rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3: Impact of RCBP**

|**Panel A: Househol**|**d Income, Assets, an**<br>Round 1<br>|**d Consu**<br>|**mption**<br>|Impact estimat<br>|es<br>|
|---|---|---|---|---|---|
||Control mean|Obs.|Round2|Round1 x RCBP|Round2 x RCBP|
|Food consumption from market purchases (birr)|71.6|2,847|64.4***|4.78|-14.6|
||[90.7]||(8.51)|(8.47)|(11.0)|
|Total value of assets (birr)|966|2,847|979***|140|338|
||[1,321]||(153)|(136)|(250)|
|Non-food consumption (birr)|3,209|2,750|2,210***|161|242|
||[2,921]||(307)|(281)|(459)|
|Nb of household members who contribute to income|2.22|2,847|.307*|-.015|.360**|
||[1.49]||(.155)|(.127)|(.166)|
|Nb of household members who work on household farm|4.10|2,847|-.558***|-.386***|.105|
||[2.28]||(.121)|(.111)|(.094)|
|Total plot area (ha)|2.20|2,847|-.663*|-.133|.382**|
||[4.35]||(.334)|(.340)|(.167)|
|Livestock ownership (=1)|.958|2,834|-.032**|-.015|-.021|
||[.200]||(.014)|(.015)|(.019)|
|Total value of livestock owned (birr)|7,718|2,834|24,430***|721|4,213|
||[9,012]||(3,401)|(1,304)|(5,258)|
|Total agricultural sales (birr)|3,961|2,847|-173|880|944|
||[6,031]||(423)|(581)|(587)|
|**Panel B: Agricultur**|**al outcomes and hig**|**h-value**|**farming**|||
||Round 1|||Impact estimat|es|
||Control mean|Obs.|Round2|Round1 x RCBP|Round2 x RCBP|
|Productivity (harvest value/ha, birr)|5,466|2,811|2,778***|-345|-11.6|
||[4,920]||(401)|(453)|(783)|
|Tried new agricultural activities in the past year (=1, head)|.473|2,513|.033|.058|-.008|
||[.500]||(.041)|(.041)|(.038)|
|Intercropping (=1)|.237|2,801|-.057|-.086**|-.059*|
||[.425]||(.041)|(.035)|(.032)|
|Irrigation (=1)|<br>.146|2,803|<br>-.087***|<br>.031|<br>.046*|
||[.354]||(.023)|(.039)|(.026)|
|Crops with improved seeds (number of crops)|.823|2,806|.007|.078|-.070|
||[1.26]||(.084)|(.105)|(.100)|

17
-87-

It is at first sight surprising that real productivity levels in Brazilian and Mexican manufacturing are as high as they appear by international standards. However, evidence from estimates at national prices appears to confirm that Brazil and Mexico have much higher productivity levels in manufacturing compared with the rest of the economy than is the case in the more advanced countries.

This is clear from table 7.4 which shows Brazilian productivity in manufacturing to be two and threequarters times as high as in the rest of the economy, and Mexican productivity twice as high. In five OECD countries, the differences between manufacturing and non-manufacturing productivity levels are very modest, and, in Germany and the UK, manufacturing levels are actually lower than the average for the rest of the economy. In this OECD group, Japan is the extreme case, with a productivity level in manufacturing a quarter above that in the rest of the economy, but the Japanese situation is closer to the OECD norm than it is to the two Latin American countries.

TABLE 7.4 Comparative Characteristics of Manufacturing Activity in 1980 Brazil and Mexico compared with Five OECD Countries

--------------------------------------------------------------

||Manufacturing<br>Share of GDP<br>at Factor<br>Cost<br>(percentages)|__------------<br>Labour Productivity<br>Level in Manufacturing<br>Relative<br>to<br>Non-Manufacturing<br>(percentage3)|
|---|---|---|
|Brazil<br>Mexico|27.1<br>22.8|278.8<br>199.9|
|France|27.8|119.8|
|Germany|33.9|97.1|
|Japan|28.2|124.6|
|UK|26.0|91.2|
|USA|21.3|102.0|

a) The German definition of manufacturing is somewhat broader than in the other countries with respect to repair services and quarrying.

