rafmacalaba/datause-encoder-data huggingface dataset:
# **_Table 8. Relationship between project design and IEG outcome rating_**

||||**Dependent v**<br>|**ariable: IEG 1-**<br>|**6 outcome ra**<br>|**ting**<br>||
|---|---|---|---|---|---|---|---|
|**Design index components**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Number of project|-0.162**|||||||
|components|(0.071)|||||||
|Number of project<br>||-0.024||||||
|sub-components||(0.016)||||||
|Number of PDO indicators at|||-0.038|||||
|entry|||(0.024)|||||
|Number of intermediate||||-0.030***||||
|indicators at entry||||(0.011)||||
|PDO rating|||||0.024|||
|(1 high - 4 low)|||||(0.155)|||
|Results framework rating (1||||||-0.226*||
|high – 4 low)||||||(0.128)||
|Extent of changes to project|||||||-0.290**|
|components|||||||(0.133)|
|Constant|2.985***|2.565***|2.640***|2.768***|2.335**|3.159***|<br>3.189***|
||(0.810)|(0.786)|(0.793)|(0.772)|(0.912)|(0.886)|(0.850)|
|Observations|120|120|120|120|120|120|120|
|Adjusted R-squared|0.09|0.067|0.067|0.110|0.046|0.073|0.086|
|||**Depend**<br>|**ent variable: I**<br>|**EG 1-6 outcom**<br>|**e rating**<br>|||
|||**(8)**|**(9)**|**(10)**|**(11)**|||
|**Design index**||**0.223*****<br>**(0.071)**||||||
|Project Characteristics Index<br>|||0.121**|||||
|for comparison<sup>1</sup>|||(0.057)|||||
|CPIA at Approval||||0.300||||
|||||(0.195)||||
|Change in CPIA over Project|||||-0.294|||
|Lifetime|||||(0.261)|||
|Observations||120|120|120|120|||
|Adjusted R-squared||0.126|0.084|0.062|0.053|||

Note: Explanatory Power of Design index = 5.54, Project Characteristics = 1.14 and Country Characteristics 2.28. This table provides estimates of IEG Outcome Ratings regressed on common design features and quality ratings. Using Principal Component Analysis , an Index is created for Design based on these features and ratings. (1) For comparison, an Index is created for Project Characteristics based on the log of original commitment, preparation cost, first and second half supervision cost, the change in project size from approval to closing, and the time it takes projects to move from one key milestone in the project lifecycle to another. The table also provides the estimates of regressing IEG Outcome on two country variables, CPIA score of project country at approval and the change in the score over the project lifetime.

29
best of our knowledge, our study is the first to explore nightly variation in light output to detect the stability of electricity provision.

<mark>To illustrate the approach, we compute annual estimates of the Power Supply Irregularity (PSI) index for all 600,000 villages in India from 1993 to 2013. We test the validity of the computed PSI using ground-based measures of power supply reliability from Indian Human Development Survey and feeder-level outage data from Maharashtra’s largest utility. We find that PSI measures are significantly positively correlated with the duration and frequency of outages reported in the ground-truth data.</mark> <mark>Our methods open new opportunities to study the determinants of power outages as well as their impact on welfare. A key strength of our method is that it can be applied to any specific location of interest using the same autonomously collected data. Moreover, since the coefficient of variation is a normalized measure of variability, values on the PSI index can be directly compared across regions. The method also demonstrates the possibility of using near real-time satellite data to monitor power outages and identify problem areas.</mark> The rest of the paper proceeds as follows: Section II describes the significance of power outages in the developing world. Section III presents the new approach to measuring power supply reliability based upon an analysis of a time series of nighttime satellite imagery. Section IV discusses ground-truth validation of our new power supply irregularity index. Section V presents power outage estimates across India using the new measure. Section VI concludes the paper.

