rafmacalaba/datause-encoder-data huggingface dataset:
Figure 9: Specification Charts for Number of Days With No Rain <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.02 .02<br>.01 .01<br>0<br>0 -.01<br>* Weather Product * -.01 * Weather Product * -.02<br>TAMSAT -.02 TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.05 .02<br>.01<br>0 0<br>-.01<br>* Weather Product * -.05 * Weather Product * -.02<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.005 .01<br>0 0<br>-.005 -.01<br>-.01 -.02<br>* Weather Product * * Weather Product *<br>TAMSAT TAMSAT -.03<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

40
Policy Research Working Paper 6831

# **Abstract**

The 2007–2008 global food crisis has renewed interest in post-harvest loss, but estimates remain scarce, especially in Sub-Saharan Africa. This paper uses self-reported measures from nationally representative household surveys in Malawi, Uganda, and Tanzania. Overall, onfarm post-harvest loss adds to 1.4–5.9 percent of the national maize harvest, substantially lower than the Food and Agriculture Organization’s post-harvest handling and storage loss estimate for cereals, which is 8 percent. Postharvest loss is concentrated among less than a fifth of households. It increases with humidity and temperature and declines with better market access, post-primary education, higher seasonal price differences, and possibly improved storage practices. Wider use of nationally representative surveys in studying post-harvest loss is called for.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region. 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 lchristiaensen@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
**<mark>Table 5: Willingness of Unemployed to Work Temporarily in Different Positions</mark>**

||All Unemployed|Males|Females|
|---|---|---|---|
|Human Resource Management|0.78|0.84|0.75|
|Public Sector Employee|0.77|0.86|0.72|
|Administrative Assistant|0.71|0.75|0.70|
|Data Entry|0.70|0.62|0.74|
|Bank Teller|0.68|0.75|0.65|
|Teacher|0.61|0.46|0.68|
|Customer Service|0.54|0.66|0.48|
|Indoor Sales|0.29|0.46|0.21|
|Telemarketing|0.25|0.16|0.30|
|Outdoor Sales|0.10|0.20|0.05|
|Waiter|0.03|0.08|0.00|

<mark>Source: May 2013 endline survey of matching experiment participants. Sample restricted to 472 unemployed, educated, youth.</mark> 35
# **5. Conclusions**

In this paper, we used data from the 2006 VHLSSs and probit regressions to estimate the effect of parental disability on education of children age from 6 to 17. Our estimates indicate that disability among parents reduces the school attendance rate of their children around 8.1 percentage points from 81.3 percent to 73.2 percent. The effect tends to be larger for boys, Kinh children, and children in urban areas. We found an especially strong effect of parental disability on children aged from 15 to 17, i.e., upper secondary school age children. However, disability of parents does not have a significant effect on the academic performance of their children in school.

There can be several reasons why children whose parents are disabled are less likely to attend school. First, parental disability can reduce the education as well as income of parents, which then decreases child education. Second, children might have increased responsibilities at home if their disabled parents cannot generate a sufficient livelihood or if they require personal assistance that they cannot access outside the family. Third, children might be reliant on their parents to physically get to school, which disabled parents – e.g., facing transportation barriers – cannot do. Fourth, the increased costs and demands on disabled parents‟ time might leave them less time and energy to look after their children‟s education. This fourth reason, however, is not supported by the fact that once they attend school, children of disabled parents have the same level of achievement as their peers.

These findings suggest clear policy implications. To achieve the Millennium Development Goal of universal primary school as well as increased coverage of secondary education, the government should have policies and programs supporting the education of children whose parents have a disability. One approach would be to implement education supports, such as a reduction or exemption of tuition fees and contributions or possibly assistance with transportation. But an approach which gets more directly at the costs of disability would be policies that provide supports to parents (such as rehabilitation) or help build a more inclusive environment so that a parent having a functional limitation imposes less of an imperative for their children to forego their education to address their family‟s immediate needs.

16
Combined, the summary statistics indicate fat tails and higher frequency of occurrences of sudden declines in financial sector value added than predicted by a symmetric normal distribution, corroborating that notion that financial industry, while growing over the long-run, is subject to abrupt, periodic contractions. Furthermore, this feature is more pronounced in emerging markets and a sub-group of developed countries, namely Denmark, Italy, Sweden, and the United States.

