rafmacalaba/datause-encoder-data huggingface dataset:
Δ _Peit_ is the volatility of electricity prices. It is the key variable of interest in gauging the effect of output price uncertainty on decision makers’ perception of future allowance prices and multi-period emissions distribution decisions. Observations of monthly fuel purchasing database reveal that plants frequently purchase coal from spot markets during the year. Therefore, decisions as to how much of each type of coal to buy and how many allowances to hold till later periods can be adjusted in response to the monthly electricity price fluctuations. I measure Δ _Peit_ as the standard deviation of the percentage change (between two adjacent months) of monthly average electricity price in the state where unit _i_ is located. As we tend to see higher overall underlying prices in highly volatile periods, this definition facilitates normalizations and reasonable comparison across high and low price levels. The calculation is described by Eq.(32).

where _t_ indexes year and _m_ indexes month. _Peitn_ is the monthly average retail electricity price to industrial customers in state _i_ during month _m_ year _t_ .

The coefficient of Δ _Pet_ provides a measure of the elasticity of annual average emission rate to electricity price volatility. A negative coefficient will provide supporting evidence for the theoretical prediction in previous sections.

The above analysis implicitly assumes current price fluctuation as a proxy for expected price uncertainty in the future. One concern with this specification is whether current price uncertainty reflects plant operators’ expectation of future price uncertainty at the time of making operation decisions. To evaluate the possibility that historical price uncertainty does not provide insights into expectation of future price changes, I also assume mangers perfectly predict price volatility in the future ( Δ _Pe_ ( _t_ + 1) ), and test for the response of current emission rate to future price fluctuation. This alternative specification does not change the result qualitatively.

Given input costs, an increase in electricity prices will increase the marginal value of allowances. According to Eq.(A6), the higher the marginal value of allowances, the higher the input ratio for low-sulfur coal, which implies a lower emission rate. Therefore, the coefficient associated with _Pe_ is expected to be negative.

_Pt_ is a price vector including the price of allowances _Pat_ ; low-sulfur coal price _Plt,_ ; highsulfur coal price _Pht_ , and retail electricity price to industrial customers _Pet_ .

Current price _Pat_ is an indicator of the market’s perception of allowances future prices. A higher _Pat_ would reduce current emissions and the coefficient of _Pat_ is likely to be negative.

The coefficient associated with low-sulfur coal price is expected to be positive, while the sign of the coefficient of high-sulfur coal price is ambiguous. Holding the output price constant, a change in input fuel prices has two substitution effects: the substitution between the two types 16
countries around the world, we hence assume that the skill content of a given occupation is comparable internationally.

This assumption might hold for some occupations but not for others – hence it is important to understand the potential bias in the computations which we are introducing. Countries differ in technology and regulatory contexts which may imply different skill profiles for specific occupations. For example, teachers in low-income settings are more likely to lack the tools (especially ICT tools) that support innovative teaching than teachers in developed countries. Similarly, doctors or nurses might have access to equipment as well as medical knowledge which impacts on the skill content and mix they can bring to bear in different settings. Generally, we can postulate that especially those occupations which use less routine type of skills are likely to be more skill intensive in more advanced economic settings than in lower-income ones. This would, hence, signal a possible upward bias in the measured skill intensity of non-routine cognitive analytical and interpersonal skills in our sample, especially so for the lower-income countries included.

The matching and adjustment process allows for the computation of average intensity scores for all the five skills considered. As each occupation is matched with a specific value for all skills, the average across all occupations for each skill represents a measure of the, composite and normalized, intensity of skills.<sup>8</sup> We then use the skill intensity computations for the United States as a benchmark value when presenting the results.<sup>9</sup> To assess the shift in skill structure through time we follow Autor et al (2003) and use the complete distribution per type of skill and focus on the behavior of the median of the base distribution. Skills per occupation are held constant so that changes in skill structure will reflect changes in the occupation structure.<sup>10</sup> Autor et al (2003) find that, over four decades, a remarkable shift in the skill intensity of the US labor force took place with a significant increase in analytical and interpersonal skills and a sharp decline in the use of routine cognitive skills. Over this time period, the United States

> 8 The ONET equivalency scale includes values for all five skills, ranging from 1 to 5, for each occupation.

