rafmacalaba/datause-encoder-data huggingface dataset:
You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. “Torrents and Trickles: Irrigation Spending Needs in Africa.” AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. “Underpowered: The State of the Power Sector in Sub-Saharan Africa.” AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

EIA. 2009. International Energy Statistics, United States Energy Information Administration, Washington, DC EIU. 2011. Uganda Country Report, Economist Intelligence Unit, London

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. “Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.” Policy Research Working Paper 4913, World Bank, Washington, DC.

- GTZ. **(** Deutsche Gesellschaft für Internationale Zusammenarbeit), 2009. International Fuel Prices 2009, 6th edition. Eschborn, Germany.

- Keener, S., and S. G. Banerjee. 2005. “Ghana: Poverty and Social Impact Analysis of Electricity Tariffs.” ESMAP Technical Paper 088, World Bank, Washington, DC.

- Okure, Eng. Dr. Mackay A. E. 2008. _EAC Strategy to Scale up Access to Modern Energy Services: Uganda Country Report and Implementation Workplan_ . Mimeo, Kampala.

- Rosnes, Orvika, and Haakon Vennemo. 2009. “Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.” AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- Tallapragada, Prasad, Maria Shkaratan, Ada Karin Izaguire, Jaakko Helleranta, Saifur Rahman, and Sten Bergman. 2009. _Monitoring Performance of Electric Utilities_ . Washington, DC: World Bank.

- World Bank. 2010a. “Performance of the Uganda Power Sector (comprising of UEGCL, UETCL, UEDCL and Umeme).” Mimeo, World Bank, Washington, DC.

- ———. 2011a. “Electricity Development Project for Uganda.” Project Appraisal Document, World Bank, Washington, DC, May 31, 2011.

- World Energy Outlook. 2010. The Electricity Database. International Energy Association, World Energy Outlook Database. <u>www.worldenergyoutlook.org/database_electricity10/electricity_database_web_2010.htm.</u>

- World Bank, forthcoming, “Uganda Strategic Review of the Electricity Sector”, Mimeo, Poverty Reduction and Economic Management, Africa Region, World Bank, Washington DC.

# Transport

- Bofinger, Heinrich C. 2009. “An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.” AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

53
<!-- Start of picture text --> Organic Carbon (%)<br><!-- End of picture text --> <!-- Start of picture text --> Total Nitrogen<br><!-- End of picture text --> <!-- Start of picture text --> 09<br>-<br>oo e 7<br>fo Le Uo<br>c<br>2 ~ “eo _ 7<br>Ss Co s “eo<br>8 e Ss 3<br>a Zee 3 e ss,<br>lo °° Oo e d we<br>e. eo<br>°o fo)<br>0 5 10 15 0 5 1 1 . 5<br>Reference Reference<br>correlation 0.985 correlation 0.983<br>o Potassium Electrical Conducivity<br>8fo} a<br>oO<br>7<br>7 “ 7<br>fo} Ce e 2 ae<br>fo} 7 Z<br>8 “ee “<br>s* v 5 uw<br>aS} e e ® aS} - oe e<br>mo} no} a<br>2 o e-<br>a 8 4 ‘ee a Ye e<br>& 7<br>e 1 9 * “ge“@°<br>ee<br>e<br>fo} fo}<br>0 20000 40000 60000 0 5 1 1.5 2<br>Reference Reference<br>——— Fittedvalues —--~-—-- 1:1 ——— Fittedvalues —--~-—-~-- 1:1<br>correlation 0.981 correlation 0.976<br>Clay (%) PH<br>fo}<br>2 ° “s<br>8 Se<br>e 0 e<br>58 e sé . 8<br>8 e 3 e<br>mo}fo) no}o e<br>agLo & oro 2<br>ee<br>e<br>8 oor lo ee a<br>Zz ta<br>7<br>°+<br>0 20 40 60 80 100 4 5 6 7 8 9<br>Reference Reference<br>—— Fitted values ----- 1:11 ——— Fittedvalues ----- 1:1<br>correlation 0.988 correlation 0 . 985<br><!-- End of picture text -->
**Figure 2: Repeated estimation of sibling mortality using different rounds of the survey: Is there a systematic recall bias?**

