrate the parametric instability inherent in cross-country regressions).

One contribution of this paper is to reassess the finance-growth relationship in a sample of sub-national economies where there are fewer omitted variable problems. In doing so, the paper addresses an issue that has received much less attention, namely, the importance of promoting geographically diffuse banking sector development across regions. The results presented here demonstrate the potential returns to efforts to develop local financial capacity both because the lack of such capacity is found to stymie growth, and because the least financially developed districts suffer a disproportionately greater penalty. I also test the interaction between human capital depth and financial development at the district level and find that human capital may help to decouple the dependence of growth from finance. This result is non-obvious, as a model based

> 3 Though it does not purport to address the connection between finance and growth, a cross country study by Aizenman, Pinto and Radziwill (2007) shows that countries who finance their growth from local savings as opposed to foreign borrowing have grown faster historically.

> 4 Significant work has also been done into the determinants of the level of financial development, including: Djankov, McLiesh and Shleifer (2007), La Porta, Lopez-de Silanes, Shleifer and Vishny (1998), and Djankov, La Porta, Lopez-de Silanes and Shleifer (2008).

2
This strategy is justified if and only if the changes observed in the control communities provide good estimates of the counterfactual ( _i.e._ , what would have happened in the treatment communities had the project not been implemented there).

Because it is impossible to directly observe the counterfactual, the literature focuses on testing if _prior_ to treatment the two groups evolved similarly, the parallel trend hypothesis. As our best estimate of the hypothesis, we test if prior to project implementation, the two groups evolved similarly in terms of poverty levels.

To do so, we take advantage of the availability of detailed poverty maps at the municipallevel (NCSB, 2005) to test if changes in poverty incidence between 2000 (date at which the data on which the poverty maps are based were collected) and 2003 (date of the baseline survey) are similar between control and treatment municipalities. We regress the changes in poverty incidence at the municipal-level between 2000 and 2003 on a dummy indicating whether a municipality was eligible for the project in 2004. A coefficient not statistically different from zero at the standard levels of confidence would indicate that the evolution observed between 2000 and 2003 was similar between the control and treatment groups. We cannot reject the parallel trend assumption. This provides some evidence that control and treatment communities would have evolved similarly over the period covered by our data had the project not been implemented.

We compare the 2003 distributions of our variables of interest of comparison and treatment villages (Table 2). We do so using T-tests of equality of means and Kolmogorov-Smirnov tests of equality of distribution. For most of the variables, there is no statistically significant difference between the two groups. However, trust toward both neighbors and strangers as well as frequency of meetings is lower in treatment villages while participation in informal collective action ( _bayanihan_ ) is higher than in comparison villages. As a result, as a further check of 16
_Table 21_

|Dependent variable: Need of fiscal|adjustmen|t based on|Escolano|et al.(20|14)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid CE minus structural as % GDP|0.064***<br>(0.015)|0.054***<br>(0.012)|0.068***<br>(0.015)|0.055***<br>(0.014)|0.054***<br>(0.014)|0.055***<br>(0.014)|0.056***<br>(0.014)|0.055***<br>(0.014)|
|Rigid SS minus structural as % GDP|0.031***|0.027***|0.031***|0.027***|0.036***|0.028**|0.010|0.026***|
||(0.007)|(0.006)|(0.007)|(0.006)|(0.008)|(0.012)|(0.016)|(0.010)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.976***||-1.848***|-1.721***|-1.841**|*-1.835**|*-1.850***|
|||(0.427)||(0.425)|(0.407)|(0.427)|(0.422)|(0.434)|
|Inflation (t-1)||-0.054||-0.012|-0.036|-0.013|-0.018|-0.012|
|||(0.053)||(0.043)|(0.049)|(0.043)|(0.042)|(0.043)|
|U.S. Interest rate||-0.016||-0.021*|-0.025*|-0.022|-0.012|-0.021|
|||(0.011)||(0.012)|(0.013)|(0.013)|(0.013)|(0.013)|
|**Political factors**|||||||||
|Gini|||0.006|0.005|0.004|0.005|0.005|0.005|
||||(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|(0.006)|
|Rule of law|||0.001|0.020|0.011|0.020|-0.045|0.020|
||||(0.031)|(0.032)|(0.030)|(0.032)|(0.039)|(0.033)|
|Margin of Majority|||0.085|0.054|0.073|0.055|0.076|0.043|
||||(0.124)|(0.126)|(0.126)|(0.127)|(0.127)|(0.162)|
|Election's year|||0.021|0.028*|0.030**|0.028*|0.029*|0.028*|
||||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.020||||
||||||(0.013)||||
|Gini * Rigidity||||||-0.000|||
|||||||(0.000)|||
|Rule of law * Rigidity|||||||0.004<br>(0.003)||
|Majority * Rigidity||||||||0.001|
|||||||||(0.012)|
|Constant|-0.255***|-0.071|-0.576*|-0.375|-0.248|-0.377|-0.324|-0.374|
||(0.069)|(0.071)|(0.327)|(0.296)|(0.275)|(0.303)|(0.288)|(0.296)|
|Observations|1,467|1,451|1,318|1,304|1,304|1,304|1,304|1,304|
|R-squared|0.111|0.169|0.127|0.176|0.181|0.176|0.180|0.176|
|Number of ifscode|83|83|74|74|74|74|74|74|
|CountryFE<br>Robust standard errors clustered b<br>*** p<0.01, ** p<0.05, * p<0.1|Yes<br>y country i|Yes<br>n parenth|Yes<br>eses|Yes|Yes|Yes|Yes|Yes|

