rafmacalaba/datause-encoder-data huggingface dataset:
child health as a consequence of the COVID-19 pandemic includes Roberton et al. (2020), who use a health care seeking and supply model, and posit reductions in care seeking and available quality of care. In contrast, our approach uses the data on past contractions to calibrate the under-5 mortality semi-elasticity with respect to the economic shock.

The rest of the paper is structured as follows. Section 2 lays out the quantitative framework. Section 3 details the calibration, and 4 presents the quantitative results. Section 5 concludes. The Appendix contains additional details on theory, quantification, and robustness.

# **2 Quantitative Framework**

This section builds a macro-SIR framework along the lines of Eichenbaum et al. (2021) with the added feature that households comprise several members in different age groups (Acemoglu et al., 2020). Our key innovations are (i) to model income shocks as a source of mortality not related to COVID-19; and (ii) to calibrate the model to 85 countries with different underlying characteristics.

## **2.1 Economic Environment**

We consider a discrete and infinite time horizon model, _t_ = 0 _,_ 1 _, · · · , ∞_ , and a continuum of households indexed by _j ∈_ [0 _,_ 1]. A model time period corresponds to one week. The measure of households is normalized to 1 in the initial period. Households are formed by individuals differentiated by age group _m ∈{_ 1 _,_ 2 _,_ 3 _}_ to which they belong. Type _m_ = 1 individuals are children ages 0-14, type _m_ = 2 are working prime-age adults ages 15-59 and _m_ = 3 household members are the elderly aged 60 and over. Denote by _l_<sup>_m_</sup> the mass of individuals of age group _m_ so that<sup>�3</sup> _m_ =1<sup>_lm_= 1.Weomitcountryindicesintheexposition</sup> to streamline notation, but the quantitative analysis uses country-specific values for many of the parameters.

5
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41
Finally, we exploit the public dataset to examine whether the correlation between aid and haven deposits has diminished in the most recent years (not covered by the restricted dataset). Since around 2009, all havens have enhanced financial transparency in response to pressure by international organizations like the OECD (Johannesen and Zucman, 2014) and individual countries like the United States (Johannesen et al., 2018). In the same period, a number of data leaks by whistleblowers in the wealth management industry, e.g. _Swiss Leaks_ and _Panama Papers_ , have increased the risk of exposure for public figures with undeclared money on foreign accounts (Johannesen and Stolper, 2017). As shown in Column (11), there are no clear signs that aid disbursements are associated with smaller increases in haven deposits in the period with more financial transparency: the coefficient on the aid variable is almost identical in the periods 1990-2008 and 2009-2018. However, the standard errors are large and we also cannot reject the null hypothesis of a zero correlation in the post-2009 period. In principle, it is possible to conduct more high-powered tests that exploit country-level variation in transparency and information exchange, but we leave that for future research.

# **5 Concluding remarks**

We document that aid disbursements to the most aid-dependent countries coincide with significant increases in deposits held in offshore financial centers known for bank secrecy and private wealth management. Aid capture by ruling politicians, bureaucrats and their cronies is consistent with the totality of observed patterns: it can explain why aid does not trigger flows to non-havens, why the capital outflows occur precisely in the same quarter as the aid inflows and why the estimated effects are larger for more corrupt countries. Other explanations are possible but we find them harder to reconcile with all the patterns in the data. We cannot exclude that firms benefiting from aid-sponsored spending receive payments in quarters with aid disbursements and deposit the funds with foreign banks; however, this mechanism cannot explain why the money only flows to havens. It seems even less likely that the results reflect profit shifting by multinational firms, the effect of aid on income through aggregate demand and portfolio adjustments by commercial and central banks. Our estimates suggest a leakage rate of around 7.5% for the average highly aid-dependent country.
<u>Table 6. Mean hours per day, by geographic area, type of activity and sex</u>

