rafmacalaba/data-use-mentions-extended huggingface dataset:
# Annex 1. Fiscal accounts in Mexico

The National Institute of Statistics and Geography (INEGI) compiles all administrative records on the source and use of all financial resources in municipalities. This information is openly available through the State and Municipal System Databases (SIMBAD) on the INEGI website. Figure A1 presents the maximum degree of disaggregation of public accounts at the municipal level available in SIMBAD.

In terms of the resources available to the municipalities, the analysis aggregated accounts the following way:

- Own income: those that are generated from taxes, contributions to social security, products, and uses

- • Unconditional transfers: federal participation (considered in Branch 28): federal resources assigned to states and municipalities that are not conditioned on their use and destination

- Conditional transfers (mostly through _Ramo_ 33) including FAIS resources. Branch 33, federal contributions respond to demands in: education, health care, basic and educational infrastructure, public safety, and social assistance; the FAIS contribution can be disaggregated from this item

- Other resources, such as unexecuted accounts from other periods and other financial instruments

- In terms of expenditure, accounts were grouped in: current expenditure, transfers and subsidies, investments, debt, and other expenditures.

# **Figure A1.** Fiscal accounts, municipal level

<!-- Start of picture text --> Ramo 33<br>Contributions of federal<br>Municipal own revenues Others resources and states<br>Total revenues<br>Total expenditure<br>Recurrent expenditure Investment expenditure Others<br>)<br>FAIS FAFM<br>Taxes<br>Social security contributions Entitlements Products User fees and charges Aprovechamientos ( Other revenues Financing Initial availability Reallocation<br>Federal contributions<br>Debt<br>transfers<br>Personal services supplies other government Movable, immovable Public investment Financial investment and other provisions Other fiscal<br>Materials and Subsides, grants and and intangible Disponibilidad<br><!-- End of picture text --> _Source
# The Hidden Potential of Call Detail Records in The Gambia<sup>1</sup>

Ayumi Arai Erwin Knippenberg Moritz Meyer Apichon Witayangkurn University of Tokyo The World Bank The World Bank University of Tokyo

**Keywords :** call detail records (CDR); data privacy; COVID-19; crisis response; The Gambia

**JEL classification :** H12 (crisis management), J11 (demographic trends), J61 (geographic labor mobility, O18 (rural, urban, regional) C81 (Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access)

1 Corresponding author : Erwin Knippenberg, <u>eknippenberg@worldbank.org. We thank Horeja Cham, Lamin</u> Dibba, Kristen Himelein, Kai Kaiser, Johan Mistiaen, Ryosuke Shibasaki, Matarr Touray, Tara Vishwanath and participants at various seminars for helpful comments. This research project has received funding from the World Bank Trust Fund for Statistical Capacity Building III which is supported by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea. We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.
informal sector workers were more likely to report receiving government assistance than public or formal sector workers. In Morocco, by contrast, public formal sector workers were significantly more likely to be receiving assistance (Krafft et al., 2021).

The World Bank, together with NSOs, collected HFPS data to monitor the impact of the pandemic on households in several African countries. These data can be used to evaluate the potential and targeting of social protection programs. Duchoslav and Hirvonen (2021) use these data to evaluate targeting of social protection and social assistance transfers in Ethiopia, Malawi, and Nigeria based on pre-COVID wealth quintiles. The targeting in Ethiopia which was primarily based on an expansion of the existing Productive Safety Net Program (PSNP) was progressive and covered about 27% of the poorest households in rural areas but was much less effective in urban areas. In Nigeria, there was a major expansion of cash transfers, but survey data showed only mildly effective targeting of government aid in urban areas, and a regressive distribution of probability of receiving aid in rural areas (Duchoslav and Hirvonen, 2021). In Malawi, major government plans to expand social protection did not materialize, so reported aid consisted of mostly uncoordinated privately run initiatives, which were poorly coordinated.

