rafmacalaba/datause-encoder-data huggingface dataset:
transfers as a share of total local revenues, which remains constant at about 5 percent<sup>8</sup> (World Bank, 2006).

# **3.5. Discussion**

In this section, we presented an overview of the deconcentration and decentralization systems in selected MENA countries and compared these countries in terms of three important aspect of decentralization: expenditure assignment, revenue assignment and intergovernmental transfers. We also compared these countries to the worldwide practice in expenditure assignments. First, we see a good potential for a rich deconcentrated and decentralized government structure in MENA countries. Most of the countries analyzed have a good number of lower tier governments such as governorates, districts and a variety of municipalities (e.g. 1000 urban municipalities and 68,000 rural municipalities in Iran). Second, MENA countries are significantly behind the worldwide practice in decentralization regarding expenditure assignments. We see in Table 4 that this is particularly true in social services, which is one of the most important government expenditures that directly affect the welfare of residents. Third, West Bank/Gaza seems to have the greatest degree of decentralization from the perspective of both expenditure and revenue assignments. Among the countries analyzed, West Bank/Gaza gives the greatest autonomy to its local governments. While this highlights West Bank/Gaza as an interesting case to consider for other countries in the region, one should approach the decentralization efforts cautiously as the observed decentralization seems to spring from special political and security circumstances of that country. Finally, there is need for improvement in intergovernmental relations in MENA countries. Intergovernmental transfer rules are largely ad-hoc. Our comparative analysis leads us to conclude that 8 This is the average figure for West Bank and Gaza municipalities in the Intifada period.

11
TABLE 111.2. FEDERAL BONDED DEBT GROWTH: 1995-1998

||December<br>1994|December<br>1998|Variation|Percent<br>Variation|
|---|---|---|---|---|
|Federal Bonded Debt<br>(R$ Millions)|60,255|322,624|**262,369**|435.7%|
|GDP (R$ Millions)|537,555|912,456|374,901|69.7%|
|Federal Bonded Debt (% GDP)|11.2|35.4|24.1|216.1%|

Initially, we will aggregate the other accounts in the federal government and Central Bank aggregate balance sheet in three groups, each one of them standing for one of the following reasons to issue federal bonded debt'<sup>8</sup> , as laid out in Table 111.1.

# 1) To finance the federal government's (+ Central Bank's) deficit;

# 2) To accumulate foreign and domestic assets; and

# 3) To repay other previous debts (non-bonded debt).

Item #1 represents the difference in the two net worth figures (a fiscal deficit is a loss, and a fiscal surplus is a profit); item #2, the asset accumulation during the period; and item #3, the decrease in the aggregate of all other liability accounts. Thus, considering the federal bonded debt as the "sources", and the other accounts as the "uses" we can observe these uses on Table III.3 and 111.4, expressed in R$ and percentage of GDP, respectively. 1<sup>9</sup>

> 18Here, we are not determining whether the debt movements resulted from the fiscal, monetary, or exchange rate policy.

'9 The total of the uses (in bold in Table III.2A) is equal to the source variation (in bold in Table 111.1), both equal to R$262,369. Since this value results from a sum of nominal values in R$ during four years with significant and variable inflation, it should be used only as an accounting reference.

16
**_<u>________________________________________________________</u>_**

country and sector.

# **_2.5.1 Information and Communications Technologies (ICT)_**

# _<u>ICT at a Glance Tables</u>_

ICT at a Glance tables provide key data on:

- Country background information includes population, GNI per capita, and adult literacy rates

- ICT infrastructure & access covers access to fixed line and mobile telephones, cost of phone calls, and TVs sets available

- Computers and the Internet presents data on access to personal computers and the internet

- • ICT expenditures shows ICT spending in per capita and absolute terms and as a percentage of GDP

- ICT business & government environment includes survey data on the effect of the Internet in business and government

-

ICT at a Glance tables compile data from several sources, including the UNESCO Institute for Statistics, the International Telecommunication Union (ITU), World Information Technology and Services Alliance (WITSA), World Economic Forum (WEF), Netcraft and the World Bank.

# _<u>OECD Key ICT Indicators</u>_

The 15 ICT indicators below are drawn from various publications and databases produced by the OECD’s Directorate for Science Technology and Industry (DSTI). They will be updated annually on a rolling basis, as data become available.