|||<br>Unaid<br>Wo|<br>men|<br>Leisure<br>Urb|<br>an|<br>Unaid<br>M|en|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|1.99|4.77|11.32|5.86|4.45|0.49|11.95|7.08|
|Argentina|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Bangladesh|2.12|5.08|13.52|3.24|6.66|1.04|12.91|3.38|
|Colombia|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Ecuador|2.86|5.24|9.70|4.83|5.71|1.27|9.44|4.94|
|Ghana|3.31|4.82|12.20|4.97|3.94|2.52|11.58|7.09|
|Guatemala|3.36|6.03|10.52|3.03|6.68|1.14|10.63|3.69|
|Iraq|0.57|4.53|n.a.|6.25|5.43|1.33|n.a.|6.58|
|Mexico|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Moldova|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|3.85|4.79|11.70|3.83|5.27|2.87|11.60|4.67|
|Pakistan|0.70|5.31|13.61|4.35|6.05|0.72|12.19|5.03|
|West Bank and Gaza|0.95|3.00|11.20|6.21|5.33|0.43|11.27|6.57|
|Peru|3.45|5.31|9.60|5.18|6.52|1.96|9.37|5.77|
|South Africa|2.83|4.20|12.22|5.03|4.60|2.17|12.03|5.79|
|Timor|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Turkey|0.65|5.00|11.08|5.37|3.75|0.42|10.79|5.15|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|

|||Unaid<br>Wo|men|Leisure<br>Rur|al|Unaid<br>M|en|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|2.13|6.32|11.30|4.18|4.54|0.71|12.07|6.66|
|Argentina|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Bangladesh|1.61|5.77|14.33|2.57|6.12|1.45|13.42|3.01|
|Colombia|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Ecuador|2.41|6.37|9.89|3.91|5.56|1.57|9.63|4.02|
|Ghana|1.88|7.00|12.78|3.40|2.07|5.41|12.40|5.06|
|Guatemala|1.85|7.44|11.47|1.88|6.84|1.15|11.49|2.54|
|Iraq|0.57|4.55|n.a.|5.77|5.43|1.57|n.a.|6.22|
|Mexico|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Moldova|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|1.41|8.85|11.17|2.50|1.32|8.14|11.50|3.13|
|Pakistan|0.56|6.91|13.41|3.08|3.36|3.69|12.64|4.26|
|West Bank and Gaza|1.15|2.64|11.24|6.26|6.19|0.29|11.17|5.95|
|Peru|2.36|7.01|10.37|3.48|5.89|2.83|10.11|4.34|
|South Africa|2.12|4.65|12.75|4.63|3.47|2.15|12.88|5.95|
|Timor|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Turkey|1.41|5.55|10.70|4.83|3.84|0.59|10.80|5.60|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

34
**Table 2: Prediction models for percentage of banked households**

||OLS<br>|Tobit<br>|
|---|---|---|
|log (Deposits per 1,000 adults)|20.02<sup>***</sup><br>(3.62)<br><sup>**</sup>|20.02<sup>***</sup><br>(3.54)<br><sup>**</sup>|
|log (Branches per 100,000 adults)|3.65|3.65|
|Constant|(1.55)<br>-87.88<sup>***</sup>|(1.52)<br>-87.88<sup>***</sup>|
||(23.05)|(22.53)|
|_σ_|-|16.53|
|||(2.08)<br>|
|_N_<br>|46|46<sup>†</sup>|
|_R_<sup>_2_</sup>|0.72|0.13<sup>±</sup>|

_Notes:_ Dependent variable is percent of households banked. † Number of uncensored observations is 46. ± Pseudo _R2_ is reported for Tobit specification. *** ** * , , indicate significance at 1%, 5% and 10%, respectively.

22
- _Growth of permanent employment_ : this is a dummy variable that takes the value of 1 if there was an increase in the number of permanent employees over the preceding three years.<sup>4</sup>

- _Labor productivity_ : the log of value added per worker, where value added is defined as sales less spending on raw materials and deflated using a PPP-based year-specific exchange rate.

- _Total factor productivity_ : this variable is constructed as a simple residual using logs of sales, employment, fixed capital and material, with weights on the latter three variables set to 0.25, 0.10 and 0.65, respectively.

