rafmacalaba/datause-encoder-data huggingface dataset:
**Table 16.  Impact of WTO Accession on Skilled Employment by Sector and Regional Market** (percentage change)

|Good|Central|Siberia|South|North|Urals|Far East|Moscow|St.<br>Peters.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|FME|33.4|13.7|45.0|24.0|16.5|19.4|45.9|355.6||11.3|
|CHM|8.4|22.1|10.6|-1.6|14.0|-0.1|4.8|5.6|46.1|29.5|
|NFM|11.3|5.1|34.3|10.8|24.3|6.0|38.0|0.9||-10.7|
|TMS|9.2|5.5|9.2|17.0|-1.3|16.6|-2.7|16.5|11.9|17.5|
|TRK|7.1|5.2|6.6|9.3|3.5|10.5|3.9|10.7|7.1|11.6|
|COL|8.0|5.3|8.0|8.0|7.6|6.9|||||
|TRD|5.2|3.8|3.5|3.2|4.7|3.5|5.7|6.0|2.1|7.6|
|OIL|5.0|-1.5|4.4|3.3|0.0|3.5|1.5|-1.2|7.3||
|HOU|1.3|1.9|2.4|2.5|1.8|2.0|3.5|2.4|3.0|2.7|
|ELE|2.1|2.1|2.1|1.3|2.6|0.5|2.1|2.6|0.8|4.8|
|RLW|3.2|0.3|1.1|0.7|1.6|0.6|0.3|5.2|1.2|3.1|
|SCI|-1.4|1.2|-0.3|2.8|-0.9|3.1|1.2|4.7|-0.3|4.2|
|HEA|0.4|1.2|1.4|1.2|1.3|0.9|2.7|1.4|1.5|1.3|
|CON|0.3|-1.3|0.6|-1.3|0.6|-1.7|3.0|-2.4|2.6|-4.5|
|CRU||-1.5|0.3|-0.4|0.1|-0.9|||1.7|-2.9|
|PST|-0.9|-0.7|-0.3|-1.1|-0.1|-1.2|1.1|-1.2|-0.4|-0.8|
|TPP|-7.3|-4.5|-10.1|-0.6|-1.8|29.3|-12.0|-8.7|15.6|-10.5|
|AIR|-2.8|-5.8|-2.4|-0.1|-3.7|-1.0|-2.1|-3.9|4.2|-4.1|
|CLI|-2.4|-4.3|-2.0|-3.0|-2.8|-3.2|-2.2|-6.1|1.9|-7.2|
|AGR|-2.7|-3.2|-1.6|-3.8|-0.3|-5.2|-1.5|-8.5|3.7|-10.2|
|OTI|-4.5|-6.1|-2.7|-4.6|-4.0|-6.3|1.2|-3.7|-1.1|-6.3|
|PIP|-3.2|-5.1|-4.0|-5.6|-2.0|-6.3|-4.7|-6.4|-0.9|-1.6|
|MWO|-10.6|-0.3|-2.6|-7.2|-9.2|-5.6|9.5|-3.5|-2.2|-12.2|
|MAR|-5.3|-12.1|-7.6|-6.0|-5.9|-5.2|-8.6|-6.9|-6.3|-9.0|
|TRO|-6.6|-11.0|-7.9|-2.7|-15.1|-2.7|-18.5|-2.3|-10.0|-1.2|
|CNM|-7.5|-11.1|-5.4|-7.7|-6.2|-9.2|-11.2|-13.6|2.2|-15.2|
|FIN|-8.8|-8.8|-8.5|-9.7|-8.0|-9.8|-7.2|-9.0|-9.3|-8.5|
|FOO|-13.1|-11.2|-10.1|-8.2|-10.1|-12.3|-15.4|-12.9|1.2|-16.1|
|OTH|-8.1|-14.3|-9.3|-13.9|-7.0|-15.6|-6.2|-17.9|-4.8|-21.9|
|GAS||-16.0|-9.6|-14.1|-12.4|-15.0|||-0.9|-22.7|

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.