# Table 2 [about here]

Table 2 shows pairwise correlation statistics for sectoral growth rates. The highest degree of contemporaneous correlation is observed between public utilities and financial sector followed by construction and financial sector (with correlation coefficients of 0.84 and 0.66 respectively).

# **3. Methodology**

# **a. Regression analysis**

Let denote the growth rate of real value added of the financial sector. First, we examine data stationary. Non-stationary data can results in spurious correlation biasing coefficients upward. We implement the Augmented Dickey-Fuller (ADF) panel unit root test to test the null of against the alternative that in the following specification: Table 3 reports the results. The p-values indicate that the null of unit root is strongly rejected, indicating that the financial sector growth series is stationary. Furthermore, Table 4 reports intermediate ADF test results, with p-values indicating that the null of unit root is rejected for each country in the sample in favor of stationarity.

7
# **Appendix A: Details of Question Design, Survey Implementation, and Item Responses Rates**

## A.1 Wording of Questions in English and Spanish, and Show Card

|**ASCD.9**|When making agreeme<br>**effective** **in resolving o**<br>N|nts with**su**<br>**rpreventi**|**ppliers**, please<br>**ng **problems.**[ **|indicate to<br>**READ OU**<br>V|what degree e<br>**T OPTIONS**|ach of the following is<br>**] SHOW CARD 8**<br>**DON'T KNOW**|
|---|---|---|---|---|---|---|
||ot at<br>all|Slightly|Moderately|ery<br>much|Extremely|<br>**(SPONTANEOUS)**|
|Personal relationsh<br>**ASCd9a**|ip and trust<br>1|2|3|4|5|**-9**|
|Mutual interest in<br>business relationsh<br>involvingothers|maintaining<br>ip, without<br> **ASCd9b**<br>1|2|3|4|5|**-9**|
|Paid, private disput<br>**ASCd9d**|e resolution<br>1|2|3|4|5|**-9**|
|Assistance of gove<br>officials|rnment<br> **ASCd9e**<br>1|2|3|4|5|**-9**|
|Intervention of oth<br>(excluding paid, pr<br>resolution<br>and government of<br>**ASCd9c**|er third-parties<br>ivate dispute<br>ficials)<br>1|2|3|4|5|**-9**|
|Legal system|**ASCd9f**<br>1|2|3|4|5|**-9**|

**ASCD.9** Por favor indique en qué medida las siguientes circunstancias son efectivas para resolver o evitar <u>problemas en los acuerdos con proveedores.</u> **LEER OPCIONES -MOSTRAR TARJETA 8**

||Para<br>nada|Ligera-<br>mente|Modera-<br>damente|Bastante|Extreme-<br>damente|**NO SABE**<br>**(ESPONTÁNEO)**|
|---|---|---|---|---|---|---|
|Relaciones personales y confianza<br>**ASCd9a**|1|2|3|4|5|**-9**|
|El interés mutuo de mantener una<br>relación de negocios sin tener que<br>involucrar a terceros**ASCd9b**|1|2|3|4|5|**-9**|
|Mecanismos de resolución<br>privados ofrecidos por terceros y<br>que sonpagados**ASCd9d**|1|2|3|4|5|**-9**|
|Ayuda de funcionarios del<br>gobierno**ASCd9e**|1|2|3|4|5|**-9**|
|Intervención de otros terceros<br>(excluyendo entes privados y<br>pagados y personas del gobierno<br>**ASCd9c**|1|2|3|4|5|**-9**|
|Recurso al sistema legal<br>**ASCd9f**|1|2|3|4|5|**-9**|

6
# **The Redistributive Effects of Fiscal Policy in Mali and Niger**<sup>**1**</sup>

**Thierry Hounsa, Mohamed Coulibaly, and Aly Sanoh**

**Keywords** : fiscal policy, fiscal incidence, social spending, inequality, poverty, taxes

**JEL Classification** : H22, I38, D31

> 1 Thierry Hounsa, Mohamed Coulibaly, and Aly Sanoh (World Bank, Poverty and Equity Global Practice) prepared this report. The authors are extremely grateful to Johannes Hoogeveen, Gabriela Inchauste, Alejandro de la Fuente, Karolina Goraus, and Mathew Wai-Poi. The findings, interpretations, and conclusions of this paper are those of the authors.
17

# B. Sector Work

During the period reviewed, the Bank's PHN sector work consisted mainly of a series of rather broad surveys at the country level. Their purpose was to familiarize the Bank with PHN conditions, the structure and organization of health services, and the main problems and issues in the sectors, preparatory to identifying first lending operations in each country. Now that projects are under preparation or implementation in most countries interested in borrowing in the PHN sectors, b.ttention is being turned to the design of a new generation of narrower and deeper subsector studies, which may in turn lead to a new generation of more focused subsector projects.

