rafmacalaba/datause-encoder-data huggingface dataset:
_1. US Treasury securities:_ According to the Federal Reserve Bulletin, the US government securities market is the largest and most active debt market in the world.<sup>2</sup> In 2002, there were over $3 trillion in direct US government securities outstanding, not including debt instruments issued by US federal agencies and government sponsored enterprises.

US Treasury securities comprise Treasury bills (which have a maturity of one year or less), Treasury notes (which have a medium-term maturity of between one and ten years), and Treasury bonds (which have a maturity of more than 10 years).

_2. Federal government agencies and federally sponsored debt:_ The total issuance of long-term debt securities by federal government agencies and government sponsored enterprises amounted to just over $1 trillion in 2002.<sup>3</sup> The Government National Mortgage Association (Ginnie Mae) is a wholly-owned federal government corporation, and its guarantees are backed by the full faith and credit of the US government. The Federal National Mortgage Association (Fannie Mae) and The Federal Home Loan Mortgage Association (Freddie Mac) are government sponsored enterprises. They are publicly chartered entities that are privately owned and operated, and their guarantees are not backed by the US government. Nevertheless, they are still considered to be extremely low in credit risk due to their government affiliation, their strong fundamentals, the authority of the Secretary of the Treasury to purchase up to $2.25 billion of securities from each, and the belief that the US government would not permit them to fail due both to their size and government affiliation. Ginnie Mae, Fannie Mae, and Freddie Mac are together referred to in this Report as “GSEs.” _3. Municipal and state government debt:_ The sub-sovereign or municipal debt market in the United States has a long history and is very large and diverse. In 2002, there was approximately $1.8 trillion in sub-sovereign debt outstanding in the United States. Approximately $350 billion in long-term municipal debt was issued that year, with an additional $80 billion in short-term debt issuance. Total issuance of sub-sovereign debt in 2002 thus amounted to $430 billion, a 25% increase over 2001. This increase was due to several factors, including moderate economic growth, the weak stock market which led to a decline in the tax revenues and investment portfolios of municipalities, and the low interest rate environment. Of the total issuance of sub-sovereign debt, about $120 billion comprised refinancing and refunding activities, with the remaining being new funds. There are over 50,000 issuers of municipal securities in the United States and 1.5 million different issues.<sup>4</sup>

> 2 Federal Reserve Bulletin, 1999.

> 3 The Bond Market Association calculates the total as $1,041.5 billion.   The figure includes long-term debt issued by Federal Home Loan Bank, Fannie Mae, Freddie Mac, the Student Loan Marketing Association, the Tennessee Valley Authority, and the Federal Farm Credit Bank.

> 4 _See_ www.bondmarkets.com, and www.bondtrac.com.

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group called other industry. These averages, with those for construction, trade, transportation & communications are shown in Table 3.1 which also shows the average for all insured employees with a breakdown on public and private sectors. Published data from the State Institute of Statistics, SIS, are available, permitting computation of a separate wage series for mining & quarrying. An SIS series for mining & quarrying was accordingly computed and entered in the same table. The first two columns show the distribution of employees. The last two columns present indexes for 1984 with 1960 and 1970 = 1. For all series data exist for all years 1960-1984 with the exception of several years in the early and late 1960s and 1971 for the SMI series. Here is shown only the years 1960. 1970, 1979, 1981 and 1984.Ihat the years 1979 and 1981 were selected rather than 1980 is related to the event of the institution of a wage arbitration board by the military government in August 1980 to settle collective agreements and issue guidelines for wage increases with certain militant unions and strikes generally banned. The 1980 wages were thus the resultant of two entirely different systems of wage formation and for the sake of analysis it wi- thought preferable to show the last full year under the old system and the first full year under the new one.

