rafmacalaba/datause-encoder-data huggingface dataset:
such as asset management companies and insurers have been preparing to invest in bank loan portfolios as they are offloaded due to deleveraging pressures. Banks could also shift exposures to non-bank entities within the group.

<!-- Start of picture text --> III.1 Based on past crisis experience, Euro Area III.2 ... and dependence on wholesale funding will<br>credit could likely decline for a prolonged period... need to decrease as well<br>Evolution of Post-Crisis Nominal Private Credit Evolution of Post-Crisis Loan-to-Deposit Ratios<br>Start of crisis=T indexed at 100 % (Start of crisis=T)<br>105<br>100 160<br>95<br>140<br>90<br>85 120<br>80<br>100<br>75<br>70 80<br>T 1 2 3 4 5<br>60<br>Finland (91) Spain (08) T 1 2 3 4 5<br>Sweden (91) UK (08) Finland (91) Spain (08)<br>Sweden (91) UK (08)<br>USA (07) Euro Area (08) USA (07) Euro Area (08)<br>Sources: IMF; ECB Sources: IMF; ECB<br>III.3 Many European banks will deleverage via run- III.4 ... and will mainly target loan portfolios in their<br>offs, divestments, and constraining asset growth... divestment strategies<br>How European banks plan to deleverage Which assets European banks are likely to divest<br>% of respondents rating these items 4 or 5 on a 1-5 scale % of respondents rating these items 4 or 5 on a 1-5 scale<br>0% 20% 40% 60% 0% 20% 40% 60%<br>Natural run-off Loan portfolios<br>Securities and trading<br>Divestments portfolios<br>Bank subsidiaries and<br>Constraining asset growth branches<br>Non-bank activities (e.g.<br>Write-offs/provisions insurance, leasing, asset<br>mngt)<br>Source: Deloitte Bank Survey 2012 Source: Deloitte Bank Survey 2012<br><!-- End of picture text -->

**In terms of magnitude, the most recent deleveraging projections are significant and point to a reduction between 5 and 10 percent of total European bank assets, although they should be seen as illustrative only since they are shrouded by market and policy uncertainties and data gaps.**<sup>**22**</sup> In October 2012, the International Monetary Fund (IMF)

> 22 For example, market conditions could improve enabling banks to raise more equity (as they have been doing so far) and avoid asset shedding and loan reductions. It is also difficult to assess whether other banks and financial institutions are able to compensate for the effects of deleveraging banks. Also, regulatory changes such as the recent easing of Basel III’s Liquidity Coverage Ratio can also affect the deleveraging outlook.

14
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countries are also of fundamental concern to the developing countrles, particularly in the trade policy area.) Some of these Interrelations are mentioned in the case of Colombia In section 4. The greater country detail and the longer time span of data examined also help to disentangle some of the policy effects better than in the overview of six cases in Table 1. World Bank (1988) indicates an association of GDP and export growth with the introduction of reform programs in two-thirds of 16 countries examined. The paper claims no causal connection.

# Conditionality and Monitoring

In the case of structural adjustment lending the elements of conditionality are macroeconosic, often supported with sectoral aspects. The question whether policy or performance ought to guide conditionality is not an easy one (see Genberg and Swoboda 1987). Four types of cases might be noted: _(1)_ Policies are agreed upon, and performance Is good; (11) Policies are agreed upon, but performance is poor; (Lit) Policies are not agreed upon, but performance Is good; and (lv) Policies are not agreed upon, and performance is poor.

The first and last cases might be viewed as straightforward, the first suggestlng that Incremental lending Is warranted, and the **last** that not. The third situation has been used by some to argue In favor of focuslng on performance, which Independent of policy agreement, Is seen as a justification for lending. This could be an Instance when the policy to be agreed upon needed to be reconsidered, and actual policy was turning out to be reasonable. However, the good performance might not be sustained in the medium-term and agreement on policy would indeed
MONGOLIA'S INFORMAL SECTOR Contents iv

