rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 10<br>South Africa<br>8<br>Vietnam<br>Kenya<br>6<br>Tanzania<br>4<br>Sub‐Saharan<br>Africa<br>2<br>0<br>1990 1995 2000 2005 2010<br>Average years of education<br><!-- End of picture text --> Note: Educational attainment for population aged 25 and over is based on authors’ calculations using harmonized microdata from the International Income Distribution Database-I2D2 version 6 (World Bank 2013). Survey year is rounded to the closest year multiple of five. The estimated average schooling for South Africa is calculated using an older version of I2D2 (December 2011). The number of countries and surveys used to estimate the regional average for Sub-Saharan Africa varies every year: for example, in 1990 it was based on eight country surveys but in 2005 it included at least 20 <u>countries.</u> While workers completing basic education are needed for the vast majority of small, informal businesses, higher-skilled workers are critical to the emergence of a diversified economy. Figure 2 shows the current distribution of the population aged 25 and over by different education levels for Tanzania compared to several other countries in SSA and Asia. For Tanzania, the proportion of the population with at least a secondary education is 11 percent and with a tertiary education, only 5 percent. These are very low figures compared to South Africa, Malaysia and Vietnam.

In fact, Tanzania has one of the lowest gross tertiary enrollment ratios in SSA<sup>14</sup> but this is beginning to change with the rapid growth of higher education, driven largely by the private sector. Progression from secondary to tertiary education is available through two routes: enrollments in universities/colleges administered by the Tanzania Commission of Universities (TCU) and in nonuniversity technical education institutions regulated by the National Council of Technical Education (NACTE). The total number of students enrolled in degree programs in universities almost doubled from 81,782 in 2008/09 to 162,510 in 2012/13.<sup>15</sup> Enrollments in technical education institutions under NACTE more than tripled, from 36,586 in 2007/2008 to 112,447 in

> 14 3.7 percent (UIS 2012 data)

> 15 TCU-administered universities and colleges also enroll students (about 20 percent of total enrollments in 2012/2013) in certificate and diploma programs.

7
<!-- Start of picture text --> 1991 1996 2001 2006 2011 2015<br>Year<br>more than 5 years more than 10 years<br>more than 20 years more than 30 years<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text --> Figure 6: Number of refugees in protracted situations (1991-2015) Finally, we look at the evolution of the average and median duration of exile in protracted situations (i.e. only for refugees who have spent at least five year in exile). Figure 7 suggests that until recently, these two numbers have been on an upward trend, driven by long-lasting situations involving large numbers of people. This includes Afghan refugees in Pakistan and the Islamic Republic of Iran, Vietnamese refugees in China, Western Sahara refugees in Algeria and Mauritania, Somalis in Kenya, and Myanmarese in Thailand. The average and median durations for these populations currently stand at 21.2 and 19 years, respectively. The slope of the curve, however, is less than one, which reflects the constant inflow of new forcibly displaced refugees: this is consistent with the trends described above. The decline in the average duration of protracted situations in 2009 corresponds to the peak of entries in these categories that is observed in figure 6.

<!-- Start of picture text --> 1991 1996 2001 2006 2011 2015 1991 1996 2001 2006 2011 2015<br>Year Year<br>All situations No AF VN−CN All situations No AF VN−CN<br>(a) Average duration of exile (b) Median duration of exile<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>Average duration (years) Median duration (years)<br>5 5<br>0 0<br><!-- End of picture text --> Figure 7: Exile duration statistics - protracted situations 15
25 generally higher for men, whereas returns to schooling are generally higher for women. However, men have much more schooling than women in MICs and LICs and women get slightly more education than men in the HICs (Web Appx. Fig. A.2).

**Steady State.** Female GDP in the HIC is 64 _._ 3 percent of male GDP in the steady state. This is in large part due to differences in labor force participation. If we compare the average earnings conditional on working, in the HIC women earn 73 _._ 2 percent of male earnings on average. In the MIC, female GDP is 45 _._ 8 percent of male GDP, or 77 _._ 2 percent conditional on working. In the LIC, the corresponding shares are 82 _._ 1 and 103 _._ 3.<sup>44</sup>

**6.3. Impact of the COVID-19 Shock – Male vs. Female**

The schooling shock is as before – that is, we assume a schooling disruption of 21, 33 and 28 percent over two years in the HICs, MICs and LICs, respectively, for both men and women. If remote learning measures are taken, there is no clear reason to assume this would affect boys and girls differently – except inasmuch as the schooling distribution is different for boys and girls, something our framework already takes into account.

