rafmacalaba/data-use-mentions-extended huggingface dataset:
prior to national elections. Second, FONCODES projects were directed to provinces in which the marginal political impact of expenditures was likely to be largest. The results are robust to a large number of specifications and controls. The Peruvian data thus support predictions made in the literature on "political business cycles" (Nordhaus 1975, Rogoff 1990) as well as the literature on political influences on the allocation of discretionary funds (Wright 1974, Cox and McCubbins 1986, Lindbeck and Weibull 1987, Dixit and Londregan 1996, Case 1997).

The rest of the paper proceeds as follows. Section 2 provides a brief description of Peruvian politics between 1990 and 1995, and FONCODES. Section 3 describes data requirements and sources for the paper. Section 4 estimates the effect of the electoral calendar on the _timing_ of FONCODES I Moncada (1996) and Graham and Kane (1998) discuss the timing and distribution of FONCODES expenditures, respectively. As I discuss below, however, both papers have serious limitations.

2
and the operational performance of utilities in the water and electricity distribution sectors of LAC. We correlated the dimensions described in the previous sections with the level and growth rates of the main performance indicators for utilities in the water and electricity sectors. We ran the governance indicators with the pool of utilities and analyzed the relationship between the governance indicators and each sector separately.

We use utilities’ sector performance data from 250 public and private utilities in the electricity sector, covering 26 countries and 89 percent of the connections in the region. It contains more than 20 variables indicating output, input, operating performance, quality and customer services, and prices. Data for the water sector includes annual information on 1700+ public and private utilities of 16 countries, covering 59 percent of the water connections in the region. Variables for water are similar to those in electricity. The results are presented in the tables in Annex A.

_Legal Framework:_ When we analyzed the correlation between these governance indicators and the growth rates of the set of performance indicators, results suggested that our measure of legal framework is associated with a decrease in average quality of service and an increase in average tariffs. When we evaluated each of the sectors separately, we observed that in water utilities there are some differences in terms of labor productivity where higher soundness is associated with 19
P a g e <u>|</u> **44**

foot, then the minimum household income level to afford construction costs alone is about INR 2100 and using the residual cost of land based on the market assessment data for Ahmedabad (see figure 10) this figure increases to 2500 per month<sup>13</sup> . This figure is roughly 90% of the All India urban poverty line (Himanshu:2009). For Ahmedabad, our income distribution data suggest this target population would amount to about 10% of the total. Performing a similar calculation for Mumbai, based on derived cost of land for Dharavi<sup>14</sup> , the required income per month is roughly 10 times the figure for Ahmedabad, or about INR 26,000. Although incomes in Mumbai are higher than in Ahmedabad, nonetheless 80% of the population is below this required income level. This simple exercise shows the dramatic difference that real estate market conditions can make for the size of even a minimalist slum program<sup>15</sup> . They also show that without supply side and regulatory measures in many cities, addressing basic needs can rapidly become an unmanageable problem.

Now we turn to a rough characterization of the housing market equilibrium using the income distribution and housing price indications developed above. Figure 12 illustrates the housing market equilibrium in Mumbai. Using house price indications, affordability estimates based on the income distribution data, and estimates of the stock of various types of housing in our typology, this figure shows how different income classes are distributed into various types of accommodation in Mumbai. Much of the middle class and all of the poor are forced into substandard housing because of the high price of land and the highly inadequate supply of housing. Most households are living in slums and chawls<sup>16</sup> . The number of such dwelling units can only grow through subdivision of the existing floor space or densification by adding additional floors. This illustrates
amounts to the assumption that the quality of education that children will receive in the future is the same as what is reflected in the most recently‐observed test scores.

It is also worth noting that for most countries, test scores are observed only at one grade level. This means that out of necessity quality measured at one grade level is assumed to be representative of the entire school system. This assumption however may however be questionable in countries where there is a lot of attrition as students move to higher grades. In this case, test scores observed in later grades reflect an element of selection, to the extent that lower‐ability students are more likely to drop out of school before reaching the level at which the test is implemented. This implies that observed average test scores in later grades likely overstate the quality of education in lower grades. Adjusting for this is difficult, as it requires some way to approximate the distribution of test scores among those students who did not take the test. However, a partial adjustment for this in the overall HCI comes through the fact that in such cases, the enrollment rates used to calculate expected years of school will also be lower, which in turn lowers the learning‐adjusted years of school measure.

