rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Results**

## **5.1. Effects of refugee presence on household welfare**

According to the OLS specifications (Table 3, Panel A), the presence of refugees is positively correlated with the household welfare represented by the consumption aggregate per adult equivalent. Within the 50km buffer, the correlation is statistically different from zero (Table 3, Panel A; Columns 1-3) until household fixed effects are controlled for when the coefficient loses statistical significance (Table 3, Panel A; Column 4).<sup>13</sup> The stability of the coefficient gives us a first hint that the efficiency of our estimates is affected by the requiring addition of household fixed effects, but not its consistency. The instrumental variable analysis confirms a positive effect of the refugee presence on the household welfare (Table 3, Panel B). The coefficient in the regression specifications with all controls including year and cluster fixed effects (column 3) is quantitatively similar to the coefficients in the regression specifications with year and household fixed effects (column 4). Doubling the presence of refugees would increase the consumption aggregate per adult equivalent by about 7.38% for households within 50km of the closest refugee settlements (Table 3, Panel B; column 4). This effect size is similar to Maystadt and Verwimp (2014) with their estimated 8% increase in consumption per adult equivalent.

Table 3: Effects of refugee presence on household welfare with analysis at cluster level and the distance weighted- <u>refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Dep. Var. : Lo|g of welfare (c<br>adult equi|onsumption a<br>valent)|ggregate per|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|||<br>Panel A (|<br>OLS)||
|Log of refugees (50km)|0.0235**|0.0236**|0.0273**|0.0217|
||(0.0108)|(0.0108)|(0.0133)|(0.0144)|
|Observations|5,453|5,450|4,105|4,102|
|R-squared|0.3897|0.3899|0.3839|0.7172|
|||Panel B (2SLS|2<sup>nd</sup>Stage)||
|Log of refugees (50km)|0.03226|0.03207|0.07808*|0.07384*|
||(0.04466)|(0.04459)|(0.04453)|(0.04302)|
|Observations|5,453|5,450|4,105|4,102|
|Kleibergen-Paap rk Wald F|19.34|19.33|14.62|14.15|
|Root MSE|0.593|0.593|0.533|0.362|
||Dep|Panel C (2SLS<br>. Var. Log of re|1<sup>st</sup>Stage)<br>fugees (50km|)|
|Log of Distance IV (mean border distance)|9.24792***|9.24997***|8.71514***|8.68000***|
||(2.10281)|(2.10376)|(2.27960)|(2.30744)|

> 13 For the 100km buffer, controlling for observed heterogeneity does not seem to affect our main variable of interest (Appendix 3, Table A8; Columns 1-3). However, the coefficient becomes only statistically different from zero when household fixed effects are included (Appendix 3, Table A8; Column 4).

20
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- 57 -

# i) The Sin e Road Pricing System

75. Finally, it is worth mentioning that Singapore - the pioneer in road pricing - ha tas en the bold step of upgrading its simple labor-intensive area licensing scheme into a capitalintensive electronic road pricing system, to be operational by 1995. The initial goal is to tum the 26 manually controled gantry points of the ALS into electronically controlled ones, with the intention of eventually turning the scheme into a more sophisticated BRP system. After inviting tenders for both an AVI and smart card system, authorities have now adopted smart card technology as the basis of their electronic road pricing system. In response to the government's first call for tenders, about 30 intnational companies submitted proposals, with five finalists being chosen. The installation cost, including an electrnic enforcement system, in the mid-price range, is S$80 [US$44.2] million and a five-year maintenance contract is about S$21 [US$11.6J million. Assuming that budget authorities act conservatively and aim for this median bid, the cost per transaction is S72.7 cents [US40.2 cents], under the assumption that the morning (7:30 - 10:15 a.m.) and afternoon (4:30 - 6:30 p.m.) peak trffic volumes still remain at the level of 69,000 in 1990 (Public Works Department, 1990).-M On the other hand, if the current average daily traffic count of 300,000 during the day (6 am. - 8 p.m.) is used when electronic road pricing becomes more encompassing, the cost per tranaction is S16.7 cents [US9.2 cents].J7 The capital costs include the costs of smart card units for 520,000 automobiles. Vehicla from foreign countres are not exempt from ERP and those who intend to drive into the Resticted Zone during peak hours are expected to obtain temporary tags at commisioning stations for M Ibc bids for Xi inatallcostAs rg fiom a low of S441 [US$22.7] million (by Phiips) for a simple AVI system to a high of _S$164 [US$90.61_ million (by dIjoint vulture of United Engineers and Vapor Canada, Inc.) for a sophistcated mit cad sysem, with numos add-on optio. Th bid submited by United Egineers and Vapor Canda, ic., for a mor basdc _mrt_ card sydem ws _$98 [US$54. 11_ million The five-year mantuance contac for _asc_ comes to _$13_ [US$7.2] and _S$14_ [US$7.71 respectively in 1990 figue.

