rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex 1: Data**

The Sri Lanka Household Income and Expenditure Survey (HIES) is conducted by the Department of Census and Statistics (DCS) under the National Household Sample Survey Program. The HIES is a yearlong sample survey which is conducted in 12 consecutive monthly rounds and an island wide representative sample of equal size is enumerated in each monthly round to capture seasonal and regional variations of income, expenditure and consumption patterns. The HIES series which started in 1980 was conducted once in every five years until 2006/07 and thereafter, as rapidly changing economic conditions demanded far more frequent monitoring of income and expenditure patterns in the country, HIES is conducted once every three years. We use the HIES 2002, HIES 2009/10 and HIES 2012/13 rounds for this paper. Generally the HIES surveys a sample of 25,000 housing units throughout the country to facilitate the information be given at district level. A two stage stratified random sample design was used in the survey. Urban, Rural and Estate sectors of the district are the domains for stratification. The sample frame is the lists of buildings that were prepared for the Census of Population and Housing 2001.

# **Comparability of HIES 2002, HIES 2009/10 and HIES 2012/13:**

# **Coverage**

HIES 2002 was conducted during the period of January 2002 to December 2002 covering seven provinces excluding Northern and Eastern Provinces due to conflicts in those two provinces. Around 20,100 housing units were planned to survey and 16,924 households were completely responded. The HIES 2009/10 was planned to have a sample of 23,631 housing units which excluded Mannar, Kilinochchi and Mullaithivu districts due to massive mine clearance and resettlement activities existed in most of the areas. HIES 2009/10 and HIES 2012/13 were conducted during the period of twelve survey months from July of the first year to June of the second one. In HIES 2009/10 the Vavuniya district in the Northern province and the entire Eastern province survey was limited to 10 months starting from September 2009 to June 2010 and the Jaffna district was further delayed till December 2009. All together 19,958 households were completely enumerated. HIES 2012/13 is the first one to cover all 25 districts in the country, surveying 20,540 households.

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location. There are also outcome measures for children that can be linked to mother‘s marital status but only for children who reside with their mothers. These include weight for height percentiles, school enrollments and whether daughters are treated differently from sons. Although I cannot link non-cohabiting children with mother‘s marital status, I can compare enrollments of children who do and do not live with their mothers.

# **Tests for selection bias**

Two drawbacks of the following analysis should be kept in mind. First, the DHS cannot shed light on the large group of widows and other women who are older than 49, and possibly include the most vulnerable. Nothing can be done to address this data problem.

Second, the DHS sample of ever-widowed or divorced women is potentially selected in the sense that it may not be random that some women experience a marriage dissolution shock at a relatively young age. One potential objection to comparing outcomes for widows and other women is that there may be selection bias whereby widows start off with worse outcomes prior to becoming widows. For example low BMI could predate marriage and widowhood. Using longitudinal data on health and retirement in the US, it has been shown that, allowing for the fact that poorer men are more likely than other men to die young (given the strong association between income and mortality), young widows are more likely to come from poorer households (Hurd and Wise 1989, Sevak et al. 2003). However, the relevance of the US case to Mali ─ where widowhood at a young age is not usually associated with the death of a young husband given large age gaps between spouses ─ is questionable. In the Mali setting, it may be that, for example, women with initially low BMI marry much older men, increasing the probability of widowhood. In either case, the ―impacts‖ on well-being predate a husband‘s demise. The key issue is then whether women who become widows were already poorer, in which case there may be no causal effect of widowhood per se.

This second problem can be studied further. In the absence of longitudinal data that can identify economic resources prior to widowhood, the best test for such selection problems is to examine individual characteristics that are correlated with living standards but clearly pre-date widowhood and even marriage. If widows are primarily poor women that married poor and hence less healthy men then we would expect this to be reflected in lower average levels of education and height, measures which are typically highly correlated with poverty but largely pre-determined to marriage. Poorer women might also be expected to marry younger.

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- Pubtlic service aployeas entitled to private medical care as a fringe benefit. Coverage **is funded** through MMt bt et. Cost to WM budget was Ksh 34.7 m t t ion in 1985-b66 or 2.2X of to_ta MON recurrent **expnditure.**

- h Does not include workers in private sector Wio receive medical care from employer or have employer psy for medical **care** directly: see Table 3.