# **Comparing first and second rounds of Comparing first and third rounds of DHS DHS**

**_Estimate of sibling adult mortality between 1975 and 1979_**

<!-- Start of picture text --> 0 .01 .02 .03 .04 .05 0 .01 .02 .03 .04 .05<br>Mortality estimate from Round 1 Mortality estimate from Round 1<br>Round 2 estimate Round 3 estimate<br>45 degree line 45 degree line<br>.05<br>.04<br>.03<br>.02<br>.01<br>0<br>.05<br>.04<br>.03<br>.02<br>.01<br>0<br><!-- End of picture text -->

# **_Estimate of sibling adult mortality between 1980 and 1984_**

<!-- Start of picture text --> 0 .01 .02 .03 .04 .05 0 .01 .02 .03 .04 .05<br>Mortality estimate from Round 1 Mortality estimate from Round 1<br>Round 2 estimate Round 3 estimate<br>45 degree line 45 degree line<br>.05<br>.04<br>.03<br>.02<br>.01<br>0<br>.05<br>.04<br>.03<br>.02<br>.01<br>0<br><!-- End of picture text --> Note: 1975-79 graphs exclude Cambodia where mortality estimate using the first round of DHS is 0.165 and the second round of DHS is 0.166.

34
Table 8 shows that there is no statistical difference between treatment and control group households in their reported amount of sleep. The point estimates are practically small and statistically indistinguishable from zero. Thus while this would seem to be one margin on which mother’s could adjust they do not.

The evidence presented thus far suggests that mothers of stipend-eligible children in the stipend district have different trajectories of time use than mothers of similar children in the comparison districts. The evidence suggests that mothers spend more time on housework and less time on children’s needs. I find no evidence of changes in the amount of time respondents sleep or participate in paid work. The inclusion of controls does not explain away the effect of the program – which in the preferred specification is an increase of about 100 minutes per typical school day of housework and a reduction of 70 minutes of time spent on children’s needs.

# **VII. Robustness Checks**

One critique that could reasonably be leveled at the analysis presented thus far is the fact that I agglomerate two districts into one comparison group. Administrative data presented in figure 7, shows total female enrollment in grades 6-8 (panel a) and all grades (panel b) for all three districts of the LEAPS data. This figure suggests that Attock is a more appropriate comparison group if baseline enrollment is taken as the yardstick for comparability.

Consequently in Tables 9 and 10, I report the results of the full specification using each district, Faisalabad and Attock respectively, separately as a comparison for the stipend district – Rahim Yar Khan.<sup>18</sup> These specifications include all control variables. I find that the results are qualitatively and quantitatively unchanged. Mothers of stipend eligible children in Rahim Yar Khan report roughly two more hours of housework and roughly one hour and forty minutes less

> 18 I also examine if there is any evidence of systematic differences within the treatment district. I compare the characteristics of households with only boys in the eligible age range with those of households with girls in the eligible age range. I find no statistical evidence of any differences between these two groups.

19
# **Table 3: Distribution of Employment, Output and Net Profits**

||**The Distri**|**bution of Emp**<br>**L**|**loyment, Ou**|**tput and Profits- **<br>**Y**|**2010**<br>**Net P**|**rofits**|
|---|---|---|---|---|---|---|
||**Total**<br>000s|**% of Total**|**Total**<br>Billion US|**% of Total**<br>D|**Total**<br>Billion USD|**% of Total**|
|>99th percentile|299|42.4%|13.08|47.3%|1.58|146.6%|
|95-99 percentile|227|32.1%|7.20|26.0%|0.57|52.8%|
|90-95th percentile|769|10.9%|2.68|9.7%|0.17|15.8%|
|90-50th percentile|865|12.3%|4.33|15.6%|0.25|23.5%|
|1-50th percentile|165|2.3%|0.44|1.6%|-1.49|-138.7%|
|Total|706||27.68||1.08||
||||**% of firms**|**that are Ben Ali**<br>|**owned**<br>||
|||**L**||**Y**|**Net P**|**rofits**|
|**Highly Regulated?**|**No**|**Yes**|**No**|**Yes**|**No**|**Yes**|
|**(de jure)**|||||||
|>99th percentile|1.9%|13.5%|2.5%|38.9%|3.8%|8.7%|
|95-99 percentile|0.8%|2.5%|1.2%|4.6%|0.6%|4.1%|
|90-95th percentile|0.9%|3.8%|0.7%|5.5%|0.4%|1.2%|
|90-50th percentile|0.3%|1.2%|0.3%|0.7%|0.0%|0.1%|
|1-50th percentile|0.1%|0.6%|0.0%|0.0%|0.4%|0.9%|
|Total|0.3%|1.1%|0.2%|0.9%|0.3%|1.0%|