I Tho number of opwating days in the you is asumed to ba 283, u with ALS. Th corresponding cost per transaction for the low and bigh bids we S39.6 cents [US2l.9 cas] and S119.3 _cents_ [U365.9 cents] respectively.

T' h coresondinS _co_ per trncio for tho low ad high bids **S** m.1 cents _[[U5.0_ culnts and S27.4 cents **_(USS3.2_** cents] epetivdy.
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE This report presents the key AICD findings for Angola, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Angola is a low-income resource-rich country, two sets of African benchmarks will be used to evaluate Angola’s situation: fragile low-income countries and resource-rich countries. Detailed comparisons will also be made with immediate regional neighbors in the Southern African Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD for Angola runs from 2005 to 2009. But financial data for comparator countries typically cover an earlier period, 2001–06, and are averaged to smooth out fluctuations, while technical data are reported for 2006. Second, in order to make comparisons across countries, it was necessary to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In recent years, Angola’s economy has been among the fastest growing in Africa. Looking ahead, the country’s GDP is projected to rise by 6.5 percent in 2011, with oil-sector growth of 3.8 percent and nonoil-sector growth of 8.1 percent (IMF 2011). A 27-year war that ended in 2002 ravaged the country and destroyed most of its economic infrastructure. Many roads, rails, and bridges were mined and obliterated; surviving infrastructure is dilapidated after years of neglect. Following the war’s end, the government turned its attention to the reconstruction of the country. And indeed, for the period 2003 to 2007, improvements in infrastructure added 1 percentage point to the per capita growth rate, which is substantial even if not as high as many countries in Africa (figure 1a). This boost to growth came predominately from the ICT revolution. Meanwhile, Angola’s road and power sectors held back the per capita growth rate by 0.2 percentage points each over the same period.

Looking ahead, simulations suggest that if Angola’s infrastructure could be improved to the level of the African leader, Mauritius, annual per capita growth rates would be 2.9 percentage points higher than they are at present. This impact would come from an increase in power-generating capacity and improved road infrastructure (figure 2b).

3
# **_3.2. Sectorial gaps in labor productivity_**

# 3.2.1. The traded and non‐traded sectors

In Table 14 we report sample values and weighted sectoral aggregates of labor productivity (defined as annual value added per worker at constant prices). Although sample value added per worker is higher for enterprises drawn from the non‐traded sector than that in the traded sector for both waves of the UBI, sample weighted aggregates show labor productivity to have been consistently higher in the traded sector than in non‐traded sector. For example value added per worker was 50 percent higher in the traded sector than in the non‐traded sector in 2002. The productivity gaps was even higher in 2009 when value added per worker in the traded sector was three times as high as that in the non‐traded sector. The shift in employment observed between 2002 and 2009 per the UBI must therefore have been reducing aggregate labor productivity in so far as it amounted to a reallocation of labor to the sector where it was less productive. However, table 14 does also show that aggregate labor productivity did in fact increase substantially regardless of that very reallocation, because aggregate productivity did increase substantially within each sector – traded as well as non‐traded – over the same period. Labor productivity grew by about 28 percent a year economywide between 2002 and 2009. It also grew by about 21 percent a year in the traded sector over the same period and even faster at 31 percent a year in the non‐traded sector.

