rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Utility ground-mounted Commercial rooftop Residential rooftop<br>8 000 ; ; 8 000 ; ; 8 000 : ;<br>= 7 000 a Tini orecast 7 000 erates een i eeorecast 7 000 an i orecastio<br>60 -------|------| 600 ,|------}------| 600 AQ-----+------<br>= 5000 }—--------~-~~-| 5000 ad SaeT 5 000 “o---t------<br>SB 4000 PA 4 000 i aa 4 000 \ =<t------<br>3 000 2 3 000 \ Resa} 3 000 NY<br>2 000 Sy SSS = 2 000 = 2 000<br>1 000 SS=~ 1 000 = | 00 enn!==><br><!-- End of picture text -->

Notes: Investment costs are overnight costs and include value-added tax or sales tax where relevant; costs are indicative and may not represent all transactions. National currencies converted to US dollars at average 2015 exchange rates. Historical data points omitted for Australia, where market was not well established.

Sources: Analysis based on IRENA (2016a), Costing Alliance, dataset provided to the IEA; IEA-PVPS (2016), PV Cost Data for IEA, dataset provided to IEA.
analytical capacity to derive benchmark targets. Increasing variation in in‡ation does not seem to a¤ect the probability of using strategic benchmarks. Higher ‡exibility of applied exchange rate regimes seems to imply lower probability of a benchmark debt strategy. This result is somewhat puzzling unless one wants to acknowledge the in‡uence of more developed economies on this ...nding, as most of these apply some type of a ‡oating exchange rate regime and often use guidelines for debt management. Also, the terms of trade volatility impact negatively on the probability of using a strategic benchmark. If developing countries are often hit by large external shocks it may be hard for them to set a conventional benchmark with rather well-de...ned ranges for selected types of risks. Volatility of CA/GDP appears to be insigni...cant in explaining the use of strategic benchmarks. Further, governments appear to be less in favor of using strategic benchmarks for debt management once facing increasing variation in o¢ cial transfers (foreign aid). Increasing variation in o¢ cial transfers can be seen as a speci...c kind of an external shock that again results in the lower use of benchmark targets (ranges) for management of the basic types of risk. Finally, when looking across the coe¢ cient estimates attached to regional dummies we ...nd that if a country belongs to the MNA region it has signi...cantly lower probability of using a benchmark strategy.

# 5 Conclusion

This paper analyzed survey data on public debt management strategies across income groups, regions and levels of indebtedness using graphical tools. Further, regression analysis was carried out to extend the graphical analysis and condition on more economic indicators possibly relevant for public debt management. More speci...cally, the graphical and regression analyses were focused on explaining how the incidence of (i) public debt management strategies, (ii) the published strategies and (iii) strategic benchmarks varies across income groups, regions, levels of indebtedness and other economic characteristics.

We found that a higher level of income in a country appears to increase its probability of having a debt management strategy. The level of indebtedness seems to 26
**Table 8: Including Indicators for Federalism and Ideology, with GOVFRAC**

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|GOVFRAC|-0.06<br>(0.035)*|-0.021<br>(0.023)|-0.001<br>(0.012)|
|Turnout|0.12<br>(0.046)***|0.075<br>(0.032)**|0.023<br>(0.02)|
|Federal|-0.04<br>(0.021)*|-0.04<br>(0.016)**|0.004<br>(0.006)|
|Right-wing government|-0.023<br>(0.015)|-0.016<br>(0.01)|-0.012<br>(0.008)|
|Majoritarian System|-0.01|-0.01|-0.002|
|(=1, 0 if Proportional)|(0.023)|(0.015)|(0.006)|
|Presidential System|-0.026|-0.032|0.012|
|(=1, 0 if Parliamentary)|(0.016)|(0.013)**|(0.006)*|
|Regular|0.005|0.019|-0.006|
|Elections<br>(=1, 0 otherwise)|(0.013)|(0.008)**|(0.007)|
|Observations|129|129|147|
|R-squared|0.67|0.77|0.42|

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in sample as in Table 7.

