rafmacalaba/data-use-mentions-extended huggingface dataset:
on behalf of 4P households - lest we would have observed lumping just below the cutoff which would have yielded beneficiary status. Still, this may indicate manipulation on behalf of program implementers. However, the income data used to determine eligibility of households was collected – as part of the _Listahanan_ – much before the 4P program was conceived and implemented. Further, the sampling for this study was done _after_ the scores had been determined and as described in Section 4, our sample was randomly drawn from the sampling frame used in Orbeta et al. 2014. In light of these considerations, it is unlikely that implementers could have exercised _precise_ control over the estimated income, and we suspect that the observed discontinuity could instead be an artifact of the targeting formula (weights used to develop the poverty score could cause jumps in the distribution) or an upshot of a non-standard sampling procedure (Ambler and Brauw 2019).

18
]***|[3.60]***|
|Returned migrant|0.237|0.240|0.237|0.542|0.550|0.541|
||[5.11]***|[5.15]***|[5.12]***|[3.38]***|[3.39]***|[3.37]***|
|Constant|-0.018|-0.004|0.005|-0.136|-0.193|-0.139|
||[-0.76]|[-0.13]|[0.19]|[-1.53]|[-2.22]**|[-1.55]|
|Observations|2122|2097|2103|2098|2073|2079|
|R<sup>2</sup>|0.706|0.705|0.705|0.398|0.397|0.397|
|Cragg-Donald F-stat|678.9|662.6|668.9|113.7|111.9|112.9|

33
most important migrants to and between the Soviet republics. This process adds many millions of migrants to the totals in the early decades and avoids the problem of a very large artificial jump in international migration between 1980 and 1990, after the dissolution of the Soviet Union.

# _Germany_

The 2005 German micro-census includes data on emigrants of German origin from Eastern Europe who arrived between 1944 and 1950 (referred to as expellees, _Vertriebene_ ) or between 19502005 (referred to as resettlers, _Aussiedler_ ). These data are recorded by year of birth and year of migration; country of birth is not recorded. As of 1950, there were 11.96 million expellees and 4.48 million resettlers residing in Germany. According to the data provided by the Max Planck Institute for the Study of Religious and Ethnic Diversity, 3.61 million were still in Germany as of 2005. Mortality data from the United Nations Population Division (United Nations, 2010) on Germany for each decade and age group were used to calculate the number of migrants who would have been residing in Germany at the beginning of each decade from 1960 to 2000, taking into account migrants‘ age and year of entry. After calculating the total number residing in Germany in each decade, shares were estimated by country of origin using the nationality 28
, our research draws from a total of over 34,000 observations. The average numbers of households in each round of data is: 2,784 in Ethiopia, 1,611 in Malawi, and 1,943 in Nigeria, though the actual number of households in the baseline and each round differ due to attrition.

# **2.3 Livelihood Diversification Indices**

Prior to describing how we construct each of our livelihood diversification indices, we present the disaggregated categories in which households engage in income generation (see Table 1). The table shows pre-COVID income sources in each country at the most detailed level provided by the classification system used by the World Bank in the LSMS surveys.<sup>3</sup> Income data is collected for all household members and we have summed across individuals to construct a household level measure. The indices we consider in this paper use aggregated versions of these income sources, though we show the most detailed level here for completeness.

The post-outbreak phone surveys collected less detailed income data than the pre-outbreak in-person surveys. The income module was also not asked in every survey round. In surveys that did included a module on sources of income, those modules asked “In the last 3 months, which of

> 3In the LSMS data, income is reported in the local currency. To allow for cross-country comparisons, we convert income values to constant 2019 US dollars.

6
|75th|BOL|50|1.9|3.9|26.5|62.4|
|Max|GNB|183|4.9|11.3|38.5|79.4|
|Difference|||||||
|Max-Min||180|0.9|2.6|7.2|25.1|

# **V. Variability in the ES results**

The next question is whether the variance across firms in the ES surveys increases or decreases with the increase in the DB indicator. Figures 5a, 5b, and 5c show the 90<sup>th</sup> -10<sup>th</sup> percentile spread for each country for imports, construction permits, and operating license. In this case, the indicators show different patterns. For construction permits the cross-country variance increases with DB at first, then flattens out entirely (consistent with the results in Table 2 showing that the 21
- Alvaredo, Facundo, Atkinson, Anthony B., Piketty, Thomas and Saez, Emmanuel (2015). The world top incomes database. http://topincomes.gmond.parisschoolofeconomics.eu/.

