rafmacalaba/datause-encoder-data huggingface dataset:
Bourguignon 2017). In EMDEs, the average Gini coefficient declined from 41.4 in 2008 to 39.8 in 2017. The downward trend since the global recession has been broad-based: in more than half of EMDEs with available data for 2005-07 and 2015-17, the Gini coefficient has declined over the decade. On average, income distributions are most equal in ECA and least equal in LAC and SSA (World Bank 2016c).

Improving income inequality is about more than reducing extreme poverty as it impacts the most vulnerable in society, women and children, and is associated with greater fragility and instability (World Bank 2016c). For example, rich children are four times more likely to be enrolled in primary education, creating a significant gap in economic opportunity later in life. More equal societies are more conducive to political and institutional stability, and greater social cohesion helps mitigate threats from extremism. Inequality can therefore aggravate output volatility but also rise with greater volatility (Stiglitz 2012; Fang, Miller, and Yeh 2015; Atkinson and Morelli 2010). Its impact on growth depends on the source of inequality.<sup>8</sup> While income inequality can create incentives for productivity growth, inequality brought about by lack of opportunity—access to healthcare, credit, and education—stifles productivity growth.

# 3. Downside risks to growth prospects

EMDEs face significant downside risks to growth over the next few years, including policy uncertainty, trade tensions, financial market disruptions, spillovers from weaker-than-expected growth in major economies, and geopolitical risks. Some risks, if they materialize, could have profound repercussions for long-run growth prospects.

# 3.1 Policy uncertainty

Global policy uncertainty has risen to its highest level in over three decades in 2019 (Figure 6; Davis 2016). This partly reflects heightened trade tensions between the United States and its largest trading partners, uncertainty related to the exit of the United Kingdom from the EU, and idiosyncratic developments in several large economies (including Brazil, France, and Italy). Heightened risks and uncertainty can lower growth and investment by depressing the expected value, and increasing the variance, of prospective future returns on long-term investment, and also by encouraging precautionary savings (World Bank 2017a; Baker, Bloom, and Davis 2016; Jurado, Ludvigson, and Ng 2015). For example, policy uncertainty in the euro area has been found to have had a statistically significant impact on investment outcomes in ECA EMDEs (World Bank 2017a).

# 3.2 Trade tensions

Much of the growth in trade since World War II has been due to the removal of protectionist measures including tariffs (Baier and Bergstrand 2001; Goldberg and Pavcnik 2016; Krugman, Cooper, and Srinivasan 1995). The commitment to trade liberalization and multilateralism has weakened recently amid growing trade restrictions. New import-restrictive measures imposed in the eight months to May 2019

> 8 See Ferreira et al. (2014) and World Bank (2006) for a survey of the literature.
as the ability to compare housing shortages across benchmarks from other regions. Previous studies that have examined housing shortages and their relationship to GDP find some similarities to the above analysis. Rojas and Medellin (2011) find that in Latin America, for example, shelter and infrastructure shortages also have a negative relationship with GDP per capita. However, it is important to keep in mind the stark differences between urban population levels in Latin America and Africa which are nearly 80 percent and less than 40 percent, respectively (World Urbanization Prospects, 2011 Revision).

# **3.3 Increases in shortages over time suggest a focus on basic service provision is still relevant for most of SSA**

As countries grow and urbanize, changes in housing conditions—either improvements or growing deficiencies—offer insight into the persistence of housing challenges in each country. For the countries in our sample where data are available for multiple time periods, we find that there is no consistent indication of improvements in housing shortages. The analysis reveals that in many cases the improvements have been small, or conditions have worsened with shortages increasing over time. Figures 13 and 14 show the housing shortages for countries have at least two comparison years; differences are shown using red bars. In terms of shelter shortages (see Figure 13), eight of the 13 countries saw increases in shortages between at least one time period. Senegal had the largest increase in shelter shortages from 2001 to 2005 when the inter-period change increased by a dramatic 102.4 percent from 33.1 percent to 66.9 percent of households facing shortages.

By contrast, only six countries saw at least one inter-period decrease in shelter shortages, with Uganda having the largest decrease of 29.3 percent from 2002 to 2005. Interestingly, the subsequent time period for Uganda, from 2005 to 2010 saw that decrease disappear when the shelter shortage then rose by 42.4 percent. For many countries, shelter shortages remained relatively constant between time periods. In Ethiopia, Kenya, Rwanda, São Tomé and Principe, Tanzania, and Zambia inter-period changes in shortages did not exceed five percent. Notably, shelter shortages in those countries were very high, exceeding 90 percent, suggesting that those countries plagued with chronic shelter shortages have a difficult time overcoming them.