A final caveat worth noting is that test scores are only an imperfect measure of learning. There are the usual concerns that test scores measure performance only on those items of the curriculum that are covered in the test. Beyond this, test scores also respond to other factors, including intrinsic motivation on the part of test takers. For example, extensive empirical evidence from schoolchildren in Chicago suggests that small immediate financial rewards for good performance have substantial effects on students’ standardized test scores (Levitt, List, Neckerman and Sadoff (2016)). To the extent that there are cross‐country differences in students’ intrinsic motivation when taking standardized tests, this will be conflated with the quality interpretation of the test scores. Student test scores also reflect the influence of the home environment
Table 3 reports the cross-sectional regression results of predicting income loss and work stoppage by real-time indicators using the first wave of data collected between April and August 2020.<sup>12</sup> Column 1 shows the results for the share of households reporting total income declines. Our preferred measure of overall model fit is the mean R<sup>2</sup> over the full set of one hundred imputations, which is 0.37. More frequent Google searches for “money” and “jobs”, and less frequent searches for “free food” are statistically significant correlates of the prevalence of total income declines. Columns 2-4 show the results for different types of income. The predictive power for a loss of farm income, nonfarm income, and wage income is significantly less, at 0.09, 0.13, and 0.24, respectively. The predictive power of real-time indicators for job loss is slightly lower than total income but substantially higher than those of the different types of income (average R<sup>2</sup> = 0.34).

The signs of the coefficients when analyzing the initial wave are not always intuitive. For example, searches for “bread” are negatively correlated with total income declines, searches for “Food Bank” are negatively correlated with the prevalence of wage income declines, and searches for “Free Food” are negatively correlated with the prevalence of work stoppage. This likely reflects correlations between Google search behavior and initial conditions of the regions, such as their overall level of development. For example, residents in poorer areas may be more likely to search for food banks or free food in response to an adverse shock, even if those shocks were more prevalent in more affluent areas. Similarly, searches for “jobs” and “unemployment” are negatively correlated with the prevalence of total income declines in the first wave, suggesting that areas hit less hard by the crisis, in terms of the prevalence of income declines, may have
6

# 3. Data

3.1. **Outcomes.** To measure our outcome variables, we will combine two administrative datasets. First, we will compute school-level enrollment, dropout rates and promotion rates using the administrative registry of all the students in Colombia, enrolled in either public or private schools. This dataset is called _R166-SIMAT_ and its source is the Colombian Ministry of Education.<sup>7</sup> It is available for the period 20122018, which shapes our sample period.

Importantly, R166-SIMAT includes the student ID that allows us to distinguish between Colombian and foreign students in order to explore the effects of the Venezuelan migration shock on both native and migrant students. Specifically, we identified as ‘migrant’ those students with IDs different to the standard ID that the government issues to underage natives. These include special residence permits, visas, and border mobility cards. We also classified as migrant students the individuals who enrolled using a provisional ID, provided by the municipal Secretary of Education to undocumented children who want to enroll in a public institution. The vast majority of undocumented children are foreigners, most of whom are Venezuelans (see section 2. Using R166-SIMAT we can construct the following school (or school/grade)-level variables:

1. **(Log) Enrollment:** the (log of the) total number of students enrolled per school (or school/grade) at the beginning of each academic year.

> 7The name of the dataset originated in the Ministry’s Resolution 166 of 2004, which created the National Enrollment System (SIMAT, from the Spanish acronym) that mandated all education institutions to report to the Ministry individual-level enrollment each year as well as the condition of each student at the end of the academic year. Importantly, this registry excludes schools that have nontraditional education models. This is the case of some indigenous communities in rural areas. It also excludes public institutions for adult education and literacy,and training colleges.
not most) low-income countries are far from stellar (Andrews et al 2017).<sup>6</sup> For example, Jerven’s (2013) important work in Africa on the numerous challenges associated with maintaining the System of National Accounts – the longest-standing economic data collection task asked of all countries, from which their respective GDP is determined – portends the difficulties facing less high-profile metrics <mark>(see also Sandefur and Glassman 2015).</mark><sup>7</sup> Put differently: if many developing countries struggle to curate the single, longest-standing, universally-endorsed, most important measure asked of them, on what basis do we expect these countries to manage lesser, lower-stakes measures?

