rafmacalaba/datause-encoder-data huggingface dataset:
infection); school performance that can be a result of risk-taking behavior (such as low school attendance) or can be a predictor of limited future integration in society; and institutional connectedness<sup>7</sup> – to school, the labor market, and to the political process (voting patterns).

The YWI is a static picture of youth today, but it is also possible to get an idea of how the youth of tomorrow will fare, by expanding the YWI to include indicators for children, via the Child-Youth Well-Being Index (C-YWI). The C-YWI expands our understanding of youth development beyond the situations facing 15-24 year old youth today and brings into consideration the importance of investments early in life that will prepare individuals once they reach the youth period. The C-YWI includes all those indicators used for the YWI as well as health, school performance, and institutional connectedness indicators pertaining to today’s children. This is useful since it gives information not only about today’s youth, but also about the next generation of youth.

Finally, a General Youth Well-Being Index (GYWI) is created, which includes the set of variables in the C-YWI, as well as factors that affect the environment in which children and youth learn their preferences, face their constraints, and make their decisions. This index is derived from a theory of youth development that posits that youth are products of their environment (Bronfenbrenner, 1979), so to measures the wellbeing of youth in a state, it is reasonable to include measures of the environment, as well. Kohler, et. al. (2005) found that low poverty, high parental education, presence of both parents in the household, and positive community well-being are important correlates of positive youth behaviors in Brazil. To capture these influences, a category of variables capturing the socioeconomic level of each state is included, which include factors such as poverty or the share of single-parent households.

> 7 The concept of “connectedness” is commonly used in the public health literature to describe the extent to which a person feels a part of a relationship, institution, community, or other group (Blum 1997).  Thus, for example, parental connectedness is not achieved by a parent simply spending time with a child, but instead it requires an interaction such that the child feels that the adult cares.  Similarly, connectedness to school is a feeling of “belonging” which often results in continued attendance).  While standard indicators do not capture “connectedness”, we can measure a result of connectedness, which is participation in institutions, which may be a proxy of the less measurable concept.

4
Policy ReseaRch WoRking PaPeR 4885

# **Abstract**

Despite their potential importance and ease of modification, impacts of monitoring and loan recovery arrangements on micro-credit groups’ repayment performance have rarely been studied. Data on 3,350 expired group loans in 300 Indian villages highlight that regular monitoring and audits, high repayment frequency, consumption smoothing support through rice credit, and having group savings deposited with the lender all significantly increase repayment rates. Estimated magnitudes of their effects vastly exceed those of members’ socio-economic characteristics. Significantly lower repayment on loans originating in externally provided grant resources suggests that stringent monitoring will be essential for these to have a sustainable impact.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to better understand the impacts of decentralized governance and local development.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org or yliu3@worldbank.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 fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
reweighting for unit non-response- to weight up those who respond with amounts so that they represent themselves and the other respondents who responded in the bracket in which the amount reported falls. This improves on single imputation methods (such as mid-point or even hotdeck) because it allows for the uncertainty in the true value, which other single imputation methods do not.

# Imputing the income distribution from consumption data

Household surveys in many LMICs collect only consumption data, since income data is assumed to be less reliable, particularly for measuring poverty. Chancel et al. (2023) attempt to estimate income inequality levels and trends in Africa between 1990 and 2010 for countries covering 60% of the population (and 80%-90% of the population from 1995-2010). But because of the complete lack of income data or harmonized and publicly available consumption microdata for most of these countries, Chancel et al. (2023) used the World Bank’s Povcalnet consumption data on shares of consumption by decile and then data for five countries where both income and consumption microdata were available was used to impute the income distribution in the rest of the countries. This lack of income data implies that estimating the income distribution in many African countries, let alone the top end of this distribution, is impossible with the data that currently exists and is publicly available. As we discuss below, there are other African countries with surveys that asked about income and consumption that were not used by Chancel et al. (2023). We also note that limited income data collection is not prevalent in all LMICs - Latin America has long had income data collected in household surveys (Lustig, 2020).

