rafmacalaba/datause-encoder-data huggingface dataset:
As case in point, in the run-up to the September 2011 elections, GRZ announced a USD 170 million program to rehabilitate urban roads. These were unplanned roads that were added to the RDA Work Plan without the normal selection process. With political pressure to commence work quickly, many of the works were executed without proper and detailed engineering designs and comprehensive bills of quantities (The Post, 6 October 2011). In the Zambian media, these roads became known as the “formula one” roads, due to the speed at which they were constructed.

Moreover, the increase in investment in the rehabilitation of urban roads conflicts with the RDA’s strategy documents that emphasize maintenance and to a lesser extent, rehabilitation. Governments around the world often face incentives to upgrade roads rather than rehabilitate them, as the former maximizes their political visibility. The economic feasibility is however questioned since the costs of upgrading are high whereas the economic benefit may be limited, especially in regions where traffic is minimal. For instance, despite political discourse on the priority given to maintain the existing network, from 2008 to 2011 around 11% was committed to maintenance and 73% to upgrading to bituminous standard.

# **Distribution of projects**

The distribution of projects funds across provinces varies significantly in Zambia (see Graph 3)<sup>9</sup> . Over the period 2008 to 2011, the Western province received the largest share of allocations (about 30%), followed by the Northern (27%) and Eastern provinces (14%), while the Copperbelt province got the lowest(less than 2%). The funds to the Western region were dedicated to high value projects such as the construction of the Mongu to Kalabo Road, the upgrading of the road between Sesheke and Sioma and the construction of the Zambezi Sioma Bridge. The funds allocated to Northern province concern a small number of high value projects including the upgrading of the road from Kasama to Isoka and Kasama to Mbesuma Pontoon among others.

> 9 Data presented in the upcoming sections derive from the 108 projects database. For a description of the database, see annex 1.

10
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE needs are relatively modest and equal only nine percent of the total. The need for capital investment is highest in the power sector at $631 million. Operations and maintenance spending needs are of the order of $200 million in each of the power, transport and water sectors (table 10).

**Table 10. Indicative annual infrastructure spending needs in Tanzania, 2006-15**

|Sector||US$ million per year||
|---|---|---|---|
||Capital expenditure<br>O|peration and maintenance|Total spending|
|ICT|181|75|257|
|Power|631|280|910|
|Transport|352|267|619|
|WSS|449|177|626|
|Total|1,613|799|2,412|

Sources: Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models at http://www.infrastructureafrica.org/aicd/tools/models.

_Note:_ O&M = operations and maintenance; CAPEX = capital expenditure.

At 17 percent of the country’s GDP, Tanzania’s infrastructure spending needs relative to the size of its economy is noticeably higher than the average for Sub-Saharan Africa average and close to the average for nonfragile low-income countries (figure 12).

**Figure 12. The burden of infrastructure needs by country typology, 2006**

<!-- Start of picture text --> Sub-Saharan Africa<br>fragile low-income countries<br>nonfragile low-income countries<br>resourch-rich countries<br>SADC<br>middle-income countries<br>Congo, Dem. Rep. of<br>Ethiopia<br>Kenya<br>Tanzania<br>Sudan<br>Uganda<br>South Africa<br>Cape Verde<br>0 10 20 30 40 50 60 70 80<br>% of GDP<br>capital expenditure operations and maintenance<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

25
ability to save cash, both of which were measured pre-treatment. These refer to actual saving behavior as reported by respondents before treatment. The ...rst is whether the respondent used a susu collector; the second, whether the respondent agrees or strongly agrees with the statement “I save regularly” (which two-thirds of respondents do). We use the principal component of these and the two discount rate measures to create an index measuring a lack of self-control. The regression in column 2 uses the index of self control in place of the discount rate measures. Those with less self control have lower baseline capital stocks.

The models also predict that individuals who are more patient and less hyperbolic should save more and grow their business faster as long as they are they below their optimal size. Conditional on the log of baseline capital, we do ...nd that the growth in capital stock between wave 1 and 2 (i.e., before treatment) is faster for respondents who are more patient and are not hyperbolic. All these results tally with the predictions of the RTI models.

Finally, in column 3 we include a dummy for high pressure to share within the household. This variable plays in principle no role in the RTI models, but we ...nd that this is also associated with signi...cantly smaller capital stock at baseline. This is consistent with the idea to pressure to share a¤ects ...rm size, and suggests that factors other than those in the basic RTI framework may have important e¤ects on ...rm size.

