rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Definitions of and data sources for variables**

|Variable Name|Data Source|Definition|
|---|---|---|
|_Firm level_|||
|Lgrow|ES|One-year employment growth rate as calculated as follows: dividing the change in<br>employment between the survey year and three years earlier by the simple<br>average of employment in the beginning and ending years.|
|TFP|ES|Industry-specific total factor productivity (in logarithm), winsorized highest and<br>lowest 1%. TFP is the firm specific residual of ln(value added) from a<br>regression of ln(value added) on ln(capital), ln(labor) and industry fixed<br>effects. For variable construction details, see Reyes et al. (2018).|
|lnLP|ES|ln(sales / number of employees in constant U.S. dollars), winsorized highest and<br>lowest 1%. For variable construction details.|
|Biggest owner share|ES|The ownership share of the largest owner.|
|Firm micro|ES|The firm had 1–20 employees three years ago.|
|Firm small|ES|The firm had 21–50 employees three years ago.|
|Firm middle|ES|The firm had 51–100 employees three years ago.|
|Firm large|ES|The firm had more than 100 employees three years ago.|
|Age 6-10|ES|The firm is between 6 and 10 years old.|
|Age 11+|ES|The firm is more than 10 years old.|
|Exporter|ES|The firm is an exporter.|
|Foreign Share|ES|The share of foreign ownership of the firm.|
|Foreign Firm|ES|Whether the share of foreign ownership of the firm is not zero.|
|_City-industry level_|||
|||City-industry average of the firm’s answer on whether corruption constitutes an<br>obstacle. The original ranking is from 0 (no obstacle) to 4 (very severe|
|ES Corruption Obstacles|ES|obstacle). 2 (moderate obstacle) and above are treated as “having obstacle”(the<br>logic is the same for skilled labor availability, access to land and informal<br>competition).|
|ES Security Cost|ES|City-industry average of the share of a firm’s sales paid for security.|
|ES Web|ES|City-industry share of firms that answer that they use websites to conduct business.|
|ES Power Outage|ES|City-industry share of firms that had ever experienced a power outage in the survey<br>year.|
|ES Skill Obstacles|ES|City-industry average of the firm’s answer on whether skilled labor availability<br>constitutes an obstacle.|
|ES Overdraft|ES|City-industry share of firms with an overdraft facility.|
|ES Land Access<br>Obstacles|ES|City-industry average of the firm’s answer on whether access to land constitutes an<br>obstacle.|
|ES Informal Competition<br>Obstacles|ES|City-industry average of the firm’s answer on whether competition with informal<br>sectors constitutes an obstacle.|
|_Country level_|||
|DB Contract<br>Enforcement|DB|Country-level Contract Enforcement score.|
|DB Get Electricity|DB|Country-level Getting Electricity score.|
|DB Get Credit|DB|Country-level Getting Credit score.|
|DB Construction Permit|DB|Country-level Dealing with Construction Permits score.|
|WB HLO|Angrist et al.,<br>(2019)|Country-level Harmonized Learning Outcome score. It is one of the factors used to<br>construct the World Bank Human Capital Index.|

28
**Figure 4  Investment Policies and Government Effectiveness**

<!-- Start of picture text --> 3 3<br>2<br>2<br>1<br>1<br>0<br>0<br>-1<br>-1<br>-2<br>-2 -3<br>0 20 40 60 80 100 0 2 4 6 8 10 12 14 16<br>Duration – Total portfolio (months) Number of eligible assets<br>N=52 and 97 respectively.<br>Source: World Bank, Transparency International.<br>Government effectiveness Government effectiveness<br><!-- End of picture text -->

# **6. Multivariate Regression Results**

We use regression analysis to delve into some of the correlation analysis on central bank governance and risk. We test the extent to which governance arrangements matter for risk taking and diversification in foreign reserves portfolios while controlling for the broader governance environment in which central banks operate, the level of reserve adequacy, the macroenvironment, or country risk.<sup>15</sup> Additional regression results are found in Appendix II.