The targeting efficiency of the social protection response in South Africa has also been found to be generally pro-poor. South Africa benefited from a pre-existing means-tested comprehensive social protection system for which payment sizes were increased after the outbreak of the pandemic. The poorest two deciles of households, however, while being most affected by the lockdowns in terms of lost income, were less likely than households higher in the income distribution to have benefited from the expanded social protection as of April 2020, due to delays in enrollment and confusion about eligibility (Kohler Bhorat, 2020). Figure 11 compares the targeting performance reported in phone surveys in Morocco, Tunisia, Egypt, Ethiopia, Nigeria, and South Africa. Notably, in spite of the limitations mentioned above, the countries with the largest
061***<br>(0.010)|
|Constant|-52.349***<br>(10.900)|-48.051***<br>(14.790)|-41.515***<br>(13.270)|-13.010<br>(11.848)|
|Observations|1,992||2,358||

Note: a) To estimate the selection equation, the following variable were added to the Mincer model: Household with children from 0 to 5 years of age; has a partner/spouse; lives with the romantic partner; and ln of the monthly income of the household. b) Corresponds to the monthly income of the household deducting the monthly income of the index young adult. /athrho of 0.046 and standard error of (0.054) for males; -0.068 and (0.089) for females. /lnsigma of -0.176***and standard error of (0.031) for males; -0.091**and (0.040) for females. Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on the data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000 In summary, the results of the Mincer model of human capital returns indicate that for the population raised in rural households with maximum exposure to PROSPERA, there is a premium in terms of labor income, the result of the gains in height and schooling. We also observe that the population with the highest levels of education earns the highest labor income, on average.

# **VIII. Conclusions**

This study focused on the analysis of the data collected as part
. This is challenging because it is not easy to find microdata with information on all dimensions of non-monetary poverty. Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collect comprehensive data on the health status of women and children but have limited employment data and no consumption data (UNDP, 2019).<sup>14</sup> The latest regional report on Arab multidimensional poverty (UN, 2017) covered seven countries and used, in most cases MICS, DHS, and Arab Family Health Project data. It did not construct MPI for Djibouti, the Syrian Arab Republic, Libya, and Lebanon because of a lack of recent data. The West Bank and Gaza was excluded due to unique problems related to blockade and movement restrictions. Table 4 presents

> 14 Many countries base MPIs on household budget surveys, which allows measuring both monetary and nonmonetary poverty. However, as noted in World Bank (2018), many surveys measure access to services and rarely the quality of services. Adding questions to measure quality of services may increase survey burden and jeopardize ability to monitor monetary poverty; so, there are trade-offs between the decision to collect monetary and/or nonmonetary information.

15
the dependent variable. The estimated coefficients also show the percentage change as the model uses a log transformation of the dependent variable. The use of PPML regressions as an alternative to OLS regressions is common practice in the trade and finance literature using bilateral country data. Countries typically do not trade with, or invest in, every other country in the world, thus yielding a data set with many zero-valued observations (Helpman, Melitz, and Rubinstein 2008). The use of standard OLS log-linear estimations might raise some concerns in this context as this approach excludes all these zero-valued observations. In contrast, the PPML estimator provides a natural way to deal with zero values in the dependent variable, while also being consistent in the presence of heteroscedasticity.<sup>12</sup> We report both OLS and PPML estimations because they provide different insights on the intensive and extensive margins.<sup>13</sup> By keeping observations with zero values, the PPML estimations capture the dynamics in international investments along the intensive margin (the growth in the value of investments) and the extensive margin (the growth in the number of bilateral links). In contrast, the OLS regressions better capture trends in the intensive margin as they estimate growth dynamics conditional on positive international investment. The C-C OLS regressions are especially informative in this regard. At higher levels of aggregation, there is less of a distinction between the intensive and extensive margins. That is, the entry and exit of bilateral links between individual country pairs affect the extensive margin at the C-C level, but at a higher aggregation (R-R and C-R) they only affect the value of the dependent variable, not whether the aggregate link is active or not.

