rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper Se, jes

||IkIQ|Author<br>Ib||Contact<br>for par|
|---|---|---|---|---|
|WPS800|The Legal Framework for Private<br>Sector Development in a Transitiona<br>Economy: The Case of Poland|Cheryl W. Gray<br>l Rebecca J. Hanson<br>Michael A. Heller<br>Peter lanachokov<br>Youssef Djehane|November 1991|CECSE<br>37188|
|WPS801|Unraveling the Mysteries of China's<br>Foreign Trade Regime: A View from<br>Jiangsu Province|Arvind Panagariya|November 1991|D. Ballantyne<br>37947|
|WPS802|Strengthening the Bank's Population<br>Work in the Nineties|Steven W. Sinding|November 1991|0. Nadora<br>31091|
|WPS803|Financial Regulation: Changing the<br>Rules of the Game|Millard Long<br>Dimitri Vittas|November 1991|W. Pitayatonakarn<br>37666|
|WPS804|Global Trends in Raw Materials<br>Consumption|Boum-Jong Choe|November 1991|S. Lipscomb<br>33718|
|WPS805|Privatization in the Soviet Union:<br>The Beginnings of a Transition|Sergei Shatalov|November 1991|CECSE<br>37188|
|WPS806|Measuring Commercial Bank<br>rEfficiency:Use and Misuse of Bank<br>Operating Ratios|Dimitri Vittas|November 1991|W. Pitayatonakarn<br>37666|
|WPS807|Moderate Inflation|Rudiger Dornbusch<br>Stanley Fischer|November 1991|S. Moussa<br>33490|
|WPS808|The New Trade Protection: Price<br>Effects of Antidumping and<br>Countervailing Measures in the United<br>States|Ann Harrison<br>|November 1991|D. Ballantyne<br>37947|
|WPS809|Openness and Growth: A Time<br>Series, Cross-Country Analysis for<br>Developing Countries|Ann Harrison|November 1991|WDR Office<br>31393|
|WPS810|Poverty and Income Distribution<br>during Adjustment: Issues and<br>Evidence from the OECD Project|Francois Bourguignon<br>Jaime de Melo<br>Christiar, Morrisson|November 1991|D. Ballantyne<br>37947|
|WPS811|Comparative Resource Allocations<br>to Human Resource Development|Peter T. Knight<br>Sulaiman S. Wasty|December 1991|D. Afzal<br>36335|
# **Table 11. Health Attainment: Input-Efficiency scores by regions across the world**

# **- Multiple Inputs,  Multiple Outputs**

||AFR|EAP|ECA|LA|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, literacy of adult) – 2<br>outputs (life expectancy,immunization DPT.)|.85|.82|.72|.82|.91|.93|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization DPT.)|.86|.82|.74|.83|.91|.94|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization measles.)|.86|.82|.77|.83|.91|.94|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 3 outputs (life expectancy,<br>immunization DPT.,DALE)|.86|.82|.80|.87|.93|.94|

**Table 12. Health Attainment: Output-Efficiency scores by regions across the world**

# **- Multiple Inputs,  Multiple Outputs**

||AFR|EAP|ECA|LA|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, literacy of adult) – 2<br>outputs (life expectancy,immunization DPT.)|.81|.91|.97|.93|.97|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization DPT.)|.81|.91|.97|.94|.97|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 2 outputs (life expectancy,<br>immunization measles.)|.80|.91|.96|.94|.98|.96|
|3 inputs (public expenditure, private spending,<br>literacy of adult) – 3 outputs (life expectancy,<br>immunization DPT.,DALE)|.82|.91|.97|.95|.98|.97|

Tables 9 and 12 show that, on average, developing nations score between .85 and .95 in output efficiency in the multiple input-output framework. This figures imply that developing countries could raise their output levels by an average of 10 percent with the same input consumption, if they were as efficient as the comparable benchmark countries. This figure is simply indicative, as the precise estimate varies with the country and with the selected indicator, and has a large variance across countries: for instance, the bottom decile of (output) efficiency scores is about .66, implying that the scope for increasing health and education attainment levels is between 3 or 4 times higher than for the whole sample average.

