rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 9. Effect of Fiscal Policy on Inequality in Mali and Niger, 2014**

<!-- Start of picture text --> 0.491 0.486 0.482<br>0.469<br>0.337 0.327 0.326 0.318<br>Mali Niger<br>Market Income Disposable Income Consumable income Final Income<br>Source: Authors<br><!-- End of picture text --> The redistributive effect of fiscal policy in Mali and Niger is lower than in other countries. In a set of 11 developing countries, the redistributive effects in Mali and Niger are below the average, with Niger having a better redistributive effect than Mali (Figure C.2). In the set of selected countries, only three (Bolivia, Indonesia, Zambia) perform worse than Mali. The other countries achieve a higher level of fiscal redistribution, with South Africa having the best fiscal redistribution.

The marginal contributions to inequality reduction of the various fiscal interventions show that (direct and indirect) taxes and basic education (primary education for Niger) are the most redistributive interventions in Mali and Niger. Direct transfers achieve a low level of redistribution in Mali given that they account for a small share of total spending, and they increase inequality in Niger given that they mostly benefit the richest, as explained earlier (Figure C.3). In addition, agricultural subsidies reduce inequality, although they have a low redistributive effect, especially in Niger. In contrast, energy subsidies (gas and electricity) increase inequality in Mali. The country’s fiscal policy would have had a greater redistributive effect without energy subsidies. Education and health spending also reduce inequality, with the exception of spending on higher education, because most of the individuals receiving higher education are from the richest deciles in developing countries in general.

Fiscal policy increases impoverishment in Mali and Niger **.** For instance, the poverty rate (measured using the national poverty line) rises by 5.9 percent (2.4 percentage points) in Mali and 5.5 percent (2.5 percentage points) in Niger from market income to consumable income because of the impoverishing effect of indirect taxes (figure 10). The trend of poverty variation observed is similar to that of other African countries. From market income to disposable income, poverty rates decrease as a result of the combined effect of direct transfers and taxes, but when indirect taxes 15
We present direct estimates of transport costs for Central Asia in Section 2.1.. The internal infrastructure of Central Asian countries is analyzed in Section 2.2., while the specific costs of landlockedness are discussed in Section 2.3.

# **2.1   Direct estimates of Central Asian transport costs**

Direct estimates of transport costs have been gathered from a survey of transport firm managers along various routes linking Europe and Central Asia (see Raballand et al., 2005). They show that the cost of transporting a 40’ container by road rises by and large linearly with the distance; a slightly higher derivative was recorded for the Central European leg of the journey (between Warsaw and Moscow). However, the cost of transporting a 20’ container by rail rises less than proportionately with the distance after the Moscow leg. Although Raballand et al. do not offer any reasons why this is the case, it is likely that these cost differences reflect the politically motivated subsidization of freight rates in the former CIS.<sup>6</sup> The same picture emerges for transport times. Unlike costs, time rises more than proportionately with distance after the Moscow leg, which reflects more difficult conditions for both rail and road cargo. Similar observations have been made for the trade route between the US and Central Asia.<sup>7</sup> Aside from a change in transport costs and time per kilometer, it is worth noting that long distances produce extremely high transport costs (USD 6,000 for a 40’ container from Paris to Tashkent, a prohibitive level for most types of cargo).

# **2.2 Internal infrastructure: a common component of transport costs across all countries**

The relation between infrastructure and trade are supported by both theoretical (e.g. Bougheas, Demetriades and Morgenroth, 1999) and empirical evidence (see Limao and Venables, 2001). Limao and Venables (2001), using a combination of shipping data and

> 6 It should be noted, however, that freight rates on routes between Moscow and Central Asian capitals are differentiated in terms of cargo, with low rates applying to primary product shipments (e.g. cotton from Uzbekistan) from Central Asia to Russia. We are grateful to Eskender Trushin from the Tashkent Bureau of the World Bank for pointing this out.

> 7 For instance, Stone (2001) notes that freight rates per mile are twice as high for a container shipped to Bishkek (Kyrgyzstan) than for one shipped to Ankara.

