rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Outcomes and Measurement

|Outcome|Measurement|Scale|Survey|
|---|---|---|---|
|VH’s knowledge of laws<br>and regulations|Proportion of questions answered<br>correctly about laws and regulations<br>governing village head’s decision-<br>making (i.e. are there are restrictions<br>on the disputes committed within<br>their geographic jurisdiction over<br>which traditional leaders can preside?)|0-1|VH Survey|
|VH’s support for<br>individual rights|Average stated support for three<br>individual rights (right to speak in defense<br>at court hearings, inheritence rights, and<br>right to protections from arbitrary authority)|1-4|VH Survey|
|VH’s adherence to<br>procedures|Index measuring whether the VH<br>adheres to fve procedures (consultation<br>of RMC, consultation of women’s council,<br>% women on court, no court fees, public<br>court records)|Standardized<br>index (mean=0,<br>s.d. = 1)|VH Survey<br>(and CL Survey)|
|Bias in VH’s decisions|Proportion of respondents with different<br>political views who received food aid<br>Proportion of respondents with different<br>political views who perceived most court<br>decisions to be fair|0-1<br>0-1|HH Survey<br>HH Survey|
|Effcacy of management<br>of problems by VH|Effect of need on receipt of food aid|OLS regression<br>coeffcient|HH Survey|
||Proportion of respondents with unresolved<br>land or livestock disputes|0-1|HH Survey|
|Legitimacy of VH|Index measuring evaluations of VH<br>(trustworthiness, quality of relationship)<br>and compliance with VH (perceptions of<br>disposition to obey within village,<br>compliance with VH’s court)|Standardized<br>index (mean=0,<br>s.d.=1)|HH Survey|

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Figure 9: Passenger cars and GDP per capita <!-- Start of picture text --> .8<br>ISL<br>LUX<br>NZL<br>MLT CAN<br>.6 POL USA<br>ESTSVN DEUAUTAUS CHE<br>ESP BEL NOR<br>LTUGRCPRT JPNFRAGBRFINNLDSWE<br>CYP DNKIRL<br>.4 BGR HRV BHR<br>HUNLVA KWT<br>BLR ISR<br>ROU<br>GEO SUR<br>MEX<br>.2 KAZ CRI CHL<br>MDA<br>TUR<br>TUNMNGAZE GUYZAFTHA CHN SGP<br>MAR COLDOM<br>0 MOZAFG MMR CMR PAK INDNIC LKAGTMNAMPRYPERECU<br>6 8 10 12<br>Log of gdp per capita<br>Number of cars per capita<br><!-- End of picture text --> Source: IRF data for passenger car ownership. Most data points are for 2017, when missing they are replaced by the latest available year between 2014 and 2016.

**Fact 7: As households get richer, they are more likely to own a car.** Figure 10 uses LSMS household data to plot the fraction of households that own at least a vehicle, being a motorcycle or a car, against household expenditures per capita within country. The dashed lines show the density of households by expenditure level. As households get richer, they are more likely to own a vehicle. For Burkina Faso, Mali, Cameroon, Nigeria, and Pakistan, most of the growth in vehicle ownership comes from the acquisition of a motorcycle. For most countries in the LSMS sample, the probability to own a car is almost null for most of the population, except for the richest. The probability to own a car is increasing in a non-linear way, in line with Gertler et al. (2016) who provide evidence of a nonlinear relationship between income and asset ownership as credit-constrained households become more likely to purchase an energy-using asset only above a certain income threshold. Figure 8 shows that for many countries a large share of households do not own a vehicle, and have yet to reach the income threshold to be able to acquire a vehicle.