34
- 7 -

# iv) record books and forms;

v) a procedure manual;

# vi) a screening checklist;

# vii) a variety of EEC materials

A few of the CBDs are also equipped with blood pressure taking machines. The functions of the CBD are to motivate, educate, initiate and resupply contraceptives. Clients are also referred to local clinics for medical examination after once cycle, clinic based methods and management of side effect and complications.

Each CBD worker covers a geographical area of between 15 and 20 kilometers in radius on a bicycle visiting clients at their homes. The CBD workers uses a three week/three months work programme which means that each acceptor is abie to see her CBD at least once a month. The programme now has a total establishment of 745 comprising of a Chief of CBD at the national level, 8 Senior Educators at the provincial level, 68 Group Leaders at the district level and 668 CBDs at the community level. Current estimates put the programme coverage at some 30 percent of the rural population.' In 1984, 24 percent of total users of modern family planning methods and 42 percent of rural family planning users obtained their contraceptives from the CBD programme.

# b) Youth Advisory Services Unit

The Youth Advisory Services (YAS programme began in 1978 when the Ministry of Education requested the Family Planning Association to provide Family Life Education (FLE) in schools as part of a national effort to deal with the increasingly significant problem of teenage pregnancy among school age girls. At present their unit educates and counsels youth between the ages of 10 and 25 years both in and out of school mainly providing education sessions at primary and secondary schools. In addition, counselling is provided at all of the eight provincial centers at the Youth Center in Harare.

A Parent Education Programme (PEP) has now been added to the programme to complement the Family Life Education. Through the PEP parents are educated on how to communicate with their adolescent children and how to handle common problems which occur during the adolescent period.

A variety of education materials have been produced by the unit, including Human Sexuality Manual, a booklet "Facts About Growing Up," a flipchart, a village television programme and posters which are used in both FLE and PEP sessions and also distributed to youths and J. ents. A weekly radio programme for youths has also been broadcast.

Currently the YAS Unit has a total staff establishment of 32 Youth Advisors 6ZNFPC- CBD Catchment Area Survey, 1988.
**Table 6: Contemporaneous and lagged effects of internet access on municipality-level employment**

**by sector and occupational skill level**

|Occupational skill tercile:||Municipality-year v<br>First|ariation|Second<br>**lag Internet**||Third<br>**lag Internet**|
|---|---|---|---|---|---|---|
|**Outcome/sector**|**Internet Coe**|**f. lag Internet Coef.**|**Internet**|**Coef.**<br>**Coef.**|**Internet Co**|**ef.**<br>**Coef.**|
|Employment - Wholesale & Retail|-0.037***|-0.073***|-0.007|-0.022*|||
|Employment - Electric, gas, & water|||||-0.026**|-0.020*|
|Employment - Manufacturing|0.015|-0.003|0.036**|0.049***|0.038**|0.089***|
|Employment - Public Administration|||||-0.041***|-0.019|
|Employment - Real Estate|||0.031|0.041**|-0.052***|-0.038***|
|Employment - Construction|||0.002|-0.008|||
|Employment - Agriculture|0.021|-0.007|||||
|Employment - Transport & Storage|||-0.004|-0.001|0.073***|0.065***|
|Employment - Other Soc. Svcs.|-0.008|-0.025**|0.002|0.011|||
|Employment - Health and Social Svcs.|||0.031*|-0.026*|||
|Employment - Hospitality|0.026*|0.009|||||
|Employment - Education|||||0.007|0.019|
|Employment - Finance and Insurance|||||0.088***|0.044***|
|<br>Employment - Mining|0.014|0.018|||-0.005|-0.012|
|Employment - Fishing|-0.046***|0.054***|||||

Note: Estimates based on RAIS employee-employer records aggregated by municipality and year merged to internet access data from the School Census. Outcome measures listed in rows are in natural log. Interpretation of coefficients relate an increase in internet access from 0% to 100% in the municipality to the percent change in the outcome measure. Rows indicate outcome used in a common specification including unreported vectors of municipality and year fixed effects and a constant term. Standard errors clustered by municipality in parentheses. * p<.1, ** p<.05, *** p<.01.