6
employment behaves very similarly across eligible and ineligible industries – it follows the same trend overall and also displays small seasonal jumps in both sets of industries. Moreover, the drop in employment during the recent crisis, which started in mid‐2008, looks similar in the pre‐program period across eligible and ineligible industries.

In the remainder of the paper, I thus use the sample of 34 caliper matched industries to measure the effect of the wage subsidy program on employment (appendix table 1 lists these industries and their names).

# **4. Results**

# **4.1. Estimation equations**

I use the following equation to estimate the effect of the wage subsidy program on employment ��� �∝���� ����������� ∗�������������� ����������� ∗������������� ���� (1) where yit is log employment in industry i and month t, αi is an industry fixed effect, βt is a month fixed effect. The variable Eligiblei is equal to one for all eligible industries and equal to zero for ineligible industries. The variable DuringProgramt is equal to one for January 2009 through August 2009 and zero for all other months, and the variable AfterProgramt is equal to one for September 2009 through November 2013 and zero for all other months. εit is an error term, clustered at the industry level. Equation 1 thus estimates the effect of the program on employment while the program was in operation and also the effect after the program ended. I also use the following related specification, to estimate the effect of the program for each post‐program year separately For both equations 1 and 2, I conduct two types of robustness checks. First, I add the fourth root of US imports (from all countries) in industry i and month t as a control variable.<sup>8</sup> Controlling for US imports addresses the concern that employment in eligible and ineligible industries may evolve differently in the post‐program period if demand for Mexican exports in these industries behaved differently. In this case, I would attribute differences in employment to the program, whereas it was actually driven by differences in export demand. I control for US imports since the US is Mexico’s largest trading partner. I chose to control for US imports from all countries instead of US imports from Mexico only since the former variable is less likely to be affected by the Mexican wage subsidy program, i.e. it is more likely to be exogenous to the program. As mentioned above, data on US imports is not available for all 34 industries in the sample. Due to mismatches in industry classifications I only have data on US imports for

> 8 I use the fourth root since it mimics a log function, but does not drop zeros and 2.72 percent of the observations are zeros. Using log of US imports instead gives very similar results.

8
|To pursue an interest, hobby, or passion of mine|-0.024<br>(0.02)|-0.024<br>(0.02)|
|---|---|---|
|I was unhappy with my previous job|-0.004<br>(0.03)|-0.004<br>(0.03)|
|To have a work and life balance satisfying my needs|0.015<br>(0.02)|0.017<br>(0.02)|
|Role model was a man (Unconditional)|0.019*|0.017|
||(0.01)|(0.01)|
|Spouse helps|0.009||
||(0.01)||
|_Ways in which your spouse helps_|||
|He contributed money||-0.024***<br>(0.01)|
|He gave me advice||0.012<br>(0.01)|
|He helped me register with authorities and get a license||0.043***<br>(0.01)|
|He is co-owner||0.115***<br>(0.03)|
|He gives me moral support||-0.119**<br>(0.06)|
|He helped me as labor force||-0.031<br>(0.06)|
|||0.150***|
|All of the above||(0.05)|
|Other||0.020|
|||(0.02)|
|Constant|0.177***|0.209***|
||(0.05)|(0.05)|
|Observations|5,916|5,916|
|Region FE|NO|YES|
|Developed countries|YES|YES|
|Developing countries|YES|YES|
|Adjusted R-squared|0.02|0.03|
|Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1|||

16
- is prone to **_manipulation_** _by powerful interests -_ it is common for people to encroach on private (or common) land during settlement camps in order to have a larger area recorded in their names; or to attempt to influence settlement officers to show a larger area on the village map. Settlement operations often result in increasing landlessness for the poor since it is easy for the more powerful to buy off settlement officers in their favour, leading to dispossession of poorer and weaker groups; and

- is likely to become _irrelevant with computerisation -_ settlement operations will become unnecessary if the mutation system is improved to as to permit regular and more rapid updating of land records (section 4.1.3).

