rafmacalaba/datause-encoder-data huggingface dataset:
policies that lead to relatively high outflows out of unemployment, but unfortunately do not prevent high (subsequent) inflows into unemployment. Slovakia and Poland represent two transition economies that, like East Germany, operate with very high unemployment rates but, unlike East Germany, have not experienced an administratively set high wage level and cross-border subsidies. For most of the 1990s and 2000s, these two economies have experienced an unemployment rate in the 14 to 20 percent range, accompanied by moderately high inflow and low outflow below 10 percent. In most years, they have also had vacancy rates significantly below 1 percent. The Czech Republic is an intermediate case, with unemployment rising from the low rate of 3-4 percent in the early to mid 1990s to 8-10 percent range since then. Its inflow rate has risen from extraordinary low levels of about 0.6-0.8 percent in the early-tomid 1990s to a still relatively low level of 1.1-1.2 percent since then. Its vacancies as a share of the labor force have declined from very high levels of 1.4-1.9 percent in the early-to-mid 1990s to 0.8-1.1 percent since then. Finally, Hungary has achieved the lowest and rather stable level of unemployment rate. After reaching an unemployment rate of about 11 percent in the mid-to-late 1990s, Hungary succeeded to lower the rate to around 8 percent in the mid 2000s, reduced its inflow rate to 1.4 percent, raised the outflow rate to 14-16 percent and kept the vacancy rate at 1.0-1.1 percent. Hungary’s relative success is hence brought about by keeping the outflow rate relatively high and inflow rate relatively low.

The Hungarian and Polish unemployment outcomes must be interpreted soberly in light of a relatively high outflow of people out of the labor force. In particular, between 1992 and 2004 the ratio of employed to the population in the 15-59 year old cohort declined by 9.9 percentage points. The corresponding decline in the Czech and Slovak Republics was 4.3 and 7.1 percentage points, respectively. The situation in Poland is especially serious because the 9.9 percentage point decline occurred from an already relatively low base.<sup>5</sup>

> official aggregate statistics published. German series exclude East and West Berlin.

> 5 In 1992 the ratio of employed to the population of 15-59 year olds was only 64.8 percent in Poland, as compared to 74.7

− 6 −
The World Bank (2011) highlighted recent trends in Dar es Salaam and São Paulo. In Dar, the population is growing roughly 6 percent a year, so its total population doubles every 12 years. Dar’s urban poor face heavy rainfall, flooding, and drought. And about 70 percent of the city’s population lives in low-quality, risky housing featuring weak infrastructure with little access to clean water and sanitation. Drainage channels are regularly blocked, flooding houses with sewage-infested wastewater and enabling water-borne diseases to come into direct human contact. Roughly 40 percent of the city is below sea level. In São Paulo, the main hazards include heavy rains, flooding, landslides, and washouts. More than 85 percent of high-risk households (890,000) are in slums. Of those households, 52 percent lack access to sanitation facilities, 33 percent lack access to paved roads in their neighborhoods, and 20 percent lack proper sewage treatment.

To protect the urban poor from hazardous environmental challenges, cities will need a new revenue source. For instance, cities could increase their property taxes. A property tax offers a consistent revenue stream, and this stability would help government officials plan capital expenditures into the medium term.<sup>46</sup> If property ownership is concentrated among a few large land owners, such a tax will be progressive. It can also improve economic efficiency if the money is used for productive local public goods, such as roads and basic infrastructure.

Assuming the government invests in improving public goods in slums, who will be the real beneficiaries? Will the urban poor’s quality of life improve, or will the value of these improvements become capitalized into local land prices so that land owners would be the winners? If housing rents are determined in a competitive market, land owners will gain most of these benefits. But if there are barriers like rent control, those who already live in the area will benefit.

# **Urban adaptation to climate change**

A third criterion for judging a city’s environmental performance is its resilience in the face of climate shocks. Residents in many developing cities face significant flood risk, disrupted electricity supply, and intense heat waves. Increased migration to these cities increases the number of urbanites at risk, and climate change will only intensify the risks. For these anticipated challenges, the goal for public policy is to implement “no regrets” policies.