**_Independent Investment Committee_** .  We examine if approving the investment guidelines at the board or the investment committee level makes any difference for reserve management operations. In most central banks, either can have this role. The most significant advantage of leaving this decision to the board is that it has more authority. However, central bank board members may not be financial experts and have less time to focus on reserve management policy. By contrast, the investment committee has less authority, but it can meet more often, and the members usually understand operational nuances and have financial expertise. We test whether the independence of an investment committee influences central bank investment policies, as suggested in our correlation exercise. As explained, as reserve adequacy, the general macroenvironment, and government effectiveness may influence a central bank's risk taking as expressed in their investment policy, we control for these factors. We do not find with any statistical significance that having the investment committee approve guidelines matters, even when different control variables are used to describe the macroenvironment (see Table 7).

15. This choice of variables was influenced by a general-to-specific regression (GETS) run as a way of selecting the most relevant variables out of a relatively large sample of variables when fitting a regression model.

14
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

## **Achievements**

The share of the population in Mali that continues to practice open defecation or use surface water is among the lowest in the continent. In 2006, about 4 percent of the population relied on surface water, which is eight times lower than the average for low-income countries in Sub-Saharan Africa. Similarly, around 20 percent of the population practices open defecation, which is roughly half the level in other low-income countries (table 7).

**Table 7. Benchmarking water and sanitation indicators**

|||Low-income<br>countries|Mal|i|Middle-income<br>countries|
|---|---|---|---|---|---|
||Unit|,<br>mid-2000s|Early 2000s|Late 2000s|,<br>mid-2000s|
|Access to piped water|% pop|10.5|8.9|10.8|52.1|
|Access to standposts|% pop|16.2|20.2|15.5|18.9|
|Access to wells/boreholes|% pop|38.3|65.1|69.5|6.0|
|of which, protected wells/boreholes|% pop||14.2|29.8||
|of which, unprotected wells/boreholes|% pop||50.9|39.7||
|Access to surface water|% pop|37.4|5.8|4.2|13.0|
|Access to septic tanks|% pop|4.9|6.0|2.4|40.8|
|Access to improved latrines|% pop|9.9|40.8|47.6|1.4|
|Access to traditional latrines|% pop|50.1|30.8|28.6|30.4|
|Open defecation|% pop|40.3|22.2|21.3|14.3|
|Domestic water consumption|liter/capita/day|72.4|39|44|165.9|
|Revenue collection|% sales|92.7|96.0|96.0|100.0|
|Distribution losses|% production|34.3|37.0|25.5|26.8|
|Cost recovery|% total costs|56.0|—|70|80.6|
|Operating cost recovery|% operating costs|65|—|98|145|
|Labor costs|connections per<br>employee|158.6|170.4|210.8|368.7|
|Total hidden costs as % of revenue|%|162.7|184|124|140.4|
|||**Mali**|Countr|ies with scarce w|ater resources|
|Residential tariff (US cents per m<sup>3</sup>)||25.2||60.26||

Source: Demographic and Health Survey and AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. DHS figures are as of 2006. Utility numbers are as of 2001 and 2007. Note: Tariff data as of mid 2000s In less than two decades, Mali has managed to provide access to improved water and sanitation to more than half of its population, or in the worst case (figure 10). The share of the population with access to improved water doubled from less than 30 percent in 1987 to 60 percent in 2007 (figure 9a). Over the same period, access to improved sanitation rose from a 40 percent share to over 50 percent. The migration of people from no services to improved services has been achieved by providing shared low-cost alternatives. For instance, 40 percent of the 60 percent of the population with improved water access, has 19
sample do not spend most of their waking time outside school in housework or paid-work; they spend the time playing. In fact, an average child who is in school spends 2.5 hours a day “playing”; an average child who is _not_ in school spends just under 3.5 hours “playing”. Given these large numbers, if a mother makes sure that her child spends an extra hour a day studying it does not imply any trade-off with other “productive” tasks that the child is responsible for, and thus probably avoids any conflict with the husband.

We feel that these results contribute to our understanding of education in low-income countries in a number of different ways. First, given low female labor force participation it is difficult to compute the rate of return to education for women in low-income countries. Our results suggest that one way to capture these non-labor market returns is through the application of time-use data in household surveys. Second, the findings suggest that some reorganization of the dominant line of thought linking maternal education to child outcomes may be necessary. Most studies of the inter-generational transmission of human capital show an association between maternal education and child enrollment, which are then assumed to be causal. Two recent studies (Behrman and Rosenzweig, 2002 and Black and others, 2005) show that in high-income countries, the causal effects of maternal education on child attainment levels are absent, or much smaller than previously believed. Desai and Alva (1998) argue along the same lines for lower income countries. Similarly, we are also unable to find any evidence for a link between maternal education and child enrollment. While this could be because our estimates are imprecise, there is a possibility that such a causal link is absent. Instead, we are able to document a causal link between child _learning_ and maternal education. This is of direct interest for policy, since recent experiments have shown that government policy can increase enrollment (Fiszbein and Schady, 2009) while methods of improving learning remain tenuous.

