rafmacalaba/data-use-mentions-extended huggingface dataset:
and more economical means of collecting data that do not rely on large-scale expensive household surveys. In particular, it would be worthwhile to investigate ways of incorporating observational data (e.g. nightlights to look at coverage and quality of infrastructure services).

Finally, in this paper we have selected our own indicators of what each level of service – nominal, effective, and quality-adjusted – may look like for each sector. These indicators could be modified to reflect specific circumstances and contexts, while also balancing the need for consistency and comparability across countries and regions. The power that this kind of easily-digestible, demand-side data offer is that they are accessible, facilitate discussions and relate well to policy. The data therefore enable more participatory discussion on investment priorities while also offering metrics against which public and private service providers can be held accountable. As countries around the world set targets for their SDGs, it is timely that we revisit how we measure access. Our research suggests that it is crucial for policy makers to set targets that extend beyond nominal access, to at least measures of ‘effective’ access, while explicitly also addressing appropriate quality and performance standards for their cities or countries.

31
typically works in the opposite direction: corrections for CPI bias in Africa would lead to faster poverty reduction during the recent period of rising food prices (Beegle et al. 2016).<sup>5</sup> Second, when food prices increase, the poor will presumably do little to reduce the quantity of food consumed, particularly if their food expenditure is already focused on the cheapest sources of calories and if their calorie consumption is already close to minimum subsistence levels. In the extreme case, food quantities consumed might be entirely unaffected by higher prices, meaning that an increase in food prices would result in a commensurately large increase in food expenditure shares. Hence, even if the original food expenditure share used in the CPI calculation were correct prior to an increase in real food prices, it would be an underestimate of the amount the poor spend on food after an increase in real food prices. This second type of CPI bias—a classic index number problem—is likely to be smaller in magnitude than the first type of CPI bias referred to above, but both types of bias raise the concern that changes in food prices and changes in poverty could be spuriously correlated because of the way POVCAL data are calculated. It is partly for this reason that we also investigate the impacts of food price changes on FAO agricultural production statistics (see below), since the latter do not suffer from these CPI biases.

# _Food Price Indicators_

The explanatory variable of interest in all our regressions is the change in real food prices, measured as the ratio of the food CPI to the total CPI. Relative to other indicators of relative food price changes, this series seemed to have little signs of substantial measurement error (though we

> 3. The median endyear of changes in poverty in this sample is 2002.

> 4. Without this restriction, around one-third of observations would be derived from Eastern Europe and Central Asia and another third from Latin America and the Caribbean.

> 5. Personal communications with the researchers managing the
strategies, it might be that households are not accurate in reporting these strategies, for example, by overestimating the frequency of using these strategies to receive more assistance from humanitarian organizations.

Figure 2 shows the predicted welfare rates for the set of different poverty lines for all three models, but this time with a focus on food security. Welfare based on food security is defined in humanitarian settings as the inability to afford the minimum expenditure basket required to purchase a food basket based on basic needs. The minimum expenditure basket is defined by the WFP “as what a household requires in order to meet their essential needs, on a regular or seasonal basis, and its average cost” (WFP 2018). The results are very similar to the overall welfare results displayed in Figure 1. The results indicate that Models 1 and 2 predict the actual welfare rates well based on food security for different poverty lines and are within the 95% confidence interval for all the arbitrary poverty lines considered. The predictions are also very similar for the two different estimation models of error terms, the normal linear model and empirical error model. Again, Model 3 overestimates the poverty rates, as the predicted welfare rates are outside the confidence interval of the survey-based rates.<sup>8</sup> In summary, Figures 1 and 2, corresponding to Models 1 and 2, underestimate welfare for low poverty lines and overestimate for high poverty lines but are within the confidence intervals. Model 3 always overestimates poverty for smaller poverty lines and its predictions are outside the confidence interval. In general, Models 1 and 2 predict poverty and food security poverty well for different arbitrary poverty lines. Based on these results, we conclude that these two models provide fairly accurate aggregate welfare estimations of refugees living in Chad, and that the variables currently available in the ProGres UNHCR registration system can be combined with other survey data to predict the aggregate welfare of refugees efficiently.

