rafmacalaba/datause-encoder-data huggingface dataset:
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ bargaining position in wage determination, so that they get paid more regardless of their productivity. But it could also be the case that union members are more productive, and therefore they are simply getting rewarded according to their productivity and not their union status.

In this section we take advantage of our data to analyze productivity differences across worker categories, and to link these differences to differences in wages. Our methodology draws largely from Hellerstein _et al._ (1999), and Van Biesebroeck (2007), who use employer-employee data to analyze wage and productivity premia respectively in the United States, and in three African countries (Kenya, Tanzania, and Zimbabwe). In this respect, our analysis in closer to Van Biesebroeck’s, with two major differences: first, we use more recent and comprehensive data. Second, we pool the countries’ data together in order to increase our sample size and hence the precision of our estimations. We perform two exercises: one with the pooled data, and another where we split the sample according to the linguistic group (English or French), as we did in previous analyses.

In essence, this exercise consists of estimating a production function at the firm level, but allowing the labor input to vary according to several “quality” characteristics of the worker. In parallel, a wage equation is estimated that includes the same quality characteristics. The coefficients from the production function and wage regressions are then compared in order to determine whether wage premia actually reflect higher productivity for a particular quality attribute.

To illustrate this, consider the following example. Suppose that workers correspond to one of two categories, that we call “0” and “1”, and let the production function take the form ~ where _L_<sup>~</sup> is quality-adjusted total labor, which equals _L_ = _L_ + (φ 1 − 1 ) _L_ 1 ; _L1_ is the total number of employees of category “1”,<sup>φ</sup> 1<sup>is the relative marginal productivity of workers of category “1”, and</sup><sup>_L_</sup> is the total number of employees. That is, workers who belong to category “1” are more productive than category “0” workers by a fraction (φ 1<sup>−</sup> 1 ) .<sup>40</sup> Then we can express (2) as:

> 40 Expressing marginal productivities in this form facilitates the convergence of the estimation algorithm.

21
Table 7: The combined effect of power outages with each level of power constraints on the firm’s revenuebased TFP

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Outages_×_No obstacle|0.016<br>(0.027)||||||
|Outages_×_Minor obstacle||-0.014<br>(0.037)|||||
|Outages_×_Moderate obstacle|||-0.035<br>(0.040)||||
|Outages_×_Major obstacle||||-0.044<br>(0.028)|||
|Outages_×_Severe obstacle|||||-0.079<sup>_∗∗_</sup><br>(0.031)||
|Outages_×_Biggest obstacle||||||-0.173<sup>_∗∗∗_</sup><br>(0.026)|
|Observations|31406|31406|31406|31406|31406|31406|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variable is the logarithm of the firmlevel revenue-based total factor productivity for all regressions (1-6). In each regression, the treatment variable represents a specific degree of power constraints for firms and outages dummy variable. Firm-level controls variables are: Firm size, Firm’s longevity, Own website, Foreign private participation. Country-level controls variables are: GDP growth (annual %), Inflation (annual %), log(Bank concentration in %), log(FD index), log(GDP per capita), log(Net ODA in % of GNI), log(Regulatory Quality). Each estimate includes Year FE, Country FE, Region FE, Income group FE and a Constant. Standard errors in parentheses are clustered at country level.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Table 8: Robustness tests – The effect of power outages and constraints on the firm’s revenue-based TFP by clustering standards errors at industry level

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Power Outages|-0.102<sup>_∗∗∗_</sup><br>(0.018)|||||||
|Power is not an obstacle||0.041<br>(0.025)||||||
|Power as minor obstacle|||0.040<br>(0.029)|||||
|Power as moderate obstacle||||0.004<br>(0.025)||||
|Power as major obstacle|||||-0.032<br>(0.022)|||
|Power as severe obstacle||||||-0.044<sup>_∗_</sup><br>(0.022)||
|Power as biggest obstacle|||||||-0.148<sup>_∗∗∗_</sup><br>(0.042)|
|Observations|31403|31406|31406|31406|31406|31406|30491|

