rafmacalaba/datause-encoder-data huggingface dataset:
# **Informality among Formal Firms:**

**Firm-level, Cross-country Evidence on Tax Compliance and Access to Credit**<sup>**1**</sup>

Roberta Gatti Development Research Group The World Bank and CEPR Maddalena Honorati Development Research Group The World Bank

> 1 We thank Reyes Aterido, Giampaolo Dell’Ariccia, Ana Margarida Fernandes, Aart Kraay, Norman Loayza, Luis Servén, and seminar participants at the European Bank for Reconstruction and Development and at the World Bank for useful comments. The opinions expressed here do not necessarily reflect those of the World Bank, its Executive Directors or member countries. All errors are our own.
collected for both males and females, and was processed accordingly. We would not expect the sex-disaggregated coverage rate to exceed the population coverage, and it never does. Moreover, if the country has a sex-disaggregated data point they also have a population estimate. But the reverse is not the case. For six of these 32 indicators (19%), while there is some reported data for the population indicator, there is no sex-disaggregated data reported. As an example, 56% of countries report a population rate for SDG indicator 10.2.1 (the proportion of people living below 50 percent of median income), yet no country reports this statistic by sex. These missing data are not the result of missing sex in underlying data source (in this case, household surveys). The measure itself (living below an income threshold) is defined at the household level and so one can produce a sex-disaggregated estimate based on the households in which individuals reside. For example, Munoz Boudet et al. (2021) report poverty rates by sex.<sup>6</sup> In the remaining 17 cases (out of 32), where there is some reported data for both the population and by sex-disaggregation, in a handful of cases there are large gaps between the percentage of countries with a recent value by sex and those reporting a population estimate (i.e. comparing the last two columns in Table 1 when both columns are non-zero). For SDG indicator 1.3.1, on the proportion of population covered by social protection floors/systems, 79% of countries have a recent value for the population, but only 8% of countries have a sex disaggregated data point. A less drastic example is SDG indicator 4.1.1, related to early childhood education: 64 percent of countries have a population estimate for this indicator but only 53 percent have an estimate by sex.

> 6 This is not related to the issue of measuring income or poverty at the individual level versus a household measure. It is simply the point that if a population estimate is produced based on a household-level measure, then there is no methodological argument against producing additional estimates for population sub-categories (be it urban/rural, male/female, for children, etc).

7
constructed by interpolating using the inverse distance weighting method.<sup>3</sup> The second is an index of climatic suitability for rice cultivation generated by the FAO, incorporating precipitation and temperature as well as other soil and topographic features. More specifically, the analysis utilizes the FAO’s index of suitability for high input-level irrigated rice, given that Chinese agriculture is characterized by a relatively high level of input use and irrigation, and estimate the mean index value within the county in which each sampled village lies (IIASA and FAO, 2012). More details on how these indices are used in the analysis are provided in Section 3.

# **2.2 Grain policy in China**

Prior to 1978, agricultural production in China was highly collectivized. The primary unit of production was the production team, a cluster of 20 to 30 households that jointly farmed agricultural land and sold the resulting output. After the death of Mao Zedong, however, major changes in agricultural policy were introduced. The household was reinstated as the primary unit of production under a system known as the Household Responsibility System. Each household was provided with an allocation of land for its own use, while land title continued to be held by the village (Brandt et al., 2002).

In addition, households were mandated to deliver a fixed amount of quota grain to the state at a preset price. Excess production could be sold to the state at a higher, above-quota price, or at rural markets (Lin, 1992). Grain thus sold to the state was primarily funneled via official grain bureaus to urban consumers, who were entitled to purchase staple consumption goods at subsidized prices (Lu, 1999; Rozelle et al., 2000).

This mandatory grain procurement by the state, widely known as the grain quota system, remained a key dimension of agricultural policy through the early post-2000 period. The volume of grain quotas did decline after 1995 relative to earlier in the

> 3Each interpolation employs only data from stations within 150 kilometers of the county centroid. The average number of stations employed to construct climatic data for a county is three. While countylevel weather data reported directly by counties to provincial weather bureaus is also available, using weather station data has the advantage of ensuring consistency of data quality and reporting methods across the sample.

