rafmacalaba/datause-encoder-data huggingface dataset:
other (Vittas 2003). Generous tax incentives, often linked to the offer of private pension plans, have played a major role in most of these countries. Fiscal incentives have a greater impact in countries where marginal rates of personal income tax are very high. High real investment returns in an environment of low inflation with economic and political stability have also been an important factor.

**Table 1: Insurance Premiums, 2001 (percent of GDP)**

||Total|Life|General|% Life|
|---|---|---|---|---|
|Jordan|1.92|0.28|1.65|15|
|South Africa|17.97|15.19|2.78|85|
|Ireland|9.14|6.30|2.84|69|
|Denmark|6.93|4.51|2.41|65|
|Cyprus|4.46|2.46|2.00|55|
|Malta|4.26|1.99|2.28|47|
|Chile|4.23|2.93|1.30|69|
|Mauritius|4.02|2.46|1.57|61|
|Malaysia|3.72|2.13|1.59|57|
|Morocco|2.82|0.81|2.01|29|
|Lebanon|2.63|0.46|2.16|18|
|Mexico|1.81|0.95|0.86|48|
|Tunisia|1.60|0.14|1.46|9|
|Turkey|1.31|0.24|1.07|18|
|United Arab Emirates|1.25|0.23|1.02|18|
|Egypt|0.58|0.18|0.40|31|
|Saudi Arabia|0.53|0.01|0.52|2|

Source: Sigma, 6/2002 In Jordan, life business has been held back by the lack of strong fiscal incentives. Only premiums for medical and term life insurance are deductible from taxable income; those for whole life and endowment policies, which generate long-term savings, are not. The poor performance of life insurance is also explained by the underdevelopment of private pension funds and housing finance. Other factors include the negative public image of insurance companies, the absence of a strong regulatory framework on market conduct and consumer protection, the weak marketing effort of most insurance companies, and the shortage of well trained selling agents.

Total per capita premiums amounted in Jordan to 26 US dollars in 2001. This compared with 144 in Lebanon, 33 in Morocco and Tunisia, and less than 10 US dollars in Algeria and Egypt. In Chile, a country that experienced a rapid growth of life insurance following the pension and insurance reforms of the early 1980s, total per capita premiums reached 176 US dollars in 2001. Per capita life premiums amounted to 122 US dollars in Chile against less than 4 US dollars in Jordan.

After rising in line with nominal income between 1997 and 2000, insurance premiums grew at an accelerated rate in the past couple of years, when annual real growth reached over 15 percent (Table 2). In 2002, premium growth reflected a significant and long overdue upward adjustment of the administered tariff on compulsory motor insurance. Gross premiums reached 146.9 million JD or 2.2 percent of GDP. The fall in nominal life premiums in 1998 was due to the reclassification of medical premiums from life to general insurance.

6
# **1. Introduction**

Governments in low- and middle-income countries are increasingly focusing on registering and taxing informal small and microenterprises (Dom and Prichard 2022, Ohnsorge and Yu 2021). Simplified or small business tax regimes that use simple and objective methods for tax assessments are often established for this purpose (Loeprick 2009). A presumptive tax on small and microenterprises is one of the simplified business tax regimes and it uses an indirect method of measuring tax liabilities, which can be assessed on turnover or other indicators (e.g., the number of tables, seats, or floor space) (Loeprick 2009). The rationale for a presumptive tax includes facilitating tax compliance, lowering compliance costs, and supporting formalization of businesses due to the simpler reporting requirements compared to the regular tax system (Mas-Montserrat et al., 2023); enabling revenue authorities to gain valuable information from businesses and to help them transition into the corporate tax system (Engelschak and Loeprick 2016); potentially raising tax revenue given the large informal sector in low- and middle-income countries (Joshi, Prichard and Heady, 2014); and increasing tax morale and tax compliance among formal taxpaying firms (Joshi, Prichard and Heady, 2014).