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# **4.3. Combined results from reform scenarios**

The effects of the reform scenarios on the GDP growth outlook are shown in Figure 14 and on the GNI per capita (relative to high income) in Figure 15. In the moderate reform scenario, headline GDP will accelerate to achieve GDP growth of 3-3.5 percent from 2030 onwards, with a growth boost of 1.6ppts by 2050. Instead, with an ambitious reform package, GDP growth accelerates to 4.6% by 2033, which is 2.6 ppts above the baseline, and HI status is achieved by 2045.<sup>15</sup>

**Figure 14: GDP growth path**

**Figure 15: Evolution of GDP per capita with the simulated scenarios***

<!-- Start of picture text --> 6.0% 32000<br>5.0%<br>16000<br>4.0%<br>8000<br>3.0%<br>4000<br>2.0%<br>1.0% 2000<br>2024 2027 2030 2033 2036 2039 2042 2045 2048<br>Baseline Moderate Ambitious Ambitious Moderate<br>Baseline HI Threshold<br>Source: LTGM simulations Source: LTGM simulations. *The series are in logarithms base 2<br>Figure 16. Moderate reform contributions to Figure 17. Ambitious reform contributions to<br>GDP growth GDP growth<br>4.5%<br>3.5%<br>1.3% 1.4%<br>0.6% 0.6% 1.4%<br>0.3% 0.7% 3.5% 0.7% 1.4%<br>2.5% 0.2% 0.5% 0.5% 0.8% 0.3% 0.7% 0.7%<br>0.4% 0.5% 0.4% 0.6%<br>0.5% 0.4% 2.5% 0.8% 0.9% 0.6%<br>0.9%<br>0.5%<br>0.8%<br>0.4%<br>1.5% 0.7%<br>2.1% 2.0% 1.8% 1.6% 1.4% 1.5% 2.1% 2.0% 1.8% 1.6% 1.4%<br>0.5% 0.5%<br>Baseline Non-res TFP<br>Baseline Non-res TFP<br>Human Capital Non-res private investment<br>Human Capital Non-res private investment<br>Source: Peru’s LTGM simulations, World Bank Staff Source: Peru’s LTGM simulations, World Bank Staff<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br>2024 2027 2030 2033 2036 2039 2042 2045 2048 2024 2027 2030 2033 2036 2039 2042 2045 2048<br><!-- End of picture text --> 15 Policies can also aim to raise the employment share of the working-age population, in particular of discouraged workers or groups with historically low participation rates, such as women. The female employment-to-population ratio was 16ppt below that of men in 2018, and closing this gap will increase long run GDP by around 12% (Pennings 2022). However, those policies are not discussed in this paper.

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_(Log of emissions in tons)_ <!-- Start of picture text --> ARE<br>SGP<br>KWT<br>SAU AUS USA<br>CANNLD<br>BEL<br>KAZ RUS HKG<br>KOR<br>ZAF CZEISRGRCJPNDEUGBR<br>AUT<br>UKR IRNBLRVEN POL ITAESPFRA<br>MYS<br>UZB CHN ROMARGMEX<br>THA TURCHL<br>IRQ DZA<br>BRA<br>IDN EGY<br>COL<br>IND MAR<br>VNM<br>PAK PHL<br>NGA<br>7 8 9 10 11<br>Log of GDP (PPP) per capita, 2008<br>4<br>3<br>2<br>1<br>Log of emissions (in tons) per capita, 2008 0<br><!-- End of picture text --> _Source: Authors’ calculations, based on data from the World Bank’s World Development Indicators._ Any commitments by developing countries to reduce emissions will lead to an increase in the price of energy and hence implicitly in the price of carbon, which is embodied in fossil fuel-based energy products. This price rise will affect not just the overall economy but also the composition of production and the distribution of consumption. On the production side, manufacturing tends to be far more energy- and carbon-intensive than agriculture and services, so any increase in the carbon price is likely to lead to a contraction of manufacturing. In India, for example, the carbon intensity of manufacturing was about 518 tons per million U.S. dollars in 2004, much more than the 301 tons in agriculture and 231 tons in services. Of course, there are big differences within manufacturing, with certain energy-intensive manufacturing sectors emitting more than twice as much carbon as others.

