rafmacalaba/datause-encoder-data huggingface dataset:
commonly held viewpoint that various kinds of inequalities interact with and reinforce each other. Multi-dimensional deprivation operates to confine some geographical pockets and social groups in ‘inequality traps’. The cause for worry is that these disadvantaged districts and blocks within a district also have relatively poorer school infrastructure.

# **Section 4**

# **State and District level financial allocations under SSA in the early years: Did investments reflect need?**

# **_EDI and PCA_**

While the construction of EDI is useful in understanding the relative development of education provision and outcomes spatially, of various states and districts, and benchmarking them, it becomes meaningful when used as evidence for targeting funds and facilitating better implementation strategies. It would be appropriate that districts with a large infrastructure gap, greater shortage of teachers, high proportion of children not attending schools, high dropout rates or high gender gaps in enrolment would need proportionately higher financial resources to make up for the greater distance they need to cover for achieving universalization of elementary education. It is not our argument that all problems of educational deprivation can be addressed by higher financial investment. But higher financial allocations to districts that have clearly identified ‘greater needs’ would be the first step in ensuring an equitable approach to the problem of large educational disparities in our country.

Per Child Allocation (PCA) under SSA as an indicator is used here to examine whether the allocations of funds are going in an equity oriented manner, i.e. in accordance with relative need as evidenced by the indicators of educational inputs and outcomes. In an ideal situation, the PCA of educationally lagging states / districts should be higher than those of better off states / districts. The PCA of SSA funds in the year 2004-05 is analyzed vis-à-vis EDI in 2003-04 both at state and district levels to see whether the more deprived regions, which are also the more deserving ones, were allocated relatively higher funds. These allocations were made before such a disaggregated district level analysis of provisions and outcomes were made through EDIs. A similar analysis that compares SSA allocations to states and districts during 2007-08 and 200809 to the EDIs are presented in the next section.

# **_EDI 2003-04 and PCA 2004-05_**

The analysis that we present below indicates that in the early years of SSA, financial investments seem to have had no relationship with educational disadvantage. In several parts of the country, educationally and economically better off regions were able to get higher resource allocations, while the ‘worse-off’ areas ended up getting a smaller share of resources. _Thus, per child allocations (PCA) in SSA were lower in a large proportion of the educationally most disadvantaged districts of the country._ 13
13 31. In the main, the economic and financial effects of privatization in transition have been positive. As elsewhere, private owners in transition are more efficient and effective than the state. There are some caveats to this conclusion, but on average, the finding is robust.<sup>One need</sup> only look at those transition countries that have delayed or avoided privatization (and other reform) to see the limits of the alternative approach. However, the vast scope and rapid pace of privatization in transition-much of it taking place in countries with ineffective<sup>public</sup> administrations, weak legal systems, and embryonic regulatory capacities, particularly concerning financial and capital markets-led to much corruption, injustice, asset-grabbing, insider trading, and defrauding of minority shareholders, particularly those who had<sup>obtained</sup> their shares in give-away, voucher schemes. This has proven costly socially and politically.<sup>In</sup> sum, the shift to private ownership was necessary, but it should ideally have been<sup>better</sup> managed.

# **(ii) Macroeconomic and fiscal effects**

32. Recent work on the fiscal and macroeconomic effects of privatization (Davis,<sup>Ossowski,</sup> Richardson, and Barnett, 2000) indicates large, significant and positive benefits<sup>from</sup> privatization. Governments tend to be financially better off after privatization than before. Proceeds from privatization have been substantial, amounting to 2 percent of GDP in<sup>a sampleof</sup> 18 countries reviewed. The fiscal situation tends to improve over time, as receipts of privatization are saved rather than spent.<sup>5</sup> Privatization produces positive impacts upon government revenue by means other than sales proceeds. Transfers decline substantially following privatization (Figure 3) and broader indicators of consolidated SOE accounts<sup>for some</sup> countries indicate a large decline in deficits.