Comparison between the SII series for manufacturing and the SMI series discloses a disturbing disparity. The SII series increased 97.7 times from 1960 to 1984, the SMI series no less

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# **3.2 Training Take-up**

Training took place immediately after the baseline surveys in each county, between June and November 2013. Of the 1,172 individuals assigned to training, 77.7 percent attended at least one day of training. Of the individuals who attended at least one day, 94.6% attended all five days. In Diwan et al. (2014) we report on a choice structure experiment intended to increase training attendance, and discuss the correlates of attendance. Age and marital status are strong and statistically significant predictors of attendance: all else equal, women aged above 35 are 35 percentage points more likely to attend training than those below 35, while married women are 24 percentage points less likely to attend than unmarried women. This potentially reflects the competing demands on their time from other household tasks. Women are also more likely to attend if they have previously participated in training (perhaps reflecting greater perceived benefits from attending), have a large household (potentially providing more people to undertake household and business tasks in their absence), and are located closer to the training venue (reducing travel time). Women who earn more profits are less likely to attend, perhaps reflecting a higher opportunity cost of time, or that they think there is less need to improve.

# **3.3 Mentoring Intervention**

After seeing the one-year follow-up results and emerging evidence suggesting that many business training programs may be too short to show sizeable impacts (McKenzie and Woodruff, 2014, 2015), we decided to add a mentoring component for half the sample assigned to training. Mentors were female business owners of a similar average age to our study sample, with 75 percent having had post-secondary education, and 68 percent having studied business or accounting, and a median of 5.5 years’ business experience. They were recruited and trained by the implementing partners Women Enterprise Fund (WEF) and Kenya Industrial Estates (KIE).

Each mentor was assigned a group of three to six mentees. The mentor program then involved the mentor meeting in a group with the mentees for 10 sessions, comprising of one session every two weeks for five months, following a standardized structure (Appendix 5). In addition, the mentor would meet with the individual mentees once a month over this period to provide individualized guidance. The mentoring process aimed to reinforce the business training by having the business owner identify core goals for their business, consider where there are gaps, explore options to fill these gaps, and then take action towards meeting these goals.

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With these caveats in mind, the results show that education is progressive in relative terms, and given the amounts spent, it is also equalizing.<sup>20</sup> In other words, the amount of spending on education is relatively high as a share of the incomes of those at the bottom of the distribution, although there is variation across educational categories, with lower secondary education being the most redistributive (Table A2.1). On average, 49 percent of all spending on education is devoted to the poorest 40 percent, however while 61 percent of primary and 66 percent of lower secondary spending is devoted to the bottom 40 percent of the distribution, only 23 percent of tertiary spending does so (Figure 13). This is because students in poorer households never make it into tertiary education.

Figure 13. Romania. Concentration of Education and Health <u>(by market income plus pensions quintiles)</u> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>In-kind education In-kind health<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Preschool Primary Tertiary<br>benefit<br>Lower secondary Upper secondary Post-secondary Net education transfers Minimum health Basic health benefit Net health transfers<br><!-- End of picture text --> <u>Source: World Bank estimates based on Romanian HBS (2016).</u> However, this does not mean that the resources devoted to education spending are adequate. Most striking is the fact that despite being progressive, the redistributive power of primary education is relatively low, not just in terms of other social spending in Romania, but also when compared to other countries (Figure 14). This is because the amount spent on education is relatively low compared to other countries (World Bank, 2018b). In Romania, only 1 percent of high-performing schools and about 83 percent of lowperforming schools are in rural areas. In fact, the gap in the quality of education between rural and urban areas (using PISA 2015 scores) is as high as 1.8 years of schooling. This is in part due to density, and the fact that public resources are allocated on a per-student basis. High-performing schools tend to have more than double the number of students than low-performing schools. This is not unexpected as it is difficult to attract and retain high-quality teachers in rural schools (and those situated in the outskirts of cities), so that they often have a higher proportion of substitute teachers, with teachers that are generally not as experienced as in those in urban schools. These factors negatively impact the quality of education and contribute to low student performance. Local authorities from large cities or rich rural areas add to the envelope of basic

> 20 We do not assess the poverty reducing impact of health and education spending as the recipients do not necessarily value these services at the cost of provision.