> 9 We followed Acemoglu and Autor (2010) in building the composite scores with the exception of the skill category ‘offshore ability’ which we did not include in our analysis.

> 10 The procedure can be summarized in three steps. First, the distribution of a given skill on the labor force in a base year is constructed and its median is recorded. Second, the distribution of the comparator year for the same skill is obtained and its median calculated. Third, the median of the comparator year is projected into the base year distribution. The difference between the median of the base distribution and the projected median from the comparator year corresponds to the percentile change. This allows for comparisons across an arbitrary scale.

**9**
Table B4: Rider audits: exit survey

|**Variable**|**Defnition**|
|---|---|
|_Adv_|_antages of reserved space_|
|None||
|More comfort|Open-ended question categorized according to most commonly|
|Less harassment|mentioned factors.|
|Less crowding<br>|Each constructed variable is = 1 if rider’s response mentioned|
|Safety|factor.|
|Other||
|_Number of occurrences of harass_|_ment in a year, by type of harassment and car ridden_|
|Physical, public space|“Imagine a woman similar to you who rides the Supervia every<br>day to work on the same commuting route as yours. How often<br>do you think a man would touch her against her will/grope her<br>at some point during a year of commuting, while traveling in the<br>public space?”|
|Physical, reserved space|“Imagine a woman similar to you who rides the Supervia every<br>day to work on the same commuting route as yours. How often<br>do you think a man would touch her against her will/grope her<br>at some point during a year of commuting, while traveling in the<br>reserved space?”|
|Verbal, public space|“Imagine a woman similar to you who rides the Supervia every<br>day to work on the same commuting route as yours. How often do<br>you think a man would make unwanted sexual comments during<br>”|
||a year of commuting, while traveling in the public space?|
|Verbal, reserved space<br>_Share of reserved _|“Imagine a woman similar to you who rides the Supervia every<br>day to work on the same commuting route as yours. How often do<br>you think a man would make unwanted sexual comments during<br>a year of commuting, while traveling in the reserved space?”<br>_space rides under hypothetical scenarios_|
|Status quo|Riders are presented with diferent scenarios for male presence<br>in reserved space and ticket prices by space.<br>They are asked|
|Current scenario, 30 cents opportunity cost|which space they would prefer to ride during they usual commute<br>|
|Current scenario, 65 cents opportunity cost|hours if they had to buy a space-specifc ticket. Options “Always|
|No men on reserved space, 30 cents oppor-|ride in public space”, “Mostly ride in public space”, “Sometimes|
|tunity cost|ride in public space, sometimes ride in reserved space”, “Mostly|
|No men on reserved space, 65 cents oppor-<br>tunity cost|ride in reserved space”, and “Always ride in reserved space” are<br>converted to 0, .25, .5, 75 and 1, respectively.<br>_Rider risk perception_|
|Physical harassment|Riders was asked to imagine a woman similar to them who rides<br>the Supervia every day to work on the same commuting route as<br>thedoontheublicsaceThewerethenaskedaboutthe|
|Robbery|y , p p. y <br>chance that inside the train diferent situations would happen to<br>them.<br>The constructed variable _high-risk perceiver_ is = 1 for|
|Verbal harassment|each situation if the response is above the median. Conversely,<br>the _low-risk perceiver_ variable is = 1 is the response is below the<br>median.|
**Figure 4: Actual and simulated poverty reduction (rank; annual percentage change), ca. 1995-2008**