The R-R regressions include R-R fixed effects (North-to-North, North-to-South, South-toNorth, and South-to-South). Analogously, the C-R regressions include C-R fixed effects. For each country in the sample, we include a set of four dummy variables capturing their investments in the

> 12 The PPML estimator presents some advantages over other estimators
characteristic. For example, one of the dummy variables created from “gender” is the share of households only having male members.

We use nine variables for the individual-level data: Hindu and age, religion (e.g., Muslim), caste (e.g., upper caste, scheduled castes, and scheduled tribes), discipline (e.g., law and medicine), marital status (e.g., married, unmarried, and divorced), literacy, and education level (e.g., primary school and middle school). Except for age, all other variables are categorical variables. Similarly, we generate the corresponding dummy variables before aggregating them to the state level. Again, we also use the average new confirmed daily cases as a share of the state population.

Using this approach, we compare the outcomes of treated states with that of a weighted combination of control states, the “synthetic” treated states without the free bus ride scheme, which has similar pre-treatment trends as the treated group. The weights applied in our regression for the household-level analysis and individual-level analysis are defined as follows: Household-Level: Weight _ijt_ = State Weight _j ×_ Time _t ×_ Household Weight _it_ , (4) Individual-Level: Weight _ijt_ = State Weight _j ×_ Time _t ×_ Individual Weight _it_ . (5) In equations (9) and (10), “State Weight _j_ ” and “Time _t_ ” are the state unit weights and time weights derived from SDID, respectively. “Household Weight _it_ ” is the sampling weight of a household in the CPHS data. “Individual Weight _it_ ” is the sampling weight of a member of a sample household in the CPHS data.

# **A.2 Delhi Primary Survey**

**Travel Purpose & Distance** The survey inquires about respondents’ primary travel the a selection of work purpose during post-scheme period, offering options including (including work and job search), education,
Doh, J. P. and Pearce, J. A. (2004). ‘Corporate entrepreneurship and real options in transitional policyenvironments: theory development’.Journal of Management Studies,41, 645–64 El-Anis, I. (2018): Jordan in 2018: Too Stable to Fail, Too Small to Flourish, Mediterranean Yearbook 2018, IE Med.

Fatás, M., & Mihov, I. (2013): Policy Volatility, Institutions, and Economic Growth, The Review of Economics and Statistics, Vol. 95(2), 362-376.

Fazekas, M; Benoit, C; & Brenner, D. (2024) An Introduction to LegDat: The global dataset of legislative processes, outputs and outcomes. Manuscript under peer review.

Gratton, G.; Guiso, L.; Michelacci, C.; & Morelli, M. (2021): From Weber to Kafka: Political Instability and the Overproduction of Laws, American Economic Review, Vol. 111(9), 2964-3003.

Gulen, H. & Ion, M. (2016): Policy Uncertainty and Corporate Investment, The Review of Financial Studies, Vol. 29(3), 523-564.

Henisz, W.J. (2000): The Institutional Environment for Economic Growth, Economics and Politics, Vol. 12(1), 1-31.

Hoffmann, V.H.; Trautmann, T.; & Hamprecht, J. (2009): Regulatory Uncertainty: A Reason to Postpone Investments? Not Necessarily, Journal of Management Studies, Vol. 46 (7), 1227-1253.

IMF (2016): IMF Executive Board Approves US$723 million Extended Arrangement Under the Extended Fund Facility for Jordan, https://www.imf.org/en/News/Articles/2016/08/25/PR16381Jordan-IMF-Executive-Board-Approves-US-723-million-Extended-Arrangement (last accessed: 16.06.2024) IMF (2019): Jordan: Second Review Under the Extended Arrangement Under the Extended Fund Faciltiy, Requests for
marital status and number of children, but this applies to the proxy assignment, and not to the short – detailed assignment, where characteristics are balanced.

The identifying variables for the control function estimation are obtained from CWIQ data collected in the same communities during the same year, but covering different households. We use the community mean distance to primary and secondary school to proxy the local supply of education at the time of the individual’s schooling.