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Policy ReseaRch WoRking PaPeR 4467

# **Abstract**

Recently, developing countries have witnessed a sharp increase in foreign bank participation. The authors examine the impact on banking outreach using newly gathered data for Mexico, where foreign bank participation rose from 2 percent to 83 percent of assets during 1997-2005. Country-, bank-, and bankmunicipality level estimations show a decline in the number of deposit and loan accounts. While countryand bank-level estimations indicate an increase in the share of municipalities with bank branches and in the likelihood of bank presence, bank-municipality regressions show that only rich and urban municipalities benefited. Overall, the evidence is consistent with a decline in outreach.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand the effect of foreign bank entry on access to financial services. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tbeck@worldbank.org and mmartinezperia@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
A second set of problems arises because, while physicians and hospitals may correctly charge for what they actually do, they might not perform or recommend the appropriate procedure, despite knowing what that is: the know-do gap. Health insurance alters the relative price of different procedures, potentially influencing provider behavior in a way that could result in lower health care quality.

# Insurance Fraud and Administration

Health Insurance fraud ranges from 3-15 percent of program costs in OECD countries and 3-10 percent in the United States (Morris 2009). It probably accounts for a larger fraction of program costs in LMICs, as periodic reports (Ngetich 2021; Begue 2018) and audits suggest, but program-wide estimates are difficult to come by. Most countries have not made their health insurance claims data public.

Two problems that may be more salient in LMICs are denial of care and “surprise” or double billing. Denial of care refers to a situation where insurance cards are not honored at participating hospitals. Dercon, Gunning and Zeitlin (2019), who were the first to study this issue, show that denial of care is frequent in their setting in Kenya. Denial of care has two important implications. First, the decision to participate in the health insurance scheme depends on trust in the insurance system. Second, since denial introduces a new risk of the insurance not being honored, the overall risk with insurance may be higher than without. There is a state of the world in which the individual has paid the premium without receiving compensation in the bad state. If individuals are forward-looking, this implies that less risk averse individuals will be _more_ likely to take the insurance. By combining lab measures of trust and risk aversion with take-up decisions for an insurance product, Dercon, Gunning and Zeitlin (2019) show that both predictions hold in their data.

Surprise or double billing is a situation whereby health care providers charge the insurance company the reimbursable amount, but then levy additional (and illegal) top-ups from patients. Rather than providing an administratively mandated price for a procedure, insurance reimbursements are then better regarded as a partial subsidy for the service, with pricing determined both through the usual considerations of supply and demand elasticities, but also possibly price discrimination and the special characteristics of health care markets. Again, there are no nation-level studies of double-billing, since it requires surveys of insurance beneficiaries in addition to claims data. One of the few studies to combine administrative data and household surveys is Jain (2021) who studies double-billing in the Indian state of Rajasthan. We will discuss this study further below.

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according to whether they care about making such decisions. This approach would build on the newly existing method of asking who the respondent thinks the decision-maker should be (as captured in the Peterman et. al. 2015 study), and allow us to understand which spheres of decision-making matter most to the respondent, as well as the degree of input they would like to have.

# **4     Perceiving control and ability to achieve goals**

## **4.1     How has this been measured?**

As Kabeer (1999) writes, for the process of defining goals and acting on them to be meaningful and constitute agency, individuals need to perceive themselves as controlling or initiating their actions. Due its importance, the measurement of “sense of agency” has been the topic of substantial research across disciplines, including within cognitive science (e.g., Friston 2012; Haggard and Eitam 2015), where measurement tools comprise brain imaging studies and sensory tests capturing individuals’ perceptual differences between stimuli that are self-generated versus externally generated.

In the social sciences, sense of agency has been conceptualized – and accordingly measured – in line with the framework laid out in Skinner (1996). This framework classifies control constructs according to how agents, resources (means) and goal-related outcomes (ends) interact. Below, we review the most commonly used control constructs and their measures. While the reviewed measures were not specifically developed for the purpose of measuring women’s agency, some of the studies that use them include an examination of gender differences. Moreover, development programs focused on women’s empowerment have begun to specifically target sense of agency as a way to improve project outcomes. For example, in a project to teach Kenyan women how to market energy efficient cook stoves, researchers found that adding a training component to increase women’s sense of agency resulted in higher sales (Shankar et al. 2015).