Women tend to be overrepresented in small and microenterprises and thus may be subject to the presumptive tax. In Ethiopia, the presumptive tax is relevant because women predominate in nonfarm enterprises as it is considered inappropriate for women to carry out certain agricultural tasks and there are limited income-earning opportunities elsewhere (Loening, Rijkers, and <mark>Söderbom,</mark> 2008, Rijkers and Costa 2013). Women’s concentration in some sectors, such as hospitality, personal services, and food processing, may also have gender implications on the tax burden if the tax rates differ by sector. This is the case in Ethiopia where the presumptive tax has a complex assessment system with divergent tax rates by the activity type and turnover - with 99 activities and 19 turnover bands.

Yet, an empirical analysis of how the presumptive tax affects low-income and female entrepreneurs has received limited attention because of a lack of data and the low priority given by governments that often generate very little revenue from the presumptive tax (Mas-Montserrat et al., 2023). Despite this, the tax can be burdensome for small and microenterprises (Bucci 2020). Given governments’ keen interest to register informal small businesses and to bring them into the tax net, it is important that the tax burdens on these businesses be examined.

This paper examines the gender and equity implications of the presumptive tax on small and microenterprises in Ethiopia by estimating the effective tax rates, disaggregated by the sex of the business owner. The study uses newly available data on household nonfarm enterprises from the 2018/2019 and 2021/2022 Ethiopian Socioeconomic Surveys (ESS) and the tax code to impute tax liabilities. The ESS also collected information on self-reported tax payments by household enterprises. The effective tax rates are 2
_Table 4: Comparing of Wages and Employment Incidence of Former Trainees by the DTS and RPs_

|||All||RP||DTS|Difference|
|---|---|---|---|---|---|---|---|
|Variables|N|Mean|N|Mean(a)|N|Mean(b)|(a)−(b)|
|Latest monthlysalary|456|11,408|212|11,044|195|12,199|−1,155|
|Currentlyemployed|833|68%|471|68%|362|69%|−0.8%|
|Currentlyemployed as wage earner|833|59%|471|57%|362|62%|−5%|
|Employed right after TESDA training|833|74%|471|70%|362|82%|−11.8%***|

Source: _DTS Tracking Survey Data, 2015, World Bank. Note: *** Statistically significant at 1% level._ 29
<!-- Start of picture text --> 35 35<br>In-<br>30 30<br>Formal Sector Informal Sector<br>and<br>25 25<br>Formal<br>20 20<br>Types,<br>Unemployment<br>of<br>15 Worker Types, y 15 Worker Types, y<br>Worker out<br>10 of 10 Rate<br>Density<br>5 5 Hazard<br>16:<br>0 0<br>0 Analytical<br>0.14 0.12 0.1 0.08 0.06 0.04 0.02 0.26 0.24 0.22 0.2 0.18 0.16 0.14 0.12<br>Figure<br>14: Sector<br>35 Figure formal 35<br>Log-<br>30 30<br>Non−parametric Lognormal vs<br>25 Data 25<br>20 Types: 20 Rate<br>15 Worker Types, y Worker 15 Worker Types, y<br>of<br>10 10 Unemployment<br>Density 15:<br>5 5<br>Figure<br>0.35 0.3 0.25 0.2 0.15 0.1 0.05 00 Analytical 0.22 0.21 0.2 0.19 0.18 0.17 0.16 0.15 0.14 0.13 0.120<br>13:<br>Figure normal<br>Analytical Density uHazard Rate out of Unemployment, h<br>Density Unemployment Rate, u<br><!-- End of picture text --> 47
<!-- Start of picture text --> Revenue TFP Differential of Credit Access by Firm Size Revenue TFP Differential of Credit Acces by Firm Age<br>£4 50.0% £3 40.0%<br>&Ee : ee E §£ 200%<br>ro 35<br>FS 3 30.0% = 3 25.0%<br>3232 3= £2 20.0%<br>£ 2 20.0% $ é<br>A 52°"<br>ig 10.0% reum<br>23 AG<br>83 0.0% A ee<br>2 3 Micro Small Medium oe £ E 0.0%<br>FS as fs <s 5-9 10-14 15-19 >20<br>x5 a R5 Age<br>mAccess toFinance —_TM Access to Financefrom Banks TMAccess toFinance _'TM Access to Financefrom Banks<br><!-- End of picture text --> <!-- Start of picture text --> Revenue TFP Premium of Firm Size for Firms with Revenue TFP Premium of Firm Age for Firms with<br>Access to Credit Acces to Credit<br>rT 0.0% S 10.0%<br>E fe<br>€ 5 8.0%<br>O -5.0% s<br>32<br>2-10.0% = 40%<br>Q 3<br>ee2 15.0% e[= a 0.0% - L<br>= ne |<br>5 -20.0% 2 .<br>oO<br>& -25.0% z -6.0%<br>Small Medium Large £ 5-9 10-14 15-19 >20<br>Size 2 Age<br>2<br>Access toFinance —‘TM Access to Financefrom Banks a mAccess toFinance —TM Access to Finance from Banks<br><!-- End of picture text -->
<!-- Start of picture text --> 2017 2018 2019<br>100<br>75<br>2<br>°<br>°<br>ad<br>u<br>un<br>6 50<br>yn<br>o—_<br>G<br>at<br>7)<br>25<br>0<br>0 1 2 3 4 5 >5 0 1 2 3 4 5 >5 0 1 2 3 4 5 >5<br>Number of teachers needed<br>| | Zero | | Less than needed than needed needed || Equalled needed needed || More than needed than needed needed<br><!-- End of picture text -->