While there are many unknowns about climate change, ongoing economic research highlights direct impacts on the urban sector and indirect impacts on agriculture.

Higher temperatures have large, negative effects on economic growth, but only in poor countries, where a 1o Celsius increase in a given year reduces that year’s economic growth about 1.1 percentage points.<sup>47</sup> In rich countries, temperature changes have no discernible effect on growth. Perhaps surprisingly, both agriculture and the urban sector suffer in hotter years. And poor countries produce fewer scientific publications in hotter years, suggesting that higher temperatures impede innovation.
further downside risks for the welfare of the Malians, but especially the sizeable vulnerable groups.

**Figure 2.1:  Poverty Rate And Vulnerability Incidence At The District Level**

<!-- Start of picture text --> (Cercles)<br><!-- End of picture text --> <!-- Start of picture text --> Welfare Effects of Increasing Household Size<br>By Wealth Category (Terciles of Durables Owned)<br>0 8 10 14 20 25<br>Household Size<br>1st Tercile of Wealth 2nd Tercile of Wealth 3rd tercile of Wealth<br>Source: ELIM 2009/10 and authors' calculations.An average increase in household size from 8 to 14 is associated with a welfare loss of27% for households in the first wealth tercile, 25% for households in the second wealth tercile and 16% for households in the third wealth tercile<br>200<br>150<br>122<br>90<br>Consumption Expenditure<br> per Household per year (1000 FCFA)<br>0<br><!-- End of picture text -->

# III. Vulnerability to poverty

## **A. Concept and methodology**

Household income in developing countries is generally very volatile because poor households are often exposed to household specific and community level shocks. Moreover, available insurance mechanisms to protect income from the influence of these shocks are often absent or imperfect. Therefore, poverty status observed at some point in time might not be the best guide to inform about households’ long term wellbeing.

In this section, we assess the households’ ex-ante vulnerability to poverty. We use Chaudhury (2003) methodology to estimate vulnerability based solely on cross-sectional data. We measure vulnerability as the conditional probability of a household’s income to fall below the poverty line in the next period. A household will be classified as vulnerable if its predicted vulnerability level is above a defined threshold. Usually a threshold of 0.5 is chosen in the literature. Under this framework, the source of vulnerability can be decomposed into high volatility (risk induced vulnerability) and low mean consumption (poverty induced vulnerability).

This approach is extended by introducing idiosyncratic and covariate risks. This is done by introducing a multilevel random effect model (Gunther and Harrtgen (2009)). 2 The consumption risk faced by households is defined by household specific risks (idiosyncratic

> 2 This approach has also been taken more recently by Imai etal. (2011) and Imai (2011).

14
**Table 4: Models by Product – Specification 1**

|**Vars**|**Carpets**|**Shawls**|**Jewelry**|**Handmade**<br>**Paper**|**Medicinal**<br>**Herbs**|**Dentifrices**|**Cardamom**|**Pipes I&S**|
|---|---|---|---|---|---|---|---|---|
|**_Lagged Log Tourists_**|1.345**|3.129*|0.628|1.439**|-2.22|1.097|-0.11|-2.771|
||(0.605)|(1.587)|(0.565)|(0.602)|(2.93)|(0.794)|(0.531)|(1.821)|
|**_Log World Imports of_**|1.113***|1.069**|0.384**|2.296***|-0.37|0.282|1.121***|2.212|
|**_the Same Product_**|(0.322)|(0.45)|(0.167)|(0.319)|(0.782)|(0.32)|(0.259)|(1.477)|
|**_Log Real Exchange Rate_**|-0.476***|-1.460***|-0.407***|-0.403*|-0.246|-1.004***|-0.154|-2.222|
||(0.138)|(0.295)|(0.133)|(0.196)|(0.841)|(0.194)|(0.101)|(1.648)|
|**_Constant_**|-25.79***|-51.13***|-8.298|-40.23***|34.17|-11.21|-3.215|6.51|
||(8.036)|(16.26)|(6.063)|(6.734)|(34.75)|(7.701)|(4.428)|(37.11)|
|**_Observations_**|99|103|63|21|10|19|21|14|
|**_R-squared_**|0.259|0.618|0.271|0.912|0.197|0.626|0.811|0.366|
|**_Number of Products_**|5|5|3|1|2|1|1|1|