# **II. Power Outages in the Developing World**

For many in the developing world, access to electricity is uneven and inconsistent, characterized by frequent and long hours of power outages. Many countries now engage in planned load shedding because of a systemic shortage of supply relative to rapidly increasing demand for electrical power. For example, the World Bank reports that the average Nigerian firm suffers 25 power outages in a typical month, with more than one outage a day reported in many other countries like Albania, Kosovo, Nepal, and the Republic of Yemen. In Bangladesh, firms reported over 100 power outages a month (World Bank Enterprise Surveys).

In India, firms report almost one outage every other day, with an average duration of 2 hours. The situation is almost certainly worse for Indian households. According to the Indian Human Development Survey, 57% of electrified households reported 3
|Table 4:|Calculating re<br>**while taking**<br>|lative cost effectiveness of te<br>**precision of estimation into a**<br>|acher to n<br>**ccount.**<br>|on-teacher inputs<br>|
|---|---|---|---|---|
|Input:|Subject:|Ratio of cost effectivene<br>size:|ss of inputs|to increasing class|
|||At point estimates of all<br>variables|Adding t<br>size|wo std. dev. to class<br>point estimate|
||Math|689||90|
|Furniture|Filipino|1243||113|
||Math|842||110|
|Workbooks|Filipino|1592||145|

Notes: In order to calculate the cost of decreasing class size, we use the fact that average teacher salary is 2.5 times GNP per capita (World Bank, 1996b) and assume that each teacher teaches an average of two classes, and that the average class size is 40.19 as reported in Tan and others (1996).

Since most investigators begin with a presumption that the inputs into education are reasonably well known, presumably the key critical question that estimation of a production function could help resolve is whether reallocating inputs given a fixed budget could improve performance. Reporting only estimates of the production 'unction with no estimates of costs is not even relevant to that question. The distressingly common practice of reporting the production function estimates of known educational inputs in quantity units (e.g. class size) text books, physical facilities and their statistical significance without comparative information on costs is nearly devoid of policy interest"<sup>6</sup> .

16 By stipulating _known_ inputs we distinguish studies of pedagogical innovations such as radio instruction, computer assisted learning, alternative teaching strategies, etc., where the question of zero marginal product over all ranges is actually a useful null hypothesis.

23
expansion as the pandemic is brought under control (Nayyar and Davies 2023; Coulibaly and Foda 2020; World Bank 2021c; Zhan et al. 2020).

The four decades prior to the global financial crisis saw a steady increase in global economic integration through trade, assisted partly by falling tariffs (figure 4). Since the global financial crisis, however, trade integration has stalled, with the COVID-19 pandemic and Russia’s invasion of Ukraine having added further obstacles. With Russia’s share of global oil production having increased considerably in recent decades, there is now a material risk that the disruptions caused by Russia’s invasion of Ukraine could lead to a major reconfiguration of global trade and investment networks, as countries look for alternative sources of energy. While this may boost trade in some parts of the global economy, it is likely to disrupt trade elsewhere. Since such a reconfiguration would be motivated by political and security considerations rather than economic considerations, it is likely to reduce global economic welfare as well as trade in the long term (Ruta 2022).

# IV. Patterns in trade costs

The fading momentum of global trade growth is diminishing its role as an engine of output and productivity growth. Countries therefore need to find new ways to reap the benefits from trade. One possibility is to cut trade costs to boost exports and encourage imports in a manner that is growth-enhancing. A number of studies have documented the negative impact of trade costs on trade growth (Anderson and van Wincoop 2003) and the boost to productivity that can result from lowering trade costs (Bernard, Jensen, and Schott 2006). Trade costs have also been recognized as an important factor in firms’ decisions to choose out-sourcing over in-sourcing (Hartman et al. 2017).