### **4.1.1     Locus of Control**

Means-ends relationships refer to classes of beliefs about how outcomes are contingent upon certain actions. The main construct used to capture the means-ends relation is that of ‘locus of control’, derived from Rotter's social learning theory (Rotter 1966; 1982). An individual’s locus of control (LOC) is defined as the degree to which an individual believes that events are caused by one’s own behavior (internal locus of control) versus external factors (external locus of control). The most widely-used locus of control scale is the original 23-item scale proposed by Rotter (1966), which was later revised by Valecha (1972) into an 11-item version. Box 4 provides a sample of the statements.

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have a more diversified income base following the shocks implies that the poor are more vulnerable to economic shocks. These findings thus strengthen the need for the public provision of well-designed safety nets before implementing significant policy changes.

# **References**

- Alderman, H.  1996. “Saving and economic shocks in rural Pakistan”, _Journal of Development Economics,_ 51:343-65.

- Alwang, J., B. Mills and  N. Taruvinga. 1999. “Changes in well-being in Zimbabwe: Nonparameteric evidence”, Poverty Reduction and Social Development Africa Region, The World Bank, processed.

- Alwang, J. 2000. “Structural adjustment, drought and poverty in Zimbabwe: Changes in the 1990s”, Poverty Reduction and Social Development Africa Region, The World Bank, mimeo.

- Alwang, J., L. Ersado, and N. Taruvinga. 2001. “Changes in poverty in Zimbabwe between 1990 and 1996: Worsening outcomes under adverse conditions”, _Development South Africa_ , 18(5):553-79.

- Barrett, C.B., T. Reardon and P. Webb. 2001a. “Nonfarm income diversification and household livelihood strategies in rural Africa: Concepts, dynamics, and policy implications”, _Food Policy_ 26(4):315-32.

- Barrett, C.B., M. Bezunh and A. Aboud. 2001b. “Income Diversification, Poverty Traps and Policy Shocks in Cote d’Ivoire and Kenya”, _Food Policy_ 26(4):367-84

- Block, S. and P. Webb. 2001. “The dynamics of livelihood diversification in post-famine Ethiopia”, _Food Policy_ 26(4):333-50.

- Binswanger, H. and J. McIntire. 1987. “Behavioral and material determinants of production relations in land-abundant tropical agriculture”, _Economic Development and Culture Change_ , 36(1):73-99.

- Bound, J., D. A. Jaeger, and R. M. Baker. 1995. “Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak”, _Journal of the American Statistical Association_ , 90(430):443-50.

- Bromley, D.W., and J. P. Chavas.1989.  “On risk, transactions and economic development in the semiarid tropics”, _Economic Development and Cultural Change_ , 37(4):719-36.

- Bryceson, D.1996. “Deagrarianization and rural development in Sub-saharan Africa: A sectoral perspective”, _World Development_ , 24(1):97-111.

- Bryceson, D.1999. “African rural labor, income diversification and livelihood approaches: a long-term development perspective”, _Review of African Political Economy_ 26(80):171-89.

- CSA (Central Statistical Office). 1997. _National Accounts 1985-1996._ Harare: Government Printing Office.

- CSA (Central Statistical Office). 1998a. _Report on the ICES_ . Harare: Government Printing Office.

- CSA (Central Statistical Office). 1998b. _Poverty in Zimbabwe_ .  Harare: Government Printing Office.

- Coate, S. and M. Ravallion.1993. “Reciprocity without commitment: characterization and performance informal insurance arrangements”, _Journal of Development Economics_ , (40):1-24.

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BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Bibliography and references

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project website: www.infrastructureafrica.org. For papers go to the document page (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

- AICD (Africa Infrastructure Country Diagnostic). ―Africa’s Infrastructure: A Time for Transformation.‖ www.infrastructureafrica.org.

Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for_

- _Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

World Bank. 2010. _Botswana Public Expenditure Review_ . Pretoria: World Bank.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

- Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

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