48
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42
# **1. Introduction**

Some people in even the poorest countries would not be considered poor in the US, say. And all developing countries have access to redistributive policy instruments. These facts have not gone unnoticed by aid donors. The government of a rich country hoping to reduce poverty in poor countries will (understandably) be disinclined to give its aid to a country that has ample internal capacity to address its poverty problem through redistribution from people at a similar standard of living to taxpayers in that rich country. Yet, while the idea that different countries have different capacities for redistribution comes up often in aid discussions, it is invariably assumed that it can be adequately proxied by a measure of average income. I do not know of any past effort to formalize and test that assumption.

The issue of country capacity for redistribution also arises (at least implicitly) in discussions of development policy within developing countries. It is often argued that “sustained poverty reduction is impossible without sustained growth.”<sup>2</sup> To accept this claim one must essentially reject its corollary: “sustained poverty reduction is impossible through income redistribution.” Yet I can find no demonstration of that point in the literature.

The governments of all developing countries have access to tax and spending instruments with distributional impacts, and significant impacts on poverty and inequality by such means appear to be feasible.<sup>3</sup> Progressive income tax systems are not yet as important in developing countries as in most developed ones, though this is changing and there appear to be feasible options in developing-country settings.<sup>4</sup> Indirect taxes with distributional impacts are widespread.<sup>5</sup> On the spending side, there are many actual or potential instruments available even in poor countries including workfare programs and targeted transfers.<sup>6</sup> Whether the available policy instruments are actually used and how effective they are— how well they are designed and implemented—depend on the redistributive efforts of countries, stemming in no small measure from their political will for redistribution. That is a different

> 2 This specific quote is from Kraay (2006, p.61) although it is just one of many statements of this view that can be found in the literature and heard in development policy discussions over many decades.

> 3 See, for example, Ferreira et al. (2009) on redistributive policies in Brazil since the mid-1990s and Robalino and Warr (2006) on their potential in Thailand.

> 4 See the discussion in Alm and Wallace (2006).

> 5 Gemmell and Morrissey (2005) provide a useful review what is known about the distributional impacts of taxation in developing countries.

> 6 The van de Walle and Nead (1995) volume contains a number of relevant papers. For an overview of the methods found in practice, with details on many examples, see Grosh et al. (2008).  For a recent overview of the evidence on conditional cash transfer policies see Fishbein and Shady (2009).

2
41 Gulnea I - PFP Projectilons and Actuals <!-- Start of picture text --> W t WOMNOM<br>0~~~~~~~~~~~~~~~~~~<br>-4~~~~~~~~~~<br>Is es 6s .>7 9 01 02<br>Yew -IP,jbne<br>-Pr.e.tlan +AMtUMe<br><!-- End of picture text --> Guinea 3 - PFP Prolections and Actuals <!-- Start of picture text --> 0m av.Mo0P<br>207- -<br>20<br>n 6@7 O' 6' 90 01 02<br>Y"er<br>Gulnea 5 - PFP Projections and Actuals<br>ma Isp. ts o b<br>16<br>*<br>t 7 as f so f D<br>.26<br>Yew.<br>-ftobodwe -1-AstW0<br>-jres: Pr4e,l1ns. .Pa U/U. /M 061.<br>ale * Coty UdefaiWr Taoles S1<br>Jai i; *"<br><!-- End of picture text --> Gulnea 5 - PFP Projections and Actuals Guinea 2 - PFP Projections and Actuals <!-- Start of picture text --> Ctm #A"<br>Yew<br>+ADIUe IL TOTa641*Usd<br>TOT Twrm o Trade<br><!-- End of picture text --> Guinea 4 - PFP Projectlons and Actuals <!-- Start of picture text --> O.ud 0mINWP<br>C r 0 7 * | 91<br>Year<br><!-- End of picture text -->

Guinea 6 - PFP Projectlons and Actuals <!-- Start of picture text --> p,smp an _<br>4<br>~~~~~~~~~~~~~~~~~2<br>-<br>YeW<br>_. @rijeU +A<br><!-- End of picture text -->
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE of the capital expending. In fact, capital investment in the transport sector is exclusively financed by the central budget (figure 27).