23
TABLE A18: Impact of leaders’ exposure on individual intermediary outcomes: preventive health

||**ITT Leaders**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Health_**||||||
|**Health index**|-0.00|( 0.04)|0.37|0.00|2,073|
|Insist on brushing teeth|-0.00|( 0.01)|0.78|0.02|2,073|
|Ensure a healthy diet|0.01|( 0.01)|0.05|0.06|2,073|
|Keep child away from the stove|-0.00|( 0.00)|0.88|0.00|2,073|
|<br>Ensure good hygiene|-0.01|( 0.02)|0.27|0.21|2,073|
|Wash clothes properly|-0.00|( 0.01)|0.71|0.12|2,073|
|Avoid giving coffee|0.00|( 0.00)|0.29|0.00|2,073|
|Make child wash hands|0.00|( 0.02)|0.90|0.27|2,073|
|Smoke at a distance from child|-0.00|( 0.00)|0.74|0.00|2,073|
|Do not give child unhealthy snacks|0.00|( 0.01)|0.72|0.01|2,073|
|<br>Cover fresh food|0.02|( 0.02)|0.86|0.13|2,073|
|Teach child to chew slowly|0.00|( 0.00)|0.80|0.00|2,073|
|Prevent child from catching the fu|-0.01|( 0.01)|0.64|0.06|2,073|
|Boil or chlorinate the water|0.00|( 0.01)|0.42|0.02|2,073|
|Ensure that child vaccines are up-to-date|-0.00|( 0.01)|0.34|0.07|2,073|
|Use a mosquito net|-0.00|( 0.01)|0.39|0.01|2,073|
|Insist child wears shoes|-0.01|( 0.01)|0.60|0.03|2,073|
|Make sure child eats regularly|-0.00|( 0.00)|0.46|0.00|2,073|
|Keep child away from dangerous products|-0.00|( 0.00)|0.85|0.00|2,073|
|<br>Show child affection|0.00|( 0.00)|0.26|0.01|2,073|
|Other|0.01|( 0.02)|0.87|0.17|2,073|

Each row corresponds to a separate estimation (equation 3) and shows the ITT estimate on the aggregate health index or on individual questions measuring whether the caregiver reported a given practice when asked how to avoid their young child getting sick. The health index is calculated based on the number of different preventive health practices. The index is standardized using the mean and standard deviation of the control household. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. Five villages without leaders were excluded from the estimation. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 66
**_Energy demand models for policy_** **_<u>formulation</u>_**

development, the linear programming models were used. One of the well-known applications of the early days was the BESOM model [Brookhaven Energy System Optimisation Model] that was developed for efficient resource allocation in the US. The first version of the model was implemented at the national level for a snap-shot analysis of a future point in time. A number of other versions were developed subsequently, that extended the capabilities of the model, including a macro-economic linkage through an input-output table [Hoffman and Jorgenson, 1977]. Similarly, multi-period or dynamic models have emerged and in fact, one of today’s best known energy system models, MARKAL, is indeed a derivative of the BESOM model.

Munasinghe and Meier (1993) indicate that many countries followed the BESOM example and developed their own model or adapted the BESOM model. Examples include TEESE model for India, ENERGETICOS for Mexico, etc. In addition to country specific models, more generic models for wider applications also came into existence. EFOM and MARKAL models come under this category. For developing countries, RESGEN was widely used [Munasinghe and Meier, 1993].

In the US, Hudson and Jorgenson (1974) pioneered the tradition of linking an econometric macroeconomic growth model with an inter-industry energy model. The input-output coefficients of the inter-industry model is endogenously determined, and the macro-model allowed a consistent estimates of demand and output.