In an international system of trading emissions rights, developing countries might have to cope with even higher carbon prices than if emission rights were not tradable. The reason is that if industrial countries undertake greater emissions cuts than developing countries and rights are not tradable, there will be international differences in carbon prices, with lower prices in developing countries than in industrial countries. But tradability---which is likely to involve producers in industrial countries’ 15
<!-- Start of picture text --> Figure 16: Histogram of the number of good predictions of ELL method at municipality level<br>FGT(0) FGT(1)<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Share Share<br>FGT(2)<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Share<br>20 20<br>15 15<br>10 10<br>Density Density<br>5 5<br>0 0<br>15<br>10<br>Density<br>5<br>0<br><!-- End of picture text --> Source: Authors’ Calculation.

Despite the encouraging results above, one might worry about the size of the confidence intervals we are simulating using the ELL approach – particularly as the conservative option of applying the cluster-effect at the municipality level in the simulations has been adopted. To what extent are the estimated standard errors sufficiently small to permit meaningful comparisons of poverty across municipalities? Coverage rates could be very high, as seen above, but if this is due to the standard errors being very large then the usefulness of the poverty map estimates becomes less obvious. Tarozzi and Deaton (2007) point out that, at least in principle, standard errors from the ELL method can explode if a sufficiently large intra-cluster correlation effect is applied entirely at the target population level. Figure 17 illustrates how estimated poverty varies across municipalities in the state based on estimates from one arbitrarily selected pseudo-survey and indicates that while, indeed, confidence errors are sufficiently large as to prevent fine pairwise comparisons of poverty across municipalities, there is a non-negligible number of municipalities 27
products exported in 2000 by some of the countries in our sample against the GDP of all the destination countries.<sup>11</sup> The graphs show that the number of products exported to markets that are closer and larger is considerably higher than to other destinations. For example, Latin countries such as Argentina and Brazil trade a basket of products significantly wider among themselves and with larger countries like USA, the EEC15, etc. Likewise, East Asian countries trade more goods regionally and with larger markets as expected, however, one point important to note for this group of countries is that in general, their basket of exports to each destination is on average more diversified (more concentration of dots in the upper part of the graphs). Finally, Tunisia and South Africa show similar patterns as those mentioned above, however, in this case, their exports basket is even more concentrated than in Latin countries’ case.

In order to check how much the level of development of the origin and destination nations matter in the trade relationship, we look at the evolution of the number of goods traded according to the directionality in trade (whether exports come from or go to the North or the South). Figure 3 shows that the number of products exported over time is growing more for the bilateral relationships involving the South (South-North and South-South). This is quite a natural result if we consider that there are few 6- digit categories that developed countries are not trading among themselves yet. However, as far as the directionality in trade within South, the number of goods traded with the North is considerably higher than that traded with the South.

**Figure 3: Evolution of the number of products exported by directionality in trade**

<!-- Start of picture text --> Number of exported products<br>By directionality in trade<br>4000<br>3000<br>2000<br>1000<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004<br>year<br>South-North North-North<br>North-South South-South<br>Source: Authors' calculations using Comtrade data.<br>Number of products<br><!-- End of picture text --> Summing up the facts above, diversification is on the rise and it seems to be more dynamic within the developing countries in our sample. The extent to which countries diversify seems to differ depending on the income level, size and proximity of the

> 11 We made graphs for years 1990, 1995, 2000 and 2004 and they all give us similar pattern. We only report the scatter graphs for 2000 for simplicity in the presentation of our results, however, graphs for the other years are available upon request.

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<mark>Vahdat, H.L., K.L. L’Engle, K.F. Plourde, L. Magaria, and A. Olawo (2013). “</mark> There are some questions you may not ask in a clinic: Providing contraception information to young people in Kenya using SMS,” International Journal of Gynecology and Obstetrics 123: e2–e6.