**Figure 27. O&M is predominantly financed by state-owned enterprises; investments by the central government**

<!-- Start of picture text --> 2.5<br>O&M CAPEX<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Central Gov Off-budget<br>Percentage of GDP<br>Total Power (trade) WSS ICT Transport (basic) Total Transport (basic) Power (trade) WSS ICT<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

# How much more can be done within the existing resource envelope?

About $83 million of additional resources could be recovered each year by improving efficiency (table 19). The three largest potential sources of efficiency gains are (i) improving the cost recovery of utilities, (ii) increasing bill collections, and (iii) reducing distributional losses. The power sector promises the highest potential efficiency dividends.

**Table 19. Potential gains from greater operational efficiency**

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Underrecovery of costs|—|37|Na.|0|37|
|Overstaffing|n.a.|10|-|2|12|
|Distribution losses|—|17|-|0|17|
|Undercollection|—|16|Na.|1|17|
|Low budget execution|0|0|0|0|1|
|Total|0|80|0|3|83|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology. — = Not available. n.a. = Not applicable.

38
witnessed a strong bank branch expansion. This expansion has coincided with the emergence of Equity Bank, a pioneering commercial bank that devised a banking service strategy targeting low income clients and traditionally under-served territories. The bank is listed on the Nairobi Stock Exchange, it has no government ownership share, and it has played a key role in integrating provision of financial services to the under privileged with formal finance. Figure 6 shows the stock performance of Equity Bank during the period of August, 2006 to August 2011. It significantly outperforms two of its main competitors on this measure, Barclays Bank of Kenya and the Kenya Commercial Bank.

The banking system in Kenya expanded greatly in terms of the number of branches in recent years. Table 9 reports that between 2006 and 2009 the total number of bank branches in Kenya increased from almost 600 to almost 1,000 (a 68% increase). This bank branch expansion occurred not only in urban districts but also in rural and arid and semi-arid districts. In percentage terms, this expansion was greater in the arid and semi-arid districts (112%), followed by rural districts (67%) and urban districts (62%). This expansion involved all ownership categories of banks, namely domestic private banks, foreign banks and government banks. However, as shown in Table 10, the expansion was particularly driven by domestic private banks, among which Equity Bank played an important role. The number of Equity Bank’s branches increased from 44 to 112 in the period 20062009, representing an expansion of 155%. No other bank experienced such growth in the same period. The number of bank branches of Barclays and Kenya Commercial Bank (i.e., the banks with the largest branch networks after Equity Bank) increased from 62 to 119 and from 117 to 169, representing growth of 92% and 44% respectively.

Although most Kenyan banks have experienced branch expansion in recent years, banks with different ownership structures have followed different expansion strategies. While foreign banks prefer urban areas (perhaps in order to cherry-pick a set of elite customers), domestic private 34
percent for girls and 64 percent for boys. The regressivity of tertiary education spending may be due to high primary and secondary dropout rates and low completion rates that depress tertiary enrollment. This could be more important for girls and children from poor households, for whom progression beyond primary education has long been difficult.

<!-- Start of picture text --> Figure 7a: Concentration of In-kind Figure 7b: Concentration of In-kind<br>Transfers, Men Transfers, Women<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Primary Market income Primary<br>Secondary Tertiary Secondary Tertiary<br>In-kind education In-kind health In-kind education In-kind health<br>Population share Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of in-kind transfers Cumulative proportion of in-kind transfers<br><!-- End of picture text -->

# _Health_

Health spending is for both genders progressive (Figure 3), though more so for women than for men. The health benefits for the poorest women and men are relatively high as a share of their market incomes (Figure 7a and 7b). However, for both men and women health spending is not propoor: about 9 percent is concentrated in the poorest decile, but the top decile takes 13 percent. The relative progressivity of health spending could be due to the recent increases in almost all neighborhoods of health extension agents, who help ensure that basic health services are available to individuals, especially women.

