rafmacalaba/datause-encoder-data huggingface dataset:
# **_Nueva España 1790_**

||Percentage of<br>population|Annual income<br>per family<br>(pesos)|Annual income<br>per capita<br>(pesos)|Average income<br>per capita<br>relative tomean|
|---|---|---|---|---|
|Spanish upper class|10|1,543|309|6.12|
|Mestizo middle class|18|300|60|1.19|
|Indigenous peasant|72|61|12.2|0.24|
|class|||||
|_Total_|_100_|_252_|_50.4_|_1_|

Note: Assumed household size = 5 for all social groups.

**Income distribution data** . In 1813, Manuel Abad y Queipo, Bishop of Michoacán, published his _Colección_ . His social tables offer information on: family size; total population; three income classes with population shares and income per capita for the bottom two (the Spanish upper class 10% ; mestizo middle class 18% at 60 pesos; and indigenous peasant class 72% at 12.2 pesos). What is missing to complete the crude size distribution is either an estimate of average income per capita for the richest class or an estimate of total income for Nueva España as a whole. Our estimates use an average of the latter from three sources: Coatsworth’s 240 million pesos in 1800 (Coatsworth 1978 and 1989); Rosenzweig’s 190 million pesos in 1810 (Rosenzweig Hernández 1989); and TePaske’s 251 million pesos in 1806 (TePaske 1985).

**Population and area.** Population estimate of 4,500,000 from _Colección_ (1813) _._ Modern Mexican borders are used to define the area of 1,224,433 km<sup>2</sup> since it appears that Manuel Abad y Queipo ignored New Mexico and California.

**Urbanization rate.** Calculated from cities with 10,000 or more inhabitants from von Humboldt (1822).

**Mean income in $PPP** : 1800 GDP per capita in 1990 international dollars (Coatsworth 2003 and 2005).

# **REFERENCES**

- Abad y Queipo, Manuel (1813/1994) _Colección de los escritos más importantes que en diferentes épocas dirigió al gobierno_ (1813, reprinted in 1994 with an introduction and notes by Guadalupe Jiménez Codinach, México, D. F.: Consejo Nacional para la Cultura y las Artes).

- Coatsworth, John H. (1978), "Obstacles to Economic Development in NineteenthCentury Mexico," _American Historical Review_ 83 (1): 80-100.

- --- (1989), "The Decline of the Mexican Economy, 1800-1860," in R. Liehr (ed.), _América Latina en la época de Simón Bolívar. La formación de las economías nacionales y los intereses económicos europeos, 1800-1850,_ Berlin: Colloquim Verlag.

- --- (2003), “Mexico,” in Joel Mokyr (ed.), _The Oxford Encyclopaedia of Economic History,_ vol. 3, New York: Oxford University Press, pp. 501-7.

60
a nonlinear effect, with a stronger effect of passport costs on emigrant stock when passport costs are below extremely high values. For countries with passport costs less than 20 percent of national income, we estimate that lowering passport costs by one percent is associated with a 0.75 percent increase in emigrants per capita. Countries with very high migration costs have less impact from a one percent decrease, since costs still remain very high. They therefore require greater reductions in costs to boost migrant numbers.

**Table 4: Do Countries with Higher Passport Costs have less Emigrants?**

Dependent Variable: Emigrants per capita (%)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Passport Cost as % of GNI|-0.272|-0.156|-0.544|-0.757|-0.422|
||(4.09)***|(2.37)**|(2.34)**|(3.95)***|(1.51)|
|Passport Cost as % of GNI Squared||||0.011<br>(3.17)***|0.005<br>(1.17)|
|Log of Population||-3.700|-3.688||-3.671|
|||(5.64)***|(5.68)***||(5.63)***|
|Log of GNI per capita||2.193|1.823||1.805|
|||(1.71)*|(1.28)||(1.25)|
|Government Effectiveness||-3.840<br>(1.97)*|-4.023<br>(2.01)**||-3.734<br>(1.89)*|
|Constant|9.452<br>(7.39)***|50.391<br>(3.67)***|54.142<br>(3.61)***|10.372<br>(7.10)***|53.635<br>(3.54)***|
|Trimmed Sample|no|no|<20%|no|no|
|p-value for test of joint significance:||||0.000|0.083|
|Observations|124|124|119|124|124|
|R-squared|0.04|0.45|0.45|0.06|0.45|
|Notes:||||||

