rafmacalaba/datause-encoder-data huggingface dataset:
belligerents.<sup>10</sup> To account for the obvious non-independence of peace events we calculate robust standard errors. A number of wars and post-conflict peace periods were still on-going in 2003 (the last year of our data); we censored these cases.

# **Data and operationalizations**

The Binningsbø, Elster & Gates (2005) data provide an overview of how post-conflict societies deal/have dealt with the wrongdoings of their recent pasts. In order to define a ‘post-conflict society’ they rely on the Uppsala-PRIO armed conflict dataset (Gleditsch et al., 2002), which defines a conflict as ‘a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths’ (Strand, Wilhelmsen & Gleditsch, 2004: 3).<sup>11</sup> To allow for time-varying covariates the dataset is disaggregated into ‘peace years’ for each individual post-conflict period. The first ‘peace year’ for each subject starts the first day of peace after conflict and ends 31st December the same year. Next ‘peace year’ starts 1st January the following year and ends 31st December, or the date the conflict resumes (failure). The subject enters the dataset again the first day of peace after (if) the resumed conflict ends. Post-conflict peace periods that did not fail (no resumed conflict) before the last day of observation in the dataset (31 December 2003) are right-censored. Begin and end dates of the conflicts and peace transition periods are based on Gates & Strand’s (2004) event dataset. Following Gates & Strand (2004: 10), ‘Our initial coding criteria only includes the periods from when a conflict reaches 25 fatalities to the last fatality in the last year of recorded conflict.’ However, a conflict period must have been followed by at least _two years_ without observation of violence in order to be defined terminated. A conflict is not reported to have terminated if there is only one year without observation of violence between two years of violence – this is one continuous conflict period. The final analyses include 200 post-conflict societies with 1130206 days at risk of failure (3191 ‘peace years’). Ninety-three failures occur.<sup>12</sup>

> 10 In the Binningsbø, Elster & Gates (2005) dataset there are 291 conflict periods, of which, 105 reoccur, indicating a relatively high risk of peace failure.

> 11  Version 3.0 (December 2004).

> 12 Due to missing data quite a few ‘peace years’ are missing, the time period under investigation consists of 3920 ‘peace years’ in total.

11
**Table 6-1. Distribution of the Estimated WTP with Normal Demand**

|**Variable**|**Obs**|**Percentile**|**Centile**|**[95% Conf.**|**Interval]**|
|---|---|---|---|---|---|
|Wtp|835|0|0.269|0.269|0.269|
|||10|11.326|6.776|15.400|
|||20|39.322|31.794|46.897|
|||30|69.673|60.000|84.881|
|||40|120.869|97.512|130.716|
|Mean:|758.8494|50|171.544|150.103|200.253|
|Stand.Err.|1586.139|60|255.959|221.416|312.481|
|||70|468.126|398.388|549.162|
|||80|879.748|746.574|1147.648|
|||90|2108.107|1793.560|2488.585|
|||100|11872.180|11872.180|11872.18|

**Table  6-2. Average Value of Risk Reduction by Sex**

|**Variable**|**Sex**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|Morbidity|Male|576|856,576.3|2168075|131.2368|2.20e+07|
||Female|249|954,089.4|1865910|438.253|1.42e+07|
|Mortality|Male|576|1,725,847|4212262|203.2255|3.45e+07|
||Female|249|2,519,660|4948185|753.1705|3.70e+07|

**Table  6-3. Average Value of Risk Reduction by Age**

|Variable|Sex|age|Obs|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|---|
|Morbidity|Male|15-44|415|1,131,665|2495159|517.131|2.20e+07|
|||45-54|116|179,377|325070.7|217.702|2287034|
|||55-64|36|71,487|102289|131.2368|388329.4|
|||65-74|8|45,707|60048.31|216.3856|147747.7|
|||75+|1|180|.|180.0348|180.0348|
||Female|15-44|213|1,062,087|1984652|1633.342|1.42e+07|
|||45-54|30|326,019|609848.5|438.253|2803215|
|||55-64|5|278,312|319122.8|37641.95|662462.8|
|||65-74|1|171,591|.|171591.3|171591.3|
|||75+|0|.|.|.|.|
|Mortality|Male|15-44|415|2,302,021|4831718|825.3475|3.45e+07|
|||45-54|116|298,932|605489.8|414.5512|4355004|
|||55-64|36|99,493|146848.8|203.2255|601343.5|
|||65-74|8|61,381|78463.8|336.9885|194385.4|
|||75+|1|247|.|247.2682|247.2682|
||Female|15-44|213|2,854,431|5264090|4593.689|3.70e+07|
|||45-54|30|545,312|1031117|753.1705|4817534|
|||55-64|5|531,069|620468|59732|1260051|
|||65-74|1|386,661|.|386661.3|386661.3|
|||75+|0|.|.|.|.|

28
**30**

to in-state projects.