## IV.1 Definition

The analysis in this paper relies on a comprehensive UNESCAP-World Bank dataset of bilateral trade costs. Following Novy (2013), Arvis et al. (2013) derive measures of annual trade costs for the period 1995-2018. For any given country pair i and j, trade costs are obtained as geometric averages of trade flows between countries i and j. They are computed according to the formula: ( _Xii Xjj_ )/( _Xij Xji_ )<sup>1/2 (σ-1)</sup> , where _Xij_ represents trade between countries _i_ and _j_ (goods produced in _i_ and sold in _j_ ) and σ refers to the elasticity of substitution. This measure assumes that international trade costs relative to domestic trade costs are reflected in international trade flows relative to domestic trade flows: when international trade costs are higher than the costs of domestic trade, countries will trade more domestically than internationally, i.e. the ratio ( _Xii Xjj_ )/( _Xij Xji_ ) will be higher. In the application of this methodology, domestic trade is proxied by the difference between gross output and total exports. Trade costs thus estimated are expressed as a proportion of the value of traded goods (comparable with an ad valorem tariff rate) and can be computed for the economy as a whole, or
to 19.4 and 0.7 percent, respectively (cells [c-nagr,gov] and [inv-gov,cap-gov]). The resulting government deficit (5.2 percent) was covered by domestic financing, shown in cell [cap-gov,capngov].<sup>4</sup> Yemen’s exports and imports amounted to 6.3 and 44.6 percent, respectively (cells [cagr,row] and [row,c-agr]), yielding a trade deficit of 38.3 percent. Thanks to a large transfer surplus – the sum of net transfers to households and government amounted to 32.6 percent (cells [hhd,row]+[gov,row])– the current account deficit (foreign savings) was much smaller, at 5.9 percent. Supplemented by domestic savings at 2.9 percent (sum of the cells [cap-ngov,hhd] and cap-gov,gov]), it financed total investment at 8.7 percent – the sum of government investment and private investment (8.0 percent; cell [total,inv-prv]). By international standards this is a very low investment level.

Table 2.1. Disaggregation of Yemen SAM and SDGSIM application

|**Group**|**Description**|**Group**|**Description**|
|---|---|---|---|
|Sectors|Qat**|Factors|Labor - Male|
|(activities &<br>commodities)*|Other agriculture<br>Fishing<br>Oil and gas**<br>Other mining<br>Manufacturing<br>Utilities (electricity, gas, and water)||Labor - Female<br>Capital - Private<br>Land<br>Natural resource - Fishing<br>Natural resource - Other mining<br>Natural resource - Oil andgas|
||Construction<br>Trade|Institutions|Households<br>Government|
||Hotels and restaurants||Rest of the world|
||Transport|Taxes and|Taxes - Income|
||Telecommunications<br>Financial serv|subsidies|Taxes - Imports<br>Taxes - Commodities|
||Real estate and business serv<br>Public administration|Institutions<br>(capital|Non-government<br>Government|
||Government education and health<br>Other services|accounts)|Rest of the world<br>Foreign reserves***|
|Trade &<br>transport<br>margins|Domestic products<br>Imports<br>Exports|Investment|Private investment (GFCF)<br>Government investment (GFCF)|

*Each sector has an activity and a commodity (output).

**Qat and Oil and gas are singled out from agriculture and mining, respectively, using CSO (Central Statistical Organization) estimates for 2017.

***Capital account for payments to foreign reserves; not associated with a current account.

> 4 The government deficit is defined as the difference between government investment and government savings. (Government savings may also be referred to as the government current account surplus.)

5
Secondly, there are positive spillovers of urban centers on the rural hinterlands, through consumption linkages, urban-rural remittances, upward pressure on agricultural wages, and the generation of rural non-farm employment (Lanjouw and Murgai, 2009; Cali and Menon, 2013).<sup>4</sup> This is especially important as 70 percent of the world’s poor are estimated to be rural (World Bank, 2008). Whether the positive spillover effects on (aggregate) rural poverty are stronger for metropoles than for secondary towns is not clear _a priori_ . The magnitude of the positive spillover effects on rural poverty in the hinterlands of metropoles could for example be larger, while the economic space and population affected by the metropoles may also be smaller than this affected by all the secondary towns taken together.

