rafmacalaba/datause-encoder-data huggingface dataset:
**7,** VWhat To Do In Future Surveys?

Our experience in analyzing the four years of the CMte d'Ivoire Living Standards Survey data in an attempt to trace the evolution of household welfare and poverty, has clearly shown that this type of survey has all the necessary data for policy oriented research on poverty. It contains in fact much more information than is needed. Our work, however, has also brought out some difficulties and shortcomings of the surveys. On the basis of this experience we would like to suggest what to keep and what to change in future surveys.

The list of what to keep includes the combination of expenditure and basic needs data in one survey, and the annual monitoring of welfare and poverty. The latter should however occur with a "lighter" survey. The full-fledged living standards survey could be undertaken once every 4-5 years, to provide benchmark data and to permit in-depth analysis of household behavior and response to policy. The collection of panel data should be continued, but in a separate survey.

The list of what to change includes a simplification of the questionnaire according to actual use of information, a doubling (at least) of the sample size and an amended sample design to reflect better analytic priorities. Improved price data collection, but independent from the survey, should receive high priority.

The table on the next page summarizes the recommendations suggested in the paper.

18
<!-- Start of picture text --> GDP Private Consumption Private Investment<br><!-- End of picture text --> <!-- Start of picture text --> 0.3<br>3 3<br>$ 02 0.2<br><!-- End of picture text --> <!-- Start of picture text --> 8 0.1 2<br>0 SS =| 0 1<br>-0.1 0<br><!-- End of picture text -->

<!-- Start of picture text --> 5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30<br>Tax Revenue Government Spending 3 Months T-Bill Rate<br><!-- End of picture text --> <!-- Start of picture text --> N 0.4<br>1.5 0.8<br>oO<br><!-- End of picture text --> <!-- Start of picture text --> f=<br>8<br>5 05 0.4<br>0 0.2<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30<br><!-- End of picture text --> <!-- Start of picture text --> Net Exports to GDP Ratio GDP Deflator REER<br>0.8<br>on<br>0.6 ; ip<br>o<br><!-- End of picture text --> <!-- Start of picture text --> sfa 0.4 0 0.5<br>8 0.2 0<br>2 ors -0.1<br><!-- End of picture text --> <!-- Start of picture text --> 5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30<br>Quarters Quarters Quarters<br><!-- End of picture text -->
sector that exports to other countries in the [South American] region” (Fonseca, 2003). In Guayas, firms mostly exported fruits (e.g., bananas and mangos) and seafood (e.g., fish and shrimp) using the infrastructure available in the port of Guayaquil. As a result, almost 50 percent of Ecuador’s exporters were either part of the apparel, textile, and leather sector or the food and beverage sector. In addition, 15 percent of its exporters were part of each of the following primary sectors: oil, chemicals, or metallic products. With respect to size (in terms of employment), statistics show one more time what was previously discussed: the share of exporters increases as firm size increases.

**Table 2: Means of selected variables for Ecuadorian exporters and non-exporters**

|**Characteristic**|**Exporters**|**Non-Exporters**|
|---|---|---|
|Employees***|169|56|
|Wage per employee (US$ per month)**|402|277|
|Age (years)***|26|20|
|Quality certificate (percent)***|31|11|
|Web-use (percent)***|72|48|
|Training (percent)***|83|63|
|Loan (percent)***|82|69|
|Plant abroad (percent)***|37|9|
|Publiclylisted companyS.A.(percent)***|63|50|

Notes: Exporters: 134 firms. Non-exporters: 307 firms. **Difference in means statistically significant at the 5 percent confidence level. ***Difference in means statistically significant at the 1 percent confidence level. Source: ICS-Ecuador.

**Table 3: Descriptive statistics for Ecuadorian exporting firms**

|**Group**|**Number of exporters**|**Share of exporters (percent)**|
|---|---|---|
|_Region_|||
|Azuay|13|10|
|Guayas|44|33|
|Manabi|10|7|
|Pichincha|66|49|
|Tungurahua|1|1|
|_Industrial Sector_|||
|Oil products|19|14|
|Food and beverage|33|25|
|Apparel, textiles and leather|29|22|
|Wood and furniture|13|10|
|Chemical products|20|15|
|Metallic products|20|15|
|_Size_ _(Employees)_|||
|Less than 10|1|1|
|From 10 to 20|12|9|
|From 21 to 50|27|20|
|From 51 to 100|30|22|
|More than 100|64|48|

Notes: Sample equals 134 exporting firms. Data for year 2002. Source: ICS-Ecuador.

