rafmacalaba/datause-encoder-data huggingface dataset:
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE There is the potential for Zimbabwe to **Table 4. Zimbabwe’s possible hydropower expansion** benefit significantly from wheeling Congolese Project Planned Lead time Cost estimate power southwards toward markets in capacity ($ millions) neighboring countries, notably South Africa. (MW) Assuming trade expansion, Zimbabwe could Kariba South 300 3 200 Extension transmit up to 20 terawatt-hours (TWh) of Mutapha Gorge* 300 7 454 power from the Democratic Republic of Congo Batoka* 800 10 1,250 to Mozambique, Botswana, and South Africa. Additional capacity 15 — — But to participate in trade, Zimbabwe would _Source:_ Derived from Rosnes and Vennemo (2009). need to develop 3,100 MW of interconnector _Note:_ * Borders Zambia <u>(capacity and cost split between countries).</u> capacity, with a price tag of $370 million over the next 10 years. Wheeling of power at $1.40 per kilowatt to $28 per kilowatt<sup>7</sup> would allow Zimbabwe to benefit from a return of anywhere between 1 and 23 percent annually on its investment.

|Project|Planned<br>capacity<br>(MW)|Lead time|Cost estimate<br>($ millions)|
|---|---|---|---|
|Kariba South|300|3|200|
|Extension||||
|Mutapha Gorge*|300|7|454|
|Batoka*|800|10|1,250|
|Additional capacity|15|—|—|

Irrespective of the evolution of trade in southern Africa, Zimbabwe will always remain a modest net importer of power, continuing to rely primarily on its own coal-based and hydropower plants for generation. Future hydropower sites that could be developed include Batoka, Kariba Soth, and Mutapha Gorge (table 4). To meet domestic demand, Zimbabwe is expected to import no more than 4 TWh of power from its neighbors for domestic consumption going forward.

**Table 5. Zimbabwe’s annualized costs of capacity expansion (over 10 years)**

|$ millions|||
|---|---|---|
||Investment cost|375.5|
||Thermal|165.2|
||Hydro|201.2|
||Other|9.2|
|ion|Rehabilitation cost|97.4|
|nerat|Thermal|57.1|
|Ge|Hydro|40.4|
||Other|0.0|
||Variable cost (fuel, O&M)|223.0|
||New capacity|106.1|
||Installed capacity|116.9|
||Investment cost|201|
|nd|Cross-border|0|
|on a<br>ion|Distribution grid|75|
|issi<br>ribut|Urban connection|20|
|ansm<br>dist|Rural connection|106|
|Tr|Rehabilitation cost|160|
||Variable cost (existing capacity)|185|
||Capital cost|834|
|otals|Investment cost|577|
|Subt|Rehabilitation cost|257|
||Variable cost|408|
|Total||1,242|

To meet its own power demand and expand access to 70 percent of the population, Zimbabwe will need to spend $1.2 billion annually over a decade. Of this total, $375 million will be needed for new investment in generation, $97 million for rehabilitation of existing generation capacity, and $233 million for generator maintenance.

_Source:_ Derived from Rosnes and Vennemo (2009). _Note:_ O&M = operations and maintenance; T&D = transmission and distribution.

> 7 These charges are used for illustrative purposes and drawn from The Potential of Regional Power Sector Integration: South African Power Pool (SAPP) Transmission and Trading Case Study (2009).

20
16

# **5 The Cost Effectiveness of Insecticide-Treated Net Allocations in Kenya**

On April 25, 2000, heads of African countries met in The African Summit on Roll Back Malaria and signed the Abuja declaration with the aim of reducing malaria mortality by one half by 2010. One of the three main measures, that was agreed on, was the expansion of ITN usage by population at risk—particularly pregnant women and children under 5 years—to 60 percent by 2005 (Abuja Declaration 2000). In order to increase the availability and usage of ITNs in Kenya, the already existing retail sale of ITNs at full cost was complemented by social marketing campaigns selling ITNs at a subsidized price, by routine distribution through antenatal and child health clinics, and by free mass distribution campaigns (Kenya Malaria Indicator Survey 2010).<sup>13</sup> Most of these programs have been funded by international development initiatives. Their cost effectiveness might be highly policy-relevant, particularly for decision making on funding of further ITN keep-up campaigns.

