rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Conclusion**

While a variety of approaches have been developed in the literature for the delineation of metro areas, there has been little effort to compare these approaches and to assess how the choice of approach affects key empirical insights on the forces that determine the productivity and growth of urban areas. In this paper, we have attempted to fill this gap by focusing on Indonesia. Because Indonesia has a national labor force survey from which an origin – destination commuting flow matrix can be derived, this allows the implementation of an algorithm attributable to Duranton (2015b) that allows for the delineation of metro areas based on these flows. Such an algorithm represents an example of what most economists would consider a “first best” approach to defining metro areas. We compare results obtained from Duranton’s algorithm with those obtained using other prominent “satellite-data based” approaches that have been developed for delineating metro areas in the absence of commuting flow data. These approaches are the Agglomeration Index, the cluster algorithm, and the thresholding of nighttime lights data.

Overall, we find that definition matters. This is true both in terms of the basic description of Indonesia’s urban landscape that the adoption of a given definition gives rise to and the estimated size of the agglomeration wage premium. A defining feature of Duranton’s algorithm using commuting flow thresholds of 10 percent and 7 percent is that it generates a relatively large number of metro areas, each of which is typically comprised of a small number of districts. The other “satellite-data based” approaches tend to generate much smaller numbers of much larger metro areas. Given the country’s high average population density, both the Agglomeration Index and the cluster algorithm with the urban cluster set of thresholds even produce results that look implausible for Indonesia. The cluster algorithm with the high-density cluster set of thresholds and the nighttime lights approach with a threshold set at the 80<sup>th</sup> percentile of the distribution of luminosity values produce descriptions that look more reasonable
tests: general science for natural sciences students and social science for those in the social science stream. After the reform, the structure of the stream specific tests changed significantly, with general science split into physics, chemistry, and biology and social science split into geography, history, and economics. Consequently, students in the natural science stream now take physics, chemistry, and biology, while those in the social science stream are tested in history, geography, and economics. Students from both streams continue to take english, mathematics, civics, and an aptitude exam. Each of these exams is scored out of 100 points.

We utilize data from the Ministry of Education, spanning 17 years between 2003 and 2019. The dataset comprises 2.47 million individuals who took the national high stakes exam, the Ethiopian Higher Education Entrance Certificate Examination (EHEECE).<sup>8</sup> The EHEECE tests are highly standardized and administered in a relatively controlled environment. Typically, the exams are administered in June or July at students’ schools of enrollment. They follow a uniform schedule across Ethiopia, with tests for each subject administered simultaneously on the same date and time. The exams’ timetable is arranged over consecutive days with two testing slots on each day – one in the morning and another in the afternoon. On some days, students from different streams take stream specific subject exams in a single slot. To minimize the possibility of cheating, test invigilators are assigned to schools other than their own.

The passing cut-off for public universities varies by gender, region, and stream. For example, in the 2021/22 academic year, the passing grade for natural science students was 363 for males and 351 for females. For students from rural areas, the cut-off grades were slightly lower, at 351 for males and 339 for females. Additionally, private universities generally have lower cut-offs compared to public universities.<sup>9</sup> Students who fail the exams are allowed to retake them once,
_|||
|Walls|0.93|0.73<br>0.40|
|Floor<br>|0.86|0.73<br>0.54|
|Roof|0.80|0.45<br>0.54|
|Electricity|0.86|0.50<br>0.14|
|Sewer|0.54|0.14<br>0.03|
|Flush toilet|0.31|0.04<br>0.16|
|Running water|0.56|0.29<br>0.12|
|_Administrative data measures_|||
|Coverage 3G mobile network (pop. shares)|0.98|0.92<br>0.78|
|Average nighttime light luminosity (2018)|15.00|1.30<br>0.03|
|Exposed to fluvial flood risks (pop. shares)|0.02|0.05<br>0.03|
|Exposed topluvial flood risks(pop. shares)|0.02|0.03<br>0.52|

_Sources:_ Calculations using commune vulnerability rates from the poverty mapping exercise; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-generalde-la-population-et-de-lhabitat-de-lagriculture-et-de-lelevage. _Note:_ Low and high poverty- and risk-induced vulnerability are defined by national medians. Variables are population weighted.