Estimates of effects on wages by sector and tercile are presented in Table 7. Positive wage effects are generally found among the lower and middle skill terciles, while internet access appears to have a negative effect on wages in the highest skill occupations. Manufacturing and real estate are an exception, exhibiting wage increases in both the middle and high skill occupations. Both the public sector and utilities show wage decreases among its highly skilled occupations. On average across municipalities, the two-year cumulative effect of an increase in internet access (from 0 to 100%) on monthly wages is 4.8% and 4.1% for medium and high-skill tercile occupations across manufacturing industries, respectively.

14
19

# V. Discussion

Agricultural growth via new seed technology is expected to reduce rural poverty because of its induced demand and linkage effects on rural wage and employment. This paper has examined whether agricultural growth in India has resulted in an increase in rural wages and employment and hence reduced rural poverty.

This paper did not attempt to relate agricultural changes with agricultural real wage and rural employment. What it attempted is to establish causal linkages between the sources of growth in agriculture and the factors that determine agricultural real wage and rural employment. The aim was to identify whether the policies that have fostered agricultural growth have promoted or discouraged agricultural real wage, rural employment and population. A reduced-form estimation technique is utilized to examine the causal factoxs that jointly influence the growth in agricultural production, real wage and rural employment. A judicious use of a panel data drawn from randomly selected 85 districts of India has circumvented both the endogeneity and unobserved variable problem that otherwise produce biased and inconsistent estimates with cross-section data.

The permanent agroclimatic characteristics are important determinants of rural wage and employment. Agricultural real wage and rural nonfarm employment grow in areas with better agroclimates favorable to agricultural growth. A positive association exists between agricultural growth and rural real wage via the agroclimatic potentials. This, of course, does not imply that the 'green revolution' influenced by the agroclimatic potentials can benefit the rural poors. Whether the rural poors can benefit from new seed technology and hence agricultural growth depends on whether agricultural growth encourages labor use both in farm and nonfarm activities.
**17**

> **23.** Table **3** shows the distribution of deviations of multiple rates from the official rate of those countries for which multiple rates are available in the IFS."

Table **3:** Distribution of Deviations of Multiple Rates from IFS_RF

|Range**(%)**|**1980**|**1981**|**1982**|**1983**|1984|**1985**|**1986**|**1987**|**1988**|**1989**|
|---|---|---|---|---|---|---|---|---|---|---|
|**0.0-5.0**<br>|2|**1**|2|2|4|2|4|**3**|**_5_**|**3**|
|**5.1-10.0**|**1**|**3**|**1**|**1**|**0**|**1**|**1**|**3**|**1**|**0**|
|**_10.1-15.0_**|**0**|2|2|**1**|4|**1**|**0**|**1**|**0**|**0**|
|**15.1-20.0**<br>|2|**1**|**1**|**1**|2|2|**1**|**1**|**0**|**0**|
|**20.1-25.0**<br>|**1**|**1**|**1**|2|**0**|**1**|**1**|**0**|**1**|**1**|
|**25.1-30.0**<br>|**1**|2|2|2|**0**|2|**3**|**1**|**1**|**0**|
|**30.1-40.0**<br>|2|**1**|**1**|**1**|**1**|**0**|**0**|**1**|2|**1**|
|40.1-50.0|**1**|2|**3**|**1**|**0**|**0**|**0**|**1**|**0**|**0**|
|**50.1-60.0**<br>|**1**|**0**|**0**|**1**|**1**|**0**|**1**|**1**|**0**|**0**|
|**60.1-70.0**<br>|2|**1**|**0**|**1**|2|**0**|**1**|**0**|**1**|**1**|
|**70.1-80.0**<br>|**0**|**0**|**0**|**1**|**1**|**0**|**0**|**0**|**0**|**0**|
|**80.1-90.0**|**0**|**1**|**0**|**0**|**1**|2|**0**|**1**|**0**|**0**|
|**90.1-100.0**<br>|**0**|**1**|**0**|**0**|**0**|2|**1**|**0**|**0**|**0**|
|**>100**|**0**|**0**|**1**|**3**|**3**|**3**|4|2|**3**|**1**|
|TOTAL<br>Source:Multip|**13**<br>lerates a|**16**<br>refrom|14<br>**IFS.**|**17**|**19**|**16**|**17**|**_15_**|14|**7**|

> 24. Almost half of them maintain multiple rates with the premium exceeding **30** percent. In recent years, the spread between multiple rates has narrowed for most countries.