- ± Pubtic fee schedule distinguishes between those with nd without insurnce, but no information on health insurwnce available.

- J Each goverreant ministry is expected to pay 80X of health care **costs** for its employee.

- k Social Security **and** Other Public Insurance are not dditive **because** tlic enployees belong to both.

- I Goverrwent etloyee end their fi_ilies receive free goverrment health care; but because cost recovery is so low, mot of the rest of the population **also** receives free health care.

- m Sanegal _has_ a **Social** Security System, the Institutione de Prevoyance Maladie (IPI4). No data **are avoilable on** the rLber of nmbers. Every fire with 100 or **ore** eplotyees is required to form an InstitutionZ that will pay most of the costs of outpatient m*dical **care and** phar_ceuticals **_w*,_**

- genrally, all of the costs of hospitalization, including deliveries of children. Firm with less then 100 eloyees can join together to form en "Institution. The actuarial construction violates most **insurence principles, because** it ignores the law of **large** mmbers, which protects againet **adverse** selection **(see** Vogel, 198).

- G Covernv nt e ployees receive smtll monthly dedution from paycheck (1$ for<sup>M</sup> regulara employees) in return for free hoalth **care at** goverraent facilities.

Doe not include workers In industry, mines, **nd** commercial farm; **see** Table **3.**
maturity (1-3 months); there are only few loans with a maturity of 1 year or more. Those few firms with sources of revenue in foreign currency are able to obtain financing from banks in US dollars at lower interest rates.

With the exception of high-income households, public servants and employees of large companies, most Zambians do not have access to financing products offered by banks. Low salaries, lack of a job in the formal economy and high interest rates have precluded Zambian workers from having access to any type of consumer credit offered by the banking system.

In Zambia, most of the consumer lending consists of salary-loans. These are personal loans granted to employees of public institutions and large private firms. The monthly payment is directly deducted from the workers’ salary by the employer and remitted to the banking institution. Other types of consumer credit, such as credit cards and loans to acquire vehicles, do not exist in Zambia. Mortgages are offered by a few banks, but the aggregate volume of mortgages is still extremely low.

The above developments have resulted in a financial system that serves only a few people and firms in Zambia. Historically, the banking sector has been unwilling to lend to the medium, small and micro sectors of the economy due to the high levels of risk associated with this sector. The harsh economic conditions that prevailed in the country in terms of high interest levels and volatile exchange rates meant borrowers found it difficult to repay their loans leading to poor repayment rates. The situation was exacerbated by the inefficient legal system which made it difficult to seek redress through the courts, besides which, the small value transactions made it uneconomical to do so.

# **1.4 Role of micro-finance institutions (MFIs) in Zambia**

Unlike other countries in Sub-Saharan Africa where micro-finance institutions (MFIs) have grown by providing financial services to low-income households, in Zambia MFIs remain extremely small. According to data from the Association of Micro-finance Institutions of Zambia, MFIs only serve 50,000 customers, representing 0.005% of Zambia’s population. The small size of MFIs in Zambia is unusual even in the context of other low-income countries where MFIs have achieved a larger outreach. Microfinance institutions are small, and all together do not represent more than 2% of the assets of banking institutions.

As noted above, MFIs are not authorized to take deposits from the public. As a result, their main sources of funds are the commercial banks and the donor community. Because of their low volume of operations and high operating costs, loans granted by MFIs have higher interest rates than those offered by commercial banks. Most households borrow only short-term (one month) from MFIs to meet family needs or short-term liquidity needs, in the case of firms.

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collecting fuelwood in rural Saint-Louis. Valuing that time at an opportunity cost of $0.40-0.80/hr, we find that the total opportunity cost of fuel for the traditional wood stove users per month would be roughly $20.96-33.10.