|The Result: Incomes of Informnal Sector Activities ..................................<br>Summary-<br>Ease of Entry is Driving Informality ...................................|24<br>25|
|---|---|
|**4.**<br>**Conclusions: What Good is the Informal Sector? ....................................**|27|
|**Appendix A.**<br>**Micro-studies of Informality in Mongolia .................................**|28|
|Informal Distribution .<br>........................................................|28|
|Bringing the Goods to the Consumers: Kiosks ...............................|28|
|Formal Facilitation of Informal Activities: Indoor Markets ....................|29|
|The Nucleus of Ulaanbaatar's Informal Sector: The "Black" Market ....|..........<br>30|
|Informal Transportation ........................................................|_31_|
|Every Vehicle is a Potential Taxicab .......................................|31|
|Informal Freight Transport .............<br>.................................|32|
|Moving the Masses: Informal Busing ......................................|33|
|From Finance to Traditional Medical Healing: Other Informal Sectors ..................|34|
|**Appendix B-**<br>**Informal Sector Survey Methodologies and Descriptive Statistics ....**|**.........**35|
|Taxicab Sample and Population ................<br>.................................|35|
|Adjusting for Over-Sampling Frequently Driven Vehicles ......................|35|
|Value Added from Ulaanbaatar's Taxicabs ..................................|37|
|Average Income of Taxicab Drivers .......................................|37|
|Taxicab Survey Instrument .............<br>.................................|39|
|Kiosk Sample and Population .<br>..................................................|41|
|Value Added from Kiosks in Ulaanbaatar ...................................|41|
|Average Income of Kiosk Proprietors ......................................|42|
|Kiosk Survey Instrument ..............<br>..................................|43|
|Informal Sector Surveys of Outdoor Markets and Street Informals ......................|45|
|Generalized Informal Sector Survey Instrument ..............................|46|
|**Notes .............................................................**|**54**|
|**References ......**|**64**|
33 These amounts are subtracted from recurrent expenditures to get the net government subsidy. Finally, the number of yearly visits to hospitals and primary health centers are derived directly from the SUSENAS, and together with the recurrent expenditure levels, used to calculate the per visit subsidies.

The earlier study (Meesook, 1984) added up the 1980/81 routine budgets from the three government levels, assumed that about two thirds of that total actually went to health care as an estimate of recurrent expenditures, and apportioned that between hospitals and public health centers. The fees collected from users and ASKES insurance were then subtracted from the expenditure estimate to get the total yearly subsidy. Next, total yearly visits to different health facilities were assessed from the 1980 census.

One difficulty arises due to the fact that hospital care is also provided by private facilities in Indonesia. Public hospitals accounted for 66 percent of total inpatient days and 72 percent of all outpatient visits in 1985 (72% of total hospital visits2). The subsidy for each hospital visit is calculated to be Rp 5,200 when no distinction is made between public and private hospitals. This appears to be what was done in the earlier study (Meesook, 1984) and thus provides the only basis for comparison with the earlier results (Table 21).

Tables 18 and 19, which present subsidy incidence estimates for 1987, adopt a different approach. Although it is not possible to identify visits to public as opposed to private facilities from the 1987 SUSENAS utilization records, this information is available from the special health module included in the 1990 SUSENAS. Public as a proportion of total hospital visits derived from the 1990 data are given in Table 17 and underlie the 1987 distribution of subsidies across deciles presented in Tables 18 and 19. This 2 Public visits as a percentage of total hospital visits are calculated from Ministry of Health data reported in Table 2.4, World Bank 1991a. The calculation is based on the total number of discharges and the total number of outpatient visits and the proportion of those which were private.
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Nevertheless with data on formal credit it is possible to quantify its effect on agricultural output and investment. Bur,<sup>because money is</sup> fungible and farmers also get loan<sup>rem</sup> the private lenders, the lack of information on informal credit may influence the effects of<sup>formal credit</sup> on output and investment. However, because the terms of credit<sup>in the</sup> formal system are better than from money lenders, farmers<sup>often try first</sup> to satisfy their credit demand by approaching the formal lending<sup>agencies.</sup> If they fail to satisfy their need for credit they then perhaps would approach the informal lenders at a higher rate of interest.<sup>This suggests</sup> that the absence of information regarding informal loans may not<sup>affect the</sup> estimates of the effects of institutional credit (Feder et. al., 1988).