Finally, another longstanding literature has highlighted the positive role of rural nonfarm activities in poverty reduction, with rural towns, which mediate the flow of inputs, goods and services between rural hinterlands and larger urban centers, seen as the most effective generators of nonfarm employment for the poor (Haggblade, Hazell, and Reardon, 2007). There are a number of reasons to believe off-farm jobs generated in nearby villages or rural towns may be more readily accessible to the poor. First, unskilled and semi-skilled workers often make up the vast majority of the workforce in rural towns, while semi-skilled and skilled workers dominate the workforce in the cities, as observed for example in Ethiopia and Uganda (Dorosh and Thurlow, 2012).

Second, while rural towns have lower wages, they may also have lower unemployment rates. This could be attractive to the poor, who may not be able to afford long spells of unemployment.<sup>5</sup> Even if the likelihood of finding a job is similar—no gradient in ILO defined unemployment rates across city size was observed by Coulombe and Lanjouw (2013) in 12 African countries—lower migration costs and the ability to maintain and exploit closer

> 4 Cali and Menon (2013) estimated the contribution of secondary town spillovers to rural poverty reduction in India during 1983-1999 to be 13 to 25 percent.

> 5 Rural unemployment was estimated around 7 percent, urban unemployment around 16 percent and unemployment in Dar es Salaam around 31 percent (Glasser et al., 2008). Introduction of migrant heterogeneity in the standard Harris-Todaro framework, sorting across cities by skill set and easier access to information about jobs, may further explain the lower unemployment rates in secondary towns and larger poverty reduction effects from migration to secondary towns. On the other hand, persisting hope to strand a high paying job or set up a thriving business, coverage of the basic expenses (e.g. housing) by the urban social networks, and the shame associated with admitting failure would all conspire against an early return when unsuccessful, inducing migrants to queue and helping explain the persistence of large informal settlements in many large cities.

4
Policy Research Working Paper 8936

# **Abstract**

This paper estimates the impact of market access liberalization in high-income countries on sub-Saharan African exports. The methodology exploits the large reduction in trade barriers that was induced by three unilateral trade liberalization initiatives: (1) the dismantling of the Multi-Fiber Arrangement, (2) the African Growth and Opportunity Act in the United States, and (3) the extension of EU trade preferences for developed countries through its Everythingbut-Arms program and the General System of Preferences. Using detailed product-level information at the 6-digit level of the Harmonized System and a triple-difference empirical specification, the usual endogeneity-of-policy critique is flexibly controlled for. The results indicate strongly positive export effects, which are especially large for textile, apparel, and leather products, and tend to be realized fully within 5 years. Each percentage point reduction in import tariffs raises exports to the EU by 0.73 percent and to the United States by 0.30 percent; effects are two to three times as large for textiles. The presence of strong Chinese imports has ambiguous effects on countries’ ability to take advantage of trade liberalization as the impact on the export effects to the EU and the United States show an opposite sign.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jo.vanbiesebroeck@kuleuven.be.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
a one standard deviation decline in real GDP growth (increase in the change in the domestic unemployment rate) under the WTO has been associated with a 25 percent (37 percent) _increase_ in TTBs in the subsequent year.

The third group of bars more precisely quantifies the results on trading partner growth. In particular, weak trading partner growth is quantitatively much less important under the WTO period.

Finally, the last group of bars shows how commitments over MFN applied tariffs – or reductions in available policy space – help push countries toward utilizing TTB policies. As the share of products with binding tariff commitments increases by one standard deviation, the number of TTBs increases by 24 percent and 48 percent in specifications (2) and (4), respectively. While these results do indicate that countries are stepping away from the liberal trade regime and their promises to lower and bind their applied tariffs, it also represents a commitment to abide by the WTO’s rules and use the WTO’s sanctioned policy tools of TTBs in response to economic shocks.