To be sure, building quantitative analytical skills in public agencies is highly desirable; for present purposes, our initial point is a slight variation on the old adage<sup>8</sup> that the quality of outcomes derived from quantitative data is only as good as the quality of the ‘raw material’ and the competence with which it is analyzed and interpreted. Fulfilling an otherwise noble ambition to build a professional public sector whose decisions are informed by ‘evidence’ requires a prior and companion effort to build the requisite skills and sensibilities. Put differently, precisely because effective data management is itself such a complex and difficult task, in contexts where agencies struggle to implement even basic policy measures at a satisfactory level (e.g., delivering mail, ensuring attendance at work) it is unlikely that, ceteris paribus, invoking such agencies to also take a more ‘data driven’ approach will elicit substantive improvement. More and better ‘data’ will not fix a problem if the absence of such data is not itself the key problem or the ‘binding constraint’; as such, the priority issue is discerning what _is_ in fact the key policy problem and its constituent elements. From this starting point, more and better data can be part of, but not a substitute
adjustments for local factors.<sup>17</sup> We considered three possible adjustments to transfer the Mexico City damage estimate to the GCMA.

First, we double the damage estimate because Cairo has a population density about double that of Mexico City. The GCMA has a population of 17 million living in an area of about 1,660 square miles (Nippon Koei 2010). The greater Mexico City metropolitan region has a population of 19 million in an area of 3,691 square miles.

Second, we made some adjustment for differences in people‘s valuation of pollution-health risks. These risks are quantified using the value of a statistical life (VSL), which measures people‘s willingness to pay for reduced mortality risk, expressed per life saved. Parry and Timilsina (2010) implicitly assume a VSL for Mexico City of about $1.5 million—about one-fourth of that assumed for the United States by its Environmental Protection Agency and Department of Transportation. The VSL is commonly transferred among countries using their relative real per capita income, raised to the power of the income elasticity of the VSL (e.g., Cifuentes et al. 2005, 40-41). Typical estimates for this elasticity vary between about 0.5 and 1.0 (e.g., Viscusi and Aldy 2003, Miller 2000). Real per capita income in Egypt is approximately 40 percent of that in Mexico, where income is measured in terms of purchasing power parity equivalent (IMF 2009, World Bank 2008). Therefore, this range of elasticities suggests the VSL for Egypt should be about 40-60 percent of that in Mexico. We lean on the conservative side by assuming the VSL for Egypt is $600,000 (or about one-tenth of that for the United States). Thus, we scale back the local damage estimate by 60 percent.

Third, we did not make any adjustment for differences in automobile emissions rates. Our estimates of average miles per gallon for autos and microbuses in the GCMA
most analyses of house prices or affordability assessments are constrained to developed economies and limited in scope. Most cross-country analyses that assess trends in house prices are based on available indices, which often aggregate to the national level, masking important within-country dynamics and regional differences. To overcome this flaw and provide more in-depth insights into housing markets in emerging economies, we demonstrate how to collect a large range of localized data through web scraping of property listing websites. Further, to compare property prices across countries, we introduce the concept of _the Basic House Price_ – which constitutes the average price of a basic one-bedroom apartment of 50 square meters in an urban area – that allows for comparability across countries.

By way of demonstrating the methodology and data processing for five EMDEs, we show that web scraping offers a cost-effective way to obtain a large amount of price data for countries where official data is absent and where alternate data sources on prices are not available. The main constraint to this approach remains the unorganized structure of listing websites and the limited information available on property features and attributes. This approach will only improve over time as the capabilities of listing websites improve and become the preferred method of listing. There is also room to improve the web scraping on several fronts. For instance, image recognition software can extract information that is not supplied systematically in listing websites and could improve model precision. In addition, addresses could be geotagged to incorporate crucial details about location and to differentiate within-city variation.

