rafmacalaba/datause-encoder-data huggingface dataset:
In truth, the concept of ―human development‖ in the HDI has never been crystal clear and nor is it clear how one defines the broader concept of ―poverty‖ that indices such as the MPI are trying to capture, and how this relates to ―human development.‖ Development policy dialogues routinely distinguish ―poverty‖ from ―human development,‖ where the poverty concept relates to command over commodities. While ―poverty‖ is typically distinguished from ―human development,‖ it can be argued that mainstream development thinking and practice is already premised on a multidimensional view of poverty (Ravallion, 2010a). The real issues are elsewhere, in the case for and against forming a mashup index.

The frequent lack of conceptual clarity about what exactly one is trying to measure makes it hard to judge the practical choices made about what pre-existing indicators get used in the composite. One can debate the precise indicators chosen, as would probably always be the case. Double counting is common,<sup>13</sup> though unavoidable to some degree. But greater guidance for users on the properties of the ideal measure with perfect data would help assess the choices made with imperfect data. For example, while we can agree that ―income‖ (as conventionally measured) is an incomplete metric, we would presumably want any measure of ―poverty‖ to reflect well the changes in peoples‘ real incomes (their command over commodities)—changes that might emanate from shocks. The MPI‘s six ―living standard‖ indicators are likely to be correlated with consumption or income, but they are unlikely to be very responsive to economic fluctuations. The MPI would probably not capture well the impacts on poor people of the Global Financial Crisis, or rapid upswings in macro-economic performance.

**What tradeoffs are embedded in the index?** We need to know the tradeoffs—defined here as the marginal rates of substitution (MRS)<sup>14</sup> —built into a composite index if it is to be properly assessed and used. If a policy or economic change entails that one of the positivelyvalued dimensions of welfare increases at the expense of another such dimension, then it is the MRS that determines whether overall welfare has risen or fallen. However, whether or not one

> 13  For example, private and public spending on health and education is a component of GDP, while measures of health and education attainments also enter separately in the HDI. In the case of the Newsweek index, mean consumption enters both directly (on its own) and indirectly via other variables, notably the poverty rate, which is also a function of inequality, which also enters on its own.

> 14  Consider any (differentiable) function _f_ of _x_ 1, _x_ 2. The MRS of _f_ ( _x_ 1, _x_ 2) is simply the ratio of the first derivative (―weight‖) with respect to _x_ 1 divided by the first derivative with respect to _x_ 2. This gives how much extra _x_ 2 is needed to compensate for one unit less of _x_ 1, where ―compensate‖ is defined as keeping the value of _f_ ( _x_ 1, _x_ 2) constant. (More general definitions are possible without assuming differentiability.)

11
|17|GFCUCO|CResident Individuals<br>(AM)|100,00|514094|0,04|0,04|||||
|---|---|---|---|---|---|---|---|---|---|---|
|18|GladzorUC|OResident Individuals<br>(AM)|100,00|475993|0,03|0,03|||||
|19|MalatiaUCO|Resident Individuals<br>(AM)|100,00|1926670|0,13|0,14|||||
|20|National Mo|Central Bank of Armenia<br>(AM)|100,00|7413622|0,52|0,52|||||
|19|Nor Horizon|Nor HorizonUCO LLC<br>(AM)<br>~~Norvik Bank (Latvia Iceland~~|100,00|1105677|0,08|0,08|||||
|20|NorvikUCO|(EU)<br> <br>bank)<br>~~PMZ NERDRUMNERUCO~~|100,00|15015313|1,05|||1,06|||
|21|PMZ NERD|<br>CJSC||1918355|0,13||||||
|22|SEF Interna|tSEF InternationalUCO LLC||3343779|0,23||||||
|23|Unileasing|UnileasingUCO CJSC||124570|0,01||||||
|||Polygraph CJSC - 99%|99,00||||||||
|||Resident Individuals|1,00|||0,00|||||
|24|VTB Leasin|gCredit Organization CJSC||167650|0,01||||||
|25|Washington|Resident Individual<br>(USA)<br>|100,00|947185|0,07||||0,07||
||**TOTA**|**L OF BANKS' AND CO's ASSETS**||**1430943793**|**100,00**||||||
|||**TOTAL OF BANKS' ASSETS**||**1355959005**|||||||
|||**TOTAL OF CO's ASSETS**||**74984788**|||||||
|||||||**ARM**<br>|**CIS**<br>|**EU**|**USA**|**ROW**|
|||**TOTAL SHARES BY REGIONS**|||**81,86**|**21,27 **|**29,61**|**12,76**|**8,28**|**9,94**|
||**ADJUSTE**|**D TOTAL SHARES BY REGIONS**|||**100,00**|**25,98 **|**36,17**|**15,59**|**10,12**|**12,15**|

88
exporter. For instance, Argentine exports account for more than 13 percent of total world exports in residues from the food industry, for 15 percent in animal fats and for 6 percent in cereals.