The 2000s in Uganda were thus not only a decade of significant structural change in the economy, but also one of fairly rapid growth in labor productivity. However, these trends did not seem to reinforce each other as the observed structural change held down the productivity gains rather than aiding them in as far as it essentially reallocated labor from relatively more productive employment in the traded sector to less productive employment in the non‐traded sector.

====== Tables 15 here==== ====== Table 15 A here==== 3.2.2. Productivity gaps within the traded sector: Comparative advantage industries vs. comparative disadvantage industries.

It is significant and to be expected that, within the traded sector, comparative advantage industries registered far higher labor productivity than comparative disadvantage industries during both waves of the UBI. This is based on the sample weighted estimates of industry aggregates of the UBI 2002 and UBI 28
a more elaborate treatment of this approach to endogenizing credibility. A key conclusion that emerges from simulation studies based on expectations formation schemes similar to (32) and (33) is that endogenous policy credibility strengthens the case for the type of forward-looking inflation forecastbased rules discusser earlier.

An alternative and conceptually appealing approach to endogenizing credibility and learning in a tractable manner is the one pursued by Drazen and Masson (1994) and Agenor and Masson (1999), who view credibility as consisting of two elements: an assessment of the central bank's "type" (which could be termed reputation), and (for a given type) an assessment of the probability that the central bank will actually decide to stick to the announced policy (maintain inflation close to target) in the presence of adverse shocks to prices.<sup>58</sup> In this setting, inflation expectations will reflect assessments about the central bank's "type", as captured by the relative weights that the authorities attach to each of their policy objectives, which are not known by the public. In addition, private agents-knowing that random shocks will alter the balance of costs and benefits associated with maintaining the inflation rate close to target-will reevaluate on the basis of observed variables the probabilities that a particular type of policymaker will decide to stick to the inflation target in the future. Put differently, if there is significant persistence in the effects of policies, then a restrictive policy carried out today (which lowers inflation but also reduces output) may make it less likely that such a policy will be continued in the future.

To illustrate this approach, consider again the per-period policy loss function under flexible inflation targeting described earlier (equation (13)). The first component of (lack of) credibility, the probability that the central bank put a high weight on limiting output fluctuations, can be modeled using Bayesian updating, under the assumption that there are two possible types of policymaker, each with a known set of weights on its objectives: a "weak" central bank, who sets A = Aw, and a "tough" central bank, whose value of A is AT < _Aw._ Thus, the policy loss function can be written as: 5"An alternative approach consists in defining credibility as the ability of the central bank to precommit its actions, that is, its capacity to convince private agents that it will carry out policies that may be time inconsistent. See, for instance, Cukierman (1992).

60
consumption is modeled through the Linear Expenditure System over composite commodities (domestic and imported); exporters of each commodity face constant-elasticity<sup>1</sup> foreign demand schedules; production for exports or domestic markets are regulated by CET<sup>2</sup> functions for each firm, production is a nested Leontief/CES structure for primary factors and composite inputs, labor is a CES function of 10 different types of labor. This non-linear model is solved with the GEMPACK software, and distinguishes between 42 sectors and 52 commodities<sup>3</sup> ; 10 labor occupational categories.

All quantity variables in the model are disaggregated according to 27 regions within Brazil, using an elaboration of the top-down regional modeling method described in Chapter 6 of Dixon _et al._ (1982). This methodology recognizes local multiplier effects: many service goods are little traded between regions, so that local service output must follow local demand for services.

The CGE model is calibrated with data from the Brazilian economy for 1996, obtained from two main sources: the 1996 Brazilian Input-Output Matrix (IBGE. http://ibge.gov.br), and the Brazilian Agricultural Census (IBGE, 1996).

