rafmacalaba/data-use-mentions-extended huggingface dataset:
roughly 50 percent of the base. We do not have detailed information on how these loans were backed. But presumably, if the quality of the assets of these banks had been high, there would have been no banking crisis to start with and no need for central bank lending. This observation suggests that these loans represent, to a large extent, subsidies to the financial sector.

# _Were the Banking Crises a Surprise?_

We now consider whether private agents anticipated the state of the banking system prior to the currency crises. Corsetti, Pesenti and Roubini (1998a), IMF (1998) and Lane et al (1999) discuss the fragile state of the financial sectors in the crisis countries prior to the recent speculative attacks. In addition they provide several example of bank failures and reorganizations that occurred prior to the crises.

While their discussion is very informative, it leaves open the question of how markets aggregated the information contained in the episodes that Corsetti, Pesenti and Roubini (1998a), IMF (1998) and Lane et al (1999) discuss. To investigate this issue we obtained daily data on stock market indices of the value of the banking or financial sectors and the industrial or commercial sectors in the crisis countries. We then constructed monthly indices of the market value of the financial sectors in these countries as well indices of nonfinancial sectors.<sup>2 5</sup> These are presented in Figure 6 and summarized in Table 10. Note that in Korea, Thailand and to a lesser extent the Philippines and Malaysia, the value of the financial sectors had been declining well before the currency crises.

> 25We used the following Bloomberg series to construct our monthly banking/financial sector indices: Indonesia-JAKFIN, Korea-KOSPBANK, Malaysia-KLFIN, Philippines-PFINC, Thailand-SETFIN, Hong Kong-AOF, Singapore-SESFIN, Taiwan Province-TWSEBKI. The Bloomberg series used to construct monthly industrial or commercial sector indices were: Indonesia-JAKMANU, Korea-KOSPMAN, Malaysia-KLPRO, Philippines-PCOM, ThailandSETCOM, Hong Kong-AON, Singapore-SESIND, Taiwan Province-TWSESTEE.

36
N</sup> 2001−2002<sup>= 62% and pN</sup> 2002−2003<sup>= 78%).Note</sup> that this is the same period in which the share of both wage cuts and freezes grows sharply in Uruguay. The rationale for this behavior must be found in the severe recession in Uruguay during these years. Uruguay enters into a long recession in 1998Q4 which reaches its trough in 2002Q3, after a temporary upswing between 2000Q2 and 2001Q3.<sup>8</sup> In 2002, the severe crisis that affected the economy forced the central bank to let the exchange rate float freely, putting an end to the use of the exchange rate as a nominal anchor. Hence, even in the presence of high inflation during these years, firms are in

> 8 Peaks and troughs are identified using the Bry-Boschan quarterly (BBQ) algorithm (Harding and Pagan, 2002) on quarterly real GDP data for the period 1988Q1-2010Q3.

17
to higher error (e.g. the subject might mark the wrong option even after evaluating the options correctly due to the change in side). The randomization within binary choice may also make comprehension harder if the exact parallel organization of each decision helps subjects understand the structure of the experiment. We believe that these risks are small compared to the potential benefits. In fact, we conjecture that within-choice randomization may increase attention, since the choices look less repetitive and are less susceptible to “automation”. That said, future work could test the optimal presentation of binary choices to minimize a potential trade-off between detecting hidden, existing error and introducing additional error. Our MPL template survey instrument (described below) allows the user to implement any pattern of randomizing the order of options within binary choice, including no randomization and full randomization.<sup>17</sup>

# **3.2 Random effects latent utility estimation**

Even if the MPL design would allow it, existing estimation approaches do not account properly for either systematic or idiosyncratic error. Building on our framework from section 2.2, we propose to estimate WTP with a latent utility binary choice model that captures both individual-level variation and choice-specific error and can also estimate the size of the systematic bias using data from a randomized MPL. This estimation approach takes seriously the structure of the data as a series of choices between two options, and it deals in natural ways with MSB, NSB, and interval-identified observations. The basic idea appeared early on in the literature on estimating risk aversion parameters from MPL (Holt and Laury, 2002; Harrison and Rutström, 2008; Harrison, 2008), and it is partially reflected in the use of interval regression for MPL data as well as in randomutility approaches found in the wider literature on WTP elicitation (e.g. Berry et al., 2020).<sup>18</sup> However, binary choice models do not seem to be commonly used to estimate preferences from survey-based MPL
- 49 -