The rest of Table 4 presents a set of robustness checks and sensitivity analysis. First, in column (3) we modify the definition for the WTO tariff binding variable. Here we redefine the share of products under WTO discipline so that any HS-06 product with an applied import tariff within 10 percentage points of its tariff binding is “under WTO discipline,” a less restrictive condition than considering only products with applied tariffs equal to the binding. Use of this alternative measure has only a small impact on the size of the estimated IRRs.

In column (4), we employ our alternative measure for the health of the domestic economy, substituting the lagged change in the domestic unemployment rate for domestic real GDP growth. The results are consistent with those obtained earlier – i.e., in the GATT period, the estimated IRR is less than one (though not significant) indicating that periods of lower unemployment were associated with heightened import protection through TTBs. While the differential between these two estimated IRRs is not statistically significant in this sample of data, part of this is likely explained by the poorer quality of unemployment data during the early period, in terms of how accurately such measures captured the health of the domestic economy, given the role of the informal sector.<sup>27</sup> Furthermore, because the sample of countries for which the unemployment data is available at all is significantly

> 27 Even for emerging economies with available unemployment rate data included in the sample, the argument is that unemployment rate itself may be becoming a more accurate and representative indicator for the overall health of the domestic economy over time due to the role that the informal sector plays in many countries. I.e., unemployment rate data for these countries may be noisier earlier in the sample if there is a general upward trend in formality within a country over time.

<mark>21</mark>
18

**Table 4.6: Poverty Incidence in Nigeria by Region, 1985-93 (Poverty Line=N395.41)**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**REGION**|**P0**|P1|P2|P0|**P1**|**P2**|
|Other Urban (Excluding Lagos)|0.320|0.070|0.038|0.306|0.101|0.048|
|Rural|0.495|0.189|0.095|0.364|0.122|0.066|
|Lagos Urban|0.240|0.070|0.032|0.279|0.161|0.095|
|AllNigeria|0.430|0.157|0.079|0.341|0.147|0.085|

**Rural/Urban Decomposition of Poverty Incidence**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**REGION**|**P0**|**P1**|P2|Po|**P1**|**P2**|
|Other Urban (Excluding Lagos)|26.3|14.8|12.4|32.6|26.9|_25.5_|
|Rural|73.1|84.9|87.3|65.7|71.9|73.5|
|Lagos Urban|0.6|0.3|0.3|1.7|1.2|0.1|
|All Nigeria|100.0|100.0|100.0|100.0|100.0|100.0|
|Source: NCS, 1985/86 and 1992/9|3.||||||

# **Table 4.7: Incidence of Extreme Poverty in Nigeria by Region, 1985-93 Poverty Line=N197.71)**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**REGION**|**P0**|**PI**|P2|**P0**|**P1**|P2|
|Other Urban (Excluding Lagos)|0.049|0.009|0.003|0.111|0.037|0.018|
|Rural|0.161|0.042|0.017|0.154|0.060|0.032|
|Lagos Urban|0.043|0.015|0.006|0.072|0.021|0.008|
|AllNigeria|0.120|0.042|0.016|0.136|0.085|0.034|

**Rural/Urban Decomposition of Incidence of Extreme Poverty**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**REGION**|P0|**PI**|P2|**P0**|**PI**|**P2**|
|Other Urban (Excluding Lagos)|14.4|3.5|2.6|29.6|20.7|19.4|
|Rural|85.2|96.3|97.2|69.3|78.7|80.2|
|Lagos Urban|0.4|0.2|0.2|1.1|0.5|0.3|
|All Nigeria|100.0|100.0|100.0|100.0|100.0|100.0|

Source: NCS, 1985/86 and 1992/93.

As seen in Table 4.8, rural Southern belt has the largest decline (16.6 percentage points) in headcount, 4.2 percentage points decline in depth and 0.9 percentage points decline in