<mark>Wilson, Susan F., Nathalie Degaiffier, Sarah J. Ratcliffe & Courtney A. Schreiber (2016). “Peer counselling for the promotion of long-acting, reversible contraception among teens: a randomised, controlled trial.”</mark> _<mark>The European Journal of Contraception & Reproductive Health Care</mark>_ <mark>, 21:5, 380-387.</mark> <mark>World Bank (2021). “Girl Empowerment and Learning for All Project,” Project Appraisal Document, Report No: PAD 4051. Education Global Practice, Eastern and Southern Africa Region.</mark> 35
Policy Research Working Paper 10226

# **Abstract**

This paper studies cartels in public infrastructure procurement and analyzes the conditions under which they succeed in generating rents. It first conceptualizes the interplay of the central actors of a procurement project, notably the contractor, the procurement agency, as well as the supervision and design consultants. By focusing on consultants, the framework includes important yet understudied actors in cartels that design tenders, evaluate bids, and supervise the implementation of projects. The paper then explores an original data set of infrastructure procurement contracts in Lebanon and analyzes the conditions under which powerful political elites can broker deals to overprice and/or over-spend contracts. To examine how cartels operate, the analysis identifies the political connections of contractors and consultants and classifies them according to their “quality” in terms of access to institutional functions of the implementing agency. The paper argues that design consultants serve as the lynchpin of the cartel by reducing transaction costs for searching, bargaining, and enforcing of corrupt deals.

This paper is a product of the Fragility, Conflict and Violence Global Theme. 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 mmahmalat@worldbank.org.

_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
past decade. Through analyzing changes in the distribution of welfare, the paper examines the extent to which economic growth in Jordan has been inclusive of the more vulnerable groups of the society throughout this period, and goes further into identifying the sources of these variations. The objectives of the study are therefore threefold. First, the paper looks to illustrate the welfare trends in the Kingdom during the decade and depict the changes in poverty and inequality measures. Second, it opts to capture the dynamics observed behind the drop in poverty and inequality measures and tries to highlight the role of welfare growth versus welfare distribution in dictating such change. Third, the study tries to investigate the determinants of welfare across time and across socio-economic groups so as to inform on the drivers of welfare growth and consequently the drivers of inequality.

To achieve these objectives the paper uses official household surveys for 2002, 2008 and 2010 to compare household consumption averages across time, income groups and regional cohorts such as rural-urban regions or across governorates. The paper then resorts to the official Jordanian poverty line to compute poverty and inequality measures<sup>6</sup> before undertaking counterfactual decompositions of these indices using the Datt-Ravallion (1991) decomposition method. Finally, using regression analysis, the paper investigates the economic determinants of household welfare changes throughout the decade and focuses on defining the role of employment, income sources and human capital services in accentuating such welfare variation from a time dimension (between 2002 and 2010), and across the different income groups in Jordan.

The rest of the paper is composed of five sections. Section II briefly presents the data used in the analysis and discusses some methodological issues. The section also defines the concept of household welfare used throughout the study. Section III examines the patterns of household consumption, poverty and inequality in Jordan throughout the last decade. The section also refers to perception issues and highlights the trend changes observed starting 2008. Section IV considers the variations in the distribution of welfare between 2002 and 2010, and reveals the dynamics observed behind the decline in poverty and inequality across the Kingdom. The section discusses counterfactual decompositions computed for the poverty and inequality measures and looks at the regional differences observed. Section V takes the analysis further and observes more closely the determinants of household welfare by estimating Re-centered Influence

> 6 The software Adept has been used to compute and tabular expenditure, poverty and inequality indices.

3
households<sup>2</sup> and measure differences in welfare gains between poor and non-poor households that may be attributable to residing in GMA and participating in CRB/VAG activities. Estimations of welfare impacts for the poor and non-poor allow us to measure the possibility and extent of elite capture of benefits derived from the GMA within the communities.

# **2. Methods and Procedures**

# _2.1 Sample design and sampling_

The “Impact of Game Management Areas on Household Welfare” (IGMAW) survey was commissioned in 2006 to study the impact of the GMA institutions on the welfare of the households living in GMAs.

The first step was to understand the nature and spatial distribution of Zambia’s national parks and game management areas. For logistical convenience, the national parks in the north and north-western parts of the country were omitted. For purposes of this survey the country’s remaining national parks were grouped into what came to be known as ‘park systems’, which in some cases constituted a combination of national parks that are within the same geographical location. The study was conducted in the GMAs of four park systems:

- a) Bangweulu (including Kasanka, Lavushi, and Isangano national parks),

- b) Kafue (Kafue, Blue Lagoon, and Lochinvar national parks),

- c) Lower Zambezi (Lower Zambezi national park), and

- d) Luangwa (North and South Luangwa national park)<sup>3</sup> .

