rafmacalaba/datause-encoder-data huggingface dataset:
It also analyzes the trends of hydropower development in South Asian countries. It then qualitatively analyzes the critical role of increased cross-border interconnections and regional electricity trade to stimulate the development of hydropower in the region. The paper also presents quantitative results of a power system planning model to measure the expansion of hydropower capacities in the region over the next two decades under various scenarios.<sup>2</sup>

# **2.  An Overview of Hydropower: Global and South Asia**

In this section we briefly reflect the current situation of hydropower development around the globe with an additional discussion on historical trends of hydropower development in the South Asia region.

## **2.1 Global Overview**

As of January 1, 2017, 1,242 GW of hydropower capacity had been installed around the world,<sup>3</sup> of which the South Asia region accounts for a small fraction, 5% (IHA, 2017). As illustrated in Figure 1, 116countries around the world have installed hydropower with capacity more than 100 MW, of which 36 countries have installed more than 5,000 MW each; 47 countries, each, have installed capacities between 1,000 MW and 5,000 MW and another 33 countries, each, have installed capacity between 100 MW and 1,000 MW. China tops the list with 331,110 MW, followed by the United States with 102,485 MW, Brazil with 98,015 MW and Canada with 79,323 MW. India, the biggest South Asian country, occupies the fifth position with almost 52,000 MW installed capacity.

> 2 This study uses the same electricity planning model used in Timilsina and Toman (2016) and Timilsina and Toman (2018). However, the quantitative results presented here are additional to those presented in the previous studies.

> 3 Installed capacity of small hydropower plants (< 100 MW) is not included.

4
operate at a larger scale with significantly higher levels of labor and capital. Crossover firms are also older, and more likely to have received business support in the form of business or technical training. There also appear to be some differences in terms of finance – crossovers are more likely to belong to a savings and credit cooperative (SACCO) and to have received business development services such as assistance on business planning, developing promotional materials, and expanding market access. In terms of business practices, crossovers are significantly less likely to be running a business that they themselves started. In terms of personal characteristics, crossovers show some differences from non‐crossovers: they are older, have a somewhat different education profile and are more likely to be widowed.

Table 4 provides the same comparison, using the KASSIDA data, for female and male entrepreneurs within the crossover sectors.

|**Table 4: Summary statistics, within crossover**|**s' sectors**<br>**Total**|**female‐m**<br>**StDev**|**ale compariso**<br>**Crossover**|**n ‐ Kassid**<br>**Male**|**a data set**<br>**Difference**|
|---|---|---|---|---|---|
|**_Financials_**||||||
|Revenuespast month(UGX '000)|3,343|5,543|2,697|3,397|‐700|
|Profitspast month(UGX '000)|513|676|494|515|‐21|
|**_Investment_**||||||
|Net capital invested(UGX '000)|3,865|7,749|5,312|3,744|1,569|
|Number of workers|5.5|4.0|3.9|5.6|‐1.7**|
|Inputs consumedpast month(UGX '000)|1,659|4,169|968|1,717|‐749|
|**_Personal characteristics_**||||||
|Age|39|10|42|39|4*|
|Married|86|35|40|90|‐50***|
|Widow|3|16|32|0|32***|
|Less thanprimaryeducation|9|28|30|7|24***|
|Primaryschool is max education|45|50|26|47|‐20*|
|Secondaryschool is max education|29|46|30|29|1|
|Completed more than secondary|17|38|13|17|‐4|
|Previouslyworker in sector|17|37|12|17|‐5|
|Received business or technical traininginpast|67|47|72|66|6|
|Financial literacyindex(0‐1)|0.5|0.1|0.5|0.5|0.0|
|Technical literacyindex(0‐1)|0.5|0.2|0.4|0.5|‐0.1**|
|Owns more than one business|24|43|24|24|0|
|Household and business activities simultaneously|12|33|44|9|35***|
|**_Business characteristics_**||||||
|Business owner started this firm|87|34|84|87|‐3|
|Saw market opportunitywhen startingbusiness|65|48|48|66|‐18*|
|Has businesspartners|13|33|0|14|‐14**|
|Business is registered|64|48|44|66|‐22**|
|Age of business|15|9|14|15|‐2|
|Size of business network(log)|4|1|3|4|‐0.2|
|**_Access tofinance_**||||||

