rafmacalaba/datause-encoder-data huggingface dataset:
# **ANNEXES**

## **Annex I.  Methodology**

1. The market price, the “cost of goods sold” (CoGS), includes: production costs, profit margins of 10 percent, distribution costs from the plants to the retailer, and a value-added tax of 10 percent.  The subsidy was calculated as the difference between the CoGs price and the actual price paid by the farmer, while the total subsidy spending  per quintile used the formula:

### ∑ (CoGs – Farmer’s price) x quantity used by the farmer.

2. The ceiling price (HET) for urea in 2003 was Rp 1,050 per kg up to July and the MoA revised the figure to Rp 1,150 for August. Therefore, the analysis takes the ceiling price of urea for 2003 as Rp 1,150 to better capture higher fertilizer consumption during the rainy season months (from September to December). The ceiling price of urea in 2007 was Rp 1,200 per kg, while for SP-36 was Rp 1,500 per kg in 2003 and Rp 1,550 per kg in 2007.

4. The source for the CoGS for urea in 2007 is the State Audit Agency Report 2008 (BPK). This analysis uses a weighted average of the CoGs prices from the four domestic producers, while the CoGS per kg = total CoGS/amount of subsidized fertilizer. These are reported below.

|**Producer**|**Production**|**CoGS**|**Weighted CoGS**|
|---|---|---|---|
|PETRO GRESIK|318,959|1,850|136.6|
|PUSRI|1,395,009|2,068|668.1|
|KUJANG|753,870|2,217|387.0|
|KALTIM|1,850,505|2,284|978.7|
|CoGS of ureaper Kg (Rp)|||**2,170.5**|

_Source_ : World Bank staff calculations.

5. The CoGS of urea for 2003 was unavailable in the State Audit Agency and is an estimate. The CoGS prices of urea in 2006 and 2007 were available, as well as the CoGS of SP36 in 2003, 2006 and 2007 from the PT Petrokimia Gresik State Audit Agency Reports (which is the sole producer). Therefore, the calculation of the CoGS for urea in 2003 maintains the price ratio of urea to SP36 (using an average of the observed ratios for 2006 and 2007) times the CoGS of SP36 in 2003. This preserves the price relationship between both fertilizers, as SP-36 was historically sold at higher prices than urea.

|**Year**|**CoGS of Urea(Rp/kg)**|**CoGS of SP-36**<sup>**b**</sup>**(Rp/kg)**|**Ratio(a/b)**|
|---|---|---|---|
|2007|2,170.5|2,395|0.906|
|2006|2,054|2,227.8|0.921|
|2003|**1,489**|1,628.8|**0.9135**|

_Source_ : World Bank staff calculations.

- a. The non-subsidy group represents farmers that paid above or equal to the CoGS prices.

- b. The partial-subsidy group captures farmers paying above the HET, but below the CoGS prices.

- c. The full-subsidy group takes farmers paying at or below the HET price.

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# **The Sovereign‐Bank Nexus in EMDEs: What Is It, Is It Rising, and What Are the Policy Implications?**

Erik Feyen<sup></sup> Igor Zuccardi JEL Classification Numbers: F34, F65, G21.

Keywords: Sovereign‐bank nexus, fiscal balance, public debt, bank exposure, probability of default, Emerging and Developing Economies.

 Erik Feyen is Head of Macro‐Financial Unit and a Lead Financial Economist in the World Bank’s Finance, Competitiveness, and Innovation (FCI) Global Practice. Igor Zuccardi is a Financial Economist in the Macro‐Financial Unit.

