rafmacalaba/datause-encoder-data huggingface dataset:
Table 10 shows the food line per month as well as the extreme poverty headcount rates for the base years of the HBS and the NPS, namely HBS 2011/12 and NPS 2010/11. The first column presents the official food line from the HBS 2011/12, estimated at TZS 26,086 per adult per month (30.4 days) and the extreme poverty rate estimated at 9.7 percent. The second column shows in line (a) the original values for the NPS 2010/11 food line and extreme poverty rate, and in lines (b) to (d) the re‐ estimated values of the food line by adjusting the differences mentioned above individually. Line (e) shows the food line and extreme poverty rates resulting from all the adjustments when they are applied simultaneously.

**Table 10: Food poverty estimates in Tanzania Mainland**

||**HBS**|**NPS**|
|---|---|---|
||**2011/12**|**2010/11**|
|**Food line based on 2,200Kcal/day**|||
|(a)Original values|**26,086**|18,719|
|% individuals below food line|**9.7**|9.0|
|(b)Period of analysis(30.4 days)||20,334|
|% individuals below food line||9.0|
|(c)Referencegroup (20‐50%)||19,678|
|% individuals below food line||10.2|
|(d)Costper calorie method||20,626|
|% individuals below food line||11.7|
|(e)Total adjustments||**22,482**|
|% individuals below food line||**11.8**|

Note: in lines (b) and (e) the reference period for NPS is 30.4 days and in line (a), (c) and (d) the reference period is 28 days.

Line (e) shows that using the HBS method to estimate the NPS food line leads to close values of both lines; however, the proportion of extreme poor population substantially increases to 11.8 percent in the NPS.

# **ii. Comparison of the food poverty lines across the survey rounds**

Table 11 compares food line values across different rounds for the HBS and the NPS. Comparison over time involves adjusting for inter‐year variations in the cost of living. This table uses food Fisher price deflators based on survey unit values and the CPI to convert the food line in the base year to prices in the survey year and make it comparable across different rounds. According to the HBS report, food prices have increased by 115 percent between the HBS 2007 and the HBS 2011/12. The 36
Policy Research Working Paper 6991

# **Abstract**

This paper uses World Bank survey data, including about 10,000 households in five countries—Burkina Faso, Kenya, Nigeria, Senegal, and Uganda—to investigate the link between - international remittances and households’ financial inclu sion in Sub-Saharan Africa. The paper finds that receiving international remittances increases the probability that the household opens a bank account in all the five countries. This result is robust to controlling for the potential endogeneity of remittances, using as instruments indicators of the migrants’ economic conditions in the destination countries.

This paper is a product of the Finance and Private Sector Development Team, Development Research 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 mmartinezperia@worldbank.org and gayanaaga@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
we provide unambiguous support for the sensitivity of the plot-level IR to the choice of the method by which maize production and yield are computed. While farmer-reported production-based maize yield regressions consistently imply diminishing returns to GPS-based plot area, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and high-resolution satellite imagery-based remote sensing point towards constant returns to scale.

<mark>In view of the aforementioned hypotheses put forth for the IR, it is important to note that the results are robust to the inclusion of objective measures of soil fertility, maize genetic heterogeneity and edge effects at the plot-level; a rich set of plot, household and plot manager attributes; as well as household and parcel fixed effects in select specifications that exploit the panel nature of the data. In other words, irrespective of the exhaustive list of controls and panel estimation, the IR exists while using farmer-reported maize production, and ceases to do so when maize yield is anchored in objective measurement methods. In fact, the IR persists throughout the distribution of plot-level maize yields that are based on farmer-reported production, while constant returns to scale prevails across the productivity distribution when one uses objective yield measures.</mark> <mark>Our core finding is driven by persistent over-estimation of farmer-reported maize production and yield vis-à-vis their crop cutting-based counterparts, particularly in the lower half of the plot area distribution. Though our findings contribute to a larger (and renewed) body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measures, the analysis, together with Desiere and Jolliffe (2017), is the first documenting how the IR could be a statistical artifact, driven by errors in self-reported survey data on crop production.</mark> The paper is organized as follows. Section 2 discusses the conceptual basis for errors in farmerreported crop production and their potential implications for the IR. Section 3 provides relevant background on the Ugandan context. Section 4 describes the data. Section 5 lays out the empirical strategy. Section 6 presents the results. Section 7 concludes.