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As is subsequently discussed, the empirical strategy relies on the geographical coordinates of communities. Consequently, the data are restricted to the communities (and associated households) for whom these data are available and correct.<sup>10</sup> Additionally, households without any consumption of food (purchased, free, or own production) or which had abnormally high holdings of land (>200 acres as compared to mean holdings of 3.7 acres with a standard deviation of 5.4) are not included.<sup>11</sup> The remaining analysis is based on 353 communities and 3,508 households for whom data were available.<sup>12</sup> The NUS data are supplemented with data from the Armed Conflict Location and Event Data (ACLED) for Uganda (Raleigh and Hegre 2005). The NUS data only include data on community level attacks in 1992, 1999, and 2004. By providing additional georeferenced data for the location of LRA attacks from 1997 until 2003, ACLED allow both for a larger set of instruments and a more accurate “map” of violence. Additionally, insofar as the behaviour of households changes based on their distance from violence, ACLED should result in more precise estimates of the effects of the risk of violence.<sup>13</sup> I use only events that are violent, involve the LRA, and occurred in 2003 or earlier. Additionally, since the precision of the geographical coordinates

> and risk. There are, however, also strong reasons to believe that the inclusion of wealthier households would lead to a larger impact of insecurity. In particular, in times of conflict and insecurity, their income generating activities would likely also suffer, and they would have more room to reduce their consumption before reaching minimal subsistence levels. Unfortunately, since the sample only contains current households, this hypothesis cannot be investigated.

> 10. For 33 communities, the recorded coordinates fall outside of the boundaries of Uganda, and therefore these communities
of tuition costs using the AMSSW 2015. The average cost for those who paid was 1,719 manat in 2015, but some individuals paid much higher tuition amounts.

**Table 4: Cost per Student/Year** (nominal _manat_ 2015)

|Level of Education|Cost|Enrollment|Per-c|apita Cost|
|---|---|---|---|---|
||||Tuition|Public<br>scholarship|
|Primary and|1,074,854,820|1,378,019||780|
|Secondary|||||
|Tertiary|243,962,964|138,301|1800|1700|
|All|1,318,817,784|1,516,320|||

Source: Azerbaijan Ministry of Education Notes: Local currency, _manat_ ; currently, 1 _manat_ is equivalent to $ US 0.59.

The discounting formula presented above was applied to the earnings profiles to estimate the returns assuming a 4-8-4 duration of primary, secondary and tertiary education. The resulting returns appear in Table 5. Estimating the rate of return using the discounting formula, the private return to investment in tertiary education is 9.7 percent, which includes the forgone earnings as the private cost of education.

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rather to guide the process of data analysis.

# **_The Jones Critique_**

A second criticism, especially relevant for the present study, is the seminal contribution of Jones (1995). Testing endogenous growth models in the context of time series implies establishing a relationship between a variable that is usually stationary? without drift? such as income growth, and a variable which is usually non-stationary, such as years of schooling. In other words, his results fundamentally call into question the implicit prediction of many endogenous growth models suggesting output growth should exhibit large permanent increases. Time series data over a very long time period for the United States and other OECD countries reveal that the growth rates of GDP per capita in these countries exhibit little persistent changes, and can be characterized by more or less constant mean.

This observation imposes a testable prediction. According to endogenous growth models permanent changes in certain policy variables, such as schooling, or the number of scientists and engineers engaged in research and development, should have

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source) is Most Probable Number<br>(MPN) in 100 mL as defined for<br>Aquagenx CBT EC+TC MPN water<br>quality testing kits and follow WHO<br>standards. For nondetects, we substitute<br>half the lower detection limit.|
|**Secondary**<br>**Outcome**|**HH Coliforms MPN/100mL**|Water quality at point of use (Drinking<br>water stored in the household) is Most<br>Probable Number (MPN) in 100 mL as<br>defined forAquagenx CBT EC+TC MPN<br>water quality testing kits and follows WHO<br>standards. For nondetects, we substitute<br>half the lower detection limit.|
|**Secondary**<br>**Outcome**|**Weight for age**|Weight for age z score for children under<br>5.|
interviewed by country, with an overall total of 10,090 firms. The aim was to have at least 15% of the firms in the “small” category (fewer than 50 employees), and in all, 40% were in this category. Nevertheless for the purpose of obtaining statistically significant results on access, the sample remains rather small in almost all of the countries.<sup>21</sup> In addition to general information about the firm and its governance, most of the questions are qualitative ones about the firm’s perception of the institutional environment in which it operates. The questions are grouped under the headings: quality and integrity of public services; rules and regulations; legal system; bureaucratic red tape; predictability; financial sector services and corporate governance; and competition.