Robust standard errors in parentheses

*** p<0.01, ** p<0.05,

*p<0.1

15
mean flood intensity from the Global Flood Monitoring System (GFMS) data set (Merz et al., 2007), elevation, and distance to river. The flood shock measure takes a value of one where the flood index is more than 30 percent above the average flood index value. The drought measure is based on the difference between the National Oceanic and Atmospheric Administration (NOAA) African Rainfall Climatology version 2 (ARC2) flowering period rainfall in the current period versus the historical average. We use a dichotomous variable that takes a value of one when current period rainfall is at least 30 percent below the historical mean and is otherwise equal to zero.<sup>8</sup> Finally, we control for climate conditions by including historical flowering period rainfall mean and coefficient of variation, as well as standard production function variables. As shown in Online Appendix 1, the flood and drought shocks have significant negative impacts on plot-level maize yields.

# **3.3. Aid Distribution and Explanatory Factors**

For aid distribution, we use monthly data on the number of households who received food and cash aid in TA’s where aid was distributed.<sup>9</sup> The vast majority of aid was distributed through a consortium of international and local NGOs, coordinated by the Ministry of Disaster Management Affairs and the WFP (Babu et al., 2018). The monthly data are aggregated, starting with the month that distribution began following the end of the current period rainy season through to March of the subsequent rainy season. We subsequently divide the number of households to whom aid was distributed by the projected total number of households in the TA to get a measure of aid availability within the TA. To simplify terms, we hereafter use “aid distribution” to refer to this variable.

> 8 McCarthy et al. (2021) use a slightly different measure for drought. That measure is semi-continuous, taking the absolute value of the percent difference between current period rainfall and the historical mean when more than 30 percent below, and otherwise equal to zero. However, as shown in Online Appendix 1, the simple dichotomous measure is also significant, and it also facilitates interpretation of coefficients.

> 9 We have data on amounts of cash distributed and quantities and caloric equivalent of food aid distributed, but we chose to use number of households receiving aid since this variable does not require us to create a variable that has the same unit across different types of aid.

13
The ownership rates in rural areas, however, are seven times lower (6%) than in urban areas (43%) in South Africa, making the rural rate more comparable to those in entire group of middle-income countries.

**_Affordability—pricing_** _._ A 3-minute local call in U.S. cents is one of the most expensive in the world in South Africa at 15 cents, which is three times the cost in South Asia and East Asia Pacific and equal to the high-income OECD countries. This reflects monopolistic market structures in local services. Local debate in South Africa has also emphasized the high cost structure, including on the international call market segment.  Nevertheless, within the cellular and international segment, some calls are very competitive. For example, the database used for this benchmarking shows that in terms of the cost of three off-peak minute cellular local calls (25 cents), South Africa has one of the cheapest rates when compared with upper-middle-income (54 cents) and high-income OECD (59 cents) countries where costs are twice as much. In addition, according to the database (with original data from the ITU—World Telecommunications Development Report) an international, 3-minute, peak rate, fixed line call to the United States in 2001 appeared lower than in upper-middle-income countries and high-income OECD countries, but this needs to be checked against most recent data within the country and a more detailed look at the rate structure before making definitive conclusions.

**_Quality—technical._** Reported faults per 100 telephone mainlines (per year) are very high (48) by upper-middle-income (18) country standards and are closer to the SSA average (57). High income OECD country rates—towards which South Africa may aspire in this highly globalized sector--are more than four times lower (11%).