Note: sample is confined to sectors covered by the investment code. USD:TND exchange rate1:1.5146. Highly regulated sectors (de jure) are those that are subject to both authorization and restrictions on FDI.

30
5 The distinction between total variability and risk Is also Important In determining how to triat any serial correlation In the price data. When defined as t'varlability around trend, total varlability also Includes any systematic but non-trend component to the data and this should not be removed through procedures that correct for serial correlation. In contrast, measurement of price risk requires removal of any systematic component to the price data that could be used to Improve the price forecasts postulated each period. Moveover, when autocorrelatlon In the world price series Is removed, the changes In the results did not alter the qualitative concluslons of this paper.

In analyzing the transformation from world to producer prices, this paper Is generally concerned with total (ex post) price variability. Linear trend regressions1 are used to obtain the normal prices for each year since no a priori reason was Identifled to expect other kinds of growth patterns In time series of real prices. However, since at the producer price level It Is the price that farmers expect to receive each year that presumably drives decisions, measures of risk In producer prices are a!so computed.

The coefficient of variation (CV) of the detrended price data Is used as a summary measure of variability. This CV is a 1 When applicable (based on Glejser's (19693 test), we corrected for heteroscedasticity using a weighted least squares approach (Neter et al. t1985]).
**Table S4.2.** Probit regression results for calculation of propensity score to predict FREG membership, used for matching with the most similar observations in control group.

|FREG membership||
|---|---|
|Gender HH head (1=male)|-0.464**|
||(0.221)|
|Age HH head (in years)|-0.003|
||(0.004)|
|No. of months per year hh head away|-0.041|
||(0.058)|
|HH head married (1=yes)|0.260|
||(0.216)|
|Education HH head (completed grades)|0.035**|
||(0.014)|
|HH head participates in off-farm wage employment (1=yes)|0.096|
||(0.138)|
|No. of HH members|0.017|
||(0.027)|
|No. of organisations involved (0-12)|0.005|
||(0.026)|
|Father of HH head important in community (1=yes)|0.132|
||(0.103)|
|Walking distance from homestead to nearest FTC in minutes|-0.000|
||(0.002)|
|No. of times talked to extension agent in the last 12 months|0.012***|
||(0.004)|
|Attended agric. training in the last 12 months (1=yes)|0.774***|
||(0.095)|
|Basic assets score (0-4)|0.015|
||(0.055)|
|Radio owned (1=yes)|-0.177*|
||(0.105)|
|Food insecurity score (0-12)|-0.071***|
||(0.018)|
|Received support from social programs (1=yes)|0.168|
||(0.117)|
|Total land size (in ha)|0.125***|
||(0.040)|
|No. of tropical livestock unit (TLU) owned|-0.025|
||(0.019)|
|No. of adopted quickwins (0-5)|0.173***|
||(0.046)|
|Grows main crop (1=yes)|0.257|
||(0.315)|
|Constant|-2.103***|
||(0.424)|
|Pseudo R-squared|0.180|
|Observations|1,513|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Probit regression results for calculation of propensity score for FREG membership. FREG stands for ‘Farmer research and extension group’. For variable definitions see notes of Table 1. Robust standard errors in parentheses. Baseline variables used as covariates. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

8
that quintile. Poor and non‐poor correspond to the bottom 20% and the top 80% respectively. ASPIRE at present does not cover developed countries, so we use data from the US Consumer Expenditure Survey (CES, 2015), Canadian National Household Survey (CNHS, 2015), Australian Household Wealth and Wealth Distribution Survey (AWWDS, 2015), and European Union Survey of Income and Living Conditions (EU‐ SILC, 2015).