The credit advanced by formal lending agencies is<sup>an outcome of</sup> both the supply of and demand for formal credit. The amount<sup>of formal</sup> credit available to farmer, his credit rations. enters into the output supply, input demand (e.g., fertilizer, employment) and wage functions as an independent argument.<sup>2</sup> We, therefore, need to disentangle the supply of formal credit from its demand. A two-stage procedure can solve<sup>this</sup> identification problem. Since financial institutions decide how many branches or offices a district should have, the number of offices<sup>is</sup> exogenous to farmer demand. In estimating the output or<sup>input effect of</sup> institutional credit, I first estimate a credit equation with credit advanced as the dependent variable with, among others, the uumber of branches of financial institutions as explanatory variables. This provides a predicted amount of credit supplied to each district by<sup>formal financial</sup> intermediaries which is then used in the second-stage estimation of output supply or input demand and wage equations.
farmer is a member of the JOHAR producer group (because the producer group members receive support and encouragement to grow HVCs) and the usual demographic controls.

We find that JCLIS beneficiaries were significantly more likely to grow an HVC in 2020-21, and that they allocated a greater share of their land to HVCs. Members of JOHAR producer groups are not more likely to grow an HVC or allocate a larger portion of land to HVCs if they are not beneficiaries of a JCLIS. Larger households with more farming members are more likely to grow vegetables, probably because vegetable cultivation is highly labor-intensive, and this confers an advantage to those with more working hands. Yet larger farming households do not increase the proportion of their land that is cultivated with HVCs. Distance to markets or mandis does not have a significant effect on the extensive or the intensive margins of the uptake of HVA by farmers. Access to irrigation from JCLIS or other sources has the largest impact on the uptake of HVA in our study area. We also find a higher probability of summer cropping among JCLIS beneficiaries versus non-beneficiaries. Farmers mostly grow fruits and vegetables in the summer season.

**Table 6. Regression Estimates for effects of JCLIS on High-Value Crop Cultivation**

|**High value crop cultivation**<br>**across all three seasons**|**(1)**<br>**Cultivated HV crops**<br>**across all seasons?**<br>**(Yes/No)**|**(2)**<br>**The area under HV**<br>**cultivation/Acre land**|
|---|---|---|
|**JOHAR Farmer (vs non-beneficiary)**|0.160***<br>(0.0463)|0.111***<br>(0.0390)|
|**Months of Operation of JOHAR System (**Base: <=18 months**)**<br>_>18 months_|0.0164|0.0184|
||(0.0516)|(0.0492)|
|Total Land Holdings (Acre)|0.00277<br>(0.00551)||
|**Pump Access**(Base: No source)|||
|_Diesel (own)_|0.467***<br>(0.0432)|0.273***<br>(0.0379)|
|_Diesel (rented)_|0.327***<br>(0.0601)|0.0796**<br>(0.0359)|
|_Electric_|0.474***<br>(0.0342)|0.196***<br>(0.0289)|
|_Pond/Canal_|0.363***<br>(0.0506)|0.220***<br>(0.0506)|
|JOHAR farmer used a supplementary source of|-0.0818**|-0.0676|
|irrigation|(0.0411)|(0.0541)|
|Household Size|0.0168***|0.00283|
||(0.00601)|(0.00636)|
|At least one member migrates for work|-0.0145|-0.00882|
||(0.0179)|(0.0137)|
|Type of JOHAR pump=Diesel (vs Solar)|0.0261|-0.0228|

21
was symmetrically distributed atop of the mode of the global distribution (exclusive of China). In other words, China and the rest of the world had about the same modal income. With each successive five-year period, Chinese distribution shifted more to the right (towards higher income levels) so much that by 2008 about four-fifths of the Chinese population has an income greater than the modal non-China global income. The income mode in China is now clearly greater than in the rest of the world. It is this rightward movement of the Chinese distribution that has most contributed to the change from a two-peaked global distribution in 1988 to a single peak distribution twenty years later. This has largely happened because China has “filled up” the relatively hollow part of the global income distribution between $PPP 2,000 and $PPP 6,000.

