rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2 Results**

Tables 2, 3 and 4 summarize our results for computation of six priority indices with equation (13). For the first three indices, we use weighted populations and pesticide volumes and vary the distance risk-decay parameter across -2, -1 and -0.5. For the second set of three indices, we use un-weighted population and pesticide volume and vary the distance risk-decay parameter across -2, -1 and -0.5. Taken together, these variations test the robustness of our methodology by assigning very different values to key model parameters.

As Table 2 shows, our approach is quite robust. For the three weighted indices, large changes in the distance risk-decay parameter have almost no effect on the results. In all cases, the rank correlation coefficients (in bold for Rank 1, 2, 3) are 0.96 or higher. The same is true for the three distance risk-decay parameter values in the case of the unweighted set (in bold for Rank 4, 5, 6). Cross-correlations between sets (Ranks 1, 2, 3 vs. Ranks 4, 5, 6) are also quite high, varying between 0.84 and 0.89.

# [Insert Table 2]

Tables 3 and 4 provide detailed results for all 197 stockpile sites and all six variations in model parameter sets.<sup>11</sup> The first column of Table 3 provides the priority ranking. All index values are normalized to a total value of 1000 for all delegations. This allows for easy interpretation of the relative magnitudes in each index. In Table 3, the next three columns display the highest, average and lowest index values for each site across the six variations, while the next three columns provide the equivalent information in the form of ranks. The following columns present index values for weighted and un-weighted population, weighted and un-weighted pesticide volume, and the proportion of pesticides in seriously-damaged containers or contaminated soil. Table 4 provides complete information on index values for the six model variations.

> 11 As stated earlier, the stockpile database is still considered confidential – thus we have preserved this confidentiality by replacing the specific location names with generic names.

11
==. **1e:.;** 13

-1 =- f

attnbutable to lost employment in the social sector and .012 to increased employment in the private sector. 'Useable observations on workspells ranged from 30,474 in 1987 to 21,198 in 1991. The decline in useable observations is due primarily to the 20 percent decline in social sector employment from 1987 through 1991. A lag in SPIF resolution of irregularities in data provided by firms contributed to a smaller useable sample in 1991. As the measurement errors are apparently random, the 1991 useable sample should still be representative. Details on sample selection and data base constucion are included in an Appendix.

The earnings data include information on earnings, regular and overtime hours, and the starting and ending date of the employment spell within the year. Hourly wage is computed as earnings divided by hours. For workers who switch employers during the year, hourly wage is computed separately for each employment spell.

# 3. EMhPIRICAL FORMULATION AND RESULS OF WAGE EQUATIONS

To summarize how the earnings structure in Slovenia changed during transition, we apply the standard earnings function approach pioneered by Mincer (1974) to the Slovenia data described above. The dependent variable is W,: the natural logarithm of average hourly earnings over a spell. The vector of independent variables, X, includes a set of dummy variables indicating different levels of formal education, years of employment, a dummy variable designating nonSlovene ethnicity, dummny variables indicating if the individual is in a temporary or internship position, a set of dammy variables for industry of employment, and a set of monthly dummy variables indicating the months in which the individual worked.

Given that wages are computed over the employment spell, monthly dummy variables are used to control for changes in consumer prices over the spell. Because of inflation, nominal wages and prices in the first half of the year differed significantly from nominal wages and prices in the last
# **Executive Summary**

This paper provides supporting documentation for the World Bank publication, Averting the Old Crisis: Policies to Protect the Old and Promote Growth, published in October, 1994. The purpose of this Annex is to clarify data definitions and sources and to correct, update and supplement the information presented in the book. In addition, this Annex accompanies the STARS data diskette for Averting the M Age Criisis available from the World Bank bookstore.

lv
green dots for males, in contrast to community norms indicated by red and yellow dots. A prominent finding is the global tendency of personal beliefs to be more progressive and counter-stereotypical than what individuals perceive as prevailing community norms, indicating a disconnect between personal beliefs and societal perceptions.

The measures for norms (red and yellow) differ from those for attitudes (purple and green). Notably, female personal beliefs (purple dots) are consistently more progressive than male beliefs (green dots) across all regions. This pattern holds true across East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), the Middle East and North Africa (MENA), North America, South Asia, and Sub-Saharan Africa (SSA). Despite this, the perceived community norms for men and women within these regions do not differ greatly. For instance, in Sub-Saharan Africa, 87% of women and 78% of men support gender equality in opportunities personally, but they perceive only 55% of their community does. With regard to the belief that expenses are a man’s responsibility, 39% of women and 52% of men agree, while 60% of the community is perceived to agree. Personal beliefs and perceived community norms about the female caregiver role are closely matched at 69%.