**Figure 13. Changes Urban Shelter Shortage over Time**

<!-- Start of picture text --> 110% 102.4%<br>90%<br>70%<br>50% 42.4%<br>30%<br>10% 5.3% 0.9% 0.0% 3.1% 1.9% 3.9% 0.8%<br>-10% -13.5% -5.6% -14.6% -0.1% -4.8%<br>-29.3%<br>-30% CIV CMR ETH GHA KEN MOZ MWI RWA SEN STP TZA UGA ZMB<br>percent difference from previous time period<br>% of households facing shortage<br>2002 2008 2001 2007 2000 2004 1991 1998 2005 1997 2005 2002 2008 2004 2010 2000 2005 2010 2001 2005 2000 2010 2000 2007 2002 2005 2010 2006 2010<br><!-- End of picture text --> 19
- 16 -

# **Nalaysia**

In addition to LNG exports to Japan, Malaysia plans initially some 150 MMcf/d of gas pipeline exports to Singapore as part of its Peninsular Gas Utilization Project and has just awarded the construction contract of its second phase. Exxon, the producer, is finalizing its price negotiations with Petronas, the Malaysian state oil and gas company although Petronas already has a sales contract with Singapore callirg for a price of about $2/MMBtu. The price is reportedly based nn mediumi quality fuel oil, which it would displace in the power sector, plus a premium. By contrast, Exxon is negotiating a domestic price with Petronas which could be more in the _$l.50/_ MMBtu range initially.

To diversify its exports, the Prime Minister of Malaysia recently announced a preliminary political accord had been reached to negotiate gas sales to Thailand although the latter has plentiful supplies of its own. He cited attractive pricing, including trade and currency concessions, and deliveries to the eastern Thai border, where a domestic line doesn't yet exist as the rationale for the project. Thai private and state oil companies however consider the possiblity remote.

## Japan

The average landed price of LNG delivered to Japan was about $3.60/MMBtu CIF in early 1988; this is a sharp reduction to the $5 price in 1985 and the high of $5.83 in 1981. (Delivered CIF prices do not include regasification and all contract prices are expressed in U.S. dollars.) Japan has retrospectively changed the price linkage in its contracts to market or actual prices rather than to official prices of crude oil. The issue is under negotiation and an interim agreement was reached whereby Japan recently paid $2.90/MMBtu based on a $15 reference price for oil. In addition, Indonesia and Malaysia have been resisting renegotiation of take-or-pay clauses in their contracts with Japan. The following table shows the price trend for Japanese LNG imports.

CIF Price of LNG Imported by Japan ($ per millions of Btu) ($/MMBtu)

|Exporter|1981|1983|Summer<br>1987 Price|February<br>1988 Price|
|---|---|---|---|---|
|Abu Dhabi|6.61|5.47|3.20|3.28|
|Indonesia|5.59|5.14|3.53|3.82|
|Brunei|5.97|5.16|3.19|3.18|
|Malaysia|--|5.19|3.33|3.36|
|Alaska|5.95|5.12|3.15|3.17|
|Average|5.83|5.16||3.60|

Source: Japanese Ministry of Finance; Cedigaz
towards goods that have become relatively cheaper over time (Deaton 1998). This _commodity substitution bias_ may be particularly relevant in Sub-Saharan Africa, where many countries lack regular HBS programs. According to metadata of the International Labour Organization (ILO 2013), which depict national practices in computing CPIs as of July 2012, 13 percent of the population in the region was living in countries in which the baskets were based on data from the 1990s, and an additional 23 percent was living in countries were basket weights dated between 2000 and 2004 (see Beegle et al 2016).

A related issue is that CPIs often do not reflect differences in the prices of the same product purchased at various outlets if distribution channels change over time. This is referred to as the _outlet substitution bias_ . In many African countries, for example, the popularity of supermarkets that offer discounted prices has been growing, at least in urban areas. Because the CPI does not account for the shift among consumers towards these more economic retail outlets (for items previously purchased in traditional stores), it overstates actual inflation.

CPIs also do not account for changes in the quality of existing goods and services in the basket, which leads to a _quality change bias_ . Over the years, as technology has evolved, many products have exhibited dramatic improvements (for example, greater functionality or safety, greater nutritional value, and so on). However, it is typically very difficult to separate out how much of the price change recorded for a specific product is associated with a change in quality and how much is associated with actual inflation.<sup>3</sup> Similarly, it often takes many years until newly available goods and services are introduced into the CPI computation, the _new product bias_ . Because the CPI fails to capture the benefits to consumers from greater availability of products and brands, changes in the cost of living are typically overstated.

The CPIs in many countries in Sub-Saharan Africa suffer from additional sources of bias and measurement error. An important example is _urban bias_ . Because a number of NSOs in the region collect price data solely in urban areas, their CPIs reflect changes in prices experienced by the urban population, in some cases, the population living in the capital city only. For example,

> 3 NSOs in developed countries often use splicing procedures, which attribute a certain fraction of the overall price change to quality, or rely on hedonic estimation techniques, which explicitly model quality. Nonetheless, there is evidence that some degree of quality improvement is typically not captured and that quality change bias often leads to an overestimation of inflation (Boskin et al 1996, 1998; Hausman 2003).