<!-- Start of picture text --> MLIMLICAFCAFBDIBDIMWIMWIMOZMOZNERNERTZATZASWZSWZMDGMDGNPLNPLNGANGAUGAUGAVNMVNMGNBGNBBGDBGDGMBGMBTJKTJKETHETHZMBZMBPAKPAKIND IND LAOLAOSENSENIDNIDN CHNCHNGHAGHAKHMKHM KENKEN MRT MNGMNGCMRCMRCIVCIV LKA PHLPHL AZE MDAMDAEGYEGYROMROM ARM NICNICHNDHNDJAMJAMMARMARKAZKAZPOLPOLNAMNAMJORJORALBALBTUNTUNZAFZAFKGZKGZVENVENGTMGTMMKDMKDMEXMEXSLVSLVECUECUPER PER TURMDVTURMDVUKRUKRBOLBOL DOMDOMMYSMYSCRICRIPANPANBGRBGRRUSPRYRUSPRYBRABRACOLCOL ARGARGCHLCHL<br>1 5 10 15<br>Average daily income per capita, 1995<br>Actual Equal initial conditions<br>80<br>60<br>40<br>20<br>Rank in poverty reduction (annual % change) 0<br><!-- End of picture text --> Note: the shaded area represents the 95 percent confidence bands.

Figure 4 suggests that initial conditions affect poverty reduction performances significantly. Nonetheless, we may be presenting a biased picture in considering only percentage changes in poverty reduction, given that richer countries – having lower initial levels of poverty – may find it easier to reduce poverty in relative terms. Figure 5 therefore presents the same simulation but ranking _absolute_ changes in poverty (i.e. annual percentage points of poverty reduction). Although actual poverty reduction performance no longer seems to vary with per capita income in a statistically significant manner, the fundamental results continue to hold: after attributing equal initial conditions to countries, poorer countries score better – and their higher score is statistically significant at the 5 percent level.

14
25 _45._ "International dollars" are calculated by the International Comparison Project (ICP), and purportedly reflect equal purchasing power parities (PPP) by correcting for price differences, somewhat like "constant dollars" correct for price changes over time. Conventionally, the US is taken as numeraire (like a given year is chosen as base year for constant dollars). Table _5_ above shows the relative price changes brought about by the currency movements of the 1980s. 24 46. While recognizing the serious practical and theoretical difficulties related to "international" dollars and their calculation, the behavior of imports relative to GDP's expressed in "international" dollars is significat!ve. Deliberately or otherwise raising the foreign exchange price of dornestic product through currency appreciation tends to draw in imports but mechanically depresses their relationship to nominal GDP (and vice versa). The use of "international" dollars tears away this exchange rate veil.

47. From 1980 to 1985, the fall in relative domestic prices in Europe and Japan mechanically raised the ratio of imports to nominal GDP; but the corresponding rise in relative import prices depressed real import demand, and thus accelerated the decline in demand for primary commodity imports and temporarily countered the secular rise in the ratio of Europe's manufactures imports to its GDP.

48. As a result, while overall imports' share in GDP remained almost stable in the US (where it was boosted both by dollar appreciation and by expansionary financial policies), the share of total imports and of manufactures imn 3rts in "real" GDP sharply fell both in Europe and in Japan.

49. Conversely, over the next period of currency appreciation, import rise was amplified; the relative cheapness of manufactures imports helped raise demand for them, almost doubling their ratio to "real" GDP in Japan, raising it by half in the EC. Meanwhile, the rise in the price of imports slowed down their penetration into the US; the ratio of manufactures imports to GDP, which had risen from .048 to .063 points in the first half of the decade rose to just .070 in the second half. By the end of a decade of see-sawing exchange rates and fluctuating but rising import ratios, both total and manufactures imports were somewhat higher in the EC than in the US in relation to "real" GDP. On both accounts, the correction reduces, but does not eliminate Japan's differences, all the more remarkable (as noted earlier) as Japan's much smaller economy would, _ceterts paribus,_ need more imports.