Each of these park systems was regarded as a reporting domain in the sampling process. A stratified two-stage cluster sampling procedure was used, involving selection of SEAs (the clusters) at the first stage and selection of sample households from each selected SEA at the second stage. To sample the SEAs within the park systems, the GMA digital maps from ZAWA were overlaid on digital maps of standard enumeration areas (SEA) from the Central Statistical Office. The sampling frame of SEAs was drawn by selecting all the SEAs in the GMAs.

ZAWA classifies its GMAs into five categories based on wildlife stock level (and tourist activity). To control for variation arising from the differences in stock levels, the sampling scheme recognized this stratification and sub-divided the frame of GMA SEAs into the five strata. However, the fifth stratum, depleted, was dropped after realizing that only one SEA was classified as such, leaving four GMA strata. To be able to decipher the impact of the interventions in the GMAs, the research design used all SEAs in non-GMA areas that border

> 2 The poor in this study are defined as households in the bottom two quintiles with respect to consumer durable asset values. Conversely, the top three quintile asset owning households are considered non-poor. This asset based indicator of poverty is based on indicators of household wealth as opposed to household income or consumption.

> 3 Some GMAs in the northern edge of North Luangwa national park were dropped from the sample due to the absence of corresponding control areas close to the park.

4
# 3 Testing theories of fiscal procyclicality

As illustrated in Figures 1 and 2, most EMDE — and, in particular, commodity exporters —are overwhelmingly procyclical. This is, of course, a puzzle since procyclical fiscal policy amplifies the underlying business cycle, which is already quite volatile, especially for CE. Why would a country’s fiscal authority pursue procyclical fiscal policy? Leaving aside sheer ineptitude (an unattractive and most likely wrong explanation), there must be some frictions in these economies that induce policymakers to pursue such misguided policies from a social point of view. In fact, there is a relatively large theoretical literature attempting to explain fiscal procyclicality. This section reviews the theories that, in our view, are potentially the most relevant ones and assess their empirical validity. To do so, we look for a single explanatory variable that captures the main friction in each of the main theories and run panel regressions.<sup>18</sup>

## 3.1 Imperfections in international capital markets

This explanation is, by far, the most popular macroeconomic explanation (as opposed to the political economy explanations analyzed below). We purposely use the term “imperfections”to capture different frictions that may prevent EMDE from borrowing in bad times. Lack of access to international credit markets in bad times will impart a procyclical bias to fiscal policy as the need to finance a primary deficit will induce the fiscal authority to cut spending and/or raise taxes in bad times.<sup>19</sup> A policy-induced imperfection is the macroeconomic use of capital controls over the business cycle. As is well-known by now, the IMF has endorsed using capital controls as a last resort.<sup>20</sup> While the primary impetus for this unexpected IMF policy

> 18Earlier and related econometric studies of factors causing fiscal procyclicality include Lane (2003), who covers only OECD countries; Calderon and Schmidt-Hebbel (2008) who also distinguish between financial and institutional variables, and Calderon et al. (2010) who focus on the role of institutions. See also Ilzetzki (2011), who provides a novel political economy explanation based on successive governments disagreeing on the desired distribution of public spending, and examines different theories of procyclicality by running numerical simulations in calibrated models.

> 19In a first-best neo-classical world, the fiscal authority would save (or repay debt) in good times and dis-save (or borrow) in bad times, leading to an acyclical fiscal policy (i.e., constant government spending and constant tax rates); see, for example, Vegh (2013), Chapter 10.

> 20For the original IMF view, see Ostry et al. (2010). For an opposite view, see Calvo (2010). For the current (essentially unchanged) view, see IMF (2022). The current view holds that while free capital flows bring substantial and well-known benefits, capital controls (referred to as capital flow management measures) can be useful in certain cases but are not a substitute for macroeconomic adjustment.

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