Still focusing on Ecuadorian exporters, it is interesting to look at the means of selected variables by industrial sector (Table 4). On average, exporters in all sectors hired at least 120 employees, but the most and the least labor-intensive industries were food 10
<!-- Start of picture text --> 7<br>6<br>w<br>© 5<br>35<br>oa)<br>wi 4 = * *<br>0 Bg Limitada<br>ao] .<br>°o 3 Intermedia<br>rT<br>£ = Buena<br>2<br>1<br>0<br>Perun=4 Ecuador n= 6 Bolivia n= 1<br><!-- End of picture text -->
welfare.<sup>26</sup> Using survey data for Russia, the authors find that income is a highly significant predictor of subjective welfare, but that this is also influenced by health, education, employment, assets, relative income in the area of residence and expectations about future welfare.

However, for many mashup indices of development there is likely to be an important normative judgment to be made about these tradeoffs. If the index is to be accepted, then some degree of political consensus will be needed. Without that consensus, there are risks in aggregating prematurely. As Marlier and Atkinson (2010, p.292) note, ―the weights are a matter for value judgments, and the adoption of a specific composite index may conceal the resolution of what is at heart a political problem.‖ The reality is that no consensus exists on what dimensions to include and how they should be weighted in any of the mashup indices of development in use today. And these are difficult issues. How can one contend—as the MPI does implicitly—that avoiding the death of a child is equivalent to alleviating the combined deprivations of having a dirt floor, cooking with wood, and not having a radio, TV, telephone, bike or car? Or that attaining these material conditions is equivalent to an extra year of schooling (such that someone has at least 5 years) or to not having any malnourished family member? It is very hard to say (as the MPI does implicitly) that a child‘s life is worth so much in terms of material goods.

And where do we draw the line in terms of what is included? In a blog comment, Duncan <u>Green</u> has criticized the MPI for leaving out ―conflict, personal security, domestic and social violence, issues of power/empowerment‖ and ―intra-household dynamics.‖ Sometimes such judgments are needed in policy making at the country level. The specific country and policy context will determine what trade off is considered appropriate; any given dimension of poverty will have higher priority in some countries and for some policy problems than for others. This will typically be a political decision, though hopefully a well informed one.

But could it be that we are asking too much of a single measure of poverty to have it include things like child mortality, or schooling, or violence, as components, on top of material living standards? It is one thing to agree that consumption of market commodities is an

> 26   Surveys of willingness-to-pay have also been widely used in valuation, including valuing lower risks of loss of life; in a developing-country context, see Wang and He (2010), whose results (for China) confirm intuition that the implicit value of life in developing countries built into the HDI is too low.

17
Policy Research Working Paper 8253

# **Abstract**

There are severe geographical disparities in pupil-teacher ratios (PTR) across Malawi, with most teachers concentrated near commercial centers and in rural schools with better amenities. Most of the variation in PTR is concentrated in small sub-district areas, suggesting a central role for micro-geographic factors in teacher distribution. Employing administrative data from several government sources, regression analysis reveals that school-level factors identified by teachers as desirable are closely associated with PTR, including access to roads, electricity, and water, and distance to the nearest trading center, suggesting a central role for teachers’ interests in PTR variation. Political economy network mapping reveals that teachers leverage informal networks and political patronage to resist placement in remote schools, while administrative officials are unable to stand up to these formal and informal pressures, in part because of a lack of reliable databases and objective criteria for the allocation of teachers. This study curates a systematic database of the physical placement of all teachers in Malawi and links it with data on school facilities and geo-spatial coordinates of commercial centers. The study develops a consistent and objective measure of school remoteness, which can be applied to develop policies to create rules for equitable deployments and targeting of incentives. Growing awareness of disparities in PTRs among district education officials is already showing promising improvements in targeting of new teachers. Simulation results of planned policy applications show significant potential impacts of fiscally-neutral approaches to targeted deployments of new cohorts, as well as retention of teachers through data-calibrated incentives.