<!-- Start of picture text --> Figure 3: Gross Budgetary Transfers and Subsidies to<br>Public Enterprises for Selected Countries<br>(in percent GDP)<br>5<br>3-<br>2<br>o~~~~~ 0 SE .i~ lO , 4'<br>'Excludes Petr6leos Mexicanos. Includes some decentralized govemment agencies.<br><!-- End of picture text --> <!-- Start of picture text --> Source: Davis, Ossowski, Richardson and Bamett, 2000.<br><!-- End of picture text --> 5 Most of the countries covered in the study had an IMF program in place, and consequent limitations<sup>onthedeficit</sup> may have influenced this finding.
In order to identify strategies that are useful for improving the lives of slum dwellers, there has been recent interest in evaluating the effectiveness of slum upgrading programs. As part of this, the World Bank recently commissioned a paper to provide guidance on how to estimate the impact of slum upgrading interventions (Field and Kremer 2005). Related to this effort, there have been several initiatives where baseline data are being collected so that they can be used to set up rigorous evaluations of project outcomes. The fly in the ointment in this evaluation strategy is the assumption that interventions happen in first-best settings. Similarly, much of the _ex-ante_ cost-benefit estimation of these interventions also assumes market clearing. Clearly, this is not the case in most developing countries where there are pre existing distortions in the land (for example, excessive zoning, development controls) and credit (higher loan rates) markets. These pre existing distortions not only tend to reduce the cost effectiveness of interventions, but also may in fact change the welfare rank ordering across interventions. Thus, assessing the benefits of interventions without accounting for pre existing distortions may in fact be misleading. The performance of **_in situ_** slum upgrading depends on the severity of land and credit market distortions, and how complementary policy initiatives are being implemented to correct for these problems.

In this paper, we develop a dynamic general equilibrium model to compare the effectiveness of alternative instruments for improving the welfare of slum dwellers in a second-best setting with distortions in the land and credit markets. With four decisionmaking agents in our economy- households, developers, financial institutions and the government, we analyze how land or credit market distortions alter the rankings of different policy instruments. We also tested the effects of building caps and infrastructure 4
3 developed and grow less rapidly than larger states. In this paper, we test this hypothesis using cross-country data ia a large sample that includes many microstates. In light of the grim predictions of theory, the picture of microstates which emerges from this analysis is somewhat surprising. After controlling for a range of factors, we find that microstates have on average higher income and productivity levels than small states, and grow no more slowly than large states. Per capita GDP growth rates are more volatile in microstates, due to their much greater exposure to international trade and fluctuations in their terms of trade. However, any growth disadvantages of this greater volatility are more than outweighed by the growth benefits of trade openness reaped by microstates by virtue of their necessarily large trade volumes. Finally, microstates are well-positioned to take advantage of opportunities international risk sharing, since the correlation of economic fluctuations in microstates with the world business cycles is surprisingly low.

These resulls contribute evidence in support of the growing view in the literature that small size might not be a disadvantage after all. Kuznets (1960) notes that small states also have advantages: primarily that many are lucky to have good natural resources and have a small and more cohesive populations which allows them to adapt better to change. Srinivasan (1986) and Streeten (1993) argue without systematic empirical evidence that small may also be beautiful. Using a sample of 48 countries Millner and Westaway (1993) fail to find evidence that the effect of a number of growth determinants varies with country size. Armstrong et. al. (1998) uses crosssectional regressions covering a large number of small states and independent regions to argue that population size. does not significantly affect growth, controlling for initial income and regional effects.

The remainder of this paper proceeds as follows. In the next section we document that small states are richer and have higher productivity levels than small states. In the following section we observe that small states suffer no obvious growth rate disadvantage, and attribute this to a number of offsetting advantages and disadvantages of small states. In Section 3 we document that although trade openness contributes significantly to the greater volatility of growth in microstates, this is not the whole story. In Section 4 we note that microstates are relatively well-positioned to take advantage of opportunities to diversify away their special risks since they currently are not particularly financially open and the shocks they receive are relatively uncorrelated with those experienced by the rest of the world. Section 5 offers concluding remarks.
March 1, 2015, March 17, 2015, and April 2, 2015. There is no evidence of smoke on March 1 image. Smoke plumes from the three oil production sites are visible in the March 17, 2015 image and portions of the sites display smoke on April 2. Large-scale smoke, evidence of fighting, is visible over the area on 18 April 2015 Landsat.