The overall costs of ITN delivery programs in Kenya are not known. As for costs per ITN delivered, the experience shows that the costs vary greatly among the different delivery channels, with the amount of subsidy per ITN, between the type of bednet, and over time. In the light of the existing literature on the costs and cost effectiveness of various ITN delivery initiatives in Sub-Saharan Africa, we estimate the cost per ITN delivered to be US$ 5.<sup>14</sup> The benefit of interest in this case is the number of infant deaths averted by the increases in ITN ownership. If we take a 13 Social marketing campaigns on ITNs have been conducted by Population Services International Kenya (PSI/Kenya) since 2002. The main focus of the campaigns has been to increase the awareness of ITNs' contribution to avoiding malaria and to deliver partially subsidized ITNs through the commercial sector (National Social Marketing Centre 2010). Distribution of heavily subsidized ITNs and long-lasting insecticide-treated bednets (LLIN) through Maternal and Child Health clinics by PSI/Kenya and Ministry of Health followed in 2004 and 2005 (Noor et al 2007). In 2006, 3.4 million LLINs were distributed for free to children under 5 years in two massive campaigns in districts of Kenya that are affected by malaria (Hightower 2010). The first distribution (1.4 million LLINs) was integrated with measles vaccination campaign; the second campaign (2 million LLINs) was conducted separately. Over time, there was a shift towards distribution of LLINs. Unlike traditional ITNs, LLINs do not need to be retreated with insecticides every 6 to 12 months. Furthermore, LLINs provide more ―treated net years‖ than ITNs which leads to protection at lower annual costs in spite of LLINs‘ higher provision costs (Yukich et al 2008). 14 The estimated cost of US$ 5 represents an average cost per ITN delivered in Kenya over a five year period 20032008. For further details on its assessment and for an overview of literature on costs of various ITN delivery initiatives in Sub-Saharan Africa see Annex.
# **1. Introduction**

Economists have discussed over the past 100 years whether or not financial development has a causal impact on economic development. Theory suggests that effective financial institutions and markets that help overcome market frictions introduced by information asymmetries and transaction costs can foster economic growth through several channels. Specifically, they help (i) ease the exchange of goods and services by providing payment services, (ii) mobilize and pool savings from a large number of investors, (iii) acquire and process information about enterprises and possible investment projects, thus allocating society's savings to its most productive use, (iv) monitor investments and exert corporate governance, and (v) diversify and reduce liquidity and intertemporal risk. However, other models show that higher returns from better resource allocation may depress saving rates, resulting in overall growth rates actually slowing with more effective financial markets and institutions.<sup>2</sup> While the finding of a positive correlation between indicators of financial development and economic growth cannot settle this debate, advances in computational capacity and availability of large cross-country data sets with relatively large time dimensions have enabled researchers to rigorously explore the relationship between financial development and economic growth. Further, as more disaggregated data sets have become available, the finance and growth literature has proceeded from using country-level data, to using industry- and firm-level data, to more recently using household data. While the cross-country literature has developed more sophisticated models to address biases introduced by measurement error, reverse causation and omitted variables, the progress to firm- and household-level data allows not only additional ways to address these biases, but also tests of the specific channels through which finance might enhance economic growth.