These conclusions may not be popular with policymakers confronting budgets in distress. Indeed a great deal of effort has recently been directed to developing new ways of _reducing_ public pension contributions, and where possible, directing pension assets so as to generate local jobs and investment. A critically important question which is only beginiing to be asked is whether the social costs of underfunding and below-market return investments are sufficient to offset their social benefits. In other words from a public finance viewpoint, "socially targeted" infrastructure investments could be justified when the social gains to such investment outweigh the social costs, taking care to note that the costs include potential retiree insecurity when public pension underfunding leads to pension and other benefit cuts. It would be useful to design and implement such a cost/benefit framework when evaluating public pension asset allocation and performance practices. In order to do this, more timely data must be collected which can be examined using a common reporting and accounting framework. Policymakers and researchers in the United States have begun to recognize this need, and have recommended standardization of pension data gathering, probably under the aegis of a federal agency (Melbinger 1992; Mitchell 1991; Munnell 1983).

Fiscal analysts in developing countries may profit from the mistakes and the lessons gleaned from public pensions in the United States. This paper shows that pension systems are extremely complex institutions, so that care must be taken when designing the regulatory and investment environment in which such plans operate. This study did not Olivia S. Mitchell - Public Pension Governance and Performance
# **The Great Proletarian Cultural Revolution, Disruptions to Education, and Returns to Schooling in Urban China**<sup>*****</sup>

John Giles<sup>**</sup> The World Bank<sup>†</sup> Albert Park Oxford University Meiyan Wang Institute for Population and Labor Economics Chinese Academy of Social Sciences Keywords: returns to schooling, wages, education, China JEL codes: I20, J24, J30, O15, O53

> *We are grateful for useful conversations with Xin Meng and Fang Cai, and for helpful comments on earlier drafts from Eric Edmonds, Belton Fleisher, Steven Haider, Hongbin Li, Djavad Salehi-Isfahani, John Strauss, Jan Svejnar, Jeffrey Wooldridge, Dean Yang and Dennis Yang. The authors acknowledge grants to support field research from Michigan State University (Intramural Research Grants Program), the Chinese Academy of Social Sciences, The Ford Foundation (Beijing), the University of Michigan (Rackham Faculty Research Grant), the International Centre for the Study of East Asian Development (ICSEAD), the Australian Research Council and the World Bank (Beijing), and support for follow-up research from the Research Support Budget of the World Bank, the Weatherhead Center for International Affairs at Harvard University (Academy Scholars Program) and the W.E. Upjohn Institute for Employment Research.

> ** _Corresponding Author:_ The World Bank MSN MC 3-311, 1818 H Street NW, Washington, DC 20433; phone: (202) 473-3451; e-mail: jgiles@worldbank.org.

†The research, discussions and conclusions presented in this paper reflect the views of the authors and should not be attributed to the World Bank or to any affiliated organization or member country.

1
policy simulations: a cut in the minimum wage, and a reduction<sup>inpayroll</sup> taxes on unskilled labor (assuming both neutral and non-neutral revenue effects). The payroll tax experiment is of particular interest in light<sup>of the</sup> previous discussion emphasizing the links between fiscal and labor market reforms. As it turns out, the unemployment and poverty effects of this policy change depend importantly on whether or not it is offset by other changes in sources of revenue, and what the precise nature of that source is. The last section summarizes the main results of the analysis and suggests<sup>various</sup> extensions. Appendices A, B and C provide a summary list of the model's equations, variable names and parameters, as well as a description<sup>of the</sup> calibration procedure.