All three regressions include controls for the age of the owner, and the education level of the owner’s mother and father. The models also predict that, other things being equal, older entrepreneurs are likely to have larger ...rms because they have had more time to accumulate and learn, and thus reach their long-term steady state. The data support this. Parental education is added as a control variable for initial household wealth and socioeconomic status. The regressions also include an indicator that the owners is female. We ...nd that female-owned businesses are signi...cantly smaller in terms of baseline capital stock.

In sum, the regressions on Table 2 suggest that baseline capital stocks are consistent with several of the predictions made by the RTI model. The signi...cance of pressure to share with others in the household suggests that factors outside the RTI framework may also be important. We now turn to the experimental data to investigate whether exogenous shocks to household capital are invested in ways which are consistent with model predictions.

21
Relying on the quasi-natural experiment of a state split raises the understandable concern about the validity of our research design. The difference in employment patterns might have existed across the border before the state split and thus before the introduction of the New Industrial Policy. Alternatively, there might have been a difference in employment growth trends across the border before the state split. Both would bias our results upward.

We address these concerns sequentially. First, we investigate employment patterns in modern sectors right before the state split. We geo-reference data from the 1998 Economic Census to match the towns and villages presented in the 2013 Economic Census and replicate the analysis of Equ.(1) for employment outcomes in 1998. Table 6 presents the results. _Target_ does not appear to affect total employment or total number of firms. The results hold across all distance buffers. Therefore, in 1998, there were no systematic differences in employment size and firm numbers for town and villages close to but on different sides of the state border that is defined in 2000.

Second, we address the issue of pre-split trends by examining the growth of employment and the change in total firms at the town and village level prior to the state split. To measure changes, we further georeference the data from the 1990 Economic Census. We then compute the changes in employment outcomes between 1990 and 1998 as the outcome variables and apply Equ.(1). As shown in Table 7, prior to the split, for towns and villages close to the border, neither employment nor the number of firms was growing faster in Uttarakhand than in Uttar Pradesh. If anything, among these places, employment in modern sectors was growing slower in Uttarakhand than in Uttar Pradesh.

Admittedly, the matching between towns and villages over the three rounds of Economic Censuses is not perfect. Depending on the distance buffers, we capture 54 to 60 percent of the places when looking at the patterns in 1998, and 29 to 31 percent of places when analyzing the pre-split trends between 1990 and 1998. In addition, Abeberese and Chaurey (2019) and Chaurey (2017) find similar results on the patterns and trends of employment in manufacturing industries before 2000, using data different from ours and for districts close to the border. Overall, the quasi-natural experiment nature of the state split is supported by our analysis and previous studies on this topic.

24
longer term contractual agreements, larger capital investments<sup>and more numerous</sup> supplier (and maybe even customer) links. Hence<sup>there is a stronger need for a more</sup> adequate legal and regulatory framework to enforce<sup>multiple-party and/or longer term</sup> contracts. Although being a public good by nature,<sup>it might perhaps be useful to look at</sup> quality of institutions and law enforcement as an<sup>input to production. Then our earlier</sup> point can be rephrased with "the more a production<sup>process relies upon suppliers, sources</sup> of credit, and technological innovations, the more _institutional-qualitxy-intensive_<sup>the</sup> production process becomes." Subsequently, scarcity<sup>of this one input makes it harder,</sup> sometimes even impossible, to invest and engage in productive<sup>activities the more</sup> institutional-quality-intensive the production process<sup>is. In what follows, the effect of</sup> developed institutions and contract enforcement on growth<sup>will be at least as pronounced</sup> in industry as it is in other sectors. Without testing the<sup>hypothesis of the relative</sup> importance of institutional quality on various sectors in the<sup>economy, the paper</sup> contributes to the empirical growth literature in that<sup>it establishes empirically the</sup> relationship between a broad range of institutional quality indicators<sup>and industrial</sup> growth.

# **III. Institutional Quality Variables**

The dataset containing measures of institutional quality<sup>and contract enforcement to be</sup> used in our study was assembled by the IRIS Center<sup>of the University of Maryland from</sup> the International Country Risk Guide - a monthly publication<sup>of Political Risk Services</sup> (PRS). The five PRS indicators used in the study are Government<sup>Repudiation of</sup> Contracts, Risk of Expropriation, Corruption, Rule of Law,<sup>and Bureaucratic Quality.</sup> For all five measures, the lower (higher) values indicate worse<sup>(better) standing.</sup> 6
Cohen, W. B. and Spies, T. A. 1992. Estimating structural attributes of Douglas-Fir/Western Hemlock forest stands from Landsat and Spot imagery. _Remote Sensing of Environment_ , Volume 41, 1-17.