The econometrics of finance and growth can be summarized in the following simple regression model: _g_ ( _i_ , _t_ ) = _y_ ( _i_ , _t_ )− _y_ ( _i_ , _t_ − 1 ) = α + β _i f_ ( _i_ , _t_ )+ _C_ ( _i_ , _t_ )γ _i_ + μ( ) _i_ + ε( _i_ , _t_ ) (1) where _y_ is the log of real GDP per capita or of another measure of welfare, _g_ is the growth rate of _y_ , _f_ is an indicator of financial development, _C_ is a set of conditioning 2 See Levine (1997, 2005) for surveys of the theoretical literature.
treatment villages than controls in the Center and seven percent higher in the South. None of the differences are statistically significant.

## **6.3.2 Labor markets**

Imbert & Papp (2015) find that the large Indian PWP increases wages in the low-skill market. A similar effect on equilibrium wages in Malawi could explain negative externalities by driving up labor costs for untreated households that hire workers, especially since planting season PWP activities coincide with peak labor demand periods. To reduce survey fatigue, we did not repeat the full IHS-3 agricultural modules about hired labor, and therefore do not have direct evidence about wages. To test this hypothesis indirectly, we interact the treatment variables with an indicator for whether the household reported hiring any agricultural labor in the IHS-3; about 20 percent of households previously hired labor.

We estimate _ψ_ 1 is the indirect effect of PWP on households that did not hire any labor during the 2010/2011 growing season and _ψ_ 2 is the indirect effect on households that hired in the past. If labor market tightening was responsible for negative spillover effects, we would expect _ψ_ 2 to be negative (and more negative than _ψ_ 1).

Instead, as reported in Table 17, the indirect effect of PWP is negative for households that do _not_ hire, a group who might benefit and should not be harmed by higher equilibrium wages. In column (9) of Panel C, the regression for the PCA index that includes the baseline value, the point estimate of _ψ_ 1 is -0.21, not statistically different from zero at conventional levels but substantial relative to the mean in the control group (0.51). The indirect effect on households that previously hired labor and would face higher costs due to an increase in ˆ wages is close to zero: _ψ_ 2 = 0 _._ 05. Thus, there is no evidence to support the labor market tightening channel for the negative spillovers.

# **6.4 Change in transfers from institutions or individuals**

Other institutions – government or NGO programs – or individuals could have changed their behavior towards untreated households or otherwise compensated control villages with other programs. In this case, the untreated households in PWP villages, which benefited from neither PWP nor these alternative programs, may be worse in comparison. We have data 24
Pakistan and Nepal but lags behind China and India. This suggests that the country has some room to expand its investment share of GDP. However, as can be seen from the graph below, the deficit in investment share with respect to India is not due to low public investment in Bangladesh. Average public investment as a share of GDP during 2011 – 2014 outpaced that of India by more than 2.5 percentage points. The targets from the 7<sup>th</sup> Five Year Plan are used to chart a reasonable path of investment. Specifically, I assume that the aggregate investment share of GDP increases from the present levels to 34.4 percent of GDP by year 2020 and remains at that level beyond 2020. This is based on the plan targets on public investment which rises from 6.5 <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Bangladesh China* India** Nepal** Pakistan Sri Lanka<br>Aggregate Private Public<br>Source: World Development Indicators<br>*Aggregate average of 2011 ‐ 2014, **All measures average of 2011 ‐ 2014<br>Investment Share of GDP (%)<br><!-- End of picture text --> percent of GDP in 2016 to 7.8 percent of GDP by year 2020. This implies that private investment as a share of GDP expands by approximately 3 percentage points to reach 26.6 percent by 2020. I assume that both public and private investment continue to remain at the same level beyond 2020.