# **2** Structure **of** Mini-IMMPA

The building blocks of the "structural" components of mini-IMMPA consist of the production structure, the labor market, the supply and demand for goods and services, external trade, sectoral and aggregate prices, profits and income formation, private consumption and savings, private investment,<sup>the</sup> public sector, and the balance of payments. The link between the<sup>structural</sup> component and a (fictitious) household income and expenditure<sup>survey, which</sup> is necessary for poverty and distributional analysis, is also explained later on.

## **2.1 Production**

The basic distinction on the production side is that between rural and urban sectors. In the rural economy (or agriculture) firms produces one<sup>good</sup> (referred to as good 1), which is sold domestically or exported. The urban economy consists of both formal and informal components. The<sup>informal</sup> economy produces nontraded services (referred to as good 2). in the<sup>formal</sup> urban economy production consists of a private good (referred to<sup>asgood3)</sup> and a nontraded public good (referred to as good 4).

### 2.1.1 **Rural Production**

Land available for rural activities is assumed to be non marketed and in<sup>fixed</sup> supply. Gross output of the agricultural good, Xi, is the sum of value added, 5
the spot check by 1/22=0.045. In the control group, however, the estimated increase is positive but small at only 0.009 and it is statistically insignificant. In all the treatment arms, the estimated increase in the probability of being absent during the spot check more than doubles and is statistically significant. In the parents’ incentive arm, this probability more than trebles and reaches 72% of the expected 0.045 coefficient. While the number of absences reported by the parents may not be exogenous, much of the heterogeneity which may lead to omitted variable bias is likely to be captured by the number of days absent in October of the previous year reported by parents at baseline. Columns (5) to (8) repeat the same analysis controlling for absences in October 2015 reported by parents at baseline, showing that results are robust.

In addition, if the effect of the weekly attendance reports was due to improved parental information about their daughter’s attendance, then one would expect the effect of this treatment to be larger where the probability of a child’s absence going unreported to the parent is higher. _Ceteris paribus_ , this probability should decrease with the size of the school since staff-per-pupil and social control are likely to be lower in larger schools. Indeed, we find that when schools are weighted by their share in the EP2 school population rather than having equal weight irrespective of their EP2 population, the effect of the information treatment increases from 4.5%-points to 6.15%-points (Table 11).

_Population-weighted estimates_ . The main analysis reported in this paper is carried out at the school level (i.e., averaging variables at the school level) without applying any sampling weights, so that each school is weighted equally whatever the number of girls interviewed in the household survey or observed during the spot checks. We also repeated the analysis but weighting each school by the relative size of its potential EP2 intake, to obtain population-weighted estimates.<sup>31</sup> 31 More precisely, we apply to each school a weight equal to the school’s population share divided by 1/173 (the school’s sample share), where the 37
# **Figure C5.1. Impact of Political Turnover on Competition** _–_ **time varying industry characteristics**

<!-- Start of picture text --> Entry Exit<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>PCM PE<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>Herfindahl MS4<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>lnN lnP<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>.4 .2<br>.1<br>.2<br>0<br>PC family PC family<br>β 0 β -.1<br>-.2 -.2<br>.2 5<br>0<br>PC family-.2 PC family0<br>β β<br>-.4<br>-.6 -5<br>.3 .2<br>.2<br>.1 0<br>PC family0 PC family<br>β β -.2<br>-.1<br>-.2<br>-.4<br>.5 .5<br>PC family0 PC family0<br>β -.5 β -.5<br>-1 -1<br><!-- End of picture text --> _Note:_ The figure depicts annual variation in the estimated impact of the market share of Suharto family firms on the outcome of interest. The dots depict the coefficient estimates ′93 ′94 ′09, estimated using the regression: = ∑ Y αα , αα , ... ... . ααY which are presented in Table jjtt YY C5.2, with 1996 as the omitted year, where ii=1993,..,2009 ii jj ii=1993,..,2009 represent industry characteristics that are allowed to vary over time.ii jjii jj ii jjii αα PC (MMSS) ∗DD(Y = tt) + ∑ γγ SS ∗DD(Y = tt) + ππ + ττ + εε measures the aggregate market share of Suharto family firms (the average of their aggregate market share in 1996 and 1997). jjii jj SS PC (MMSS) is a vector of industry fixed effects, and is a vector of year fixed effects. The vertical bars indicate the 95% confidence interval associated with the estimates. jj ii ππ ττ 66
# **Table A2: OECD Development Center African Economic Outlook (AEO)**