While most of the above initiatives were at the national level, the pioneering works of large-scale global modelling started with the efforts of Jay Forrester for his World Dynamics and its application in Limits to Growth by Meadows et al (1972). As is well known now, the doomsday prediction of this report fuelled a fierce debate about resource dependence for economic growth and the issue of sustainability. Despite its limited representation of the energy sector and the limited following of the report, this initiated a new trend of global modelling. At a collective level, the efforts of the Workshop on Alternative Energy Sources (WAES, 1977), of US Energy Information Administration 124
examined gender wage gaps in Vietnam between 1993 and 2009, Ganguli and Terrell (2005) used data from Ukraine to examine the earnings differentials between men and women during the 1986 to 2003 period, and Badel and Peña (2010) used survey data from 2006 to estimate gender wage differences in Colombia. All of these studies find that women’s earnings are significantly lower than those of men, men earning up to 30 percent more than women in these countries. The authors note that education and experience account for a small proportion of the estimated wage gap, and that the bulk of the gap is attributed to differential returns to education and skills for men and women. Three of these studies use quantile regression-based decomposition methods, allowing researchers to examine wage differentials at different points of the wage distribution. In the case of Colombia, Badel and Peña (2010) found that women at the top and bottom of the wage distribution are those primarily affected by differences in returns to endowments or skills. For Ukraine, the gaps are widest at the top end of the wage distribution, while the opposite is true in Vietnam, where the wage gaps decrease as one moves along the distribution from bottom to top.

A limited set of studies on gender wage discrimination examines the contribution of cognitive and noncognitive skills in explaining the gender wage gap (Fortin, 2008; Mueller and Plug, 2006; Nordman, Sarr, and Sharma, 2014; Nyhus and Pons, 2012; Sakellariou, 2012; and Semykina and Linz, 2007). Most of these studies are based on data from OECD countries. To our knowledge, Nordman, Sarr, and Sharma (2014) present the only available evidence for developing countries. They used matched employer-employee data from Bangladesh to examine discrimination in male-female wages, and found that, when controlling for educational attainment, the mean wage gap is about 8 percent. The mean wage gap reduces marginally when cognitive and noncognitive skills are added to the model. Examining the wage gaps at different points of the wage distribution, they found that the gap in Bangladesh is wider at the bottom end of the distribution and that cognitive and noncognitive skills explain about 88 percent of the gap at the upper end of the wage distribution and about 69 percent at the bottom of the distribution.

Mueller and Plug (2006) and Fortin (2008) provide evidence for the United States. Mueller and Plug used data from the Wisconsin Longitudinal Study, and although they found significantly strong associations between their measure of cognitive skills (IQ) and earnings (especially for men), they found no gender differences in responses to cognitive skills. For noncognitive skills, they report that about 3 percent of the gender gap is explained by differences in, and returns to, personality traits. Fortin’s study examined the effect of noncognitive skills—self-esteem, locus of control, the importance of money/work, and the importance of people/family—using data from two 6
Campbell, J. Y., H. E. Jackson, B. C. Madrian, and P. Tufano. 2011. “Consumer Financial Protection.” _The Journal of Economic Perspectives_ 25(1), 91-114.

Choi J, D. Laibson and B. C. Madrian. 2010. “Why does the law of one price fail? An experiment on index mutual funds.” _The Review of Financial Studies_ 23(4) :1405–32.

Choi J, D. Laibson and B. C. Madrian. 2011. “$100 bills on the sidewalk: suboptimal investment in 401(k) plans.” _Review of Economics and Statistics_ 93(3): 748-763.

Cole, S. A., and G. K. Shastry. 2010. "Is High School the Right Time to Teach Selfcontrol? The Effect of Financial Education and Mathematics Courses on Savings Behavior." Mimeo.

Cole, S. A., T. Sampson, and B. Zia. 2011. “Prices or Knowledge? What Drives Demand for Financial Services in Emerging Markets?” _Journal of Finance_ 66(6): 1933–1967.

DellaVigna, S. 2007. “Psychology and Economics: Evidence from the Field.” _Journal of Economic Literature_ 47(2): 315-72.

Dranove, D. and G. Z. Jin. 2010. “Quality Disclosure and Certification: Theory and Practice.” _Journal of Economic Literature_ 48(4): 935-63.

Duarte F. and Hastings J. S. 2012. “Fettered consumers and sophisticated firms: evidence from Mexico’s privatized social security market.” _NBER Working Paper 18582_ .

Fernandes, D., J. G. Lynch Jr and R. G. Netemeyer. 2014. “Financial literacy, financial education, and downstream financial behaviors.” _Management Science_ 60(8): 1861-1883.