- _R&D-firm_ : this is a dummy variable that takes the value of 1 if the firm reports that it does R&D.

- _Process or product innovation_ : this is a dummy variable that take the value of 1 if the reports that it introduced a new process or product during the previous three years.

- _Sales to MNCs_ : the percentage of sales reported to have been made to multinational companies. We interpret this as an indicator of external evaluation of the firm’s quality.

- _Education top level of the manager_ : this is measured in years of education.

- _Average education level of the production workforce_ : this is also measured in years of education.

- _Training offered_ : this is a dummy variable that indicates whether the firm has an in-firm training program.

- _Per cent of production workers trained_ : this relates to the firm’s in-firm training.

# _The reported costs of public input constraints_

Measures of _Rij_ , the reported cost or shadow price of public inputs, are available in the SAR data: “How much of an obstacle is XX to the operation and growth of your business?” The model predicts that these will be positively related to measures of productivity and growth: they will be increasing in _Aij_ and positively correlated with

> 4 If permanent employment data is not available for a firm for 3 years earlier, a shorter period is used.

9
**-** **<u>Appendix Table 1: Estimation of Cobb Douglass crop production function (1982 data)</u>**

||**OLS**|**Frontier**|
|---|---|---|
|Total crop sown area (log)|0.488***<br>(22.95)|0.494***<br>(31.74)|
|All labor in crop production (log)|0.208***<br>(11.15)|0.188***<br>(14.15)|
|Seed expenditure (log)|0.093***<br>(8.49)|0.078***<br>(10.02)|
|Fertilizer expenditure (log)|0.054***<br>(10.73)|0.045***<br>(10.91)|
|Pesticide expenditure (log)|0.023***<br>(5.11)|0.023***<br>(5.36)|
|Other expenditures (log)|0.025***<br>(4.11)|0.026***<br>(5.26)|
|Value of farming assets (log)|0.040***<br>(6.25)|0.039***<br>(8.28)|
|Share of crop area irrigated|0.342***|0.315***|
||(5.03)|(5.51)|
|Head’s age|0.001|0.002**|
||(1.63)|(2.24)|
|Head attained primary or above education|0.009|0.034|
||(0.33)|(1.48)|
|Female headed|-0.032|-0.024|
||(0.67)|(0.54)|
|Constant|5.847***|6.595***|
||(61.98)|(88.92)|
|Observations<br>|3328|3328|
|R<sup>2</sup>or log-likelihood|0.77|-2721.08|

Absolute value of t statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1% Regional dummies included throughout but not reported.

24
1.3 joint trade-fiscal reform: a case study

21

1.3.3 _Data_

The empirical analysis uses a new data set extracted from unpublished customs records from Nepal for the calendar year 2008. It contains 417,715 import transactions. In addition to the date when the import shipment was processed, the data set lists the value of each import in Nepali Rupees inclusive of cost, insurance and freight (c.i.f.) and tariffs levied on that import. Customs also raise a substantial share of additional revenue at ports of entry by levying a range of domestic taxes. The main ones in Nepal are the excise duty and VAT, as well as the Agricultural Reform Fee (ARF) imposed on agricultural imports from India only. A range of other charges and taxes (para tariffs) are levied as follows: demurrage, customs service fee, fine, special fee, Road Construction Fee, and the Local Development Tax.<sup>24</sup> The data set lists applied Most Favored Nation (MFN) and preferential tariff rates set for each import at the Harmonized Commodity Description and Coding System (HS) 8-digit level.

I check for the consistency of entries and adjust the data set as follows. All import transactions worth Rs. 10,000 (approx. US$140) or less are dropped.<sup>25</sup> Goods entering the country under customs procedure codes which do not compete in the local market are dropped. These are mainly diplomatic and governmental imports that are tax-exempt. Next, I compute the applied tariff rate, applied excise duty, and applied VAT by dividing the actual amount of such taxes collected by their respective base.<sup>26</sup> Those “applied” values that abnormally deviate from the statutory

> 24 As of 2011, the local development tax, road construction fee, and special fee have been phased out.