# **2. Conceptual Basis for Potential Errors in Farmer-Reported Crop Production**

The quality of farmer-reported estimates of production is degraded by a myriad of issues, summarized in Table 1. Human performance and behavior are subject to a number of constraints, one of them being finite memory. Recall bias can, therefore, pose a serious threat to the quality of farmer-reported estimates of production. This is especially relevant if there is partial green harvest (for immediate consumption or sale) or if permanent or extended-harvest crops, such as cassava, 4
# Introduction

The objective of this paper is to shed light on the poverty, malnutrition and vulnerability profile in Mali before the March 2012 coup and assess the impacts of some production and price shocks the country has sustained recently. The Malian economy was buffeted by a partial drought in 2011. Furthermore, by summer 2012, the coup and internal strife had led to some 320,000 refugees, of which some 40-45% was internally displaced refugees. That is about 27% of the population of the North of Mali. The disruption of the normal life and production capacity of a large number of Malians are expected to have considerable effects on their vulnerability and ability to cope.

The paper identifies the poor, food poor and malnourished in Mali, providing a short profile of their characteristics and where they are. It assesses the impact of the recent food price and food production shocks on these different groups. We also estimate the impact of the internal refugees staying with relatives, finding that this arrangement affects the welfare of host families. The study then analyzes the vulnerability of Malians, looking at whether this vulnerability is poverty induced (low mean consumption) or risk induced (high consumption volatility), as well as whether it is induced by idiosyncratic risk or covariate risk. A final section provides suggestions for further analytical extensions. The data used for this analysis are from the household survey ELIM/MICS 2009/2010, the latest available for such an analysis.

A household is considered poor if its per capita consumption is below the national poverty line. This was set at 165,431 FCFA (approx. 346 USD) per capita per year for the ELIM/MICS 2010 survey. A household is said to be ‘food poor’ (or extremely poor) if its level of total expenditure per capita is below the national food poverty line. In 2010, this food poverty line was set at 118,173 FCFA per person per year (approx. 247 USD). This food poverty line is defined as the minimum consumption expenditure needed to meet the cost of consuming 2,450 kilocalories per day, reflecting Malian consumption habits.

Malnutrition indicators are based on nutritional outcomes (weight and height) measured for all children aged 0-59 months in the ELIM-MICS 2009/10 survey. Three basic malnutrition indicators are considered here (stunting, wasting and underweight). Stunting is based on a child’s height and age. It is a measure of chronic nutritional deficiency.<sup>i,ii</sup> Wasting is based on a child’s weight and height. It is a measure of short-term nutritional deficiency.<sup>iii</sup> Underweight is based on a child’s weight and age. It is a composite measure of chronic and acute nutritional deficiency.<sup>iv</sup> 3
**Figure 13: Beneficiaries of private health insurances, by quintile**

<!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>Q5<br>0.5<br>Q4<br>0.4 Q3<br>Q2<br>0.3<br>Q1<br>0.2<br>0.1<br>0<br>2008 2009 2010<br>Source: Bank staff calculations, ENFT survey<br>% enrolled in private health insurances<br><!-- End of picture text --> How has private health affiliation varied over the past three years? Much alike the case of private schools, enrolment in private insurances decreased in 2009, to quickly rise again in 2010, especially among the middle and high class. Quintile 4 and quintile 5 present also a higher level of affiliation to private health insurances, and they are the only significant quintile dummies when a logit regression is applied to test opting for private health insurances across quintiles. As can be seen in the graph, differences across quintiles in private health affiliation are much wider that in the case of private education.

We also include a dummy for informal workers; against the intuition, formal workers show more inclination to sign up in private health insurances than informal ones. It seems that informality levels are lower among middle and high class employees, groups that usually have the purchase power to acquire private insurances, and they prefer to do so, since public services have relatively low quality standards.

To sum up, the Dominican Republic Social Insurance System is still recent and the ambitious set of reforms reforms aimed at expanding its coverage and improving its quality has not been fully implemented yet. Additionally, it can be argued that the system is “fragmented” between people in the contributory and subsidized regimes. These are some of the reasons why the upper intermediate strata of the Dominican population seems to be motivated to individualize and internalize the cost of health services by opting for private centres.

[19]
# **Can non‐monetary incentives induce more teacher effort?**

Policy‐makers can contribute to teacher performance through the provision of non‐monetary incentives. These can range from providing teachers increased support in the classrooms, to increasing training opportunities, to improving the infrastructure of schools as well as the availability of teaching materials. These interventions may not only have direct effects on student learning, but also improve the motivation of teachers by feeding into their sense of professionalism and efficacy.

While the evidence on the impact on these policies is relatively scarce, a recent quasi‐experimental study of a mentoring program in New York City found some evidence that mentoring could reduce teacher absence and improve retention (Rockoff, 2008). In particular, the study found that teacher retention within a particular school is higher when a mentor has previous experience working in that school, a finding that the author attributes to the hypothesis that an important part of mentoring may be the provision of school specific knowledge. Importantly, the study found that the time spent working with the mentor matters: student achievement in both reading and math were higher among teachers that received more hours of mentoring. This finding has implications for developing countries. Research has consistently shown that during the first years of teaching, teachers tend to be less effective (though the relationship is not linear; that is, after only 2‐3 years of experience, the “experience premium” fades) (Darling‐Hammond, 2000).