Giné, X. and R. Mazer. 2017. “Financial (Dis-)Information: Evidence from a MultiCountry Audit Study.” _Policy Research Working Paper 6902, The World Bank_ .

Gigerenzer, G. and A. Edwards. 2003. “Simple tools for understanding risks: from innumeracy to insight.” _BMJ: British Medical Journal_ 327: 741-744.

Gross, D.B. and N.S. Souleles. 2002. “Do liquidity constraints and interest rates matter for consumer behavior? Evidence from credit card data.” _Quarterly Journal of Economics_ 117(1): 149-185.

Hastings, J. S. and L. Tejeda-Ashton. 2008. “Financial literacy, information, and demand elasticity: survey and experimental evidence from Mexico.” _NBER Working Paper 14538_ .

Hastings, J. S., B. C. Madrian and W. L. Skimmyhorn. 2013. “Financial Literacy, Financial Education and Economic Outcomes.” _Annual Review of Economics_ 5(1): 347373.

24
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure 4. Parallel Trend Assumption – Presidential Elections<br>Continuous Treatment Variable<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β β<br>0 0<br>-.05 -.05<br>-.1 -.1<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β 0 β 0<br>-.05<br>-.05<br>-.1<br>-.1<br>.2<br>.15<br>.1<br>.05<br> (95% CI)<br>β<br>0<br>-.05<br>-.1<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation. We interacted the standardized values of the variable PEP holders with presidential electoral years excluding the election of 2018, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

23
# 3.2 Results

## 3.2.1 Efficiency of truthful equilibria

As with the standard common agency game discussed in DGH (1997), this game comports a multiplicity of subgame perfect Nash equilibria. We follow Bernheim and Whinston (1986), Grossman and Helpman (1994) and DGH in restricting our attention to truthful symmetric Nash equilibria only, since truthful contribution schedules are always a best-response strategy and are the only coalition-proof equilibria of these games. In this subsection we therefore turn to the efficiency properties of truthful Nash equilibria within this model.

First let us characterize in Proposition 1 the constrained efficient allocation in this economy<sup>20</sup> , which will serve as a benchmark for comparisons with the political equilibrium allocation.

The efficiency condition established in Proposition 1 follows directly from the first-order condition of the problem faced by a hypothetical social planner who wished to maximize a Benthamite social welfare function (corresponding to total output) in this economy, by choice of public expenditure, subject to the government budget constraint. The expressions g<sup>∗</sup> +kR<sup>∗</sup> αs<sup>∗andk</sup> sP<sup>∗∗represent</sup> what may be called the private-public capital ratios of the rich and the poor, respectively. In other words: how many units of private capital are invested per unit of public capital obtained by a given individual. Proposition 1 says that those ratios must be related in a precise manner in order to obtain an efficient allocation. The term 1−α(1p −p)<sup>isequalto</sup> (1−p)[1p(1−−αp(1) −p)]<sup>,whichisexactlythe</sup> ratio between the marginal cost to the group of the poor of an increase in the rich-specific type of expenditure g (i.e. a change of the composition of government expenditures) and its marginal benefit to the group of rich. The term<sup><u>B</u>ameasureoftheproductiveefficiencyofthepoorrelativetothat</sup> A<sup>gives</sup> of the rich. Therefore an efficient allocation must equate one group’s marginal cost to the other’s marginal benefit, taking into account their relative efficiency on production.

On the other hand, a truthful political equilibrium may be characterized as follows: Proposition 2 (i) A feasible allocation {kR<sup>0, k</sup> P<sup>0, g0, s0} is a truthful equilibrium</sup> only if

> 20 What we are calling an ”efficient allocation” takes as given the fact that there are some individuals that are restrained in their productive possibilities, to a worse technology, given the absence of credit markets in which they could possibly have access to k<sup>∗</sup> . We could otherwise consider the outcome with perfect credit markets as being the efficient one, and our present notion of efficiency would be a second best. More on this will come later.

11
-43-

# Adjustment of Comparisons of Gross Value of Output to a Value Added Basis

In order to avoid double-counting in aggregating the individual industry results, it is desirable to measure value added rather than gross output. This requires separate comparisons of output and inputs separately. Unfortunately, the Brazilian and Mexican production censuses do not give figures for individual inputs at the product level, and the product detail given for "materials consumed" in the US census cannot be related to output of individual commodities. This problem can be met only by adjusting the gross output comparisons by the value added - gross output ratios for the countries, as explained below.