The intentionally broad scope of the first generation of sector work meant that no deep analysis of service provider/client relations could be attempted. This section is therefore correspondingly brief, merely noting that, insofar as the first generation studies dealt with management issues, they tended to focus on the structure of services; the staffing situation and manpower gaps; and the role and duties of different health cadres, mainly as all of these pertained to government services. Less attention was given to issues of service efficiency and effectiveness; processes and incentives, as opposed to structures; or to commercial, traditional and private not-for-profit PHN services. Insofar as these studies addressed client-related issues, they focused more on variation in PHN status by region and over time; and less on PHN status by income group, client perceptions of PHN problems. or client attitudes to PHN services from different types of provider.

One exception to this broad picture was the Ecuador Population, Health and Nutrition Sector Review (1986) which for the first time presented a systematic framework for analyzing the sector's institutional strengths and weaknesses. This included a "management audit" covering organizational designs (including issues of structure, centralization, autonomy, and coordination); organizational culture; planning systems; client population; provision of services; logistics of materials and maintenance; financing and budgeting; personnel; leadership; and control systems. However the breadth of this review--as indicated by the subject matter of the audit--prevented a detailed analysis of the particular design and management issues which are the concern of this paper.

# C. Policy and Research Work

# Completed Work

Three broad reviews covering respectively population, health, and nutrition sector work and lending were undertaken by the former PHN departmental front office. In addition, the PHN Policy and Research Division's Technical Note (TN) Series includes about 15 studies (out of a published total of about 135 titles) relevant to the management of PHN services at the periphery, and a few other relevant studies have been issued in the now-discontinued Bank-wide Staff Working Paper (SWP) Series. This section summi.rizes the general scope of these studies through FY87. It
**Figure A.5. Infrastructure and Business Activity, Location by Gender** **_(%)_**

<!-- Start of picture text --> a. Types of Location b. Water, electricity, phone<br>Men Women Men Women<br>30<br>27.08 25 23.69<br>25<br>20<br>21.3<br>19.64<br>20<br>18.36<br>15.49<br>17.42<br>15<br>15 12.9<br>10.6<br>12.15 12.64 12.73 10.03<br>8.59<br>10<br>10 8.9 8.22<br>8.27<br>6.78<br>6.33<br>5<br>5 3.43 3.61<br>2.98<br>1.51 1.78<br>1.49 0.7<br>0 0<br>Water Electricity Land line,<br>phone<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

**Figure A.6. Firm Location, by Quartile of Capital**

<!-- Start of picture text --> 45 42.16<br>40<br>35<br>30 28.42 27.36 25.45 27.89 25.82<br>24.12 23.73<br>25 21.44 22.54 20.49<br>20<br>15<br>10.58<br>10<br>5<br>0<br>Q1 Q2 Q3 Q4<br>Fixed location Mobile location Other<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

31
“actual” stock of public capital created in Eurasia may be less than half of what traditional estimates of capital stock using cumulative investment spending suggest.

# **3. TRANSFORMATION OF RESOURCE WEALTH INTO PUBLIC CAPITAL**

For resource-rich countries, the conjunction of high global commodity prices and natural resource discoveries provides a major new source of finance. The governments of these countries possess legal entitlements to the rents generated by the depletion of natural assets. They have the opportunity to tax these rents and use this revenue to acquire the public capital crucial for economic development. Since the revenues from the exploitation of non-renewable natural assets are intrinsically unsustainable, countries should offset such depletion by accumulating more physical capital and other forms of productive assets. Those countries that build a solid stock of physical capital are more likely to develop along a path of relative prosperity. In contrast, countries in which resource rents contaminate the political process of public investment decision-making, and thereby lower the accumulation of public capital, are more likely to achieve sub-optimal economic performances.