The majority of these top sectors comprise commodities and primary products, although there may be a manufacturing component in such sectors as 27, 23, 15, 10, 12, and 02 in Table 1, which together account for more than 50 percent of total exports. In fact, a significant portion of exports are derived from petroleum products, soy bean and its derivatives, cereals and bovine meat. The largest industrial manufacturing sector is vehicles, which accounts for around 6 percent of total exports, followed by plastics, with almost 3 percent of exports, and nuclear reactors and their components, with 2.2 percent of total exports.

From the more disaggregated data of Table 2, we can see that oil cake and other solid residues were the largest export of Argentina in 2004, accounting for more than 10 percent of exports with a total value of US$3.6 billion. However, it is interesting to note that this product is the only one of the top 50 export products belonging to this sector. This product by itself accounts for 93 percent of the exports within its own 2-digit category. The second and fifth products by rank are obviously closely related, although belonging to different groups. Again, the majority of export products are agricultural and commodity based, although automobiles, trucks and tubes and pipes account for 5 percent of total exports. We return to the analysis of these industrial products in a subsequent section below.

**Table 2 Argentina: Export Composition 2004, 4 Digit Level**

|**Code**|**Product Name**|**Trade Value**<br>**$ '000**|**Share**<br>**%**|**Cumm. Share**<br>**%**|
|---|---|---|---|---|
|2304|Oil-cake and other solid residues,|3,597,953|10.5|10.5|
|1507|Soya-bean oil and its fractions|2,335,749|6.8|17.3|
|2709|Petroleum oils and oils obtained fr|2,255,856|6.6|23.9|
|2710|Petroleum oils, etc, (excl. crude);|1,940,005|5.7|29.5|
|1201|Soya beans|1,740,114|5.1|34.6|
|1001|Wheat and meslin|1,365,480|4.0|38.6|
|1005|Maize (corn)|1,193,805|3.5|42.0|
|2711|Petroleum gases and other gaseous h|1,106,768|3.2|45.3|
|2603|Copper ores and concentrates|904,184|2.6|47.9|
|4104|Leather of bovine or equine animals|804,059|2.3|50.3|
|8703|Motor cars and other motor vehicles|676,554|2.0|52.2|
|8708|Parts and accessories of the motor|658,787|1.9|54.1|
|1512|Sunflower-seed, safflower or cotton|570,612|1.7|55.8|
|8704|Motor vehicles for the transport of|545,764|1.6|57.4|
|7304|Tubes,pipes and hollow profiles,sea|485,482|1.4|58.8|
|0202|Meat of bovine animals, frozen|421,734|1.2|60.0|

Source: WITS Database-Comtrade

# **IV. Concentration Analysis**

7
WPS3968

# **Measuring Corruption in Eastern Europe and Central Asia:**

# **A Critique of the Cross-Country Indicators**

## Stephen Knack*

### World Bank Policy Research Working Paper 3968, July 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Address correspondence to Stephen Knack, Senior Research Economist, World Bank, 1818 H Street, N.W., Washington, D.C. 20433. Email: <u>sknack@worldbank.org. Phone:</u> 202-458-9712. Fax: 202-522-1154.

This paper benefited from enormously valuable suggestions and comments from James Anderson and Cheryl Gray.
# **b. Sanitation**

In the sanitation sector, there is no clear case of concentration neither at the global level nor at the regional level. Although Ondeo and Veolia clearly dominate the market and are followed closely by the large German group RWE, there are other private operators (OPP). These OPP include many local players in many of the countries and serve about a third of the population of the world.

North America, is, once more, a clear border line under both types of market definition, suggesting that its sanitation market may be subject to some degree of market power by the few operators who dominate the market. The indicator is however formally inclusive. Eastern and Western Europe are also border cases when relying on the urban population indicator to define the market.

The developing regions of the world are much less concentrated than sometimes argued. This is largely due to the fact that coverage rates are fairly low. In many countries of the developing world, sanitation is a much less formal activity than in the developed part of the world.