Source: Brazil: output from Contas Nacionais do Brasil: Metodologia e Tabelas Estatisticas, Vargas Foundation, Rio, 1984; employment in manufacturing from IBGE, Censo Industrial, Dados Gerais, 1980, Rio, 1984; non-manufacturing employment from Anuario Estatistico do Brasil, IBGE, Rio, 1985. Mexico: INEGI, Sistema de Cuentas Nacionales de Mexico: Principales Variables Macroeconomicas, Periodo 1970-1982, Mexico, 1983. OECD countries (except USA) from OECD, National Accounts 1972-1984, Paris, 1986. USA from US Dept. of Commerce, Survey of Current Business.

There are several reasons for this relatively high level of labour productivity in the manufacturing sectors of Brazil and Mexico. One is that in many sectors of manufacturing, the nature of technology is such that it is often rational to use processes which are labour saving and capital intensive, even in countries with low wages. Low income countries do have
# Relationship Between Real per Capita GNP and Paved Road Density in the U.S.

I-' <!-- Start of picture text --> GNP/Capita in '82 Constant Dollars (000)<br>18 -<br>16 - PGNP--3.4 1.24xLPR<br>14- R squared: 0.93<br>1950 to 1988<br>~12-<br>10<br>8-<br>6<br>4- I l l<br>8 9 10 11 12 13 14 15<br>Paved Road Density (km/thousand pop)<br><!-- End of picture text --> FIGURE 3
target year of 2000, which allows for comparison with the studies carried out in later years that forecasted beyond 2010.

(2) Relative shares in t+30 years from the year of reference in each study: This indicator allows for capturing the implementation lag of any major energy policy shift, given the diffusion/maturation period of the underlying technologies. How the t+30 indicators evolve made it also possible to assess the trends for a given technical change (i.e., 30 years ahead).

The first section presents our chosen methodology and details of the data processing step. Section two presents our main results, with the support of detailed graphs. Section three derives some general lessons of interest in the reflection about the nature of any energy forecast.

# **1) Methodology**

In this section we describe the method used to compute the results. Note that this study comes along with a data spreadsheet file that may allow the reader to reproduce our results or work out their own.

# **1.1) Energy unit conversion**

Different scenarios often use various energy units. For example, US scenarios most often use the Quadrillion BTU (Quad), or sometimes the Million Barrel per Day (mbd), while the Shell company scenarios use Exa Joules, and the book _Energy for a Sustainable World_ uses a seldom used unit related to generation capacity (i.e., Tw.yr). For comparison purposes it was deemed preferable to convert the original scenarios’ energy units into a unique and homogeneous unit. Therefore, all energy units were converted into Million tons of oil equivalent (Mtoe) using conversion coefficients from the American Physics Society website<sup>3</sup> which is retained in the IEA’s annual World Energy Outlook reports results in Mtoe and in the IPCC emission scenarios.

# **1.2) Scope of scenarios and use of median shares**

The different studies provide a set of contrasted scenarios. Instead of picking a few scenarios in each of them more or less arbitrarily, we retained all of them when possible.<sup>4</sup> However, we took the median shares across all the recorded scenarios (i.e., primary energy shares for nuclear, RE and fossil fuels, in 2010 and t+30). This avoids the drawback of conferring to each study a weight directly proportional to its number of alternative scenarios and to increase the results variance as a simple consequence of the fact that computation capabilities increased over time, as did the belief that the objective of energy forecasting lies less in predicting the future than in clarifying the field of possibilities.