There are five financial sector services questions in the WBES survey, relating to (i) the firm’s confidence in the ability of the financial sector to deliver needed services; (ii) a breakdown of the source of the firm’s financing; (iii) time taken to transfer funds; (iv) firm’s rating of the extent to which 11 identified financial sector service difficulties such as bank collateral requirements; high interest rates, corruption of bank officials etc. represent an obstacle to the firm. (v) whether the firm uses international accounting standards (IAS) and provides shareholders with audited accounts.

The survey’s findings have been summarized by Batra et al. (2003) and they reveal the importance of perceived financial constraints in most regions (though not in Africa). There are differences by size of firm: unsurprisingly, the use of internal funds, family and friends and of money lenders declines as firms get larger, while the use of local and foreign commercial banks and state funds increases. Small and medium sized firms have a greater propensity to report moderate or major financial obstacles than do large firms, though not in relation specifically to high interest rates which is overall
Statistical Life.” _Journal of Human Resources_ 45 (Summer): 749-771.

Hildebrandt, Nicole, and David J. McKenzie. 2005. “The Effects of Migration on Child Health in Mexico.” _Economia_ 6 (1): 257–89.

Juranek, Steffen, Jörg Paetzold, Hannes Winner, and Floris Zoutman. 2020. “Labor market effects of COVID-19 in Sweden and its neighbors: Evidence from novel administrative data.” NHH Dept. of Business and Management Science Discussion Paper 2020/8.

<mark>Khamis, Melanie, Daniel Prinz, David Newhouse, Amparo Palacios-Lopez, Utz Pape, Michael Weber. 2021. “The Early Labor Market Impacts of COVID-19 in Developing Countries: Evidence from High-Frequency Phone Surveys.” Policy Research Working Paper No. 9510. World Bank, Washington, DC. © World Bank.</mark> Kramarz, Francis and Oskar Nordström Skans. 2014. “When Strong Ties are Strong: Networks and Youth Labour Market Entry,” _Review of Economic Studies_ , 81 (3): 1164–1200.

<mark>Kugler, Maurice; Mariana Viollaz, Daniel Duque, Isis Gaddis, David Newhouse, Amparo Palacios-Lopez, Michael Weber. 2021. “How Did the COVID-19 Crisis Affect Different Types of Workers in the Developing World?” Jobs Working Paper No. 60. World Bank, Washington, DC. © World Bank.</mark> 30
# **5. Conclusions**

The results outlined above, based on concurrent nationally-representative surveys from Malawi, highlight how respondent selection can affect levels of ownership and rights constructs across men and women within households. In particular, our findings further bolster the UN (2017) recommendations to expand intra-household data collection on individual-disaggregated asset ownership and control, and to interview adult household members in private regarding their personal ownership of and rights to physical and financial assets. Doing so has significant gender implications. Asking the so-called most-knowledgeable household members on land ownership and rights of other adults, for example, raises the levels of exclusive reported and economic ownership of agricultural land among men, and lowers the levels of ownership among women. For younger to middle-age women, we find that the best-practice, private individual interviews also raise women’s reported and economic ownership (exclusive and joint), as well as rights, relative to men. Further, under individual interviews, we find substantial agreement on parcel ownership among married/cohabiting spouses, and discrepancies that arise are typically associated with proxies of greater household status for women.

While the findings point to the value of following international best practices in individualdisaggregated data collection on ownership of and rights to land, further research is needed on better understanding how men and women within households (and across country/regional contexts) comprehend and interpret survey questions on asset ownership, often due to gender norms and other social customs. Doss et al. (2019), for example, suggest cognitive interviewing pilots in the field to shed light on these issues, as well as on correlates of discrepancies in intrahousehold reporting on land ownership and rights. In general, this is part of an important area of work on survey question design that needs to be explored further.