5
**13**

# **Table 4. Budget Expenditures by Level of Government as Percent of GDP, %**

|**BudgetExpenditures/Year**|1992|1993|1994|1995|1996|
|---|---|---|---|---|---|
|**Total Expenditures**|**56.06**|**46.14**|**46.14**|**36.06**|**40.98**|
|- federal|40.91|25.10|24.24|18.45|22.17|
|- regional|7.38|10.51|11.71|9.08|9.42|
|- local|7.77|10.54|10.20|8.53|9.40|
|Total Expenditures Net of||||||
|**Interbudgetary Transfers**|**51.63**|**_NIA_**|**38.64**|**31.19**|**35.30**|
|- federal|39.24|22.53|20.39|16.31|19.57|
|- regional|4.70|N/A|8.15|6.52|6.49|
|- local|7.69|10.19|10.10|8.36|9.23|
|Transfers to OtherLevels<br>of||||||
|**Government**|**4.43**|N/A|**7.51**|**4.87**|**5.68**|
|**-** federal|1.67|2.57|3.84|2.14|2.59|
|- regional|2.68|N/A|3.56|2.56|2.92|
|- local|0.08|0.35|0.10|0.17|0.16|
|**NationalEconomy**|**26.42**|**13.45**|**10.60**|**8.34**|8.56|
|<br>- federal|21.35|6.53|3.22|2.20|2.35|
|- regional|2.31|3.31|3.68|2.96|2.77|
|- local|2.76|3.61|3.70|3.19|3.43|
|**Education**|**3.58**|**4.06**|**4.51**|**3.48**|**3.82**|
|- federal|1.21|0.79|0.90|_0.55_|0.56|
|- regional|0.52|0.77|0.86|0.69|0.69|
|- local|1.85|2.50|2.75|2.24|2.58|
|**Health and Sport**|**2.47**|**3.11**|**3.25**|**2.37**|**2.61**|
|- federal|0.28|0.33|0.38|0.23|0.27|
|- regional|0.70|0.94|1.08|0.79|0.86|
|- local|1.49|1.85|1.78|1.35|1.48|
|Social Protection|**1.08**|0.74|**0.78**|**1.28**|1.76|
|- federal|0.77|0.38|0.39|0.24|0.55|
|- regional|0.22|0.26|0.27|0.40|0.47|
|- local|0.08|0.10|0.12|0.64|0.74|
|Administration and Justice|1.84|2.32|2.96|2.40|2.78|
|- federal|1.48|1.85|2.41|1.51|1.77|
|- regional|0.10|0.14|0.18|N/A|0.53|
|- local|0.26|0.33|0.37|N/A|0.48|
|**Culture and Mass Media**|**0.61**|0.57|0.73|0.55|0.59|
|- federal|0.31|0.20|0.29|0.17|0.21|
|- regional|0.13|0.17|0.21|0.17|0.17|
|- local|0.17|0.20|0.23|0.21|0.21|
|Defence|4.50|4.20|4.67|3.04|3.47|
|- federal|4.50|4.20|4.67|3.04|3.47|
|- regional|0|0|0|0|0|
|- local|0|0|0|0|0|
|**Loans**|**5.04**|**N/A**|2.72|**0.99**|**0.91**|
|- federal|4.61|1.70|2.30|0.58|0.28|
|- regional|0.20|N/A|0.34|0.34|N/A|
|- local|0.23|N/A|0.08|0.06|N/A|
|**OtherExpenditures**|6.09|N/A|8.42|8.73|10.78|
|- federal|4.72|6.55|5.83|7.77|10.12|
|- regional|0.52|N/A|1.51|N/A|N/A|
|<br>- local|0.85|<br>N/A|<br>1.08|<br>N/A|N/A|
# **An Anatomy of Urbanization in Sub-Saharan Africa**