11
Table 4. Robustness checks: The role of political connections

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|PS YearsInOffice|<br>0.096<sup>***</sup><br>(0.029)|<br>0.103<sup>***</sup><br>(0.030)|<br>0.098<sup>***</sup><br>(0.029)|<br>0.096<sup>***</sup><br>(0.029)|<br>0.096<sup>***</sup><br>(0.029)|<br>0.105<sup>***</sup><br>(0.030)|
|PS YearsInOffice<sup>2</sup>|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|-0.009<sup>**</sup><br>(0.005)|
|Connected to PPS|-0.139<sup>**</sup>|-0.031|-0.141<sup>**</sup>|-0.139<sup>**</sup>|-0.137<sup>**</sup>|-0.030|
||(0.069)|(0.082)|(0.069)|(0.069)|(0.069)|(0.082)|
|Prov Exper.|0.089<sup>*</sup><br>(0.048)|0.104<sup>**</sup><br>(0.048)|0.086<sup>*</sup><br>(0.048)|0.089<sup>*</sup><br>(0.048)|0.117<sup>**</sup><br>(0.051)|0.128<sup>**</sup><br>(0.051)|
|CYL Exper.|0.051|0.058|0.050|0.051|0.052|0.057|
||(0.057)|(0.057)|(0.056)|(0.057)|(0.056)|(0.057)|
|Mayor Exper.||0.126<sup>*</sup><br>(0.071)||||0.128<sup>*</sup><br>(0.071)|
|Prov PSC Mem.||-0.188||||-0.186|
|||(0.184)||||(0.186)|
|Vice Governor||-0.154<br>(0.227)||||-0.155<br>(0.224)|
|PPS Hometown|||-0.427<sup>***</sup>|||-0.415<sup>***</sup>|
||||(0.116)|||(0.123)|
|PPS Workplace|||-0.058|||-0.041|
||||(0.141)|||(0.133)|
|Both PS & PPS Female||||0.093||0.114|
|||||(0.278)||(0.289)|
|Hometown Ties with PPS||||0.006<br>(0.132)||-0.024<br>(0.131)|
|Shared Prov. Exper. with PPS|||||-0.098<br>(0.073)|-0.093<br>(0.073)|
|Shared City Exper. with PPS|||||-0.056|-0.050|
||||||(0.145)|(0.144)|
|# of Observations|3642|3642|3642|3642|3642|3642|
|# of Cities|281|281|281|281|281|281|
|Adj R<sup>2</sup>|0.60|0.60|0.60|0.60|0.60|0.60|

_Notes_ : This table presents the political cycle of GDP manipulation with more measures of political connections included. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. All the specifications include city socioeconomic controls, leader controls, city FEs, and province-year FEs. Leader controls and city controls are the same as those in Table 2. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

42
11 We considered two alternative views of child persistence in school: parental decisions can be driven by the child's performance in school as in equation (1), or by both performance and promotion as in equation (2). Note that promotion will be positively correlated with child performance as long as γ > 0 in equation (7), even if promotion is not solely merit-based. Consequently, distinguishing between equations (1) and (2) requires that the incidence of promotion be decomposed into a component correlated with merit and a component that is uncorrelated with merit. This can be accomplished by predicting promotion on the basis of equations (3) and (4’), the strict merit-based model. Then, student performance will be measured by the merit-based component of the promotion, _E_ ( _Pijt_ | _Aijt_ , _M ijt_ , _Lijt_ ) . The difference between actual promotion and the predicted merit-based

> promotion, [ _Pijt_ − _E_ ( _Pijt_ | _Aijt_ , _M ijt_ , _Lijt_ )] , can then be used as the incidence of promotion that is

uncorrelated with _qijt_ . The parents' decision to keep the child in school approximates equation (2) by where δ is a set of parameters to be estimated, υ _tp_ is the observable component of parental marginal utility from an additional year of child schooling and ξ _ijt_ is a random error. Child persistence in school is governed by where _Eijt+1_ =1 if the child is enrolled in school the following year.