- 50 -
Table 3: Total education and health aid

||Public edu|cation exp.|Public h|ealth exp.|
|---|---|---|---|---|
||OLS|FE|OLS|FE|
|Education aid|0.047|0.0042|||
||(0.082)|(0.068)|||
|Health aid|||0.47<sup>_∗∗∗_</sup>|0.26<sup>_∗∗_</sup>|
||||(0.18)|(0.12)|
|General aid|-0.0032|0.032|0.016|0.0037|
||(0.053)|(0.029)|(0.030)|(0.019)|
|Support to NGOs|-0.41|-0.38<sup>_∗_</sup>|-0.13|-0.18<sup>_∗∗_</sup>|
||(0.33)|(0.21)|(0.17)|(0.091)|
|Other non-education aid|0.0026|-0.0041|||
||(0.022)|(0.018)|||
|Other non-health aid|||0.0084|-0.012|
||||(0.017)|(0.012)|
|GDP per capita|0.085|0.26<sup>_∗_</sup>|0.17<sup>_∗∗∗_</sup>|0.14<sup>_∗_</sup>|
||(0.059)|(0.14)|(0.048)|(0.085)|
|GDP per capita growth|-0.049<sup>_∗∗∗_</sup>|-0.028<sup>_∗∗∗_</sup>|-0.025<sup>_∗∗_</sup>|-0.020<sup>_∗∗∗_</sup>|
||(0.016)|(0.0093)|(0.012)|(0.0074)|
|Urbanisation|-0.010|0.080|0.0026|0.056<sup>_∗_</sup>|
||(0.0083)|(0.056)|(0.0053)|(0.033)|
|Trade|0.015<sup>_∗∗∗_</sup>|-0.014<sup>_∗∗_</sup>|0.010<sup>_∗∗∗_</sup>|-0.0075<sup>_∗_</sup>|
||(0.0038)|(0.0068)|(0.0031)|(0.0041)|
|PV debt|-0.0038|-0.0025|0.00025|0.000032|
||(0.0035)|(0.0017)|(0.0022)|(0.00056)|
|Public debt service|0.050|-0.063<sup>_∗∗∗_</sup>|-0.040<sup>_∗∗_</sup>|-0.024<sup>_∗∗_</sup>|
||(0.062)|(0.022)|(0.019)|(0.012)|
|_R_<sup>2</sup>|0.178|0.207|0.294|0.171|
|Hausman||0.000||0.000|
|_β_ ⩽0|0.285|0.475|0.005|0.019|
|_β_ ⩾1|0.000|0.000|0.002|0.000|
|Countries|108|108|108|108|
|Observations|1082|1082|1087|1087|

Note: OLS and fixed effects (FE) results, annual data, 1990-2003. All regressions include time dummies, coefficients not reported. Heteroskedasticity-robust standard errors, clustered by country, in brackets. _∗_ , _∗∗_ , and _∗∗∗_ denote significance at 10, 5 and 1%, respectively. In the case of FE estimation, _R_<sup>2</sup> refers to the within _R_<sup>2</sup> . Hausman shows the p-value of a generalized Hausman test of the null hypothesis that _ηi_ is uncorrelated with the regressors. _β_ ⩽ 0 ( _β_ ⩾ 1) is the p-value for the test of full (no) fungibility for total sectoral aid. Source: Author’s analysis based on data described in the text.

15
Page 74

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59 Employment and Immigration Canada). Given all of this information (which admittedly is quite incomplete), I would think that a lower net immigration assumption of 25,000 might be more appropriate for 1980-1985. Twenty-five thousand is also the migration assumption for the official medium projection for each five-year period from 1985-2010 (Central Bureau of Statistics, 1988).

The World Bank's previous net emigration assumption for 1980-1985 is twice as high as that of the United Nations. Moreover, for 1985-2000, the World Bank previously assumed no net migration while the United Nations assumed emigration of 100,000 in 1985-1990 and 50,000 in 1990-1995. Although the U.S. Bureau of the Census (1983) estimates in 1983 assumed no net international migration for Egypt, I prefer the United Nations estimate for 1985-1990 and continuing net emigration of 80,000 for 1990-1995 and 60,000 for 1995-2000.

# Other Middle Eastern Countries

The previous World Bank assumptions of net international migration for the other countries in the Middle East seem quite reasonable, but I would question whether or not the sex ratio of immigrants and emigrants will continue to remain so highly skewed toward uiales as time progresses. For most five-year periods, the sex ratio is assumed to be about 250 for Kuwait and 300 for Bahrain, Egypt, Libya, Oman, Qatar, Saudi Arabia, the United Arab Emirates, the Yemen Arab Republic, and the People's Democratic Republic of Yemen. While these high sex ratios may prevail at the beginning of the period, it is likely that these international migration flows will become somewhat less dominated by males as time progresses. Therefore, I would suggest a reduction of the sex ratio for Kuwait to 210 in 1990-1995 and 190 in 1995-2000 and substantial reductions in the sex ratios of the other countries mentioned above as well.

# LATIN AMERICA AND THE CARIBBEAN

Illegal immigration tends to dominate in this region, so it is difficult to obtain accurate immigration and emigration data. There are some indications that illegal immigration was increasing in Latin America through the early 1980s (United Nations, 1982), but economic difficulties in some major receiving countries since that time have probably dampened illegal flows in the last few years. Refugees are not a major force in the region, although in general the refugees who are resident in the region have taken up permanent residence in their destination countries (Balan, 1988). The largest numbers of refugees are in Mexico (about 165,000 from El Salvador and Guatemala), Honduras (about 52,000, mostly from Nicaragua and El Salvador), and Costa Rica (about 32,000, mostly from Nicaragua). Overall, the U.S. Committee for Refugees (1988) estimates that there are only 290,000 refugees in Latin America and the Caribbean.