29
Policy Research Working Paper 8664

# **Abstract**

“Leaving no one behind” is an overarching principle of the Sustainable Development Goals. Many countries are prioritizing resources for those who are furthest behind. Existing malnutrition indicators—underweight, stunting, wasting, overweight, and severe wasting—are headcount ratios. They do not capture how far behind malnourished children are relative to the World Health Organization growth standards. To understand the severity of malnutrition, this study develops a new malnutrition measurement, using the method originally developed for estimating poverty. This study estimates the prevalence, gap, and gap squared for stunting, wasting, overweight, and underweight, using data from 94 developing countries over 20 years. The results show that although in most cases the headcount measures and gap measures are moving in the same direction, in many other cases, they are moving in opposite directions. Moreover, employing the new measures, the study can identify countries that have low levels of headcount for a malnutrition measure but comparatively high severity of malnutrition according to the gap measures, and vice versa. This suggests that these new malnutrition measures provide additional information on the severity of malnutrition that is not possible to be known from headcount measures. These new measures of the severity of malnutrition can therefore improve the monitoring of child malnutrition across countries, and consequently help countries to achieve their Sustainable Development Goals.

This paper is a product of the Development Data Group, Development Economics. 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/research. The authors may be contacted at juan.feng@fao.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
Applying Machine Learning and Geolocation Techniques to Social Media Data (Twitter) to Develop a Resource for Urban Planning<sup>∗</sup> Sveta Milusheva<sup>†</sup> Robert Marty<sup>_†_</sup> Guadalupe Bedoya<sup>_†_</sup> Sarah Williams<sup>‡</sup> Elizabeth Resor<sup>§</sup> Arianna Legovini<sup>_†_</sup> _JEL Classification:_ R41, R42, O18, C80 _Keywords:_ Big Data, Machine Learning, Road Safety, Urban Mobility, SDGs

> ∗We thank Robert Tenorio and Amy Dolinger for their field coordination and research support. We are also grateful to Andrew Muriithi, Purity Kimuru, Rodgers Avuya, Salome Omondi and Pheliciah Mwachofi for their field support. DIME Analytics provided technical support throughout the analysis with Luiza Andrade and Luis Eduardo San Martin conducting code review and reproducibility checks. We appreciate comments from anonymous reviewers and participants at the ACM COMPASS Conference and the Netmob Conference. The research has been funded with UK aid from the UK government through the ieConnect for Impact program and the World Bank’s Knowledge for Change program.

> †Development Impact Evaluation Department, World Bank, Washington DC

> ‡School of Architecture and Planning, Massachusetts Institute of Technology, Cambridge MA

> §School of Information, University of California, Berkeley CA

1
the potential measurement error in the production data for petroleum re fi neries (sic 353) and miscellaneous petroleum and coal products (sic 354). Given that these are sensitive sectors, the production data for them can be inaccurate and is in some cases missing. To calculate the EU weighted average production variable used in the main speci fi cations, we imputed the missing data for a few countries in 1992 by using information from previous years.<sup>51</sup> However, since these sectors produce outlier values for the political economy variable, ∆x, we re-ran the basic speci fi cations by dropping the two industries. They account for less than 0.8% of all products in the sample and so dropping them does not affect the results.

As we argue in section 4.1, there are good reasons to expect the main regressors to be subject

> 49 We treat the initial tariff as an endogenous regressor.

> 50 Again, a simple explanation for why the basic estimates are not too sensitive is that, to the extent that the change in tariffs for that industry was not already accounted for by the political economy or reciprocity variables, it indicated the existence of an omitted variable but since we instrument for the preference variable, this omitted variable should not bias the estimates.

> 51 For these industries we do not have data in 1992 (or surrounding years) for France (354), Germany and Italy (353, 354). We impute it based on the 1978 values by assuming a similar share in total output. For example if the value of output is available for all sectors 1, 2, and 3 in 1978 but missing for sector 2 in 1992 in country c. We impute the value for output, v2<sup>92</sup> c<sup>with[v</sup> 2<sup>78</sup> c<sup>/(v</sup> 1<sup>78</sup
in Amman, Beirut, and Cairo. In Amman, for our sample of households, the average value of the index is 41%. In Beirut, the same figure is at 29%, and Cairo it is at 52%.

> 22 Benchmarking data is only available for Africa.

> 23 To normalize, we divide the number of runs by 60 and truncate the values equal to or over 1 to be equal to 100%. This means that we have an index of an average number of bus reachable each minute between 8 am and 9 am, at a maximum distance of 10-minute walk, with the maximum being 1 bus per minute (in this case the index equals 100%).

15