# Robustness checks

This subsection assesses the difference in the variance estimations and the associated implications for the predicted vulnerability rates between the Günther and Harttgen (2009) approach and the small area estimation method. To render the results comparable between survey estimates and imputations into the census, the vulnerability
# 1. Introduction

Poor sanitation has been identified as one of the major causes of malnutrition (Briend 1990; PrüssÜstün and Corvalan 2006; Humphrey 2009). Following up on the United Nations’ Sustainable Development Goals (SDGs), put forward in 2015, there is a global effort to end open defecation by 2030 by providing universal access to adequate and equitable sanitation and hygiene (United Nations 2016). In line with this effort, the Government of India launched the Clean India Mission for Villages (most commonly known as the Swachh Bharat Mission–Gramin; hereafter, SBMG) on October 2, 2014, with the aim to achieve universal sanitation coverage and eradicate the practice of open defecation in rural India by 2019. Completed on October 2, 2019, the SBMG emphasized a community-based approach with a focus on raising awareness and encouraging collective behavior change, and thus generating demand for toilets in rural areas. It also provided an incentive of Rs. 12,000 to eligible households for the construction and use of toilets (Government of India 2017).

Punjab, with a population of 27.7 million living in 22 districts, is one of the richest states of India. Even before the initiation of the SBMG, it had made great strides in implementing water supply, sanitation, and hygiene (WASH) reforms in rural areas, while delivering services appropriate to the specific needs of local communities. Punjab was perhaps the first state in India to provide safe water supply to all its 12,258 villages. According to the 2011 Census, rural sanitation coverage was impressive, at about 71.9 percent (the average across India was 32.7 percent), mainly because of state government efforts to prioritize sanitation.

Yet, despite this high coverage rate, before the initiation of the SBMG, only 1 percent of all Gram Panchayats (GPs; “village councils”) in the state had received the Government of India’s Nirmal Grama Puraskar (“Clean Village Award”) for the achievement of a 100
7 government, (iv) commercial, and (v) industrial.<sup>5</sup> Initially, the rate of increase for social and residential tariffs was somewhat higher than for other uses, which reduced cross subsidies slightly, but this has been reversed somewhat since 1993 (Figure 1). For example, in 1989 industrial rates were 170 percent higher than residential; this fell to 130 percent in 1993, then rose to 160 percent in 1996.6 Although rates are highest for industrial users, these fall mostly on smaller industries, since large industries can afford to drill their own boreholes.

_Figure 1. Average Water Tariff_ <!-- Start of picture text --> 0.90<br>>7 0 .7 5 - -- - -- - -- -- -- - -- - -- - - -- - -- - -- - -- -- -- - - -- - -- -- - - -- - -- - -- - -<br>0.60 - - -- - - - E<br>0 .4 5 - -- - - - -- -- --- -- - -- - -- -----.<br>0 .3 0 -- - ------ --- -----<br>F--|<br>- 0.15 E<br>1989 1990 1991 1992 1993 1994 1995 1996<br>E| flSocial (1996 US) E Domestic (1996 US) r Goverrnment<br>Commercial Industrial - Weighted Avg<br>Source: Author's Calculations based on data from SEDAPAL Annual Reports<br><!-- End of picture text -->

Only 39 percent of connections were metered by 1996, and only 15 percent of connections had meters that were read (see statistical appendix table 3
learning proficiencies that should arise from undergraduate economics education: (1) access existing economics knowledge; (2) display understanding of economic knowledge; (3) interpret and evaluate economic knowledge; (4) interpret and manipulate economic data; (5) apply economic knowledge to an analysis of an economic problem; and, (6) create new economic knowledge.