With regards to investment, there is a concern that public investment is not as efficient as private investment. This means that a percentage point expansion in public investment will deliver lower growth compared to a percentage point expansion in private investment. On the other hand, it is possible to generate additional growth by increasing the efficiency of public investment. To capture the differences in efficiency across public and private investment, it is assumed that a unit of public investment ��� equals only a fraction �∈�0,1� of private investment ��� which is perfectly efficient. The total effective investment �� is given by �� ����� ���� (9) There are many studies that have studied the efficiency of public sector (for example Afonso, Ludger Schuknecht and Tanzi (2005, 2010) etc.). The quantitative findings from these studies can be used to discipline the parameter governing the efficiency of public investment � . Afonso, Ludger Schuknecht and Tanzi (2010) report that for the sample countries, the same level of public sector output can be produced 12
focused on these. Rather, reviews have focused on outcomes in schooling (Baird, et al. 2014, Saavedra and García 2013), health (Leroy, Ruel and Verhofstadt 2009, Ranganathan and Lagarde 2012), consumption (Fiszbein, et al. 2009), or a combination of these (IEG 2011). At the same time, many individual evaluations of cash transfer programs have included analysis of the impact on some set of temptation goods within their consumption analysis.

Across 44 estimates from 19 studies, we find that almost without exception, studies find either no significant impact or a significant negative impact of transfers on expenditures on alcohol and tobacco. This finding is similar whether the analysis includes experimental and quasiexperimental designs or if it is restricted to randomized trials alone. Likewise, studies that have tried to quantify the proportion of beneficiaries who spend transfers on temptation goods find negligible effects. This result is consistent across the world, supported by data from Latin America, Africa, and Asia. It is also consistent across conditional and unconditional cash transfer programs. The evidence suggests that cash transfers are not used for alcohol and tobacco at any significant levels.

# **Cash, Spending, and Consumption of Temptation Goods**

If alcohol and tobacco are normal goods, then as incomes rise, consumption of these goods will likewise rise (i.e., the income effect). Evidence from the United States suggests that alcohol is a normal good, whereas tobacco is an inferior good (Decker and Schwartz 2000); evidence from the United Kingdom suggests that alcohol expenditures rise with income, at least to a point (Banks, Blundell and Lewbel 1997). Banerjee and Duflo (2006; 2007) show expenditures for households living on under $1.08 per day and for households living on under $2.16 per day in 11 countries divided into urban and rural areas, resulting in 21 country-urban or country-rural combinations, due to missing data on urban Guatemala (Table 1). Of these 21 combinations, 14 increased or maintained the same percentage of spending on alcohol and tobacco combined when comparing $1.08 to $2.16 daily income, suggesting a likely increase in the total spending.<sup>2</sup> That number rises to 20 out of 21 if one includes settings with minor decreases in the proportion, still consistent with increases in the total spending on these goods given the rise in income. These numbers suggest that alcohol and tobacco, when examining regular income, are normal goods.

Beyond the income effect, there are at least three reasons that cash transfer income may affect spending on temptation goods differently from other income. First, _conditional_ cash transfers in particular may induce a substitution effect, increasing the value of schooling and health

> 2 These percentages are best for distinguishing luxury goods (for which the proportion of spending increases with income) from necessity goods (for which the proportion falls), but they are suggestive of an increase in absolute spending (i.e., normal goods).

