rafmacalaba/datause-encoder-data huggingface dataset:
signals involving fairness and equity call for looking at family income and demographic characteristics of those participating in schooling and training, and of course, the labor market outcomes related to participation.

The absence of adequate private training capacity can be determined by surveying public and private training institutions, their curricula, and training capacity. Manpower planners can develop this information as part of their effort to produce labor market signals for guiding training decisions. This information can be gathered while conducting cost studies of schools and training institutions or while conducting tracer studies. The more difficult task is that of addressing the potential problem of labor markets anticipating structural changes in the economy and attendant effects on labor market signals and skills development. The ability of labor markets to look ahead in time and anticipate through relative wage movements the emerging skills requirements of new industries and production technologies is critical for skills that require long lead times to acquire and which are of strategic importance to economic development. These are important areas of potential market imperfections and failures justifying public interventions in schooling and training.

The monitoring of sectoral employment shifts, production technologies and innovations within sectors, new investor activity, and trade and investment policies are a means for manpower planners to anticipate changes with skills demand and supply implications. Armed with this information, manpower planners can monitor labor market signals for these skills to determine if markets are properly anticipating future skill needs and putting the appropriate economic incentives for skills development in place. If not, then planners can adjust these signals and advise policy makers on appropriate public investments in schooling and training. The scope of this task will be narrower than that undertaken by central planners using manpower requirements forecasting. The focus will be on skills that require long lead times to acquire and skills that are considered strategic to economic development. Other skills, particularly those requiring shorter periods of training, perhaps two years or less, can rely on labor market signals and the planning of managers of schools and training institutions.

20
development planning and macro topics than is the case in the top-20 U.S. programs. We control for government expenditure as a percentage of GDP to allow for this possibility.

A third potential explanation is that the difference reflects differences in the educational level of the students. We have seen that there are few differences between masters level and undergraduate level classes in terms of topics in developing countries, suggesting this might not be so important. Nevertheless, we control for the average years of tertiary schooling for individuals aged 15 and above as a proxy for schooling achievement in the country.<sup>7</sup> A final difference lies in differences in the backgrounds of the instructors, and how close they are to the research frontier.<sup>8</sup> As a proxy for whether the course instructor is an active researcher, we consider whether they have a Google Scholar profile. The instructor is not listed for a number of classes, which can often reflect that it is taught by non-regular faculty who are unlikely to be researchers. We therefore also create a variable for no instructor listed.

We then examine the correlates of being more similar in topic coverage to the top-20 U.S. schools by means of the following regression for undergraduate course _i_ in developing country _j_ : �����������,� ����∗������� ��∗�������� ��∗����� ��∗��������������,� �� Where the standard errors are clustered at the country level.

The first column of Table 8 reports the results. We see that courses in countries with higher levels of per-capita income are more similar to those offered in top-20 U.S. departments, while courses in countries with more involvement of the state in the economy are less similar. Instructors who are more research active also are more likely to cover topics closer to those in top-20 departments, while courses without an instructor listed (and for which the instructor is less likely to be a researcher) are further from the frontier. The general tertiary attainment level in the country is positively correlated with similarity to the top programs, but this is not statistically significant. The R<sup>2</sup> of 0.33 shows that just a few correlates account for a sizeable share of the variation in topic choice across classes.

> 7 This data is taken from the World Bank education statistics, and is the Barro-Lee measure. We also attempted to use PISA test scores but they were not available for most of the developing countries in our sample.

> 8 Ideally it would be useful to correlate the age and place of PhD of these instructors with their course content, but we were unable to collect this information for the majority of our sample.

19
# **Figures and tables**

Figure 1: Trends in monetary and multidimensional poverty measures <!-- Start of picture text --> Panel A Panel B<br>Poverty Indicators Decomposition of Change in M0<br>Adjusted Headcount Ratio<br>1.5<br>.448<br>.42 .431 .425 10.8<br>21.4<br>.372<br>.345<br>.327<br>.302<br>.27<br>.154<br>.13<br>.115<br>77.1 90.9<br>2008 2010 2012 -1.7<br>Year 2008-2010 2010-2012<br>Years<br>H - Multidimensional Pov. Headcount<br>A - Average Deprivation Share (poor) Contribution of H<br>M0 - Adjusted Headcount Ratio Contribution of A<br>Monetary Pov. headcount Interaction<br>.5<br>100<br>.4 80<br>60<br>.3<br>Percentage 40<br>.2<br>20<br>.1 0<br><!-- End of picture text --> Source: DANE. Author’s calculations 27
Figure 6: Incomes convergence - education versus no education wave <!-- Start of picture text --> 48.1 49.3<br>50.0<br>40.0 36.2<br>35.1<br>33.0 33.7<br>30.0<br>20.0<br>13.6 13.9<br>11.5 11.7<br>8.1 8.3<br>10.0<br>0.0<br>East Asia & Europe & Central Latin America & Middle East & South Asia Sub-Saharan<br>Pacific Asia Caribbean North Africa Africa<br>no-wave wave<br>income countries (2030)<br>Incomes per capita as % share of high<br><!-- End of picture text --> Source: CGE and GIDD simulations results.