ASPIRE does not cover all countries in the world. To expand the set of countries in this study, we proceed as follows. First, we estimate an econometric model that explains the share of income from transfers, for poor and nonpoor people in each country by the share of GDP spent on social protection at the national level (from the International Labour Organization), an indicator of government effectiveness (from the Worldwide Governance Indicators), and whether the country used to be in the Soviet Union. (Other explanatory variables we tested include World Bank income category of the country, region, and GINI coefficient, but they turn out to have no explanatory power.) This provides an estimate of income from transfers in 26 additional countries (from 98 using only ASPIRE data).

Diversification also comes from financial inclusion. We use the fraction of the population with savings at a financial institution, from the Global Financial Inclusion Database (FINDEX, 2015). The Findex database provides these values for the bottom 40% and for the top 60%, which we use for poor and nonpoor people respectively. We assume that the fraction of income that is diversified increases by 10% for people who have bank accounts.

Figure 6 shows the diversification of income thanks to remittances and social protection for poor and non‐ poor people as a function of GDP per capita. It shows that generally, poor people receive a large share of their income from transfers (which sounds logical but is not the case in all countries). Here, we see that most countries below $10,000 GDP per capita have limited social protection systems, but there are exceptions, and a large variability for countries above this level. The figure also show the fraction of poor and nonpoor people with bank accounts, showing that financial inclusion is still very low in low income countries.

16
Table A2: Top and Bottom 20 Industries by Complexity Measure

|||10 Least Complex||10 Most Complex|
|---|---|---|---|---|
|Rank|SIC|Description|SIC|Description|
|1|2449|Wood Containers, Nec|3711|Motor Vehicles And Car Bodies|
|2|3262|Vitreous China Table And Kitchenware|3714|Motor Vehicle Parts And Accessories|
|3|3942|Dolls And Stuffed Toys|3081|Unsupported Plastics Film And Sheet|
|4|2074|Cottonseed Oil Mills|3089|Plastics Products, Nec|
|5|2083|Malt|3721|Aircraft|
|6|2999|Petroleum And Coal Products, Nec|3812|Search And Navigation Equipment|
|7|2298|Cordage And Twine|3731|Shipbuilding And Repairing|
|8|3161|Luggage|2819|Industrial Inorganic Chemicals, Nec|
|9|3493|Steel Springs, Except Wire|2869|Industrial Organic Chemicals, Nec|
|10|2021|Creamery Butter|2813|Industrial Gases|
|11|2823|Cellulosic Manmade Fibers|2816|Inorganic Pigments|
|12|2892|Explosives|2621|Paper Mills|
|13|3021|Rubber And Plastics Footwear|2051|Bread, Cake, And Related Products|
|14|2091|Canned And Cured Fish And Seafoods|3312|Blast Furnaces And Steel Mills|
|15|3931|Musical Instruments|3841|Surgical And Medical Instruments|
|16|3652|Prerecorded Records And Tapes|2834|Pharmaceutical Preparations|
|17|3261|Vitreous Plumbing Fixtures|2833|Medicinals And Botanicals|
|18|2098|Macaroni And Spaghetti|3999|Manufacturing Industries, Nec|
|19|3255|Clay Refractories|3949|Sporting And Athletic Goods, Nec|
|20|3111|Leather Tanning And Finishing|3669|Communications Equipment, Nec|

Notes: Table lists top and bottom 10 industries according to the number of intermediate inputs use (from other 4-digit industries) from Levchenko (2007).