| | Zero | | Less than needed than needed needed || Equalled needed needed || More than needed than needed needed
# **Table 6 Question 4: Multinomial Logit Regression**

This table reports the marginal effects of a multinomial logit regression. The dependent variable is _Loani,_ 4, where _Loani,_ 4 = 1 if the student _i_ chooses the Debt in question 4, _Loani,_ 4 = _−_ 1 if the HCC is chosen instead, and _Loani,_ 4 = 0 if the option “indifferent” is chosen. The coefficients should be interpreted as the marginal effect on the probability of choosing the alternative labeled in the column.

||HC|C|Indife|rent|L|oan|
|---|---|---|---|---|---|---|
|||N|=767||||
||Coef.|Std. Error|Coef.|Std. Error|Coef.|Std. Error|
|**Treatment**|**0.1299*****|**0.0361**|**-0.0742*****|**0.0233**|**-0.0557**|**0.0354**|
|Mexico|-0.0412|0.0571|0.0449|0.0404|-0.0037|0.0562|
|Chile|-0.0945|0.0632|0.0080|0.0425|0.0865|0.0640|
|Age|0.0048|0.0049|0.0005|0.0033|-0.0053|0.0048|
|Male|0.0502|0.0370|0.0348|0.0229|-0.0850**|0.0360|
|Student|-0.0620|0.0428|0.0035|0.0260|0.0585|0.0413|
|Children|0.0494|0.0762|-0.0695**|0.0333|0.0200|0.0752|
|Married|-0.0545|0.0826|0.0558|0.0627|-0.0012|0.0835|
|Mother Education|0.0474|0.0490|0.0416|0.0286|-0.0890*|0.0482|
|Father Education|0.0057|0.0484|-0.0112|0.0319|0.0055|0.0465|

- 10% Significance Level

- ** 5% Significance Level

*** 1% Significance Level

26
# Household Impacts of Tariffs: Data and Results from Agricultural Trade Protection<sup>_∗_</sup>

Erhan Artuc<sup>_†_</sup> Guido Porto<sup>_‡_</sup> Bob Rijkers<sup>_§_</sup> The World Bank Dept. of Economics The World Bank DECTI UNLP DECTI

> _∗_ We thank J. Angbazo, S. Fernandez, N. Gomez Parra, W. Kassa, H. Liu, A. Luo, M. Saleh and N. Santos Villagran for providing excellent research assistance. This research has been supported by the World Bank’s Research Support Budget, the ILO-World Bank Research Program on Job Creation and Shared Prosperity and the Knowledge for Change Program. It has also been supported by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID) through the Strategic Research Partnership on Economic Development. 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 of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. All errors are our responsibility.