The cross-survey imputation literature<sup>9</sup> stresses the importance of selecting a
–<br>financial market development (WEF GCI 8.01-8.08);|
|**Imp**|**roving other domestic infrastructure, including storage and energy**|•<br>Procedures and time to build a warehouse (WDI)<br>•<br>Time required to get electricity (WDI)|
|•<br>•|Storage infrastructure<br>Reforms pertaining to access, regulation, competition in the field of<br>energy (production and distribution) and other natural resources<br>essential to certain activities (e.g. water in agriculture)|•<br>Energy statistics / Access to electricity (IEA, WDI)<br>•<br>Quality of electricity supply (WEF 2.07)<br>•<br>Power outages in firms / value lost in power outages (WDI)<br>•<br>Electricity cost (WTI 4.6)<br>•<br>Pump price for fuel(WTI 4.6)|
|**Imp**<br>**perf**|**roving business support and the organization, connectivity and**<br>**ormance of markets, including e-commerce**|•<br>Logistics performance index and its indicators – quality of<br>transports and IT infrastructure, international transport costs,<br>logistics competence, trackability and timeliness of shipments,|
|•|Export and investment promotion and incentives|domestic transportation costs (WB, WTI 4.1)|
|•<br>•<br>•<br>•|Analyses and information on markets, opportunities, threats, etc.<br>Marketing, branding, international presence and promotion efforts<br>Sectoral, professional or other forms of associations (e.g. chambers<br>of commerce) and consultations<br>Development of trade corridors, and other
on advertising and R&D are represented by their ratio to the total sales. The latter are directly derived from balance sheet data.

_Taxonomy II_ is based on occupational data, distinguishing first the two types of whitecollar and blue-collar workers, and then for each, the shares of respectively high- and low-skilled labour. The data source stems from the OECD and covers employment shares for a sample of developed economies.

> 60  WIFO ( _Österreichische Institut für Wirtschaftsforschung_ ) analyzes national and international economic trends and supplies short- to medium-term economic forecasts. Most of its activities include commissioned research and consulting for domestic and international decision-making bodies, the European Commission, OECD, major business and financial institutions.

> 61  Peneder, M. (2000), _External Services, Structural Change, and Industrial Performance_ , paper prepared for the Competitiveness Report 2000 on behalf of the European  Commission. Peneder, M. (1999), ‘Intangible Investment and Human Resources. The New WIFO Taxonomy of Manufacturing Industry’, _WIFO Working Papers_ No. 114.

71
hectare and the incidence of organic fertilizer application and cash crop cultivation is 23 percent and 6 percent, respectively. The residential parcels are smaller and dwellings on them have been occupied for an average of 7 years. Thirty-nine percent of household residential dwellings have improved roofs, walls, or floors.<sup>10</sup> As only 2.3 percent (in IHPS) or 1.6 percent (in IHS4) of households report to have formal documentation to agricultural land, or 6.5 percent or 3.5 percent, respectively, to residential land, we focus on individual (co)-owners’ rights at the parcel level irrespective of whether or not formal documents exist.<sup>11</sup> Information on rights for agricultural parcels in Table 2 highlights several interesting observations. First, at the household-level, (co)-owners often can only exercise a subset of the rights generally associated with full ownership and the incidence of short-term rights is higher than that of long-term ones. The IHPS data

> 7 Data, questionnaires and basic information documents for the IHS4 2016/17 and the IHPS 2016 can be accessed <u>here and here. Both surveys were</u> implemented with technical and financial assistance from the World Bank Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) using the Surveys Solutions Computer-Assisted Personal Interviewing (CAPI) platform. Implementation of individual interviews as part of the IHPS 2016 received technical and financial support from the World Bank LSMS Plus <u>(LSMS+) initiative, aiming to improve the</u> availability and quality of individual-disaggregated survey data on asset ownership, work and employment and entrepreneurship in IDA countries. 8 A parcel (referred to as ‘garden’ in Malawi) is a contiguous piece of land under a specific type of tenure that can comprise multiple plots, defined by the types of crop grown.

> 9 The basic information document for the survey (https://microdata.worldbank.org/
48 van Doorslaer et al. (2006b; 2007) come closest to our work, but the poverty line in van Doorslaer et al. (2006b) is the old international poverty line, and in any case the number of surveys analyzed is 11 compared to our 600+ surveys.<sup>48</sup> Therefore, instead of comparing our financial protection results to published numbers, we perform external and internal consistency checks.<sup>49</sup> For the former, we perform three checks to determine the following:

1. Whether the welfare aggregate is aligned with the welfare aggregate reported in PovcalNet. Data sets are flagged if the absolute value of the relative difference between the measure of welfare derived from the survey is more than 10% apart from the value reported in PovcalNet (in log terms).

2. Whether the poverty headcount is aligned with the poverty headcount reported in PovcalNet. Data sets are flagged if the poverty headcount derived from the survey is more than 10 percentage points apart from the value reported in PovcalNet.