Notes: Table shows the average treatment effects on the treated obtained by weighted least squares regressions (Entropy Balancing). The dependent variable is the logarithm of the firm-level revenue-based total factor productivity for all regressions (1-7). In each regression, the treatment variable represents a specific degree of power constraints for firms. Firm-level controls variables are: Firm size, Firm’s longevity, Own website, Foreign private participation. Country-level controls variables are: GDP growth (annual %), Inflation (annual %), log(Bank concentration in %), log(FD index), log(GDP per capita), log(Net ODA in % of GNI), log(Regulatory Quality). Each estimate includes Year FE, Country FE, Region FE, Income group FE and a Constant. Standard errors in parentheses are clustered at industry level.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 21
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**<mark>Table 3 (Cont'd)</mark>**

|_Variable Name_|_All_|_Male_|_Female_|_Difference_|
|---|---|---|---|---|
|_Hired labor_|||||
|Used hired male labor†|0.28|0.29|0.17|0.11<br>**|
|Hired male labor days|6.25|6.49|3.93|2.56|
|Hired male labor days/HA|25.46|26.25|17.58|8.67|
|Hired male labor days/HA (Conditional on use)|91.87|91.31|100.98|-9.67|
|Used hired female labor†|0.14|0.14|0.14|0.00|
|Hired female labor days|2.90|2.99|2.00|0.99|
|Hired female labor days/HA|13.71|13.43|16.54|-3.11|
|Hired female labor days/HA (Conditional on use)|100.46|98.33|121.81|-23.49|
|Used hired child labor†|0.02|0.02|0.02|0.00|
|Hired child labor days|0.77|0.81|0.44|0.37|
|Hired child labor days/HA|2.39|2.36|2.68|-0.32|
|Hired child labor days/HA (Conditional on use)|109.82|106.52|150.87|-44.36|
|_Number of observations_|2,050|1,855|195||

<mark>Note.-*** indicates significance at 1% level, ** at 5% level, * at 10% level. † indicates dummy variable</mark> Estimates are weighted.

29
Policy Research Working Paper 7152

# **Abstract**

This paper assesses inequality of opportunity in educational achievement using the Human Opportunity Index methodology on data from the Programme for International Student Assessment. The findings suggest that there are large inequalities in learning outcomes as measured by demonstrated proficiency in Programme for International Student Assessment test scores in math, reading, and science. Differences in wealth, parental education, and area of residence explain a bulk of this inequality in most of the countries in the sample. Consistent with what has been documented previously in the literature, the paper also finds a strong and stable correlation between inequality of opportunity and public spending on school education. An exploration of the changes in inequality of opportunity between the 2009 and 2012 rounds of the Programme for International Student Assessment, using parametric and nonparametric techniques, suggests that there has been little progress.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cbalcazarsalazar@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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direction from what one might expect if “young and inexperienced” teachers were drafted into the profession at the time of FPE. While also varied across countries, to the extent that there were statistically significant effects for teacher behavior results, these seem to go in the opposite direction from what would be implied if post-FPE teachers were of worse “quality:” they tend to use “good pedagogical practices” more, and to be absent less often.

In sum, this analysis has revealed a mixed set of findings and suggests that the processes that played out over the FPE period and beyond was clearly complex. An intriguing initial finding of learning deficits associated with having a teacher who was hired post-FPE is not systematically matched by the expected corresponding trends at the teacher level (including teacher test scores, teacher characteristics, and teacher in-class behaviors). It is likely that there are a variety of countervailing forces at play—for example recruiting teachers locally might have led to increased commitment and local accountability, perhaps lower absence, and perhaps greater ability to communicate in the local language. At the same time, these might have been offset by lower mastery of subject knowledge or other countervailing forces. Last, for the results in this analysis, it is not just what occurred at the time of the FPE reform that matters, but also what transpired after that—including potentially differential attrition of teachers hired pre- and post-FPE. At the end of the analysis we are therefore left with something of a puzzle that will take more analysis, and perhaps more or different data, to investigate.

# **References**

- Ajayi, Kehinde F., and Phillip H. Ross. 2020. “The effects of education on financial outcomes: Evidence from Kenya.” _Economic Development and Cultural Change_ 69(1): 253-289.

- Atuhurr <mark>a, Julius F. 2016. “Does community involvement affect teacher effort? Assessing learning impacts of Free Primary Education in Kenya.”</mark> _<mark>International Journal of Educational Development</mark>_ <mark>49: 234-246.</mark>

- Avenstrup, Roger, Xiaoyan Liang, and Søren Nellemann. 2004. _Kenya, Lesotho, Malawi and Uganda: Universal primary education and poverty reduction_ . Africa regional educational publications. Washington, DC: World Bank.

- Behrman, Julia Andrea. 2015. “Does schooling affect women’s desired fertility? Evidence from Malawi, Uganda, and Ethiopia.” _Demography_ 52(3): 787-809.

17
- Ceritoglu, E., H. B. G. Yunculer, H. Torun, and S. Tumen (2017). The impact of Syrian refugees on natives’ labor market outcomes in Turkey: evidence from a quasi-experimental design. _IZA Journal of Labor Policy 6_ (1), 5.

- Chiswick, B. R. (1999). Are immigrants favorably self-selected? _American Economic Review 89_ (2), 181–185.