In a global dataset on public capital and resource rents in 45 developed and developing countries, including some Eurasian countries, Battachyrrya and Collier (2011) found that resource rents did not augment the public capital stock. If anything, higher rents were associated with lower stocks of public capital. They also found evidence of such an outcome in the case of point source natural resources (minerals, oil and gas), but not in the case of less appropriable agricultural and forestry resources. In other words, non-renewable point source resources reduced public capital, while potentially sustainable forestry and agriculture did not. They interpreted this negative association between resource rents and public capital as evidence of governments plundering natural resources, rather than converting them into more productive forms of assets.

The accumulation of physical capital is undermined by weak government policies and institutions that are associated with resource rents. Battachyrrya and Collier (2011) used the Hall and Jones (1999)’ social infrastructure index as a combined measure of economic institutions. They found that countries below a certain threshold of institutional quality are more likely to suffer from a low stock of public capital.<sup>7</sup> We find that Eurasia’s institutional score is well below that threshold. Thus, the accumulation of

> 7 The un-adjusted institutional index score threshold is 3.1. For comparison, Mexico averages 3.3 under the original methodology. After adjustments for the availability of data, the score for Mexico is 4.1.

18
# 6 Policy Implications

Our findings on the salience of geographic boundaries and the caste-composition of settlements have important implications for school allocation decisions in rural Pakistan. We illustrate this point by comparing the following hypothetical policies targeted to the population of 9-12 year-old children (the most recent 3-year cohort) in all PRHS-II villages: Policy 1: Provide a girl’s school to every settlement currently lacking one.

Policy 2: Provide a school to a low-caste dominant settlement in every village currently lacking one.

Based on our estimates (using only statistically significant coefficients), Policy 1 will induce only more high-caste girls to enroll in school, whereas Policy 2 will draw in only additional low-caste boys and girls. To level the playing field somewhat on the cost-side, we restrict the first policy to settlements with at least a 10% high-caste population and the second policy to villages with at least a 10% lowcaste population, thus avoiding the construction of a large number of "superfluous" schools.<sup>14</sup> The question is: Which policy increases enrollment rates most costeffectively?

Table 5 summarizes the horserace. Our analysis uses estimates from column (4) of Table 4 combined with village census data to obtain population weights for each settlement as well as the proportion of low-caste households within each settlement.<sup>1516</sup> As mentioned, the enrollment impacts of Policy 1 are zero for boys as well as for low-caste girls. High-caste girls increase their enrollment, but not by much (5.3 percentage points). The reason is that, since higher population settlements typically already have a school, Policy 1 disproportionately serves smaller settlements and thus does not greatly increase school access on a population-weighted basis. Policy 2, by contrast, does not suffer the same defect; more populous settlements tend to be in villages without a low-caste dominant school. Thus, Policy 2

> 14From the village census, we know that only 0.1% of the high-caste population resides in settlements that are more than 90% low-caste and that 3.7% of the low-caste population resides in villages that are more than 90% high-caste.

> 15 Throughout our simulations we ignore the fact that building new schools will also reduce distance to school. In this sense, the enrollment gains from the policies that we consider are somewhat understated.

> 16The population proportion of girls among 9-12 year old children is calculated from our sample and, along with household size, is assumed to be constant across settlements and caste-types.

17
relevant in the short term since, as shown in Ravallion’s (1990) study of Bangladesh rice pricing policy, the short-run effect on wages of food price changes may be considerably smaller than the long run impact.

## **_4.2 Impacts of higher food prices on poverty_**

In the second scenario, we consider the impact of the recently observed food price increases on poverty. The scenario again includes the same set of agricultural commodities as before with the exception of beef and sugar, whose prices did not change much between 2005 and 2007. For the remainder of the commodities, we obtained the average annual prices from the FAO website and calculated rough percentage changes over this two-year period. The imprecise and rounded estimates are the result of the existence of various varieties of the same commodity and different prices at various points of sale. However, because the prices of similar commodities changed at a roughly the same rate, our scenario broadly captures the overall development of the relevant food commodities in the reference period. The price changes considered in this scenario are shown in Table 4.

# **5 Results**

## **_5.1 Impact of a 10 percent increase in food prices_**

In this analysis, we first considered the direct impacts of changes in the prices of the commodities, and then the combined effect of changes in the prices of commodity prices and induced changes in wages.

### **5.1.1 Commodity Price Impacts**

The results presented in Table 2 show the impacts on poverty rates of raising the prices of particular goods by 10 percent from their initial levels. These results are shown first for the individual commodities and then for all commodities amongst the products considered. Corresponding results are presented in Table 3 for the poverty gap.

10