One final point on the regional aggregate growth rates. The education wave, as discussed above, is a scenario where the growth of the global pool of skilled workers is almost entirely due to the expansion of education in developing countries. Indeed, by 2030 for all developing countries incomes per capita are higher in the wave versus than in the no-wave scenario, while for the rich countries incomes are unchanged. Further to the rising inequality in high-income countries observed by several authors (see footnote 4), the micro results below suggest that increasing pressure on the middle-income classes of high-income countries will continue.

Changes in the skill and sector premium of wages are the other crucial macroeconomic results of the simulations that affect global inequality through its effects on incomes dispersion within each country. Wages will follow the interaction of the demand and supply of labor by skill and sector as well as the economy-wide repercussions of other factors in general equilibrium.

For each country, a higher supply of skilled workers in 2030 relative to 2012 will lead to a larger reduction in the skill premium (Figure 7). The regression that relates the relative size of the skill premium against the relative supply of skilled labor has a well-defined negative slope. The causality in this framework runs from the changes in the quantities of workers to the changes in the wages because all labor supplies are exogenous. Turkey, Brazil, and South Africa are amongst the countries with the largest drops in the skill premium. In Turkey, the skill premium by 2030 is reduced by 20 percent of what it was in 2012. At the opposite end, the premium increases by about 5 percent in the Russian Federation. In general, the skill premium will tend to decrease across countries.

20
# **References**

- **Aguilar, Arturo Alberto, Emilio Gutierrez, and Paula Soto Villagran** , “Benefits and Unintended Consequences of Gender Segregation in Public Transportation: Evidence from Mexico City’s Subway System,” _Economic Development and Cultural Change_ , 2021.

- **Akerlof, George A. and Rachel E. Kranton** , “Economics and Identity,” _The Quarterly Journal of Economics_ , 08 2000, _115_ (3), 715–753.

- **Ashraf, Nava, James Berry, and Jesse M. Shapiro** , “Can Higher Prices Stimulate Product Use? Evidence from a Field Experiment in Zambia,” _American Economic Review_ , December 2010, _100_ (5), 2383–2413.

- **Banerjee, Abhijit, Raghabendra Chattopadhyay, Esther Duflo, Daniel Keniston, and Nina Singh** , “Improving Police Performance in Rajasthan, India: Experimental Evidence on Incentives, Managerial Autonomy and Training,” NBER Working Papers 17912, National Bureau of Economic Research, Inc March 2012.

- **Beaman, Lori, Raghabendra Chattopadhyay, Esther Duflo, Rohini Pande, and Petia Topalova** , “Powerful Women: Does Exposure Reduce Bias?,” _The Quarterly Journal of Economics_ , 11 2009, _124_ (4), 1497–1540.

- **Berry, James, Greg Fischer, and Raymond P Guiteras** , “Eliciting and Utilizing Willingness-to-Pay: Evidence from Field Trials in Northern Ghana,” _Journal of Political Economy_ , 2019.

- **Besley, Timothy and Hannes Mueller** , “Estimating the Peace Dividend: The Impact of Violence on House Prices in Northern Ireland,” _American Economic Review_ , April 2012, _102_ (2), 810–33.
# **<u>STANDARD & POOR’S</u>**