Robust t statistics in parentheses

* significant at 10%; ** significant at 5%, *** significant at 1%

Column 3 trims countries with passport costs which are above 20% of per capita national income

- 13 -
|Welfare quintile 1|ref.|ref.|ref.|ref.|ref.|ref.|
|---|---|---|---|---|---|---|
|Welfare quintile 2|0.028***|0.026**|0.031**|0.025***|0.025*|0.025*|
||(0.009)|(0.012)|(0.014)|(0.010)|(0.013)|(0.015)|
|Welfare quintile 3|0.057***|0.032**|0.084***|0.056***|0.034**|0.079***|
||(0.009)|(0.013)|(0.013)|(0.010)|(0.014)|(0.014)|
|Welfare quintile 4|0.080***|0.074***|0.086***|0.081***|0.077***|0.084***|
||(0.009)|(0.012)|(0.014)|(0.009)|(0.013)|(0.014)|
|Welfare quintile 5|0.105***|0.106***|0.104***|0.103***|0.103***|0.103***|
||(0.009)|(0.012)|(0.014)|(0.009)|(0.013)|(0.014)|
|Observations|23,752|11,975|11,777|23,752|11,975|11,777|

Note: Robust standard errors in parentheses. Regressions are done using probit displaying marginal effects but no constant. Enrollment: child is enrolled in current school year. Attendance: child attends school at least three days (out of five) a week.

Wage work and household chores: children not engaged in these activities recorded as doing 0 hours of work. HH edu at least secondary: at least one household member has completed secondary education or higher. High income hh: households in the 4th or 5th income distribution quintiles. *** p<0.01; ** p<0.05; * p<0.1

# 6. Conclusion

This study finds that child work to differing degrees, depending on the type and intensity, negatively affects school enrollment and attendance, regardless of gender. In 2015, there is a clear trade-off between child work and schooling,<sup>12</sup> especially after the age of 11. The results suggest that children participating in wage work are less likely to be enrolled in school, regardless of the number of hours spent working and gender. However, wage work could reduce in a greater magnitude the likelihood of being enrolled and attending school for boys than for girls, whereas household chores affects girls disproportionately more. Hours spent working has different outcomes for both enrollment and attendance depending on the type of work and gender: for both girls and boys wage work intensity does not seem to matter, working less than or more than 40 hours significantly negatively affects enrollment and attendance. Intensity of household chores matters more for girls than for boys when looking at both schooling outcomes, affecting girls disproportionately more than boys. Focusing on the whole sample, wage work intensity does not seem to have a strong relationship with either enrollment or attendance probably because, regardless of the time actually spent participating in wage work, this most likely occurs during school hours, thus making it impossible for the child to do both. Intensity, as we mentioned, does matter for girls when looking at the effect of household chores on both schooling outcomes.

> 12 With schooling we refer to both the measures of enrollment and attendance.