The ILO (2020) reports the decrease in activity by gender. We use it to differentiate between the magnitude of the employment shock by gender. See Web Appx. Section G for details and see Table 2 for the resulting values. The shock disproportionately affects female workers in all country groups, but only by about 2 percentage points more.

Given the more severe employment shock, the initial impact on female income is greater than that on male income (Web Appx. Fig. A.9). After that, however, it is not easy to distinguish between the impact on male and female workers in each group. This is reflected in the welfare impact as well (Ibid.), which is similar after an initial stage where women are worse off relative to the steady state than men. However, these similarities mask the competing influence of the shocks to schooling and employment, which affect male and female workers differently. This is best observed by looking at changes over time in _relative_ income, and by decomposing it based on the two different shocks.

In order to decompose relative income, we could simply plot deviations in relative income from its value in the steady state for female divided by male income. However, 44 Conditional on working, income inequality is smaller for lower levels of income. Indeed, the only potential source of differences in our framework is due to differences in human capital and in labor force participation. The lower the income group, the less human capital there is in the first place, and thus there is less scope for differences in earnings across gender net of labor force participation. Interestingly, conditional on working, women in the LIC earn slightly more than men, due to the fact that returns to schooling are higher for women, so those who do work earn slightly more than men.
# **Re-evaluating Microfinance: Evidence from Propensity Score Matching**

_Inna Cintina and Inessa Love_ JEL Classification codes: O12, O16, G21 Keywords: microfinance, poverty, propensity score matching.

Inna Cintina is a Senior Economist with The Lewin Group (inna.cintina@lewin.com); Inessa Love (contact author) is Professor at the University of Hawaii at Manoa; her email address is <u>ilove@hawaii.edu, 808-956-7653. We thank Tim Halliday and participants of the World Bank</u> DECRG seminar series for useful comments. All errors are our own. The arguments discussed in the paper do not represent the views of the World Bank or its member countries or The Lewin Group.
consumption variation over time would show a decline in poverty during the last five years for both HBS and NPS. However, the decline in poverty revealed by the HBS data would remain much higher than that observed with the NPS data. Given the greater degree of commodity detail in the consumption module that was added to the HBS 2011/12 questionnaire—which would suggest a better capture of consumption information—it is possible that the HBS under‐estimated consumption in 2007 and hence overestimated poverty then and its subsequent decline. Also, the increase in poverty between NPS 2008/09 and NPS 2010/11 continues to be observed and could potentially be explained by the financial crisis and international food price variations.

- IV. The mismatch in inequality trends between HBS and NPS could not be resolved. The analysis of the variation of consumption distribution over time using HBS and NPS data shows that both surveys indicate significant improvements in households’ endowments over time. However, while HBS reveals that endowments increased faster for the poorest groups, NPS shows that the richest groups experienced higher improvements in their endowments. Also, while HBS shows a slight increase in returns for the poorest groups, NPS reveals a deterioration. This might be partly driven by the inter‐year deflator but would need further investigation and confirmation. All these results point to the importance of examining the sampling design.

Based on these findings, we would suggest the following recommendations:

- 1) Enhance closer collaboration inside NBS between the teams working on HBS data and those processing NPS surveys and harmonize the methodologies for the evaluation of the poverty lines and price indicators;

- 2) Attempt as much as possible to harmonize the HBS and NPS design, particularly the methods for household consumption data collection;

- 3) Examine further the sampling procedure and the potential differences resulting from sampling design.

- 4) Further explore the underlying causes of the divergence between both surveys in growth and distributional patterns.

47
## **Figure 3 Fuel Price trends in South Africa and Zambia (in USD)**

<!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>1<br>Zambia<br>0.8<br>RSA<br>0.6<br>0.4<br>0.2<br>0<br>Month<br>Price in USD<br>Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07<br><!-- End of picture text -->

The fuel price differences affect the structure of operator costs. The South African RFA Vehicle Cost Schedule records the fuel and lubricants costs for a seven axle interlink to be 32.6 % of total operating costs. Within Zambia, the fuel costs component reaches 45% of total operating costs. Diesel fuel costs are the major cost elements in Zambia’s operators transport costs. Otherwise, if fuel costs were similar to neighboring countries, domestic transport costs would be lower than South African operators transport costs.