1. Access lines and channels in total for OECD

2. Mobile subscribers in total for OECD Data compiled from original and/or official 3. Internet subscribers in total for OECD Data compiled from original and/or official

- 4a. Broadband subscribers per 100 inhabitants in OECD countries

- 4b. Availability of Digital Subscriber Lines (DSL) in OECD countries

5. Cable TV subscribers in total for OECD Data compiled from original and official

- 6a.Households with access to the Internet in selected OECD countries

- 6b.Households with access to a home computer

7a. Internet penetration by size class. Percentage of businesses with ten or more employees using the Internet

- 7b. Businesses using the Internet and businesses receiving orders over the Internet

8. Share of ICT-related occupations in the total economy in selected countries 9a.Telecommunication services revenue in total for OECD 9b.Mobile telecommunication services revenue in total for OECD 9c.Telecommunication infrastructure investment in total for OECD 10a. Share of ICT value added in the business sector value added 10b. R&D expenditure in selected ICT industries 34 __________________________________________________________________________________________________________
- e. Social inclusion issues show up in prioritization as recommendations to broaden access to markets and services, but SCDs do not usually recommend special attention to excluded groups, except in some cases, women. There are notable exceptions such as Panama, and some countries in Eastern Europe that address Roma exclusion (e.g. Albania, Bulgaria and Serbia).

- f. There seems to be a link between depth of analysis in the diagnostic and the specificity _as_

- of prioritization of social inclusion. SCDs that identify social inclusion as a priority _well as_ suggest actions to advance it are also those, which have analyzed processes of exclusion and have asked the “why question” upfront.

This final section provides **<u>lessons and directions</u>** for those who seek to integrate issues of social inclusion into future analytical products.

1. **Setting the country context** . Saying that context matters is a truism, but one that needs repetition because social exclusion is indeed more context specific than say poverty, or inequality. The same issues that may be critical in one context may not be so in another. For instance, some countries, such as Bolivia may consider indigenous people as being excluded, but in another country, the indigenous issue may not be as important. Similarly, a current social exclusion issue may not have been as salient a decade ago, hence lending a strong temporal dimension to the context. Thus, the first step in analyzing who is excluded and why is to set the context.

2. **Engaging early** . A SCD is a collaborative effort. The WBG staff develops SCDs in consultation with country partners and other stakeholders. Staff members who have worked on SCDs emphasize the importance of establishing a diverse SCD team early in the process, starting before the concept note stage. Commenting on a concept note or a draft SCD after it is written can be useful, but the likelihood of making significant change, is lower, because SCDs follow a very short timeline. For social inclusion issues to be well reflected, SCD task leadership should bring experts on board early on.

3. **Building collective understanding** . Collaboration across teams is likely to lead to a richer collective understanding of social inclusion from both empirical and policy standpoints. Creative use of household survey data where social experts and poverty specialists collaborate, can lead to the kind of analysis that this paper has highlighted from Bulgaria, Myanmar, Sri Lanka, Panama and other countries. Intensive discussions within SCD teams also orient those members who may not reflect naturally about social exclusion, and help them realize that outcomes in their sectors may be stymied because of the exclusion of certain groups.

4. **Deepening coalitions and in-country partnerships** . While desk reviews and data analysis are an essential first step, they are not substitutes for in-country engagement and consultation. The SCD process can help country teams in deepening partnerships in an atmosphere of trust and transparency, especially around issues that may have earlier seemed too political, too fuzzy or out of bounds. Clearly, such engagements are 25
<!-- Start of picture text --> Fig 2 Orissa: distribution of household<br>operational and holdings, 1953-54 to 1982<br>100%<br>0 0)2 860%% ! e :<br>m<br>40% ~<br>Ml 1-2 ha<br>o 20% l]0.4 -I ha<br>Cu<br>1953-54 1961-62 1971-72 1982<br><!-- End of picture text --> Source: NSS data, reported in Sharma (1994) Agricultural land accounts for 59 percent of the total land area of the state. The share of total land area under various forms of agricultural land use is shown in Table 1. The changing proportions of total operated area accounted for by each size class of land holding is shown in Figure 3, for the period 1953-54 to 1982. Land reforms, demographic and socio-economic change together appear to have brought about little net change in the share of the total operated area accounted for by sub-marginal, marginal and small land holdings (i.e. those of 2 ha or less). Within this broad group, marginal land holdings (0.4 - 1 ha) increased their share of total operated area at the expense of small holdings (1-2 ha). Medium sized holdings (2-6 ha) accounted for the greatest proportion of total operated area, having gained at the expense of large (>6 ha) holdings owing to ceilings restrictions and estates abolition.