# **3.  Data Sources**

The empirical analysis uses data from a series of survey questionnaires designed by the authors and fielded by NEMIS staff under the supervision of Rafiq Jaffer. The survey covered a
straddling the reforms, the headcount went down by about 74’485 individuals, or a full 17.4% of the regional population. The Gini index also showed a favorable trend, shrinking from 0.427 to 0.39 (only to go up again between 1999 and 2001). Both numbers indicate large and favorable shifts in the regional distribution of income which stand in contrast with the seemingly endless descent into poverty that characterized Madagascar over the previous quarter-century.

Table 7 Poverty headcount, Poverty <u>gap and Gini indices, vanilla region</u>

||Headc|ount|Headco|unt ratio|Pove|rty gap|G|ini coef.|
|---|---|---|---|---|---|---|---|---|
||Actual|Counter.|Actual|Counter.|Actual|Counter.|Actual|Counter.|
|93|333'879|n.a.|0.749|n.a.|0.380|n.a.|0.427|n.a.|
|97|271'424|n.a.|0.596|n.a.|0.227|n.a.|0.366|n.a.|
|99|259'394|n.a.|0.575|n.a.|0.213|n.a.|0.39|n.a.|
|01|261'618|282'510|0.674|0.728|0.276|0.320|0.418|0.418|

Source: authors’ calculations from EPM.

Headcount: number of individuals; headcount number: proportion of individuals (using EPM’s survey design). Counter. : Counterfactual values obtained from baseline scenario (σ = 4, εs = 0.5, εv = 1.0). See text and annex A.2 for description of the simulation model.

The fact that this reversal takes place precisely during the reform period is remarkable. Before crying victory, however, one needs to take a hard look at how much of it can reasonably be attributed to the vanilla reforms. One can only be suspicious that at least some of it has little to do with the reforms for two reasons. First, the trend is the same nationwide. Second, vanilla output and producer prices evolved, in fact, _unfavorably_ during the period 1995-98. For all its limitations, only a simulation-based counterfactual analysis recreating the old policy environment under current market conditions can give a beginning of an answer to the real question: how much did the reforms achieve by themselves?

# **3.2 A simple counterfactual**

Our description of Madagascar’s vanilla sector suggests that domestic competition in processing and intermediation was limited both by technical factors and by the GOM’s policies. Internationally, competition was also limited with only two major producing countries, at least until recently, each producing a differentiated good. We exploit this characteristic of the vanilla market described 27
# **_(b) Access Barriers_**

# **Inequities Linked to Geographical and Insurance Regime Issues**

The latest ENDS survey of 2010<sup>46</sup> showed that 72% of adults who had suffered from health problems during the previous 30 days had been treated in a health center by either a doctor or a nurse. A problem of access (distance, transport costs, or copayments) was the reason given by 7% of those who did not attend a health center.

These figures might lead to the conclusion that there are no serious problems of access to health services that might require attention and solutions. However, on the basis of the various interviews conducted during the Study, the Study Team arrived at a different conclusion. The Team concluded that the quantity, variety and quality of HIV/AIDS care services in Colombia: Is higher in urban than in rural areas; Is higher in the contributory than in the subsidized insurance regime; Varies in the associated regime to the extent that—in places such as Bogotá, the capital city—care in the associated regime can be far better than care provided by either the contributory or subsidized regimes.

The graph provided in Figure 5 below aims to reflect the discrepancies in quality of service available for people with HIV/AIDS, depending on the insurance scheme they happen to belong to and the place where they happen to live (rural or urban setting).

Paradoxically, it so happens that persons from urban areas who are not registered in any insurance scheme—and are therefore treated at ESEs—receive the best care of all. This seems to be due to a variety of reasons. First, ESEs are generally better equipped than most IPSs. Second, ESEs can provide key support HIV/AIDS services—such as nutrition, social care, pharmacy, and diagnostics—in the same building, which benefits patients notably by saving them time and expenses that would otherwise be incurred by travelling to multiple locations.

Persons from urban areas who are affiliated with the contributory insurance regime receive the second best care. Persons from urban areas who are affiliated with the

> 46 MPS (February 2011) Encuesta Nacional de Demografía y Salud 2010, Bogotá: MPS.

45
For reasons that are discussed in the next section, our prior is that aggregate volatility has asymmetric, non linear effects: a sharp contraction exerts a more than proportional influence on employment, poverty and inequality –that a sharp expansion does not undo completely. In order to identify these nonlinearities and asymmetries, good precise measures of aggregate volatility are critical. We focus on the post-Bretton Woods era (1975-2005).