2
Table A6 Shapley Decomposition Urban- Rural Differences in Poverty in 2001

|||Difference|Scale|Inequality|
|---|---|---|---|---|
||Headcount|-22.30|-22.25|-0.05|
||Poverty Gap|-8.51|-8.20|-0.31|
|Urban|Squared Poverty Gap|-3.96|-3.80|-0.16|
||Watts|-11.90|-11.39|-0.50|
||Headcount|11.90|19.87|-7.97|
||Poverty Gap|4.54|9.40|-4.86|
|Rural|Squared Poverty Gap|2.11|5.03|-2.91|
||Watts|6.35|13.94|-7.59|

Source: Authors’ Calculations Table A7 Shapley Decomposition Urban- Rural Differences in Poverty in 2007

|||Difference|Scale|Inequality|
|---|---|---|---|---|
||Headcount|-27.73|-22.05|-5.68|
||Poverty Gap|-9.50|-7.69|-1.81|
|Urban|Squared Poverty Gap|-4.06|-3.44|-0.62|
||Watts|-12.60|-10.42|-2.18|
||Headcount|15.14|21.41|-6.27|
||Poverty Gap|5.19|10.47|-5.28|
|Rural|Squared Poverty Gap|2.22|5.64|-3.42|
||Watts|6.88|15.34|-8.46|

Source: Authors’ Calculations 46
**Policy, Research, and External Affairs**

_ - **aIT** -

**Macroeconomic Adjustment and Growth**

WPS 632

- This paper a product of the Macrocconlomic Adjustment and Growth Division, Country Econiomlics Departmc1t- is part ofa PRE research project, ThecMacrocconiomics o'ithc Public Sector Delicit (RPO 675-31). Copiesare availalble f'reefroi the World Banik, 1818f iStrectNW,WashlillgtolnDC 20433. Please contact Raquel Luz, room NI 1-057, extensioni 34303 (90 pages).

Argentina hias had a quarter century without growth at a time of' rapid economic growth in thie rest ol' the world and government spending systematically grows fEster than GDP. Spendinig declined when the final crisis of **thc** Argenltine economy began in 1982, but more because of' resource constraints than delibcrate political action - and too late to avoid the 1'ina Jal crisis that brought hyperinflation (approaching 5,00(0) percent in 1989). Thc government **rani** a primary dcf-icit (not including interest paymentis) evcry y( r from 1961 to 1989, SO it issued money anid inteirest-bearing debt. As a result, the cconomy expericnced high real interest rates and inflation.

Despite heavy fiscal prcssurc, fiscal spending has continued to grow, systematically exceeding revenues. From 1964 to 1975, the deficit was financed by creating money; witlh **thc** fall of Peron and the beginning of a military regime, debt financing became significanit. Rodriguez' regression study shows that every I percent of primary deficit is linanced with 0.7 percent of revenue f'rom creating moncy - the eff''ct o 'collectitig which is an additional 67.9 percent of inflation.

Public debt plays a peculiar role **in** Argentina's finances. The central bank has become the chief borrower ol' about 80 percent of thc private banks' lending power. In this context, a policy of tight moncy to reduce aggregate demand basically increases the tratnslers from the public to ihe private sector because of' **the** highlCI deficit that the rise in interest rates generates.

The pressure that government debt puts on the financial markets is best captured by evaluating that debt at the commercial exchange ratc. When the stock of'debt gets out of linie with available reserves, pressures mount against the currcncy and devaluation follows. Then thlc remainirng stock of debt rises at rates Far bey(,nd levels consistent with a fixed exchange **raltc** -- and a new crisis begins to develop.

Thc PRE Working Paper Series diss-rninates thc inclings of work under *wa\ in **the Bailk's Policy,** Research. and Extenal AffairsComplex. Anobjectiveofthcscries istoge tihesefindingsootitquicklv, c\ en ifprcsenta:tionsre less than full\ polished. The Findings, interpretations, and conclusions in thesc papers do not necessarily represcnt official Bank polic\.

