rafmacalaba/datause-encoder-data huggingface dataset:
Reduced-form results of empirical models on school participation and attainment show that droughts reduce the likelihood of school-aged children to attend school regularly. Partly as result, affected children accumulate 0.2 fewer years of schooling, equivalent to a drop in their school attainment of 6% relative to the control mean (3.3 years) (Table 4). Similarly, data from the 2007 census allow us to distinguish individuals who accumulated some level of education from those who do not have any education (variable “Any Education” in the right panel of Table 4). Performing the analysis on this sample confirms the results. Affected individuals are 1.4 percentage points (2.8%) less likely to accumulate some education. Thinking about the result chain, it is unlikely that rainfall anomalies occurring around the time of birth exerted a direct impact on school enrollment, attendance, progress and attainment of the children because school participation for affected infants began 4 or 5 years after the shocks happened. A more plausible explanation is that the negative effects of droughts and floods on the schooling of affected individuals are mediated by the influence that these shocks exerted on their nutritional status as infants.

# _The long-term human capital and productivity channel_

The nutritional deprivations suffered in infancy appear to have long-lasting consequences on the physical development of affected individuals. In trying to connect the pathways linking early life health shocks –caused by extreme weather– and wellbeing in adulthood, the analysis looks at the height of adults several decades after they experienced droughts and floods around the time of birth. While comprehensive data on adult anthropometrics are rarely available in household surveys, the DHS constitutes an exception. However, the DHS normally collects anthropometrics data only for women 15 to 40 years old, and not for men. This is the case for the DHS’s implemented in Mozambique. Another limitation of the DHS is the lack of information concerning the place and date of birth of adults in the sample. An attempt to circumvent this is made by restricting the sample to include only districts that exhibit permanent migration rates below 20%. To do so, the 1997 census is used to calculate the share of adults born in the same district of permanent residence (see Figure 5). Reduced-form results on height (measured as a percentage of an international reference group) for this sample are shown in columns 5 and 6 of Table 5.<sup>2</sup> The

> 2 The internal validity of these results may be compromised by the fact migration decisions are likely endogenous to weather shocks. However, a district-level regression of migration rates on weather shocks and district fixed effects does not reveal systematic differences in migration patterns between affected and non-affected villages.

12
a markedly increased interest in agriculture, yield growth dominated area growth, and total factor productivity rose. Thus recent developments are cause for cautious optimism. Efforts to raise factor productivity must be intensified and sustained, however, to secure lower prices for consumers, higher earnings for farmers, and good opportunities for young people to enter farming.

**Table 2: Wholesale Price of Maize and Rice in Selected Countries (average Jan.-Apr. 2012)**

|**Maize**<br>||**Ric**<br>|**e**<br>|
|---|---|---|---|
|**Markets in Africa**|**US$/ton**|**Markets in Africa**|**US$/ton**|
|Ethiopia|390|Benin|1,055|
|Kenya|393|Burkina Faso|738|
|Malawi|400|Madagascar|593|
|Mozambique|378|Mali|690|
|Rwanda|318|Mozambique|865|
|Tanzania|334|Niger|850|
|Togo|453|Senegal|810|
|Uganda|334|Togo|1,097|
|Zimbabwe|300|Uganda|1,368|
|**International prices**||**International prices**||
|South Africa|293|Vietnam|434|
|U.S.|276|Thailand|556|
|Ukraine - Black Sea Region|267|India|378|

_Source_ : FAO GIEWS.

# **5. Young People Do Not Yet Recognize Agriculture as an Opportunity**

Agriculture in Africa has untapped potential to create jobs, both directly and indirectly. In order to attract young people, agriculture will need to be more dynamic and appealing than it is now, and young people will need to view the sector more positively than they do now (Institute of Development Studies 2012). The farms that offer attractive opportunities will have to be quite different from those that most young Africans know. Worldwide and historically, farming as a profession has rarely carried high prestige. Colloquial terms for farmer in English, such as “hayseed” and “clod-hopper,” reflect the low status of the profession even where it yields incomes higher than the national average. Thus, it is not surprising that most young people in the developing world express a desire to leave farms. When 32 focus groups of young, rural Africans were asked about the best and worst ways to earn a living in their communities, agriculture was rarely mentioned as a “best job,” although it was not considered to be the worst either (Figure 3).