|**1.**|**Economy**|
|---|---|
||Demographics/Population<br>|
||(total and as a % of national population; growth rates; % of young and<br>|
||elderly population)|
||Economic Structure<br>|
||GDP (nominal and output)|
||Employment (rate; distributionby sector; bylargest employers)<br>|
||Unemploymentrates|
||Wealth|
||Percapita GDPand as % of nationalor regionalaverage|
||Growthprospects|
||RealGDPgrowth|
||Exports as a % of GDP, and export growth|
||Investment growth(yearonyear) %|
|**2**|**S S d Pdibili**|
|**.**|**ystem upport an rectaty**|
||Predictability of political and administrative system, intergovernmental relations, public sector<br>reforms|
||Revenue/expenditure match (tax, equalization, central government financial assistance,<br>expendituremandates|
||Legal frameworkandformal/informal rulesregarding transparency and accountability|
||Fiscalpolicyframework includinglegal restrictions ondebt andfinancialpolicy|
||Extraordinary supportfromother levels ofgovernment|
|**3.**|**Management Sophistication and Institutional Legitimacy**|
||Transparency and disclosure|
||Budgeting|
||Long termcapitalandfinancialplanning|
||Revenue and expendituremanagement|
||Debtmanagement|
||Reserve andliquiditymanagement|
||Management ofgovernmentrelated entities|
|**4.**|**Financial Flexibility**|
||Legal limits on fiscal flexibility|
||Politicaland competitivelimits on flexibility|
||Limits on flexibility due tofuture spendingrequirements|
||Otherpotentialsources ofpressure and one-off revenues|
||Modifiablerevenues as a % ofoperatingrevenues|
||Capital expenditure as a % of total expenditure|
||Personnel as a % of operating expenditures|
||<br>Operating expenditure growth(%)|
||Operatingrevenuegrowth(%)|
|**5.**|<br>**Budgetary Performance**|
||BudgetaryPerformance|
||OperatingBalance/OperatingRevenues(%)|
||<br>Balance after capital expenditures/ Total Revenues (%)|
||Balance afterborrowing/Total Revenues (%)|
|**6.**|**Liquidity and Debt Management**|
||Liquidity analysis|
||Free cashandliquid assets as % ofdebt service|

43
maximum is lower than this (see Table 9). In a few cases (3% of the total) the maximum is greater than 50,000 baht, which is in excess of the upper limit established by the guidelines produced by the VF headquarters.

A modest amount of interest-rate flexibility may sometimes be possible. If every household in a village could borrow at some market interest rate i0, and the VF lends at iV0<i0, then the benefit accruing to a borrower is (i0-iV0)L, where L is the value of the loan. Now suppose that from time to time households are hit by (unpredictable) shocks – a death, illness, crop failure – and need cash at these moments. If the cost of borrowing at such moments is i1>>i0, then an altruistic lender may want to keep some of its funds available to lend in such times of difficulty. Given that such loans have a shorter duration than standard loans, we might expect the VF to extend emergency loans at an interest rate iV1>iV0.

**<mark>Table 8.  Breakdown of VF loans by size</mark>**

|Loan amount(baht)in 2009|% breakdown|
|---|---|
|< 10,000|12.8|
|10,000|24.3|
|> 10,000 but < 15,000|8.5|
|15,000|12.1|
|> 15,000 but < 20,000|5.2|
|20,000|27.8|
|> 20,000 but < 30,000|2.6|
|30,000|3.2|
|> 30,000|3.6|
|Memo: Mean loan size (baht)|15,790|
|Memo: Standard deviation of loan size(baht)|8,335|
|Source: Thailand Socio-Economic Survey 2009||

**<mark>Table 9.  Maximum loan sizes for regular and emergency loans</mark>**

|Maximum size for regular|% breakdown|Maximum size for emergency|% breakdown|
|---|---|---|---|
|loan(‘000 baht)||loans(‘000 baht)||
|< 20|9.9|<3|7.7|
|20|73.2|3|25.3|
|-30|8.8|-5|34.4|
|-40|2.4|-10|16.3|
|-50|5.2|-20|11.2|
|>50|0.5|>20|5.1|
|Sample size|2,811|Sample size|1,501|
|Mean interest rate|6.1|Mean interest rate|7.8|

Note: * This includes non-rounded amounts (such as 20,500, etc.). Source: Village Fund Survey, 2010 This is indeed what we observe. Many village funds distinguish between their ordinary loans, and emergency loans; the latter tend to be smaller, and represent a modest part of the portfolio, with relatively low minimum amounts, as Table 9 shows. The median number of emergency loans approved was 14 per village fund per 11
**Table 1. Social Progress in Selected Developing Countries**