Figures 8a and 8b summarize the progressivity and pro-poorness of government transfers (direct and in-kind transfers plus subsidies), which are assessed using concentration coefficients. For both men and women, PSNP is the most progressive and the only pro-poor spending. Direct transfers have variable progressivity and pro-poor impact across the genders (Figure 3 and Figure 8a&b), mainly driven by the PSNP. However, only PSNP transfers are pro-poor for men (Figure 8a); for them, transfers other than PSNP appear to be regressive. Primary education is progressive and pro-poor for men, and all women benefit from this transfer equally. Spending on tertiary education, the wheat subsidy, and indirect subsidies in the aggregate are all found to be regressive 15
cities: Eldoret, Emu, Garissa, Kakmega, Kericho, Kisumu, Kitui, Machakos, Malindi, Mombasa, Nairobi, Naivasha, Nakuru, Nyeri and Thika. The sample is representative of households in both formal and informal (slum) settlements. Field data collection began in July 2012 and continued through to March 2013. Two caveats are important here. This survey was completed before the onset of devolution under the new Kenyan constitution – that is, prior to the establishment of counties and abolition of municipalities in Kenya in 2013. Although the process of reestablishing municipalities has commenced in Kenya in 2018, the new jurisdictions may not necessarily align with the ones that existed prior to 2013. Second, we use the term “city” broadly to refer to all the urban areas studied, rather than use administrative terminology, specified in Kenya’s Urban Areas and Cities Act, that distinguishes between municipalities, cities and towns.

Unlike prior research in Kenya, this survey provides a more comprehensive picture of living conditions and infrastructure service throughout urban Kenya. It offers previously unavailable data on formal and informal urban settlements in 15 of Kenya’s major urban centers and is one of only a few known multisectoral studies of this scale that covers urban Africa (Arimah 2003, Steckel et. al. 2017).

# **2.1 Survey Methodology**

Details of the survey sampling and data collection methodology can be found in the _Kenya State of the Cities Baseline Survey: Overview Report_ (World Bank 2016). Table 2.1 summarizes the household composition of the final sample by location and income poverty level.<sup>2</sup> Overall, the sample draws on a data set of 14,583 household surveys. Of these, 14,204 are classified as urban households; almost a third of the households interviewed (32.7%) were located in informal areas and over half the households (51.1%) surveyed were poor.

> 2 For the purposes of this survey, ‘poor’ households are defined as those below the national expenditure-based criteria for the poverty line.   The Kenya national poverty line was adjusted for inflation at the start of the survey.  This poverty line varies according to number and age of household members, but it is calculated based on the following: 5,547 Ksh/month for each adult>15 years; 3,619/month for youth 5-14 years; and 1,336/month for children<5 years (NORC study, 2012).

4
In order to use (12) to identify a causal effect of consanguineous marriage, � , on child quality, _Q_ , we must be confident that, after controlling for total current household wealth, �� , land ownership of a child’s grandparent (in the past if not no longer alive, or at present if still living) is uncorrelated with both child endowments and parent investments in child quality. Contrary to liquid wealth, markets for land wealth are thin in rural South Asia (Griffin, Khan and Ickowitz, 2000). In addition, as pointed out by De Soto (2000), financial institutions in South Asia are typically reluctant to accept land as collateral for a loan. Land wealth is therefore unlikely to be converted into child quality inputs and therefore to directly affect child quality. Since in our model _wl_ (land ownership) only affects _Q_ indirectly via its effect o _n_ � in (12) but does not enter (10) directly (exclusion restriction), it will identify the effect of � on _Q_ as long as errors in measurement of wealth are not systematically related to both land wealth and child outcomes. As grandparents may have had a strong influence on the marriage decision of a child’s parents (Holy, 1989), we also explore using indicators of whether a grandparent was alive at the time the child’s parents were married as instruments.