The motivation for using the median share stems from the fact that it is a more robust statistic than the conventional arithmetic mean. Concerning the SRES scenarios, we considered not the

> 3 http://www.aps.org/policy/reports/popa-reports/energy/units.cfm

> 4 It was found that some studies do not display usable figures for our purpose, or that they provide scattered, crude information. In this case, the results were not used.

3
the huge majority by which _Chama Cha Mapinduzi (CCM)_ candidate Jakaya Kikwete was elected to office (over 80% of the overall vote, and higher than that in the mainland part of the country studied here) the presidential vote share is not particularly informative. Similarly, 72% of the votes cast in parliamentary elections went to the CCM; however, in ward councilor elections the ruling party is less dominant. We therefore use the vote share for the CCM at the ward councilor level to measure _intensity_ of local-level support for the ruling party. We also construct two co-party indicator variables, which signify that the ward councilor and the parliamentarian are from the same party. The first of these is a dummy indicating any cases in which the national and local-level politicians are from the same party. Given the dominant and unique position of the CCM, we then define a separate co-party dummy for cases in which the councilor and parliamentarian are both from the same party but this party is not the CCM. The omitted category for these two dummies is any ward in which the councilor is from a different party than the parliamentarian.<sup>10</sup>

# _3.4.Household Survey Data_

The survey data come from a listing exercise and household survey conducted in five districts of Tanzania between June and December of 2008. First, five districts were selected that were intended to be broadly representative of the country as a whole: these are Moshi, Kwimba, Lushoto, Makete, and Nzega. Within these study districts, TASAF officials were asked to provide 20 villages in which „Vulnerable Groups‟ applications had moved through the submission process to the point at which they were ready to be funded, but had not yet received any money. Thus while the districts that form the study are in some general sense representative, the sample of villages was selected based on an endogenous criterion: being at a specific stage in the VG application process. Within the 100 study villages, we interviewed every household using a short listing survey, providing a census of 61,611 households. Each household was then sorted into one of the following strata:

- **Village elites** (Village Executive Officer, or VEO, and the village chairman, or the VC),

- **Ineligible households** (i.e. households with no vulnerable individuals as defined by TASAF and described above in Section 1),

> 10 Co-party dummy always equals one when the non-CCM co-party equals one, so this dummy tests for a differential effect of non-CCM co-parties versus CCM co-parties.

9
health, they are also a useful instrument in the fight against poverty” (Bosch and Koch 2014, p. 3). On the other hand, another recent study found that the deeper empirical consideration of how people responded to such tax changes possible with more detailed U.S. data casts a cloud on this happy conclusion. In the short term, Chiou and Muehlegger (2014) find that increasing cigarette taxes definitely reduced consumption especially of cheaper cigarettes, both because of stockpiling before the expected increase and substitution after it took place. However, the short‐term and long‐term effects of the tax increase were markedly different. After the initial post‐tax substitution of lower for higher priced cigarettes, the ‘flight to quality’ stressed in the literature (Barzel 1976) ensued as the consumption of pricier cigarettes rose while that of cheaper cigarettes declined. Moreover, the evidence shows that because the shift was not only towards more expensive cigarettes but to cigarettes higher in tar, nicotine and carbon monoxide, the net result of increasing the tobacco tax was to increase rather than reduce the negative effect on health outcomes (Chiou and Muehlegger 2014).

However, this analysis does not consider the possibly offsetting effect of higher taxes on discouraging beginning smokers. Taxes that discourage people from taking up unhealthy habits in the first place may not be regressive across the population as a whole if one takes into account their effect in reducing the level of addiction: that is, such taxes may not only improve health outcomes for the population as a whole but also reduce both poverty and ignorance. Without going further into the ‘rational addiction’ literature (Gruber 2010), as before the main conclusion that seems to emerge from the partial and conflicting evidence on all these matters is that it is unlikely to make sense to clutter up the tax structure with complicated excise taxes aimed at e.g. imposing minutely higher taxes on a few people who drink imported whiskey or smoke expensive cigars or delivering a small nudge to deter potential abusers by raising the price of such ‘introductory’ delivery systems as e‐cigarettes or wine coolers. Detailed excise tax policies targeted to refined equity aims, like those aimed at achieving an empirically (and usually politically) unsupported degree of economic efficiency, may all too easily divert attention from the two main, and achievable, objectives of good excise policy: to reduce the consumption of harmful goods and to raise revenue in a relatively efficient way.