This paper is a product of the Education Global Practice Group. 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 sasim@ 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
is a dummy indicator variable for the global financial crisis of 2008‐09, and _Post_Crisis1011_ is a dummy indicator for the years 2010 and 2011.<sup>12</sup> Our coefficients of interest are μ0 and μ1 since these capture the behavior of firms’ capital structure during the global crisis and subsequently.<sup>_f_</sup> _i_<sup>is a set of firm fixed effects and</sup>  _ijt_ is the firm level residual that is assumed to be potentially auto‐correlated of order one and correlated in the cross‐section at the country level as well.<sup>13</sup> Indeed, the firm capital structures are quite persistent over time. In our complete sample, the correlation of the ratio of total debt to total assets, long‐term debt to total assets, and long‐term debt to total debt with their own lags are respectively 0.82, 0.84, and 0.85. Because of this persistence, standard OLS or fixed effects estimators will be biased. To correct the resulting bias, we use a feasible generalized least square estimator where the error terms are assumed to be serially correlated (Prais‐Winsten estimator). Specifically, the error terms are assumed to follow a first‐order autoregressive process  _ijt_   _ijt_  _ijt_ where  _ijt_ is the error term and  is the autocorrelation coefficient. The equation (1) is transformed by using the autocorrelation coefficient from the Durbin‐Waston statistics. We also cluster observations of the transformed model at the country‐year level. This

> 12 This period also coincides with the crisis in the euro area, which started with the first bailout of Greece in May 2010.

> 13 Various papers in the recent cross‐country comparative literature on corporate capital structures have relied on firm specific data, including Booth et al (2001), Fan et al. (2010), Beck et al. (2008), or on individual loan data, including Bae and Goyal (2009) and Qian and Strahan (2007).

15
MORFE **DEVELOPED COUNTRIES**

# **1. zstiuates**

# **and** Projections

|To|**_papatim_ **|**_atl**|**Ped**|**rtoe**|_U_|_U_|TF4 ll|tlm|**FPmtl**|In|KIM|
|---|---|---|---|---|---|---|---|---|---|---|---|
|195<br>1990<br>1995<br>2000<br>**2005**<br>2010<br>2015<br>2020<br>2025<br>2050<br>2075<br>2100<br>2125<br>2150|117679<br>1210961<br>1242929<br>1272611<br>1301060<br>1325732<br>134657<br>1364789<br>137965<br>13979<br>13914<br>'403753<br>1414370<br>1419620|50.6<br>50.2<br>51.0<br>51.1<br>51.6<br>52.2<br>54.6<br>58.5<br>62.7<br>72.4<br>72.0<br>73.0<br>74.0<br>74.6|1965-09<br>1990-94<br>1995-99<br>2000-04<br>2005-09<br>2010-14<br>2014-19<br>2020-24<br>2025-49<br>20so-74<br>207n-s9<br>2100-24<br>2125-49|0.57<br>0.52<br>0.47<br>0.44<br>0.36<br>0.31<br>0.27<br>0.22<br>0.05<br>-0.02<br>0.03<br>0.03<br>0.02|14.7<br>13.7<br>13.0<br>12.6<br>12.6<br>12.4<br>12.2<br>12.1<br>11.9<br>11.9<br>11.6<br>11.7<br>11.7|9.6<br>9.1<br>6.9<br>6.8<br>9.2<br>9.6<br>9.7<br>9.9<br>11.3<br>12.0<br>11.5<br>11.4<br>11.5|1.90<br>1.6<br>1.86<br>1.69<br>1.93<br>1.97<br>2.00<br>2.03<br>2.07<br>2.06<br>2.06<br>2.06<br>2.06|70.5<br>71.7<br>73.0<br>74.1<br>74.9<br>73.6<br>76.4<br>77.3<br>79.0<br>60.7<br>61.6<br>6.3<br>42.6|77.7<br>79.0<br>00.3<br>51.5<br>62.2<br>62.9<br>83.6<br>64.3<br>66.0<br>67.7<br>U.6<br>89.3<br>_69.6_|Is<br>13<br>11<br>9<br>6<br>7<br>6<br>5<br>3<br>3<br>2<br>2<br>2|ls<br>16<br>13<br>12<br>10<br>9<br>a<br>6<br>5<br>4<br>3<br>3<br>3|