# **Section 1: Data Description**

We use a unique dataset on households and children from 112 villages in 3 districts of Punjab— home to 56 percent of Pakistan’s population. The Learning and Educational Achievements in Punjab Schools (LEAPS) study follows a panel of households from 2003-2005 in 3 districts of the province—Attock, Faisalabad and Rahim Yar Khan. These districts represent an accepted stratification of the province into North (Attock), Central (Faisalabad) and South (Rahim Yar Khan). The villages were chosen randomly from 7
the likelihood of returning, while having children in the home country and being married increase the likelihood of returning. Constant and Massey (2003) find the likelihood of emigrants returning home from Germany increases if migrants remit home, have strong ties with home, are not wage-employed or have been a host resident for less than five years. Indicators of greater social assimilation within Germany reduce return likelihood.

Neckby (2006) differentiates between return migrants and migrants who emigrate from the host country to another host country and finds the likelihood of both increases with education. What kind of economic activities return migrants choose has been analyzed by Dustmann and Kirchkamp (2002), Radu and Epstein (2007) and Shima (2010) in Turkey, Bulgaria and Romania. Although the first two studies found that the more educated return migrants choose selfemployment activities, something that is explained by higher returns to education in this activity compared to an employee, Shima finds that skills acquired abroad and the intention of a permanent return increase the likelihood of participating in the labor market upon returning.<sup>6</sup> Piracha and Vadean (2010) find that returning Albanian migrants who have achieved their savings goal and are more educated are more likely to become entrepreneurs compared to nonmigrant working age Albanians. Less skilled returning Albanian migrants are, at least initially, more likely to be self-employed (own-account workers) than employees. Co, Gang and Yun (2000) find no evidence of returning male migrants earning more than male non-migrants in Hungary, but do find an earnings gap for females. This is consistent with females receiving higher returns to the occupation-specific human capital accumulated in the host country, in Hungary. Hazans (2008) finds a clear earnings gap between return migrants and non-migrants in Latvia again consistent with the theory that human capital acquired abroad has greater returns in the home country while also arguing that the savings of return migrants means they can search longer. The study by Mintchev and Boshnakov (2006) finds that Bulgarian households with a return migrant are more likely to have benefited from remittances in terms of household earnings and are more likely to have their own business, consistent with the work of Mesnard (2004). Iara (2006) and Martin and Radu (2012) find that returning migrants from across Central and Eastern Europe earn more than non-migrants in both self-employment and paid employment, while there is mixed evidence on the type of economic activity return migrants are likely to undertake.

> 6 The World Bank Survey data Shima uses in his work is drawn from highly educated return migrants so the findings of Dustmann and Kirchkamp (2002) and Radu and Epstein (2007) are not likely to be found.

8
Table A1: Additional returns to schooling from the effect of education in reducing the risk of HIV infection for males in rural Uganda..

||**Same mortality**<br>**schedule for**<br>**primary and**<br>**secondary**<br>**education **|**Lower mortality**<br>**schedule for**<br>**secondary**<br>**education**|**Extra return**<br>**to schooling**|
|---|---|---|---|
||**A**|**B**|**= B-A**|
|Future income<br>stream only|10.23%|10.71%|0.48%|
|**Add non-**||||
|**income part of**<br>**the value of life**||||
|Ashenfelter,|10.23%|11.54%|1.31%|
|Greenstone||||
|Murphy,Topel|10.23%|13.74%|3.51%|
|Based on:||||

- Income schedules by age and education, and cost of secondary education per pupil estimated from the Uganda National Household Survey, 1999/2000.

- HIV prevalence in 1999/2000 by education category and HIV incidence by age group estimated from the data of the General Population Cohort of the Medical Research Council Program on AIDS in Uganda.