||**Economic ind**|**icators**|**iEducat**|**ion**||||
|---|---|---|---|---|---|---|---|
||Average<br>annual<br>growth of<br>GNP per<br>capita|Average<br>annual<br>growth of<br>exports|Adult i<br>(<br>1|lliteracy<br>%)<br>995|Primary<br>gross<br>enrollment'|Secondary<br>gross<br>enrollmen|<br>Average<br>years of<br>t schooling for<br>labor force|
|**Country**|1970-95|1980-95|Male|Female|<br>1993|1993|1992|
|**East Asia**||||||||
|China|6.9|12.4|10|27|109|52|5.86|
|Korea, Rep. of|10|11.6|1|3|101|93|8.70|
|Malaysia|4|13.6|11|22|93|59|6.92|
|Philippines|0.6|_5.3_|5|6|III|79|8.34|
|Thailand|5.2|16.7|4|8|98|37|6.27|
|**South Asia**||||||||
|India|2.4|_6.5_|_35_|62|105|-|4.01|
|**Latin America**||||||||
|Brazil|..|6.3|17|17|Ill|43|4.95|
|Chile|1.8|7.3|_5_|_5_|98|70|7.69|
|Colombia|1.9|8.1|9|9|119|62|5.69|
|Mexico|0.9|13.0|8|13|112|58|7.12|
|Peru|-1.1|2.4|6|17|119|65|7.48|
|Venezuela|-1.1|1.0|8|10|96|35|8.13|

a. Gross enrollment rates are used because net enrollment rates are not available for all countries. Source: World Bank 1997b. Nehru, Swanson, and Dubey (1995) for the last column.

Education alone is not a guarantee for sustainable economic growth. Sri Lanka had higher per capita income than the republic of Korea in the 1960s, and its social indicators outstripped those in many low-income countries. But its income growth stagnated in the 1970s and 1980s. For some time East Asia has scored high both in the importance people attach to basic education (the demand side) and its broad availability (the supply side). But the recent financial crises have exposed the need for East Asian countries to restructure their economies and upgrade their supply of high-skilled labor and their regulatory capability.

# _Evidience_ **_on_** _Access to_ **_Education: The_** _Case of Korea_

Through a massive national literacy campaign, the Republic of Korea increased its literacy rate from 55 percent in 1945 to 68 percent in 1960 and 88 percent in 1970. Korea achieved universal enrollment in primary schools by 1965, in middle school by the mid-1980s, and in high school by the late 1980s. Enrollment in higher education has also expanded rapidly, reaching 40 percent in 1990.

Korea's experience is marked by a rapid expansion of primary education and equal access to education. Korea closed the gap between genders at the primary school level in the mid-1960s and at the secondary school level in the late 1980s. Korea vastly reduced education inequality between 1970 and 1995. Its Gini coefficient of education started at a higher level than that of Brazil in 1970, 0.439, but declined dramatically to 0.189 in 1995, the lowest among the 12 countries in this study (table 2. For methodology see appendix 1). Korea's economy also grew rapidly during this time, soaring ahead at double-digit rates between 1965 and 1979. As government revenue and household income grew, more public and private funding was devoted 6
25 indicated before we use tq = 0.25 as estimated in Lopez and Riveros (1990).2 The average elasticity 8ln I/aln we fbund by Lopez and Riveros for Chile was -0.87 indicating that any increase in the market clearing wage rate causes a smaller increase in the a!,tual wage rate. This, in turn, implies that capital accumulation that increases w (i.e., that increases the marginal social product of labor) will ceteris paribus necessarily decrease unemployment.

Row 1 in Table _5_ shows the private returns of capital (Sk) that would prevail if there were no labor market distortions at each tariff level. As the tariff rate decreases from 50% to 0%, the private returns of capital also decline (from 0.53 to 0.50) due to the fact that the tariff protect domestic production of intermediate goods, a highly capital intensive s"-tor. The empirical estimates suggest that the intermediate goods effects dominate the negative effect on the private return of capital of protecting the final import substitutes which are not capital intensive activities.

The private returns of capital need to be corrected to consider the effects of tariffs. This is shown in row 2 of Table 5.1Y This correction decreases as the tariff rate is reduced. Lowering the tariff from 50% to 0% with no wage distortions increases the social return of capital about 20% from 0.42 to 0.50 (row 3). There is an 11 percentage point gain due to the capital allocational effect of the tariff but since the private return of capital declines 3 percentage points, there is a net gain of 8 percentage points as shown in row 3.

The social return of capital shows a net increase because the term R33k = z is positive and highly significant dominating the negative value of R2., = #mm (see equation 10), while the effect of the ether third order terms is negligible. What happens is that in Chile capital accumulation causes a very dramatic increase in domestic production of imported intermediates and a large reduction of its imports.

- _15/_ A 25% wage distortion would imply according to our estimated labor demand elasticity of -0.14 that such a distortion would explain about 3.5 percentage points of unemployment beyond its natural rate.