**_Quality—perception._** Interestingly, perceptions of ICT are more closely aligned to the other indicators of performance. Commercial perception of _telephone infrastructures_ rating is 5.1 out of 7 compared to 6.6 for high income OECD countries and 5.6 in upper-middle-income countries. Commercial perception of availability of mobile telephones is much higher at 6.3 (vs. 6 for upper-middle-income group), which is only better in high-income OECD at 6.6. Commercial perception of _internet access_ in schools is also low by high income OECD standards, 3.6 vs. 5.4 and slightly lower than upper-middle-income countries (3.8). Commercial perception of _postal efficiency_ is even worse at 3.3 which is only just above the average for low-income countries (3.1), and almost half the rating given to high-income OECD, and compared to 4.4 for uppermiddle-income countries.

22
**Table 3 - National-level drivers of cropland loss**

|**Dependent variable: Fixed effe**|**ct recovered fro**<br>|**m the pixel-leve**<br>|**l cropland loss re**<br>|**gression**<br>||
|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|
|Cropland per capita|-0.0347***|-0.0314***|-0.0244***|-0.0249***|-0.0178***|
||(0.00657)|(0.00645)|(0.00601)|(0.00638)|(0.00575)|
|Quality of land administration||-0.00481**|-0.00703***|||
|||(0.00218)|(0.00266)|||
|Rural land tenure insecurity||||0.0339**||
|||||(0.0157)||
|Enabling the business of agriculture|||||-0.00221**|
||||||(0.000938)|
|GDP per capita|||1.58e-06†|2.11e-06†|1.23e-06|
||||(1.04e-06)|(1.32e-06)|(1.16e-06)|
|Gini Index|||0.00401*|0.00536**|0.00385*|
||||(0.00223)|(0.00241)|(0.00222)|
|Constant|-0.668***|-0.602***|-0.765***|-0.927***|-0.771***|
||(0.0191)|(0.0362)|(0.106)|(0.101)|(0.102)|
|Observations|184|168|145|122|96|
|R-squared|0.133|0.171|0.228|0.230|0.253|

_Note: The explained variable in these regressions are the fixed effects recovered from the pixel-level regression of cropland losses. Cropland per capita is from MODIS 2018; the Quality of Land Administration is from Doing Business 2020; GDP per capita is in current US$, the Gini Index uses the latest value reported in the World Development Indicators. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1;_ † _p<0.15_ .

We now turn to the assessment of the factors that drive movements into cropland, adopting the same two-stage procedure. For this, we focus on pixels within a 3-km distance of existing cropland in 2003 and estimate a regression similar to (1) but where the left-hand side variable is a dummy equal to 1 if the pixel transitioned to cropland in 2018 and 0 otherwise and where explanatory variables are the same as in Table 1. Results are presented in Table 4 below. All coefficients are highly significant, and the signs of these coefficients for distance to the city, the number of drought months, land suitability, and distance to the coast are opposite to what we reported in Table 1 for transitions out of cropland. Here, a one standard deviation decrease in proximity to a city is associated with an increased probability of 8.7 hundredths of a percent that the pixel would transition to cropland. The effect is in line with theory and previous work that showed that cropland expands in place with better market access (see Berg et al., 2017, for an assessment in Sub-Saharan Africa). Droughts are of course a major deterrent to putting land into cultivation, with a one standard deviation increase in the number of droughts reducing the likelihood of 13
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- d) Technology and sustainability: Are women and the broader community being consulted on the actual choice of technology? Once chosen, are they being taught how it works? To what extent can potential users and the broader community finance or help with initial construction and maintenance requirements? Will women be involved in construction, maintenance, management, and finance/cost recovery?

- e) Training: Are training programs being organized to reach women not just once but enough to maintain repair skills and knowledge about water uses over time? Will training sites, times, and staff be selected to make it easy for women to participate? It may be possible to link this training with the health system, school feeding programs, or similar efforts. Many clinics give presentations on hygiene and other preventive health measures for groups of clients, usually mothers and children, who are waiting to be seen.