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# **S8 Additional IV Results Analyzing the Impact of Areas Incidentally Connected to the RSDP**

Our main specification examining the impact of the RSDP on areas incidentally connected to RSDP roads uses Kebeles as the unit of analysis and uses a minimum spanning tree (MST) constructed from regional least cost distance MSTs appended together as an instrument. In this section, we test the sensitivity of results to using 1x1km pixels as the unit of analysis and using different versions of the MSTs. Section S8.1 shows sample sizes and the number of units near the RSDP and MSTs. Section S8.2 shows OLS results, section S8.3 shows first-stage results from IV models, section S8.4 shows second-stage results, and section S8.5 shows second-stage results when using two groups of baseline nighttime lights as opposed to four.

## **S8.1 Treated and Control Summary Stats**

For instrumental variable models, units that are near targeted areas are removed. Table S3 shows the number of units after removing targeted areas. In addition, S3 shows the number of units treated (near an RSDP road) and near the MST networks.

**Table S3:** Number of Treated and Control Units for IV Analysis Across Different Units

||Sam|ple Size||N Units|Near RSDP and D|iferent MSTs||
|---|---|---|---|---|---|---|---|
|Unit|N Total|N, Targeted|RSDP|MST Least|MST Least|MST Least|MST Least|
|||Areas Removed||Dist|Dist: Regional|Cost|Cost: Regional|
|1x1km Grid|1,337,567|113,908|1,271,094|95,603|106,534|124,674|136,769|
|Kebeles|15,714|3,157|10,220|1,956|2,233|2,781|3,020|

## **S8.2 OLS Results**

Table S4 shows results from OLS models; models (1)–(6) show results using 1x1km pixels as the unit of analysis and models (7)–(12) show results using Kebeles as the unit of analysis. The results show road upgrades associated with gains in nighttime lights and urban land, particularly in areas with medium-tohigh initial baseline nighttime lights.

S12
received little empirical attention. Fourth, the gender dimension of temporary migration and its gender-differentiated effects, for example on adult household members and children left behind, is an area that remains significantly understudied via rigorous causal analysis.

Another important limitation of available evidence on the impacts of temporary migration on origin countries is that it is largely based on static models and analysis. While providing very useful insights, cross-section data and analysis do not shed adequate light on the dynamic effects of temporary migration. These dynamic effects are expected to impact workers and their families’ entire life cycle, and to differ prior to, during and after the migration episode. Recent papers attempt to identify some of these time-varying effects, but rigorous answers to these questions require a dynamic modeling approach with granular data on employment and migration history. For example, when looking at the impacts of temporary migration on the outcomes of household members left behind, the analysis would greatly benefit from having information on the full employment and migration history of temporary migrants, but also of other household members.

For policy purposes, the main drawback of the non-experimental reduced-form approach to temporary migration is its inability to examine the impacts of policy changes on the welfare of migrants themselves and of the origin country more broadly. In contrast, a more structural and dynamic approach allows to model the complex linkages between the decisions and outcomes of migrants at different stages of the migration life cycle. This allows the researcher to simulate the impacts of a change in policy parameters on the migration decisions and outcomes of workers and to estimate impacts on specific aggregate variables of interest in the origin or destination country. For example, Bossavie, Gorlach, Ozden, and Wang (2021) simulate the impacts of changes in migration recruitment costs, domestic loan interest rates, and information provision on emigration rates, migration duration, repatriated savings and the extent of business creation in Bangladesh.

Existing microdata typically capture temporary migrants at one point in time in one location (abroad or at home after return), while a dynamic analysis of temporary migration phenomena requires information on the entire migration and employment history of the migrants and household members. Ideally, surveys collecting this data would also include questions on expectations of the workers on their wages, employment, savings and return decisions. While comprehensive panel data information remains very sparse and takes time to collect, dedicated migrant surveys capturing retrospective information on workers’ full employment and migration history and that of their household members is a promising alternative. Policy-relevant research on temporary migration would also greatly benefit from the inclusion of more comprehensive modules on past migration episodes overseas in standard national household surveys in origin countries, in the spirit of those included in the Egyptian 34
# **Table 1.3 Trade structure, Central Asia and Southeast Europe, 2003 and 2008**