Using survey data and parametric distributions estimated from survey data Imputation for item non-response and reweighting for unit non-response are specific solutions for specific problems. A more general solution to the more general problem of missing top income recipients (whether due to sparsity, measurement error, non-random unit non-response etc.) is to replace the incomes at the top of the distribution with values drawn from a specific distribution. For example, if top incomes are assumed to have a Pareto distribution it is possible to estimate inequality measures for the top X% of observations assuming the incomes are Pareto distributed, where the Pareto parameter is estimated using the survey data above the Xth percentile. An overall measure of inequality, like the Gini coefficient or the share of the top 1%, is then estimated from the top X percentiles and the bottom (100-X) percentiles.

Jenkins (2017) argues that this method of replacement of top incomes does not work very well in the UK for solving under-capturing of top incomes, by comparing it to methods using tax data. Hlasny and Verme (2018) use this method to estimate income inequality in Egypt. In their preferred results Hlasny and Verme (2018) replace the top 10% of the distribution with a Pareto distribution estimated using 16
education and the new level. This is a relative effect. The return to primary education increases by 1 percentage point, Figure 8. With no unemployment, the return to secondary education increases substantially, 4 percentage-points. This is a result of the positive level effect strengthened by a positive relative effect. Workers with secondary education suffer most from joblessness in Colombia. The return to tertiary education decreases by a half percentage point as the relative unemployment effect (compared to secondary education) dominates, since graduates with secondary diploma tend to face higher unemployment than graduates with a tertiary diploma. The findings show that if the unemployment rate declines in Colombia, the return to secondary education would in particular increase.<sup>12</sup>

**Figure 8 - Impact of Unemployment on Rates of Return**

<!-- Start of picture text --> 5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>age 25 age 35 age 45 age 25 age 35 age 45 age 25 age 35 age 45<br>-1%<br>Primary Secondary Tertiary<br>Impact on Rate of return<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000 _<u>Taxation.</u>_ The tax rate only influences the rate of return if costs or benefits are taxed differently. In our base scenario, only returns to tertiary education changes with the tax rate as we assume that tuition cost cannot be deducted. The impact of taxes seems marginal for the returns to education. The impact of paying the top marginal tax rate of 23 percent compared to paying no taxes does not exceed 2 percent-points. The reduced impact and the already existing option of tax deduction of educational costs could 12 In contrast to the Colombian labor market, no unemployment increases returns to tertiary education for the five OECD for which we have data, see Annex 4.

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result to Damgaard et al. (2024), who separate _phantom FDI_ , investments into empty corporate shells with no link to the local real economy, and _real FDI_ . They find a positive impact of lower CIT rates on total FDI across countries, but zero impact on _real FDI_ .

The rest of the paper is organized as follows. The next section provides the institutional context including the background of the tax reform; sections 3 and 4 describe the data and the empirical methodology, respectively; section 5 presents the results at the sectoral- and firm-levels; and section 6 concludes and discusses the policy relevance of the findings.

# **2 Institutional Context and Policy Background**

Tunisia has long maintained a preferential regime aimed to attract and facilitate the activities of exporting firms, which are defined as those that export at least 70% of their output. This regime comprised generous tax and fiscal benefits since its creation in 1972, including duty-free imports and income tax exemptions. Additional benefits include "streamlined customs procedures, corresponding to significant costs savings" and the possibility of holding bank accounts in foreign currency, which protects against currency risks (Nucifora and Rijkers, 2014).<sup>4</sup> While the establishment of the offshore regime was associated with growth in export oriented investment, it also came at a high fiscal cost. A study by the IFC and ECOPA (2012) estimates that the offshore income tax exemption accounted for two-thirds of the total fiscal and financial incentives granted by Tunisia in 2009. Using administrative firmlevel data (described below), we estimate that the fiscal cost of this offshore exemption was worth 2.4 billion Tunisian dinars in 2013, equivalent to 6.8% of GDP or 14.7% of overall tax revenues.<sup>5</sup> While this is an upper-bound estimate, it illustrates the significant order of magnitude of the offshore tax holiday scheme.<sup>6</sup>

> 4The benefits of the offshore regime are such that Nucifora and Rijkers (2014) notes that "domestic companies that start to export tend to divide themselves into two distinct entities: one dedicated to the onshore market and the other under the fully exporting offshore regime."

