rafmacalaba/datause-encoder-data huggingface dataset:
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Comparing performance across the SADC member countries, South Africa and Seychelles are far ahead of the others (table 4.2). Performance is mixed everywhere else, with the mobile footprint ranging from 23 percent in Madagascar to 93 percent in Malawi, mobile penetration ranging from 7 percent in Malawi to 39 percent in Namibia, and Internet subscribers ranging from less than 0.1 percent in Malawi to 1.8 percent in Swaziland. The price of a monthly mobile basket of services ranges from $4 in Zimbabwe to $15 in Zambia, while the price of a monthly Internet dial-up subscription ranges from $7 in Madagascar to $148 in Tanzania.

Perhaps more relevant than fixed-line international calls are regional roaming arrangements for mobile services—an area where the SADC is not as advanced as the Economic Community of West African States (ECOWAS). Given that fixed-line services have largely been overtaken by mobile services in southern Africa, the regional availability of roaming arrangements is in many ways a more relevant measure of the cost of internal communications. Although there are a number of roaming arrangements in the SADC (table 4.3), restrictions and limitations apply. Not every country has a roaming arrangement with all other SADC countries, and where roaming does exist, it may not be across all operators within a given country. In a number of cases, roaming is restricted to postpaid subscribers, which are a minority of total mobile subscribers in the region. Prepaid roaming requires real-time billing and is not always implemented. In some cases roaming is one way (subscribers in one country can use their mobile in another but not vice-versa) or subscribers cannot make international calls from the roamed country (except back to their home country).

**Table 4.1 Benchmarking ICT infrastructure across Africa’s regional communities**

||ECOWAS|CEMAC|COMESA|EAC|SADC|
|---|---|---|---|---|---|
|Broadband subscribers (per 100 inhabitants)|0.03|0.01|0.04|0.02|**0.36**|
|International Internet bandwidth (per capita)|16|11|9|11|**19**|
|Internet subscribers (per 100 inhabitants)|0.24|0.06|0.09|0.05|**0.53**|
|Main telephone lines outside largest city (per 100 inhabitants)|0.39|0.20|0.53|0.24|**1.89**|
|Mobile telephone subscribers (per 100 inhabitants)|25|22|12|21|**31**|
|**Prices**||||||
|Prepaid mobile price basket ($ per month)|14.04|15.11|9.09|12.18|**11.32**|
|Price of a 3-minute call to the United States ($ per 3 minutes)|0.83|5.68|2.20|1.37|**1.50**|
|Price of the 20-hour Internet basket ($ per month)|79.98|67.97|50.91|95.70|**75.60**|
|Price of fixed telephone price basket ($ per month)|9.35|12.59|6.85|13.33|**13.27**|

_Source:_ Ampah and others 2009. Note: CEMAC = Economic and Monetary Community of Central Africa; COMESA = Common Market for Eastern and Southern Africa; EAC = East African Community; ECOWAS = Economic Community of West African States.

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**Figure 4.4: Outages frequency, duration, and impacts compared with average wind speeds.**

<!-- Start of picture text --> 12 16 600 16<br>14 14<br>10 500<br>12 12<br>8 400<br>10 10<br>6 8 300 8<br>4 6 200 6<br>4 4<br>2 2 100 2<br>0 0 0 0<br>160,000 16<br>140,000 14<br>120,000 12<br>100,000 10<br>80,000 8<br>60,000 6<br>40,000 4<br>20,000 2<br>0 0<br>(hours)<br>outages (2000-2017)<br>Average wind speed (km/h)<br>Average number of days with Average wind speed (km/h)<br>January February March April May June July August September October November December Average outage duration 2000-2017 January February March April May June July August September October November December<br>affected 2000-2017<br>Average number of consumers Average wind speed (km/h)<br>January March May July September November<br><!-- End of picture text --> Figure 4.4 demonstrates that outages do not match the monsoon season that is captured by average wind speeds from the ERA5 climate data (see 2.2.2). However, the significant increase in the variance of wind speed from April to May suggests the occurrence of more high intensity storm days caused by severe local convective storms (Nor’wester). As described in section 2.2.2, these storms can be proxied by data on lightning strikes. The seasonal pattern of lightning strikes resembles strongly the occurrence of power outages, thus confirming that most power outages that occur in Bangladesh due to natural causes, are indeed caused by Nor’wester storm in the months April and May. This observation is consistent across the entire data period from 2000 to 2014, as the high correlation between outages and lightning strike time series confirms (Figure 4.6).