> _†_ Development Economics Research Group, Trade and Integration, The World Bank. email: eartuc@worldbank.org

> _‡_ Universidad Nacional de La Plata, Departamento de Economia, Calle 6 e/ 47 y 48, 1900 La Plata, Argentina. email: guido.porto@depeco.econo.unlp.edu.ar

> _§_ Development Economics Research Group, Trade and Integration, The World Bank. email: brijkers@worldbank.org
**Acy** RFSFARCH WORKING PAiFR 14W8

# Summary findings

Addressing the **question,** 'What is th, work **stattis** of the w,,!orld's workiing-age populatioi and stbgr **ups** tlercof h Filmer gathers data for many countries anti infers dara where it is missing (which requires making 'heroic assumptions"). The results are of couirse onily as good as the data are representative and accurate. Data are least reliable for Sub-Saharan Africa.

The high-inconme grout is dominated (in population) bv the lUniited States, Germany, and Japan, which account for 58 percent of i hat grotip's work ing-age population.

1T1c middli-incomei groLup is dominated by Indonesia, til Ru5 sian Federatio)n, and Brazil. whiich. account for 40 percent of that grouip's working-age population.

The low-inconme grouip is dominated 1y China and India. which account for 70 percelt of that group's workinig-page population.

Aniong (othetr things, Filnlert' charts and tables show the breakdown on wvor ;king-age erriplomnient -in ,ertvices, indlstry, agriculture -- and uinemiiploymient in varioujs parts of the world.

Thispaper-aproduct of the OfficeoftheViccPresident, Deveiopment Economics--waspre paredasabackground paper for _World Development Report_ 1995 on labor. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Michael Geller. oo0m T7-(079, telephone 202-473 1 39.3, fax 202-676-0652, internet address M FLLFRf\aVORLD)BANK.OR(, ( 2 pages). jul y _1995._

> _The Policy Research Working P'aper .S,erics d-ssenzinates the findings ot fwork_ **_in_** _progress to encourage the exchange of ideas about development issues. An objectivie /the cet- s_ **_is_** _t -get the findings_ **_iust_** _quicrkli. even i/tlJhe p7esentations are less t/an fully polished. The papers tarry the_ **_names_** _of the_ **_author s and_** _should be used and_ **_citedt_** _at_ **_ordingly._** _The fitndinigs._ **_interpretations,_** _and c_ **_onc_** _luisions are ft'e authors' own and should not he_ **_attributed._** _to the Worid Bank., its ILxeC_ **_i.it_** _B1/aid (,I )ireftors. or_ **_ny,i_** _of its_ **_niernher_** _countries._

**IPrOdUced** bsv tIlc Prdicv Rejrdareh l)Iv, -iisv'.iton Center
# The Next Frontier in Water Supply Service Delivery:

# An Assessment of the Performance of Water Sector Service Providers in Pourashavas in Bangladesh

Aroha. Bahuguna; Luis. Alberto Andres; George. Joseph; and Mainul. Huq

## Background

Bangladesh is one of the most densely populated countries in the world, with over 163 million people living in an area of 147,570 km with the overall population growth rate of about 1.2 percent.<sup>2</sup> However, the population in urban areas has grown over 3.5 percent on average,<sup>3</sup> and the total urban population in Bangladesh was estimated to be about 57 million in 2016.<sup>4</sup> Bangladesh is divided into eight administrative divisions and 64 districts. There are 491 sub‐district units (Upazilas) and 4,554 Union Parishads (UPs), the lowest tier of government in rural areas.<sup>5</sup> In urban areas, there are two types of local governments: 11 City Corporations for metropolitan and larger cities; and 329 smaller municipalities (pourashavas, hereinafter). The large metropolitan cities like Dhaka, Chittagong, Khulna, and Rajshahi have populations between 0.84 million and 17.6 million<sup>6</sup> ; the smaller City Corporations have populations between 0.3 million and 0.5 million; and the 329 pourashavas have populations between less than 0.01 million and 0.5 million.<sup>7</sup> While Dhaka is still the center of the country’s economy, the garment industry, which is its most important industry, has been moving out to the peri‐urban areas in recent years,<sup>8</sup> leading to a population influx in these areas. Moreover, as Dhaka and Chittagong have started to exhibit the adverse impact of congestion and failing infrastructures, pourashavas have experienced accelerated population growth.<sup>9</sup> An important driver of the migration to pourashavas is that the potential migrants do not have to move too far from their ancestral homes, and their existing support networks remain useful in these new locations. Without investment in basic urban services, the increasing pressure of urban growth can lead to deterioration in quality of services in these areas.