> However, the spread has remained consistently high in **Egypt** (exceeding **100** percent),

> Paraguay and Venezuela (about **90** percent), Guatemala (averaging **60** percent), and Romania (about 40 percent).

> **'3** As trade and exchange systems have been liberalized, and multiple exchange systems have been unified, the number of countries with multiple rates shown in the **IFS** has decreased.
# **Table 7. Accuracy of different variables in targeting poor households**

||Ru|Targeting <br>ral|accuracy (ar<br>Urb|eaunderR<br>an|OCcurve)<br>All Vi|etnam|
|---|---|---|---|---|---|---|
|TargetingVariable|Food<br>Poverty|Overall<br>Poverty|Food<br>Poverty|Overall<br>Poverty|Food<br>Poverty|Overall<br>Poverty|
|PoororRemoteComnune|0.585|0.559|0.554|0.520|0.589|0.559|
|Categories inNationalPoverty Map|0.641|0.622|0.620|0.645|0.663|0.650|
|Communesrankedbymedianexpenditure|0.829|0.790|0.726|0.808|0.849|0.827|
|Landallocated(quintiles)|0.529|0.542|n/a|n/a|0.619|0.646|
|Livestockowned(animaleq.units)|0.474|0.448|0.591|0.541|0.467|0.441|
|EducationalLevelofHouseholdHead|0.601|0.579|0.715|0.685|0.625|0.609|
|EducationalLevelofSpouse*|0.570|0.554|0.739|0.727|0.602|0.597|
|NumberofChildrenunder15|0.733|0.690|0.753|0.789|0.742|0.714|
|NumberofFemales|0.636|0.618|0.578|0.671|0.632|0.616|
|Ethnicity|0.642|0.612|0.495|0.500|0.649|0.614|
|Floor Type|0.696|0.665|0.694|0.773|0.734|0.720|
|RoofType|0.630|0.585|0.687|0.658|0.637|0.594|
|ToiletType|0.597|0.577|0.773|0.730|0.650|0.648|
|RadioandTVOwnership|0.736|0.711|0.876|0.792|0.771|0.751|

Source: Analysis based on VLSS 1998.

Notes on targeting variables: Poor or remote commune: 0=Commune not included in CEMMA's list of remote communes or MOLISA list of poor communes; I=Commune included in either CEMMA difficult mountainous and remote communes or MOLISA poor communes lists; Categories in National Poverty Map: 0= Provincial poverty headcount < 25%; 1= Headcount 25- 45%; 3= Headcount 45-60%; 4=Headcount > 60% Communes ranked by median expenditure: Ranking of 194 communes and urban wards in VLSS sample by median per capita expenditure of the sample households in that commune Livestock owned: number of livestock multiplier by their livestock equivalents units: 0.7=cow, horses and water buffalo; 0.1=goats, pig and deer; 0.01=ducks and chickens Educational Level: 0 = Post-secondary; I=Advanced Technical; 2=Upper Secondary; 3=Lower Secondary; 4=Lower Secondary; 5=Primary; 6=Less than Primary (* Note: 1284 households do not have spouses present) Ethnicity: 0=Kinh or Chinese Head; 1= Ethnic minority head Floor Type: 0=Earth; I=Other, 2=Bamboo/Wood; 3=Lime and Ash; 4=Cement; 5=Brick; 6=Marble or Tile Roof Type: 0=Other; I=Leaves/Straw; 2=Bamboo/Wood; 3=Canvas/Tar Paper; 4=Panels; 5=Galvanised Iron; 6=Tile; 7=Cement or Concrete Toilet Type: 0=Flush: l=Other: 2=None Radio and TV Ownership: 0=Color TV; I=Black and White TV; 2=Radio; 3 =None conceal ownership of radio or televisions if it become known that their ownership would exclude household from being selected as program beneficiaries.