Taking these proficiencies as the goal also of development economics education, what is taught and how it is taught can then be related back to the extent to which students are then able to perform these tasks. Doing so requires that students have knowledge of a range of key topics, an understanding of theory, and the ability to be critical readers and users of data analysis.

# **2.4 Implications**

Based on the above, we should expect undergraduate development economics courses that are closest to the frontier in the profession to have a stronger focus on empirical methods and interpretation of data, along with applied theory, rather than a study of development as stylized facts, case-studies and descriptive patterns. They should include more of the more recent topics, and test and assess students in a way that allows them to demonstrate the proficiencies outlined above.

# **3. Data Collection**

We collected data on development economics courses via two sources: an online survey of instructors, and collection of course syllabi. We gathered information on both undergraduate and masters level development economics classes in developing countries. A comparison of the content of the undergraduate and masters level courses provides one way of seeing whether the education level of the students is a key factor in determining what is taught.

# **3.1 Online Survey**

Our initial approach was to field an online survey directed at instructors of development economics classes, asking questions on the range of development economics classes offered at their institution, their background and on the specific development economics courses they teach. The online survey was available in English and French. For the identification of the relevant institutions we
POLICY RESEARCH WORKING PAPER 2437

# **Summary findings**

Vulnerability is an important aspect of households' experience of poverty. Many households, while not currently in poverty, recognize that they are vulnerable to events-a bad harvest, a lost job, an illness, an unexpected expense, an economic downturn-that could easily push them into poverty.

Most operational measures define poverty as some function of the shortfall of current income or consumption expenditures from a poverty line, and hcnce mcasure poverty only at a single point in time.

Pritchett, Suryahadi, and Sumarto propose a simple expansion of those measures to quantify vulnerability to poverty. They define vulnerability as a probability, the risk that a household will experience at least one episode of poverty in the near future. A household is defined as vulnerable if it has 50-50 odds or worse of falling into poverty.

expenditures below which a household is vulnerable to poverty. The VPL allows the calculation of a "headcount vulnerability rate" (the proportion of households vulnerable to poverty), a direct analogue of the "headcount poverty rate." The authors implement this approach using two sets of panel data from Indonesia. First they show that if the poverty line is set so that the headcount poverty rate is 20 percent, the proportion of households vulnerable to poverty is roughly _30-50_ percent. In addition to the 20 percent currently poor, an additional 10-30 percent of the population is at substantial risk of poverty.

They illustrate the usefulness of this approach for targeting by examining differences in vulnerability between households by gender, level of education, urban-rural residence, land-holding status, and sector of occupation of the head of household.

Using those definitions, they calculate the "vulnerability to poverty line" (VPL) as the level of This paper-a product of the Environment and Social Development Sector Unit, East Asia and Pacific Region-is part of a larger effort in the region to develop a national poverty reduction strategy for Indonesia. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washi
12 banking standards – such as Basel Core Principles, BCPs - helps in limiting financial crises at the national level (see for example Demirgüç-Kunt, Detragiache and Tressel 2008; Demirgüç-Kunt and Detragiache 2011).

# **_Symptomatic treatment vs.  underlying incentive distortions_**

Most regulations of course affect incentives in one way or the other. Indeed, the recent regulatory reforms at the global level as well as those at the country level have included measures aimed at reducing the role of systemically important financial institutions, improving compensation policies, reducing the role of credit ratings, fill in various data and information gaps, all of which go some way towards addressing the weaknesses highlighted by the crisis (World Bank, 2012). But, as pointed out by Čihák, Demirgüç-Kunt, Martínez Pería, and Mohseni-Cheraghlou (2012) based on a detailed analysis of the World Bank’s bank regulation and supervision survey, the regulatory responses to the crisis at the individual country level have been slow and in most areas gradual at best. The crisis did not trigger a major change in national regulatory and supervisory frameworks. While some measures adopted in the crisis (such as improvements in resolution regimes in some countries) are encouraging, others (such as extension of blanket guarantees, and increase coverage of deposit insurance schemes) are likely to be less so. The survey results suggest that there is substantial room for further improving the regulatory and supervisory frameworks as well as private incentives to monitor risk-taking.