**<u>Table 7: Regional HHI - Sanitation</u>**

||<br>**GLOBAL**|<br>**Africa**|<br>**Asia &**<br>**Oceania**|**Latin**<br>**America**|**North**<br>**America**|**Eastern**<br>**Europe**|**Western**<br>**Europe**|
|---|---|---|---|---|---|---|---|
|**Market indicator**<br>||**1. Urban p**<br>**2. Country**<br>|**opulation (**<br>**population**<br>|**upper boun**<br>**(lower boun**<br>|**d concentra**<br>**d concentr**<br>|**tion)**<br>**ation)**<br>||
|**HH min(urban)**|**782**|231|457|260|1738|1016|989|
|**HH max(urban)**|**2 246**|951|1063|1 466|3 504|3 072|3416|
|**HH min(country)**|**476**|51|156|171|1 407|536|534|
|**HH max(country)**|**1 542**|452|565|1095|2837|1914|2391|
|**Aggregate market share(urban)**|**31%**|19%|21%|25%|45%|35%|40%|
|**Aggregate market share(country)**|**23%**|13%|11%|20%|40%|24%|31%|
|**Number of firms**|**7**|2|1|12|5|5|7|
|**Number of OPP**|**19**|7|0|0|7|13|13|
|**Number of countries**|**37**|6|5|8|2|5|11|
|**Cases**|**102**|9|15|27|12|2|37|
|**Main actors**|**AWG, RWE,**<br>**SAUR, SET,**<br>**ONDEO,**<br>**VEOLIA,**<br>**UUT and**<br>**Other Private**<br>**Providers**<br>**(OPP)**|ONDEO,<br>VEOLIA,<br>OPP,|AWG,<br>RWE,<br>ONDEO,<br>UUT,<br>VEOLIA,<br>OPP|AWG,<br>RWE,<br>SAUR,<br>ONDEO,<br>VEOLIA,<br>OPP|RWE,<br>ONDEO,<br>SET,<br>VEOLIA,<br>UUT,<br>OPP|AWG,<br>RWE,<br>SAUR,<br>ONDEO,<br>VEOLIA,<br>OPP|AWG,<br>RWE,<br>SAUR,<br>SET,<br>ONDEO,<br>UUT,<br>VEOLIA,<br>OPP|

19
# **Figure 5: Domestic Mobility as Measured by Current Residence Migration History**

<!-- Start of picture text --> Toshkent City Year of Last Departure<br>Sirdaryo<br>Andizhon % of total migrants<br>Karakalpakstan 5.0<br>Horazm<br>4.5<br>Buhoro<br>4.0<br>Navoiy<br>Jizzakh 3.5<br>Fargona 3.0<br>Namangan 2.5<br>Kashkadaryo 2.0<br>Surhondaryo 1.5<br>Toshkent region<br>1.0<br>Samarkand<br>0.5<br>0% 5% 10% 15% 20% 25% 30%<br>0.0<br>Destination Out-Migration<br>1940 1954 1958 1963 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016<br><!-- End of picture text --> _Notes: Mobility estimates are derived from survey responses in the L2CU Baseline. Author’s Calculations. The left shows source/destinations as a share of all those people who report ever having moved (domestically). The right-hand graph reports the year that they report last having moved._ Most self-reported migrants came from other regions of Uzbekistan (Figure 6). However, about 23 percent moved from another country, most commonly the Kyrgyz Republic, Tajikistan, and the Russian Federation. About 71 percent report that the primary reason for their move was family-related (either moving with family, or to join family) followed by employment-related reasons at 19 percent. Relatively small shares of respondents reported moving due to government relocation or security concerns, and only about four percent for the purposes of studying.

There is imprecision associated with these population estimates, which is magnified by the lack of census data for Uzbekistan (no census has been conducted since independence, though one is planned for 2022). In particular, Tashkent’s population has clearly increased since independence, but incentives around registration are such that administrative records are likely inaccurate to some degree. However, there is broad consistency between the last Soviet Census and current official population estimates. The 1989 Census estimated the population of Tashkent at 2.1 million people. Current official statistics estimate that the population of Tashkent city included 2.14 million people in the year 2000, rising to 2.5 million in 2019. This means that as a share of the total population, Tashkent has in fact become smaller, despite remaining the most popular destinations of the small number of domestic migrants recorded over this period. The city represented 8.7 percent of the total population of Uzbekistan in 16
calibrated such that simulations of estimated historical productivity volatility of coarse grains for the 1971-2001 period replicate observed historical price volatility, as described in Ahmed et al. (2011).