34
economy, and how they may affect relative prices (e.g. the real exchange rates between domestic and foreign goods), and various components of GDP that utimately determine revenue generation by individual tax instruments. This is often a heroic assumption. Governments in Eastern Europe are confronted by pressing reforms in all fronts and are just learning GDP accounting properly. What if simplistic and wrong assumptions are made in the budgetary preparations? Do import tariffs minimize the downside risks relative to other taxes?

Take the case in which the government anticipates **the** external shocks correctly but the economy is less responsive that what government originally expected. In particular, Case II is repeated: the governmet fonnulates the same tax policy given the same expectations about the shocks and parameters; however, except for the Armington elasticity, all expectations are correct; the correct Armington elasticity is only half of what is anticipated and has greater standard deviation. i.e., a is changed from N(0.80, 0.02, 0.50, 1.10) to N(0.40, 0.05, 0.25, 0.90). The results are sununarized as follows:

**Case m:** The Government is Overly Optimistic

|**Tax**Instrument|Tax/GDP|Rev/er|Consumption|Investment|
|---|---|---|---|---|
|**1)**Dom. Indirect Tax|65.21 ± 0.29|72.51 ± 4.84|83.99 ± 1.64|65.12 ± 5.25|
|2) ImportTariff|64.23± 0.20|74.62± 4.81|84.51± 1.78|64.26 ±5.38|
|3) Capital Income Tax|65.28 ± 0.32|72.08 ± 4.80|83.49 ± 1.61|65.83 ± 5.30|
|Memo:Base-YearLevel|59.24|100|100|100|

The tax-to-GDP ratios are similar to those in Case II. The results suggest that **the** target of raising the ratio by 5 percentage points is sdll achievable and appear comparable for altemative tax instruments.

**_Go: Revenie Uncerairnt_**

_23_
et al. (1998). In all, we add 5 different<sup>variables.</sup><sup>_ACCOUNT_is an index of the</sup> strength of accounting standards ranging from<sup>a low of 0 to a high of 90. High val-</sup> ues of _ACCOUNT_ indicate that companies<sup>in that country are required to have more</sup> comprehensive financial statements. _ENFORCE_<sup>is an indicator of the enforceability</sup> of contracts. It is actually an average of two<sup>separate indices-one which assesses the</sup> law and order tradition of an economy and<sup>the second which assesses the chances that</sup> a given government will change the provisions<sup>of a contract once it has been signed.</sup> _ENFORCE_ ranges from 1 to 10 with higher<sup>values indicating stronger enforcement of</sup> contracts. _CORRUPT_ is an index of corruption<sup>ranging from one to ten with lower val-</sup> ues indicating a greater incidence of government<sup>officials demanding special payments.</sup> _STRUCTURE_ is an index of the extent<sup>to which the financial system is based on the</sup> stock market rather than banks. Higher values<sup>of</sup><sup>_STRUCTURE_imply that the market</sup> is relatively more important than banks.<sup>Finally, our data contains dummy variables for</sup> the legal origin of a country, _ORIGIN_ (i.e., French,<sup>English, Scandinavian or German)."5</sup> To implement our cross country estimation, we<sup>collapse our data into two time</sup> periods and modify equation (1) to estimate where Zi,t_j includes _ACCOUNT, ENFORCE, CORRUPT,_<sup>and</sup><sup>_STRUCTURE._</sup> We measure Z2,t_j in the initial time period<sup>(1976-1980) and then measure subse-</sup> quent volatility and Xi,t over the period 1980<sup>to 1998.16 Thus, our cross-country data</sup> set gives us some advantages. It allows the inclusion<sup>of additional variables and gives us</sup> the ability to measure volatility over a much longer<sup>period of time. Moreover, the addi-</sup> tional variables in our cross-country data set also<sup>allow us to assess whether or not our</sup> main results are affected by endogeneity. Reverse<sup>causation may also be an issue in this</sup> 1<sup>5</sup> For a more detailed discussion of the definitions<sup>of these vaxiables, see also Demirguc-Kunt and</sup> Levine (1999).