26
childlessness in the EU(26) only explains less than 10 percent of the completed fertility gap. Figure 3 shows that the absolute gap in the proportions is highest for women having at least four children, but this difference between Europe and Turkey explains less than 20 percent of the gap in completed fertility, as even in Turkey, only a minority of women is concerned.

Our calculations suggest at the same time, that if completed fertility is going to drop below replacement level in the near future due to an assimilation process towards Europe, the drop is likely to be caused in main parts by parents deciding against second and third child arrival. Our further analysis of completed fertlity by education and the econometric analysis of child arrival probabilities will shed light on the question which kind of families are most likely to decide against family enlargment in Turkey, especially for child ranks numbers two and three.

Finally, we apply the same calculation logic when comparing the completed fertility rates and the according parity rates of cohort 1960 (figure 1, figure 2) with those of cohorts 1969 to 1974 (figure 1, figure 3). We find that the drop in completed fertility is explained firstly by fewer women having at least three children.

**figure 3. Parity fertility by cohort in Turkey in comparison to the EU average**

Data Source: EU SILC CS 2011 (women aged 37 to 42)

# _Completed fertility by education_

The fertility decline in Turkey can be observed for all levels of education, but is most pronounced for low educated women. Figure 4 illustrates two trends: Low educated women (less than primary education or primary diploma) experience a drastic fertility decrease, while more educated women (secondary and tertiary) show a much less dramatic fertility decline. At the same time, figure 4 reveals that the average number of children has always been much lower for educated women (below replacement level).

9
- (v) It is assumed that all assets have a life span of 25 years to ensure service standards are maintained. While this period is short it reflects the Soviet standard that is practiced in all countries of the paper.

# **_Calculation of Average Cost Recovery Price in the Water Sector_**

Cost-recovery tariffs are defined as the cost of supplying water 24 hours a day, including the cost of operation, maintenance and necessary investments. Where an average cost-recovery price has not been available the following assumptions have been made in its calculation: ACRP = _[Current cost of operation] + [24 hours-a-day supply factor] + [Investment component]_ Where: _Current cost of operation, C -_ The actual reported operation and maintenance cost collected from the OECD data set<sup>8</sup> or by using the IB-NET<sup>9</sup> toolkit.

_24 hours-a-day supply factor, T_ **-** To reduce short-term costs, especially electricity costs, water utilities initiate intermittent water supply. Reducing operational hours results in some savings that reduce costs in the short run, but long-run costs increase dramatically due to hydraulic shocks that destroy the water infrastructure and other effects that reduce water quality. It has been assumed that savings incurred through interrupted water supplies are equal to one-quarter of the cost of maintaining supplies round the clock due to energy savings and reduced short-term maintenance. As a rule, intermittent water supply does reduce consumption.

Here, T = 0.25 C (1 – (t/24)) Where, t = number of hours of water supply _Investment Component, I -_ Assumed to be equivalent of 4 percent of the value of the assets per year on the basis that assets have a life span of 25 years to ensure service standards are maintained. The fixed asset value is reported in IB-NET.

I = 0.04 A / P Where, A = fixed asset value in US$ P = annual water production in m<sup>3</sup> Therefore the Average Cost Recovery Price, ACRP equals ACRP = C + T + I ACRP = C + 0.25 C (1 – (t/24)) + 0.04 A/ P

> 8 <u>www.oecd.org/document/49/0,2340,en_2649_34343_1839281_1_1_1_1,00.html</u> 9 http://www.ib-net.org/

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provides a degree of protection against storms and its tidal ecology supports a wide variety of birds, reptiles, mollusks and crustaceans.

On average, incomes are low in Ca Mau, with about one-third of the province’s 1.2 million people classified as poor in 1996. Most households in the province still depend on agriculture, fishing or forestry for their livelihoods. Traditionally, farming households have used the area’s abundant water supplies for irrigated rice. In recent years, following the lead of national policies that promote export-led growth, local authorities have departed from their long-held emphasis on rice production, to encouraging shrimp production intended for export. The study examines the consequences of this policy shift on incomes and the local environment.