> 25 This excludes nearly 30 percent of the observations, which accounts for 3.2 percent of total import value.

> 26 In Nepal, excise duty is levied as a percentage of import value. VAT is paid as a percentage of the base comprising of import value plus excise and other taxes. The Agricultural Reform Fee (ARF) is levied (in lieu of tariff) on the value of agricultural imports from India. If VAT is additionally levied on such agricultural goods from India, it is a fixed percentage of the import value, _not_ import value plus the ARF.
# **Heterogeneous Impacts of Main and Feeder Road Improvements:**

# **Evidence from Ethiopia**

Atsushi Iimi<sup>¶†</sup> Haileyesus Mengesha<sup>†</sup> James Markland<sup>†</sup> Yetmgeta Asrat<sup>§</sup> Kefargachew Kassahun<sup>§ ‡</sup> Key words: Rural access, Agricultural production, Transport service demand, Local job creation.

JEL classification: R41; Q12; C33; C34.

> ¶ Corresponding author

> † Transport & Digital Development Global Practice, World Bank Group

> § Ethiopian Roads Authority

> ‡ We would like to acknowledge both TPO consultant and URRAP Impact Assessment teams for their efforts to carry out various surveys and data collection.
generates an astounding 39 percent<sup>of GDP (1984). For such sectors as commerce</sup> mind personal services this share exceeds<sup>60 percent. Litan and others (1987)</sup> report that the official national<sup>accounts estimate of the informal sector's</sup> contribution to GDP it, 3'84 resulted<sup>in an understatement of total GDP of 23</sup> percent. Perhaps even more striking<sup>is the estimate that 439,000 Lima residents</sup> depend on the underground commercial<sup>economy, and almost three fourths (314,000)</sup> of these individuals depend on street<sup>sales. According to de Soto, these</sup> activities generated about $25<sup>million a month in gross sales in 1985 and an</sup> average ret per capita profit of $58 a<sup>month, about 40 percent more than the</sup> legal minimum wage. The 314,000 street<sup>merchants include 91,455 street vendors,</sup> are 42 percent of the Lima work force involved<sup>in commerce.</sup> The ILD surveys show that women make up<sup>54 percent of the street vendors.</sup> Eighty-six percent of the street merchants<sup>occupy curbside sites, while the</sup> remaining 14 percent rove the streets.<sup>Business is also conducted in (illegal)</sup> cooperative markets -- collections<sup>of kiosks, stalls and booths. Of the 331</sup> markets in Lima, 274 were put up illegally;<sup>only 57 were built by the government.</sup> It is estimated that $41 million has<sup>been invested in these illegal markets,</sup> which employ about 125,000 people (including<sup>sc-40,000 vendors). More than 80</sup> percent of the street vendors and 64 percent<sup>of the informal markets are found</sup> in low-income districts.

According to recent studies that used the Peru<sup>Living Standards Survey8,</sup> half of the 5,100 households surveyed owned<sup>at least one nonagricultural family</sup> enterprise. Of the 27,000 individuals in the<sup>survey, 13,600 were in the labor</sup> force and 97 percent were employed.<sup>More than 4,500 worked in nonfarm family</sup> 8 See Stelcner and Moock (1988), and<sup>Moock, Musgrove, and Stelcner (1989).</sup> 5
strategy are crucial to the outcome, and it is difficult to capture these complex dimensions through quantitative measures of policies.

Honohan and Klingebiel (2003) construct a database with estimates of the fiscal cost of 40 banking crises and catalogue the policies adopted in each episode, classified according to five broad categories: blanket guarantees to depositors, liquidity support to banks, bank recapitalisation, financial assistance to debtors, and forbearance. With this database, the authors explore how the different intervention policies affect the fiscal cost of the bailout, after controlling for country and crisis characteristics. They conclude that more generous bailouts resulted in higher fiscal costs.