# **Taxation and Regulation**

Taxation and regulation are sometimes complementary and sometimes substitutes. Many countries impose regulatory policies such as age limits for the sale of alcohol and tobacco as well as special 15
|**Table D.6: Logistic regression results forp**|**robability of investment**<br>(1)|**, by investment categ**<br>(2)<br>|**ory**<br>(3)<br>|
|---|---|---|---|
|VARIABLES|P(total investment >0)|P (disaster-related<br>investment >0)|P (disaster-unrelated<br>investment >0)|
|**Region**(Dar es Salaam)||||
|Dodoma|2.301***|3.253***|1.247***|
||(0.391)|(0.384)|(0.317)|
|Tanga|0.145|1.326***|-0.363|
||(0.286)|(0.362)|(0.253)|
|**Direct disaster risk perception**(no risk)||||
|Low risk|-0.139|-0.087|-0.156|
||(0.272)|(0.380)|(0.271)|
|Moderate risk|-0.310|0.290|-0.273|
||(0.314)|(0.418)|(0.323)|
|High risk|-0.640*|0.515|-0.374|
||(0.368)|(0.482)|(0.383)|
|Very high risk|-1.294***|0.447|-0.593|
||(0.458)|(0.534)|(0.443)|
|**Indirect risk perception**(no risk)||||
|Low risk|0.076|0.109|0.228|
||(0.395)|(0.573)|(0.401)|
|Moderate risk|-0.099|0.040|0.136|
||(0.443)|(0.612)|(0.466)|
|High risk|0.733|0.281|0.623|
||(0.480)|(0.683)|(0.497)|
|Very high risk|0.909*|-0.405|0.760|
||(0.481)|(0.761)|(0.536)|
|**Experienced flooding on business premises**:||||
|Yes|0.460|0.515|0.171|
||(0.297)|(0.384)|(0.274)|
|Firm age (years)|-0.012|0.007|-0.011|
||(0.009)|(0.009)|(0.008)|
|Number of employees|0.033***|0.012|0.000|
||(0.013)|(0.009)|(0.000)|
|Log (total operational costs, $/month)|0.253***|0.276***|0.299***|
||(0.067)|(0.092)|(0.060)|
|**Time since last serious disaster**(months)|0.008*|0.005|0.006*|
||(0.005)|(0.004)|(0.003)|
|**Sector**(Accommodation and food service||||
|<br>activities, Construction):||||
|Communication and other services|-0.053|-0.528|0.301|
||(0.389)|(0.418)|(0.338)|
|Manufacturing, Mining, Water, Energy,||||
|<br>Agriculture|-0.174|-0.871*|0.421|
||(0.310)|(0.462)|(0.283)|
|Transportation and storage|-0.952*|-2.365**|-0.290|
||(0.490)|(0.923)|(0.561)|
|Wholesale and retail trade Repair of motor||||
|,<br>vehicles and motorcycles|-0.656**|-0.918**|-0.265|
||(0.303)|(0.389)|(0.292)|
|Constant|-1.590***|-3.769***|-2.415***|
||(0.564)|(0.809)|(0.521)|

45
<!-- Start of picture text --> Dhaka - Population (1000s)<br>by Skill level<br>25000 -| |<br>20000 -<br>15000 | 14460 teste |<br>10000 | | |<br>5000 +~ | 10099 10770 |<br>o- |<br>Scenario A Scenario B<br><!-- End of picture text -->