The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank. This paper first appeared as a Special Topic in the October 2018 issue of the _Macro‐Financial Review_ report which is co‐produced by the Finance, Competitiveness and Innovation (FCI) and Macroeconomics, Trade, and Investment (MTI) Global Practices under the auspices of the Equitable Growth, Finance and Institutions (EFI) Vice Presidency. We are grateful to Paloma Anos Casero, Mariano Cortes, Doerte Doemeland, Alfonso Garcia Mora, Nigel Jenkinson, Lars Jessen, Naoko Kojo, Bill Maloney, and members of the Financial Stability Board’s Analytical Group on Vulnerabilities for discussions and comments. We thank the following Focal Points of the _Macro‐Financial Review_ for regional perspectives which are reflected in Box 5: Ana Maria Aviles and Richard Record (East Asia and Pacific region), Eva Gutierrez, Raquel Letelier, Alena Kantarovich, and Elisa Gamberoni (Europe and Central Asia region), Syed Mehdi Hassan and Emmanuel Pinto Moreira (Middle East and North Africa), and Marius Vismantas and Muhammad Waheed (South Asia). Box 6 and Box 7 are in part based on work by Krishnamurti Damodaran and Barend Jansen, respectively. We also thank Federico Diaz Kalan for assistance with the data, and Diego Sourrouille and Jiemin Ren for research assistance. For questions, please contact the authors at efeijen@worldbank.org and <u>izuccardi@worldbank.org.</u>
attacks. The consumption basket of exposed households was 3% more expensive for these items, providing some evidence that higher food prices could also be another relevant mechanism through which a terrorist incident affects consumption levels.<sup>26</sup> Figure 4: Distributional effect across consumption percentiles in Mogadishu <!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>p=0.0 p=0.1 p=0.2 p=0.3 p=0.4 p=0.5 p=0.6 p=0.7 p=0.8 p=0.9 p=1.0<br>Consumption percentile<br>Estimated impact on log of core consumption<br><!-- End of picture text --> Source: Authors’ calculations based on data from the SHFS and ACLED. Note: The vertical lines depict 95% confidence intervals.

We extend the analysis to investigate how the impact on consumption differs across consumption percentiles and the spatial variation of this negative effect.<sup>27</sup> For these extensions, we use our most complete specification from the DiD approach, the sample of households from Mogadishu and the sampling weights of surveys. Figure 4 plots the point estimates for the effect of incidents on core consumption from a quantile regression. For the negative and immediate impact of terrorist attacks on consumption, we find a heterogeneous effect across different parts of the consumption distribution. The point estimates increase with the consumption decile. Incidents affect exposed households from most of the consumption distribution, except those in the top 20%. The estimates for deciles 1, 2 and 3 are either significant at the 10% level or not significant because these groups are underrepresented in the survey sample considered.<sup>28</sup> Most of the households

> 26 It is unlikely that the difference in cost of consumption baskets is explained by a seasonal pattern as all interviews of households in these groups were conducted within a period of 13 days.

> 27 We cannot extend the analysis to measure the effect beyond 7 days after an incident occurred as only one exposed household was interviewed in this period during data collection of Wave 2.

> 28 Households in the bottom 30% of the consumption distribution accumulate 40% of the sum of sampling weights from the total sample, ultimately indicating they are underrepresented in the survey data.

19
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

within the margin of error to that found by Deville et al (2014) for France and Portugal, 0.77 ± 0.055, suggesting a stable relationship across countries. In addition, the R^2 value suggests that 85 percent of the variation in density in the World Pop data is explained by variation in the CDR data. The R^2 is lower for the census, given that the data is older. These results confirm that CDR data are valid for examining the population distribution in terms of their residential locations. By extension, shifts in CDR data can capture both short term and long-term shifts in population over time, with implications for disaster risk management and urban planning.

<mark>Table 2.</mark> Correspondence between known population data and CDR data

||(1)<br>Log (WorldPop density)|(2)<br>Log (Census density)|
|---|---|---|
|Log (Subscriber density)|0.675***|0.596***|
||(0.066)|(0.12)|
|Constant|2.98***|4.76***|
||(0.619)|(1.339)|
|Regional fixed effects|Yes|Yes|
|R^2|0.846|0.640|
|N of observations|45|45|

a Standard errors are reported in parentheses. *, **, and *** indicate significance at the 90%, 95%, and 99% level, respectively.