On the income generation side of the model, workers are divided into 10 different categories (occupations), according to their wages. These wage classes are then assigned to each regional industry in the model. Together with the revenues from other endowments (capital and land rents) these wages will be used to generate household incomes. Each activity uses a particular mix of the 10 different labor occupations (skills). Changes in activity level change

> 1 For the simulations reported here, we set the export demand elasticities to values derived from the GTAP model, so as to increase consistency between results for the world and Brazil models.

> 2 The domestic/exportable CET was set to infinity for the simulations reported below, to fit in with the assumptions of the GTAP model.

> 3 One of the activities (Agriculture) produces 11 commodities.

7
<!-- Start of picture text --> Policy, Research, and External Affaire<br><!-- End of picture text --> <!-- Start of picture text --> Edualon and Employment<br>WPS 701<br><!-- End of picture text --> This paper -a product of the Education and Employment Division, Population and Human<sup>Resources</sup> - Department is part of a larger effort in PRE to understand the education sector in the broader<sup>context</sup> of Bank operations, particularly adjustment programs. It is the second<sup>step in a research agenda that</sup> includes analysis of how the education sector should be treated in public expenditure reviews<sup>in the context</sup> of adjustment (see WPS 510) and of how adjustment-related operations<sup>affect the education sector. Copies</sup> are available free from the World Bank, 1818 H Street NW, Washington DC 20433.<sup>Please contact Cynthia</sup> Cristobal, room S6-035, extension 33640 (68 pages with figures and tables).

Many recent stuties evaluate the effects of adjustment on ecoinomic growth and on the poor, but few assess the specific impacts of adjustment on the education sector. Noss assesses what is known about how adjustment (particularly World Bank adjustment lending) affects education.

He concludes that reliable evidence about the effect of adjustnent policies on education is limnited.

Most critics of adjustment programs say little about education directly and do not distinguish the effects of adjustment measures from the effects of international recession, fiscal constraints, or structural problems. Early adjustment programs ignored education issues but adjustment lasted longer than expected, so the Bank has broadened its approach to protect education from the negative effects of adjustment.

Relevant data are scarce and of poor quality. The most common indicators aggregate financing and enrollment indicators - are difficult to interpret. Moreover, analyses may compare indicators between two before-and-after points but say nothing about how or why indicators change.

The effects of changes in financing on coverage, quality, and equity of education are by no means obvious. Education has a long gestation period, so the impacts of adjustment may not yet be evident. Country studies are probably the best framework for analyzing the adjustment process. The database of kev education indicators must be improved.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's<sup>Policy, Research, and External</sup> AffairsComplex. Anobjectiveoftheseries is to getthese fmdings outquickly, even if presentations<sup>arc Icss than fullypolished.</sup> The findings, interpretations, and conclusions in these papers do not necessarily represent<sup>official Bank policy.</sup> Produced by the PRE Dissemination Center
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24
Table 2: Relationship between mentioning <u>green</u> technologies in ECTs and CO2 emissions, 2012-2021

|Dep. var.: log Scope 1 emissions|All s|ectors|Constr|uction|En|ergy|Farming a|nd Forestry|PM|P|Transp|ortation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Green tech frm|0.052|0.090**|0.083**|0.095**|0.121**|0.218***|0.026|-0.072|0.002|-0.004|-0.053|-0.014|
||(0.035)|(0.040)|(0.041)|(0.048)|(0.053)|(0.069)|(0.134)|(0.116)|(0.078)|(0.117)|(0.066)|(0.077)|
|Green tech frm _×_|||||||||||||
|High-emission frm||-0.210**||-0.123||-0.352***||-0.244**||-0.087||-0.249*|
|||(0.084)||(0.085)||(0.103)||(0.115)||(0.158)||(0.151)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time varying controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|

Notes: Table reports OLS estimates on the relationship between having an ECT mentioning keywords related to green technology and CO2 emissions. The variable ”green tech firm” takes the value of 1 in the year the firm mentions keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively. Log sales included as time varying control.