# <u>South Africa</u>

The South African study site is a portion of the Incomati River basin, which also stretches into Mozambique and Swaziland.<sup>15</sup> The Incomati River originates in South Africa, flows into Swaziland, reenters South Africa and flows into Mozambique, entering the Indian Ocean near Maputo. The river supports a rich set of ecologies in all three countries. The basin contains Kruger National Park, South Africa’s large game reserve, located near the headwaters of the Incomati, and feeds a large estuary and mangrove forest at the mouth of the Incomati. The game reserve is home to cheetahs, giraffes, elephants, hippopotamus, lions, leopards, rhinoceros and zebra. Hundreds of species of resident and migratory birds dwell along the Incomati and its tributaries as well. The mangrove forest near Maputo protects against ocean storms and provides a breeding ground for shrimp and other important aquatic life. The Incomati River Basin is an important source of fresh water for the river basin’s human population, providing water for drinking and for irrigating crops. Demand for the basin’s water is high and current water use exceeds the Incomati River’s annual runoff by more than 26 percent.

The economic focus of the study is on sugar production in the poor province of Mpumalanga, which abuts Kruger National Park. Household income in the province averages around $257 per year, inequality rates are high, and life expectancy is under 50 years. The sugar industry employs nearly 70,000 people in the province where

> 15 This section draws on Lorentzen, Cartwright and Meth (forthcoming).

9
However, there have been recent innovations in how job training and job search programs in developing countries are designed, targeted, and implemented.<sup>1</sup> We argue that there is a real, if more limited, role for government in providing these programs. For example, government action may be warranted when private firms are underinvesting in training of workers due to the possibility workers will leave, and when search and matching frictions slow down or prevent the reallocation of workers across sectors and geography that are critical for structural transformation. Equity concerns may also justify the use of these policies to help disadvantaged jobseekers find jobs. We begin with an overview of the worse and better reasons why governments in developing countries have become involved in job search and job training. We then outline lessons from recent experiences in developing economies with vocational training, policies designed to overcome spatial and informational search frictions, interventions aimed at overcoming psychological barriers to job search, and government efforts to encourage the development and use of online jobs platforms.

# **Good and Not-So-Good Reasons for Governments to Provide Job Training and Search Assistance**

Firms typically have strong incentives to find and retain good workers, and many workers likewise have strong incentives to look for jobs and seek training in the skills demanded by the market. As a result, the vast majority of jobs are filled without government intervention. Table 1 illustrates this point, using some of the relatively few available labor force surveys that directly ask employed workers in developing countries how they found their jobs. The vast majority of jobs are found by workers either learning about jobs through their social networks of friends and relatives, or through workers directly approaching employers in their business or worksite and asking if they have openings. Public (government) agencies and private employment agencies are

> 1 We focus on job training and job search assistance as two of the most common types of active labor market policies undertaken by governments. There are several other types of active labor market policy that we do not cover. For example, another common form of skills training is to teach jobseekers business skills in order that they can start their own small businesses (for a review, see McKenzie et al.  2021). Governments can also directly provide jobs to workers through large public works programs (reviewed in Gehrke and Hartwig, 2018) or make it cheaper for firms to hire workers through wage subsidy programs (reviewed in McKenzie, 2017). We also restrict our focus to largely urban labor markets, reflecting government efforts to integrate workers into wage jobs, and do not discuss policies to increase agricultural employment.

4
# **<mark>Table A2D – Parallel Trend Test of Bank Lending to Local Governments Across States of Varying Public Debt Before</mark> FD Law**

<mark>This table reports OLS estimates of the trends of bank lending to local governments across states of varying public indebtedness</mark> prior to the introduction of the FD Law. The variable _Loans-Govts,m_ is the total bank lending (in logs) of state government _s_ in month _m_ . _Term Loan-Govts,m_ is the bank lending (in logs) in term loans of state government _s_ in month _m. Credit Line-Govts,m_ is the bank lending (in logs) from credit lines of state government _s_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendm_ is a linear trend over time from the start of our sample in January 2014 up to March 2016, the month prior to the implementation of the FD Law. Observations at the state-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided <u>in Table A1 in the Appendix.</u>

||Loans -<br>(1)|Govts,m<br>(2)|Term Loan<br>(3)|- Govts,m<br>(4)|Credit Li<br>(5)|ne- Govts,m<br>(6)|
|---|---|---|---|---|---|---|
|Trendm|0.00||0.00||0.00||
||(0.01)||(0.01)||(0.01)||
|Trendm*DebtStates,16Q1|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|450|450|450|450|450|450|
|R-squared|0.98|0.98|0.98|0.98|0.98|0.98|
|Bank-State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|No|Yes|No|Yes|No|Yes|
|State-Month FE|No|No|No|No|No|No|