22
# **Table** of Contents

## PUBLIC SECTOR PRICING POLICIES:

## A Review of Bank Poliev and Practice

|I.|INTROD|UCTION<br>...........................................|
|---|---|---|
||Object<br>LimLta<br>Approa|ives<br>...................................<br>..................<br>1<br>tions<br>...........................................<br>1<br>ch<br>..........................................<br>2|
|II.|TTHEM|M'S<br>POLICYON PUBLIC SECTOR PRICIN ......................<br>2|
|II-.|APPLIC|ATION OF THE POLICY<br>..<br>4|
||A.|Use ofthe Two-Stop Approach.<br>4|
|||Sectors<br>with External<br>Economies in Consumption<br>.<br>4|
|||Sectors<br>with External<br>Economies in Production.<br>6<br>Public<br>Sectors<br>without<br>External<br>Economies.s<br>8|
||B.|The Calculation of Efficiency Prices<br>.10|
|||Capital IndiviLsibilitis<br>.11|
|||Project versus Systen Costs<br>.11|
|||Joint Cost Allocation<br>.13|
|||Information Problems<br>.13|
|||Externalities<br>.15|
|||Shadow Pricing<br>.17|
|||Ratianing Prices<br>.18|
|||Issues of Price Structur .19|
||C.|Constraints to Implementing<br>Efficiency Prices<br>.20|
|||Metcring Problems<br>.20|
|||Lack of an Efficient Capital Market<br>.21|
|||Lack of an Insurance Narket<br>.21|
|||Uninformed Consumers<br>.22|
|||Second Best considerations<br>.22|
||D.|Adjustments for Distributional<br>Objectives<br>.<br>22|
|||Explicit Distributional<br>Weights<br>.23|
|||Percent of Project Beueficiaries Below Income Target....<br>23|
|||Rules of Thumb on Prices**as**Percent of Income<br>...........<br>23|
|||Differential Prices<br>.....................................<br>24|
|||Access versus Affordability<br>..............................<br>25|
# **2.4 Auxiliary Data Sources**

We combine the main sample described in the previous section with four administrative data sources. First, the biannual Mexican school census which collects information on school-level yearly trajectories - e.g. enrollment, failure and drop-out rate, school infrastructure, teachers’ and school principals’ main characteristics as well as detailed information on the attendance fees that students have to incur upon registration in a given school: tools, uniforms, monthly payments, registration and tuition. This information can be conveniently linked with the full list of COMIPEMS schooling options through a unique school identifier ( _Claves de Centros de Trabajo_ - CCT) during the period 2005-2010. In addition, geocoded information allows us to compute geodesic distances between students’ places of residence and the high school programs offered by the COMIPEMS system.

Second, we employ a sub-module of the third round of the 2008 national employment survey (ENOE) which records retrospective information on the academic trajectories of individuals in the age group 15-34, who passed at least one year of upper secondary education, including the track attended in high school and whether college education was attended or not. Wage and employment information comes from the ENOE that is considered the most reliable source of wage data for Mexico. This survey is representative at the State-level and further allows to distinguish between rural, semi-urban and urban areas depending on the population size of the communities of residence of the surveyed individuals. We thus consider as the relevant labor market for the COMIPEMS applicants in the city of Ecatepec the urban sections of the State of Mexico and the Federal District.

Third, we use data from a 2009 survey collected on a nationally-representative sample of students who take a nationally standardized end of high school test (ENLACE). The so called _ENLACE de contexto_ gathers, among others, information on expected monthly earnings five years from the moment of the survey depending on two hypothetical scenarios of educational attainments - high school completion and university degree. The answers are given using a pre-codified set of brackets.<sup>15</sup> As before, we restrict the sample to the urban sections of the State of Mexico and

> 15The earnings brackets for both questions are: i) 4,000 MX$ or less; ii) 4,001 MX$ to 7,000 MX$; iii) 7,001 MX$ to 10,000 $; iv) 10,001 MX$ to 15,000 MX$; v) 15,001 MX$ to 20,000 MX$; and vi) more than 20,000 MX$. The questions are the following:

> 1. If you do not obtain a university degree, what monthly income do you expect to have on average five years from now?

10
And they frequently include labor training costs – in the absence of other firms in the market, the specific skills required are unlikely to be available. But firms cannot prevent trained labor from being poached by later entrants, so they are left with a cost of training labor that is of benefit to all firms in the market.

Access to finance can mitigate some of pioneers’ start-up costs if there is enough information and predictability for pioneers to capitalize on their potential. First-mover benefits in fragile states should be reflected in higher access to capital at lower cost. And this is the case for most developing countries, yet in fragile states the reality is much bleaker.

The higher probability of young pioneers getting a bank loan or credit is reversed in fragile states where young pioneers are _less_ likely, by half as much, to have access to credit (see Table 3). The shares of their investments and working capital financed by banks are disproportionally lower too, which forces them to rely on internal funds, and thus start up with a much higher probability of default. While pioneers in other countries are able to capitalize on their potential in terms of lower cost of capital per unit of value-added, pioneers in FCS face the same credit terms as everyone else. In both cases, first-mover costs are significantly higher. Nor is this surprising: banks avoid exposing themselves to radical uncertainty. Although all firms operating in fragile states face distinctively higher uncertainty due to political instability, pioneers face an additional layer of ‘unknown unknowns’.