**Pierre-Philippe Combes, Clément Gorin, Shohei Nakamura, Mark Roberts and Benjamin Stewart**<sup>*****</sup>

## **JEL codes:** R12, R23, O55

**Keywords:** urbanization, Sub-Saharan Africa, dartboard approach, satellite imagery, population density

**Acknowledgments:** The authors gratefully acknowledge comments provided by participants at the 2021 and 2022 North American Urban Economics Association meetings and the 2021 SMU Urban & Regional Economics conference, as well as additional comments provided by Yue Li and Harris Selod. They further acknowledge the generous assistance provided by Andrew Tatem, Maksym Bondarenko and Alessandro Sorichetta from the University of Southampton’s WorldPop group in generating constrained WorldPop gridded population data for 2015. This paper is part of a set of two Policy Research Working Papers that have been produced under the World Bank’s “A Move to Consistency – Integrating the Consistent Measurement of Urbanization into Global Poverty Measurement” Advisory Services and Analytics project (P175622). The accompanying paper – entitled “Where is Poverty Concentrated? New Evidence Based on Internationally Consistent Urban and Poverty Measurements” – is available as Policy Research Working Paper No. WPS10620.  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.

* Pierre-Philippe Combes is affiliated with the Department of Economics, Sciences Po, and CEPR, and Clement Gorin with the University of Paris 1. Shohei Nakamura, Mark Roberts, and Ben Stewart are affiliated respectively with the World Bank’s Poverty & Equity Global Practice; Urban, Resilience, and Land Global Practice; and Geospatial Operations Support Team. Shohei Nakamura and Mark Roberts are the World Bank Task Team Leaders for the project. **Corresponding author:** Mark Roberts (mroberts1@worldbank.org).
<!-- Start of picture text --> 0.00<br>0.000.00 = l —_—a ia l |_| i —<br>0100 voll 1982-1 1991-2003 BB3-202<br>os<br>0.300<br>0.400<br>mG mEP wOR mEL mToT<br><!-- End of picture text -->

<!-- Start of picture text -->
1.300<br>-<br>0.300<br>-0.2000.300 1960-1982ie = 1982-19! 1991-2008 His2003-2020<br>-0.700<br>mEP wDR mEL<br><!-- End of picture text -->
of a standard deviation. As shown in Figure 2 (panel A and B), the kernel densities of the treatment and control groups generally overlap during the baseline survey. During the endline survey, the mean test score of the treatment group of students was higher than that of the control group. Considering each test individually, the intervention improved writing and math test scores the most (by 27.1 and 25.9 percent of standard deviation, respectively). Reading and Peabody test scores increased, respectively, by 25.6 and 20.9 percent of standard deviation, respectively.<sup>10</sup> The interventions did not improve performance on listening test.

Table 3 Impact of teacher training and books on test score

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Peabody|Math|Listening|Reading|Writing|Total Score|
|ATE|0.481<sup>_∗∗∗_</sup>|0.617<sup>_∗∗∗_</sup>|0.225|0.989<sup>_∗∗∗_</sup>|0.555<sup>_∗∗_</sup>|2.867<sup>_∗∗∗_</sup>|
||(0.00)|(0.00)|(0.10)|(0.00)|(0.02)|(0.00)|
|_N_|2424|2424|2424|2424|2424|2424|

b coecients; p in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05 , _p <_ 0 _._ 01 , _p <_ 0 _._ 001 Note: The standard errors are clustered at aimag level, with wild cluster boostraping" (Cameron et al., 2008). The matching variables are characteristics of the student, household, teacher and school. Student's characteristics includes gender, age, number of books she owns, frequency of extra lessons after school, frequency of accomplishing household chores before and after school and a dummy for residing more than one hour walk from school and commuting by foot. Characteristics of the student's household encompasses household size, the household head's relationship with the child, wealth indicators (such as housing condition, dummy for phone and car ownership) and education level. Teacher's formal education and years of experience makeup characteristics of the student's current teacher. Teacher's formal education and years of experience makeup characteristics of the student's current teacher. School characteristics encompasses dummy for existence of hygiene infrastructure (like toilet and hand-washing facilities).