- Age specific mortality rates for Uganda published in the final reports of the Demographic and Health Surveys 1995 and 2000 for Uganda.

44
_Since 2013, the number of Sub-Saharan African countries in risk of debt distress has increased sharply._ The Low-Income-Country Debt Sustainability Framework (LIC DSF) analyzes and evaluates the public debt situation in LICs on an annual basis and assesses the countries’ risk of debt distress. In 2006, 18 countries in the region were at a high risk of debt distress, roughly equaling the number of countries at moderate and low risk combined ( **Figure 16** ).<sup>10</sup> After the region received more than US$100 billion in debt relief under HIPC and MDRI in nominal terms, debt sustainability ratings improved. The number of countries at high risk of debt distress decreased to eight in 2013-14. The trend reversed afterwards, and the region started to borrow rapidly —notably, borrowing from non-Paris Club governments and private creditors (bondholders and commercial banks). In line with this changing composition, LIC-DSF risk ratings started to deteriorate and the number of countries at high risk of debt distress or in distress more than doubled to 18 by October 2018.

**Figure 16. Evolution of the risk of debt distress: SSA LIC DSF countries**

<!-- Start of picture text --> 40<br>35<br>8<br>8 10<br>30 13 12 10 13<br>18 17 18 15 17 18<br>25<br>20 14 17 18<br>11 12<br>21<br>15 11 18<br>11 8<br>12 15 13<br>10<br>5 10 10 12 12 12 11 11<br>8<br>6 6 6 5 6<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Low Risk Moderate Risk High Risk<br>Number of Countries<br><!-- End of picture text --> _Source: World Bank, Low-Income Country Debt Sustainability Framework_

> 10 The number of Sub-Saharan African countries for which LIC Debt Sustainability Analysis (DSAs) have been prepared has changed over time. As of now, DSAs are available for 34 active International Development Association– eligible countries, excluding inactive countries and countries in arrears to the World Bank, that is, Eritrea, Somalia, Sudan, and Zimbabwe.

18
9 various hospital payment reforms, where we have used the _HiTs_ series to classify a country’s predominant hospital payment method in a given year as falling into one of three categories: (i) fixed budgets/block grants (the prevailing method under the communist Semashko system), (ii) fee-for-service/payment by bed days, or (iii) patient-based systems (mainly DRG-based) (cf. Ellis and Miller 2008). Of the 18 countries that adopted SHI, 12 switched from the use of budgets, though in three cases the switch occurred with a lag. Some switched to FFS and stuck with it, while others switched subsequently to a PBP. A few switched immediately to PBP. Interestingly, some countries without payroll-based contributions also switched from budgets. The lag in provider payment reform, the fact that different countries opted for different payment methods and sometimes switched a second time after SHI adoption, and the fact that some nonSHI countries also switched from budgets during our period all help to create an opportunity to see how far any impact of SHI adoption is due to the shift to payroll finance and the setting up of a SHI agency, rather than to provider payment reforms which could have occurred (and in some cases did occur) even without the adoption of SHI.

# **3. Methods**

Let _yit_ be the outcome of interest in country _i_ at time _t_ , _Xit_ be a vector of covariates thought to potentially influence both the outcome and the SHI adoption decision, and _SHIit_ be a dummy variable taking on a value of 1 if country _i_ has a SHI health financing system at time _t_ . An obvious model to estimate is:
which declined by about 4 percentage points, from 12.5% in 2000 to 8.5% in 2014. Another important measure of child health improvement over the last 15 years is the declining trend in child mortality rates. During this period, U5MR declined by over 83 deaths per 1,000 live births, from 164 deaths in 2000 to 80 deaths in 2014, and IMR declined by about 35 deaths per 1,000 live births, from 95.9 death per 1,000 live births in 2000 to 59.3 death per 1,000 live births in 2014.<sup>10</sup>