These shortcomings raise serious doubts as to the need to persist with settlement operations which might have outlived their utility. Some of these shortcomings and their distributional impacts are highlighted in a case study of farmers' experiences in Khurda and Ganjam districts in Box 7.

# **3.4 Conclusion**

Land settlement is a carry-over from the British period when its main purpose was revenue assessment at what were then lucrative levels for the colonial administration. Under _zamindari_ tenure, in which single proprietors possessed large estates, the State revenue was assessed on the ascertained or assumed rental value. The revenue, though fixed with reference to acreage rates on the land actually cultivated, was assessed on, and payable by, the estate as a whole. The assessment remained unchanged for the period of the settlement. The proprietor could bring as much of the wasteland under cultivation as desired, and it was only on re-assessment at the end of the term of the settlement that the state could obtain any increase of revenue on account of the extensions of cultivation during the settlement period. The regular practice of revenue assessment, updating and maintenance of land records were absent in the _zamindari_ tracts since the tenants and sub-tenants were 'tenants-at-will' and as such had no heritable or transferable rights in land. By contrast, land revenue administration systems in _khasmahal_ and _ryotwari_ tracts were in much better shape.

Periodic settlement operations were the most cost-effective way of settling a large area in a short time. There was little need for sporadic settlements because land was rarely transferred or partitioned<sup>17</sup> . Moreover, settlement made sense when new lands were being brought under cultivation, i.e. without any transfer of ownership. The survey and settlement process may have outlived its utility is the same work can be done more efficiently by a smoothly functioning registration and mutation process. The system of deeds registration is examined in the next section.

> 17 This is not to sugges **t** hat land sales markets were absent during the late 19th and early 20th centuries. There were several classes of tenancies in _zamindari_ areas (especially privileged tenancies based on service) that were heritable. Rent-collection contracts were also bought and sold (K.C. Shivaramakrishnan, personal comm.).

26
sector, including crop insurance. Entry into the insurance market by private sector providers finally began in 2002 when twelve private sector insurers entered the market.

All subsectors of _transport services_ were operated primarily by public sector companies prior to liberalization. Air transport was run by two publicly owned carriers, states controlled the ports for maritime industries, and a large segment of the shipping sector was heavily regulated and dominated by publicly owned companies. In 1997, foreign direct investment up to 40 percent was allowed in airlines, 74 percent foreign direct investment was allowed in port construction, and private sector companies were allowed to contract for infrastructure maintenance and construction. In air transport, for example, the remarkable increases in passenger traffic can be attributed almost entirely to private entrants (OECD, 2011). Yet transportation sectors remain subject to state level regulations which vary significantly across states, with trucking particularly susceptible to local political pressures.

_Professional services_ including accounting, legal, and other services sectors such as _retail distribution, postal_ and _rail transport_ services are formally closed to foreign participation.<sup>8</sup> FDI is not allowed in the accounting and legal sectors. Within distribution services, FDI is not allowed in the retail segment (with some narrow exceptions, such as single-brand retail) but there are no limits in other areas, except the requirement of approval for commission agents, franchising services and wholesale trade. The closed sectors are characterized by domestic firms that are sub-optimal in size and handicapped by an inhibiting and weak regulatory environment. Many Indian services in closed sectors are highly fragmented by international standards.<sup>9</sup> Here adjustment and employment concerns are the dominant factors impeding liberalization.

A more detailed survey of the liberalization reforms is provided in on-line Appendix A (attached at the end of the paper).

> 8 As an exception to this general rule, single-brand retailers are allowed.

> 9 For example, there are 100,000 chartered accountants in India and 43,000 audit firms, with an average of two chartered accountants per firm as compared to an average of between 350 and 1500 chartered accountants in the typical affiliates of the “big four” accounting firms. In retail distribution, the penetration of supermarkets in India is only 2 percent compared to 55 percent in Malaysia and 36 percent in Brazil (World Bank, 2004).