3
|P130819|2013|2017|12/28/2016|8 Closed|Honduras|
|---|---|---|---|---|---|
|P130819|2013|2017|4/26/2017|9 Closed|Honduras|
|P130819|2013|2018|11/21/2017|10 Closed|Honduras|
|P130819|2013|2018|6/22/2018|11 Closed|Honduras|
|P130819|2013|2019|2/13/2019|12 Closed|Honduras|
|P130824|2013|2013||0 Closed|Tonga|
|P130824|2013|2019|5/9/2019|1 Closed|Tonga|
|P130835|2013|2013|4/13/2013|1 Closed|Pakistan|
|P130835|2013|2014|12/14/2013|2 Closed|Pakistan|
|P130835|2013|2014|6/6/2014|3 Closed|Pakistan|
|P130835|2013|2015|11/26/2014|4 Closed|Pakistan|
|P130835|2013|2015|4/14/2015|5 Closed|Pakistan|
|P130835|2013|2016|12/5/2015|6 Closed|Pakistan|
|P130835|2013|2016|6/8/2016|7 Closed|Pakistan|
|P130835|2013|2017|12/14/2016|8 Closed|Pakistan|
|P130835|2013|2017|6/7/2017|9 Closed|Pakistan|
|P130835|2013|2018|12/20/2017|10 Closed|Pakistan|
|P130835|2013|2018|6/6/2018|11 Closed|Pakistan|
|P130835|2013|2019|2/10/2019|12 Closed|Pakistan|
|P130835|2013|2020|10/10/2019|13 Closed|Pakistan|
|P130840|2014|2015|11/24/2014|1 Active|Nigeria|
|P130840|2014|2015|6/4/2015|2 Active|Nigeria|
|P130840|2014|2016|12/7/2015|3 Active|Nigeria|
|P130840|2014|2016|5/25/2016|4 Active|Nigeria|
|P130840|2014|2017|12/14/2016|5 Active|Nigeria|
|P130840|2014|2017|6/27/2017|6 Active|Nigeria|
|P130840|2014|2018|12/21/2017|7 Active|Nigeria|
|P130840|2014|2018|5/10/2018|8 Active|Nigeria|
|P130840|2014|2019|12/4/2018|9 Active|Nigeria|
|P130840|2014|2019|6/7/2019|10 Active|Nigeria|
|P130840|2014|2020|12/17/2019|11 Active|Nigeria|
|P130840|2014|2020|6/12/2020|12 Active|Nigeria|
|P130843|2011|2012||0 Pipeline|Latin America|
|P130843|2011|2013||0 Pipeline|Latin America|
|P130847|2014|2016|10/7/2015|1 Closed|North Macedonia|
|P130853|2013|2013|4/2/2013|1 Closed|Yemen,Republic of|
|P130853|2013|2014|8/1/2013|2 Closed|Yemen,Republic of|
|P130853|2013|2014|2/26/2014|3 Closed|Yemen,Republic of|
|P130853|2013|2015|8/23/2014|4 Closed|Yemen,Republic of|
|P130853|2013|2015|2/5/2015|5 Closed|Yemen,Republic of|
|P130853|2013|2015|6/19/2015|6 Closed|Yemen,Republic of|
|P130853|2013|2016|12/20/2015|7 Closed|Yemen,Republic of|
|P130853|2013|2016|6/16/2016|8 Closed|Yemen,Republic of|
|P130853|2013|2017|12/14/2016|9 Closed|Yemen,Republic of|
|P130858|2012|2014|7/9/2013|1 Dropped|Argentina|
|P130864|2013|2014|10/19/2013|1 Closed|Turkey|
|P130864|2013|2014|6/25/2014|2 Closed|Turkey|
In exploring policy-related determinants of importing, we find that increases in tariffs are detrimental to import growth of exporters overall in Peru, and in particular of those in the agribusiness sector. For the cross-section of exporter-importers in 2012—the year when non-tariff measures (NTMs) are available—we show that those that are subject to NTMs more intensively tend to import less intermediate inputs, not only in value but also in terms of numbers of products and varieties imported. Finally, the use of the advanced clearance procedure as the modality to clear customs for imports is favorable to the import performance of exporter-importers. Exporters who import intermediate inputs using this modality import more and import a more diversified bundle of inputs than those that do not use it. All these results are robust even after controlling for the fact that some firms are larger than others. Given the importance of import levels and variety for export and productivity performance, these findings lend support to Peru’s path of extensive trade policy liberalization. They also underscore the importance of effective de facto implementation of trade policy measures and efficient trade facilitation procedures, and to ensuring that this is available to firms of all sizes across all sectors.