Dasgupta, P. 1995. Population, poverty and the local environment. _Scientific American_ , vol 272, no. 2: 26-31.

Dasgupta, S. U. Deichmann, C. Meisner, and D. Wheeler. 2005. Where is the povertyenvironment nexus? Evidence from Cambodia, Lao, PDR and Vietnam. _World Development_ , vol 33, No. 4: 617-638.

Dewees, P. 1989. The woodfuel crisis re-considered: Observations on the dynamics of abundance and scarcity. _World Development_ , vol 17, no. 8: 1159-1172.

Dewees, P. 1995. Trees on farms in Malawi: Private investment, public policy and farmer choice. _World Development_ , vol 23, no, 7: 1085-1102.

Ekbom, A. & J. Bojö 1999. Poverty and Environment: Evidence of Links and Integration in the Country Assistance Strategy Process. World Bank Africa Region Discussion Paper No. 4. Washington, DC: World Bank.

Duraiappah, A. 1998. Poverty and environmental degradation: A review and analyses of the nexus. _World Development_ , vol 26, No. 12: 2169-2179.

Ezzati, M and D. M. Kammen. 2001. Indoor air pollution from biomass combustion and acute respiratory infections in Kenya: an exposure-response study. _The Lancet_ , vol 358, 619-24.

Fisher, M. 2004. Household welfare and forest dependence in southern Malawi. _Environment and Development Economics_ 9: 135-154.

French, D. 1986. Confronting an Unsolvable Problem: Deforestation in Malawi. _World Development_ , vol 14, no. 4: 531-540.

26
Figure A1: BOP Data Structure <!-- Start of picture text --> by flow type a<br>? ?<br>? ? ? ? Other Other ?<br>Direct Portfolio Portfolio Financial Investment Investment<br>Reserves a<br>Investment Equity Debt Derivatives Debt Equity<br>? ? ?<br>Equity Debt by sector b - by instrument<br>? ? ? ?<br>General<br>Govern- Central Banks Other ? ? ? ?<br>ment Banks Sectors Currency Trade Accounts<br>Loans Credit & Payable/<br>& Deposits Advances Recievable<br>?<br>by sector b<br>? ? ? ?<br>General<br>Central Other<br>Govern- Banks<br>Banks Sectors<br>ment<br><!-- End of picture text -->

> _a_ This structure is the same for inflows and outflows. Reserves are only classified as outflows.