In the model, the impact of the production deviation shocks on trade will be driven by changes in relative prices between alternative suppliers. The percentage change in demand for imports of commodity _i_ from a specific country _r_ into a country _s_ ( _qxsirs_ ) is a function of the change in aggregate imports of the commodity into _s_ ( _qimis_ ), the percentage change in the domestic price of _i_ imported from _r_ into _s_ ( _pmsirs_ ) _,_ the composite import price of all _i_ imported into _s_ ( _pimis_ ), and the rate of import augmenting technological change ( _amsirs_ ), which captures the impact of changes in trade facilitation on any particular trade flow. Equation 1 describes this function.

Increases in the aggregate import demand for _i_ in _s_ will encourage more exports from country _r_ . We term this the expansion effect. However, it is moderated, or perhaps strengthened, by the substitution effect which hinges on the change in the price of _i_ from the exporting country _r_ relative to the change in the aggregate import price (a weighted average of prices from all sources) in the importing country _s_ determines whether the importing country will source more of commodity _i_ from country _r_ .

The parameter  _i_ is the so-called Armington elasticity of substitution amongst imports of the same commodity across different sources and it governs the responsiveness of the substitution effect amongst exporters. This elasticity is estimated in Hertel et al. (2007), using the data and approach of Hummels (1999). The approach exploited cross-sectional variation in delivered prices, by conditioning on an exporter and commodity. The elasticity of substitution was then identified from variation over importers in delivered prices which arose from bilateral variation in ad-valorem trade costs.

In order to understand how climate shocks can affect household welfare and poverty, we use the household micro-simulation model from Ahmed et al. (2011). That approach involves augmenting the CGE simulation framework with the household model of Hertel et al. (2004), to estimate changes in income and consumption of households in the neighborhood of the poverty line. For poverty analysis, the utility of the household at the poverty line is then defined as the poverty level of utility. If an adverse climate shock pushes households’ utility below this level, they enter poverty. Conversely, if they are 12
a system with lower financial efficiency. As a result, rationing phenomena and cuts in investment funds could be limited.

This prediction, for which no empirical evidence is referred to in the next section, will be tested in a subsequent study.

# _5._ Evidence from the literature

Although specific investigation will have be to be carried out to provide empirical corroboration of the theoretical predictions of the model above,'<sup>2</sup> recent studies seem to bear interesting evidence in their support, thereby encouraging further research along the lines of this study. This section briefly reviews such evidence.

First of all, it is interesting to learn from Demirguc-Kunt's and Levine's (1999) recent cross-sectional analysis of a 60 country data set that the financial systems of countries with less developed financial infrastructure (i.e., less investor-prone law system, poor protection of shareholder and creditor rights, weak contract enforcement, high levels of corruption, poor accounting standards, restrictive banking regulation) tend to be bank-dominated. In particular, the analysis study finds that countries with lower levels of corruption tend to have more market-based financial systems. It also finds that, as financial infrastructure develops, financial systems tend to become more market-based and less bank-dominated.