Figure 4 focusses on the change in the regional composition of the global income distribution between 1988 and 2008. The chart shows the regional composition of the population in each ventile of the global distribution.<sup>64</sup> As before, we can see a clear upward movement of China. The top decile in China reaches as far as the 17<sup>th</sup> ventile (i.e. between 80<sup>th</sup> and 85<sup>th</sup> percentile) of the global distribution in 2008, whereas in 1988, the richest Chinese were only between the 65<sup>th</sup> and 70<sup>th</sup> percentile. Conversely, in 2008 China has entirely graduated from the bottom 5% of the world, while in 1988 it made up almost 40% of the population in that group.

As the bottom incomes in China have moved up the global distribution, Sub-Saharan Africa and to a smaller extent India have expanded their population shares in the bottom ventile. The distribution of Sub-Saharan Africa is very spread out with some decile groups (from South Africa and Seychelles) reaching the top 10% of the global distribution. India has not moved dramatically which is explained by the fact that its growth rate has been similar to the global average.

The global 20-year GIC showed that fractile groups between the 75<sup>th</sup> and approximately 95<sup>th</sup> percentile grew slower than the global average (Figure 1(a)). In 1988, the percentiles between the 70<sup>th</sup> and the 85<sup>th</sup> (ventile groups 15, 16 and 17) originated primarily from the mature economies and Latin America, and to a smaller extent from the Middle East and North Africa. By 2008, China and to a lesser extent Russia<sup>65</sup> had moved into these percentiles, reducing the shares of the

> 64 The ventile categories correspond to different absolute money amounts in 1988 and 2008. 65 The first observation that we have for Russia is for benchmark year 1993.

34
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# **REFERENCE**

Amemiya, Takeshi. 1975. Qualitative models. _Annals of Economic and Social Measurement_ , Vol. 4, pp. 363-372.

- Bajari, Patrick, Ali Hortaçsu. 2003. Winner’s curse, reserve prices and endogenous entry: empirical insights from eBay auctions. _The RAND Journal of Economics_ , Vol. 34(2), pp. 329-355.

- Bajari, Patrick, Steven Tadelis. 2001. Incentives versus transaction costs: a theory of procurement contracts. _The RAND Journal of Economics_ , Vol. 32, pp. 387-407.

- Bajari, Patrick, Robert McMillan, Steven Tadelis. 2006. Auctions versus negotiations in procurement: an empirical analysis, _Journal of Law, Economics and Organization_ , forthcoming.

- Brannman, Eric, Douglass Klein, Leonard Weiss. 1987. The price effect of increased competition in auction markets. _The Review of Economics and Statistics_ , Vol. 69, pp. 24-32.

- Bulow, Jeremy, Paul Klemperer. 1996. Auctions versus negotiations, _The American Economic Review_ , Vol. 86(1), pp. 180-194.

- Che, Yeon-Koo. 1993. Design competition through multidimensional auctions. _Rand Journal of Economics_ , Vol. 24, pp. 668-680.

- Cripps, Martin, Norman Ireland. 1994. The design of auctions and tenders with quality thresholds: the symmetric case. _The Economic Journal_ , Vol. 104, pp. 316-326.

- De Silva, Dakshina, Timothy Dunne, Georgia Kosmopoulou. 2002. Sequential bidding in auctions of construction contracts. _Economics Letters_ , Vol. 76, pp. 239-244.

- De Silva, Dakshina, Timothy Dunne, Georgia Kosmopoulou. 2003. An empirical analysis of entrant and incumbent bidding in road construction auctions. _The Journal of Industrial Economics_ , Vol. 51, pp. 295-316.

- Estache, Antonio, Jose-Luis Guasch, Atsushi Iimi, Lourdes Trujillo. 2008. Multidimensionality and renegotiation: evidence from transport-sector public-privatepartnership transactions in Latin America. Policy Research Working Paper No. 4665, the World Bank.

- Estache, Antonio, Atsushi Iimi. 2008a. Procurement efficiency for infrastructure development and financial needs reassessed. Policy Research Working Paper No. 4662, the World Bank.