63
**Table 5: Regressions for Financial Constraints and Loan Application**

||**Dep. Var: Fi**<br>**Obs**|**nance is NO**<br>**tacle**|**Dep. Var: App**|**lied for Loan**|
|---|---|---|---|---|
||**Probit**|**OLS**|**Probit**|**OLS**|
|Observations|920|920|748|748|
|**Sector Dummies**|Yes|Yes|Yes|Yes|
|**Year Dummies**|||||
|2002 dummy<br>|-0.068<br>|-0.051<br>|-0.115<br>|-0.093<br>|
|(dummy)|(0.092)|(0.088)|(0.135)|(0.129)|
|2008-9 dummy|-0.125|-0.107|0.057|0.043|
|(dummy)|(0.096)|(0.092)|(0.104)|(0.092)|
|**Firm Characteristics**|||||
|Firm is medium-sized|0.046|0.043|0.209***|0.178***|
|(dummy)|(0.041)|(0.038)|(0.043)|(0.038)|
|<br>Firm is large|0.190**|0.172*|0.297***|0.182**|
|(dummy)|(0.093)|(0.089)|(0.100)|(0.072)|
|<br>Firm is Large*2005|<br>-0.003|<br>-0.001|<br>-0.071|<br>-0.033|
|(interaction term)|(0.090)|(0.091)|(0.079)|(0.094)|
|<br>Firm is Large*2008/9|<br>-0.144*|<br>-0.156|<br>0.215*|<br>0.234**|
|(interaction term)|(0.075)|(0.092)|(0.135)|(0.105)|
|Firm Age|-0.022|-0.021|-0.038|-0.028|
|(natural log)|(0.034)|(0.032)|(0.035)|(0.030)|
|<br>Percent private-owned|<br>-0.0004|<br>-0.0003|<br>0.003***|<br>0.0025***|
|<br>(percent)|(0.001)|(0.001)|(0.001)|(0.0005)|
|<br>Percent foreign-owned|<br>0.0002|<br>0.0003|<br>0.001|<br>0.001|
|<br>(percent)|(0.001)|(0.001)|(0.001)|(0.001)|
|Firm was privatized|0.012|0.011|-0.047|-0.048|
|(dummy)|(0.059)|(0.056)|(0.038)|(0.036)|
|<br>Exports|<br>-0.0002|<br>-0.0002|<br>0.001|<br>0.001|
|(% of sales)|(0.001)|(0.001)|(0.001)|(0.001)|
|**Firm Performance**|||||
|Firm Growth|-0.001|-0.007|0.046*|0.037**|
|(employment growth, %)|(0.020)|(0.017)|(0.025)|(0.016)|
|<br>**Country Characteristics**|||||
|<br>GDP Growth*2002|0.004|0.004|-0.022|-0.019|
|(interaction term)|(0.010)|(0.010)|(0.014)|(0.011)|
|GDP Growth*2005|-0.010|-0.007|-0.009|-0.008|
|(interaction term)|(0007)|(0004)|(0011)|(0007)|
|<br>GDP Growth*2009|.<br>-0.008|.<br>-0.008|.<br>0.005|.<br>0.005|
|(interaction term)|(0.008)|(0.007)|(0.007)|(0.006)|
|<br>GDP Per Capita|<br>0.003|<br>-0.004|<br>0.030*|<br>0.025*|
|(1000s, 2002 US$)|(0.008)|(0.008)|(0.016)|(0.014)|
|KKM Instit. Development|0.104*|0.105*|-0.087|-0.072|
|(index)<br>|(0.059)<br>|(0.058)<br>|(0.083)<br>|(0.071)<br>|
|Population Density|-0.002***|-0.002***|0.0002|0.0002|
|<br>(residents per square mile)|(0.001)|(0.001)|(0.001)|(0.001)|
|Population|0.001|-0.001|0.001|-0.001|
|(millions)|(0.001)|(0.001)|(0.001)|(0.001)|
|Inflation|-.00005|0.0001|-0.005|-0.005*|
|(percent)|(0.002)|(0.002)|(0.003)|(0.003)|
|<br>**Banking Sector**|||||
|<br>state bank assets|-0.0004|0.0005|-0.001|-0.001|
|(percent)<br>|(0.001)<br>|(0.001)<br>|(0.001)<br>|(0.001)<br>|
|foreign bank assets 1999|-0.002*|-0.002*|0.003|0.0017|
|<br>(percent)|(0.001)|(0.001)|(0.002)|(0.0013)|
|<br>% Foreign in 1999*2005|<br>0.003**|<br>0.0031**|<br>-0.002|<br>-0.0017|
|(interaction term)|(0.001)|(0.0014)|(0.002)|(0.0020)|
|<br>% Foreign in 1999*2008/9|<br>0.004***|<br>0.0042***|<br>-0.004***|<br>-0.003**|
|(interaction term)|(0.001)|(0.001)|(0.002)|(0.001)|