Figures 2 to 4 present an analysis of gender attitudes and norms across countries.<sup>19</sup> These figures compare aggregate personal beliefs, represented by green dots, with the average perceived community norms, shown by purple dots, at the country level. The horizontal line within these figures quantifies the gap between personal beliefs and average perceived community norms, thereby illustrating the extent of norm misperception in each country. Countries in the figures are arranged based on the magnitude of this gap.

Figure 2 demonstrates the variance in the agreement with the norm that “men and women should have equal opportunities”, which spans from 80.84% in Algeria to 97.69% in Portugal for personal beliefs. The perceived community norms for this statement range from a low of 41.25% in Iraq to a high of 81.44% in Denmark. Meanwhile, Figure 3 depicts the belief that “household expenses should be the man’s responsibility, even if his wife can help him,” ranging from 10.99% in Denmark to 78.06% in Egypt, suggesting a wide spectrum of beliefs about the male breadwinner norm. The perceived community norm ranges from 29% in Denmark to 74.97% in Mali. Notably, Chile shows the largest misperception, with 20% of respondents personally subscribing to the male breadwinner norm, while 57% are perceived to do so by the community. In contrast, Nigeria displays close alignment between personal beliefs and perceived community norms at around 60%. Finally, in terms of the female caregiver norm, as shown in Figure 4, agreement levels vary significantly, from 13.63% in Denmark, indicating more progressive views, to 87.25% in Pakistan. Perceived community norms on this issue also

> 19Data by region and country is also provided in Tables A7 to A9.

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Table 3: Average school characteristics in 2007 (before the matching) by treatment status Panel A: Fifth-grade schools Panel B: Ninth-grade schools

|Variable|Schools in<br>_Mais_<br>_Educação_<br>(2008–2011)|Nonparticipating<br>Schools|Difference<br>(1)−(2)|Schools in<br>_Mais_<br>_Educação_<br>(2008–2011)|Nonparticipating<br>Schools|Difference<br>(4)−(5)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|∆ Portuguese grades|1.0|1.0|0.0|5.0|3.4|1.6***|
|∆ Mathematics grades|10.5|8.6|1.9***|1.6|1.0|0.6|
|IDEB Index|3.5|4.3|-0.8**|2.9|3.8|-0.9***|
|∆ IDEB|0.4|0.3|0.1**|0.3|0.2|0.2***|
|Municipal population|1,180,426|788,482|391,943***|1,364,439|777,606|586,833***|
|Students' enrollments|924|593|331***|1159|849|310***|
|IDHM|0.8|0.7|0.0**|0.8|0.7|0.0***|
|Urban|1.0|1.0|0.0|1.0|1.0|0.0|
|Basic sanitation|1.0|1.0|0.0***|1.0|1.0|0.0***|
|Library|0.8|0.6|0.1***|0.9|0.8|0.1***|
|Sports court|0.5|0.5|0.0|0.7|0.7|-0.1***|
|Number of classrooms|13.0|10.8|2.2***|14.3|12.7|1.6***|
|Number of computers|0.9|0.9|0.1***|1.0|0.9|0.0**|
|Science lab|0.1|0.1|0.0***|0.3|0.3|0.0|
|Computer lab|0.5|0.4|0.1***|0.6|0.7|-0.1***|
|Teachers room|0.9|0.8|0.1***|1.0|0.9|0.0***|
|DVD|0.9|0.9|0.0*|0.9|0.9|0.0|
|Overhead projector|0.7|0.6|0.1***|0.9|0.9|0.0**|
|Number of employees|65.3|44.8|20.5***|76.2|60.1|16.0***|
|School meals|0.9|1.0|0.0***|0.9|1.0|0.0***|
|Number of classrooms used|13.6|10.7|2.9***|14.4|12.5|1.9***|
|Morning lessons|0.5|0.6|-0.1***|0.5|0.6|-0.1***|
|Child care|0.0|0.0|0.0*|0.0|0.0|0.0***|
|Kindergarten|0.4|0.4|0.0|0.2|0.2|-0.1***|
|State schools|0.3|0.3|0.0|0.6|0.7|-0.1***|
|High school|0.1|0.1|0.0*|0.4|0.5|-0.1***|
|Share classes in school with full-time<br>day (7 or more hours)|0.0|0.0|0.0|0.0|0.0|0.0|
|Total duration of school day (hours)|4.3|4.4|-0.1***|4.3|4.5|-0.2***|
|N|611|17,263||555|13,404||