- Alvaredo, Facundo and Londo ̃no V ́elez, Juliana (2013). High incomes and personal taxation in a developing economy: Colombia 1993-2013. Working Paper 12. Commitment to Equity-CEQ.

- Alvaredo, Facundo and Piketty, Thomas (2014). Measuring top incomes and inequality in the middle east: Data limitations and illustration with the case of egypt. Working Paper 832. ERF.

- Anand, Sudhir and Segal, Paul (2015). The global distribution of income. In _Handbook of Income Distribution_ (eds Anthony B. Atkinson and Franois Bourguignon), volume 2A. Elsevier.

- Atkinson, Anthony B. (2007). Measuring top incomes: Methodological issues. In _Top Incomes over the Twentieth Century: A Contrast Between Continental European and English-Speaking Countries_ (eds Anthony B. Atkinson and Thomas Piketty). Oxford University Press.

- Atkinson, Anthony B., Piketty, Thomas and Saez, Emmanuel (2011). Top incomes in the long run of history. _Journal of Economic Literature_ , **49,** number 1, 3–71.

- Belhaj Hassine, Nadia (2015). Economic inequality in the arab region. _World_ –

- _Development_ , **66,** 532 556.

- Burricand, Carine (2013). Transition from survey data to registers in the french silc survey. In _The Use of Registers in the Context of EU-SILC: Challenges and Opportunities_ (eds Markus J ̈antti, Veli-Matti T ̈orm ̈alehto and Eric Marlier). European Union.

- Diaz-Bazan, Tania (2014). Measuring inequality from top to bottom. Working Paper.

- Douidich, Mohamed, Ezzrari, Abdeljaouad, van der Weide, Roy and Verme, Paolo (2015). Estimating quarterly poverty rates using labor force surveys: a
Using this data and a combination of value-added estimates, instrumental variables, and regression discontinuity methods, Singh (2020) finds that the causal effect of an additional year of primary school in Vietnam is 0.76 _σ_ , the largest value among the four countries. This is likely a lower bound for “high performance” on a global scale, since Vietnam—while an excellent performer for its income class—ranks in the second decile of average Harmonized Learning Outcomes (which, as noted above, covers 164 countries from 2000-2017). We can compare these results to an alternative high-benchmark year-on-year comparison: changes analyzed in the United States by Bloom, Hill, Black, and Lipsey (2008), building on methods used by Kane (2004). The largest year-on-year learning gains are between grade 1 and 2, and range from 0.97 _σ_ in reading to 1.03 _σ_ in math. Finally, we can derive approximate year-on-year changes for global high performers. We assume that the appropriate high-performance rescaled HLO benchmark is a score of 325 at the primary level. This score is assumed to be obtained over four years, since most primary international assessments occur in grade 4; average high-performance learning per year is thus 81.25 points. We then assume a within-country standard deviation of 85 points, based on the values for the five highest-performing countries using 2006 PISA microdata. Taking the ratio of these two values yields a year-on-year gain of 0.96 _σ_ .

The second approach examines large, system-level gains. Here, we explore what would constitute a large learning gain in systemic terms, as a way to benchmark what high-performing learning progress would look like. One example is to consider cross-country learning gaps in terms of HLO scores used for the World Bank Human Capital Index. A gain of 0.8 _σ_ would enable the United Kingdom or Vietnam to catch up to Singaporean learning levels: because the cross-country standard deviation is equivalent to 70 HLO points, a 0.8 _σ_ gain for the United Kingdom (517
within the household. Among the former, are questions about regional location, demographic composition, quality of the dwelling, ownership of durables, etc. The latter include age, gender, race, educational attainment, labor force status, sector of occupation and incomes, both in cash and kind, and from various sources. The main variables used in our analysis are the those related to incomes, education, the demographic structure of the household and labor force participation. Tables A6 - A9 summarize the main items in the questionnaire concerning these variables, and the changes from 1976 to 1996.