**Table 7.  Comparing ethanol and traditional woodstove cooking in rural Saint-Louis region, Senegal**

|Parameter|Traditional wood stove|Ethanol|
|---|---|---|
|Stove purchase cost|0|$50-80<sup>&&</sup>|
|Fuelwood collection time<br>(for 1⁄4 of total use)|23 hours/month<sup>*</sup>|NA|
|Opportunity cost of<br>fuelwood collection time<br>(at $0.40-0.80/hr)<sup>**</sup>|$9.20-18.40/month|NA|
|Total fuel consumption|98 kg/month|0.5 liters/month<sup>#</sup>|
|Fuel price|$0.16-0.20/kg|$0.64/liter?<sup>##</sup>|
|Total opportunity cost of<br>fuel<sup>***</sup>|$20.96-33.10 per month|$19.20 per month?|
|Cooking duration<sup>&</sup>|156 hr/month|109 hr/month|
|Opportunity cost of<br>cooking time (at $0.40-<br>0.80/hr)<sup>**</sup>|$62.40-$124.80/month|$43.60-87.20/month|
|Total non-stove costs|$92.56-176.30/month|$62.80-106.40/month|

Sources: See notes.

*Based on collection time per week in Table 6.

**See text for discussion of the range of unit time values.

***Equal to the opportunity cost of collection time for one-fourth of total fuelwood use plus total purchase cost for three-fourths of fuelwood use.

&Entry for wood stove comes from Table 6. The figure for ethanol assumes that this cooking fuel and technology has about the same cooking time savings relative to a traditional wood stove as a kerosene stove, which in turn are taken from Jeuland and Pattanayak (2012) (the figures behind the summary cost numbers in Table 3).

&&Cost range for a more sophisticated stove; could significantly overstate the actual cost of a smaller and less durable stove using alcohol gel.

#See text for explanation of this figure.

##As noted in text, the actual price of ethanol fuel for consumers in Saint-Louis is uncertain.  This figure does not include distribution costs but likely is an upper bound for the cost of fuel at the plant gate.

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83. In interpreting the effect of internal supervision, the field research of Wheeler et al (in press), carried out in cooidination with the survey reported here, is instructive. For purposes of that field work, four matched pairs of schools were identified from our survey data, with one matched pair in each of the four broad geographic regions of Thailand (the North, the South, the Northeast, and the Central areas). All were small, rural primary schools, and each pair was matched on community socioeconomic status, modernity of infrastructure, and linguistic background of the students. However, within each pair, schools were selected to be maximally different on total achievement. Teams of field workers assigned to each pair intensively interviewed staff ana observed classes.

84. Their observations and interviews support our findings and elaborate their meaning. They found that in several of the most effective schools, regular internal supervision was a critically important component in the school principals' strategy to create and sustain a strong academic focus. Such schools were characterized by an "ethos of improvement" that encouraged teachers, for example, to come to school on time; to provide academic instruction in the afternoon as well as the morning; to use test results to evaluate instruction; and to discuss teaching in learning during lunch breaks. In some schools, principals who were effective at encouraging such an ethos were also active in mobilizing community resources to purchase instructional materials and in identifying resources at the district level that could support academic learning. In these settings it would be hard to imagine effective leadership without classroom supervision, but it is also clear that supervision is linked with a broader constellation of strategies for supporting the academic

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BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 18. Financial flows to Benin’s infrastructure**

<u>US$ million per year</u>

||Operations and<br>maintenance|||Capital expenditure<br>|||Total<br>di|
|---|---|---|---|---|---|---|---|
||Public sector|Public sector|ODA|Non-OECD<br>financiers|PPI|Total CAPEX|spenng|
|ICT|48|41|0|1|41|83|131|
|Irrigation|--|--|--|--|--|--|--|
|Power|71|29|13|1|0|43|114|
|Transport|41|16|46|6|0|68|109|
|WSS|15|11|59|0|13|83|98|
|Total|174|97|118|7|54|277|452|

_Source_ : Derived from Foster and Briceño-Garmendia (2009).

ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

Note: The public sector figures are averages of actual spending for 2007–09. In the case of SOEs, the average spans 2004–08 and in some cases 2004–09. Funding from external financiers is averaged over the 2002–07 period.

**Figure 21. Benin’s existing infrastructure spending**

<!-- Start of picture text --> As percentage of GDP<br>SSA<br>LIC-NoFragile<br>LIC-Fragile<br>MIC<br>ECOWAS<br>Resource-Rich<br>Zimbabwe<br>Liberia<br>Cape Verde<br>Ethiopia<br>Kenya<br>Ghana<br>Benin<br>Mali<br>Sierra Leone<br>Uganda<br>South Africa<br>Nigeria<br>Cote d'Ivoire<br>Congo, Dem. Rep.<br>0 2 4 6 8 10 12 14 16 18 20<br>% of GDP<br>Capex O&M<br><!-- End of picture text --> _Source_ : Derived from Foster and Briceño-Garmendia (2009).