> 5Approximately USD PPP 3.5 billion, using the GDP PPP conversion rate of 0.67 in 2013. 6This is estimated from the firm-level data described below multiplying each offshore firm’s reported accounting profit by the general corporate tax rate. This is obviously an upper bound estimation as it takes into account neither the tax exemptions/incentives that reduce the effective rate, nor the possible

6
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 39 Productivity

- Lee, B; Gordon, P (2011). Urban structure: its role in urban growth, net new business formation and industrial churn, _Région et Developpement_ , 33: 137-159.

- Li, X; Zhou, Y (2017). Urban mapping using DMSP/OLS stable night-time light: a review. _International Journal of Remote Sensing_ , 0(0): 1–17. https://doi.org/10.1080/01431161. 2016.1274451

- Lo, CP (2001). Modeling the Population of China Using DMSP Operational Linescan System Nighttime Data. _Photogrammetric Engineering and Remote Sensing_ , 67(9): 1037–1047.

- Lowe, M (2014). The Privatization of African Rail. Working Paper, Massachusetts Institute of Technology, Boston MA.

- Ma, L; Wu, J; Li, W; Peng, J; Liu, H (2014). Evaluating saturation correction methods for DMSP/OLS nighttime light data: A case study from China’s cities. _Remote Sensing_ , 6(10): 9853– 9872. https://doi.org/10.3390/rs6109853

- Meijers, E; Burger, M (2010). Spatial structure and productivity in US metropolitan areas, _Environment and planning A_ , 42: 1383-1402.

- Melo, P; Graham, D; Levinson, D; Aarabi, S (2017). Agglomeration, accessibility and productivity: Evidence for large metropolitan areas in the US. _Urban Studies_ , 54(1): 179-195.

- Michalopoulos, S; Papaioannou, E (2013). Pre-Colonial Ethnic Institutions and Contemporary African Development. _Econometrica_ , 81(1):113-152.

- Nunn, N; Puga, D (2012). Ruggedness: the blessing of bad geography in Africa. _The Review of Economics and Statistics_ , 94(1): 20–36.
(Arbia, et al., 2023). To enable comparisons, all prices were converted to unit price per kilogram (i.e. ₦/kg). Note that this is a simple rescaling that does not control for possible discounts that are factored into larger quantity prices such as those tracked by the AIestimated data. The AI-estimated data contain monthly Open, High, Low and Close (OHLC) price estimates, while the crowd-sourced and enumerator data is available at an intra-month interval. For easier comprehension, we first present the overall summary of the correlative relationships between paired price datasets (by commodity, and their sub-types), based on monthly averages.

We hone-in on the results for enumerator versus crowdsourced commodity prices to position the crowdsourced data relative to enumerator-submitted ground truth data. We then position the AI-estimated data relative to the crowdsourced data, leveraging the longer time interval for which these data are available. The focus is initially on yellow maize prices, which shows similar trend with white maize prices, to elucidate important ramifications of the observed correlative relationships between ground truth and crowd sourced prices at different time intervals. We then contrast monthly maize prices (yellow maize and white maize) in the crowdsourced data with same commodity prices in the AI-estimated dataset (Figure 2). Additional insights for rice (as a whole and by sub-types) is presented in the appendix sections.

**Figure 2:** Comparison of data volume, frequency, and temporal trend of maize grain prices from AI-estimated and ground truth sources in the northern region of Nigeria over three years (2019–2021) – **(a)** ; and during validation period (Mar. 2021 – Oct. 2021) – **(b)** and **(c)** .

**_Ground Truth Enumerator versus Crowdsourced Commodity Prices:_** During the 8-month data validation period, the trend and progression of daily averaged submitted prices were comparable between enumerator-submitted and crowdsourced ground truth data, notwithstanding indicative differences in the daily price range and mean values, during the 8-month validation period (Figures 2 and 3a-c). Specifically, the overall mean price from both sources were not significantly different ( _p=_ 0.40), as ground truth and crowdsourced maize

6
The current study uses the benefit-transfer value of a statistical life (VSL) approach to estimate the COVID-19 mortality costs over time in Côte d’Ivoire. We also review the health policies implemented by policy makers to curb COVID-19 and propose effective health policies to help the country better prevent, prepare for, and respond to future pandemics.