Most importantly, the distributions analyzed in this paper (except where explicitly otherwise indicated) have as welfare concept total household income per capita (regionally deflated). It is constructed from summing all income sources for each individual within the household, and across all such individuals, except for lodgers or resident domestic servants. The latter two categories constitute separate households. Total nominal incomes are spatially deflated to compensate for differences in average cost-of-living across different areas in the country, according to the spatial price index given in Table A3 below:
Figure 3.18 Household expenditure on tutoring of Grade 9 students (marginal change in probability) <!-- Start of picture text --> Child's school performance top<br>Child's school performance upper middle<br>Child's school performance middle<br>Child's school performance lower middle<br>Family economic condition-well off<br>Family economic condition-middle<br>Parent has post-secondary education<br>Parent has upper sec or equivalent education<br>Child attending school in rural area<br>Child attending school in city area<br>-1000 -500 0 500 1000 1500 2000<br>CNY per term<br><!-- End of picture text --> _Source_ : Authors’ calculation based on the CEPS.

Exam-driven systems can inhibit intrinsic motivation and stifle creativity. Such abilities are often gained beyond traditional classrooms and through extracurricular activities. Preparing for the highstake test at Grade 9—sorting students into secondary school tracks—basically has become the dominant and often the sole activity toward the last year of junior secondary school, during one of the most important formative periods of their lives. The CEPS data show the time allocation of Grade 9 students. Students in urban areas tend to have the highest pressure—on average they sleep a little over seven hours—with more than three hours of homework per day. More than half of them study during school vacation. While over half of the total students reported participation in extracurricular activities, the extracurricular activities in the survey also include further studies of selected subjects. Excluding these advanced studies, only 18 percent of the surveyed Grade 9 students spend any time on any extracurricular activities.

Table 3.2 Grade 9 student activities by school location

|**School location**|**Hour/d**<br>**ay**<br>**sleep**|**hour/d**<br>**ay**
countries over 23 _,_ 000 like Kazakhstan, already attained 0% poverty rates in the published data. After, 49 _._ 54% of poverty remains missing because most countries are not complete in the time dimension. We first interpolate these variables by taking a weighted average over time. This works well for most variables, but may yield poor results for poverty, as we have seen a tremendous improvement in most countries in past years. We improve the time dynamics in the interpolated poverty data by using information about time dynamics contained in our other variables. We vectorize the interpolated values, and fit the kernel model using the full set of undernourishment, logarithmic GDP per capita, the share of manufacturing, services, urban population shares, and bottom 40 income shares. The model reaches an _R_<sup>2</sup> ’s of _._ 91. We use this model to smoothen the interpolated poverty values by taking an average of the interpolated values and the values predicted by this nonlinear model.

Table 1: Summary of the data used in our empirical application. Statistics are not weighted and not necessarily representative of the world averages.

|Statistic|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|
|Annual % Tree loss|0.437|0.406|0.009|2.924|
|Urban PM2_._5 mcg/m3|18.924|12.609|0.311|63.498|
|CO2 kg / $|0.239|0.199|0.014|1.990|
|CO2 ton p.c.|3.402|4.162|0.015|20.208|
|GDP ppp p.c. 2011 international $|13,468.880|15,056.710|555.560|64,979.840|
|
.407|0.423|0.430|0.260|0.311|0.389|
|_Standard errors in par_|_entheses_||||||
|_*** p<0.01, ** p<0.05_|_, * p<0.1_||||||

When the regression is estimated with pooled country data over time, trade openness and external risk are not robust and are only statistically significant for central government employment (Table 5).

Table 5: Pooled OLS: Rodrik’s framework

|_(1)_<br>_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|

13
and TBCA, based on food items categorizations by IBGE.