Benin’s existing spending amounts to almost 10.5 percent of GDP, if 2005 GDP is taken into account (figure 21). This represents an average level of effort, almost at par with the average spending burden for low-income, nonfragile countries. Relative to its peer group, Benin is much more reliant on ODA in capital funding for the water sector.

Benin’s ICT and power sector receive larger shares of public funding than those of the low-income countries as a group (figure 22). All considered infrastructure sectors in Benin, except for irrigation, receive comparable funding than non fragile countries. A slightly larger share of infrastructure spending goes to ICT (29 percent), followed by power (25 percent), transport (24 percent), and water and sanitation (22 percent).

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10

# 2.2 The Financial Sector

To capture the distributional implications of adjustment programs, we distinguish five financial units: government, households, firms, the consolidated banking system, and the foreign sector. We assume that governments do not lend and that households do not borrow. Because of thin or nonexistant equity markets in most developing countries, it is not included here, and the endogenously determined proportion of household savings allocated to physical capital is made directly available to firms. Household savings is first allocated to cash balances, the remainder being allocated in a first stage between bonds and physical assets. In a second stage, expenditure on bonds is allocated between domestic and foreign bonds. Firms' financial requirements are for investment expenditures, working capital, and interest payments on their stock of domestic and foreign debt. The distinction between firms and households allows us to separate productive and distributional implications of adjustment packages. However, to avoid modeling the details of the process of creating inside money, we integrate the commercial banks and the Central Bank into an aggregate monetary survey, following IMF practice. The resulting simplified Zinancial structure is shown in Table 1.

# 2.3 The Government Sector

Critics of Bank-Fund supported programs point out that excessive reductions in government expenditures fall disproportionately on capital expenditures, and within current expenditures, disproportionately on health and education expenditures. In an analysis with a macroeconomic focus, it is not possible to capture meaningfully a direct link between type of government expenditure, productivity, and income distribution. Thus, we
Russian scientific community, it is acknowledged that the opportunity costs to be a scientist are high as other occupations, such as finance or real state are more profitable and carry a higher social status. Moreover, survey data shows that, at least at the beginning of 2000s, about 40% of researchers had R&D as a part time job (Yegorov, 2009).

Nevertheless, the rate of engineers (and scientists to a smaller degree) graduating from Russian universities is very high. The share of engineers among new university degrees in Russia (27.3 percent) is lower only than in Korea (Figure 8). Ukraine, Mexico, Japan, the United Kingdom, and the United States all had lower rates than Russia. By contrast, the equivalent figure for science degrees (5.8 percent) was lower than most comparators, at roughly half the share in Mexico and less than half the share in the United Kingdom.

There is evidence of a brain drain from Russia to the West, with a large share of science and engineering graduates studying abroad and not returning. Russia had the eighth-largest number

**Figure 8: Share of all new university degrees awarded in science or engineering, 2007**

<!-- Start of picture text --> Korea<br>Finland<br>Russia.<br>Mexico<br>Ukraine<br>Czech...<br>Engineering<br>UK<br>Turkey Science<br>Japan<br>Romania<br>U.S.<br>Brazil<br>0 10 20 30 40<br>%<br><!-- End of picture text --> <!-- Start of picture text --> Source: UNESCO.<br><!-- End of picture text --> of science and engineering graduates receiving PhDs in the United States in 2002 (Table 6). By 2007, less than one-fourth of those graduates had returned to Russia. The share of Russian graduates remaining in the United States is higher than graduates from other economies such as Korea and Turkey (and above the all-country average of 62 percent). Russia may better leverage the skills and knowledge of those graduates who remain in the United States and other countries where they study, through stronger and wider business and research Diaspora networks, similar to those established by China and India.