<!-- Start of picture text --> Fig 3 Orissa: share of total operated area<br>by holding size, 1963-54 to 1982<br>2! 100%<br>~ 80%<br>a 60% 0 ~ ~ , ~ ~ ~~ > 6 ha<br>.9 40% ~ 2 -6 ha<br>0<br>20 ~1- 2 ha<br>20% Ej 0.4-1 ha<br>0% 00%<br>~~0 ~ 1953-54 1961-62 1971-72 1982<br><!-- End of picture text --> Source: NS S data, reported in Sharma (1 994) 8
**Table 8. Family and income structure by deciles and types of households.**

||average num|ber of people in t|he household|Average net market<br>income,<br>|a|verage share of (in|<br>percent)|
|---|---|---|---|---|---|---|---|
||total|children|pensioners|leu per person per<br>month|wages|remittances|incomes from<br>agriculture|
||||deciles byma|rket income||||
|1|2.0|0.2|1.0|49|0.2|3.6|92.5|
|2|2.6|0.6|0.8|281|6.7|11.8|76.2|
|3|3.2|1.1|0.5|608|23.7|22.4|43.2|
|4|3.5|1.2|0.3|916|37.8|23.3|26.6|
|5|3.6|1.2|0.3|1202|47.9|22.0|18.3|
|6|3.5|1.1|0.2|1514|54.8|19.5|13.7|
|7|3.2|0.9|0.2|1828|60.6|17.4|10.0|
|8|3.1|0.9|0.1|2219|65.3|13.3|7.7|
|9|2.9|0.6|0.1|2863|74.0|12.9|3.7|
|10|2.2|0.3|0.1|4695|76.7|11.2|2.3|
|||dec|iles bymarket inc|omepluspensions||||
|1|4.0|1.7|0.2|385|21.4|18.0|50.3|
|2|3.4|1.2|0.3|658|29.5|16.8|44.1|
|3|3.1|0.9|0.5|758|30.0|17.8|43.7|
|4|2.9|0.8|0.6|846|38.6|10.2|43.6|
|5|3.0|0.8|0.5|1094|44.0|13.9|31.3|
|6|3.1|0.8|0.5|1327|47.8|16.7|26.5|
|7|2.9|0.7|0.3|1708|51.7|18.5|17.1|
|8|2.8|0.7|0.3|2118|58.2|16.5|11.3|
|9|2.5|0.4|0.2|2752|67.6|16.3|7.0|
|10|2.1|0.2|0.2|4533|70.1|16.1|3.2|
||||types of ho|useholds||||
|two adults (or more) and<br>1child|3.7|1.0|0.2|1759|59.8|15.9|15.2|
|two adults (or more) and<br>2children|4.4|2.0|0.1|1475|54.8|15.3|17.5|
|two adults (or more) and<br>3+children|5.8|3.4|0.2|1008|36.1|15.0|31.4|
|one adult and child(ren)|2.8|1.8|0.0|1726|27.4|51.1|12.5|
|onlyworkingage adults|2.1|0.0|0.0|2278|53.3|13.3|23.6|
|only pensioners|1.5|0.0|1.5|568|9.7|12.3|69.1|
|mixed adults,no children|2.5|0.0|1.2|1067|42.3|9.8|41.0|
||||types of l|ocation||||
|bigcities|2.8|0.7|0.3|2272|74.2|10.6|2.5|
|small towns|2.9|0.8|0.4|1659|53.8|15.9|14.7|
|rural|3.1|0.9|0.4|1324|32.5|18.3|41.7|
||||geograph|ic zones||||
|north|2.9|0.8|0.4|1377|35.8|16.9|37.0|
|center|3.1|0.9|0.3|1398|40.0|17.9|31.6|
|south|3.1|0.9|0.4|1377|36.4|20.5|35.5|
|Chisinau|2.8|0.7|0.3|2361|75.0|9.0|3.9|

Source: authors’ calculations using HBS-2017 data.

# **4. Results and discussion**

# **4.1. Main results – incidence of the tax benefit system**

Most households contribute and benefit from the system at the same time, but the net effect is not the same for all. On average, households are net receivers from the tax-benefit system in Moldova - they receive 5 percent of their final income as net benefits from the system (Table 9, Figure 7).<sup>7</sup> The most important components of the benefit system are pensions (21 percent). Other direct transfers, in-kind education and health benefits are relatively less important (5, 6 and 5 percent of final income respectively). The average burden of social contributions and

> 7 This section relies on the scenario when pensions are treated as transfers. The redistribution effect of the tax-benefit system in Moldova is much smaller in the alternative case when pensions are treated as deferred income. The magnitude of redistribution power under that case could be received from Table 8, if market income plus pension is considered as initial level.