Next, we calculate the household-specific cost per calorie of each food item consumed by dividing the expenditures reported on each food item by the product of the associated quantity purchased in kilogram and the calorie intake per kilogram. We then calculate household-specific average costs per calorie by computing a weighted average of the costs per calorie of the items consumed by each household h , as follows: _Reference group and consumption aggregate._ We choose a reference population for the estimation of the poverty line on the basis of their position in the welfare distribution. As noted above, the average cost per calorie of this reference population is used to estimate the food poverty line. Although in Brazil welfare and distributional analysis is usually based on income, for the purpose of estimating the CBN poverty lines, we use a consumption aggregate, as this would be consistent with the poverty line we are estimating. For this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services. It considers monetary expenditures (made in cash or 8
Road information is presented in Table 2. For 1976 and 1986, the road information we have captures road quality, divided into four categories, plus a small ’quality unknown’ category. At that time, most of the roads included in this list are unpaved. According to these data, the proportion of cells with at least one road was 3% in 1976 and 3.4% in 1986. The 2008 data use a more stringent road definition, according to which 1.8% of cells had at least one road. The 2014 data includes two new categories: highways, which at the time only include on short stretch of motorway/freeway between Accra and a nearby town; and local roads. The meaning of the other three categories also seems to have changed between the two years, precluding a direct comparison. In the analysis we rely heavily on the 1976 data because it captures the predetermined road infrastructure at the beginning of our study period. Averages for road availability in the 8 _n_ 1 and 16 _n_ 2 neighboring cells are not shown to save space but, by construction, they are identical to the cell averages reported here.

In Table 3 we summarize our main time-invariant variables. Longitude and latitude are presented in decimal degrees. They are used to measure the distance between cells in the subsequent analysis. A tiny fraction of cells are on the border with a neighboring country or on the seashore, but 2.7% are partially covered by a water body such as Lake Volta. The predominant soil quality is either poor or moderate, a situation that typifies much of Sub-Saharan Africa and reflects its tropical nature: heavy rainfall combined with high evapotranspiration means considerable leaching of nutrients from the soil. Only 7.4% of soils are categorized as good. There is a small fraction of cells with hydroponic soils, which means they are located in river estuaries or flood plains. Some of these soils can be made fertile, but often at some cost in terms of infrastructure and equipment. Some
1.28|0.00|1.78|0.87|y|

Source: World Bank Databank

# **_Qard-al-Hasan_ as a tool for financial inclusion**

Qard-al-Hasan (QH henceforth) is defined in _Shariah_ as an interest free loan. It is usually granted from well off lenders to poor borrowers. It can also be directed from borrowers to intermediaries that can redirect it on their behalf to poor borrowers. QH is therefore a non rewarding loan (with no expected return) and the borrower is under obligation to repay the loan depending on the borrower’s financial capacity to do so. Loan procedures are usually informal and social capital is the basic collateral for this instrument.

The main objectives of Qard al-Hassan could be:<sup>23</sup>

- To help the needy fellow people.

- To establish better relationship among poor and the rich.

- The mobilization of wealth among all people in the society.

- To perform a good deed that is encouraged and appreciated by the _Allah_ (swt) and His messenger.

- To strengthen the national economy.

- To facilitate the poor to create new jobs market and business ventures by using their merits, skills and expertise.

- To establish a caring society.

- To eradicate unemployment problem from the society.

> 23 Please see (Hossain 1955) for a comprehensive exposition of al-qard-al-hasan based on Quran and Sunnah.

22
-|
|Self-employed|Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed|Dummy variable = 1 if primary work activity is wage-employment, 0 if<br>primary work activity is self-employment, out of labor force, or<br>unemployed|
|**Table A.5 Definition of variables used from**|**3-month follow up survey**|
|Variable|Definition|
|Fixed Location Outside of the House (FLOH)|(Self-employed only) Dummy variable=1 if respondent’s place of work is<br>outside of the home in a permanent structure, 0 otherwise (e.g. when<br>the respondents works either at home or in a mobile location outside of<br>the home).|
|Informal|(Wage employees only) Wage employees who do not get paid for doing<br>overtime, do not have a pension, do not get paid sick leave, nor paid<br>holidays. This definition of informality is ad hoc but allows us to identify<br>roughlyeven-sizedgroups of informal and formal wage workers.|