3. Whether the budget share of health expenditures derived from the survey is aligned with the aggregate budget share of health out-of-pocket expenditures. Data sets are flagged if the health budget share derived from the survey data is more than 5 percentage points apart from the aggregate budget share for health payments. The aggregate budget share for health is constructed by taking the ratio between aggregate out-of-pocket expenditures expressed in local currency units (in nominal terms for the year of the survey), and aggregate consumption, also expressed in nominal local currency units. The information for aggregate out-of-pocket expenditures is extracted from the WHO Global Health Expenditure Database

> 48 The data in Wagstaff et al. (2018a; 2018b) are not a potential comparator source either, as 80% of the datapoints there come from a preliminary version of the 2018 HEFPI database, the rest coming from WHO. 49 These checks were refined during the collaborative studies with WHO staff (Wagstaff et al. 2018a; Wagstaff et
**Table 1. Share of migration in an average village and a Taobao Village**

|**Percentage**|**of migrants**|**in all rural vil**|**lages**<br>|**Percentage of**|**migrants in T**|**aobao Villages**||
|---|---|---|---|---|---|---|---|
|**All**<br>**migrants**|**Different**<br>**village**<br>**same**<br>**county**|**Different**<br>**county same**<br>**province**|**Different**<br>**province**|**All migrants**|**Different**<br>**village same**<br>**county**|**Different**<br>**county same**<br>**province**|**Different**<br>**province**|
|5.3%|1.7%|1.4%|2.2%|29.0%|4.7%|4.7%|19.6%|

Note: Social registration ( _hukou_ ) information for all rural villages is derived from module 1-1c and 1-4c of China Population Census in 2010. Migrants are defined as individuals who live in this village but are registered outside of the village.

# **_2.3 Characteristics of e-households and non-e-households in Taobao Villages_**

E-commerce participation is not random. E-households and non-e-households differ in many dimensions. Table 2 shows the descriptive statistics for these two household types in the Taobao
Until 2003–04, the government’s allocation for both seasonal and year-round safety-net programs was fairly low and about the same, after which time the allocation to seasonal programs rose considerably, reaching Tk. 6 million per year by 2007–08, more than 10 times the amount allocated in 2003–04. Over the same period, allocation to year-round programs increased by only half (Figure 2). Thus, the relative importance of short-term versus longer-term programs is a point worth exploring.

# **3. Survey and Data Description**

This study is based on a large household survey conducted in the greater Rangpur region in 2006–07 by the Institute of Microfinance (InM), with support from the Palli Karma Shahayak Foundation (PKSF), Bangladesh’s wholesale outlet for microfinance. The InM survey aimed to identify the hard-core poor, the group most vulnerable to monga, and to design and implement appropriate mitigating interventions.<sup>1</sup> The survey covered roughly 70 percent of rural residents in the greater Rangpur region,<sup>2</sup> and was conducted immediately following the 2006 monga season (September– November). A total of 482,928 households were surveyed, representing 23 sub-districts ( _upazilas_ ) and some 2,300 villages; after data cleaning, 480,918 households were retained for the purpose of this paper’s analysis. Survey data on the extent of seasonal food deprivation and coping mechanisms adopted by the poor to mitigate it were limited.

The survey characterized poor households in the greater Rangpur region by a high dependency ratio (0.63), a high rate of wage employment (54 percent), and a low rate of selfemployment (16 percent) (Table 5).<sup>3</sup> Since most of the poor are wage employed in the agriculture

> 1 In the InM survey, the hard-core poor are defined as those households that own less than half an acre of land, whose monthly incomes
The value of the index has been reversed so that the higher levels represent larger governments and by extension more extensive provisions of ERs.

The index of civil and political rights _(CPR)_ is computed as the simple average of Freedom House’s Civil Rights (CR) and Political Liberties (PL) indices.<sup>16</sup> Civil rights indicates whether citizens are able to participate freely in the political process, including the right to vote for distinct alternative in legitimate lections, compete for public office, join political parties and organizations, as well as elect representatives that have a distinct impact on public policies and are accountable to the public. Political liberties allow for freedom of expression and belief, association and organization rights, rule of law and personal autonomy. The indices has been reversed as compared to the original Freedom House indices, so that the higher the level of index the higher the level of freedom. Because the indexes of civil rights and political liberties are highly correlated,<sup>17</sup> we create a joint index of civil and political rights (CPR).

A number of control variables are used to depict exogenous factors, such as geography, landlockedness, and remoteness. To control for geography, we use the data from Gallup, Sachs and Mellinger (1998) on the extent of land located in the geographical tropics. To control for landlockedness, we also use the data from Gallup, Sachs and Mellinger (1998) on the proportion of the country’s populations living within 100 km of the coastline or ocean-navigable river. To control for remoteness, we include a measure of the average distance to the world markets in line with the work of Redding and Venables

> 16 <u>http://www.freedomhouse.org/template.cfm?page=594</u> 17 The Spearman rank correlation between the two indices over the period of 1970-2007 amounts to 0.93.