> _b_ The breakdowns of these variables by sector exist in the BOP data but the coverage is sparse for many countries and quarters.
<!-- Start of picture text --> Figure 19: Governance indicators scores (-2.5 to +2.5) Figure 20: Factor floor costs in Sub-Saharan Africa<br>1996 and 2005 compare well with China and India<br>0 0.70<br>Voice and Political Government Regulatory Rule of law Control of<br>-0.1 accountability stability effectiveness quality corruption 0.60<br>-0.2 0.50<br>-0.3 0.40<br>-0.4 0.30<br>1996<br>2006 0.20<br>-0.5<br>0.10<br>-0.6<br>0.00<br>-0.7<br>-0.8<br>-0.9<br>Source: Arbache and Page (2007a). Data from the World Bank various years Source: Cadot and Nasir (2001).<br>i.<br>Figure 21: Nett productivity is lower than "factory floor" Figure 22: African firms are sharply disadvantaged by<br>(gross) productivity due to high costs of doing business higher indirect costs<br>1.20 Gross Net Share of total costs (%) Materials Labor Capital Indirect<br>Source:<br>1.00 Mozambique<br>Eritrea<br>0.80 Kenya<br>Tanzania<br>Zambia<br>0.60 Uganda<br>Bolivia<br>0.40 Nigeria<br>Ethiopia<br>0.20 China<br>Nicaragua<br>0.00 Morocco<br>India<br>Senegal<br>Bangladesh<br>0 10 20 30 40 50 60 70 80 90 100<br>Source: Eifert, Gelb, and Ramachandran (2005).<br>Source: Eifert, Gelb, and<br>Ramachandran (2005).<br>Madagascar Kenya GhanaMozambique LesothoSouth Africa India Chinaa<br>ZimbiaEritreaMozambiqueEthiopiaNigeriaBoliviaUgandaKenyaTanzaniaNicaraguaBangladeshSenegalMorocco IndiaChina<br>a l e s h i r t ($ )<br>D ir e c t c o s t p e r m<br>T o ta l fa cto r p ro d u ctiv ity (C h in a = 1 )<br><!-- End of picture text --> The picture is somewhat brighter for additional reforms, albeit still uneven. Fortysix Sub-Saharan countries introduced at least one business environment reform in 2006/07, and Ghana and Kenya were among the top 10 reformers (Tanzania was also on the list in 2005/06). Kenya rose from 82 to 72, and Ghana from 109 to 87. But all others had ranks of 90 or higher. Nonetheless, several countries saw improvements: Mozambique went from 140 to 134, Madagascar from 160 to 149, and Burkina Faso from 41
||East Asia &<br>Pacific<br>(N=35)|South<br>Asia<br>(N=19)|Sub-Saharan<br>Africa<br>(N=52)|
|---|---|---|---|
|Annual change in $1.25/day poverty rate<br>(% points per year)|-2.2|-1.2|-1.5|
|Annual change in $2/day poverty rate<br>(% points per year)|-2.3|-1.2|-0.9|
|Annual percentage change in domestic food prices<br>(% per year)|0.6|0.7|0.6|
|Annual percentage change in food production<br>(% per year)|4.4|3.3|4.0|
||Europe &<br>Central Asia<br>(N=41)|Latin America<br>& Caribbean<br>(N=157)|Middle East &<br>North Africa<br>(N=4)|
|Annual change in $1.25/day poverty rate<br>(% points per year)|-1.6|-0.5|-0.4|
|Annual change in $2/day poverty rate<br>(% points per year)|-1.7|-0.7|0.0|
|Annual percentage change in domestic food prices<br>(% per year)|0.2|0.4|0.0|
|Annual percentage change in food production<br>(% per year)|0.9|3.5|4.2|

_Source_ : Author’s estimates from World Bank (2013) poverty data and ILO (2013) food price data.

# _Methods_

For systematically testing the relationship between poverty and food prices, we use both least squares regressions and instrumental variables (IV) approach. In the least squares approach the first difference in the poverty headcount ( _P_ )<sup>7</sup> is regressed against the first difference in the log of domestic food prices ( _p_<sup>_d_</sup> ) and series of time dummies ( _T_ ), with _v_ denoting the error term: One concern with equation (1) is that changes in food prices might be correlated with the error term because of omitted variables. The 1998 Indonesian financial crisis, for example, increased 7. First differences is preferable to percentage differences for two reasons. First, poverty headcounts are already measured in percentage terms, and taking percentage changes of very small values produces results that are arguably counterintuitive. For example, an increase in the poverty rate from 1% to 3% in one country and 30% to 32% in another country should arguably be regarded as equivalent. In contrast, the two changes are very different in percentage terms: 200% in the first case, and 6.67% in the second case. See Deaton (2006) for a discussion of this problem in the context of mortality rates. A second problem is that taking percentage changes can sometimes produce outliers.

7
publishing LCOEs annually for various technologies since 2009. Lazard’s estimations of LCOEs reflect investors’ perspectives, and the analysis uses data for the United States. The International Energy Agency (IEA) has been doing the same in five-year intervals since 1982. IEA conducts analysis by collecting data from selected countries, but the sample includes mostly the OECD member countries, with only a few non-OECD countries included. The International Renewable Energy Agency (IRENA) publishes its LCOEs of renewable energy technologies every alternate year. IRENA’s analysis covers only renewable technologies; it does not include fossil fuels and nuclear technologies. The US National Renewable Energy Laboratory (NREL) and the financial institution Bloomberg also publish their LCOEs. These studies are, however, focus on the United States.

Based on LCOE, several studies claim that renewable energy technologies are cheaper than fossil fuel technologies. For example, Aghahosseini et al. (2020) find that solar photovoltaics (PV) and wind energy are the most cost-competitive technologies for electricity generation in the Middle East and North Africa (MENA) region. Collecting data from many countries around the world on the capital costs and other input data to calculate LCOE, IRENA (2020) finds that renewables are cheaper than fossil fuels to produce electricity on the basis of relative LCOEs. Lazard (2019) shows that LCOEs of utility-scale PV and wind recently have been smaller than that of the cheapest fossil fuel-based technology, gas-fired combined cycle (CC), in the United States despite historically low gas prices.