2. **Aqe** structure (percent): 1990 and 2025 <!-- Start of picture text --> 75 +<br>70.74<br>65-69<br>60-64<br>55.5,<br>50-54<br>45.49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>mxxx~~~~~~~ 5-9<br>. . . , 04.. . . . . mgrm-l . . ~ ~~~0-4 . . . . .<br>10 8 * * 2 0 2 * * | 10 10 6 6 4 2 0 2 4 6 6 10<br><!-- End of picture text -->

# 3. Distribution of population (percent):

1990 and 2025 <!-- Start of picture text --> WX Cc l 3M)<br><!-- End of picture text --> 4
45 missing data do arise from seasonal lack of availability (Gilbert et al. 2017). In related work we collected a survey in all the markets for which we have price data and asked about the usual availability of each item every month. Comparing the missing data in the CPI price data set to the results of this survey from August 2016 – July 2017, we found concordant results 79% of the time where there was a price observation present (absent) when the item was reported to be available (unavailable) (Kaiyatsa et al. 2021). This study also confirmed that the potential impact of discordant missing prices (missing when reportedly available) on the CoNA estimate was likely small in practical terms.

Third, the food composition data are the best available for Malawi, however more of certain nutrients may be available than we estimate but would only affect the results if that item would have been selected into the diet. One way to examine where this might be the case is to model scenarios. Doing so in a related study for the shared diets scenario, we found selenium to be the limiting nutrient (the nutrient causing the diet to be infeasible) (Schneider 2021b). Selenium is the nutrient for which there are more foods with no data than for any other nutrient, however there is also strong evidence that selenium is lacking in Malawian soils and diets, and there is widespread deficiency in the population (Joy, Kumssa, et al. 2015; Joy, Broadley, et al. 2015; Phiri et al. 2019; Schneider 2021a; Schneider 2021b). So there is some evidence that little selenium is present and available to consumers in Malawi, but also note that our results show this does not hinder the individualized diets, so there is sufficient selenium present given the data to identify a solution to meet individual needs. What Schneider (2021b) demonstrates – to support the intuition behind these results – is that there is enough selenium present, but it comes from sources that also add copper to the diet, and when attempting to meet the shared nutrient requirements with lower upper tolerance for copper density of the diet relative to most
Figure 3: Brazil's Sugarcane Industry Policies are Costly to Brazil <!-- Start of picture text --> Brazil sugar demand and Brazil sugar or Brazil demand Rest of the world world Rest of the world<br>ethanol supply for ethanol demand and supply of demand and supply of<br>ethanol<br>sugar<br>DB DROS SRW<br>DBS D\BE DROWE 8 ROWE<br>SBSE |8E \<br>DP N<br>| A . C b g : g . B Il | § C-A-8<br>| O I B j I C-A-B3<br>A' c' B'D-A--'<br>+ 0111 -net no- cost of policy to Brazil<br>tun <+ , nel cost to lhe rest of the word<br><!-- End of picture text -->
# Road Investment and Violence in DRC: Perishable Peace Dividends<sup>∗</sup>

Mathilde Lebrand<sup>1</sup> , Hannes Mueller<sup>2</sup> , Peer Schouten<sup>3</sup> , and Jevgenijs Steinbuks<sup>1</sup>

> 1The World Bank

> 2The Barcelona School of Economics

> 3Danish Institute for International Studies

**Keywords** : DRC, mining, remote sensing, road infrastructure, violence **JEL Classification** : O18, O19, O55, Q34

> <u>∗The</u> authors thank Jay Taneja and Aggrey Muhebwa for sharing the estimates of road quality based on the remote sensing analysis and Muneeza Alam, Quy-Toan Do, Nicolas Peltier, Stephane Straub, Peter Taniform, and Augustin Tapsoba for helpful comments and suggestions. Nuri Bae, Miguel Connor, and Dominik Wielath provided outstanding research assistance.
Finally, it is important to recall that during the URA, and in the context of the Agreement on the Implementation of GATT Article VI on antidumping, the developed countries committed themselves in Article 15 of the Agreement, to give special regard to the "special situation of developing county Members when considering anti-dumping measures under the this Agreement. Possibilities of constructive remedies provided for by this Agreement shall be explored before applying anti-dumping duties where they would affect the essential interests of developing country Members." There is little guidance regarding how this article is to be implemented. In particular, it is not clear how "the special situation of developing country members is to be taken into account."