'<sup>6</sup> Since we view these additional variables<sup>included in Z as country characteristics that are essen-</sup> tially fixed over the time period of the data,<sup>the exact timing of when we measure them should be</sup> inconsequental.

15
- BASE: Moderate growth; variables follow their historical trend; no additional investment or productivity gain.

- INF-FL: Front-loading of additional infrastructure investments (CFAF 2,000 billion in 2013-2015); no productivity gain.

- INF-BL: Back-loading of additional infrastructure investment (CFAF 2,000 billion in 2016-2022); no productivity gain.

- INF-BL(15), INF-BL(30), INF-BL(45): Delayed additional infrastructure investment similar to INFBL with an annual investment productivity gain of 1.5 percent, 3.0 percent and 4.5 percent, respectively.

- GOVEFF-INF: Improved government efficiency; gains are used to scale up government services for infrastructure.

- GOVEFF-HE: Improved government efficiency; gains are used to scale up government services for health and education.

- GOVEFF- HE+INF: Improved government efficiency; gains are used to scale up government services for health, education and infrastructure.

# 4.1 <u>Baseline scenario</u>

Under the baseline scenario (BASE), it is assumed that growth remains moderate with on average 5.6 percent per year between 2010 and 2025 and with most variables following their historic paths. The base year of the model is 2009, but all available information and estimates for 2010 and 2011 have been taken into account, especially regarding the government budget.

**Table 5:** Real macro indicators by simulation (average annual growth from 2010 to 2025, percent)

|||Front-loading<br>|B<br>|ack-loa<br>|ding (B<br>|L)<br>|Increase<br>|d government<br>|efficiency<br>|
|---|---|---|---|---|---|---|---|---|---|
||BASE|INF-FL|0|15|30|45|HD|HD-INF|INF|
|GDP at factor cost|5.6|6.1|6.3|6.5|6.6|6.8|5.5|6.5|7.0|
|Private consumption|6.4|6.9|7.2|7.3|7.5|7.6|6.2|7.1|7.5|
|Government consumption|5.7|5.9|6.0|6.1|6.1|6.2|7.1|6.6|6.3|
|Private investment|2.6|3.0|3.2|3.3|3.4|3.5|2.5|3.1|3.4|
|Government investment|4.3|4.4|4.5|4.5|4.5|4.6|3.2|5.2|6.3|
|Exports|3.4|3.9|4.1|4.2|4.4|4.5|3.3|4.2|4.6|
|Imports|5.6|6.0|6.2|6.3|6.5|6.6|5.5|6.2|6.7|
|Absorption|5.5|6.0|6.1|6.3|6.4|6.5|5.5|6.3|6.6|
|Real exchange rate(index)|2.1|2.1|2.1|2.1|2.1|2.1|2.4|2.3|2.3|

_Source:_ Authors’ calculations According to the latest available projections, oil production is going to decline starting in 2012. To maintain an average real GDP growth rate of 5.6 percent until 2025, rapid growth is required in the other sectors to compensate for the decline in oil growth. In the baseline, growth for agriculture and forestry is constrained by the land supplies, which are assumed to grow by 1 percent annually for each of the two sectors. Given the small share of industry, high growth in the services sector has to be the main contributor to overall growth (Figure 6). Growth in the services sector will be driven to some extent by the investment projects of the government, especially the construction sector.

The real exchange rate depreciates due to the reduction in oil production and related export receipts, encouraging production of non-oil tradables and discouraging production of non-tradables. However, this effect is mitigated by a reduction in Congo’s net foreign assets, aimed at smoothing Congo’s 8