**Figure 4. Labor productivity – pioneering versus non-pioneering firms**

# **Labor productivity**

<!-- Start of picture text --> Labor productivity<br><!-- End of picture text --> _(Average, in thousands of 2009 dollars) (Median, in thousands of 2009 dollars)_ <!-- Start of picture text --> 40 14<br>35 12<br>30<br>10<br>25<br>8<br>20<br>6<br>15<br>10 4<br>5 2<br>0 0<br>Pioneering Other firms Pioneering Other firm Pioneering Other firms Pioneering Other firm<br>firms firms firms firms<br>FCS Non-FCS FCS Non-FCS<br><!-- End of picture text --> _Source:_ Calculations using World Bank Enterprise Survey data. _Note:_ Labor productivity is the ratio of value added by the number of workers.

These conditions are reflected in lower productivity premia for pioneers in FCS: higher returns are offset by important costs and uncertainty. Estimates from the World Bank Enterprise Survey suggest that the productivity of pioneering firms in FCS is lower than that of their peers in nonFCS. As illustrated in Figure 4, the median value added per worker in pioneering firms in FCS ($6,986) is lower than that of pioneering firms in non-FCS ($11,651).

15
**3. MENA governments’ budgets dramatically improve with either a moderate carbon tax ($25 per ton of CO2) or removal of fossil fuel subsidy.  The improvements are often sustained through 2035 or 2036.**

**4. In most cases of the select MENA countries chosen here, under either of the two fiscal instruments, growth is stabilized in the long run, after a small initial drop.   The long run result is consistent with empirical panel results as well.**

# **5. Detailed Empirical Results**

## **5.1. Oil subsidies among MENA oil producers strongly and positively impact CO2 emissions**

In Table 1, the regressions are based on fixed effects panel estimates that control for country and time fixed effect and use robust error.<sup>7</sup> For comparison, this is done for the full sample, for the MENA subsample, and for the subsample of MENA oil group. The typical regression is of the form,  where for each country i and time t, Y represents CO2 emissions, S is oil subsidy, _X_ is (m x 1) column vector of  all other controls (GDP, population, gas flaring) and _c_ ' is (1 x m) row vector of corresponding coefficients. The regression errors ui and vt represent country and time specific error terms and μit represent regression error. The period covered is 2011-2018. The results are perhaps not surprising. Fossil fuel subsidies are associated with more CO2 emissions _only_ among MENA oil exporters.

As for the measure of the dependent variable, rather than focusing on CO2 as a fraction of GDP or relative to population, we instead control for both GDP and population as independent variables and use total CO2 production as the dependent variable of interest. The advantage of this method is that the result no longer depends on which measure of CO2 emission we use. Naturally, the high degree of significance of both variables indicates that size matters both in terms of population and in terms of output. The measures of

> 7 The xtreg command is used in STATA.

15
Large firms have higher productivity growth. Our results are robust to sub-sample analysis by country income group and by looking at countries with large versus small informal sectors. When we examine the role of age conditional on firm size, we find that while large mature firms (firms with over 100 employees and 11+ years) have the largest proportional share of total employment compared to other size-age groupings, job creation rates are highest amongst small young firms (firms with <20 employees and ≤ 5 years old). We also find that young firms (≤ 5 years) have higher employment growth, sales growth, and productivity growth rates than older firms in regressions controlling for firm size, country, industry, and year fixed effects.

Our cross-country database improves upon existing databases along several dimensions. First, the data are comparable across countries since they are all sourced from the World Bank Enterprise Surveys (ES) database which samples formally registered firms from over 100 countries to study the business climate constraints to private sector growth and performance. The surveys use standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across countries.<sup>3</sup> Second, for the first time ever, we are able to compute statistics on SMEs for a large sample of developing countries. While statistics on size distribution are more easily available for the developed countries from sources such as the OECD, there is little to no information available for many developing countries. Finally, the data set allows us to compute contribution to total employment, labor productivity, and employment generation across the entire size distribution and conditional on size, we can also compare the role of young versus mature firms.