> 10For training and books intervention group of students, the standard deviations of test scores in Peabody, math, listening, reading, writing and total score are 2.30, 2.38, 1.93, 3.85, 2.05 and 8.19, respectively (see Table A.3 in the annex).

18
excellent evaluations of PROGRESA have thus far not focused on the differential impact on indigenous peoples. Therefore, the objective of this paper is to analyze the impact of PROGRESA/OPORTUNIDADES on indigenous children’s progress in school and work activities. The determinants of schooling and work will be modeled according to Grootaert and Patrinos (1999).

We use the ENCASEH97, ENCEL99N and ENCEL00N household surveys.<sup>16</sup> These surveys are part of a round of surveys to evaluate PROGRESA.<sup>17</sup> It consists of a base survey and six consecutive surveys of the same household in a three-year period. This panel data is representative of the rural disadvantaged populations with 20 to 2,500 individuals in 7 states. It includes approximately 138,000 individuals in 26,000 households in 506 localities, with 320 as the treatment group and 186 as the control group. It includes micro-data on household characteristics, especially those that refer to education and health. It includes demographic characteristics of the household, with individual information for all family members.

In both years, population is separated into three groups: indigenous, Spanish and bilingual. Indigenous is defined as people that only speak an indigenous language but not Spanish. Bilingual is defined as the people that speak both an indigenous language and Spanish. Finally, Spanish stands for people that only speak Spanish but not an indigenous language. Children are defined as individuals between 8 and 16 years.

For the description of changes in incidence before and after the program, the groups are also divided into control and treatment groups. As explained in Behrman and Todd (1999), the process by which the treatment and control samples were collected is as follows. First, a subset of localities in Mexico was chosen to participate in the experiment. Randomization is being implemented at the locality rather than at the household level because PROGRESA benefits such as improvements in local schools and

> 16 ENCEL00N: Encuesta de Evaluación de los Hogares, November 2000; ENCEL99N: Encuesta de Evaluación de los Hogares,  November 1999; ENCASEH97: Encuesta de Características Socioeconómicas de los Hogares, November 1997.

> 17 Programa de Educación, Salud y Alimentación (http://www.progresa.gob.mx/).

18
<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Share of Votes for Center Share of Votes for Right<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Electoral Competition<br>2002 2006 2010 2014 2018 2022<br>Election Year<br>PEP No-PEP<br>.55 .4<br>.5 .3<br>.45 .2<br>.4 .1<br>.35 0<br>.5 .7<br>.4<br>.6<br>.3<br>.5<br>.2<br>.4<br>.1<br>0 .3<br>.65<br>.6<br>.55<br>.5<br>.45<br>.4<br><!-- End of picture text --> Figure D.2. Presidential Elections’ Evolution of Outcomes by PEP and Non-PEP Status _Notes:_ The figure illustrates the yearly evolution of the electoral outcomes in municipalities with and without PEP take-up.

69
measure of quality of care during those visits). We also find substantial improvement in birth outcomes. Specifically, we estimate that being a beneficiary reduces the probability of low birth weight by 19 percent and in-hospital neonatal mortality by 74 percent. About half the reduction in neonatal mortality comes from better prenatal care that prevents low birth weight and half from better postnatal care available to low-birth weight babies. Our results also suggest that there are no negative spillovers onto the birth outcomes among the nonbeneficiary population receiving care in clinics covered by Plan Nacer.

Overall, we find that Plan Nacer is hugely cost-effective. The cost of a DALY saved through Plan Nacer’s financing of maternal health services was $814, which is hugely cost-effective in the Argentine context when compared to GDP per capita, $6,075, over this period. Plan Nacer uses a relatively small amount of resources (2 to 4 percent of total expenditure) to provide incentives to health providers to use resources more efficiently and for higher-quality care to program beneficiaries.