Table A.4: Ajil field

|**Dates Removed**|**Temperature(K)**|
|---|---|
|March 5,2015|1356|
|March 6,2015|1297|
|March 7,2015|1350|

First, Ajil is situated northeast of Tikrit near the Hamrin Mountains in Iraq. Daesh took control of the field in June 2014 and continued production without interruption until January 2015. The field was set on fire by the group to counter an attack by Iraqi forces in starting March 5, 2015 [9] and Daesh ceded control of the site on March 8, 2015. Fires reportedly continued for a few weeks as Iraqi engineers worked to put the site back to production and data corroborate this activity at the site: temperature observations after Daesh lost control generally remain below 1400 degrees K until 19th March 2015.

Table A.5: Hamrin area 1

|**Dates Removed**|**Temperature(K)**|
|---|---|
|March 5,2015|1270|
|March 6,2015|1282|

Second, we reached similar conclusions for nearby Hamrin sites. Like Ajil, Daesh fighters set fires in wells in the Hamrin Mountains during March 2015 to curb the same advance of the Iraqi forces before they lost control of the area on March 7th, 2015. The same Landsat images depict Hamrin on March 1, 2015, March 17, 2015, and April 2, 2015. Smoke plumes from the oil field are visible in the March 17, 2015 image and there is no definitive evidence of smoke on March 1 and April 2.

35
marketers of ACP fruit have not needed to emphasize the need for productivity gains and new technology to the extent of competitive marketers of bananas in the European Union.. In competitive exporting countries big productivity gains have been achieved through technology transfer and highly integrated production and rnarketing systems. By some accounts, the same banana marketing companies operating in both protected and unprotected markets have been much more active in investing in productivity enhancing techniology in the unprotected markets than in the protected ones.

In as much as the 'aid' component of banana prices is captured by landowners in the preferred supplying countries the distnrbution of the aid is inefficien **t** oo. Far better would be to use aid for infrastructure developmen **t** hat will assist in long term development of efficient industries upon which small island economies can stage their own development.

Further, to the extent that aid is transferred through _quota rents_ it is unreliable. The aid is not transparent, and it could be captured by others. So long as it is not delivered direcdy it cannot be guaranteed or channelled to most productive uses. When looldng ahead twenty to thity years, the futility of protecting inefficient industries seems more apparent. Over such a period, sometiing is bound to occur to jeopardize specific product assistance - as occurred with US sugar aid.

Chart 5.1 reinforces the eventual futility of granting aid through prices. EU national policies fix the prices to preferred suppliers, yet real banana prices have been on a downward trend. As efficient producers become more efficient in a competitive market and world prices falL EU and ACP producers receiving assured and higher rea prices become less compeiive and more dependent on aid. They are under lttle pressure to adjust and resources are increasingly being locked into an industry which has no long term hope of being competitive.

<!-- Start of picture text --> Chchart 5.1 The downward tend in wolld baxnana plices<br>.~~~~~~I<br>I. :.. 15:17: . . .- 196. -.95 ::9<br><!-- End of picture text -->

**_Data soncN_** World **Bart (199.**

Currently the ACP counties have a short term incentive to increase banana production to ensure they have sufficient quantities to fill their quotas in bad years. Failure to fill them may give EU officials reason to realocate the quota Further, the requirement that 30 per 23
Savings Defaults and Payment Delays for Cash Transfers: Field Experimental Evidence from Malawi<sup>_∗_</sup> Lasse Brune,<sup>_†_</sup> Xavier Gin ́e,<sup>_‡_</sup> Jessica Goldberg,<sup>_§_</sup> and Dean Yang<sup>_¶_</sup>

> _∗_ The authors gratefully acknowledge the financial support of the Yale Savings and Payments Research Fund at Innovations for Poverty Action, sponsored by a grant from the Bill & Melinda Gates Foundation. We thank Stacy Pancratz for her excellent oversight of field work, and we appreciate helpful comments from Joshua Blumenstock, Tarek Ghani, Susan Godlonton, Margaret McConnell, and Lore Vandevalle. All errors and omissions are our own.