11
vaccination. There is overwhelming willingness among those vaccinated to do so: In Malawi (80.7%), Tanzania (76.4%), and Nigeria (72.1%), more than 7 out of 10 vaccinated individuals would be “very likely” to encourage others to get vaccinated. In Burkina Faso, most are either very likely (22.9%) or somewhat likely (58.4%).

_Figure 6: Decisionmaker about vaccine uptake among adult household members._ <!-- Start of picture text --> Burkina Faso Malawi<br>0.1% 0.6%<br>42.3% 39.4% 42.0%<br>50.9%<br>17.9%<br>6.6%<br>Nigeria Tanzania<br>0.3%<br>6.0%<br>18.3%<br>37.1%<br>6.6% 44.4%<br>69.0%<br>18.2%<br>Each adult for themselves All adults together Household head Other household member<br><!-- End of picture text -->

# **Discussion**

## **Contribution**

Since the start of efforts to vaccinate the world population for COVID-19, vaccine coverage in Sub-Saharan Africa has trailed the rest of the world. In this study, we use data from five national phone surveys conducted by each country’s national statistical agency to provide comprehensive, cross-country comparable, and timely insights into the factors that hold back the progress of vaccination campaigns. Our findings on vaccine hesitancy, uptake, last-mile delivery barriers, and possible promoters of vaccine demand come at a critical moment for vaccination campaigns: vaccines are now more widely available in the region but have generally failed to reach beyond a fraction of the population.

Our findings supersede potentially outdated results from previous studies that were conducted before the wider availability of vaccines<sup>14–16,25,26</sup> and add an up-to-date empirical basis to recent discussions of vaccine hesitancy and last-mile delivery barriers in the uptake of vaccines.<sup>3–8,10,12,13,27,28</sup> The analysis spans all five dimensions of vaccine hesitancy that have been hypothesized to underly the low take-up of vaccines in SSA and elsewhere<sup>17</sup> : Trust in the safety and efficacy of vaccines ( _confidence_ ), perceived severity of COVID19 and risk to fall ill ( _complacency_ ), access to COVID-19 vaccines ( _convenience_ ), the level and sources of information ( _communications_ ), and sociodemographic characteristics ( _context_ ). Furthermore, we provide insights into the last-mile delivery barriers that have kept vaccine-willing people from getting vaccinated 10
6 It is necessary to go beyond primary agriculture, however, when evaluating the consequences of reforms under WTO. This is because the WTO negotiations on agriculture involve potential liberalization of a wide range of processed agricultural products as well. In OECD countries domestic subsidies are not paid to processed agricultural products, but the extent of border protection to processing activities is substantial – even after netting out the effect on processors’ costs of having to pay higher than border prices for many primary agricultural product inputs.<sup>7</sup> According to the GTAP database, in 2001 that assistance amounted to $198 billion, which is greater than the estimated $136 billion in total support to primary agriculture for that year (final row of Table 1).<sup>8</sup> The remaining important element to consider is the market price support provided to the agricultural and food sectors of non-OECD countries. At $76 billion for primary agriculture plus $82 billion for food processing, this support is a substantial addition to the support through import barriers of $46 billion provided to OECD agriculture and $198 billion to OECD processed food (row 2 of Table 1). But there are almost no export subsidies in non-OECD countries (row 3 of Table 1), further increasing the prominence of market access.

In summary, the OECD and GTAP databases are very similar in their estimates of the extent of direct support to farmers in OECD member countries (columns 1 and 2 of Table 1); but the GTAP database also includes support via the food processing sector in those countries plus the support to both sets of activities in non-OECD countries. In total, the GTAP database suggests only 19 percent of the dollar value of the transfers to those producers from taxpayers and consumers is in the form of domestic support and only 6 percent comes via export subsidies (see final column of Table 1).<sup>9</sup>

> 7 Again this follows the effective protection concept mentioned above in footnote 5. 8 As well, any given nominal protection rate for agricultural processing activities delivers a higher rate of effective protection and hence a greater welfare cost than the same nominal protection to primary agriculture, other things equal. This is because of the difference in value added shares of output (VASO) in the two sets of activities. According to the GTAP model, VASO is 32 percent in processing and 45 percent in primary agriculture in OECD countries, while in non-OECD countries the difference is even greater: 24 percent compared with 54 percent, making for global averages of 30 and 50 percent, respectively.