18
and formal workers have jobs more amenable to working from home, and that home-based-work amenability is higher in richer countries. Their measures of working-from-home amenability are positively correlated with those of DN2020, but their geographic coverage is constrained by the availability of skills surveys.

The remainder of this paper is organized as follows. The next section briefly reviews existing measures of home-based-work, explains why they may not be appropriate for developing countries, and how we adjust them to appropriately reflect ICT constraints. Section 3 presents estimates of the prevalence of jobs amenable to working from home across countries and shows that poorer countries have more jobs at risk. Section 4 explores implications of COVID-19 for inequality, showing that the pandemic will likely exacerbate both spatial and income inequality since lagging regions have more jobs at risk, and because poorer workers are less likely to be able to work from home. Section 5 presents robustness tests. Section 6 examines which workers are most at risk and demonstrates that labor market risk is inversely correlated with education; skilled workers are more likely to have jobs amenable to home-based work. Workers on temporary contracts, who are more vulnerable to start with, are less likely to have jobs that can be performed from home. Section 7 concludes and points out that our analysis suggests that COVID-19 will likely exacerbate pre-existing socio-economic disparities, both within and across countries.

# **2 Data and methods**

Labor market vulnerability depends on the nature of the jobs that workers have. The main criterion used in the literature is the feasibility of home-based work. Dingel and Neiman (2020) use information from characteristics of more than 900 occupations based on two surveys from the US Department of Labor/Employment and Training Administration’s Occupational Information Network (O*NET). When answers reveal that an occupation requires daily activities such as “working outdoors” or “operating vehicles, mechanized devices,” or “contact with the public,” they determine that the occupation cannot be performed entirely from home. DN2020’s measure, which is based on the Standard Occupational Classification (SOC) system used in the United States, needs to be concorded to the International Standard Classification of Occupations (ISCO08) that is widely used globally at the 2- (or 3)-digit level of granularity (depending on the country). As DN2020 acknowledge, their Home-Based Work (HBW) index is likely to present an “upper 6
Assuming that ε is independently Gumbel distributed and the profit function can be written linearly in its parameters, the probability can be calculated as follows: which gives the probability that farmer _i_ will choose crop j from among J species (McFadden 1981).

The marginal change in probability of selecting a crop with respect to climate is therefore: The marginal probability of choosing a new crop depends on the underlying climate of the farm.

A critical assumption of the multinomial logit is _Independence of Irrelevant Alternatives_ . We assume that the relative probability of any two alternatives is not affected by adding a third choice.

The same discrete choice model applies to irrigation except in this case there are only two choices. In the irrigation analysis, we rely on a logit regression to estimate the probability of irrigation. The logit model predicts that the probability of irrigation can vary between 0 and 1 depending on the values of the independent variables. We again test the influence of climate controlling for a number of other independent variables such as soils, household characteristics, and farm characteristics.

# **III. DATA**

The climate data (monthly temperature and precipitation) were obtained from the National Meteorological Information Center in China. The data are based on actual measurements in 753 national meteorological stations that are located throughout China. The temperature and precipitation data were collected from 1951 to 2001. We rely on the mean values of these variables (climate normal) over this time period for each month. We average the monthly climate data into four seasons in the following way: winter is the 6
**Figure 4:** Weak vs Strong State Capacity (Latinobar ́ometro)

<!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year<br>Strong State Weak State Strong State Weak State<br>(a) Unfair Distribution (b) Generalized Trust<br>2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year<br>Strong State Weak State Strong State Weak State<br>(c) Democracy is Better (d) Satisfied with Democracy<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text --> 10
<!-- Start of picture text --> =<br>Ce)<br>~ industry<br>Gee CRSSSS88SpHbBBBE4 Services<br>°<br>1980 1990 2000 2010 2020<br><!-- End of picture text --> <!-- Start of picture text --> =<br>co<br>+ eo eeee.<br>. or err<br>°<br>2000 2005 2010 2015 2020<br><!-- End of picture text --> <!-- Start of picture text --> =<br>co<br>ge Bag<br>oa a OPCEECES8 85 Sg<br>.<br>°<br>1990 2000 2010 2020<br><!-- End of picture text --> <!-- Start of picture text --> =<br>co<br>+<br>~ ar a<br>°<br>2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> <!-- Start of picture text --> =<br>co fe<br>© ae h z 0)<br>t POSES oy a d<br>a5, See J<br>Ss p<br>N Se, d<br>°<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> <!-- Start of picture text --> =<br>co<br>o<br>+<br>N 9? sd<br>°<br>1990 2000 2010 2020<br><!-- End of picture text -->
# _Indirect Taxes: VATs, Import Taxes, and Other Consumption Taxes_