> 2 (The World Bank, 2017)

> 3 (Central Intelligence Agency, 2017)

> 4 (The World Bank, 2017)

> 5 (Bangladesh Cabinet Division, 2019)

> 6 (Central Intelligence Agency, 2017)

> 7 (Bangladesh Bureau of Statistics, 2017)

> 8 (Burke, 2014)

> 9 (Khan Chandan, 2017)

2
# **Cognitive Development among Young Children in Ecuador: The Roles of Wealth, Health and Parenting**

# **Abstract**

This paper examines the relationship between early cognitive development, socio-economic status (SES), child health, and parenting quality in a developing country. We use a sample of over 3,000 predominantly poor pre-school age children from Ecuador, and analyze determinants of their scores on the Spanish version of the Peabody Picture Vocabulary Test (TVIP), a widely used test of language ability. We show that median age-normed test scores on the TVIP are much lower for older than younger children, and there is greater dispersion in scores among older children. We find that household socio-economic characteristics, in particular wealth and parental education, are “protective”—children from wealthier households and with more educated parents have higher scores. The associations of test scores with wealth and maternal education are larger for older children, suggesting that these factors have cumulative effects on cognitive ability. Last, we show that child health and measures of parenting quality are associated with performance on the TVIP. Children with lower hemoglobin levels perform worse on tests. Measures of parenting quality, in particular the degree to which parents are “responsive” and “harsh” toward children, and whether children are read to, account for a portion, although not the majority, of the association between SES and cognitive development.

JEL codes: I12, O12 Christina Paxson 316 Wallace Hall Princeton University Princeton NJ 08544 <u>cpaxson@princeton.edu</u> Norbert Schady Development Research Group Room MC 3-551 World Bank Washington DC 20433 <u>nschady@worldbank.org</u> ii
may be problematic because of assumptions about foregone earnings, costs, assumptions, comparators – like university versus secondary. What might be more telling are the trends. This includes the trends on the returns on years of schooling and returns by level.

Primary school enrollment has gone up from 72 percent in 1970 to 90 percent in 2017 (according to UNESCO and listed on the World Bank’s EdStats website: <u>https://data.worldbank.org/indicator/se.prm.nenr), and from 41 percent to 77 percent at the</u> secondary level (according to UNESCO and listed on the World Bank’s EdStats website: <u>https://data.worldbank.org/indicator/se.sec.enrr). The returns to schooling follow a predictable</u> pattern at those levels: they decreased, based on evidence from 746 observations between 1970 and 2014 (Montenegro and Patrinos 2014; Psacharopoulos and Patrinos 2018) from 116 economies, representing more than 98 percent of the world’s population. The returns to primary education have decreased by 21 percent and the returns to secondary have decreased by one-third (Figure 4).

<!-- Start of picture text --> Figure 4: Changes in retuns to education since 1980<br>1.20<br>1.00<br>0.80<br>0.60<br>1980 1990 2000 2014<br>Primary Secondary Higher<br><!-- End of picture text --> Source: Montenegro and Patrinos 2014 10
measure household incomes (for Brazil and China) and household consumption expenditures (for India). (I will return to this difference.) For most of the discussion, a common poverty line is used, set at $1.25 a day converted using purchasing power parity (PPP) exchange rates for consumption in 2005; this is the average poverty line found in the poorest 15 countries. I will also use a line of $2.00 a day at 2005 PPP, which is the median poverty line for all developing counties with the available data (Ravallion et al., 2009). Differences with national poverty lines will also be noted. Poverty is measured by the headcount index, namely the percentage of the population living in households with income per person below the poverty line. Inequality is measured by the Gini index, given by half the mean absolute difference between all pairs of incomes normalized by the overall mean.<sup>2</sup> Growth rates are measures from national accounts. There are a number of issues concerning the data sources, as reviewed in the Appendix.