# **3.2.2 Truck Import Rules**

In South Africa the importation of used and depreciated vehicles is prohibited. Such imports require an import permit, which is very rarely granted except for special circumstances (returning residents, immigrants and special motivated circumstances<sup>31</sup> ). Elsewhere in the other SADC countries, Zambia included, import permits are issued freely, as long as tariffs and duties are paid.

Imports of used trucks result in savings in the initial purchase price and the subsequent financing and depreciation costs. A Zambia operator can purchase a 3 to 4 years old heavy truck with about 320,000 – 480,000 kms on the odometer for about USD 25,000 USD to USD 40,000, whereas a South African operator will have to pay between USD 100,000 and USD 150,000 for a new unit.

> 31 Import permits are granted only if such goods or substitutes are not manufactured domestically, constituting a _de facto_ ban on such goods.

18
largely depends on temporal and spatial diversification of rainfall to smooth weather-induced volatility in incomes, especially since only 30% of land used for agriculture is irrigated (Gadgil et al. 2002; Rao et al. 2000).

Several papers show that household incomes in India are sensitive to rainfall variation. Parchure (2002) estimates that around 90% of variation in Indian crop-production levels is due to rainfall volatility. Using macrodata from 1951 to 2003, Gadgil and Gadgil (2006) find that despite substantial decreases in the contribution of agriculture to Indian GDP, severe droughts have resulted in decreases between 2% and 5% of GDP throughout the period. A World Bank (2006) study of adaptation strategies to droughts in Andhra Pradesh finds that, based on simulations of a crop model, severe droughts (one in 30 years) are likely to decrease rice yields from 29% to 62%, depending on the district. Yield losses of rain-fed crops also appear high, with different crops being particularly vulnerable in different districts. Rosenzweig and Binswanger (1993) also present evidence that weather shocks significantly affect agricultural profits, based on ICRISAT panel data from the 1980s.

[Figure 6.1 here] Figure 6.1 shows the actual onset of the monsoon over Kerala as announced by the Indian Meteorological Department (IMD) as deviations in days from the normal onset of June 1. It is clear that there is significant volatility. This volatility, however, would be less of a concern if available forecasts of the onset were accurate, since if they were, farmers could use them to adapt their agricultural production decisions accordingly. The IMD issues a single long-range forecast of the onset of the monsoon over Kerala around May 15. Despite recent advances in forecasting techniques, even the onset conditional on the IMD forecast still displays substantial volatility. Figure 6.2 shows the difference in days between the forecast and the actual onset for each year. (For example, in 2005 the IMD forecast that the monsoon would arrive in Kerala on June 13; as
> <sup>2</sup>

Though Africa is a central focus of research on ethnicity, voting and development, data from Afrobarometer surveys in 16 countries are inconsistent with these predictions.<sup>1</sup> First, ethnic clustering of partisan support appears to be less pronounced than is implied by the literature on ethnicity in Africa. Five different definitions of clustering are explored here. Under the one which comes closest to capturing theoretical descriptions of the role of ethnicity in politics, only 25 percent of respondents belong to clustered ethnic groups. No ethnic groups at all meet this standard in approximately half of the 16 countries.

Second, respondents from clustered ethnic groups are not substantially more likely to express a partisan preference than respondents from non-clustered parties. In country-bycountry estimates, half or more of the countries exhibit no statistically significant difference between clustered and non-clustered respondents in the likelihood of expressing a partisan preference. In only two countries where they consistently exhibit a significant difference (South Africa and Mozambique) does the evidence support the claim that parties have made credible commitments.

The third indication of the lack of credibility of partisan appeals to ethnic groups is that even respondents from clustered ethnic groups express high rates of partisan indifference. A large fraction of these respondents indicate that they are “not close to any party” (around 35 percent, almost three times the rate at which American voters declare themselves to be independents). Finally, fourth, the magnitude of other determinants of 1 The 16 countries are typical. They exhibit the high levels of ethnic fractionalization (.69) associated in the literature with poor development outcomes, similar to levels in the rest of Sub-Saharan Africa and far higher than in the rest of the world (.37) (Alesina, et al. (2002); fractionalization is the probability that two randomly selected individuals are not from the same ethnic group).
that have not been documented. This has significantly limited potential demand, as the high price of insurance was a principal reason given by 64 percent of non-participating farmers for not purchasing the insurance – they faced liquidity constraints.