Before discussing the results, we consider the differences in wages across treatments. The plots in Figure 1 present the kernel density for the log of daily earnings for the different subsamples. Figure 1A(i) and 1B(i) draw the earnings obtained from the detailed and short modules for men and women respectively, with the distribution generated by the latter mostly to the left of that generated by the former. Figure 1A(ii) and 1B(ii) suggests that the difference is less outspoken for the wages generated by proxy and self treatments.

The descriptive statistics in Table 3 reveal further differences. While the detailed module yields a lower proportion of labor force participants compared to the short module for both men and women, it generates a higher relative proportion of wage workers for both sexes. The detailed module also produces lower mean daily earnings than the short module. Proxy response, on the other hand, leads to relative lower labor force participation and a lower proportion of wage workers, but produces higher mean wages compared to self response, again for both sexes. Although the differences in wages between treatments may seem small, they are actually substantial, as is perhaps more clear when expressed as monthly rather than daily wages. The average daily wage reported in Tanzanian Shilling (TSH) in Table 1 for men (3987, 4781, 3634, 6255) correspond to monthly income expressed in USD of respectively 75USD, 89USD, 68USD and 117USD, while the respective daily wages in TSH for women (3912, 4388, 3552, 5367) correspond to the monthly wages of
# Japan Case Studies

## 1.9. Case 1: Construction of Bypass Road in a Flood-Prone Area

The first case project for testing the calculation of resilience indicators is a proposed construction of a new bypass road intended to save travel time and to develop an alternative route for the existing route, which is located in a flood-prone area (see Figure 7 ).

The existing route of National Highway No. 312 experienced a major flood of the Maruyama River when Typhoon No. 18 hit in October 2004. This road section is a major highway connecting two local cities in the northern Hyogo prefecture, Toyooka City and Asago City. Toyooka City is a regional base in the North Tajima region wherein a first-aid station hospital, Toyooka Hospital, is located. Some residents in the flooded area are isolated and lose access to the first-aid station hospital. Moreover, the increase in travel time between the two cities due to necessary detouring is not negligible. The proposed bypass route passes through a mountainous area with some tunnels and, hence, is not exposed to flood risk.

### Indicator Data

Table 1 shows a list of necessary data for the calculation of resilience indicators for each dimension. The historical record of flooding in 2004 is set as an expected scenario for the disrupted state of the existing route.

21
***|
|_t_=2020|0.160|(0.032)|***<br>0.161|(0.032) ***|0.162|(0.031) ***|
|constant|0.344|(0.080)|***<br>0.350|(0.074)***|0.369|(0.073)***|
|Obs.|1,374||1,374||1,374||
|Wald stat|549658||552955||552778||
|No. of groups|255||255||255||
|0.250***|0.232***|0.231***|
||(0.0259)|(0.0227)|(0.0227)|(0.0227)|(0.0258)|(0.0260)|
|_DVAR*Internet_|0.0010*<br>(0.0006)||||||
|_DVAR*LPI logistics_||0.0513**<br>(0.0211)|||||
|_DVAR*LPI customs_|||0.0550***<br>(0.0204)||||
|_DVAR*LPI overall_||||0.0621***<br>(0.0232)|||
|_DVAR*Time to export_|||||-0.0053***<br>(0.0019)||
|_DVAR*Time to import_||||||-0.0033**<br>(0.0014)|
|_Constant_|4.844***|4.671***|4.742***|4.965***|5.059***|5.029***|
||(0.165)|(0.166)|(0.157)|(0.159)|(0.174)|(0.181)|
|Observations|7,164|7,060|7,060|7,060|7,164|7,164|
|R-squared|0.872|0.879|0.879|0.879|0.872|0.872|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes:
once again given that key fault lines<sup>18</sup> that gave rise to the Euro Area crisis are not yet sufficiently addressed and significant financial market and political tail risks remain compounded by recessionary conditions.