31
000 3,500<br>3,000<br>6,000<br>2,500<br>4,000 2,000<br>1,500<br>2,000 1,000<br>‐ 500<br>Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year ‐<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year Year<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Suzhou Cost Rica DR Bangladesh<br>Vietnam Ghana Kenya Lesotho Cost Rica DR Bangladesh Ghana Kenya Lesotho<br><!-- End of picture text --> Sources: Author’s calculations based on data from individual country EPZ authorities; Arce‐Alpazar et al (2005) for Costa Rica; Zhao & Farole (2010) for Suzhou Of the three African programs, only Ghana appears to be showing evidence of shifting to the higher growth path, although it is unclear how sustainable this growth will be as it is dependent to a large degree on processed commodities (cocoa and timber) which are both limited in their availability and face significant cyclical price fluctuations. Figure 7 removes China and Vietnam to give a clearer picture of the growth paths of the African zones against some relevant comparators<sup>17</sup> . Here it becomes clearer that exports from both Kenya and Lesotho are growing, but they are failing to take off. In contrast the formerly slow‐growing EPZ program in Bangladesh appears, after 10‐12 years, to have moved to a much faster growth path. This is driven by the increasing competitiveness of Bangladesh in the global

> 16 Unfortunately, we have insufficient time series data on Senegal and Nigeria to include
<!-- Start of picture text --> Bond Fund Flows by Region<br>(% of GDP}<br>1<br>EAP ECA ae LAC MINA ee SAR —+—SSA<br>os<br>— ad<br>a o eeeeic2 2 2 = 3 ASEeesfort ce wi22ee22= fm. a “me a88<br>8 a ee ee /, ee eee<br>2-05 S FS SESS EVRSRa S SSS SESBSEBSB RSa<br>-2<br>e eeeN Sr Guceteont ee Ge Am Ge a a a &<br>-L.5<br>Note: Regional aggregates of 33 GEM eligible countries;<br>excluding Kenya, Sri Lanka, Morocco<br>-2 Source: Emerging Portfolio Funds Research, aggregate of<br>bond funds flows by country<br><!-- End of picture text --> <!-- Start of picture text --> 350,000 Trading Volume of Local Instruments Q4 2009<br>300,000<br>250,000<br>8€ 200,000<br>5<br>150,000<br>100,000<br>50,000<br>, (<br>oe ¢ SF ESFEEESE PE ELSEEELE<br>Source:éfFPOEPEP; IP EPISoe EPS ESSé* ANG?x<br> EMTA Q4 2009 Debt Trading Volume Survey<br><!-- End of picture text --> <!-- Start of picture text --> 25,000 Trading Volume of Local Instruments Q4 2009<br>20,000<br>= 15,000<br>=<br>=<br>$10,000<br>5,000<br>: (_\<br>2 o> o @ @ iS 9<br>Nate: ExeludingPEPBe the tap 11EL cuntriesinGOSEELSthesurveyELES LIESBOOSIESN)ISG SE RIS
.0076)|(0.0053)|(0.0020)|(0.0012)|
|Constant|0.153<sup>_∗∗∗_</sup>|0.201<sup>_∗∗∗_</sup>|0.210<sup>_∗∗∗_</sup>|0.186<sup>_∗∗∗_</sup>|0.137<sup>_∗∗∗_</sup>|
||(0.0069)|(0.0447)|(0.0353)|(0.0124)|(0.0073)|
|Industry FEs|No|No|No|No|No|
|State FEs|No|No|No|No|No|
|Obs.|2,266|81|112|625|1,448|
|R<sup>2</sup>|0.294|0.228|0.291|0.440|0.255|

*** Source: World Bank staff using RAIS. Note: Robust standard errors in parentheses. _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

# **4.2 Conditional convergence**

A multiplicity of factors influences the pace of regional convergence in productivity. Some are geographic (e.g., resource endowment, sea access), institutional (e.g., regulatory capacity, market distortions), and policy. To this end, the regional convergence regressions are now estimated after controlling for industry fixed effects (the levels and productivity growth rates inherently differ due to differences in production and market structure) and state fixed effects (to capture the effect of mostly geographic factors on regional development). In addition, these regression account for states’ initial level of
, 2018).