|**Data Provider**<br>OECD Development C|enter||||||||
|---|---|---|---|---|---|---|---|---|
|**Description**<br>Multilateral organizati|on headqu|artered i|n Paris,|France|||||
|**Website**<br>www.oecd.org|||||||||
|**Data Source**<br>African Economic Out|look||||||||
|**Type**<br>Expert Assessment<br> <br>|||||||||
|**Respondents**<br>OECD Development<br> <br>|enter Staf|f|||||||
|**Frequency**<br>Annual since 1996<br> <br>|||||||||
|**Coverage**<br>African countries|||||||||
|**Public Access**<br>Publicly available thro<br>**Description**<br>Indicators are based o<br>two indicators: "har<br>reports are taken from<br>and are either coded<br>severity of reported in<br>and the distribution is<br>reported events. We<br>observations in the firs|ugh Africa<br>n the freq<br>dening of<br>the weekly<br>as 0-1 for<br>cidents. T<br>highly ske<br>therefore<br>t, second,|n Econ<br>uency o<br>the reg<br>newsp<br>the oc<br>otal sco<br>wed by<br>conver<br>and thir|omic Ou<br>f newsp<br>ime" an<br>aper Ma<br>currenc<br>res are<br>a few co<br>t to a<br>d tercile|tlook re<br>aper rep<br>d "poli<br>rchés T<br>e of rele<br>reported<br>untries<br>three-po<br>s of the|port tab<br>orts on<br>tical tro<br>ropicau<br>vant st<br>for eac<br>with ver<br>int scal<br>distribu|les.<br>inciden<br>ubles".<br>x et Méd<br>ories or<br>h count<br>y high f<br>e corre<br>tion in e|ts relate<br>Newp<br>iterran<br>0-3 on<br>ry and<br>requen<br>spondin<br>ach yea|d to<br>aper<br>éens,<br>the<br>year,<br>cy of<br>g to<br>r.|
|**Voice and Accountability**|2006|2005|2004|2003|2002|2000|1998|1996|
|Hardening of the Regime|X|X|X|X|X|X|X|X|
|**Political Stability**<br>Political troubles|X|X|X|X|X|X|X|X|
|**Government Effectiveness**<br>NA|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|..|..|..|..|..|..|||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Control of Corruption**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Country Coverage**<br>**Year of Publication**|**33**<br>**2006**|**33**<br>**2005**|**32**<br>**2004**|**26**<br>**2003**|**26**<br>**2002**|**26**<br>**2000**|**26**<br>**1998**|**25**<br>**1996**|

40
# Firm-Level Input Distortion in Indian States

Ritam Chaurey<sup>1</sup> Ruchita Manghnani<sup>2</sup> Viviana M.E. Perego<sup>3</sup> Siddharth Sharma<sup>4</sup>

**_Keywords:_** Misallocation, Firms, Distortions, Land Markets, Labor Markets, India **_JEL Codes:_** O12, O43, L20, D22

> 1 SAIS, Johns Hopkins University

> 2 The World Bank

> 3 The World Bank

> 4 Corresponding Author, The World Bank. Email: ssharma1@worldbank.org. The views expressed are those of the individual authors and do not necessarily reflect those of the World
|Secf|At least some secondaryeducation/Female|
|---|---|
|Rch|Rural as child|
|Uch|Urban as child|
|Rchm|Rural as child/Male|
|Uchm|Urban as child/Male|
|Rchf|Rural as child/Female|
|Uchf|Urban as child/Female|
|Edltp|Less thanprimaryschooling|
|Edpom|Primaryor more schooling|
|Edltpm|Less thanprimaryschooling/Male|
|Edpomm|Primaryor more schooling/Male|
|Edltpf|Less thanprimaryschooling/Female|
|Edpomf|Primaryor more schooling/Female|
|Edltpu|Less thanprimaryschooling/Urban|
|Edpomu|Primaryor more schooling/Urban|
|Edltpr|Less thanprimaryschooling/Rural|
|Edpomr|Primaryor more schooling/Rural|
|Edltpmu|Less thanprimaryschooling/Male/Urban|
|Edpommu|Primaryor more schooling/Male/Urban|
|Edltpmr|Less thanprimaryschooling/Male/Rural|
|Edpommr|Primaryor more schooling/Male/Rural|
|Edltpfu|Less thanprimaryschooling/Female/Urban|
|Edpomfu|Primaryor more schooling/Female/Urban|
|Edltpfr|Less thanprimaryschooling/Female/Rural|
|Edpomfr|Primaryor more schooling/Female/Rural|

**M** refers to whether the variable is the number of observations used to calculate the estimate (in which case **M** takes on the value “n”) or whether it is a mortality estimate (in which case **M** takes on the value “m”).