**In the medium term, it is necessary for European banks to deleverage further** as they remain highly levered and reliant on wholesale funding, despite some progress made since 2008 (Exhibits I.1 and I.2). In a recent Deloitte survey, more than 70 percent of European banks indicated that deleveraging will take an additional 5 to 7 years.<sup>19</sup> A protracted deleveraging process is also consistent with past crisis experiences in which credit growth resumed after several years of a stagnant or negative trend (Exhibit III.1). Similarly, at the onset of various past crises, loan-to-deposit ratios were over 100 percent and often fell precipitously for many years before reaching more sustainable levels (Exhibit III.2). These patterns are also more broadly confirmed by past economy-wide deleveraging processes. These episodes can be characterized by a first phase of debt reduction and deleveraging of corporations, households, and financial institutions while the economy is negative and government debt rises. In the second phase, growth is restored and government debt is gradually reduced<sup>20</sup> (McKinsey Global Institute (2012)).

**Deleveraging can affect credit conditions and loan growth, given that the effectiveness of other deleveraging options is currently more limited.** Deleveraging may mostly occur to an important degree via the asset side as liability-side options currently appear to have been largely exhausted or limited due to adverse market valuations and economic conditions, as described earlier. Indeed, over 40 percent of the surveyed European banks have indicated they will deleverage and de-risk by naturally running off their assets, divestments, and constraining asset growth (Exhibit III.3). Most banks also indicate that loan portfolios are an important target of their divestment plans, although weak economic conditions
that the production of non-reference priced goods requires a relatively high level of relation-specific investments so that these goods are more subject to the hold-up problem than goods sold on an organized exchage or reference priced goods.  As suggested by Nunn (2007), the fact that a good is traded on an organized exchange indicates that its market is thick, hence there is limited scope for the hold-up problem to emerge. Similarly, the fact that a good is reference priced in a publication may be thought of as an indication that there is a reasonable number of potential buyers and sellers of that good,limiting potential hold-up problems. Following Hummels (2001), we define time-sensitive products according to their probability of being transported by air. We measure timeliness

> <sup>_t_</sup> _j_<sup>of industry</sup><sup>_j_as the share of US imports shipped by air.  Data on US imports and shipping</sup> mode at the HS10 digit level are collected by the U.S. Census Bureau and are taken from the homepage of Peter Schott. We use the HS10 to NAICS 1997 correspondence table from Feenstra et al. (2002) to calculate

> timeliness at the NAICS industry level. Since both institutional intensity<sup>_q_</sup> _j_<sup>and timeliness</sup><sup>_t_</sup> _j_<sup>are based on</sup> product level data, we can use the BEC classification to calculate intensities for intermediate and other types of goods that belong to the same NAICS industry.

> Country endowments of capital _Ki_ and human capital _H i_ are measured by the natural logarithm of capital stock per worker and the logarithm of human capital per worker for the year 1988 taken from Hall and Jones

7
while 20% of the area in the average district is unsuited for agricultural production. On average, almost 50% of a district’s area under cultivation is irrigated. Over this period, a district’s probability of receiving a positive rainfall shock was 0.15, and a negative rainfall shock, 0.19.

# **2.2 Non-Farm Enterprise Data**

Our data on non-farm enterprises comes from two rounds of a nationally representative survey on unincorporated enterprises. These surveys are conducted by the National Sample Survey Organisation (NSS) in 2010-11, and 2010-15, and includes data from every state and district in India. The surveys are in the form of repeated cross-sections and the enterprises covered are enterprises not registered under the Companies Act of 1956. Loosely, the survey can be considered to cover informal enterprises in India.

Each survey covers in excess of 290,000 enterprises and we restrict our sample to a total of 13,000 enterprises located in the state of Rajasthan. The surveys provide enterprise weights which can be applied to make the figures representative at the national level. Based on the enterprise weights, the 2010-11 survey covered a total of 50 million enterprises and 100 million workers, reflecting approximately a fifth of the national workforce.

> 1 Three new districts were formed in the 1990s in Rajasthan, limiting us to using data since 1996.

5