# **3. Literature review**

In Vietnam the General Statistics Office (GSO) regularly collects household consumption data as part of the longitudinal household living survey. The first Vietnam Living Standards Survey (VLSS) was conducted in 1992 and the second in 1997. Since 2002, the Vietnam Household Living Standards Survey (VHLSS) is conducted every two years. This generated an abundance of household consumption data with respect to other countries that conduct household surveys less frequently. As a result, there exist a relatively high number of studies analyzing Vietnamese household consumption patterns and elasticities, especially with respect to studies fousing on Sub-Saharan African or Caribbean countries. In this review we focus on studies conducted in the last 20 years.

An advantage of looking at the literature on consumption elasticities in Vietnam is that, thanks to the above mentioned data availability and number of studies, it is possible to analyze differences across applications of different demand systems and econometric strategies. However, most of the studies on 5
|**Data Pro**|**perties**||||**M**|**odeling App**|**roach**|||
|---|---|---|---|---|---|---|---|---|---|
|**Sample**<br>**and**|**Frequency**|||**Type**||||||
|**Author (year)**<br> <br>**Source**||**Variables**|**Integration**||**Specification**||**Estima**|**tes**||
|1995Q1-<br>2007Q4|1995Q1-<br>2007Q4 are<br>|real money<br>balances,<br>|_ln_Yt_and<br>_ln_Et_are||_Et+ b4 Rt +_<br>_b5 CPIt-1 +_<br>|C|3.3065|2.9401|0.005|
|IMF -<br>|not available<br>for Egypt<br>either in this<br>source or any<br>other source,|(_ln_RM2t_),<br>real GDP<br>(_ln_Yt_), FX<br>rate (_lnEt_)|integrated<br>of order<br>one, i.e. I~<br>(1) both_Rt_||_et ._|LN_Y|0.70235|8.2304|0.000|
|IFS|we used<br>statistical<br>methods to<br>extrapolate<br>quarterly data|___,<br>three-month<br>deposit rates<br>(_Rt_) and|, <br
an advance premium of 1.5% of insured deposits. The co-insurance was abolished in 2001. The fund has the right to borrow, including from the government in the last resort to receive donations and foreign assistance.

_Sources_ : Central European (1998), Own survey of deposit insurers, Garcia (1999), Law on Bank Deposit Guaranty-Bulgaria (1998).

**Cameroon, Chad, Central African Republic, Gabon, Equatorial Guinea, and Republic of Congo.** A proposal was drafted in 1999 but only ratified by two out of these six CEMAC countries. Thus, they are not considered to have deposit insurance systems unlike earlier sources Demirgüç-Kunt and Sobaci (2000) and Garcia (1999). These African countries share a common central bank. The features of the proposed system are as follows: mandatory membership, joint administration, a permanent fund in place, exclusion of deposits of foreign currencies. The assessment bases for the premiums are deposits and non-performing loans, and the premium rate is 0.15% of deposits plus 0.5% net non-performing loans. When necessary, budgetary resources will be available from member countries. _Sources_ : Own survey of deposit insurers, Garcia (1999).

**Canada.** ( _Canada Deposit insurance Corporation, Deposit Insurance Corporation Act of Canada_ ) The deposit insurance scheme in Canada was established in 1967 and the initial coverage was $Can 20,000. In 1983 the deposit coverage was raised to $Can 60,000, while retirement accounts and deposits held in trust received separate protection with an additional $Can 60,000. The scheme applies to the aggregate amount held per depositor per institution. The system is jointly administered and the membership is compulsory. The covered deposits are savings and demand deposits, term deposits such as guaranteed investment certificates and debenture issues by loan companies, money orders, drafts, checks, and traveler’s checks issued by member institutions. The fund can borrow from the markets and the government, but is charged at