Table 1 provides summary statistics for all three countries for 1981, 1993 and 2005; 1993 is the mid-point, and is also a natural choice given the changes in the policy regimes of Brazil and India around that time. Notice that the table gives results for both the surveys mean and a “mixed mean” given by the geometric mean of the survey mean and its predicted value based on private consumption expenditure (PCE) per person from the national accounts (NAS); see the Appendix for further details. This method is not considered better, as it makes some strong assumptions (notably that the relative distribution based on the surveys is appropriate for the mixed mean). Rather it provides a sensitivity test, motivated in large part by concerns about the large and growing gap between the survey-based consumption aggregates from India’s National Sample Surveys and those from India’s NAS.<sup>3</sup> The following discussion focuses mainly on the survey-based measures, though noting any important differences with the mixed method, notably for India.

Figure 1 gives the headcount indices for nine reference years. Figure 1(a) is based on the national household surveys, while 1(b) uses the mixed method.

Given the very different initial levels of poverty, I shall measure the rate of progress by the proportionate annual rate of poverty reduction—the difference between the growth rate in the number of poor and the overall population growth rate—rather than in percentage points per

> 2 The headcount index and the Gini index have been the most popular measures in the literature and policy discussions, but they are not necessarily the best. The headcount index does not reflect distribution below the line and the Gini index need not reflect well how distributional shifts impact on poverty.

> 3 Note that the mixed method may well exaggerate the extent to which economic growth (as measured from national accounts) reduces poverty.

3
# **Table 3: Descriptive statistics by province**

|**Province**|**Distance**|**Remote**|**Populatio**|**n PCI **|**Infrast**|
|---|---|---|---|---|---|
|**Aceh**|424|0.037|3,990|2,714|0.45|
|**North Sumatra**|0|0.000|11,600|1,878|0.49|
|**West Sumatra**|460|0.123|4,396|1,617|0.71|
|**Riau**|291|0.078|3,734|4,880|0.35|
|**Jambi**|304|0.082|2,498|1,210|0.58|
|**South Sumatra**|424|0.047|6,512|1,714|0.53|
|**Bengkulu**|566|0.063|1,520|1,069|0.72|
|**Lampung**|195|0.022|6,836|933|0.49|
|**Jakarta**|0|0.000|9,000|6,298|0.98|
|**West Java**|121|0.000|34,900|1,526|0.70|
|**Central Java**|258|0.007|31,400|1,216|0.64|
|**Yogyakarta**|264|0.008|3,040|1,542|0.76|
|**East Java**|0|0.000|35,000|1,566|0.58|
|**West Kalimantan**|607|0.163|3,817|1,667|0.31|
|**Central Kalimantan**|624|0.018|1,837|2,066|0.15|
|**South Kalimantan**|485|0.014|3,032|1,854|0.56|
|**East Kalimantan**|583|0.084|2,543|7,915|0.21|
|**North Sulawesi**|953|0.136|1,982|1,235|0.72|
|**Central Sulawesi**|484|0.069|2,072|1,046|0.54|
|**South Sulawesi**|0|0.000|6,985|1,150|0.51|
|**SE Sulawesi**|367|0.053|1,755|901|0.45|
|**Bali**|317|0.009|3,085|2,223|0.97|
|**West Nusatenggara**|402|0.012|3,843|858|0.76|
|**East Nusatenggara**|726|0.104|3,828|682|0.40|
|**IrianJaya **|2381|0.341|1,633|3,132|0.15|

**Notes:** Population is expressed in thousands. PCI in rupiah at constant prices of 1993, in thousands. Distance is in kilometers, to one of the 5 main cities. Infrastructure is the % of asphalted roads in the province. Remoteness weights distance to the main city by the inverse of the population of the main city. **Source:** BPS and CEIC Data Company Ltd **.**

26