29
In a subsequent and last set of regressions, we test whether the results thus far capture other factors that drive sectoral growth across countries. Fisman and Love (2004; hereafter FL) have argued that it is not financial development but rather the presence of growth opportunities that drives the differences in actual growth rates of industries across countries. In their view, other factors (which happen to be correlated with financial development) determine growth opportunities across countries and so the RZ regression results simply reflect that countries with greater financial development also have a better ability to make use of such growth opportunities. It could be the same for competition: perhaps it is not a more-competitive banking system that provides more or better financing for industries that are financially dependent but rather the presence of growth opportunities, which happen to be correlated with greater competition, that allow financially dependent firms to grow faster. In this robustness test we therefore include, as FL did, the interaction between the actual sales growth rates of benchmark U.S. firms over the 1980—1990 period (using data from FL) with our competition measure. To assure comparability, we also re-run, for our sample of countries, the FL specification with financial development and growth opportunities. Table 9 reports the regression results.

The first regression result replicates the FL results (for the larger sample of 41 countries): including the measure of growth opportunities interacted with financial development is statistically significant and, importantly, after including this interaction term the interaction variable between financial development and external financial dependence is no longer statistically significant (column 1). When we next include the interaction between our competition measure and financial dependence (for the smaller sample of 16 countries), we find this interaction to be significant, while the financial development and growth opportunities 37
Rodriguez, Sanzo-Pérez and Suárez Pandiello, 2005; Molero and Pujol, 2005; Alm and Torgler, 2006; Alm and Gomez, 2009) and as a novelty Martinez-Vazquez and Torgler (2009) focus on the inter-temporal evolution of tax morale in a country using data from different WVS survey waves.

The last 30 years represent a period of radical transformation for Spain, which could have profoundly affected the way citizens view the state and ultimately, their tax morale. The state of tax morale was quite low at the end of Franco‘s regime. As reported by Comin (2007), at the end of the 1970s, tax evasion in Spain was estimated at 40 percent of tax receipts and above 90 percent of all Spanish taxpayers recognized the existence of fiscal fraud. In addition, at the time the fiscal system provided ample tax evasion opportunities for the wealthy, and there was no political will to prosecute fiscal fraud. In particular, tax evasion was not a criminal offense and evaders only received small administrative sanctions. After Franco‘s death, the first democratic elections took place in 1977 and were won by a center-right coalition (the UCD — Unión de Centro Democrático). The highlight of this period was the coalescence of all political forces in the country in what became known as the Moncloa Pacts of 1977. These Pacts set the foundations for political reform, with a new Constitution being approved in 1978. These Pacts also set the blueprints for deep fiscal reform, including legislation making tax evasion a crime. The goals of the Moncloa Pacts were to introduce policy and institutional reforms in the public and private sectors, that would bring Spain up to par with the system Spain‘s European neighbors had put in place almost three decades earlier following the end of World War II (see Comin, 2007). The period subsequent to the Moncloa Pacts was one of political indecision, poor economic performance and political turmoil - including a failed coup by a small group of security forces in 1981. It appeared that the government had
**11**

|||**La**|**Table**5<br>**bor Regulations**|||
|---|---|---|---|---|---|
|_Country_<br>____|_Payroll_<br>_Taxes'_<br>__________<br>_________|_Severance_<br>_Paymentsb_|_Employment_<br>_Growth_<br>**_(1992-1995)c_**|_Unemployment_<br>_Rate (1996)_|_Collective_<br>_Bargaining_|
|**Australia**|27.8|Low|1.0|9.0|Centralized|
|**Chile**|20.9|Low|2.3|6.3|Firm Level|
|**Japan**|22.9|None|0.6|2.5|Firm Level|
|**Malaysia**|24.3|Low|3.3|2.8|Firm Level|
|**New Zealand**|_11.5_|None|1.4|8.0|Firm Level|
|**United States**|20.1|None|1.8|_5.5_|Firm Level|
|**Argentina**|50<br>0 <sup>d</sup>|High|-0.7|17.2|Centralized|
|**France**|54.7|High|-0.4|11.6|Centralized|
|**Italy**|52.8|High|-1.7|10.2|Centralized|
|Spain|38.2|High|-1.6|22.4|Centralized|

**a** Payroll taxes are firm donations plus obligatory personal contributions. The values