**C** refers to whether the variable is for the unadjusted mortality rate calculation (in which case **C** takes on the value “u”) or whether it adjusts for the number of surviving female siblings (in which case **C** takes on the value “a”).

**AAAA** refers to the age group that the mortality estimate is calculated for.  It takes on the values:

1554 Ages 15-54 1524 Ages 15-24 2534 Ages 25-34 3544 Ages 35-44 4554 Ages 45-54 Other variables that are in the databases are: 31
fl uctuation near the time of measurement, there is no advantage in splitting the sample by age group.

While these results are extremely strong, our second strategy provides us with another and more direct test of our main hypothesis.

## 4.2 The Impact of Migration on Child Growth: Household Fixed Effects Estimates

Columns 1 and 2 present the results by child gender. While the effect of being born before migration is negative and signi fi cant at the 5% level for girls, there is no discernible effect on boys. Column 3 reports results for the pooled sample. Now β3, the coefficient on the the interaction term, is negative and highly signi fi cant (p-value <.05), while β1, is actually positive, though not at all signi fi cant. In sum, girls who are born before fi rst migration in the same household do much worse on height for age than girls who are born after fi rst migration-while there is absolutely no effect of the timing of migration on the heights of boys.

# 5 Conclusions

Migration is one mechanism through which poor rural households in many developing countries attempt to cope with income risk. In this paper we ask whether temporary economic migration by a household member allows rural households to avoid costly risk coping strategies. We focus on early child growth since there is considerable epidemiological evidence that very young children are particularly vulnerable to shocks that lead to growth faltering, with substantial long-term health consequences.

The data come from rural Pakistan, where, as in the rest of Asia, son preference is substantial and there are large gender gaps in most developmental outcomes. As such, our interest is in examining also whether migration induced resource fl ows allow households to extend better nutrition and health care protection to girls. Recent work on the intrahousehold allocation of resources and risk has also shown that gender differences in the relative burden of risk may be important and that the allocation of resources to daughters is often one margin along which poor households adjust to uninsurable transitory income shocks.

The main child growth measures we use are weight for age (WAZ) and height for age (HAZ) z-scores. For our purposes, the child’s height for age, a measure of stunting, is 15
49 understanding of the welfare implications of

# turbulence.

3) Broad issue: The apparent trend favoring a larger economic role for smaller production units is another phenomenon that is not well understood. In this case, much of the ambiguity arises from the multiplicity of underlying factors that could be contributing to a phenomenon observed at a macroscopic level. A plausible research objective would be to identify circumstances where this trend is clearly present, is autonomous rather than induced by other changes, and appears to depend importantly on generally operating factors such as the declining costs of communication and computation. The larger objective is to understand the new forces operating on the economic role of SMEs on the ground that policy toward SMEs in developing countries should take these forces into account For this purpose, useful research could be conducted with data from advanced countries,<sup>1 6</sup> supplemented to the extent possible with data from developing countries.

Topics / actionable items.

(i) The evolution of the inter-firm division of labor could be tracked by looking at how value added/ sales ratios are developing by sector and by firm size. Declining ratios of value added to sales among large 1 For the general purpose of studying aspects of interfirm transactions, it would be worth exploring the possibility that countries with value-added taxes might have better statistical sources than the U.S. does -- or at least a better starting point for an effort to establish a sampling frame.
FIGURE 2

# Prevalence **of** Anemia in Pregnant Women

<!-- Start of picture text --> AFRICA ! | g i X EAST ASIA 4 SOUTH ASIA<br>11.3 Million aS Million . 27.1 Million<br>LATIN AMERICA DEVELOPED COUNTRIES<br>3 Million 2 Million<br><!-- End of picture text -->

_soutrce VVHO_