> 9 The value of the transfer to producers via export subsidies, as reflected in the GTAP database, is estimated at $28 billion globally (row 3 of Table 1). This includes not just the budget cost of export subsidies ($4.4 billion in OECD and $0.5 billion in non-OECD countries) but also the transfer from domestic consumers to producers, because export subsidies raise consumer prices as well as producer prices.
PSP). Some commonly used measuring rods do not do this (such as the mean monetary transfer, or the change in the number of the poor). Without imposing an explicit formula for aggregation, much can be learnt by forming a simple tabulation-a quick and dirty incidence of benefits table-of what the likely monetary gains will be for each broad subgroup of the poor-"ultra-poor", "poor", "nearpoor". The ability to make clear quantitative assessments will vary greatly, depending on data availability. However, a reasonably well researched qualitative picture may often be informative.

One should also look closely at how benefits can be enhanced, and costs reduced, such as through more care in designing and implementing targeted schemes.

# 4.2.3 Design and implementation can help reduce targeting costs

A key lesson from experience and many of the studies under review, is that the costs and benefits from fine targeting depend critically on program design. Subsidizing a food staple heavily consumed by the rich, or setting the wage rate too high in a public works scheme, can really destroy targeting performance. Under ideal circumstances the policymaker would know incomes and distribute transfers to eliminate poverty accordingly. All of the poor and none of the nonpoor would be covered and the cost of bringing everyone to the poverty line would exactly equal the poverty gap. In practice, the most extreme form of targeting, means testing, is difficult and costly to do well in developing countries. Incomes are particularly hard to measure in poor agricultural settings where they are also often subject to extreme variability from one season to the next. Effective means testing would require collection of detailed and comprehensive information coupled with continual updating and verification. This is well beyond the capabilities of most administrative agencies. Reliance on local agents with intimate local knowledge has sometimes been found to work well, and sometimes to flounder in nepotism and corruption. Ravallion and Datt (PSP) provide evidence on the feeble connection between living standards and benefits received from the Integrated Rural Development Programme credit 38
sample set includes 19,398 households, 8,469 of which are rural. The information was collected between February and March of 2010.

The LCMS VI also identifies beneficiaries of public spending programs, though not always precisely. The survey identifies individual and household beneficiaries of public health and education services and identifies whether a household received fertilizer and seeds from a public program, but not which one, FISP or FSP. In addition, the LCMS does not include information on beneficiaries of the Public Welfare Assistance Scheme (PWAS), either those receiving food subsidies or other cash transfers, nor does it record beneficiaries of school feeding programs or programs providing support to orphans and vulnerable children.

The benefit incidence analysis (BIA) presented here combines LCMS data on government expenditures in education, health care, and fertilizer subsidy programs at the provincial level. The latest available information for the education sector is for 2009 and covers both total spending and the number of beneficiaries enrolled in basic, secondary and tertiary institutions; enrollment data for 2010 are also available. For healthcare, the latest provincial-level information is from 2008 and includes expenditure data and the number of beneficiaries (counted as patients or recorded as services provided) at tier 1 and 2 healthcare providers. Finally, the 2010 budget allocations for all fertilizer and seed packages provided by the Fertilizer Support Program and/or the Fertilizer Support Program were used.

# **4.2. Allocation of Education Benefits**

The LCMS VI collects information about school attendance in 2010 and 2009 of each household member; additionally, it collects each household‘s aggregate expenditure in education<sup>15</sup> .The information about 2009 is not as complete as the 2010 one; mainly it does not contain information about the type of school attended (public or private). In order to calculate the allocation of school benefits we face data limitations and we need to make some assumptions explained in Table 8.

> 15 Appendix 3 provides detailed  information about the education related questions in LCMS VI as well as a figure with the education data flow.

17