A second important point is that there are differences in the definition of value added in the three countries involved in our comparison (see also chapter II above). In the United States manufacturing _ensus, only inputs directly related to the production process (i.e. raw materials, energy consumption, and packing expenses) are reported. Information on overheads and general expenses, which cannot be allocated directly to a product group is not given. So the "US census concept" of value added is gross of these non-allocable inputs, and is therefore a grosser concept than used in the national accounts.

In the Brazilian census the standard concept of value added ("valor de transformacao") is the same as in the US Census. However, at the level of major industry groups, of which there were 24 (including mining) in Brazil, enough detailed information is provided to permit derivation of a rConcept of value added compatible with that in the national accounts, which we have used in chapter IV below (see also chapter II for a discussion of this point).

In the Mexican census a distinction is made between direct inputs ("materias primas y auxiliares consumidas") and other costs ("otros insumos"). The first category is smaller than US or Brazilian. census inputs so the Mexican census concept of value added is different from that in the USA and Brazil (see chapter II). However, there is enough detail in the Mexican census to permit construction of a measure of value added conceptually equivalent to that in the US census (which we use here) or alternatively to measure Mexican value added in a national accounts sense (which we do in chapter IV).

For the detailed value added comparisons in this chapter for our 17 industries, we use the "US census concept" of value added. However, in the final section where we make estimates at branch levels and for manufacturing as a whole we were able to make estimates on a national accounts basis. Paige and Bombach discussed the possibilities of making value added (or to use their terminology "net output") comparisons (see also chapter I). One possible approach is the "double deflation" method, which makes separate measurements for output and inputs.
``` Decompositionofgrossexports: Perspective:sectbil Exporter:CHN Importer:USA Sectorofexport:17 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`107292.76`|_`100.00`_|
|`Domestic content (DC)`|`79824.00`|_`74.40`_|
|`Domestic Value-Added (DVA)`|`79815.64`|_`74.39`_|
|`VAX -> DVA absorbed abroad`|`79373.64`|_`73.98`_|
|`Reflection`|`442.00`|_`0.41`_|
|`Domestic double counting`|`8.36`|_`0.01`_|
|`Foreign content (FC)`|`27468.76`|_`25.60`_|
|`Foreign Value-Added (FVA)`|`27465.88`|_`25.60`_|
|`Foreign double counting`|`2.88`|_`0.00`_|

According to WIOD 2014 data, Chinese value-added potentially exposed to this tariff turns out to be around $79.8 billion, as shown by the value reported for the `Domestic Value-Added (DVA)` . This is higher than the $77.4 billion of Chinese value-added traced in the same export flow using an “exporting country” perspective (see the previous `icio` output above). Thus, if we had used the latter perspective, we would have understated the Chinese exposure. Again, each empirical question calls for the best suited perspective: the _default_ “exporting country” is more useful if the aim is to assess GVC participation or to retrieve measures of value-added trade consistent with the figures at a more aggregate level; the perspective in line with the selected trade flow is more suited to encompass the entire value-added that might be affected by a shock hitting that particular flow.

The same reasoning applies when the objective is to choose the best suited perspective for bilateral aggregate exports. Here the choice will be, again, between the _default_ “exporting country” perspective and the bilateral one. For example, Figure 4.9 of the World Bank World Development Report (WDR) 2020, reported here as Figure 1, aims at quantifying the potential exposure of other countries to a US-China trade war. In fact, US and Chinese exports embed a non-negligible amount of other countries foreign value-added that would be indirectly exposed to new tariffs. For instance, around 2% of the value-added in the Chinese exports to the United States consists of Japan’s GDP and almost 1.8% of the Republic of Korea’s GDP. In turn, around 2.5% of the value-added in the US exports to China is Canadian GDP. These numbers can be easily obtained using `icio` by selecting a bilateral perspective and retrieving the value-added by country of origin in a particular bilateral flow. The entire value-added, domestic and foreign, that crosses 22