# _a. First glance_

Figures 1 characterizes aggregate volatility for different income (high, middle, low) and development (industrial, non-industrial) categories.<sup>8</sup> As can be seen, output volatility in developing countries (as measured by the standard deviation of real growth rate over the period 1975-2005) almost triples that for the developed group. Similarly, high-income countries enjoy greater stability than low- and middle-income economies, even after excluding small countries which are likely less diversified and more sensitive to external real shocks.

The same picture is obtained when we look at extreme output events, measured in this case as a 5% cumulative decline in output relative to its level prior to the decline. Again, the propensity to suffer sharp output contractions in non-industrial economies is many times larger than in the industrial world, suggesting that the greater output volatility may reflect the incidence of these extreme downturns.

# **III. The ultimate toll: From aggregate volatility to income distribution**

A critical –and often overlooked– degree of vulnerability concerns the capacity of countries in general, and developing ones in particular, to forestall the negative welfare consequences of the adverse swings in economic activity documented in Figure 1. Higher exposure to exogenously-triggered output contractions or financial disarray does not imply, per se, larger

> 8 Categories are drawn from the World Bank’s classification. The middle-income sample include both low- and high-middle income countries. Variable definitions and sources as well as summary statistics for all the relevant variables in the paper are reported in the Appendix.

6
# **A New Approach to Producing Geographic Profiles of HIV Prevalence: An Application to Malawi**

## **Oleksiy Ivaschenko and Peter Lanjouw**<sup>**1**</sup>

**Key words** : HIV prevalence, census, GIS, sentinel data, small area estimation, bootstrap.

> 1 The affiliation and contact details of the authors are as follows: Oleksiy Ivaschenko, Economist, Europe and Central Asia Human Development Department, The World Bank, email: oivaschenko@worldbank.org; Peter Lanjouw, Research Manager, Research Manager, Development Research Group, The World Bank, email: planjouw@worldbank.org. The authors would like to thank Livia Montana for providing generous support for this research. We are also very grateful to all those who provided comments and suggestions on the earlier version of this paper. All the views expressed in this paper are solely those of the authors, and should not be attributed to the World Bank group.
benefits or by redistributing a given total of benefits more in favor of the poor (Lambert 2001). This is analogous to the case of a progressive income tax.

The extent to which the expenditure program is _targeted_ to the poor can be judged on the basis of the comparison of the concentration curve for benefits with the line of equal distribution (the 45 degree line). The further the concentration curve lies above the 45 degree line, the better the targeting. In this case<sup>26</sup> , the poorest 100 **p** percent of the population receives more than 100 **p** percent of the total subsidies. Targeting is weaker when the concentration curve of the benefits lies below the 45 degree line. The results presented in table 3.1 reveal that the poorest received 12 percent of overall public health spending compared to 33 percent for the richest. This is an indication of poor targeting of public health spending in Ghana in 1992.

Table 3.2. Incidence of Primary and Secondary Public Education Expenditure in Malawi (1997/98)

||1<sup>st</sup>Quintile|2<sup>nd</sup>Quintile|3<sup>rd</sup>Quintile|4<sup>th</sup>Quintile|5<sup>th</sup>Quintile|
|---|---|---|---|---|---|
|||Incid<br>|ence<br>|||
|Primary|25|23|19|18|14|
|Secondary|18|19|22|20|21|
|||Share of School|-Age Population|||
|Primary|24|22|20|18|16|
|Secondary|24|21|20|19|16|

Sourec Al-Samarrai and Zaman (2007) Another important way of assessing the equity of a spending program is to compare benefits received to needs. Comparing the distribution of benefits to that of household income or expenditure does not tell the whole story as there could be significant differences in needs across income levels. This point is well illustrated by the case on primary and secondary public education spending in Malawi presented in table 3.2 (Al-Samarrai and Zaman 2007). The results of this incidence analysis show that the poorest quintile has the greatest demand for both primary and secondary education. Their share of the school-age population in both education sectors is higher than that of any other income group. When compared specifically with the richest quintile, the

> 26 The associated concentration curve is concave and the corresponding concentration index is negative.