<!-- Start of picture text --> (R505) a<br>iB! 7)<br>Dhan 9302<br>SIRE<br>3<br>Sav = 20000<br>at A, 10000<br>0<br>190) -10000<br>- -20000<br>y u sf<br>Sreenagar Munshig<br>ce SATS Hrs J<br><!-- End of picture text --> <!-- Start of picture text --> East-Dhaka - Population(1000s)<br>by Skill level<br>5000 |<br>4000<br>2500<br>3000 |<br>21 000 986 2161 |<br>0 580 |<br>Scenario A Scenario B<br><!-- End of picture text -->

<!-- Start of picture text --> (R505) I<br>3315) (Za<br>Dhan 9302<br>aid<br>3<br>Sav = 20000<br>at A, 10000<br>0<br>190, -10000<br>‘ = -20000<br>ij t TM<br>cee SreenagarNTA Munshig.WATS ,}<br><!-- End of picture text -->
estimate the impact of P4P as jumps in time trends of the amount of services providers by treatment facilities.<sup>4</sup> There are, however, three well-identified evaluations. Hospital-based physicians in the Philippines who received extra bonus pay based in part on knowledge of clinical appropriate clinical procedures, reported increases in clinical knowledge (Peabody et al, 2011). In Indonesia, performance incentives to _villages_ for improvements health and education outcomes led to an increase in labor supply from health providers (Olken et al. 2011). Finally, this paper builds on our previous work that demonstrated P4P in Rwandan increased prenatal care quality, institutional delivery, and use of preventive child medical care (Basinga et al 2011).

# **2. INSTITUTIONAL CONTEXT**

Rwanda is one of the poorest countries in the world with a GDP of US$340 per capita in 2007 (World Bank, 2008). Since the end of the 1994 genocide, Rwanda has made remarkable progress in improving maternal and child health. Between 2000 and 2005, infant mortality fell from 107 to 86 deaths per 1,000 live births, while maternal mortality fell from 1,071 to 750 per 100,000 live births (Institut National de la Statistique du Rwanda and ORC Macro 2006). Despite this progress, the health system continues to grapple with serious shortages of qualified personnel and low levels of service delivery, especially in rural areas (World Bank 2010b).

# **a. P4P**

In 2005, the Rwandan Ministry of Health (MoH) used the opportunity of an increase in the health sector budget to scale up nationally a P4P scheme for maternal and child health care services (Logie et al 2008; World Bank 2010a). The decision was based on positive reports of P4P schemes in two Provinces that had been developed by a number of NGO’s (Kalk et al. 2005; Soeters et al. 2005 and 2006). The P4P scheme provides bonus payments to primary care facilities based on provision of various types of services and the quality of those services. P4P payments go directly to facilities and are used at each facility’s discretion. The overall amount of P4P payments is large in comparison to facilities’ budgets: a study of 68 facilities receiving P4P payments shows that P4P payments represent an increase in funding of 24.6% above the base budget. On average, 77 percent of P4P funds were used to compensate personnel resulting in an increase of 38 percent in staff compensation (Basinga et al 2011).

> 4  See Witter et al., (2012) for a recent systematic review of health care P4P in low and middle income countries

4
growth also experiences sizeable and significant impacts due to conflict; however, the impacts of conflict on annual GDP are not seen to be significant.

_Table 4: Across districts, conflict reduces GDP growth for up to a quarter._

||**2005-2016**<br>**Annual**|**Annual**|**2014-16**<br>**Quarterly**|**Monthly**|
|---|---|---|---|---|
||GDP growth|GDP growth|GDP growth|GDP growth|
||(1)|(2)|(3)|(4)|
|Killed and injured (per<br>1,000)|-2.009|0.986|-5.541**|-8.675**|
||(3.025)|(2.141)|(2.572)|(3.309)|
|District andyear FE|yes|yes|yes|yes|

Note: The regression is estimated for Afghan districts. GDP is predicted using night lights as described above. The number of injured and killed is from the Global Terrorism Database. The results do not change without fixed effects. ** p<0.05

# c. DEMONETIZATION IN INDIA

In early November 2016, all 500- and 1,000-rupee banknotes were declared invalid in India. For several months following this declaration, liquidity was severely constrained. Demonetization, as it came to be known, aimed at curbing corruption and encouraging the use of electronic payments. There is clear agreement that it will take time to assess the extent to which these benefits materialize. But there is considerable disagreement on how large the short-term cost of demonetization was, and which population groups were most affected. The shortage of relevant data partly explains why these issues are still being so lively debated. Nightlight data provides interesting insights on this topic.

