rafmacalaba/datause-encoder-data huggingface dataset:
55 become marginalized in education, housing, employment, and healthcare. For instance, some schools refuse to accept Romani and Balkan Egyptian students, particularly if they appear poor. When admitted, Romani students are often separated physically from the rest of the class<sup>93</sup> .

**Annex Table 2‐ Country statistics High Intensity Conflict Countries (.. indicates data not available)**

|Country‐ High Intensity Conflict|Afghanistan|Syria|Somalia|South<br>Sudan|Iraq|
|---|---|---|---|---|---|
|Population (million)<br>|31.6|22.2|10.5|11.9|34.8|
|GNI, Atlas $ bn|21.4|..|..|11.6|227.3|
|GNI per capita, PPP, $|2,000|..|..|1800|15100|
|Life Expectancy at birth in 2010|59|72|54|54|68|
|Life Expectancy at birth in 2014|60|70|55|56|69|
|Labor force participation rate in<br>2010 (% of population ages 15+)|48|44|56|..|42|
|Labor force participation rate in<br>2014 (% of population ages 15+)|48|44|56|..|42|
|Gross capital formation in 2000 (%<br>of GDP)|12|17|..|..|29|
|Gross capital formation in 2010 (%<br>of GDP)|18|28|..|10|16|
|Gross capital formation in 2014 (%<br>of GDP)|18|..|..|10|18|
|Individuals using the internet in<br>2010 (% of population)|4|20.7|1.3|7|2.5|
|Individuals using the internet in<br>2014 (% of population)|6.4|28.1|1.6|15.9|11.3|
|Merchandise trade in 2000 (% of<br>GDP)|72|44|..|..|..|
|Merchandise trade in 2010 (% of<br>GDP)|35|65|..|..|70|
|Merchandise trade in 2014 (% of<br>GDP)|41|..|..|..|64|
|Electrification<br>%<br>of<br>total<br>population in 2013, *in 2012|43%*|96%|15%|1%|98%|
|Real GDP CAGR 2001‐2010 (years<br>mayvary), %|*9%|N/A|N/A|***5.3%|3.1%|

93http://www.refworld.org/topic,50ffbce528c,50ffbce52f8,58ec8a7e4,0,,ANNUALREPORT,ALB.htm <u>l</u>
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<u>Table 2: Data and sources. Variable Description Units (Yr.) — Source</u> Ag. % GDP Agriculture value added as % GDP. % (2005) WDI Col_* Equal to one if a country was colonized by *(UK, NA CEPI France, Spain, Portugal, the Netherlands, or Russia). Distance Average of the great circle distances between the Km CEPI main cities of the exporting country and Germany, weighted by population shares. Entry Cost Official cost of starting up and formally operatUSD (2006) Doing Business ing an industrial or commercial business in the exporting country. Export Cost Official fees levied on a 20 foot container leavUSD (2006) Doing Business ing the exporting country. Includes document preparation costs, administrative fees for customs clearance and technical control, terminal handling charges, and inland transit. GDP Gross domestic product. USD (2005) WDI GDP Defi. GDP deflator. % (2005) WDI GDPPC Per capita GDP. USD (2005) WDI Lat. Latitude of the main city in the exporting country Deg. CEPI (absolute value). Lines Number of 8-digit product lines in which a counNA (2005) — Eurostat try has strictly positive exports to the EU. Lines_CN2 Number of 8-digit product lines in a 2-digit sector NA (2005) — Eurostat for which a country has strictly positive exports to the EU. Manuf. % GDP Manufacturing value added as % GDP. % (2005) WDI Real Int. Rate Real interest rate. % (2005) WDI Tariffs Average applied ad valorem tariff by HS2 sector. % (2005) MAcMap Aggregated from 6-digit data using the reference group methodology of Laborde et al. (Forthcoming).