_Sources: Prova Brasil,_ School Census, and INEP. _Note:_ Table reports the average characteristics in 2007 for the schools that joined _Mais Educação_ in 2008 and remained until 2011 (columns (1) and (4)) and for nonparticipating schools (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
their overall performance, linkages with the local economy, and supplier assistance which all influence the firms’ potential to generate productivity spillovers. Second, we also study the relationship between foreign investor characteristics and linkages with the local economy as well as assistance extended to local suppliers. In the second part of the paper, we shift the focus to domestic suppliers and examine the role of supplier firm characteristics – the so-called absorptive capacities – for their linkages with multinationals. Fourth, focusing on assistance and demand effects, we assess how factors within the transmission channels between multinationals and local suppliers affect FDI spillovers.

Studies on FDI spillovers that focus on direct supplier-multinational linkages based on foreign investor or supplier survey data are rare. Focusing on foreign affiliates in five transition economies, Giroud, Jindra and Marek (2012) find that foreign firm characteristics have a positive impact on backward FDI linkages and spillovers. Javorcik and Spatareanu (2009) find evidence of “learningby-supplying” for a sample of Czech manufacturing firms, although there is also evidence of selfselection into supplying due to a higher productivity ex ante. Jordaan (2011) also confirms the existence of positive backward spillovers on manufacturing suppliers in Mexico. Specifically, positive spillovers are facilitated through supplier firms’ absorptive capacities and the level of support from the multinational. Studying the Polish automotive sector, Gentile-Lüdecke and Giroud (2012) examine the mechanisms behind knowledge spillovers of suppliers. While the authors don’t find evidence of a supporting role of suppliers’ absorptive capacities on knowledge acquisition, they find evidence of a supportive role on performance improvement and new knowledge creation.

This study is structured as follows. The next section provides a literature review that identifies major foreign investor characteristics and suppliers’ absorptive capacities which have shown to influence FDI spillovers. It also discusses the main transmission channels through which FDI spillovers can be generated. Section 3 compares foreign investors and domestic producers in terms of their potential to generate productivity spillovers and also studies the role of foreign investor characteristics for their FDI spillover potential. Section 4 then evaluates the role of suppliers’ absorptive capacities for FDI linkages, while section 5 analyzes various factors within the transmission channels between suppliers and multinationals that increase FDI spillovers. Section 6 concludes.

3
that the model estimates in specifications where only the tail hypotheses parameter is included and GDP per capita is not controlled for tend to be larger than the estimates of Machin and Pekkarinen (2008). This suggests that not accounting for the role of the mean hypothesis in the model may lead to overestimating the tail hypothesis parameter value.

# **6.5 Interpretation and discussion**

In the full sample of both high-income and developing economies, our results highlight that the combined forces of the tail and mean hypotheses have been jointly contributing tertiary enrollment dynamics. For secondary completion, however, the mean hypothesis appears to play a weaker role. The joint influence of the mean and tail hypotheses for tertiary enrollment dynamics could help explain why the extent of the gender gap reversal in education has been stronger for tertiary enrollment than for secondary school completion. As evidenced in Section 2, the female-to-male ratio in tertiary enrollment increased faster over time and reached higher levels than the gender ratio among secondary school completers.

Overall, the significance of the tail hypothesis parameter estimate _σ_ is quite robust across estimation samples. Its value estimated using our model is close to prior estimates of male-to-female test score variance ratios from international student assessments, and is fairly stable across specifications. This provides comfort on its validity and suggests that it has been an important determinant of the gender gap reversal in educational attainment worldwide. Accounting for the tail hypothesis parameter in the model strongly increases predictive power for the gender gap in tertiary enrollment, compared to a specification with the mean hypothesis only. Its explanatory power is stronger than that of the better known mean hypothesis in the full sample, and is especially strong in developing economies. We therefore find strong evidence that the tail hypothesis has played a role in the dynamics of the gender gap in educational attainment, which is a key contribution of the paper.