Households purchase the insurance at the beginning of the growing season, a time when many uses compete for available cash. Similar to the Malawi case, there is a lack of trust in the insurance process and limited education and financial literacy in the region. Formal legal protections are also viewed as relatively weak, further deteriorating the trust necessary for the success of the program. Limited salience of the insurance product can be improved by using different selling techniques, including door-to-door sales. The continued success of the program and its ability to scale, expand, and become self-sufficient is dependent on overcoming these barriers (Cole et al., 2010).

In Gujarat, India, the Self-Employed Women's Association (SEWA) has pioneered index weather insurance distribution, beginning in 2006 (Cole et al., 2011). Leveraging their longstanding, trusted presence as a trade union, SEWA's insurance sales operation has grown by an order of magnitude over this time, initially targeting 15 households in each of about 30 villages and in 2011 targeting about 70 households in each of 60 villages. Each year SEWA has used village meetings to introduce the insurance sales, followed by door-to-door marketing visits.

Between 20 percent and 40 percent of visited households have purchased the insurance, and the trend line is upward-sloping, but for research purposes the marketing outreach and the policy prices have been subsidized. A long-term randomized impact evaluation is ongoing, and will shed light on the dynamics of demand, the opportunities for greater production efficiencies, and the implications for consumption-smoothing. Consistent with the view that ambiguity aversion may influence adoption of index insurance (Bryan, 2010), in the Gujarati data the partial correlation of insurance demand with risk aversion is negative initially, but positive in the more recent data. This suggests that ambiguity about index insurance can fall over time, with salutary consequences for risk management.

_Initiative in Peru to reduce losses associated with El Niño._ In a new collaboration between GlobalAgRisk, with the support of the Bill and Melinda Gates Foundation, and the United Nations Development Programme, Peru has initiated a new insurance model to mitigate losses 16
results do not change. The same questions are asked for primary public schools, health centers and food markets.

# **_Descriptive Statistics_**

Cameroonian households closely relate the fact of being poor to the ease of accessing a developed and well-maintained tarred roads network. Whatever the poverty status, the first reported root of poverty in Cameroon is the lack of employment. The next causes reported by the households surveyed are decreasing or insufficient revenues and the lack of roads. Indeed the density of tarred roads in Cameroon is less than one meter<sup>11</sup> of tarred road per squared kilometer of arable land<sup>12</sup> . In order to overcome this issue, the Road Fund Cameroon plans to increase the tarred network by 75% during the 15 next years. An initiative also concerns the rural network, which connects the production areas to local markets or commercial centers. The total network under consideration is long of 24 310 kilometers, almost the half of the entire Cameroonian road network (see Figure 3). The Road Fund's argument is that thanks to this development strategy "many areas will be open to trade and a great progress will be made in the fight against poverty, insecurity and malnutrition".

The households surveyed share this argument, as showed in Table 1, which presents the three major actions reported by households when they are asked about the first initiative to fight against poverty. Employment is by far the first action against poverty underlined by surveyed people. Therefore, we decided to dedicate an important place to the relation between poverty and labor in our analysis.

> 11 0.00000679 kilometers

> 12 The tarred network measures 4047.8 km (Fonds Routier du Cameroun). Data on arable land come from WDI (World Bank, 2009.3).

11
Policy Research Working Paper 5275

# **Abstract**

This paper combines 172 Demography and Health Survey data sets from 70 countries to estimate the effect of water and sanitation on child mortality and morbidity. The results show a robust association between access to water and sanitation technologies and both child morbidity and child mortality. The point estimates imply, depending on the technology level and the sub-region chosen, that water and sanitation infrastructure lowers the odds of children to suffering from diarrhea by 7–17 percent, and reduces the mortality risk for children under the age of five by about 5-20 percent. The effects seem largest for modern sanitation technologies and least significant for basic water supply. The authors also find evidence for the Mills-Reincke Multiplier for both water and sanitation access as well as positive health externalities for sanitation investments. The overall magnitude of the estimated effects appears smaller than coefficients reported in meta-studies based on randomized field trials, suggesting limits to the scalability and sustainability of the health benefits associated with water and sanitation interventions.

This paper—a product of the Prospects Group, Development Economics—is part of a larger effort in the group to do forward-looking analyses of development strategies, including strategies for achieving the Millennium Development Goals.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at isabel.guenther@nadel.ethz.ch and gfink@hsph.harvard.edu.

_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