Good jobs were identified by the focus groups as those that commanded good pay and respect, features not typically associated with farming under the conditions most familiar to young Africans. Bad jobs were characterized as those with poor, insecure returns, physically damaging/demanding conditions, and/or illegal status. But the focus groups gave mixed messages regarding the desirability of farming as a livelihood, and their perception varied widely across SSA. "Farming is a good job because it is For example, within the broad categories of job types in where I can get food to eat and live Figure 3, “family farming” was the single most often good." _Young woman in Woimah,_ named desirable job. Yet with the exception of a women’s _Liberia_ group in North Darfur, none of the focus groups from South Africa, Sudan, or Togo mentioned any farming activities as good jobs (Petesch and

|"Farming is|a good job because it is|
|---|---|
|where I can|get food to eat and live|
|good."_You_|_ng woman in Woimah,_|
|_Liberia_||

9
Table 4 shows conditional probabilities: the proportions of the population that move out of poverty or fall into poverty given their initial poverty status. The table presents results for the long panel data from 2007 to 2011 in panel A of the table and the short panel data from 2010 to 2011 in panel B of the table. Since both numerator and denominators are estimated in this figure, results are less accurate than those shown in Table 2 and 3 (Dang and Lanjouw 2013). Again, with few exceptions, most of the point estimates fall within the 95 percent confidence intervals. For instance, actual panel data in panel A shows that about four to 11 percent of the initial non-poor in 2007 were poor in 2011 (based on the 95 percent confidence interval), while according to the synthetic panel technique with interaction terms (specification 3) about 5 percent of the initial non-poor fell into poverty after five years.

# _4.2. Sensitivity checks_

Given that we are randomly drawing from the empirical distribution of residuals from the first round, all previous estimates are based on 50 replications. Figure 1 analyzes the robustness of results to different number of repetitions for the 2007-2011 period.<sup>7</sup> The graph shows results for specifications 3 increasing gradually the number of repetitions from 1 to 300. In general, estimates are robust to the number of repetitions; the figure suggests that estimations fall within 95 percent confidence intervals independently of the number of replications used. This result is extremely important because it simplifies the analysis by allowing us to obtain just only one predicted vector of incomes for the first round instead of 50 vectors as in Tables 2 and 3. Given this result, all remaining estimates are based on only one repetition.

All results have been based on an unweighted average (i.e., γ = 0.5) of residuals from the income models in the first and second round of data. Even when results have been encouraging and γ = 0.5 is a reasonable assumption based on empirical findings in the literature,<sup>8</sup> one could think of different scenarios where this assumption does not hold. Dang and Lanjouw (2013) obtain a point estimate of poverty dynamics by assuming normality of the error terms and using the agecohort correlation of residuals obtained from cross-sectional data in the first and second round to produce a point estimate of inter-generational poverty mobility. We could infer the value of γ by

> 7 Results are similar for the 2010-2011 period.

> 8 Residual correlations from panel data estimates are on average about 0.65 in Dang and Lanjouw (2013), about 0.50 in Dang et al. (2014), and about 0.45 in Cruces et al. (2015). Tables 2 and 3 show that correlation is about 0.50 in Peru between 2007 and 2011. These results provide support to the assumption of γ = 0.5.

10
as certain outcomes (specific durable categories IT items and services) are not collected at baseline. However, even if specific outcomes are not recorded at baseline we could still implement both other approaches by replacing the baseline missing information with durable items that are correlated with the missing ones (e.g. the full set of durables). In equation (2) we use a standard difference-in-differences approach, where the parameter of interest is _α_ 3 describing the excess variation in the outcomes between treated and controls with **1** _{_ 2017 _t}_ an indicator dummy for the 2017 wave. Equation (3) is commonly referred to as an ANCOVA specification which allows for some extra flexibility with respect to the DiD and is more appropriate from an inferential viewpoint. In the latter specification the parameter of interest is _β_ 1, while _Yij_ (1997) indicates the outcome value at baseline (1997) (see McKenzie 2012).