Over the last two years, utilities have signed power purchase agreements (PPAs) with private suppliers for renewables at very low prices. Deign (2019) reports on solar project auctions in eight countries at prices below $25/MWh. These are: (1) China’s Jinko Power offer at $24.89/MWh for 150MW in Jordan, (2) Norwegian company Scatec Solar’s bid for a 200MW plant at $24.4/MWh in Tunisia, (3) a joint offer of Abu Dhabi Power Corporation, China’s Jinko Solar, and Japan's Marubeni Corporation for a 1200 MW project in Abu Dhabi at $24.20/MWh; (4) an undisclosed developer’s offer for a 900 MW solar power project in Dubai at US$16.9/MWh; (5) Saudi Arabia’s ACWA offer for a 300 MW project at $23.42/MWh; (6) Italian developer Enel’s offer for a 116MW project in Chile at $21.48/MWh; (7) French developer Neoen’s offer for a 375MW project at $18.93/MWh in Mexico; and (8) Brazilian Milagres project of 163 MW at $16.95/MWh. In Portugal, 24 utility-scale PV projects with a combined capacity of 1,150 MW 4
Table 6: Percentage of Female Enrollment by Faculty

|Faculty|1973/74|1982/83|1988/89|
|---|---|---|---|
|Arts|15.2|23.2|44.6|
|Law|0.5|2.4|1.9|
|Science|14.8|28.5|27.2|
|Commerce|6.7|9.7|9.5|
|Medicine|-|^|44.3|
|Engineering<br>|||9.1|
|Agriculture|-|-|4.5|
|Education|10.6|17.8|23.3|
|Total|10.1|11.7|14.5|

Source: University of Sana'a 52. The establishment of Faculties of Education in the Governorates of Taiz, Hodeida, Ibb and Hajjah, has made higher education accessible at the local level to female students and increased their participation rate. For example, the female share of student enrollments to males in Taiz, Hodeida and Ibb in 1988/89 has improved -- they were 27.8%, 21.3% and 31% respectively (see Appendix, Table 1). As an incentive to join the teaching service, female students from the Faculty of Education are provided free accommodation, while those in the other faculties enjoy a subsidized rent which amounts to YRI 150 (US$15) a month.

<!-- Start of picture text --> Graph Ilk Total Graduate Output,<br>1974-1989<br>Male-Femaj Compoaltion<br>No. of Graduates (Thousanda)<br>4-<br>2i<br>1 _<br>0<br>74-77 78-81 82-86 88e9<br>War<br>Male Femals<br><!-- End of picture text -->

**Source:** Univrelty **of Sang's**

20 -
<!-- Start of picture text --> 2%<br>0%<br>-2% —Baseline scaling-up<br>4% scenario<br>6% —Reference scaling up<br>-BY8% scenarioi<br>-10%<br>42%<br>-14%<br>-16%<br>“18%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 5%<br>o%10,<br>5% —Baseline scaling up<br>scenario<br>-10910% —Reference scaling up<br>° scenario<br>-15%<br>-20%<br>-25%<br>2012 2015 «2018 2021 2024<br><!-- End of picture text -->
# **Figure 3. Type of Products Imported and Exported by Exporter-Importers in Peru Panel A. Type of Products Imported**

<!-- Start of picture text --> Goods Imported by Exporter‐Importers (X‐M)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2004 2008 2012 2000 2004 2008 2012 2000 2004 2008 2012<br>Agribusiness Apparel All<br>Capital Consumption Intermediate Not Classif.<br><!-- End of picture text -->

**Panel B. Type of Products Exported**

<!-- Start of picture text --> Goods Exported by Exporter‐Importers (X‐M)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2004 2008 2012 2000 2004 2008 2012 2000 2004 2008 2012<br>Agribusiness Apparel All<br>Capital Consumption Intermediate Not Classif.<br><!-- End of picture text --> Next, focusing only on imports of intermediate inputs by Peruvian exporter-importers we show the portfolio of origin countries from which they source those inputs in selected years in Panel A of Figure 4. The portfolio of destination countries to which exporter-importers sell in selected years is shown in Panel B. Exporter-importers have a diversified portfolio of sourcing markets with the major ones being the United States (US), the European Union (EU), Japan, and Latin American countries. Over the 2000-2012 period China grows in importance as a source of imported intermediate inputs, especially for exporter-importers in apparel. Exporter-importers in 9