**Table 17**

# Sectoral Distribution of Anti-Dumping Investigations by Developed and Developing Countries 1987-1997

(in number of cases and in %)

|||Developed|%|Developing*|%|
|---|---|---|---|---|---|
|I|Animal products|<br>8|0.6|<br>17.|2.2|
|II.|<br>Vegetables|1 1|0.8|18|2.3|
|III.|<br>Fats and oils|4|0.3|13|1.7|
|IV.|Prepared foodstuff<br>|52<br>|3.7|9|1.2|
|V.|<br>Minerals<br>|T<br>41|2.9|11|1.4|
|VI.|Chemicals|210|15.0|157|20.2|
|VII.|Plastics|147|10.5|102|13.1|
|VIII.|Leather.|8|0.6|1|0.1|
|IX.|Wood|16|1.2|.8|1.0|
|X.|Pulp and paper|65|4.6|.<br>47|6.0|
|XI.|<br>Textiles|95|6.8|_5S_|7.1|
|XII.|Footwear|24|1.7|**9.7**|1.2|
|XIII.|Glass|59|4.2|_15-_|1.9|
|XV.|Base metals|374|26.7|178|22.9|
|XVI.|Machinery and electrical equipment|212|15.1|. 83|10.7|
|XVII.|<br>Vehicles|27|1.9|7|0.9|
|XVIII.|Instruments|19|1.4|21|2.7|
|XIX.|Arms|3|0.2|0|0.0|
|X.X.|Other Manufactures|27|1.9|27|3.5|
|||_<br>1402|100|778|100|
|Explanati|on:|||||

*Includes Poland (25 investigations) and Israel (I5 investigations).

_Source: WTO, Antidumping database._ 57
**Table 4** Impact of Treatments on Sexual Activity

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|A. Female Data|||||
|Sexual activity index<br>treatment x post|-0.125**<br>|0.065<br>|-0.000|5,182|
|treatment x post x Goal|(0.058)<br>-0.027<br>(0.081)|(0.064)|||
|Ever had sex|||||
|treatment x post|-0.047<br>(0.031)|0.046<br>(0.031)|0.372|5,182|
|treatment x post x Goal|-0.009<br>(0.041)||||
|Currently has partner|||||
|treatment x post<br>treatment x post x Goal|-0.110***<br>(0.036)<br>0.027<br>(0.056)|0.000<br>(0.044)|0.337|5,182|
|Total sex partners ever|||||
|treatment x post|-0.025|0.088**|0.491|5,182|
||(0.037)|(0.038)|||
|treatment x post x Goal|-0.064<br>(0.049)||||
|B. Male Data|||||
|Sexual activity index<br>treatment x post|-0.098|-0.006|-0.000|2,314|
||(0.078)|(0.071)|||
|treatment x post x Goal|-0.009<br>(0.106)||||
|Ever had sex|||||
|treatment x post|-0.028|-0.006|0.610|2,314|
||(0.039)|(0.044)|||
|treatment x post x Goal|0.000<br>(0.057)||||
|Currently has partner|||||
|treatment x post|-0.060|-0.006|0.564|2,314|
||(0.056)|(0.045)|||
|treatment x post x Goal|-0.044<br>(0.065)||||
|Total sex partners ever|||||
|treatment x post|-0.116*|0.003|0.820|2,314|
||(0.067)|(0.063)|||
|treatment x post x Goal|0.081||||
||(0.087)||||

_Notes._ This table presents the estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1 in Panel A and from equation 2 in Panel B. See notes from Table 2 for detail on table structure and control variables in Panel A and notes from Table 3 for detail on table structure and control variables in Panel B. Ever had sex is an indicator that the female (male) has ever had sex, currently has partner is an indicator that the female (male) currently has a partner, and total sex partners ever is the total number of sex partners the female (male) reports. Sexual activity index is the unweighted mean of the indicators after they have been standardized at baseline and endline separately to the mean and standard deviation among females (Panel A) or males (Panel B) in ELA only communities among females who were not assigned to the _Goal_ treatment (or among males whose connected female was in a ELA only community and not assigned to the _Goal_ treatment). Standard errors, clustered at the club level, are presented in parentheses below coefficient estimatesin columns 1–2. .01 ***, .05 **, .10 *. _Source._ Panel A: Female baseline and endline data, balanced panel. Panel B: Male baseline and endline data, balanced panel.

51