**Lifecycle and Intergenerational Effects.** For the analysis of the lifecycle and intergenerational effects of PROSPERA, we employ a modification of equation 3, as is standard in this literature, by relating baseline outcomes (for the original household when appropriate) to current ENCEL 2017-18 outcomes. We explore the effects on mobility over generations and across treatment status using:

where ∆ _Yij_ = _Y_ ( _G_ 2) _ij_ (2017) _− Y_ ( _G_ 1) _ij_ (1997) gives the change in the outcome through time for the children with respect to their parents. _Y_ ( _G_ 2) _ij_ (2017) is the outcome of the next generation or new households (denoted with _G_ 2) observed in 2017, while _Y_ ( _G_ 1) _ij_ (1997) is the outcome of their parents ( _G_ 1) observed at the time the program was about to start: the baseline (ENCASEH 1997). The coefficient _η_ 2, is essentially one-minus-the coefficient of intergenerational persistence as it indicates the stability of outcomes across generations. In this formulation we are also interested in both _η_ 1 and _η_ 3, as the first is the baseline effect of the policy with respect to control individuals or households while _η_ 1 + _η_ 3 is the corresponding parameter for the early treated. If we take equation (4) and replace _Y_ ( _G_ 2) _ij_ (2017) with _Y_ ( _G_ 1) _ij_ (2017) (parents’ outcome observed in 2017), _η_ 1 and _η_ 3 would give information about lifecycle effects for the treatment group. Following Chetty et al. (2014), in this analysis we employ as outcomes the relative position of the household using the percentile of three different measures: schooling, a durable asset index and income. Each percentile is calculated within groups formed by 10-year window age ranges.

# **V.B Analysis Using the Intercensus**

To avoid the small window provided by the early versus late treatment identification, we complement the previous analysis with a difference-in-difference (DiD) strategy that employs the program’s roll-out and different age groups. For this purpose, we create four groups of localities based on their enrollment year into PROSPERA: (i) E1, which indicates localities enrolling 17
Policy Research Working Paper 9813

# **Abstract**

This paper investigates the relationship between the use of service inputs and integration in global value chains. Using macro and detailed firm-level data (for 1990–2017), the study documents the extent of India’s integration into global value chains. Older, larger, and more productive firms and firms with a higher leverage ratio are more likely to be deeply integrated into global value chains. Firms in the information technology services and electronics industry are more deeply integrated into global value chains, compared with textiles. Services are the engine for many global value chain industries as they help coordinate the different stages of production across geographical locations. The findings suggest that both the intensity of service usage as well as the composition or type of service used are important. Firms using service inputs, particularly complex services and information technology and information technology–enabling services intensively are typically more deeply integrated into global value chains.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rmanghnani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
three parameters, the user can specify a variety of models. For instance, the random walk process for the logs of exchange rate can be easily specified by putting a unit coefficient for the lagged value and zeros for all other. If the user does not know the values for some parameters, he can check the _estimated_ option, then _INFRISK_ will use the data specified at the _data for estimation_ textbox to obtain its own estimate.

The _Random Component_ section allows the user to specify the distribution function, and other parameters associated with it. If the data are expected to possess a stochastic term, the user can provide the parameters for the error term, namely its distribution and the standard deviation. Several options can be used to specify the standard deviations for the random process:

- User-defined standard deviation

- Estimated from the historical data (the location to historical data is specified in the _data for estimation_ text box) using their residuals from the trend or deviations from a random walk model (first differences), when the projected values are used as the mean.

- Estimated from the ranges provided by the user in the input sheet. The ranges must be labeled following the format <VarLabel>Range (for instance, "ORRange").