I Some **_may_** have **expected greater pr3ce divernces between** the US and the **EC.** but **in** 1980 **eveal** EC member **countes acaaly had** lower **price** levels **than** the US **(Ireland,** lly, Greece, **Portugal,** Spain).
# **Inequality of Opportunity in Early Childhood Development in Morocco over Time**

By Safaa El-Kogali,<sup>1</sup> Caroline Krafft,<sup>2</sup> Touhami Abdelkhalek,<sup>3</sup> Mohamed Benkassmi,<sup>4</sup> Monica Chavez,<sup>5</sup> Lucy Bassett,<sup>6</sup> and Fouzia Ejjanoui<sup>7</sup>

**Keywords:** Early childhood development, inequality, health, nutrition, pre-primary, Morocco

**JEL codes:** J13, I4, I24

# **Acknowledgements**

The authors would like to acknowledge the assistance of the National Human Development Observatory ( _Observatoire National du Développement Humain_ , ONDH) of Morocco, the Ministry of Health in Morocco, and the Moroccan office of UNICEF. We particularly appreciate the generosity of these institutions in sharing data. We are also grateful for the questions, comments, and suggestions provided by Sophie Naudeau and World Bank seminar participants. Research for this paper has been supported by the World Bank Strategic Research Program, a multi-donor trust fund.

1 World Bank.

> 2 Department of Economics, St. Catherine University, 2004 Randolph Avenue, St. Paul, Minnesota 55105. 3 National Institute of Statistics and Applied Economics (INSEA), Rabat, Morocco.

> 4 National Human Development Observatory, Rabat, Morocco.

> 5 Consultant, World Bank.

> 6 World Bank.

> 7 Mohammed V University, Rabat, Morocco.
**In Turkey, the servicification of manufacturing firms is happening, albeit slowly.** Turkish firm‐level data<sup>9</sup> allow for an analysis of which manufacturing firms show servicification and which only produce goods. It does so by recording the secondary services activities for each manufacturing firm using the revenue generated from services affiliates. The data therefore give a good indication of the degree of servicification of Turkish goods firms, particularly the production and output of services (see the center and right‐hand elements in Figure 3.1). Services inputs in manufacturing, which is the left‐hand element of Figure 3.1, are partially captured through the indirect domestic services value added in exports discussed in the previous section. (In other words, while service inputs are an important element of the servicification phenomenon, they are not covered by the analysis in this section due to data limitations.) The services affiliates are recorded using their 4‐digit NACE code, as is the manufacturing activity of the firm. A share of the revenue of the entire firm is assigned to each affiliate based on the number of employees in that affiliate. Over time, manufacturing firms in Turkey have seen a slow shift away from generating revenue from goods toward more services (Figure 3.2). When only manufacturing firms with a services affiliate are considered, average revenue from the services affiliate increased from about 20 percent in 2006 to 21.5 percent in 2014.

**Figure 3.2: Servicification of manufacturing firms: 2006‐2014**

<!-- Start of picture text --> In-house servicification of manufacturing firms<br>2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Services (left) Manufacturing (right)<br>15 100<br>13<br>95<br>11<br>90<br>9<br>85<br>7<br>Share revenue generated from goods<br>Share revenue generated from services<br>5 80<br><!-- End of picture text --> Source: EIS data from Turkey; author’s calculations.

# **Box 3.1: Where does servicification take place?**

Servicification involves all kind of services activities such as R&D, design, and marketing, as well as construction and transportation services. Depending on where in the supply chain the manufacturing firm is located, it adds or sells different services to downstream users (or consumers). Studies using aggregated value‐added of exports data show that industries such as chemicals and motor vehicles use the most services value‐added while coke and petroleum use the least (Miroudot and Candestin, 2017).

Across countries, business services and distribution services are most prominent in terms of services activities by manufacturing industries. Across sectors, however, capital‐intensive industries seem to be

> 9 From the Ministry of Science, Industry and Technology (MoSIT) Enterpreneurial Information System (EIS) database.

14
of information provided in budget documents, public access to budget documents, and the interaction between executive and legislative branches in the budget process. Many of these indicators arguably have normative value on their own: having public access to budget documents is desirable by itself; and having streamlined business registration procedures is better than the alternative.