# **5. The Labor and Skills Survey**

This paper uses data from the rural sample of the of the Labor and Skills Survey (LSS) wave 2, conducted in Pakistan in 2013.<sup>10</sup> The survey is representative at the national and provincial level and covers all regions of Pakistan except Balochistan and the Federally Administered Tribal Areas, which represent less than 7 percent of the total population.<sup>11</sup> The final sample used to estimate the impact of consanguinity on cognitive development outcomes consists of 1,411 children aged 5-13 from 60 rural villages. When looking at the effect of the treatment on children’s height-for-age, the sample size is 1,285 children.<sup>12</sup> The LSS consists of a detailed household roster, a female and male questionnaire, and a cognitive assessment taken by all adults in the household and all children aged 5-13.<sup>13</sup> The household roster collects general information on all household members including demographics and education, but also anthropometrics and overall health status, parental land ownership and parental mortality. It also

> 10We also conducted the analysis in the urban sample of the survey. However, our set of instruments used in rural areas lack power to identify the impact of consanguinity in urban areas. Therefore, the causal impact of consanguinity in urban areas could not be identified. As a result, they are not reported in the paper but are available upon request.

> 11Those areas could not be covered by the survey due to security reasons.

> 12Following international standards, children with z scores below -6 or above 6 were excluded from this estimation sample.

> 13The female and male questionnaires were administered to one randomly selected male and one randomly selected female in the household aged 15-65.

10
<mark>goods that proxy standards of living (cooking fuel, drinking water, and electricity, among others). While these indices are a step-up from poverty headcounts in that they measure the “intensity” of the poverty and allow for granularity in exploring “how the poor are poor,” these data are also ill-suited for our task. The most widely used index, Oxford’s MPI, only starts in 2010 and observations are not available every year; this is the same issue we face with poverty headcount data if we try to rely only on survey data as opposed to imputations.</mark> <mark>We therefore turn to alternative indicators that proxy some of the components of these indicators, but for which more complete time series exist to try to complement the current picture. Taken in concert, these indicators suggest that defaults have persistent negative effects on benchmark indicators and therefore the general welfare of the population. Food availability is curtailed, electricity consumption drops, and even child mortality and life expectancy seem to have worse-than-control outcomes more than a decade after default. The picture of welfare that emerges from an exploration of these variables is one of stagnation with respect to the counterfactuals, and thus with respect to the world.</mark> <mark>The recent case of the República Bolivariana de Venezuela after its 2017 default puts in stark relief the insidious association between a major macroeconomic crisis and the welfare of the population. One study by the country’s leading universities and NGOs found that 64 percent of Venezuelans had lost an average of 11 kilograms in 2017 (Encovi 2017).</mark><sup>26</sup> <mark>Nearly 90 percent of respondents also reported that their households’ income was not sufficient to cover the purchase of food and infant mortality had grown to about 70 percent, among other deprivations.</mark> <mark>Additionally, in 2019, the country suffered nation-wide blackouts (Morales-Arilla 2021). If only to state the obvious, curtailed access to power in a modern society can prove disastrous, not least because access to other services like water is dependent on it.</mark> <mark>We begin by proxying access to nutrition with the calories available for consumption by the population – aggregate calorie supply per capita indexed to 100 for the year before default. Over the 1961-2015 period, global average daily calorie availability has increased steadily from roughly 2,200 per day to about 2,850. Yet, when we compare defaulting countries to their controls, calorie supply stagnates relative to the synthetic control. The gap grows steadily to</mark>

> 26 <mark>Deriving the overall weight loss for the entire sample from the responses, one still finds that the population lost an average of 6.8 kilograms.</mark>

22