- f) Local organization: Are women's groups, NGOs, or other local groups being mobilized to help facilitate cooperation between the community and the government or other institutions providing the water? Are women being given a chance to serve as leaders or spokesmen for such groups? (One advanA age of working with women's groups is that women will indeed lead them and voice their real views.) Such groups can often help efficientl, with planning, construction, maintenance, and even cost sharing.

- g) Cost Recovery: Are women being given greater opportunity to earn income that will help them contribute to the costs of rural water supplies? Bank projects in such diverse areas as Mexico and Gambia are planning to combine community-based rural water supply programs with greater efforts to enable women to earn. Other efforts in Africa and Asia suggest that a community-based approach focused on women can be effective, despite the need to spend time and resources to understand and work with the community. The records of programs such as KWAHO in Kenya compare favorably with those of many other efforts to provide water through more technologically sophisticated and capital-intensive systems that involve clients less (see box).

179. Forestry. Women are major actors in the forestry sector throughout the world. They and their children are the primary collectors of fuel and fodder for home consumption and for sale to urban markets. With increasing population pressure on the land, women in such diverse settings as Kenya and Nepal must daily travel longer distances, often for more than two hours, to find their family's fuelwood. But women's role in forestry goes well beyond their responsibility for fetching the family fuel. Women also work actively on forest plantations and are often a major source of knowledge on the growth, use, and management of forest resources. In many countries they are also the main managers of such "minor forest products" as plant fibers, medicinal plants, fruits, and edible and industrially usable seeds.
central bank, was used as the starting point.<sup>23</sup> The following assumptions were made (1) GDP growth rises from 1.5% in 1997 to 4.5% and 5% in 2005; (2) inflation is kept at around 3% p.a.; (3) stabilization and financial sector reform leads to an increase in money demand so that the monetization ratio rises from 14.5% in 1997 to 24% in 2005; the ratio of public foreign debt to GDP is kept at around 30% and foreign reserves are increased to reach 3 months of imports.<sup>24</sup> Table 9 below shows a possible scenario. The sustainable deficit is estimated around 1.5-2% of GDP. This is not a large number but it is larger than the cash deficit of today.

Table 9: Estimating a Sustainable Fiscal **Deficit**

||**1997**|**1998**|**1999**|**2003**|**2005**|
|---|---|---|---|---|---|
|Money creation|0.3|_0.5_|0.4|1.0|0.8|
|Foreign fnance|0.1|0.3|1.6|1.3|1.5|
|Debt finance|0.3|1.6|3.0|2.7|3.0|
|Reserves|-0.2|-1.3|-1.4|-1.4|_-1.5_|
|Domestic finance|-0.3|-0.4|-0.2|-0.4|-0.4|
|Gov. borrowing (from the rest of the economy).|-2.3|0.0|_0.5_|_0.5_|0.5|
|National Bank of FYR Macedonia credit|-2.0|0.4|0.7|0.9|0.9|
|Public sector deficit|0.0|0.4|1.8|1.9|1.9|

Source: 1997 data from **the Govermment** of **Macedonia. Other data are** Bank **staff projections** In the absence of other adjustments, the realization of some of the fiscal risks described here could easily push the deficit beyond "sustainable" levels. The debt implied by the frozen foreign currency deposits would alone add 2.7% of GDP to the deficit. Suppose the uncovered exposure in the financial sector, amounting to 9% of GDP, and 1/3 of the implicit liabilities in the enterprise sectors, are paid by the government. Assume the authorities issue two bonds. The maturity structure and interest rates assumed are: 15 years, at 8.9% 25 and 10 years 7% 26

> 23 Profits and losses of public enterprises as well as capital transfers are included in the budget. To the extent that public enterprises' activities (and prices) differ from market values these transfers will be net of the subsidy element embodied in these prices.

> 24 If inflation were 10%, base money could grow much faster and revenues from the inflation tax would be higher. With a more aggressive external debt strategy, the external debt stock might grow faster, allowing a higher deficit to be financed. On the other hand, the GDP growth assumptions and assumptions regarding money demand are quite optimistic given recent trends.

> 2 The discount rate of the central bank.

23