Addressing incentive problems with regulations has been far from trivial. For one, many regulations provide only a symptomatic treatment. For example, ceilings on credit growth have been used to slow down credit growth in some countries. This measure is aimed at the symptom—rapid credit growth—without addressing the underlying incentive issue, which is the incentive of commercial bank managers and shareholders to boost profits by rapidly expanding credit portfolio, without sufficient regard to risk. If regulations address the symptoms but not these underlying issues, the result may be just another, sometimes even more
Weil, D. N. (1992). A contribution to the empirics of economic growth. _Quarterly Journal of Economics_ , _107_ (2), 407.

- Miller, G. (2010). Contraception as development? New evidence from family planning in Colombia. _The Economic Journal_ , _120_ , 709–736.

- Natali, L., & Kabeer, N. (2013). Gender Equality and Economic Growth: Is there a Win-Win? _IDS Working Paper_ (417).

- OECD. (2015). _Education at a Glance 2015: OECD Indicators._ Paris: OECD Publishing.

28
for example, measures include whether DOH has budget preparation guidelines and are disseminated on time (policy), but also whether funds are released on time and how the capital budget is allocated (performance).

The Philippine ABI analysis provided policy-relevant findings that can be used to improve service delivery. For example, in the area of budget preparation, the assessment found that the process is constrained by frequent changes to the budget calendar and by the limited time that individual units are given to prepare their budgets. In the area of financial reporting, the analysis found that while the DOH publishes public reports about its programs, it makes available little of the information necessary to assess value for money. This effort shows the usefulness of an agency-specific tool for assessing financial performance. The ABI could be modified for use in other countries.

This type of analysis of financial management performance at the _organizational_ level could be usefully complemented by tools for analysis of policies and performance at the _sectoral_ level. In the health sector, for example, resources flow to local health facilities from central and sub-national governments as well as, in many cases, a health insurance fund. Each of these may have its own rules, institutional culture, and level of efficiency. Comprehensive sector analyses such as public expenditure reviews (PERs) and public expenditure tracking surveys (PETS) can complement organizational analyses to provide a more comprehensive assessment of the quality of financing of service delivery. PERs and PETS are also useful tools for measuring the performance dimension of financing and resource management (Koziol and Tolmie 2010). PETS measure leakage by tracking resources from the central budget to the facility level.

21
presidential elections since the independence of Guinea. Following the presidential elections, the new regime has undertaken several reforms, including the adoption of a new mining code in 2011, the construction of Kaleta dam, as well as the implementation of an Extended Credit Facility with the International Monetary Fund, launched in January 2012. Moreover, the country reached the completion point under the Heavily Indebted Poor Countries program in September 2012. Thus, for a deeper analysis, we split the period 2006-15 into two periods of five years each: 2006-10 and 2011-15.

The main findings show that structural change has made a positive contribution to productivity growth, during each of the two periods under consideration. However, the contribution of structural change to productivity growth has declined over time. While structural change has contributed to productivity growth to 0.5 percentage point per year on average during the period 2006-10, that contribution was reduced by 0.2 percentage point during 2011-15. Such finding raises a question about the driving factors of the dynamics of structural change in Guinea.