34
39 Table A2.4: Shares of total household expenditure on LPG for all households and for purchasing households in Guatemala (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|<br>**Rural**|**User households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.1|0.8|0.2|6.2|6.8|6.6|
|**2**|0.3|1.7|0.8|4.1|4.4|4.3|
|**3**|0.8|2.5|1.6|3.8|3.6|3.7|
|**4**|1.3|2.3|1.9|2.8|2.7|2.8|
|**5**|1.0|1.4|1.3|1.6|1.6|1.6|
|**All**|0.6|1.8|1.3|2.7|2.5|2.5|

_Source:_ Authors’ calculations.

# **India (National Sample Survey, 2004–05)**

The data are taken from the 61<sup>st</sup> round of the National Sample Survey of India administered by the government’s National Sample Survey Organization. The fieldwork for the survey was carried out between July 2004 and June 2005.

The value of all non-purchased items was about 14 percent of total purchased expenditure, while non-purchased food accounted for about 82 percent of all non-purchased items. The former ratio tended to decline at the upper quintile levels. The latter was approximately constant across the quintile groups. The value of non-purchased non-food items may be underestimated, as the only expenditure values reported for non-purchased non-food items were fuel and lighting items and some durable goods. The expenditures on all items (both food and non-food items) were not deflated because adjustment factors were not available.

The household survey asked how much LPG the household had consumed in the previous 30 days, and also what the main cooking fuel was. Figure A2.1 shows the distribution of households by main energy source for cooking. LPG was the most common source of fuel after firewood and chips. Kerosene was used by a small proportion of the population, while electricity was scarcely used.

Figure A2.1: Main source of energy for cooking across all households in India <!-- Start of picture text --> Firewood and chips 61<br>LPG 22<br>Dung cake 7.1<br>Kerosene 3.7<br>Coal 1.4<br>Gobar gas 0.2<br>Electricity 0.1<br>Charcoal 0<br>Others 2.8<br>No cooking arrangement 2.2<br>0 10 20 30 40 50 60 70<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.
# **3 Study Design**

This study involved 1,210 small and medium-sized businesses spread across urban and periurban areas in Tanzania. These businesses were randomly selected within each of their representative wards and can be considered representative of typical small and medium-size business in these locations, covering sectors such as retail and wholesale trade, manufacturing, education, accommodation and food service activities, human health and social work, and other services. The sample design ensured representation across different business sizes, including small businesses with 1–4 employees, medium-sized enterprises with up to 49 employees, and various economic activities.

Dar es Salaam had the highest concentration of surveyed businesses, reflecting its role as a major commercial hub, while regions like Katavi and Simiyu had smaller sample sizes, highlighting regional disparities in business density.

The selection process involved stratifying businesses by size and sector to ensure comprehensive coverage of Tanzania’s economic landscape. For instance, the manufacturing and retail sectors accounted for the largest shares of businesses sampled. The distribution of businesses also considered regional and sectoral diversity, capturing variations in business operations and economic activities. In total, these businesses originated from 119 wards in 84 districts and 25 regions.

All of these business completed a 20 to 30 minute in-person survey providing details about their business activities and attitudes towards paying tax. The survey was in the field for a period of 15 days inclusive of weekends from December 8 to December 22, 2022. Most of the firms that were surveyed had a monthly turnover of less than one million Tanzanian shillings and were predominantly from the wholesale and retail sectors (see the appendix for more details about the sample).

We embedded a clustered randomized field experiment as part of this survey activity. In this experiment, a randomly selected subset of businesses completed the survey with tax officials present. Specifically, the random assignment took place at the ward level, stratified 10
Policy Research Working Paper 8437