The remainder of the paper is organized as follows. Section 2 describes the data and provides descriptive evidence on the importance of imports for exports in Peru. Sections 3 and 4 present the results on baseline and further export performance premia for exporter-importers. Section 5 discusses general performance premia for importing firms in Peru. Section 6 explores the role of policies in explaining why Peruvian exporters do not import more. Section 7 concludes.

# **2. Data and Characterization of Exporter-Importers in Peru**

# **2.1 Data Description**

Our analysis uses transaction-level customs data for Peru covering the period 2000-2012 obtained from Peru’s National Tax Administration (SUNAT) and collected by the Trade and Integration Unit of the World Bank Research Department as part of their efforts to expand the Exporter Dynamics Database described in Fernandes et al. (2015). The data cover the universe of Peruvian export and import transactions in the agricultural, mining—excluding HS Chapter 27 (hydrocarbons such as oil, petroleum, natural gas, coal, etc.)—and manufacturing sectors. The data include unique firm identifiers that allow us to follow firms over time and allow us to match across export and import transactions so as to generate a final data set at the firm-HS 6-digit product5
Table 2. Effect of a 1 Percentage Point Increase in the Income Share Held by the 3<sup>rd</sup> Quintile on Log GDP per capita

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|1.08|1.66|
|Cambodia|-0.40|0.46|
|Indonesia|-0.58|0.67|
|Laos|-0.61|0.54|
|Malaysia|-0.16|1.11|
|Philippines|-0.18|0.60|
|Singapore|0.30|1.64|
|Thailand|-0.17|0.95|
|Vietnam|-0.62|0.56|
|ASEAN Average|-0.15|0.91|
|ASEAN Median|-0.18|0.67|

Note: The table reports effects on log GDP per capita of a 1 percentage point increase in the income share held by the 3<sup>rd</sup> quintile. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015), see there column (4) of Table 1, with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015), see there column (4) of Table 1, with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is ln(GDP per capita)ct = -243*Q3ct + 37*Q3ct*ln(initial GDP per capita)c.

16
|___________________<br>Country|_________<br>Model|_____<br>|_____________<br>All|__________________<br>20 – 49|______________________<br>50 – 249|_____________________<br>250 – 499|_____________________<br>500 and more|
|---|---|---|---|---|---|---|---|
|___________________|_________|_____|firms<br>_____________|employees<br>__________________|employees<br>______________________|employees<br>_____________________|employees<br>_____________________|
|Republic of Ireland|Pooled|ß1|0.097|0.104|0.307|-0.920|-0.498|
|1991 - 2004||p|[0.01]|[0.03]|[0.00]|[0.00]|[0.17]|
|||ß2|0.134|0.100|-0.094|1.129|1.457|
|||p|[0.00]|[0.06]|[0.17]|[0.00]|[0.00]|
||Fixed|ß1|0.220|0.229|0.279|-0.415|-0.240|
||effects|p|[0.00]|[0.00]|[0.00]|[0.12]|[0.47]|
|||ß2|0.011|0.051|-0.104|0.621|0.356|
|||p|[0.82]|[0.48]|[0.16]|[0.02]|[0.28]|
||N||3,680|2,244|1,218|148|70|
||NxT||27,232|14,004|11,094|1,441|693|
|Slovenia|Pooled|ß1|0.052|0.122|0.149|0.259|0.065|
|1994 - 2002||p|[0.83]|[0.01]|[0.00]|[0.01]|[0.51]|
|||ß2|0.001|0.006|0.001|0.008|0.002|
|||p|[0.06]|[0.15]|[0.08]|[0.01]|[0.33]|
||Fixed|ß1|0.009|0.011|0.043|0.179|-0.088|
||effects|p|[0.34]|[0.77]|[0.10]|[0.02]|[0.17]|
|||<br>ß2|-0.0004|0.007|-0.001|-0.004|0.005|
|||p|[0.07]|[0.11]|[0.19]|[0.33]|[0.15]|
||N||1,566|581|746|138|97|
||NxT||9,909|3,389|4,841|966|701|

43