Similarly, the multi‐country absence study cited earlier found evidence in some cases that the quality of the school’s infrastructure may help predict attendance. Schools that scored higher on an index of available infrastructure and equipment had significantly higher attendance rates than those that scored low, and in some cases the difference was quite large. On average across the six countries in the study, schools with the best infrastructure and equipment had a predicted teacher absence rate only about half that of those with the worst infrastructure, even after controlling for other factors (Chaudhury _et al_ , 2006). There are a variety of reasons why this might be, but one plausible story highlights the non‐financial returns to teaching. If teachers find their work environment to be more pleasant and more conducive to promoting student learning, they are less likely to avoid it.

To improve attendance, policymakers’ best bet is likely to be to focus on improving teachers’ marginal incentives – that is, the incentives that they face each morning when deciding whether to call in sick that day (Kremer _et al_ , 2005). Until teacher compensation is made more dependent on performance than it currently is, salaries will not provide that incentive. So education administrators will likely need to move ahead with both financial and non‐financial incentives, as they seek to
# **References and Notes**

1- Chen, L.C. (1986), 'Primary health care in developing countries: overcoming operational, technical, and social barriers', The Lancet, vol.328, no.8518: 1260-1265.

2- Wolfers, I. (1988), 'Limitations of the primary health care model: a case study from Bangladesh', Tropical and Geographical Medicine, 40: 15-53.

3- Rahman MM, Aziz KMS, Patwari Y & Munshi MM. (1979), 'Diarrheal mortality in two Bangladeshi villages with and without community based Oral Rehydration Therapy', The Lancet vol.314, no.8147: 809-812.

4- Kabir M, Moslehuddin M.(1986), 'Mortality levels and patterns in the oral therapy extension program areas of the Bangladesh Rural Advancement Committee', Food Nutr Bull 1986, vol.8, no.3: 8-11.

5- M. El Rafie, W.A Hassouna, N. Hirschhorn, S. Loza, P. Miller, A. Nagaty, S. Nasser, S. Riyad (1990), 'Effect of diarrhoeal disease control on infant and childhood mortality in Egypt', The Lancet, vol. 335, no. 8685: 334-338.

6- National Control for Diarrheal Diseases Project (1988), 'Impact of the national control of diarrhoeal diseases project on infant and child mortality in Dakahlia, Egypt', The Lancet, vol.332, no.8603: 145-148.

7- Tekce,B. (1982), 'Oral rehydration therapy: An assessment of mortality effects in rural Egypt', Studies in Family Planning, vol.13, no.1: 315-327.

8- Kielmann A.A., Mobarek A.B., Hammamy M.T., et al (1985), 'Control of deaths from diarrheal disease in rural communities. I. Design of an intervention study and effects on child mortality', Tropical Medicine and Parasitology, 36: 191-198.

9- Norbert Hirschhorn and William B. Greenough III (1991), 'Progress in Oral Rehydration Therapy', Scientific American, vol.264, no.5: 50-56.

10- _In both trials the treatment vllages had extensive actvites quite different from the regular health system. The latter served as control villages. Dakahlia trial was conducted by the strengthening of rural health services delivery project. Menoufla trial was conducted to evaluate the ORT component of wider project aimed to promote family planning, health and social welfare services through an integrated appt2ach._ 11- National Control of Diarrheal Disease Project (1988), 'The clinical epidemiology of acute diarrhoeal disease in Egyptian children', Journal of Tropical Pediatrics, vol. 34: 150-157.

12- NCDDP internal reports.

24
documented by Ceritoglu et al. (2017); Rozo and Winkler (2019); Altindag et al. (2020). Returnee inflows may be fully absorbed by the informal sector, increasing the informal market share, as informal businesses are able to offer lower wages than the formal sector, as found in Rozo and Winkler (2019) and Altindag et al. (2020).

There are at least three reasons that justify a separate analysis for the informal sector in the context of El Salvador (Banegas and Winkler, 2020). First, informality in El Salvador is high. About 70 percent of workers are employed in the informal sector and most jobs are created by micro firms and self-employed in the informal sector. Second, informality limits the growth of formal firms. Unfair competition from informal businesses is claimed as a barrier for formal firms to grow. This is part of a vicious cycle that is difficult to break, as informal businesses provide most of the jobs, but still pose a negative externality to firms that are the most productive. Moreover, micro firms in the informal sector do not tend to grow to become medium-sized firms or to formalize and among the higher productivity sectors, those that expanded the most are those with higher informality levels. Finally, informal employment is considerably more sensitive to economic growth than formal employment: while a 1-percent growth in GDP was accompanied by a 0.8 percent increase in the number of informal jobs, such figure with respect to formal jobs was only about half that value.