At the aggregate level, a comparison of nightlight intensity in FY2015 and FY2016 suggests that demonetization had a small and short-lived effect on economic activity in India. There is a dip in nightlight intensity, but it only lasts for about two months.

20
Korea, the ratio of total non-performing loans to real GDP (obtained by summing the entries in the fifth columns of Table 6 and Table 7) is roughly 25 percent. This is the number we used to calibrate our model. In the case of Thailand, this ratio equals 30 percent.

Table 7: Estimated Liabilities from Non-bank Foreign Borrowing

||Non-<br>Perform.<br>Loans<br>(%of<br>all loans)|Non-bank<br>Private<br>Sector<br>Borrowing<br>from<br>Abroad*<br>(6/97)|No<br>% f<br>MB|n-Performi<br>(estimat<br>% of<br>1997<br>GDP**|ng Credit<br>ed)<br>% of<br>1997<br>Central<br>Gov't.<br>Revenue**|
|---|---|---|---|---|---|
|Indonesia|17|92|34.6|2.5|16.8|
|Korea|16|23|18.0|0.9|4.1|
|Malaysia|16|19|4.1|1.1|4.5|
|Philippines|14|134|8.3|0.8|4.0|
|Thailand|19|343|12.6|1.3|7.7|
|Hong Kong|4|198|8.2|0.6|3.0|
|Singapore|4|12|2.6|0.3|0.9|
|Taiwan|4|88|0.2|0.0|0.4|

*For Indonesia and Korea, expressed in trillions of local currency units. For the others, in billions of local currency units.

**Estimates.

The previous two tables summarize information on default rates and implicit liabilities prior to the currency crisis. J.P.Morgan (1998) has estimated nonperforming loans as a percentage of total loans and GDP in Indonesia, Korea, Malaysia and Thailand, as of June 1998. In addition they provide estimates of the amount of capital, as a percent of GDP, needed to restore bank capital to the 8% Bank of International Settlement's Capital Adequacy Requirement level.

33
traders, landlords, and relatives and friends had increased their share of debt to about 32 percent. According to the All-India Debt and Investment Survey of 1981/82, the expansion of institutional lenders not only further rd4uced moneylenders to a share of 16 percent of the market, but also reduced the share of lending from traders, relatives and friends, and from landlords.- It is therefore clear that any analysis that ignores the substitution of informal credit by formal credit programs will overestimate the benefits of those programs.

Until 1971, despite the large effort to increase institut.3na **l** ending in rural areas, the growth in the agricultural capital stock per farm was not impressive. Between 1951 and 1971 the capital stock rose by a mere 18.5 percent--at less than one percent per year. Debt equity ratios, which were at the low level of about six percent, rose slightly but declined again to four percent in 1971.

During the 1970s credit outstanding to agriculture grew rapidly. The aggregate data are shown in Table 2. In 1972/73 (the first year for which commercial bank data are available), total credit outstanding to agriculture from all formal sector sources was Rs 21,697 million, which rose more than fourfold in nominal terms to Rs 87,978 million. Using the implicit GDP deflator for agricultural production, it rose to Rs 46,549 million, a 115 percent real increase during a decade.

2/ **The data of the All-India Debt and Investment Survey of 1981/82 appear to seriously underestimate** debt **of cultivators and is, therefore, not further used. For a discussion see Bell,** 1990.

**8**