- Estache, Antonio, Atsushi Iimi. 2008b. Auctions with endogenous participation and quality thresholds: evidence from ODA infrastructure procurement. Mimeograph.

- Estache, Antonio, Atsushi Iimi. 2008c. Bidder asymmetry in infrastructure procurement: are there any fringe bidders? Policy Research Working Paper No. 4660, the World Bank.
32 reinterviewed in 1986; (3) the 1986 data from the half of the 1985 sample reinterviewed in 1986; and (4) the 1986 datd from the households newly included in the 1986 sample. This separation highlights differences between the 1985 and 1986 data. The infant mortality rates obtained for group (2) and group (3) can be expected to be similar if no significant distortions exist in the data.

Table 3. External Checks of 1985/86 LSMS Mortality Data for Cote dl'voire

|Survey|Period|Infant mortality<br>rate|
|---|---|---|
|LSMS- 1985 data|||
|Interview:|||
|1985 only|1975-79|106|
|1985 and 1986|1975-79|126|
|LSMS - 1986 data|||
|Interview:|||
|1985 and 1986|1975-79|84|
|1986 only|1975-79|82|
|IFS<sup>8 </sup>1981-82 (i)|1975-79|119|
|EPRb 1978-79 (ii)|1978-79|103|

Ilvorian Fertility Survey. _bEnquete a Passages Repetes_ Sources: i. Rutstein 1984. ii. Rep. de Cote d'Ivoire 1984b.

This comparison of the LSMS data which can be viewed as an internal check, yields two observations. The 1985 LSMS data give higher estimates of infant mortality than the 1986 data. And the comparison of infant mortality rates based on the same set of households in 1985 (group 2) and 1986 (group 3) suggests that infant deaths have been omitted disproportionately compared to live births still alive
# **Analysis of the Mismatch between Tanzania Household Budget Survey and National Panel Survey Data in Poverty & Inequality Levels and Trends**

Nadia Belhaj Hassine Belghith, Maria Adelaida Lopera, Alvin Etang Ndip and Wendy Karamba<sup>1</sup> Key words: Data collection, Survey, Poverty, Inequality JEL classification: C81, C83, I32, D63

> 1 Nadia Belhaj Hassine Belghith is a Senior Economist in the Poverty and Equity Global Practice of the World Bank; Alvin Etang is an Economist in the Poverty and Equity Global Practice of the World Bank; Wendy Karamba is an Economist in the Poverty and Equity Global Practice of the World Bank; Maria Adelaida Lopera is a PhD Candidate at University Laval, Quebec Canada. The authors wish to thank Isis Gaddis for providing support to finalize this work.
18 were working, while about 21 percent were in the formal sector.<sup>11</sup> In terms of industrial sectors, about 28 percent were in agriculture. The manufacturing, commerce and service sectors each employed about 11 percents of workers; while the construction sector employed about 4 percent of workers. Our covariates, also reported in Table 3, include province-level minimum wage, non- municipal area dummy, number of children under 6 years old in the household, number of children between 6-15 years old in the household, and number of elderly older than 64 years old in the household. The covariates also include the individual gender, age and education level.

The minimum wage in Thai Baht per day is obtained from the Thai Ministry of Labor. The Ministry of Labor reconsiders the minimum wage at least once a year. This reconsideration is done at the province level. Thus, minimum wage could potentially control for the trend of living cost, labor scarcity and the relative attractiveness of jobs in each province. The non-municipal area dummy variable, on the other hand, should highly correlate with the industrial sector the worker is in. For example, agricultural workers are more likely to live in non-municipal areas. The number of children under age 6, the number of children ages 6-15, and the number of elderly older than 64 are to control for the claims on resources of the household.

As for individual-specific covariates, we include gender, age and education. For the ease of analysis but without much loss of generality, we group age into 15-29 years old, 3049 years old, 50-64 years old and over 64 years old. We also group education attainment based on the highest level of education achieved. The categories include: less than primary

> 11 We categorize workers who were covered by the Civil Servant Medical Benefit Scheme (CSMBS) and the Social Security Scheme (SSS) as formal. This 21 percentage approximation is consistent with Na Ranong et al. (2004) as shown in Table 1.