In the next section, we describe the methods used to estimate the mortality costs of COVID-19 over time in Côte d'Ivoire. In Section 3, results are presented whereas in Section 4, the findings are discussed with some policy recommendations. Section 5 concludes.

# **2. Methods**

In this paper, we first estimate the COVID-19 mortality costs in Côte d'Ivoire using the VSL, and then conduct a desk review to better understand the health policies implemented by policy makers to curb COVID-19.

The VSL is an individual's marginal rate of substitution between money and the risk of dying over a given time period (Hammitt, 2000). The VSL represents the monetary value of risk reduction that would save one statistical life, rather than an identified life<sup>2</sup> (Andersson, 2008; Lisa A Robinson et al., 2019b). We use the benefit-transfer VSL approach to estimate the mortality costs due to COVID-19 over time in Côte d’Ivoire. The benefit-transfer VSL approach extrapolates from values from higher income countries, adjusting for differences in income, and the rate at which VSL changes as income changes (income elasticity of the VSL). The transfer is based on 2013 estimates for the US. All VSL estimates are adjusted for inflation using the GDP deflator, as well as for future changes in real income. We follow best standard practices by taking into account the low and central values of VSL (Lisa A Robinson et al., 2019a; Lisa A Robinson et al., 2019b). The COVID-19 mortality costs are calculated by multiplying the total COVID-19-related deaths by the

> 2 The word "statistical" refers to small reductions in the likelihood of dying. As stated by Cohen et al. (2015), statistical lives are those that will be lost to known risk factors in the future unless we intervene, but whose identities we will never know, at least for the time being. Identified lives are those whose identities we currently know.

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Table 13. Changes in Participation Rates, Annual Hours of Work, Earnings, and Consumption as a Result of Wage Increments (Percentage Changes From Base Case)

|Percentage<br>increase||Sector<br>partici|specifi<br>pation|c<br>|Sect<br>cific<br>nual<br>of wo<br>cond<br>naD*|or spe-<br>an-<br>hours<br>rk (un-<br>itio-<br>)|Wa<br>ear<br>(un<br>tion<br>(inti|ge<br>nings<br>condi-<br>a)<br>s)|W<br>ear<br>(int|age<br>nings<br>is)|Con<br>tio<br>(int|sump-<br>n<br>is)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Wa|ge wor|k SeHf<br>me|emplo<br>nt|<br>y- Wa|<br>ge work|Selfe<br>m|mp,oy-<br>ent|||||
||F|M|**F**|**M**|F|M|F|M|F|M|F|M|
|Base case ..........|0.32|0.62|0.34|0.35|414|1165|414|492|2300|8100|1790|0 27800|
|20 percent Increase<br>In female wages .3.2<br>20 percent increase||-0.6|-0.9|-1.2|5.8|-0.7|-0.5|-0.4|30.0|-1.2|6.3|5.0|
|<br>In males wages<br>20 percent increase in both|-1.9|1.6|-2.0|-1.2|-2.2|2.7|-2.4|-2.0|-4.6|22.3|17.1|11.9|
|<br>females and males wages|0.6|0.6|-1.8|-1.4|1.9|1.9|-1.5|-2.4|19.8|20.5|21.2|11.5|
|Female wage rates increased|||||||||||||
|by 20 percent of the<br>mean wage<br>.<br>Male wage rates Increased<br>by 20 percent of the<br>mean wage<br>.<br>Female and male wage rates<br>increased by 20 percent of<br>the mean wage|........<br>8.8<br>.....**.**..<br>9 9<br>1.6..|.<br>-1.4<br>.<br>2.1<br>....**.**<br>1 0|-0.9<br>-0.9<br>-2.3|0<br>-2.3<br>-2.9|8.0<br>-3.6<br>3.4|-1.4<br>3.8<br>2.0|-1.5<br>-0.5<br>-2.7|0<br>-3.5<br>-4.3|25.0<br>-4.5<br>19.7|-0.8<br>17.6<br>15.1|5.0<br>14.0<br>16.2|4.7<br>7.0<br>8.6|

*) Recall that conditional hours in the respective sectors can be obtained by dividing the unconditional hours by the corresponding participation rates.