> 3 The mean response rate across our sample of countries is 70% which is superior to most other survey based studies. For instance, Campello, Giambona, Graham, and Harvey (2010) report response rates of 3 to 7% in their survey of how companies use credit lines during a financial crisis. Other studies in corporate finance report response rates between 7%-9% including Graham and Harvey (2001), Brav, Graham, Harvey and Michaelu (2005), Graham, Harvey, and Rajgopal (2005), and Lins, Servaes, and Tufano (2010)

4
||**Qua**|**ntitative reaso**|**Depende**<br>**ning**|**nt variable:**<br>**Nativ**|**Z-scores in Sa**<br>**e language (Sp**|**ber PRO**<br>**anish)**|**Foreig**|**n language (**|**English)**|
|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**Ordinary**<br>**Least**<br>**Squares**|**Non-**<br>**parametric**<br>**matching**|**Instrumental**<br>**Variables**|**Ordinary**<br>**Least**<br>**Squares**|**Non-**<br>**parametric**<br>**matching**|**Instrumental**<br>**Variables**|**Ordinary**<br>**Least**<br>**Squares**|**Non-**<br>**parametric**<br>**matching**|**Instrumental**<br>**Variables**|
|**Teacher program has emphasis in**<br>**the assessed subject in Saber**||||||||||
|**PRO * Saber 11 scores in**||||||||||
|Quantitative reasoning|0.0885**<br>(0.0391)|0.0365<br>(0.0664)|0.9171***<br>(0.2670)|||||||
|Native language||||-0.0639**<br>(0.0292)|-0.0391<br>(0.0660)|0.2160<br>(0.1853)||||
|Foreign language|||||||-0.1584***<br>(0.0546)|-0.3296***<br>(0.0915)|0.8286***<br>(0.2707)|
|Constant|0.5234*|-0.0763|0.5001*|0.5810**|0.4350|0.5826**|0.3200|-0.2840|0.2879|
||(0.2833)|(0.4170)|(0.2772)|(0.2517)|(0.2900)|(0.2537)|(0.1994)|(0.5065)|(0.1983)|
|**Observable characteristics**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Reliability of instruments†**||||||||||
|Kleibergen-Paap rk Wald F statistic|||145.7668|||91.5435|||105.2551|
|P-value|||0.0000|||0.0000|||0.0000|
|Hansen J statistic|||21.3165|||46.2364|||82.4951|
|P-value|||0.0003|||0.0000|||0.0000|
|Angrist-Pischke multivariate F test<br>of excluded instruments|||35.64|||35.93|||39.79|
|P-value|||0.0000|||0.0000|||0.0000|
|**R-squared**|0.3255|0.3026|0.2856|0.3057|0.3098|0.2724|0.5216|0.5233|0.4419|
|**Observations**|**50772**|**12431**|**50772**|**50772**|**12520**|**50772**|**50772**|**12463**|**50772**|

Robust standard errors in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent. Note: Programs with emphasis in mathematics are those related to the study of mathematics, physics, biology and the like; programs with emphasis in native language are those related to the study of social sciences, humanities, philology and the like; programs with emphasis in foreign language are those that are focused on the study, mainly, of a foreign language (including English). Observable characteristics include: gender; parents' education; family size; semester in which the student took Saber 11; and dummies by municipality where the student took Saber 11. Source: Authors’ calculations based on ICFES data 23
# GREEN PRICES

Ngoc Bich Tran The World Bank Eduardo Ley The World Bank JEL Classification—Q510, E010 This paper builds on work prepared for the joint PREM-SDN Knowledge Platform on Green Growth. Alex Bowen, Milan Brahmbhatt, Mark Dutz, Antonio Estache, Marianne Fay, Adrian Fozzard, Stéphane Hallegatte, Kirk Hamilton, Geoffrey Heal, Urvashi Narain, Diego Rodriguez, David Treguer, Michael Toman, Jeff Vincent provided useful comments.