# **Abstract**

The technical complexity of ensuring that tax rolls are complete and valuations current is often perceived as a major barrier to bringing in more property tax revenues in developing countries. This paper shows how high-resolution satellite imagery makes it possible to assess the completeness of existing tax maps by estimating built-up areas based on building heights and footprints. Together with information on sales prices from the land registry, targeted surveys, and routine statistical data, this makes it possible to use mass valuation procedures to generate tax maps. The example of Kigali illustrates the reliability of the method and the potentially far-reaching revenue impacts. Estimates show that heightened compliance and a move to a 1 percent ad valorem tax would yield a tenfold increase in revenue from public land.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at dali1@worldbank.org, kdeininger@worldbank.org, and mwild@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
Figure O8: Changes in synthetic SNAP benefits per population, SUA, energy usage and prices, and temperature by state, Farm Bill (a) Changes in synthetic benefits per population and HCSUA, Farm Bill (b) Changes in synthetic benefits per population and temperature, Farm Bill <!-- Start of picture text --> 20 ME NHVT 20 VT ME NH<br>OH CT CT OH<br>MA OR MA OR<br>15 UT KSKYMNOKMTLAWVNYTNNCRIILSCINGAIDARMDFLIA NVMOPAMSCASDAZMI NDDCWI NE ALTXWA CO DE VA N MJ 15 ND MI NYNJPAWI DE RIMDWVVADCILINNCKYTNSDSCFLGAARMNALMSIAMOLAOKTX NE KS CAAZNV NM MTWA IDCOUT<br>10 10<br>WY WY<br>5 5<br>-20 0 20 40 -20 -10 0 10 20<br>(% change) HCSUA per pop (% change) Average temperature<br>Fitted values % change in synthetic benefits per population Fitted values % change in synthetic benefits per population<br><!-- End of picture text --> (c) Changes in synthetic benefits per population and total residential energy usage per population, Farm Bill <!-- Start of picture text --> 20 ME NH V T<br>OH CT<br>MA OR<br>15 PA WIIDNYTNMIINILNVNMAR NE ALMOIACOAZMNOKMTMSSCKYUTCAGANJVANDKSNCSDTX DE WAMDFL LARIDC WV<br>10<br>WY<br>5<br>-10 -5 0 5 10 15<br>(1-mo % change) Total energy residential, Btu per pop, SNAP FY<br>Fitted values % change in synthetic benefits per population<br><!-- End of picture text --> (d) Changes in synthetic benefits per population and residential energy prices, Farm Bill <!-- Start of picture text --> 20 ME VT NH<br>CT OH<br>MA OR<br>15 LA RI IL ND MTMNNYSDNM PAOKTXWICODIAMSNJ C A ARKS NE GAIN DE MDKY NCMIMOVAWAALTNUT SCWV IDNVFLAZ<br>10<br>WY<br>5<br>-20 -15 -10 -5 0 5<br>(1-mo % change) Total energy residential, price per MBtu, SNAP FY<br>Fitted values % change in synthetic benefits per population<br><!-- End of picture text --> (e) Changes in synthetic benefits per population and electricity usage per population, Farm Bill (f) Changes in synthetic benefits per population and electricity prices, Farm Bill <!-- Start of picture text --> 20 VT ME NH 20 ME VT NH<br>OH CT CT OH<br>MAOR MA OR<br>15 IL TNNJIN KY MOMI KSCORI ARWIIAAL DE UTDCNVPACAOK NE AZNYMNGASCWV MDMSID VATXNCWA NMFL SD MTLA ND 15 LA RI OK TXNY MTMSARCO NMND DE WAILGAALSDPAUTMNWINJAZTNNCIAKYSCFLMOCAINKSDCNVMIMD NE VA IDWV<br>10 10<br>WY WY<br>5 5<br>-6 -4 -2 0 2 4 -20 -10 0 10<br>(1-mo % change) Electricity residential, Btu per pop, SNAP FY (1-mo % change) Electricity residential, price per MBtu, SNAP FY<br>Fitted values % change in synthetic benefits per population Fitted values % change in synthetic benefits per population<br><!-- End of picture text --> 89
|Table 4.3: Reasons for Youth not to A|ttend School,2001(percent)|
|---|---|
|Too costly|38.8|
|Not interested in school|10.2|
|Illness|4.5|
|Repeated failure|4.2|
|Left school for marriage|3.0|
|Family does not allow girl|2.5|
|Family disintegration|2.4|
|Housework|1.9|
|Work to support family|1.3|
|Other reason|31.2|

_Source:_ Own calculations based on ECVH 2001.