**Table 6: Foreign science and engineering PhD recipients in the United States and share staying there**

||**Foreign doctorate**<br>**recipients in 2002**||**% remainin**<br>|**g in the Unit**<br>|**ed States**<br>||
|---|---|---|---|---|---|---|
|**Country of origin**||**2003**|**2004**|**2005**|**2006**|**2007**|
|China|2,139|97|93|92|92|92|
|Korea|814|61|53|47|44|41|
|India|615|91|89|85|83|81|
|Turkey|315|53|49|46|44|42|
|Canada|258|65|64|58|52|55|
|Mexico|173|36|34|28|28|32|
|Germany|164|58|58|54|51|52|
|Russia|161|83|85|82|80|77|
|Japan|144|44|38|36|32|33|
|Romania|121|88|88|89|87|86|
|Brazil|119|32|34|32|30|31|
|United Kingdom|89|68|65|64|68|64|
|France|83|54|50|50|49|45|
|All countries<br>|7,850<br>|69|66|64|62|62|
|Source: Oak Ridge Associat|ed Universities, 2010.||||||

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**<u>Table 5:  Government Interactions and Family Management Practices</u>**

|**Model**||**OLS with Country**|**FE (Cross-section)**||
|---|---|---|---|---|
|**Outcome Variable**||**Overall Mana**|**gement Score**||
||**(1)**|**(2)**|**(3)**|**(4)**|
|All Family Management x Political connections|-0.083||||
||(0.053)||||
|Family Management share x Political connections||-0.092*|||
|||(0.051)|||
|All Family Management x Informal payments to get<br>things done|||-0.069**||
||||(0.031)||
|Family Management share x Informal payments to get<br>things done||||-0.074**|
|||||(0.031)|
|All management is family Y/N|-0.040***||-0.035***||
||(0.013)||(0.013)||
|Share of family management (0 to 1)||-0.038**||-0.032*|
|||(0.018)||(0.018)|
|Firm expected to make payment to get things done Y/N|||0.004|0.015|
||||(0.015)|(0.017)|
|Owner, Manager or Board Political Connection Y/N|0.009|0.017|-0.017|-0.018|
||(0.020)|(0.021)|(0.018)|(0.018)|
|Share of same family ownership (0 to 1)|0.013|0.015|0.011|0.012|
||(0.015)|(0.018)|(0.014)|(0.018)|
|Female top manager Y/N|0.011|0.010|0.011|0.009|
||(0.011)|(0.011)|(0.011)|(0.011)|
|Firm Formally Registered when Started Operations|0.048**|0.047**|0.044**|0.043*|
||(0.022)|(0.022)|(0.022)|(0.023)|
|Top manager experience in sector (years)|-0.001|-0.001|-0.001*|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Log of age of firm|-0.013*|-0.014*|-0.012|-0.012|
||(0.008)|(0.008)|(0.008)|(0.008)|
|Log of size|0.035***|0.035***|0.034***|0.034***|
||(0.004)|(0.004)|(0.005)|(0.005)|
|Direct exports 10% or more of sales Y/N|0.031***|0.031***|0.034***|0.034***|
||(0.010)|(0.010)|(0.010)|(0.010)|
|Foreign ownership Y/N|0.026**|0.025**|0.024**|0.024**|
||(0.011)|(0.011)|(0.012)|(0.012)|
|Establishment has checking or savings account Y/N|0.019|0.016|0.019|0.018|
||(0.022)|(0.022)|(0.023)|(0.022)|
|Establishment has a line of credit or loan Y/N|0.027***|0.028***|0.033***|0.034***|
||(0.009)|(0.009)|(0.008)|(0.008)|

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# **Table 2. Proportion of countries with explicit deposit insurance to total by category**

(in percent, as of 2003)

||**Proportion based**|**on**|
|---|---|---|
|**Category**|**Number of countries**<br>**GDP**|**GDP per capita**|
|_By income level_|||
|High income|75.00<br>96.35|83.45|
|Upper middle income|60.71<br>86.20|63.26|
|Lower middle income|58.82<br>57.56|64.25|
|Low income|16.39<br>78.11|17.26|
|_By geographical region*_|||
|Asia & Pacific|38.46<br>48.76|53.78|
|Europe & Central Asia|74.07<br>97.24|93.40|
|Latin America & Caribbean|66.67<br>98.34|71.11|
|Middle East & North Africa|28.57<br>16.36|42.84|
|Sub-Saharan Africa|10.87<br>17.12|3.63|

*Regional breakdown excludes high income countries

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