Table 14. Changes in Mean Level and Inequality in the Distribution of Per Capita Household Consumption as a Result of Wage Increments (percentage change)

||Mean level|A-coefficient|Gini-coefficient|
|---|---|---|---|
|Base case .........<br>.............|7,600 (in intis)|0.566|0.438|
|20 percent increase in female wages<br>....|5.3|2.1|3.2|
|20 percent increase in male wages<br>.....|11.9|0.6|0.7|
|20 percent Increase in both female and<br>male wages .........<br>............|11.6|-1.3|-1.6|
|Female wage rates increased by 20||||
|<br>percent of the mean wage .....<br>......|4.9|0|0.7|
|Male wage rates Increased by 20 percent||||
|<br>of the mean wage ......<br>...........|6.4|-3.0|-3.4|
|Male and female wage rates Increased||||
|by 20 percent of the mean wage ......|8.6|-3.0|-3.4|
|**Table A1- Variable Def**<br>_State/Municipalities_<br>DebtStates,16Q1|**initions**<br>Ratio of total liabilities of local governments of state_s_over net income of state_s_in<br>the first quarter of 2016.|
|---|---|
|Maturitys,16Q1|Outstanding maturity of state government_s_’ bank debt in the first quarter of 2016<br>(in logs)_._|
|DebtBankMunim,Mar16|<br>Average_BankExposureGovs,b,Mar16_ of banks operating in municipality_m_in March<br>2016, weighted by the share of total lending in the municipality of each bank.|
|DebtMunicipalitym,16Q1|Total public debt per capita of the municipality_m_in 2016Q1.|
|Employments,q|Quarterly growth of employment in state_s_in quarter_q._|
|Employment Primarys,q<br>|Quarterly growth of employment of primary sectors in state_s_in quarter_q_.|
|Employment<br>Secondarys,q|Quarterly growth of employment of secondary sectors in state_s_in quarter_q_.|
|Employment Tertiarys,q<br>GDPs,q|Quarterly growth of employment of tertiary sectors in state_s_in quarter_q_.<br>Real GDP index (relative to 2014) of state_s_in quarter_q_(in logs).<br>Share of population in state_s_that is poor in year_y_. The government defines<br>|
||poverty when a person cannot fulfill one of these six basic needs: Education|
|Povertys,y|,<br>access to health, access to social security, basic housing services, access to food,<br>and basic income.|
|Poverty – Moderates,y|Share of population in state_s_that in year_y_that cannot fulfill at most two basic<br>needs.|
|Poverty – Extremes,y|Share of population in state_s_that in year_y_that cannot fulfill three or more basic<br>needs.|
|Ginis,y|Gini coefficient of state_s_in year_y_that takes values between zero (lowest<br>concentration) and one (highest concentration).|
|_Fiscal Balance Compone_<br>|_nts_<br>|
|Total Expenditures,y|Ratio of total public spending over GDP in state_s_in year_y_.<br>|
|Infrastructures,y|Ratio of spending on public projects towards infrastructure over GDP in state_s_in<br>year_y_.|
|Social Servicess,y|Ratio of spending on social services (_ayudas sociales_) over GDP in state_s_in year<br>_y_.|
|Debt Servicings,y|Ratio of spending on debt costs over GDP in state_s_in year_y_.|
|Transferss,y|Ratio of federal transfers over GDP in state_s_in year_y_.|
|Tax Rate|Ratio of tax reene in statein earoer GDP|
|s,y|vu_s_y_y_v .|
|Non-Infrastructures,2015|Share of public spending in all categories except for infrastructure over the total<br>public spending of state_s_in 2015.|
|_Lending to Local Govern_|_ments_|
|Loans – Govts,m|Volume of total loans extended to state_s_in month_m_(in logs).|
|Credit Line – Govts,m|Volume of loans from credit lines extended to state_s_in month_m_(in logs).|
|Term Loan – Govts,m|Volume of term loans extended to state_s_in month_m_(in logs)_._|
|Interest Rates,m|<br>Average interest rate of loans extended to state_s_in month_m_(percent).|
|Collaterals,m|Share of loan that state_s_has in a month_m_that is guaranteed_._|
|Maturitys,m|Outstandingmaturity of theloan thatstate shasin a month_m _(in logs)_._|

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