29
# **Digital divide**

The UK has relatively high Internet penetration, but the ONS nonetheless reported 15% of adults did not use the Internet in 2014 (when most of these studies were conducted). Additionally, there are still substantial gaps in digital literacy even among those with access to the Internet (Office for National Statistics, 2019).

As Figure 4 shows, digital literacy varies across demographic groups. The largest demographic splits are by age and education. Older British people are much more likely to be digitally illiterate in the UK and lack Internet access (Office for National Statistics, 2019). In 2017, 13% of those aged over 50 lacked Internet access, compared with 2% of those aged 50 or under (Internet Access, Opinions and Lifestyle Survey (OPN), 2017). The education gap is even stronger: 26% of people with no qualifications lack Internet access compared with 1% of those with a degree (Office for National Statistics, 2017).

Other demographic groups show less dramatic splits. Women have slightly lower digital literacy than men on average (Office for National Statistics, 2019), but broadly similar levels of Internet access (Office for National Statistics, 2017). Ethnic minorities are _more_ likely to have Internet access in the UK, although this difference disappears after accounting for age.

# **Digital divide → participant profile**

We analyze FMS users’ demographics by comparing a survey conducted by Cantijoch, Galandini and Gibson (2016) to the UK Annual Population Survey to look at how over or under-represented different groups are. Men are substantially overrepresented on FMS (66% of users) and the average age of FMS users is much higher than the whole adult population (55 years compared to 48 in the adult population). Ethnic minorities are substantially underrepresented on FMS (2% compared to around 12% of UK adults in the 2016 Annual Population Survey). However, the single largest overrepresentation on the platform is of highly educated people: 80% of FMS users reported attending 17
supply must be distributed over intermediate demand (ID) , final private consumption (C), government consumption (3), gross fixed capital formation (FKF), stocks accumulation (SA) and exports (x). The following table illustrate this description.

Table A.l:

|Supply<br>Value<br>and<br>In<br>Prod.<br>Imp.<br>Demand<br>De|tern.<br>mand|Priv.<br>Cons.|Gov.<br>Cons.|Fix<br>Inves.|Stck.<br>Accus. Exp.|
|---|---|---|---|---|---|
|VP1<br>+<br>=sector,<br>=ID,|+ C|+ 01|+ F7F|+ SA<br>|+xi|
|.<br>.<br>m<br>.<br>=|.....|||||
|VPJ +N j<br>sectorj = IDj|+ C|J + GJ|+ FKFj|+ SA|+SJ|
|VP<br>+ M<br>=<br>Total = I|D<br>+|C +|G<br>+|FKF +|SA +X|

The notation in consistent with equation (2) after we define Y = VP - ID, and I = FKF + SA.

The usual problem with the distribution of demand components is that the relevant information is generally not available, and some assumptions must be made. The balance of payments accounts normally provide fairly good information on imports and exports. Except for the exchange rate measurement difficulties of transforming foreign denominated series into local currency accounts, these serieo are fairly accurate. This means that measured total supply is a good measure of actual supply of goods and services at the sector level. For several sectors (and/or products), however, the problems of assigning the total demand into its demand components is very difficult. The discussion below states why some series can be trusted more than others.

Assigning total supply to the demand components can be facilitated by doing the supply/demand compatibilization at a level as disaggregated as possible. For instance, several firms, or even sectors, snecialize in exports. Consequently the value of production would mostly be assigned to exports. The only uncertainty could be, if the commodity is storable, how much of total supply goes to stock accumulation. If good exports series are available, stock accumulation can be accurately obtained by difference (all produced goods not exported,.

Something similar could be done if the company produces mostly machinery, or inputs for some other sector, which are easy to assign due to the characteristics of the good. The value of production of the construction sector, for instance, is another sector which is mostly assigned to fixed capital formation, and does not produce mayor measurement problems to other components. Government consumption is another variables relatively easy to assign. G is mostly the value of productions of sectors associated to the government. These are typically public administration plus governmont health and education production (expenses).

The more serious problems appear with those sectors or products which can go to several different demand compone"ts. Most food production, for instance, can go to intermediate demand (purchases of other firms), private consumption, to stock accumulation (if storable) and exports. Similar problems occur with several investment goods. While again exports are generally well measured, the distribution to the other components can be very cumbersome.