This leads to two related difficulties in using rules-based indicators to design and monitor governance reforms. The first is that absent good information on the links between changes in specific rules or procedures and outcomes of interest, it is difficult to know which of these rules should be reformed, and particularly in what order of priority. Will establishing an anticorruption commission or passing legislation outlawing bribery have any impact on reducing corruption, and if so, which one would be more important? Or should instead more efforts be put into ensuring that existing laws and regulations are implemented as intended, or that there is greater transparency and access to information, or greater media freedom? And how soon should we expect to see the impacts of one or more of these interventions? Given that governments typically operate with limited political capital to implement reforms, these tradeoffs and lags are important.

The second difficulty when designing or monitoring reforms arises when aid donors, or governments themselves, set performance indicators for governance reforms. Performance indicators based on changing specific rules, such as the passage of a particular piece of legislation, or a reform in a specific budget procedure, can be very attractive because of their clarity -- it is straightforward to verify whether the specified policy action has been taken.<sup>8</sup> Yet it important to underscore that "actionable" indicators are not necessarily also "action-worthy" in the sense of having a significant impact on the outcomes of interest. Moreover, excessive emphasis on registering improvements on rules-based indicators of governance leads to risks of "teaching to the test", or worse, "reform illusion", where specific rules or procedures are changed in isolation with the sole purpose of showing progress on the specific indicators used by aid donors.

> 8 Indeed, this is reflected in the terminology of "actionable" governance indicators emphasized in the World Bank's Global Monitoring Report (World Bank, 2006).

10
# **Literature**

Cameron, Colin; Trivedi, Pravin (2005). “Microeconometrics: methods and applications.” Cambridge University Press.

Cruces, Guillermo; Lanjouw, Peter; Lucchetti, Leonardo; Perova, Elizaveta; Vakis, Renos; Viollaz, Mariana (2011). “Intra-generational mobility and repeated cross-sections: a three-country validation exercise”. Policy Research Working Papers. The World Bank. Number 5916. Washington D.C..

Cuesta, Jose; Ñopo, Hugo; Pizzolitto, Georgina (2011). “Using pseudo- panels to measure income mobility in Latin America.” Review of Income and Wealth. Volume 57. Number 2.

Dang, Hai-Anh; Lanjouw, Peter; Luoto, Jill; McKenzie, David (2014). “Using repeated cross-sections to explore movements in and out of poverty”. Journal of Development Economics, Volume 107.

Dang, Hai-Anh; Lanjouw, Peter; (2013). "Measuring poverty dynamics with synthetic panels based on cross sections". Policy Research Working Paper Series. The World Bank. Number 6504. Washington D.C.

Datt, Gaurav; Ravallion, Martin (1992). "Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil and India in the 1980s". Journal of Development Economics, Volume 38. Number 2.

Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter (2005). “Imputed welfare estimates in regression analysis.” Journal of Economic Geography. Volume 5. Number 1.

Statistica Moldovei (2007). “Technical note on the 2006 household budget survey”. National Bureau of Statistics Chisinau, Moldova.

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<!-- Start of picture text --> 8 as : LI KOS i :<br>Big Five Alpha cost Cognitive<br><!-- End of picture text --> <!-- Start of picture text --> a. inpyg a , :<br>10 7) ae E<br>‘ °<br>Big Five Beta oo Cognitive<br><!-- End of picture text --> <!-- Start of picture text --> ‘<br>Grit °° Cognitive<br><!-- End of picture text -->
# **<mark>Table A2E – Parallel Trend Test of Bank Lending to Firms Across States of Varying Public Debt Before FD Law</mark>**

This table reports OLS estimates of the trends of bank lending to private firms across states of varying public indebtedness prior to the introduction of the FD Law. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendm_ is a linear trend over time from the start of our sample in January 2014 up to March 2016, the month prior to the implementation of the FD Law. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Volu<br>|mef,b,m<br>|Investm<br>|entf,b,m<br>|Working<br>|Capitalf,b,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Trendm*DebtStates,16Q1|-0.000|-0.000|-0.002|-0.001|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Observations|524,710|524,710|29,383|29,383|502,403|502,403|
|R-squared|0.840|0.840|0.895|0.897|0.834|0.834|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month|No|Yes|No|Yes|No|Yes|

64