- 16/ Corrections in row 2 corresponds to the sum of th 2nd and 3rd right-hand terms in equation (8).
**Figure A.1. Predicted Poverty Rates Based on Imputation, from NPS 2019/20 and NPS 2020/21 to TA, Empirical Distribution of the Error, Tanzania**

<!-- Start of picture text --> TA 1 TA 2<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m8 m9<br>Imputation model Imputation model<br>TA 3<br>NPS 2019/20<br>NPS 2020/21<br>m1 m2 m3 m4 m8 m9<br>Imputation model<br>30 30<br>25 25<br>20 20<br>15 15<br>Estimated poverty rate (%) 10 10 Estimated poverty rate (%)<br>30<br>25<br>20<br>15<br>Estimated poverty rate (%)<br>10<br><!-- End of picture text -->

**Note:** 1000 simulations are implemented. Model 3 in TA 2 and Models 3 and 4 in TA 3 use standardized variables. The dashed lines represent the true poverty rates for TA 1 using the 2019/20 and 2020/21 poverty lines. The dotted lines represent confidence intervals of the true poverty rates.

68
|**Table 18 Comparis**|**on of repo**<br>**Unconditi**|**rted constraints**<br>**onal means**|**by formal,**|**small forma**|**l and informal**<br>**Survey fixed e**|**sector firms**<br>**ffects and l**|**(manufactur**<br>**og(L) (center**|**ing & India**<br>**ed on L=5)**|**only)**|
|---|---|---|---|---|---|---|---|---|---|
||All formal|Small formal|Informal|Diff?|Small formal|Informal|Diff?|Obs (SF)|Obs (I)|
|Electricity|1.790|1.818|1.572|Smaller|1.817|1.574|Smaller|2,247|1,951|
|Telecoms|0.515|0.497|0.920|Greater|0.522|0.998|Greater|2,144|1,951|
|Transport|0.905|0.826|1.836|Greater|0.838|1.840|Greater|2,244|1,951|
|AccessLand|0.713|0.752|1.672|Greater|0.901|1.686|Greater|2,149|1,951|
|InadEducLabor|1.013|0.974|1.714|Greater|0.860|1.720|Greater|2,151|1,951|
|MacroInstability|0.913|0.832|1.149|Greater|0.677|1.160|Greater|2,104|402|
|GovPolicyUnc|1.033|0.954|1.192|Greater|0.809|1.184|Greater|2,114|402|
|AccessFinancing|1.203|1.168|1.533|Greater|1.113|1.567|Greater|2,238|1,951|
|Competit|0.863|0.824|1.376|Greater|0.767|1.382|Greater|2,127|1,951|
|TaxAdministration|1.551|1.486|1.455||1.432|1.414||2,149|1,951|
|TaxRates|1.682|1.639|1.531|Smaller|1.586|1.492||2,152|1,951|
|LaborReg|1.169|1.061|1.576|Greater|0.879|1.588|Greater|2,234|1,951|
|Customs|1.011|0.878|0.590|Smaller|0.783|0.595|Smaller|2,021|402|
|BusLicensing|0.891|0.802|1.826|Greater|0.710|1.818|Greater|2,032|1,549|
|Courts|0.441|0.336|1.591|Greater|0.272|1.585|Greater|1,129|1,951|
|Corruption|1.681|1.637|1.451|Smaller|1.507|1.431||2,241|1,951|
|CrimeTheftDisorder|0.960|0.993|1.582|Greater|0.994|1.561|Greater|2,148|1,951|
|bribe|0.444|0.438|0.237|Smaller|0.312|0.266|Smaller|2,230|1,936|
|bribe1|0.175|0.173|0.084|Smaller|0.158|0.096|Smaller|1,966|1,623|
|mng_time|6.624|6.436|1.624|Smaller|5.685|1.704|Smaller|2,538|1,942|
|generator|0.588|0.417|0.197|Smaller|0.246|0.226||2,410|1,951|
|insufficient_water|0.213|0.207|1.000|n.a.|0.205|1.000|Greater|-999|5|
|num_insp|8.743|5.832|5.063||4.140|5.125||2,309|128|
|days_customs|8.470|7.136|7.136|n.a.|5.928|5.928|n.a.|295|0|
|m_power_out|0.686|0.719|0.942|Greater|0.671|0.943|Greater|2,179|1,320|

Small numbers of observations are noted in red. Differences are heteroskedasticity-robust tests with a 5% significance level.

62