- Clemens, M. A. and J. Hunt (2017). The labor market effects of refugee waves: Reconciling conflicting results. _NBER Working Paper N. 23433_ .

- Del Carpio, X. V. and M. C. Wagner (2015). The impact of Syrians refugees on the Turkish labor market. _World Bank Policy Research Working Paper_ (7402).

- Depetris-Chauvin, E. and R. J. Santos (2018). Unexpected guests: The impact of internal displacement inflows on rental prices in colombian host cities. _Journal of Development Economics 134_ , 289–309.

- Dustmann, C. and A. Glitz (2015). How do industries and firms respond to changes in local labor supply? _Journal of Labor Economics 33_ (3 Part 1), 711–750.

- El Espectador (2009, April). La situaci ́on de los desplazados. http://www.elespectador.com/articulo137806-situacion-de-los-desplazados.

- El Tiempo (2010, August). Fuerza laboral de desplazados impacta salarios informales. http://www.eltiempo.com/archivo/documento/MAM-4088271.

- El Tiempo (2014, February). Desempleo entre los desplazados es del 35,5%. http://www.eltiempo.com/archivo/documento/CMS-13528618.

- Engel, S. and A. M. Ib ́a ̃nez (2007). Displacement Due to Violence in Colombia: A HouseholdLevel Analysis. _Economic Development and Cultural Change 55_ (2), 335–365.

- Galiani, S. and F. Weinschelbaum (2012). Modeling informality formally: households and firms. _Economic Inquiry 50_ (3), 821–838.

27
## **Figure 8. Skills developed in training (among firms with training programs)**

<!-- Start of picture text --> Job-specific technical skills<br>Managerial and leadership skills<br>Numeracy/math skills<br>other<br>Problem solving/critical thinking skills<br>Foreign languages skills<br>Interpersonal and communication skills<br>0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%<br>Source: Enterprise Survey data, authors<br><!-- End of picture text -->

# _Shaping factors of firm capabilities_

The dynamics on firm capabilities are often shaped by underlying structural factors, especially in differences by industry, location and type of firm. Bloom and Van Reenen (2010) point to the importance of competition in shaping management practices. They hypothesize that in a competitive economy, firms with low management practices would be outcompeted by more productive firms with higher levels of firm capabilities. It is therefore expected that in sectors/locations with higher degrees of competition, higher levels of firm capabilities are found.

For Mozambique, our analysis does not find evidence for this relationship between competition and firm capabilities (see also McKenzie and Woodruff, 2017, for similar finding in developing countries). Table 5 shows regressions of management practices and skills on the HerfindahlHirschman Index (HHI)<sup>11</sup> , a measure of market concentration, calculated at the nationwide industry level, province-industry level and district-industry level. There is little evidence for a negative relationship between market concentration and management practices: most of the coefficients are positive and insignificant. This lack of significant relationship does not necessarily

> 11 The Herfindahl Hirschman Index (HHI) is a commonly used measure of market concentration, among others (Syverson 2019). It is calculated by taking the sum of squared market shares of a firm in a particular market (H 12 22 2) and takes a value between 0 and 1. A value close to zero means low market concentration, while a value close to one means a high degree of market concentration. HH= nn ss + ss + ⋯+ ss

22
to 35% of median wage, among lowest in Latin America, non complaince is less that 5%, and volatitlity in the past decades has been below 5% (IDB 2004).

Djankov et al (2004) compare labor regulation for 85 countries in 1997, by constructing severeal indexes. The employment law index measures effective protection of employed workers (rater than just statutory protection. The Collective relations law index measures the power of labor unions. Both indexes are measured in a scale of 0 to 1 with higher values indicatig more protection and larger worker power. The difference in the index of employment protection between Indonesia and Mexico seems rather small, with only a 0.1 difference (0.59 for Mexico and 0.68 for Indonesia. The difference in collective relations law index is a little higher: In Mexico it takes a value of 0.57 while in Indonesia the value is 0.39.

The above data suggests that at the time of the crises i) Both Indonesia and Mexico faced realtively flexible markets compared to their regional counterpart, and ii) although its difficult to compare the level of flexiblity we can safely conlude that at least, there were no starking differences between the two countries in terms of labor protection or collective agreements iii) If there is any area where the two markets can be considered different, it would be in terms of minimum wage, with indonesia having a higher minimum wage and a higher non‐ compliance rate.

Surprisingly one would expect that the country with the more rigid wage mechanism (Indonesia) would adjust through employment rather than wages. But as discussed above exactly the opposite was true. This brings into question the hypothesis that, compared to Mexico, wages in Indonesia where rigid.