# **B. Patterns of phone usage remained near-constant in terms of the number of active subscribers**

Overall cell-phone use remains stable over the period of observation. We use the number of active subscribers computed for Indicator 3 to examine whether the pattern of phone usage changed against interventions and events over the period of observation. Figure 4 shows the number of active subscribers, which is presented as the ratio to the baseline. We use the average of the number of active subscribers for the first two weeks of March. While we observe a short term, sharp decrease at the beginning of the pandemic, with some erratic behavior in the following weeks, these fluctuations soon subside with a return to baseline levels of activity on average.

<mark>Figure 4.</mark> The number of active subscribers in The Gambia - ratio to the baseline

**12** | P a g e
We find a far more muted cross-country relationship between grammatical gender and women’s educational attainment. This may be due to the fact that the average withincountry gender gap in educational attainment is much smaller than the gender gap in labor force participation — since many wealthy countries have no gender gap in educational attainment, particularly at the primary school level. The prevalence of gender languages is negatively associated with the gender gap in both primary and secondary school completion after controlling for continent fixed effects, but the estimated relationship is not statistically significant at conventional levels.

Using data from the World Values Survey (WVS), we show that grammatical gender predicts support for traditional gender roles. The coefficient estimate is large in magnitude, suggesting that differences in language could explain the entire gap in gender attitudes between Ukraine (at the 55<sup>_th_</sup> percentile of WVS countries in terms of support for gender equality) and Trinidad and Tobago (at the 80<sup>_th_</sup> percentile). As Whorf might have hypothesized, gender languages are associated with greater support for traditional gender roles among both men and women.

Though our analysis uses much richer country-level data on grammatical gender than has previously been available, mis-measurement of our independent variable of interest is still a concern. We would typically expect measurement error in the independent variable to bias the estimated association toward zero, but the interval nature of our measure of the country-level prevalence of grammatical gender (when the gender structure of the native language is not known for the entire population) can also lead to invalid inference. Using a bounding technique proposed by Imbens and Manski (2004), we show that our results are robust to correcting for the censored nature of our independent variable of interest.

A more serious inference concern arises from the fact that languages are not independent. Within a language family, individual tongues have evolved in parallel over many centuries. While this slow process of language development may help to address potential concerns about reverse causality, it complicates statistical inference. Intuitively, languages need to be clustered within families, but countries draw from many different clusters. We 5
the average value of each variable for each MCU (weighted by population size in each city), and assigned it to each city in the MCU.

Fourth, we use information on the institutional development of the city, published by IBGE, used in Naritomi, Soares and Assuncao (2007), and kindly made available by the authors. These measures include an index of the access to justice in the city, an index of managerial capacity in the city and an index of political concentration in the city (based on a HirshmanHerfindhal index of the shares of the political parties). The index of managerial capacity in the city measures the quality of local administration, and is used by the Ministry of Planning to monitor the administrative performance of cities. Access to justice measures the penetration of the rule of law, in particular the existence of courts or justice commissions in the city. We also consider state aggregates of these variables, by averaging across cities.

Fifth, we compute the distance and travel time (by car) between each city and the nearest _subdelegacia_ in the state. The transportation of inspectors from the _subdelegacia_ to each firm is made using ground transportation, usually car. Hence, the enforcement of labor regulation will be easier and less costly the closer a _subdelegacia_ is from the city where the firm is located. We construct a measure of the accessibility of inspectors to firms by using the travel time from each city to the nearest _subdelegacia_ within the state (minimum distance). Data on travel times and travel distances between any two Brazilian cities is available from one of the largest Brazilian auto insurance companies (BB), which collects very detailed information on distances across cities.<sup>27</sup> When firms are located in cities that have a _subdelegacia_ the measure assumes the value zero. We also construct the distance between each city and the state capital. In the remaining of the paper we focus on travel time as the most relevant measure of distance. A third measure of the remoteness of the city, or of its access to markets, is an index of transportation costs between each city and the nearest capital city taken from IPEA (1995). Sample statistics for the main variables we use are presented in table 1.