To test this channel, we separate our sample of formal firms by the probability that a sector is more or less prone to informality competition. We use data from the household surveys and split the sample according to industry codes in two groups of firms using the 2011 STEP survey. If a firm was operating in a sector that tends to have high levels of informality (e.g., agriculture, construction, wholesale and retail, restaurants, and others) or low levels of informality (e.g., financial services, real state, electricity, gas, water, manufacturing, among others). If our hypothesis is correct, we should observe that formal firms operating in highly informal sectors are more affected by the returned migrant inflows relative to the others firms.

The results are presented in Tables 6 and 7. Estimated effects confirm our hypothesis that the impacts of a positive shock of returned migrants are driven by firms that operate in sectors with 17
**Policy** Reaeuch|

**National Economic Management|**

# WPS **1166**

This paper-aproduct of the National Economic Management Division, Econom;c Development Institute - was funded by the Bank's Research Support Budget under the research project "Labor Markets in Transitional Socialist Economies" (RPO 677-30). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Olga del Cid, room M3-047, extension 35195 (August 1993,46 pages).

The authors surveyed 41 firms In and around Moscow In the last two weeks of November 1992 to get an empirical handle on how firms are responding to the changing economic environment. They found that:

- There were large negative (supply and demand) shocks to output for a significant number of firms and branches.

Profitability was remarkably buoyant in real terms; there was clear evidence that firms with market power rapidly adjusted producer prices, Lying to maintain or increase their markup.

* There was no evidence of a strategic change **in** pricing rules.

* Beneflmt firms provl'ed account for large shares of labor income and 40 to 45 percent of firms' costs. Firms may have tried to squeeze benefits, particularly in housing, but allocations to the Social Fund have generally stayed constant.

* Employment adjustmaents were limited, despite the downward pressure on output and the lack of growth In firms surveyed. Net employment separations were relatively restricted. Firms continued to hire at significant rates in 1992, In part because of fixed factors technology, in part because of the reluctance of firms to discard workers. Consequently, firms have shed few workers - mostly ancillary and clerical staff, usually women.

* Most firms experienced relative stability in earnings and In the distribution of revenues. There was no substantial evidence of - decapitalization at least through greater bofrowing or predatory wage settlements.

* Some firms chose to place workers on minimum wages, reducing labor costs significantly. The result is that unemployment benefits are provided de facto within the firms rather than through labor offices.

* **The** upward shift in interfirm arrears was smaller than aggregate numbers might have led one to expect.

* Inertia in the wage system should not be ignored. Real wages were cut back sharply by the great price shock of January 1992, but real statistical wages then climbed back toward early 1991 levels.

* In short, the status of the so-called production worker, the core of the Russian industrial firm, remains untouched. Clearly, there was a large "employment overhang" at the end of 1992. The next stage of the transition will be difficult.

ThePolicyResearchWorking PaperSeriesdisseminates thefindingsof workunderwayin theBatc. Anobjectiveofthe series is to get these findings out quickly, even if **presentations** are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Folicy Research Dissemination Center
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Unaccounted losses (mostly nontechnical) constitute the most obvious challenge, followed by collection inefficiencies. Inadequate bill collection has been an increasing burden for ENERCA in recent years (figure 13a). Relative to other low-income countries in Central and West Africa, however, ENERCA’s hidden costs in 2009 were better than several other countries (figure 13b).

**Figure 13. Hidden costs, as percentage of revenues**

<!-- Start of picture text --> a. ENERCA has massive hidden costs largely due to unaccounted b. Hidden costs are large compared to several countries in Central<br>losses and West Africa<br>400 400<br>350 350<br>300 300<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>DRC Niger Mali CAR Congo, Burkina<br>Congo Rep. Faso<br>Collection inneficiencies Under-pricing Losses Collection Inefficiencies Underpricing Losses<br>Percentage of the revenues Percentage of the revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

# Information and communications technologies

## **Achievements**

The CAR pressed ahead in its commitment to liberalization and private participation with the creation of a legal and institutional environment for the ICT sector. In 1996 the country enacted a telecommunication law that provides for the liberalization of the sector; a new sector regulator started functioning in 2004. This took place in parallel with and in spite of the ongoing conflict, making it an even more remarkable achievement. The country has achieved a genuinely competitive market with no real dominant player, except in the landline market.

29