Our model estimates also evidence that the mean hypothesis, put forward by previous literature, has strong explanatory power for the gender gap reversal in educational attainment. The mean 28
# **Table 2. Regression Results**

(Dependent variable: InTFP)

|Variable|(i)|(ii)|(iii)|(iv)|
|---|---|---|---|---|
|_In(NRDN)_|0.361|0.37||0.403|
||_(3.01)***_|_(3.27)*_||_(2.52)**_|
|**_lnNVRDOT)_**|0.041||0.233|0.062|
||_(0.21)_||_(1.26)_|_(0.27)_|
|**_In_**_(NRDN)*DR_||||-0.070|
|||||(-0.37)|
|**_In(NRDOT)_**_*DR_||||**0.058**|
|||||(0.27)|
|**AdjustR**<sup>**2**</sup>|_0.80_|_0.80_|**_0.79_**|_0.80_|
|**No.ofObservations**|_282_|_282_|_282_|_282_|

Note: Figures in parenthesis are t-statistics. The *** (**) (*) means that the coefficient is significant at the 1% (5%) (10%) significance level. NRD N is the traderelated R&D from NAFTA countries (USA and Canada), and NRDOT is the traderelated R&D from other OECD countries. DR = 1 for high R&D-intensity industries, and DR = 0 for low R&D- intensity industries.

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schooling _r_ is equal to 7.8%, following the meta-analysis for Central and Eastern European countries (Wincenciak et al., 2022). Using this value and accounting for the 26 weeks of school closures, the value of the adjustment factor to account for the period in which schools were closed _α_ is equal to 0.52. According to the CSO data, the mean annual earnings Y in 2021 are PLN 72,000. Following Psacharopoulos et al. (2021), the present value is calculated over a student's working life period of 45 years, assuming a 3% discount rate. The number of students _S_ equals 6 million (UNESCO, 2022). Finally, the efficiency of distance learning _β_ was set at 0.6 following the Polish school closures duration (UNESCO, 2022) and arguments of Kuhfeld et al. (2020) and Psacharopoulos et al. (2021). The yearly economic loss at the individual level equals PLN 2,927. The present value for the 45 years of working life amounts to a loss of PLN 74,693 for each student. It sums up to PLN 179 billion for the affected population, which equals 7.2% of the 2021 GDP.

# **Conclusions**

The Polish success story of rapid social and economic progress relied strongly on human capital improvement. Unfortunately, this factor is now under significant distress. Significant learning losses have been experienced by Polish students due to the COVID-19-induced school closures. In mathematics and science, the learning losses are equal to more than a year's worth of schooling, even though schools were closed for only part of an academic year. In addition, we show that the 2016 reforms also had a negative impact on student learning. These skills losses are likely to affect the future economic success of the students as well as the country as a whole. Future earnings are projected to decline by PLN 74,693 (more than US$15,000) per year for the affected students. The country would then lose the equivalent of 7.2% of GDP over time.

6
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finance from the introduction of credit bureaus affects job growth, then we should expect to see larger effects for firms in industries that are more dependent on external finance.

We use two complementary sources of firm level data: cross-country World Bank Enterprise Surveys that include information on 52,231 firms operating in 70 developing countries; and Bureau Van Dijk’s Orbis database on over 1 million firms operating across 29 developing countries. Both databases include large and small, and listed and unlisted firms. While the Enterprise Surveys provide data across a larger number of countries, the panel dimension in the Orbis database allows us to control for unobserved heterogeneity at the firm level, using firm fixed effects.

We find a strong positive relationship between access to finance and job growth. Overall, firms with access to a loan exhibit employment growth between 1 and 3 percentage points larger than firms with no access to finance across the two databases. These results are robust to controlling for firm fixed effects. When identifying the relationship using the introduction of credit bureaus (CB) as an exogenous shock to the supply of credit, we find that the introduction of CBs increases employment growth by over 5 percentage points compared to countries where CBs do not exist, a result that holds when estimated with the matched sample. This effect is particularly large for firms in industries that are more dependent on external finance.

We also find the association between finance and job growth to be stronger among MSMEs than among large firms. MSME firms with access to a loan have between a 1 and 4 percentage point larger employment growth than MSMEs without a loan across the two databases. This difference in job growth among MSMEs with and without access to finance is at least three times larger than the differential among large firms with and without a loan.<sup>7</sup> In our

> 7 We find similar results in robustness tests using alternative measures of access to finance such as growth of financing and debt ratio. A one standard deviation increase in the growth of financing among MSME’s is associated

4