**Table 2.  Trends in MNCH outcomes in Ethiopia, 2000-2014**

|||National|||Rural|||Urban||
|---|---|---|---|---|---|---|---|---|---|
||Earlier<br>Survey|Latest<br>Survey|Change|Earlier<br>Survey|Latest<br>Survey|Change|Earlier<br>Survey|Latest<br>Survey|Change|
|Stunting|57.0|40.6|-16.4|58.2|42.5|-15.7|46.5|27.0|-19.5|
|Wasting|12.5|8.9|-3.6|13.0|8.9|-4.1|7.8|9.0|1.2|
|Underweight|41.9|26.6|-15.3|43.4|27.8|-15.6|28.4|18.7|-9.7|
|NMR|48.4|34.3|-14.1|48.8|32.8|-16.0|44.5|44.4|-0.1|
|IMR|95.9|59.3|-36.6|114.2|65.7|-48.5|96.3|60.7|-35.6|
|U5MR|163.9|80.1|-83.8|191.3|91.8|-99.5|148.3|67.3|-81.0|
|Measles vaccination|27.0|56.7|29.7|43.2|48.5|5.3|63.1|79.6|16.5|
|Full immunization|14.6|24.9|10.3|11.2|20.9|9.7|43.2|48.5|5.3|
|Contraceptive|7.2|45.2|38.0|3.8|42.0|38.2|32.9|61.8|28.9|
|ANC4+|10.4|24.2|13.8|3.2|37.1|33.9|28.5|56.2|27.7|
|SBA|5.7|15.5|9.8|2.3|9.1|6.8|34.6|58.4|23.8|

Source: Authors’ compilation from the DHS (2000, 2005, 2011 & 2014) data. Note: The values are number of births per 1000 live births for NMR, IMR and U5MR and percentages for the rest. Earlier survey year is 2000 for all indicators. Latest survey year is 2014 for all but full immunization and measles vaccination, for which our latest source of information is the 2011 DHS.

10 The mortality results reported here are based on birth history records collected as part of DHSs. Birth histories usually go back 3-5 years before the date of the survey. Hence, the results might refer to a period 1.5- 2.5 years before the survey report date. It is more than likely that under- five mortality level in 2014 could be even lower. The main focus of the current paper is on the distribution, which is consistent over the four surveys. However, for the actual levels of mortality readers can also refer to the UN Inter Agency Group estimate <u>http://www.unicef.org/publications/files/Child_Mortality_Report_2015_Web_8_Sept_15.pdf</u> accessed on 7 November 7, 2015 (UNICEF, 2015).

11
Figures 11 a) to i) display a clear urban rural and north south divide, with lower shares of children of school going age being out of school in the urban and southern areas. Moreover, the figures demonstrate a difference in age group behavior. While in urban areas the share of children not in school doubled in wave 3 when moving from the 10-14 year olds to the 15-17year olds age group; in rural areas, children appear to start attending school in larger shares only at 10-14 years and results do not display the a similar decrease in school attendance among the older children. When comparing northern and southern areas this pattern is even more extreme. A potential reason might be that in urban and southern areas the opportunity costs of staying in school are higher with more job opportunities available than in rural and northern areas. The observed pattern is consistent among the GHS and MICS surveys and does not seem to have changed much over time until wave 4. Interestingly also school attendance patters do not seem to differ by gender until wave 3, not even in rural areas - at least not at the aggregate level, despite education projects oftentimes targeting rural girls. Wave 4 shows a jump in drop-out rates for children aged 15-17 in all areas and most extreme in urban and southern areas, whereby the surge of southern girls compared to a limited increase in southern boys dropping out of school is particularly noteworthy. More research is needed whether this is a result of higher opportunity costs for girls staying in school due to the availability of jobs, or girls being forced to work to provide for their families as monetary poverty rates started to rise for households with children in this age group in the South East and South South, as displayed in Figure 5.

Looking beyond aggregates into the potential heterogeneities across the six regions, Figure 12 shows in addition to the rural-urban divide a north-south divide at a regional level. The highest share of children not in school, hence, the lowest school enrollment rates are observed for rural children of any age groups in the North-East and the North-West, with the shares comparing the third GHS wave and the MICS data being relatively close. Moreover, similar to the rural-urban observations discussed in relation to the national level findings, children in the North-East and North-West seem to start attending school at a later age but not facing such a surge in non-attendance in the older age categories at the children in South. Besides, school dropout rates from the middle to the oldest age category appear to rise faster in the rural areas than in the urban areas in the North East and North-West, whereas the reverse seems to hold in the North-Central and South-West, and to a lesser extent also in the South-East and South-South where there is a surge in non-enrollment rates in the urban areas for the oldest children. Further research may want 25