43
person consumption floor below this level. This procedure results in bottom-coding 0.3% of the population covered by both income and consumption surveys in 2019.

# **3.2 The evolution of global welfare, inequality, and poverty over time**

We begin by illustrating the features of our new measures in the setting of the world interpersonal income distribution. Table 3.1 reports our new welfare index W, comparing it with mean income in Panel A with the help of our new inequality measure I (Panel B). We also compare our new censored poverty measures C and P with the headcount ratio (Panel C). While we compare our new measures to other distribution sensitive welfare indices and poverty measures later in this section, we start with the mean and the headcount index as comparators, precisely because they are simple, commonly used, and not distribution sensitive by any of the three definitions in the literature (see Table 2.1). W is calculated using z= $6.85 as the reference income level, corresponding to the World Bank’s poverty line for upper-middle income countries, while the censored measures in Panel C are presented for z= $6.85 as well as z= $2.15, the World Bank’s international poverty line capturing extreme poverty.

**Table 3.1: Global Welfare, Poverty, and Inequality Measures 1990-2019**

||**1990**|**2019**|**Avg Annual**<br>**Change, %**||**1990**|**2019**|**Avg Annual**<br>**Change, %**|
|---|---|---|---|---|---|---|---|
|**Panel A: W**|**elfare Me**|**asures (z=$6**|**.85 per day)**|**Panel B**|**: Inequalit**|**y Measures**||
|_W_|2.95|1.36|-2.67|_I_|5.02|3.58|-1.16|
|Mean|11.6|18.0|1.51|||||
|Sen|3.53|6.88|2.30|Gini|0.70|0.62|-0.41|
|_A1_|4.29|8.76|2.46|_AI1_|0.63|0.51|-0.71|
|_A2_|2.32|5.03|2.67<br>|_AI2_|0.80|0.72|-0.36|
|**Panel C: Po**<br>**(i) z = $6**<br>|**verty Mea**<br>**.85 per da**<br>|**sures**<br>**y**<br>||**(ii) z = $**<br>|**2.15 per d**<br>|**ay**<br>||
|_C_|3.15|1.66|-2.20|_C_|1.31|1.05|-0.74|
|_P_|2.15|0.66|-4.06|_P_|0.31|0.05|-5.95|
|_FGT0_|0.69|0.47|-1.34|_FGT0_|0.38|0.08|-5.20|
|_FGT1_|0.45|0.21|-2.56|_FGT1_|0.14|0.03|-5.84|
|_FGT2_|0.32|0.12|-3.36|_FGT2_|0.07|0.01|-6.06|
|Watts|0.85|0.34|-3.18<br>|<sup>Watts</sup>|0.20|0.04|-5.91|

**Notes:** This table reports the global estimates of W(y, z), I(y, y̅), C(y, z), and P(y, z) and selected comparators. The welfare measure, W, is reported using the $6.85 per person per day threshold in 2017 $PPP (Panel A). C and P are reported for the $6.85 and $2.15 thresholds (per person per day in 2017 $PPP). The _Avg Annual Change_ column reports the average annual log differences. Abbreviations: Aε – Atkinson Social Welfare Index with ε= 1 or 2; FGT – Foster-Greer-Thorbecke poverty measure with α= 0, 1, or 2; AIε  – Atkinson Inequality Index with ε= 1 or 2.

17