> _†_ Yale University, lasse.brune@yale.edu

> _‡_ World Bank, xgine@worldbank.org

> _§_ University of Maryland, goldberg@econ.umd.edu

> _¶_ University of Michigan, deanyang@umich.edu
**<mark>Table 6– Labor Force Participation of Grandmothers and Grandfathers by 1996</mark>**

||**Children’s Grand Mothers’**<br>**Labor Force Participation**|**Children’s Grand Fathers Labor Force**<br>**Participation**|
|---|---|---|
|Refugeearea#in1996sample<br>(Kagera and Kigoma)|0.0562**|0.0346|
||(0.0238)|(0.0234)|
|_N_|_3317_|_724_|
|Adj. R<sup>2</sup>|0.035|0.004|

**<mark>Source:</mark>** <mark>1991/1992 and 1996 Tanzania DHS. Standard errors in parentheses, Standard Errors clustered at the cluster level. In addition to cluster fixed effects, the regression controls for the gender of the head, the birth order and gender of the child but also mother and child year of birth fixed effects; * p < 0.10, ** p < 0.05, *** p < 0.01.</mark>

**<mark>Table 7 – Labor Force Participation, Education and Assets of Mothers and Fathers in 2015/2016 (Model 6.1)</mark>**

||**Labor Market Participation**|**More than Secondary**|**Own land**|**Own house**|
|---|---|---|---|---|
|Q=1, Mothers|0.127***|-0.0365*|-0.0644***|-0.0827***|
||(0.0421)|(0.0196)|(0.0212)|(0.0229)|
|N|4910|4912|4912|4912|
|Q=1, Fathers|0.0698|0.00343|-0.0985***|-0.0869***|
||(0.0524)|(0.0351)|(0.0343)|(0.0321)|
|_N_|_2191_|_2197_|_2197_|_2197_|

**<mark>Source:</mark>** <mark>2015/2016 Tanzania DHS. Standard errors in parentheses. Standard errors are clustered at the cluster level. In addition to cluster fixed effects, the regression controls for the gender of the head, the birth order and gender of the child but also mother and child year of birth fixed effects; * p < 0.10, ** p < 0.05, *** p < 0.01.</mark>

33
Policy Research Working Paper 6563

# **Abstract**

This paper provides an overview of the state of access to bank financing for SMEs in five Sub-Saharan African countries and analyzes the drivers behind banks’ involvement with SMEs. The paper builds on data collected through five in-depth studies in Kenya, Nigeria, Rwanda, South Africa, and Tanzania between 2010 and 2012. The paper shows that the share of SME lending in the overall loan portfolios of banks varies between 5 and 20 percent. Reasons for this finding vary, but key contributing factors are the structure and size of the economy and the extent of Government borrowing, the degree of innovation mainly as introduced by foreign entrants to financial sectors, and the state of the financial sector infrastructure and enabling environment.

This paper is a product of the Finance and Private Sector Development Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gberg@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
% College 11% 13% 9% 14% 16% 12% 16% 17% 15%