Further evidence on the determinants of the fiscal costs of crises is provided by Keefer (2001), who focuses on the political economy of crises resolution. He finds that when voters are better informed, elections are close, and the number of veto players is large, governments make smaller fiscal transfers to the financial sector and are less likely to exercise forbearance in dealing with insolvent financial institutions. Thus, transparency, information dissemination, and competition among interest groups play an important role is shaping crisis response policies.

The relationship between intervention policies and the economic – rather than fiscal – costs of crises is explored by Claessens, Klingebiel, and Laeven (2003). Costs are measured by the output loss relative to trend during the crisis episode. The main finding is that generous support to the banking system does not reduce the output cost of banking crises. However, since omitted exogenous shocks may simultaneously cause a stronger output decline and more generous intervention measures, the interpretation of the results is ambiguous. Nevertheless, the results survive even after the authors control for a large set of variables such as GDP growth prior to crisis, existence of deposit insurance, inflation rate at the onset of the crisis, state ownership of banks, degree of dollarisation and others.

26
Pharmaceutical Patents and Prices: A Preliminary Empirical Assessment Using Data from India Mark Duggan and Aparajita Goyal<sup></sup> _JEL:_ O31, 034, L24, L43, I18 _Keywords:_ Intellectual Property Rights, Technological Innovation, Patents, Pharmaceutical Industry

>  We are extremely grateful to Pete Lanjouw for bequeathing Jenny Lanjouw’s background material on India’s pharmaceutical industry to us, and to Daniel Lederman for help in obtaining the proprietary data from IMS Health. Thanks to a number of Intellectual property rights lawyers and industry experts, in particular Tahir Amin, Ramesh Adige, Usha Rao, Manoj Kamra, Sudip Chaudhuri, Jayashree Watal for explaining the institutional details of the pharmaceutical market and legal implications of TRIPs reform in India.  Funding from the World Bank Research Support Budget, and the World Bank executed Trust Fund on Trade is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Directors, or the countries they represent. Contact: Aparajita Goyal at World Bank, 1818 H Street, NW, Washington, DC 20433, Email: agoyal3@worldbank.org; Mark Duggan at the Wharton School of  the University of Pennsylvania,  3620 Locust Walk, Philadelphia, PA 19104, Email: mduggan@wharton.upenn.edu
# **2.3 Mobile Phone Metadata**

In a follow-up survey conducted in 2018, we requested informed consent from survey respondents to obtain their mobile phone CDR and match them to the survey data collected through the TUP project. CDR contain detailed information on:

- **Calls:** Phone numbers for the caller and receiver, time and duration of the call, and cell tower through which the call was placed

- **Text messages:** Phone numbers for the caller and recipient, time of the message

- **Recharges:** Time and amount of the recharge

For participants who consented, we match baseline survey data (collected November 2015 - April 2016) to CDR covering that same period, obtained from one of Afghanistan’s main mobile phone operators. For households with multiple phones and a designated household head (N=65), we match to CDR for the phone belonging to the household head. For households where the household head does not have a phone and someone else does (N=17), we match to CDR for one of the households’ phones selected at random. In total, for the 535 households in our sample, 629,543 transactions took place in the months of November 2015 to April 2016, broken down into 310,883 calls, 305,756 text messages, and 12,904 recharges.

From these CDR, we compute a set of 797 behavioral indicators that capture aggregate aspects of each individual’s mobile phone use (de Montjoye et al., 2016). This set includes indicators relating to an individual’s communications (for example, average call duration and percent initiated conversations), their network of contacts (for example, the entropy of their contacts and the balance of interactions per contact), their spatial patterns based on cell tower locations (for example, the number of unique antennas visited and the radius of gyration), and their recharge patterns (including the average amount recharged and the time between recharges). The distributions of a sample of these indicators are shown in Figure S4.

# **2.4 Machine Learning Predictions**

**CDR-Based Method** Extending the approach described in Blumenstock et al. (2015), we test the extent to which ultra-poor status can be predicted from CDR. This analysis uses the 535 households who match to CDR to train a supervised machine learning algorithm to predict ultra-poverty status from the mobile phone features. The intuition — also highlighted

8