We believe that given the apparently small differences described above regarding the institutional setting of the labor market, the wide discrepancies in the response of the labor market to the economic crises, may mean that the crises were qualitatively different and so had differing effects. In what follows we will explore this hypothesis.
of primary data sources, 2) an innovative estimation procedure used to impute data where they are otherwise missing and 3) a reformulation of existing high-killed migration measures, which in turn form the basis of our global analysis. Broadening our analysis from the more orthodox approach of focusing solely upon OECD destination countries, yields many important insights that have previously been overlooked. Perhaps most pertinently, migration to non-OECD countries accounts for 20% of all high-skilled migration and these movements comprise relatively large numbers of individuals from low income and least-developed nations in many regions of the world. In the wake of the recent global financial crisis and the shifting balance of power in the global economy, no doubt these migratory patterns will become more pronounced in the years to come.

The database in this paper allows us to paint a picture of human capital mobility around the globe and perform other interesting empirical exercises. We hope our analysis and the data we provide will pave the way for further analytical and empirical work. It is important for those wishing to use the data to familiarize themselves with the methodology we have followed (see Section 3), the adjustments that we have subsequently made (see Section 2) and the detailed list of data sources as described in Appendix A1. Among the primary data sources, users should have confidence in those that are not marked with an asterisk in Appendix A1. The bilateral (cell-level) data for these destination countries can be used for empirical analysis with comfort as the adjustments have been minimal. The data for countries marked with an Asterisk in Appendix A1, however, should be treated with more caution, given our preceding discussions. Disaggregated data for this second set 41
# **Figure 3.1. Sovereign risk by country groups, 2000-21 averages**

<!-- Start of picture text --> A. 5-year CDS spreads (log) B. Sovereign ratings C. Sovereign ratings in EMDEs, by<br>income groups<br><!-- End of picture text --> _Sources:_ Bloomberg; Kose et al. (2017).

_Note:_ The figure shows boxplot charts for 2000-21 averages of sovereign risk variables. AE = advanced economies; EMDE = emerging market and developing economies; HIC = high-income EMDEs; LIC = low-income EMDEs; MIC = middle-income EMDEs.

**Figure 3.2. Sovereign ratings by EMDE regions and groups**

<!-- Start of picture text --> A. Sovereign ratings by EMDE groups, simple<br>averages<br>Index<br>2009-2013 2017-2021<br>17<br>14<br>11<br>8<br>Commodity Commodity EMDEs without LICs<br>exporters importers LICs<br>C. Sovereign ratings by EMDE regions, simple<br>averages<br>Index<br>2009-2013 2017-2021<br>17<br>15<br>13<br>11<br>9<br>7<br>5<br>3<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> B. Sovereign ratings by EMDE groups, GDP-<br>weighted averages<br>Index<br>18 2009-2013 2017-2021<br>15<br>12<br>9<br>6<br>Commodity Commodity EMDEs without LICs<br>exporters importers LICs<br>D. Sovereign ratings by EMDE regions, GDP-<br>weighted averages<br>Index 2009-2013 2017-2021<br>17<br>15<br>13<br>11<br>9<br>7<br>5<br>3<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> _Sources:_ Bloomberg; Kose et al. (2017).

_Note:_ EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; LICs = low-income countries; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa. The figure shows combined numeric sovereign ratings ranging from 1 = lowest risk to 21 = highest risk. The sample includes 88 EMDEs (strongly balanced panel), of which 5 are LICs, 49 are commodityexporting EMDEs, and 39 are commodity-importing EMDEs. The sample includes 10 EAP, 19 ECA, 24 LAC, 11 MNA, 4 SAR, and 20 SSA EMDEs. A.C. Simple averages. Orange whiskers show medians and interquartile ranges. B.D. GDP-weighted averages based on real GDP averaged over the respective periods.

27
Table 6: Growth in Central Government Employment

|Country|Years|Growth in<br>Employment<br>(%/Annum)|
|---|---|---|
|Ghana|1975-82|15.0|
|Haiti|1979-82|10.0|
|Costa Rica|1973-79|9.8|
|Nigeria|1977-83|8.6|
|Malawi|1977-83|7.9|
|Ethiopia|1974-81|6.5|
|Thailand|1979-84|6.2|
|Bolivia|1975-85|5.6|
|Senegal|1976-80|5.4|
|Malaysia|1975-85|4.7|
|Peru|1970-78|4.3|
|Sudan|1975-83|2.6|
|SinaP,_e|1975-R4|1.8|
|Sri Lanka|1975-85|1.7|
|Zambia|1975-80|0.7|
|Guyana|1981-85|-1.2|

Source: Various World Bank Reports.

33