|B. Working sample||||||||||
|---|---|---|---|---|---|---|---|---|---|
|N|536,771|345,765|191,006|530,018|320,346|209,672|268,357|160,115|108,242|
|Age|38.5|38.8|37.8|39.7|40.0|39.1|40.3|40.4|40.0|
|% Full-time work|85%|92%|73%|82%|89%|71%|82%|88%|72%|
|Monthly Earnings|$6,133|$6,449|$5,330|$6,937|$7,386|$6,003|$5,714|$6,018|$5,116|
|Hourly Wage|$29.3|$30.0|$27.1|$32.5|$33.5|$30.0|$26.8|$27.4|$25.6|
|% Married|72%|80%|57%|71%|79%|58%|70%|77%|58%|
|% Rural|33%|36%|28%|32%|34%|28%|33%|36%|28%|
|% Primary or less|49%|51%|46%|41%|42%|38%|32%|34%|30%|
|% Junior High||||||||||
|School|20%|22%|18%|25%|26%|22%|29%|30%|26%|
|% High School|17%|14%|22%|18%|16%|22%|21%|20%|23%|
|% College|14%|13%|15%|16%|15%|18%|18%|16%|21%|

Notes: Authors’ calculations using quarterly Labor Force Surveys. Each year uses information from all available quarters: 2000 uses quarters 2-4, 2007 uses quarters 1-4, 2014 uses quarters 1-2. Full sample uses all individuals ages 23-65, and working sample uses all individuals with positive hours of work ages 23-65. Monthly earnings and hourly wages are restricted to full-time workers (at least 30 weekly hours of work) and are in constant Mexican Pesos of the first quarter of 2014. Married includes cohabitation. Rural refers to individuals living in localities with less than 15,000 inhabitants. Primary or less refers to individuals with less than 9 years of schooling, Junior High School refers to individuals with at least 9 years but less than 12 years of schooling, High School refers to individuals with at least 12 but less than 16 years of schooling, and College refers to individuals with at least 16 years of schooling.

# **3. Results**

Our results can be summarized as follows. The Datt-Ravallion decomposition (Table 2) shows that most of the change in the proportion of workers below the baseline wage is due to a shift in the distribution rather than to a change in its shape. The skewing towards the lower end of the wage distribution exists but it is very small (see Appendix). Based on this result, there does not seem to be evidence consistent with a degraded tertiary effect or demand-supply mismatch of skills for the younger workers in particular. However, this exercise is not sufficient to rule out the presence of labor market dynamics that hurt the younger age group in particular. This is because the Datt–Ravallion decomposition is anonymous in the sense that we are not able to track who is losing relative to other workers. For example, it is possible that changes in the age composition of the workforce are such that although younger workers are increasingly receiving lower wages, their share in the workforce is declining.

**Table 2: Datt-Ravallion Decompositions: Log Hourly Wage. Restricted to workers with college.**

5
# **Figure 6a.  Optimal risk-financing strategy for a farmer with large asset base**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60% self-retention<br>50% Savings<br>40% Insurance<br>30%<br>20%<br>10%<br>0%<br>90 80 70 60 50 40 30 20 10 5 4 3 2<br>return period (years)<br>Assumptions: β = .098 , r = 5% , γ = 2 , λ= 3.0 , pl = 1% .<br>Source: Authors.<br>average loss per event<br><!-- End of picture text -->

**Figure 6b.  Optimal risk-financing strategy for a farmer with small asset base**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60% self-retention<br>50% savings<br>40% insurance<br>30%<br>20%<br>10%<br>0%<br>90 80 70 60 50 40 30 20 10 5 4 3 2<br>return period (years)<br>average loss per event<br><!-- End of picture text --> Assumptions: β = .098 , _r_ = 5% , γ = 2 , λ= 3.0 , _pl_ = 1% , internal financial capacity: 3%. Source: Authors.

# **_4.3. Combining Insurance and Contingent Debt_**

Contingent borrowing is a post-loss financing instrument offered by financial markets as an alternative risk-financing solution. It is arranged on an ex ante basis. The agent first pays a loan contingent fee and, in the event of a loss, it receives the full principal of the prearranged loan that will be repaid over time. At the farmer’s level, such an arrangement can be viewed as a pre-agreed emergency loan. Gurenko and Mahul (2003) develop an analytical model to examine the optimal risk-sharing arrangements using insurance, self-retention and contingent debt for different layers of risk. They show how the risk financing structure as well as the risk sharing rule within each layer of risk are driven by the cost of internal capital, the cost of insurance, the cost of external capital and the risk characteristics. Under some 18