There are, however, a number of limitations to our analysis. First, while we have high-quality data on birth outcomes, we have limited information on the use and quality of services that act as mediators between Plan Nacer and ultimate health impacts. Second, the neonatal mortality data are for a subset of the population. While the causal impacts are internally valid, they may not be completely externally valid. However, the neonatal mortality rates for the subsample used in our analyses are not different from the neonatal mortality rates for the region overall.

32
disparities—males in the minority group fare roughly the same as their counterparts in the reference group (46 and 50 percent, respectively), but females in both of these groups fare substantially worse than men (25-29 percent).

**Table 2. Literacy** **<u>and educational attainment in the reference and minority groups, by gender</u>**

|Country|Outcome|Referen|ce group|Minori|ty group|
|---|---|---|---|---|---|
|||Male|Female|Male|Female|
|Bolivia|Lit|97.6|94.4|87.4|63.0|
||PSC|80.1|72.3|47.0|20.1|
||SSC|45.5|39.9|11.6|3.7|
|Mexico|Lit|93.3|91.2|77.1|61.8|
||PSC|67.8|65.5|38.2|26.0|
||SSC|21.6|19.6|5.6|3.0|
|Peru|Lit|97.8|94.2|90.3|68.9|
||PSC|78.7|70.9|51.8|29.3|
||SSC|62.3|55.4|30.3|14.6|
|Senegal|PSC|32.7|20.6|22.0|9.8|
||SSC|10.2|3.9|5.5|1.3|
|Sierra Leone|Lit|46.0|25.0|49.8|28.8|
||PSC|31.4|14.0|29.7|14.3|
||SSC|5.3|1.6|6.8|1.7|

Note: Literacy rate for ages 12 and above. Primary and secondary school completion rates for ages 25 and above. Lit=Literacy rate, PSC=Primary school completion, SSC=Secondary school completion.

Source: IPUMS-I.

In primary and secondary school completion rates, similar patterns exist in each of the five countries in the sample. Female members of the minority group usually do worse than the male members of the same group, who in turn fare worse than females and males in the reference group. In all cases (except for secondary school completion in Sierra Leone), males in the reference group have the highest school completion rates. However, there is some variation across countries in the relative sizes of gender-based and ethnicity-based differences. In Latin American countries, for example, gender-based differences in primary and secondary school 16
<u>Table 22. The determinants of R&D adoption and ICT intensity</u>

||Ne|pal|Bangl|adesh|Ind|ia|Pak|istan|
|---|---|---|---|---|---|---|---|---|
||R&D|ICT index|R&D|ICT index|R&D|ICT index|R&D|ICT index|
|Firms Size - in log|0.3303***|0.3447***|0.2848***|0.2537***|0.2251***|0.1574***|0.1784**|0.2181***|
||(0.106)|(0.023)|(0.039)|(0.015)|(0.021)|(0.008)|(0.071)|(0.022)|
|Log Firm Age|0.2336|0.0526|0.0577|0.0212|0.0071|-0.0136|0.4247**|0.0946**|
||(0.199)|(0.039)|(0.069)|(0.025)|(0.030)|(0.012)|(0.171)|(0.044)|
|Firm Exports|0.1673|0.1565**|-0.0011|0.3138***|0.1286**|0.1035***|0.0109|0.3080***|
||(0.304)|(0.071)|(0.148)|(0.056)|(0.063)|(0.026)|(0.247)|(0.076)|
|Working capital|-0.0061*|0.0003|-0.0004|-0.0007|-0.0019***|-0.0002|-0.0086*|-0.0038**|
||(0.004)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.005)|(0.002)|
|Duopoly/Monopoly|0.3389|0.2058**|-0.0602|0.2111**|0.0093|0.0299|0.0430|0.0114|
||(0.493)|(0.099)|(0.245)|(0.086)|(0.080)|(0.033)|(0.253)|(0.077)|
|New Capital previous Year|0.3052|0.1820***|-0.0271|0.0174|0.0155|-0.0249|-0.0733|-0.0764*|
||(0.230)|(0.050)|(0.098)|(0.046)|(0.050)|(0.018)|(0.226)|(0.045)|
|Informal Sector as Obstacle|0.1104|0.0448|-0.2199|-0.0557|-0.2187***|-0.0074|-0.0860|0.0814|
||(0.269)|(0.041)|(0.181)|(0.065)|(0.070)|(0.013)|(0.299)|(0.058)|
|License_foreign|1.9250***|-0.1022|-0.1771|0.1327***|0.1884**|0.0277|0.8201***|<br>-0.0831*|
||(0.462)|(0.135)|(0.147)|(0.051)|(0.087)|(0.029)|(0.249)|(0.049)|
|Constant|-2.9221***|-1.4913***|-1.6676***|-1.3925***|-1.3279***|-0.5659***|-1.3310|-0.6241*|
||(0.620)|(0.499)|(0.297)|(0.108)|(0.149)|(0.060)|(0.941)|(0.353)|
|Observations|470|470|990|990|3,481|3,481|499<br>ISIC-|499<br>ISIC-|
|Sector dummies|ISIC-1digit|ISIC-1digit|ISIC-2digit|ISIC-2digit|ISIC-2digit|ISIC-2digit|1digit|1digit|
||Stan|dard errors in|parentheses. *|** p<0.01, **|p<0.05, * p<0.1||||