Indirect taxes on consumption of goods and services are considered through their effective rate derived from each country’s social accounting matrix (SAM). This allows informality to be corrected for when assessing the effect of the various indirect taxes. In reality, because of the high level of informality in developing economies such as Mali and Niger, the statutory rates of indirect taxes cannot provide an accurate estimate of the actual indirect taxes collected because tax evasion can be significant, but the effective rates from the SAM indicate the actual taxes collected, meaning that they correct for informality. In Mali, indirect taxes are estimated by analyzing price multipliers using the 2005 SAM, calibrated and updated based on 2012 macroeconomic indicators; in Niger, indirect taxes are estimated using the effective rates derived from the 2011 SAM.

The direct and indirect effects of the VAT are considered in this assessment. The effective rates applied to each product in the SAM represent the direct effect of VATs on consumers. In an economy with exempt sectors, VATs have indirect effects on prices through VATs paid on inputs. In general, the producers can claim VAT refunds for the inputs they used. The VAT is therefore levied only on the final product, but for sectors that are exempt from the VAT, no VAT is paid directly on final goods, although the VAT has an indirect effect. A VAT paid on inputs results in higher producer prices because producers cannot claim VAT refunds for the inputs they used to produce the final (exempted) goods. In this regard, the input-output matrix is used to determine the indirect effects of VATs in exempt sectors. The total effect of the VAT is equal to the sum of the direct and indirect effects.

Although the richest individuals in Mali and Niger pay the majority of indirect taxes, the poorest individuals pay a nonnegligible amount. The richest decile is the main contributor to indirect taxes in the two countries, paying more than 50 percent of all indirect taxes in Mali and approximately 30 percent in Niger (figure 4.b). Nonetheless, in contrast to direct taxes, the poorest individuals pay a significant amount of all indirect taxes. For instance, the poorest 30 percent pay more than 8 percent of all indirect taxes in Mali and more than 12 percent in Niger. Moreover, total indirect taxes paid is not negligible, especially with respect to the incomes of the poorest individuals. As a share of market income, all indirect taxes represent approximately 7 percent for the poorest deciles in Mali and 4 percent in Niger and approximately 10 percent for the richest decile in Mali and 5 percent in Niger (figure 4.a). In addition, indirect taxes are slightly progressive because they increase overall as a share of market income with increasing income decile.

7
<!-- Start of picture text --> Cost Coverage<br>18<br>1.6 ee<br>1412 WT3 oc ~e se . on<br>21<br>&<br>& 08<br>06<br>.<br>0.4<br>02<br>2010 2011 2012 2013 2014 2015 2016<br>=== Pourashava_cost_coverage = = = WASA_CityCorp_cost_coverage<br>———= Global_cost_coverage<br><!-- End of picture text --> <!-- Start of picture text --> Unit Costs<br>0.7<br>Zz=ovg205a 0.6 eee<br>.@ 04<br>5 0.3<br>© 0.2<br>a#01E SS<br>=) 0 —_—_—“—“—_—a——YS—_s<br>2010 2011 2012 2013 2014 2015 2016<br>== Pourashava_unit_cost = = = WASA_CityCorp_unit_cost<br>=== Global_unit_cost<br><!-- End of picture text --> <!-- Start of picture text --> Average Revenue<br>1.6<br>14<br>zQa 12<br>1<br>3<br>2 08<br>a<br>E06<br>><br>F 04<br>6 —<br>2010 2011 2012 2013 2014 2015 2016<br>=== Pourashava_avg_rev = = = WASA_CityCorp_avg_rev === Global_avg_rev<br><!-- End of picture text --> <!-- Start of picture text --> Collection rate<br>100<br>80 rreeePP teiete ee<br>Py= 60<br>& 40<br>20<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>=== Pourashava_collection = = = WASA LityCorp_collection<br><== Global_collection<br><!-- End of picture text --> <!-- Start of picture text --> Metering<br>90<br>80<br>B EEae ee a<br>al<br>w 60<br>& 50<br>Es<br>= 40<br>+»c 30 SL ,<br>8 20<br>& 10<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>== Pourashava_metering =— + WASA _CityCorp_metering<br>———= Global_metering<br><!-- End of picture text -->