Besides the self-reported reasons for not attending school there is generally a strong connection between parents’ level of education and that of their children. This is the case as more educated parents tend to have more information, know the educational environment and what it requires, motivate their children to learn, and have better connections or sometimes better abilities. Moreover, there is a connection because of the correlation between parents’ education and wealth, i.e., they can afford schooling. The situation in Haiti confirms this hypothesis. Figure 4.1 shows the proportion of uneducated household heads in relation to youth enrolled in school or not. Clearly the heads of household<sup>16</sup> of youth who do not attend school are less educated than the heads of those who attend. Compared to household heads with youth in the household attending school, 23 percent more of household heads with youth in the household out of school have no primary education.<sup>17</sup> It is also clear from Figure 4.1 that education of household heads is not only decisive for youth undertaking education or not, but also for how long time youth spend in school, and thus the level of education they achieve. Those who stay longer in the school system come from households with higher educated heads.

A multivariate probit model is used to examine school dropout in further detail. The probit model measures the marginal impact of a set of characteristics on the probability of school dropout and push out (henceforth dropout).<sup>18</sup> Data only allow a cross-section analysis, thus not taking dynamics into account such as changes in the schooling system over time. This model’s results should be seen as correlates since causality is not established. The analysis will help target specifically vulnerable groups of youth. Regressing dropout on a set of variables gives the findings presented in Table 4.4.

> 16 Because many children live away from their biological parents (see Section 5) the household head is more likely to have a parental role. Because of this and matters of consistency in the data, household heads are used instead of parents. The results are conditioned on the household head not being a youth. 17 On average for each year and group of youth.

> 18 School dropout and push out are not directly observed in the sample. Thus, youth out of school is used as a proxy for dropout and push out.

12
improvements in women's education also contributed to reducing child mortality rates and improving their children's education (Iregui-Bohórquez, et al., 2021). Since the late 1990s, the estimated coefficients have decreased, suggesting that the effect of infant mortality on female labor participation is losing importance over time as the infant mortality rate is reduced.

**Figure 9: Effect of the infant mortality rate on the female labor force participation rate**

<!-- Start of picture text --> 2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Infant mortality rate<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

# **iv) Marriage rate**

We also consider the changing relationship between the marriage rate (per 100,000 inhabitants) and women's labor participation in Colombia (Figure 10). From 1960-1979, a mild positive correlation was observed between these two variables, suggesting a moderate positive effect of higher marriage rates on labor force involvement from women during this period when marriage was more prevalent. However, a significant shift in this relationship occurred from 1980 to 2009. The coefficient dropped to -0.36pp in the 1980s, deepened to -1.06pp in the 1990s, and settled at -0.42pp in the early 2000s. This negative partial correlation indicates that as the marriage rate decreased, there was a corresponding increase in women's labor participation. According to Valdés and Gomáriz (1993), the proportion of cohabitating couples increased from 8% in 1970 to 13% in 1985. Moreover, Esteve, Saavedra, López-Colás, López-Gay and Lesthaeghe (2016) found that Colombia was a notable example of the surge in unmarried couples choosing to live together. In fact, over the period of 1973 to 2005, the percentage of women aged 25 to 29 who opted for cohabitation rose dramatically from 20% to 66%. This trend towards cohabitation could have contributed to the marginal decrease in marriage rates while enabling women to participate more fully in the labor force. However, the increase in marriage rates since 1990 coincided with the approval of divorce by Law 25 of 1992. As explained by Iregui Bohórquez et al. (2021), before the enactment of this law, many couples chose to 17
conservation and also provide a source of funds for urban planning and for public transportation investments.

China is already the second largest emitter of greenhouse gases, after the U.S., and is projected to emerge by 2010 as the largest emitter. So, an important question for the world will be whether China and the U.S., together with the other major industrial producers, can cooperate on energy efficiency and control of greenhouse gases. Under any scenario, demand for oil and gas (and hence prices) are likely to remain high, and the global climate is likely to become warmer. With sound energy policies and new technologies, the world can achieve a result of high but stable energy prices and mitigation of climate change. Without better cooperation and policies, on the other hand, the world could face some bad scenarios of climate change and competition over ever scarcer oil and gas resources.