31
# DIAGRAAM El

# MARGINAL REVENUE<sup>PRODUCT OF EXPENSE'</sup> SAMPLE VALUES:<sup>ALL ENTERPRISES</sup>

<!-- Start of picture text --> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~0.01&7<br>I<br>0.0107<br>a r , 0.0087 0.0067<br>L0.007<br>0 J<br>-t 3. \<br> j<br>,, I \ t0.0067<br>S [~~~~~~~~~~~~~~~~~~~~~~~~~~~~0.005?<br>0.0027<br>100 200 300 400 500 600 700 800 900 1000 1100<br>EUtERPRISES ORDERED BY MAP Of EXPENSES<br><!-- End of picture text --> <!-- Start of picture text --> 0-<br><!-- End of picture text -->
|**Enu**<br><br>|**merator questionnaire**<br>|
|---|---|
|Q1:<br>(a) Respondent first name:|(b) Respondent second name:|
|Q2:<br>Respondent Gender:|1. M<br>2. F|
|Q3:<br>Location of main residence:||
|(a) District:|(b) County:|
|(c) Urban=1, Rural=2:||
|Q4:<br>Have you ever worked as an<br>enumerator before?|1=yes<br>2=no |____||
|Q5:<br>If yes, for how many months?||____||
|Q6:<br>What year and month were you<br>born?|Month |_______| Year |___|___|___|___||
|Q7:<br>What is the highest level of<br>education you have completed?|1. None<br>2. Some primary<br>3. Completed primary<br>4. Some secondary<br>5. Completed secondary (S6)<br>6. Some university<br>7. Completed university<br>8. Completed Teachers College,<br>Vocational Training Institute<br>9. Master’s or PhD|
|Q8:<br>What is/ was the highest level of<br>education attained by your biological father?|1. None<br>2. Some primary<br>3. Completed primary<br>4. Some secondary/junior (up to S5)<br>5. Completed secondary (S6)<br>6. Some university<br>7. Completed university<br>8. Don’t know|
|Q9:<br>What is/ was the highest level of<br>education attained by your biological<br>mother?|1. None<br>2. Some primary<br>3. Completed primary<br>4. Some secondary/junior (up to S5)<br>5. Completed secondary (S6)<br>6. Some university<br>7. Completed university<br>8. Don’t know|
|Q10:<br>Generally speaking, would you say<br>most people can be trusted OR that most peop<br>reallybe trusted?|that<br>le can’t<br>(a) Most people can be trusted<br>(b) Most people can’t really be trusted.|

2