There are time discrepancies between the different variables. Notably, enforcement is measured in 2002, while labor market outcomes are measured in 2000. This is due to limitations

> 27 This information is available online at www.bbseguroauto.com.br. When collecting information on distances. We have faced two obstacles First, could not find information online for those cities that have only recently been recognized as cities. In these cases, we have located the closest nearby city (using maps) and used that information instead. Second, most on the cities in Amazonas use the maritime rather than the ground transportation both for goods or persons. Hence, the travel distance by car is meaningless for this state and, hence, we have excluded it from the analysis.

18
19 <!-- Start of picture text --> 1200<br>800<br>400<br>0<br>-400 71291094" ' iYi&945'5'95"" _ 9i225""2'i09 6' "'&28/96<br>___ DIFF=MEXDIS-POLDIS<br><!-- End of picture text --> _Figure 7_ The Johansen Cointegration Test is reported below: _Sample: 7/29/94 - 7/12/96_ _Included observations: 101_ _Test assumption: No deterministic trend in the data Series:_ **_MEXDISI POLDIS_** _Lags interval: I to I_

||Likelihood|5 Percent|I Percent|Hypothesized|
|---|---|---|---|---|
|Eigenvalue|Ratio|Critical Value|Critical Value|No. of CE(s)|
|0.259056|32.22498|19.96|24.60|None|
|0.019045|1.942089|9.24|12.97|At most I|

**L.R. test indicates 1 cointegrating equation(s) at 5% significance level**

Unnormalized Cointegrating Coefficients:

|MEXDIS1|POLDIS|C|
|---|---|---|
|0.000880|-0.000924|0.152483|
|-5.53E-05|-0.000259|0.084977|
|Normalized|Cointegrating|Coefficients: 1 Cointegrating Equation(s)|
|MEXDIS1|POLDIS|C|
|1.000000|-1.050131|173.3165|
||(0.10657)|(62.6566)|
|(Log likelih|ood -1273.92|7)|
in poorer countries over the past few decades has come in the form of improved health and that the economic contribution of these longevity increases is important.

A formal approach used to calculate the economic value of increased longevity is based on translating increases in survival rates into incremental annual incomes required to yield the same utility level as with the original survival probability (Becker, Philipson et al. 2003) and is presented in Annex 1 in Laxminarayan (2006).

# **Valuing improvements in health**

To value the improvement in health status represented by TB-control programs, an appropriate “price” to place on health must be found. Although most would agree that saving someone from certain death is a moral imperative that a value cannot be placed on, preventing every single probabilistic death is unaffordable and infeasible, even in the wealthiest countries in the world. The term "value of a statistical life" (VSL) is used widely in economics and regulation to denote not the value placed on a particular life but on the public-health measures that can reduce the statistically expected number of deaths by one.

Three principal approaches are used to evaluate VSL or willingness to pay for reducing risks to life. The most common approach is based on wage-risk tradeoffs, whereby workers are paid risk premiums to accept jobs with a higher risk of death or injury. The VSL is defined as the willingness to pay for a risk reduction divided by the risk reduction. Therefore, if lifetime wages for a high-rise construction worker with a 1/1,000 greater probability of death on the job are $500 more than for a worker with a similar job but with a lower risk of death, VSL is calculated as $500,000. VSLs are estimated through revealed-preference approaches (as distinct from stated-preference methods where respondents are asked how much they would hypothetically pay for lower risk of death), such as hedonic wage studies that use labor-market data to estimate the effect of morbidity and mortality risk on wage differences between occupations with differing levels of risk, after controlling for other variables that would explain wages. For example, all else being equal, a construction worker employed on a high-rise building 10
# **The Impact of a Community Development and Poverty Reduction Program on Early Childhood Development in Morocco**

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:** Community development; poverty reduction; early childhood development; impact evaluation