# **_C. Can the transformation be harmonious?_**

China should be praised for achieving the most rapid poverty reduction in history. The World Bank estimates that the number of people living on $1 per day (measured at PPP) declined from over 600 million at the beginning of economic reform to 135 million in 2004 (World Bank, 2007). The country is on track to eliminate $1 per day poverty by 2015. While poverty has been reduced, however, there has been mounting inequality in recent years that has generated social tension. Roughly speaking, urban real incomes have been growing at about 12% per year while rural real incomes have grown at 5–6%. The rural-urban income gap in China is one of the largest observed in the world, fueling massive migration from the countryside to cities. Already about 200 million people have 20
deflate nominal expenditures into "real" expenditures, we have to be very careful in defining how exactly "real" is calculated.<sup>2</sup> Table 1 illustrates the problem. Median nominal consumption expenditures increased by 1 10 percent from February 1996 to February 1999. How much did median "real" expenditures rise? If one made the mistake of defining "real" expenditures as purchasing power over non-food items only, then median "real" expenditures have actually _risen_ by 16.2 percent. If, in contrast, a price deflator was defined only as purchasing power over rice, then "real" expenditures have fallen by 26 percent. But this "rice only" deflator is just as unrealistic as a "non-food only" as all households actually consume a mix of goods.

If one uses the standard approach and deflates nominal expenditures by the consumer price index (CPI), this implies that "real" expenditures were only 1.3 percent _lower_ in February 1999 than in February 1996. But the share of food in the CPI basket (which is around 40 percent) is much lower than in actual consumption expenditures as recorded in the Susenas, and certainly understates the importance of food for the poor.<sup>3</sup> If one constructs a price index using the CPI price series but with weights for prices based on the actual consumption basket of the poorest 30 percent of households then inflation in that consumption basket was 136 percent and median "real" expenditures fell 10.9 percent. This sensitivity of the measurement of "real"

> **2** See Suryahadi and Sumarto (1999).

> 3Susenas is the "National Socio-Economic Survey" conducted by Statistics Indonesia (BPS). The "Core" of this survey, which contains summary characteristics of households and individuals, is conducted annually. The detailed "Consumption Module" of the survey, which forms the basis for the official poverty statistics, is conducted every three years. For more details about Susenas, see Imawan and Ahnaf (1997).

_5_
number of observations, mean, standard deviation, and quartiles for each of the variables described above. The average number of tax inspections is just under 2 and 62% of firms receive at least one tax inspection visit in the year. The average compliance rate with taxes is 81%, and 57% of firms report full compliance. The average informal payment corresponds to 1.6% of firms’ sales and 26% of firms make such informal payments. Since the survey over-samples from larger firms it is not surprising to see that 22% of firms are exporters. Since the sampling frame is defined at the two digit ISIC level, manufacturing firms, which occupy a disproportionate share of the ISIC 2 categories, represent 58% of the sample of 3-digit industries. Finally, the average ISIC3 industry has 69 workers in the WBES, while the average industry has 53 workers in the 2001 US Census.

Two points worth highlighting concern sample size and survey weights. First, while the core tax inspection variable is always available, data on tax compliance are only available for earlier surveys.<sup>15</sup> Second, when possible we apply survey weights. However weights are missing from some early surveys. The core tax inspection results are drawn from the sample with survey weights, but to preserve sample size, we report results on tax compliance without dropping observations with missing weights. We show in the appendix that results remain unchanged in the smaller sample with full survey weights.

# **3 Identification and Econometric Specifications**

## **3.1 Identification and First-Stage**

Our objective is to estimate how firm size impacts tax inspection:

> 15The question on formal tax compliance was dropped from the harmonized survey after 2007. In surveys after 2007 administered in Angola, Botswana, Congo (Dem. Rep), Ethiopia, Iraq, Mali and Rwanda, we extracted the tax compliance question from the non-harmonized raw data.

10