12
|Source|Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Aliaga and Ren (2006)<br>Pettersson and Sisouphanthong (2005)|Pettersson and Sisouphanthong (2005)<br>Humphreys (1979)|Grosh and Mu ̃noz (1996)|Personal communication|Personal communication<br>Personal communication|Personal communication|
|---|---|---|---|---|---|---|
|Survey|Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Demographic and Health Survey<br>Third Lao Expenditure and Consumption Survey|Third Lao Expenditure and Consumption Survey<br>Ada Baseline Survey|Generic LSMS|Malawi Third Integrated Household Survey 2010/11|Nigeria General Household Survey 2012/13<br>2011 National Survey on Household Living Conditions and Agriculture|Household Budget Survey 2012|
|_c_|10<br>10<br>12<br>12<br>15<br>27<br>48<br>52<br>3.9|6.1<br>7|12|39|24<br>31|10|
|ntry/Area|bodia<br>nda<br>an<br>opia<br>i<br>key<br>kina Faso<br>o<br>s-Urban|s-Rural<br>opia|_−_|awi|eria<br>er|nia|
|
Policy Research Working Paper 5640

# **Abstract**

Skill shortage remains one of the major constraints to continued growth of the Indian economy. This employer survey seeks to address this knowledge-gap by answering three questions: (i) Which skills do employers consider important when hiring new engineering graduates? (ii) How satisfied are employers with the skills of engineering graduates? and (iii) In which important skills are the engineers falling short? The results confirm a widespread dissatisfaction with the current graduates—64 percent of employers hiring fresh engineering graduates are only somewhat satisfied with the quality of the new hires or worse. After classifying all skills by factor analysis, the authors find that employers perceive _Soft Skills_ ( _Core Employability Skills_ and _Communication Skills_ ) to be very important. Skill gaps are particularly severe in the higher-order thinking skills ranked according to Bloom’s taxonomy. In contrast, communication in English has the smallest skill gap, but remains one of the most demanded skills by the employers. Although employers across India asks for the same set of soft skills, their skill demands differ for _Professional Skills_ across economic sectors, company sizes, and regions. These findings suggest that engineering education institutions should: (i) seek to improve the skill set of graduates; (ii) recognize the importance of _Soft Skills,_ (iii) refocus the assessments, teaching-learning process, and curricula away from lower-order thinking skills, such as remembering and understanding, toward higher-order skills, such as analyzing and solving engineering problems, as well as creativity; and (iv) interact more with employers to understand the particular demand for skills in that region and sector.

This paper is a product of the Education Team, South Asia 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 ablom@
cycle effects vary considerably across countries, but they are generally quite small. One limitation of the data used by Sorensen (2013) is that the alternatives available in the questionnaire for additional government expenditure are not mutually exclusive. In other words, individuals can choose to increase or decrease expenditure in all categories. In contrast, in a more realistic scenario individuals would be faced with trade‐offs, so that increasing expenditure in one category would require offsets in others.

Against this background, our paper makes three contributions to the literature. First, it tests the “grey peril” hypothesis for a larger set of countries. Using data from LITS II, we use comparable data for 34 economies, mostly developing ones. This is important, as most papers in the literature focus on advanced economies. Second, even though Sorensen (2013) allows for age and cohort effects, we use

> 5 There is of course a large literature showing how individual preferences do not necessarily translate into actual policy. See for example Gilens and Page (2015) for a recent example.

6
Policy Research Working Paper 9404

# **Abstract**

Many Sub-Saharan African countries have instituted free primary education policies, and this has led to a significant increase in the primary school enrollment rate. However, many children who are in school are not learning. It is not clear whether free primary education policies have contributed to the decline in the quality of education and whether these learning effects are long-lasting. This paper addresses the latter question and estimates the long-term effects of free primary education on educational achievement in Lesotho where the program was phased-in on a grade-by-grade basis, beginning with grade one in 2000. The timing of the implementation created changes in program coverage across age (and grade) groups over time. A semiparametric difference-in-differences strategy is employed that exploits these variations to identify the long-term effects of the free primary education policy on educational achievement, using university examinations records data for student cohorts with and without free primary education. The results indicate that the effect of free primary education on academic performance is bounded between 2 and 19 percentage points, implying that the program increased enrollment without hurting education quality.

This paper is a product of 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://www.worldbank.org/prwp. The author may be contacted at rmoshoeshoe@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