In this section, the user can also specify the following:

- _Distribution._ Allows the user to select the probability distribution function for the error term. Presently, _INFRISK_ can handle four different error distributions: Normal, Lognormal, Beta, and Uniform. The "Beta distribution" option allows the user to model the errors around the trend using a right-skewed distribution based upon a member from the _beta_ family (as an example, see Box 4 for a formal description of _Beta_ distribution, its probability density function, and some important parameters).

- _Positioning._ Specifies whether the selected distribution is positioned around, above, or below the trend. Also the user can _truncate_ all generated values above or below the mean value of the given distribution.

- _Correlated With_ option allows the user to impose a certain correlation between pairs of random variables. For instance, in our example, the random component of _Revenues_ can be correlated with the random component of the _Maintenance costs_ (for a toll road project, it is safe to assume that toll road revenues are positively correlated with maintenance costs). The value of the correlation coefficient can be any number from -1 to 1 and is specified in the _correlation coefficient_ edit box.

18
where ����� ∆ �������� �����,� is the log change in tradable jobs instrumented by Bartik�,�.

# **4. Data**

County employment data by industry are from the County Business Patterns (CBP) dataset. Data are from 1998 to 2015, where the NAICS classification is available. CBP data are recorded in March each year. CBP employment data at the four-digit industry level are used. County data at the fourdigit industry level are sometimes suppressed for confidentiality reasons. However, the Census Bureau provides a range within which the employment number lies. As in Mian and Sufi (2014), we take the mean of this range as a proxy for the missing employment number in such cases.

Following Mian and Sufi (2014), a 4-digit NAICS industry is defined as _tradable_ if it has international imports plus exports equal to at least $10,000 per worker, or if total exports plus imports exceed $500 million. There are 82 fourdigit tradable sectors. They consist of all manufacturing, oil and gas extraction, and mining. Tradable industries on average account for about 15% of a county’s total employment. _Construction_ industries are those that are related to construction, real estate, or land development. On average, they account for 12% of a county’s workforce. _Retail and restaurants_ include local retailers and restaurants. On average, they account for 21% of the county’s employment. The remaining industries are classified as _other services_ . They consist of wholesales, transportation, finance, schools, hospitals etc. They do not include government services. On average, they account for about 52% of a county’s workforce.<sup>3</sup>

> 3Please see <u>http://faculty.chicagobooth.edu/amir.sufi/data-andappendices/unemployment_miansufi_EMTRA_final_APPENDIX.pdf for a complete industry</u> classification.

11
# WITHIN-SCHOOL TRACKING IN SOUTH KOREA AN ANALYSIS USING PISA 2003

Kevin Macdonald<sup>1</sup> and Harry Anthony Patrinos<sup>2</sup>

> 1 Consultant, Human Development Network – Education Team, World Bank; <u>kadmacdonald1@worldbank.org</u> 2 Lead Education Economist, Human Development Network – Education Team, World Bank (corresponding author) <u>hpatrinos@worldbank.org</u>

The opinions expressed in this paper are that of the authors alone and should not be attributed to that of the World Bank Group.
**Table 6.6 Health Benefits of Ozone Air Pollution Reduction in the ZMVM** _(in million US$ per year_ **_a_** _for income elasticity 0.4, 2010 values in 1999 prices)_