As regards the relationship between financial development and the quality of capital accumulation, Carlin and Mayer (1998), using data from 27 industries in 20 OECD countries over the 1970-1995 period, find that equity financed industries tend to grow faster, carry out more R&D, and employ higher skilled workers in countries with relatively better accounting standards. Interestingly, under better accounting standards, equity finance industries also tend to undertake less fixed capital accumulation and to invest more in intangibles. This latter result can be explained by the lesser need - made possible by the improved financial infrastructure - for enterprises to accumulate reputational capital in the form of non-salvageable (productive and/or nonproductive) capital assets necessary to signal the enterprises' commitment to honest and prudent behavior."<sup>3</sup> Unlike equity-financed industries, Carlin and Mayer find that bank-financed industries grow more slowly in countries with developed financial infrastructure and tend to undertake less R&D. Consistent with Carlin and Mayer's results, Hoshi _et al._ (1990, 1990b) also observe that industries dependent on bank finance tend to be physical capital intensive, smoke-stack industries. Furthermore, studying the financing arrangement of 250 US public firms, Houston and James (1995) find that firms with relationships to single banks tend to use less bank debt in 16
7 The importance of record keeping, in the Nigerian context, stems from several factors. First and foremost is the fact that the Government of Nigeria has struggled to reliably track how much it spends on primary health care. This is partly because classifications of expenditure in the health sector tend to be too broad to capture such granular but vital information, but also because the resources (cash and in-kind) that flow to primary health care in the country are heavily fragmented, with no system in place to aggregate them into central databases. This means that it is important to devise alternative ways of tracking income and expenditure in the sector. In an effort to help Nigeria with that development, the World Bank introduced an innovative continuous Public Expenditure Tracking Survey (PETS) and Resource Tracking (RT) exercise in two states (Ekiti and Niger), which recorded and aggregated information on resource flows in real time, rather than following the more established practice of retrospectively generating such data. In both states, the intervention and the monitoring were conducted by the same local firm.<sup>3</sup> The need is especially great at the primary facility level, for which no entity above the facility level – including local government and state authorities – has reliable information on income and expenditure streams. One of the central issues identified by previous studies is the weakness of accountability structures at the facility level – a gap that a World Bank-financed intervention is attempting to address. The main contribution of the PETS/RT has been to design and introduce relatively simple-to-fill-out forms (spreadsheets; other resource tracking tools) to track various income and expenditure streams. This includes, for example, creation of a standardized cash book form (for the recording of cash receipts from user charges e.g., consultation fees, laboratory tests, and drug sales and expenditures such as due to purchases of drugs, equipment, materials, or supplies, wages, and facility maintenance), accompanied by training for staff at the facility level on how to fill out these forms on a daily basis.

However, absent sufficient motivation, the introduction of resource tracking forms (and instruction in their use) is not likely to change bureaucratic behavior at the facility level. In an important sense, the larger agenda regarding transparency and anti-corruption in countries such as Nigeria depends, in part, on the motivation of bureaucrats to complete their record keeping tasks. Indeed, accountability and the reduction of ‘leakage’ is another explicit factor in the importance of careful tracking of monetary inflows and outflows among civil servants.

> 3 The local firm carrying out this work was Hanovia Medical Ltd., Nigeria.
**High potential countries for mobile phone banking in MENA** include Egypt, Yemen and Morocco. The table below suggests a characterization of countries in terms of potential to benefit from mobile phone banking, and to support viable mobile phone financial products. Market potential is influenced by the number of unbanked potential customers, wireless penetration rates, and relatively conducive regulatory frameworks that allow private sector activity and innovation. Egypt has the largest population in the Arab world, with very low financial access, but 65 percent of the population (55.4 million) already has a mobile phone. (Michaels, 2010) Mobile and agent-based financial services could also transform access to finance in Yemen. Nearly 70 percent of the population is living in rural areas which are without basic infrastructure. There are close to 8 million mobile phone subscribers in Yemen and less than 1 million bank accounts (CGAP, 2010). While West Bank and Gaza does not have a large population, the difficulties of moving cash in and out of Gaza may provide a business case for the introduction of mobile phone banking. In Iraq, mobile phone banking may help commercial banks overcome constraints of limited branch networks (state banks control the majority of branches) and uncertain security of movement.

**Table 6. Which MENA Countries have the Most Potential for Mobile Banking?**

|**LOW POTENTIAL**|**MEDIUM POTENTIAL**|**HIGH POTENTIAL**|
|---|---|---|
|Low rural or unbanked population<br>Low mobile penetration<br>Uncertain regulatory regimes<br>High state ownership of bank and/or<br>telecom assets (can limit innovation)|High state ownership and control<br>of banking and telecoms sectors<br>Uncertain regulatory and<br>business environments|High rural populations<br>Low levels of banked people<br>High mobile penetration<br>Increasingly conducive regulatory<br>regimes|
|Djibouti, Libya, Saudi Arabia, Syria|Algeria, Iraq|Egypt, Jordan, Morocco, Yemen,<br>Tunisia|

Source: Michaels, 2010. Data presented in Annex 3.3.

**Mobile phone banking in MENA has not yet taken off** , with few mobile phone-based financial services yet launched. Regulators in high potential markets such as Egypt and Morocco are starting to ease regulatory constraints and allow banks to link with MNOs to launch financial services through mobile phones.  An emerging example of the use of technology in the MENA region is the use of electronic wallets (e-wallets), linked to cards and mobile phones, that can be used as a form of payment, to withdraw cash, to pay bills, recharge airtime, make deposits, and send or receive money transfers. Emerging mobile phone banking examples in MENA are outlined in the box below.

17