lP odued h! th ic I'KR. **Di)iinmatnon CU** cicr
|**Trade**|||
|---|---|---|
|_Exports_|Exports of a certain product (HS6) from a specific country (s) at a year t|COMTRADE|
|_Imports_|Imports of a certain product (HS6) from a specific country (s) at a year t|COMTRADE|
|**Evasion Gaps**|||
|_Value Gap_|Log Export Value Reported by Partner – Log Import Value Reported in<br>Tunisia, measured at the HS6-country-year level.|COMTRADE|
|_Quantity Gap_|Log Export Quantity Reported by Partner – Log Import Value Reported in<br>Tunisia, measured at the HS6-country-year level.|COMTRADE|
|_Price Gap_<br>**Definition of Tariff Cate**|Log Export Unit Price Reported by Partner – Log Import Unit Price<br>Reported in Tunisia, measured at the HS6-country-year level.<br>**gories**|COMTRADE|
|_High Tariff_|The tariff rate is greater than or equal to 36||
|_Low Tariff_|The tariff rate is strictly smaller than 36.||
|_(T≥40)_|Dummy variable taking the value 1 if the tariff rate is at least 40% and zero<br>otherwise.|WITS|
|_(20≤T<40)_|Dummy variable taking the value 1 if the tariff rate is equal to or greater than<br>20% but smaller than 40% but zero otherwise|WITS|
|_(T<20)_|Dummy variable taking the value 1 if the tariff rate is smaller than 20% and<br>zero otherwise.|WITS|
|**Predicted Import Share**|||
|_Predicted import share_|The predicted import share of different groups of firms (Ben Ali firms,<br>offshore firms, onshore firms, and public firms) based on the consumption<br>weighted average of their market shares across industries, with industry<br>consumption weights depending on how intensively a particular HS6<br>product is used by a given industry. Specifically, comprehensive tax data were<br>used to compute the aggregate market shares of the different types of firms<br>in each 5-digit sector in a given year. Subsequently, an IO table was used to<br>identify what share of imports of a particular HS6 product is used by a given<br>sector; consumption weights (summing to 1) were assigned to each of the<br>different sectors (note that if a certain sector does not use these imports the<br>consumption weight is zero by construction). The predicted import share of<br>firms of type j was calculated as the sum of the aggregate market shares of<br>firms of type j across sectors multiplied by the relevant consumption weights<br>for those sectors. Not all products were covered by the IO table, so the<br>predicted import shares could only be calculated for a subset of all products<br>(mostly comprising intermediate inputs).|Own calculations<br>with help from<br>the INS|

INS=Institut National de la Statistique, MoF=Tunisian Ministry of Finance, CC=La Commission Nationale de Gestion d'Avoirs et des Fonds objets de Confiscation ou de Récupération 50
- 22 -

# **(c)** Using Flrm Behavior to Monitor Regulatory Activity

The previous examples have investigated ways of inducing local enforcement of central policies. This has been motivated by the observation in Section 2 that, _ceteris parlbus,_ local monitoring may be expected to be more efficient than central. Local governments can also be expected to have access to information regarding the potential for abatement, as well as whether it actually occurs. This knowladge is useful in policy choice, and is valuable to a central policy maker.

Suppose the central government wishes to give tax incentives to firmns to invest in abatement technology. The size of the appropria,e tax break depends on the costs of adopting the technology.' For example, if the adoption will cause serious disruption to production processes and other input markets, the required incentive is necessarily high.

The local government may be in a better position to determine these costs of adoption, but will likely Fur a cost of its own in doing so. The center must then give the local authority sufficient incentive to become, informed about abatement possibilities, and to transmit that information to the center.

One way to make it attractive for the local agency to become informed is to base its incentive payment on a comparison of the agency's ex ante prediction of pollution control, and the actual ex post outcome. If it is informed about the costs of adoption, then it will have a better chance of predicting the outcome. The center is then able to implement a more accurate tax incentive for the firm, since it has better knowledge about its adoption costs.

An advantage of this scheme is that the actions of the local authority need not be monitored by the center.<sup>24</sup> The only measurement that has to be made is, in the case of airborne emissions for example, that of the ambient air quality.

'If the technology does not exist, the adoption costs also include R&D expenditures.

24In fact, often it may be expected that they are not observable.