|||_Sc_<br>|_enario_<br>||
|---|---|---|---|---|
|_Endpoints_|_10%_|_20%_|_AQSI_|_AQS2_|
|**Morbidityimpacts(Prod.Loss+ COI+ WTP)**<br>|||||
|_Hospitaladmission_|||||
|Respiratory<br>|7.43<br>|14.86<br>|45.95<br>|50.90<br>|
|Cardiocerebrovascular|5.25|10.50|32.45|35.94|
|Emergencyroomvisits|||||
|Respiratory|6.30|12.60|38.95|43.14|
|_MinorRestrictedactivitydays_|||||
|Total (adults)<br>|86.49|172.98|534.79|592.28|
|_Effectsinasthmatics_|||||
|Somerespiratorysymptoms (children)|0.02|0.04|0.11|0.12|
|Lowerrespiratorysymptoms|0.01|0.02|0.06|0.06|
|_Respiratory symptoms_|||||
|Upperrespiratorysymptoms<br>|3.67<br>|7.34<br>|22.69<br>|25.13<br>|
|Lowerrespiratorysymptoms|2.14|4.28|13.22|14.64|
|Wheeze|1.23|2.46|7.60|8.41|
|**Morbidityimpacts(Prod.Loss+ COI)**|||||
|_Hospitaladmission_|||||
|Respiratory|6.34|12.69|39.22|43.44|
|Cardiocerebrovascular|4.97|9.94|30.74|34.04|
|_Emergencyroomvisits_|||||
|<br>Respiratory|2.65|5.29|16.37|18.13|
|_Effectsinasthmatics_|||||
|Somerespiratorysymptoms(children)|0.00|0.01<br>|0.02<br>|0.03<br>|
|Lowerrespiratorysymptoms|0.00|0.00|0.01|0.01|
|_Respiratory symptoms_|||||
|Upperrespiratorysymptoms|1.19|2.37|7.33|8.12|
|Lowerrespiratorysymptoms|0.69|1.38|4.27|4.73|
|Wheeze|0.19|0.39|1.20|1.33|
|**Mortalityimpacts-WTP**|||||
|<br>Mortality-acuteexposure(total)|70.07|140.13|433.25|479.83|
|**Mortalityimpacts-Humancapitallosses**|||||
|Mortality-acuteexposure(total)|1.67|3.34|10.33|11.44|
|Total- Morbidity(Prod.Loss+COI+WTP)and|183|365|1129|1250|
|WTPformortality|||||
|Total- Morbidity(Prod.Loss+COI+WTP)and|114|228|706|782|
|humancapitallossesmortality|||||
|Total- Morbidity(Prod.Loss+COI)and|18|35|109|121|
|humancapital losses mortality|||||
|a.Discountrate 3%.|||||

_38_
# **Table 1. The payoff tables**

# **A Trader’s payoff if the Government chooses a LOW quantity**

||<br>**20**|<br>|**The other trad**<br>**40**|<br>**er’squantity**<br>**60**|<br>**8**|**0**|
|---|---|---|---|---|---|---|
|**20**<br>**ty**|2763|2763|2367<br>4733|1971<br>5912|1575|6299|
|**40**<br>**quanti**|4733|2367|3941<br>3941|3149<br>4724|2357|4715|
|**60**<br>**y own**|5912|1971|4724<br>3149|3536<br>3536|2348|3131|
|**M**<br>**80**|6299||4715|3131|1547||
|||1575|2357|2348||1547|
|My ownp<br>a<br>**A Trader’s**|yoff is wr<br>**payoff i**<br>|<br> <br>itten in<br>**f theGo**<br>|**red**, th<br>eother tra<br>**vernment choos**<br>**The other trad**<br>|der’spayoff in**b**<br>**es aHIGH**<br> **qua**<br>**er’squantity**<br>|**lue**.<br>**ntity**<br>|<br>|
||**20**||**40**|**60**|**8**|**0**|
|**20**<br>**ty**|614|614|218<br>436|-178<br>-534|-574|-2296|
|**40**<br>**quanti**|436|218|-356<br>-356|-1148<br>-1722|-1940|-3880|
|**60**<br>**y own**|-534|-178|-1722<br>-1148|-2910<br>-2910|-4098|-5464|
|**M**<br>**80**|-2296|-574|-3880<br>-1940|-5464<br>-4098|-7048|-7048|

My own payoff is written in **red** , the other trader’s payoff in **blue** .

# **The Government’s payoff**

|**Government’s**<br>|||**The trade**|**rs’ TOTA**|**Lquantity**|||
|---|---|---|---|---|---|---|---|
|**quantity**|**40**|**60**|**80**|**100**|**120**|**140**|**160**|
|**Low**|913|1111|1309|1507|1705|1903|2101|
|**High**|1528|1726|1924|2122|2320|2518|2716|

10