# 4.2 What are the driving factors of the dynamics of structural change in Guinea?

# _4.2.1 Strategy of investigation_

For the analysis of the drivers of the dynamics of structural change, we rely on the findings of McMillan _et al_ . (2014), and Martins (2019). Using cross-section data over the period 1990-2005 and covering 38 countries (including 9 SSA countries), McMillan _et al_ . (2014) find that three factors significantly affect structural change. These include: (i) natural resources abundance, (ii) currency undervaluation, and (iii) labor market rigidity. Natural resources abundance negatively affects structural change because the mining sector does not generate much employment, unlike manufacturing industries. Likewise, labor market rigidity does not facilitate structural change. Structural change is facilitated when the existing regulatory framework allows the easing flow of labor across sectors. Finally, countries that maintain competitive or undervalued currencies tend to experience more growth enhancing structural change, because undervaluation
|[0.57]||[0.62]|[0.54]||
|Food Consumption Score|58.84|56.08|2.76|57.72|58.65|-0.93|58.13|57.50|0.63|57.29|57.18|0.11|55.43|57.55|-2.11|
||[21.17]|[19.60]||[21.57]|[21.72]||[21.13]|[22.60]||[23.30]|[21.75]||[23.03]|[20.90]||
|Reduced Coping Strategies Index|8.62<br>|8.92<br>|-0.30|12.31<br>|12.73<br>|-0.42|13.85<br>|12.84<br>|1.00|14.08<br>|15.01<br>|-0.93|14.51<br>|16.56<br>|-2.05*|
||[9.85]|[7.87]||[13.59]|[13.65]||[15.05]|[15.20]||[15.94]|[15.34]||[14.21]|[16.41]||
|Livelihoods Coping Strategy Index|<br>3.88|<br>4.05|-0.16|<br>4.05|<br>4.15|-0.10|<br>4.62|<br>4.59|0.03
**Figure 1. Household income and the fraction of malnourished children (0-5 years)**

<!-- Start of picture text --> Fraction of stunted children Fraction of underweight children<br>.62<br>.4<br>.25<br>.2<br>0 0<br>8000 15000 25000 50000 100000 8000 15000 25000 50000 100000<br>Per capita consumption Per capita consumption<br>Fraction with height for age z-score less -2 Fraction with weight for age z-score less -2<br><!-- End of picture text -->

**Figure 2. Nutrition z-scores for children aged 0-5 and presence of program interventions in the community** Weight for age z-scores Height for age z-scores

<!-- Start of picture text --> Nutrition program in village Nutrition program absent Nutrition program in village Nutrition program absent<br>-1 -1<br>-2 -2<br>-3 -3<br>8000 15000 25000 50000 100000 8000 15000 25000 50000 100000<br>Per capita consumption Per capita consumption<br>Weight for age z-score Height for age z-score<br><!-- End of picture text --> 17
# **5. Testing the hypotheses using data on SP responses to the pandemic**

In Section 3.2, we saw that the SPECs have not changed over time in a way that is suggestive of a shift in development priorities in poor and middle-income countries such that more of their national income is devoted to SP. Yes, there has been an increase in the SP share of GDP over time among initially poor countries, but this has been driven by the fact that they are no longer quite so poor. However (as noted above), there may well be a degree of stickiness over time in the SPECs. (Line ministries are known to be resistant to cuts in their budgets.) Possibly public spending decisions simply adjust very slowly to the new priorities.

The COVID shock induced SP responses in varying degrees across the world, as documented by <u>Gentilini et al. (2021). This shock-induced change in policies may better reveal</u> the true change in the underlying policy priorities (even prior to the pandemic), in that the pandemic gave policy makers a new opportunity to implement the policies they truly want—a break from the past.

Studying the pandemic responses might also provide a new perspective on what countrylevel factors influence the success of pro-SP policy reforms. It may well be that the country characteristics relevant to SP spending in normal times differ from those that matter most in responding to a large shock, or in policy reforms. For example, ICT infrastructure is likely to matter to the scope for scaling up existing programs while general government effectiveness may matter more to the capabilities for introducing new programs, which can be administratively demanding.

We find that poorer countries devoted a smaller share of their GDP to social protection in response to the pandemic. The bold line in Figure 6 plots the relationship.<sup>32</sup> (We explain the dashed line below.) The pattern is similar to Figure 1, although the mean shares of GDP are