**JEL codes:** I38; I32; I15; I25; J13

# **Acknowledgements**

The authors would like to acknowledge the assistance of the National Human Development Observatory ( _Observatoire National du Développement Humain_ , ONDH) of Morocco. We particularly appreciate the generosity of ONDH 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.
Policy Research Working Paper 7478

# **Abstract**

How much do poor rural households rely on environmental extraction from natural ecosystems? And how does climate variability impact their livelihoods? This paper sheds light on these two questions with household income data from the Poverty and Environment Network pantropical data set, combined with climate data for the past three decades. The study finds that extraction of wild resources (from natural forests, bushlands, fallows, etc.) provides on average as much income (about 27 percent) as crops across the smallholder sample. The cross-section data on past reactions to household self-perceived economic shocks and observed production reactions to climate anomalies can, respectively, provide hints about livelihood vulnerability to current climate variability, which is likely to worsen with climate change. Forest extraction did not figure among the most favored response strategies to households’ selfperceived economic shocks, but households undertake subtle substitutions in sector production in response to weather anomalies that accentuate suboptimal climatic conditions for cropping. By relying more on forest extraction and wages, households compensate quite successfully for declining crop incomes. This paints a cautiously optimistic picture about fairly flexible rural livelihood reactions to current climate variability, and featuring forests as potentially important in household coping strategies.

This paper was commissioned by the World Bank Group’s Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank Group’s flagship report: “Shock Waves: Managing the Impacts of Climate Change on Poverty.” 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://econ.worldbank. org. The authors may be contacted at frederik.noack@gmx.de and S.Wunder@cgiar.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
# **D.1.1 Economic intuition from Markov GEEs**

The GEEs derived here provide another way to understand the intuition behind Proposition 3. Rewriting (D.11): This condition characterizes the marginal welfare effects of increasing the transfer rate _τ_ . The signs in parentheses in the GEEs are based on calibrated values of Section 3. Using Implicit Function Theorem, ratios of _η_ -derivatives have the interpretation of marginal effects holding other variables of the local government’s Euler equation constant. For example, _−ητ_ / _ηbn′_ gives the effect of changing _τ_ on _b_<sup>_n′_</sup> holding constant _b_<sup>_n_</sup> and _d_<sup>_′_</sup> .<sup>54</sup> The right-hand side of (D.13) comprises of three marginal welfare effects of increasing the transfer rate _τ_ . First, intra-temporally, a higher transfer _τ_ shifts spending from central to local governments in the current period. Second, _τ_ changes _b_<sup>_n′_</sup> (by _−ητ_ / _ηbn′ <_ 0 amount), which shifts spending inter-temporally. Third, _τ_ changes _τ_<sup>_′_</sup> through changing _b_<sup>_n′_</sup> , which affects within-period spending sharing between central and local governments in the next period.

The consolidated government achieves perfect resource sharing: _u_<sup>_n_</sup> _g_<sup>=</sup><sup>_uo_</sup> _g_<sup>=</sup><sup>_θ_/(1</sup><sup>_−θ_)</sup><sup>_vc_(MULG = MUCG).</sup> This essentially corresponds to the first line of (D.13). In addition, at the steady state, the third line of (D.13) also collapses into the perfect resource sharing condition. This leaves the second line of (D.13) as the major difference between the consolidated government allocation and the non-commitment (Markov) equilibrium. Specifically, the second line demonstrates that the central government’s marginal value of increasing _b_<sup>_n′_</sup> is different from the marginal value perceived by local governments. To see this, we decompose the inter-temporal effect of raising _b_<sup>_n′_</sup> (second line of D.13) into three parts: the marginal benefit from higher local spending today, the marginal cost of lower local spending tomorrow, and the additional marginal cost to the central which captures the marginal welfare effects of increasing _τ_ , through changing _d_<sup>_′_</sup> while holding _b_<sup>_n′_</sup> constant. For the purpose here we only need to focus on (D.13).

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