(percentage of total exports, unless otherwise indicated)

|**Region**|**Food and**<br>**live animals**|**Beverages**<br>**and tobacco**|**Crude**<br>**inedible**<br>**materials**<br>**except fuels**|**Mineral**<br>**fuels,**<br>**lubricants,**<br>**and related**<br>**materials**|**Animal and**<br>**vegetable**<br>**oils, fats,**<br>**and waxes**|**Chemicals**<br>**and related**<br>**products**|**Manufact-**<br>**ured goods**|**Machinery**<br>**and**<br>**transport**<br>**equipment**|**Textile**<br>**products**|**Commodities**<br>**not classified**<br>**elsewhere**|**Total trade**<br>**($ millions)**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_2003_||||||||||||
|Central Asia|6.47|0.32|14.38|47.41|0.03|3.02|23.31|1.75|1.23|2.08|16,730|
|Southeast Europe|5.58|1.19|6.75|4.71|0.18|6.25|21.54|16.95|35.92|0.94|35,946|
|Europe and Central<br>Asia|5.19|0.63|5.23|20.39|0.27|6.49|21.79|24.92|12.63|2.48|455,874|
|_2008_||||||||||||
|Central Asia|4.36|0.13|8.42|61.70|0.02|3.16|16.69|1.99|0.51|3.03|70,187|
|Southeast Europe|6.08|1.59|6.88|8.11|0.45|7.50|24.35|24.53|18.56|1.96|94,668|
|Europe and Central<br>Asia|4.66|0.63|4.54|29.09|0.32|6.80|18.95|23.69|7.10|4.22|1,343,808|

_Source_ : United Nations Commodity Trade Statistics Database.

_Note:_ Column headings reflect Standard International Trade Classification categories, revision 3.

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# **Figure 21: Revised Framework to Inform Reforms on the Financial Viability of Power Sectors**

<!-- Start of picture text --> Need to be<br>(a) aligned Price formation<br>with...<br>• Competitive markets<br>New public<br>• De-politicized and technically investment<br>sound price setting by regulators or • Grid-based (g) Enable ...<br>the government electrification<br>• Transmission &<br>Financially viable distribution Sector outcomes<br>(in most cases)<br>power sector • Electrification<br>Boundary conditions (d) Cost-recovery prices • Reasonable cash flows for new • Security of supply<br>• Economy-wide (c) Need to be aligned investment • Quality of service<br>governance institutions with... • Cost-recovery in • End-consumer<br>• Purchasing power Cost optimization / ongoing operations affordability<br>(e) efficient operation • Healthy balance New private investment • Fiscal affordabiltiy<br>sheet • Environmental<br>• Off-grid<br>sustainability<br>electrification<br>• Generation<br>Utility governance & (f) Enable ... (h) Enable ...<br>decisionmaking • Transmission &<br>• Private sector participation in utility distribution (in<br>some cases)<br>• Corporatized SOE held<br>Need to be<br>accountable through state-of-the-<br>(b) aligned with... art governance arrangements<br>Source: World Bank Staff.<br><!-- End of picture text --> 43
primary education policy choices. If they do, then na ̈ıve estimates of the effects of revenue and its interactions with income and inequality would be biased by omitted variables.

There are several possible sources of omitted variable bias (I detail three below). First, having an older population may mean more working adults as a fraction of the population, and thus more tax revenue. However, if having fewer children reduces the need for pre-primary education, ordinary least-squares (OLS) estimates of the effects of revenue and its interactions with income and inequality would be biased. The magnitude of the bias would shrink as measures of population-by-age are added to equations (11), (12), (13), and (14), but it would be impossible to know when all bias had been eliminated. This would downward-bias estimates of the effects of revenue on pre-primary enrollment and quality, and education investment. Second, mayors with high discount rates or who are corrupt may generate higher revenue to maximize private gains.<sup>30</sup> However, short-sightedness may also lower investment in education, which has long-term payoffs. This, too, would downward-bias the estimates. Finally, municipalities with higher revenue may pay higher wages to public officials. Ferraz and Finan (2009b) show that, in Brazil, this leads to more political competition, and to the election of better-educated, more experienced, more productive candidates. If these qualities increase public pre-primary investment, this would upward-bias estimates of the effects of revenue on such investment.

Municipal revenue per capita can also be a response to education policy. This is a classic problem of reverse causality. Municipalities that invest heavily in pre-primary may need to level more taxes to pay for it. The best response to these identification problems is a set of valid instruments. These instruments should affect revenue and its interactions with per capita income and inequality, but should be uncorrelated with factors affecting the demand for revenue.

# **5.2 Instruments for per capita revenue**

To address these threats to identification, I exploit the federal FUNDEF/B education finance reforms described in Section 3, which generated exogenous variation in municipalities’ revenue.

> 30de Janvry _et al._ (2009) suggest such a possibility. They find that second term mayors in Brazilian municipalities, who are not eligible for reelection, have less transparent policies and are less likely to reduce school drop-out rates using federal funds designated for this purpose. Ferraz and Finan (2009a) show that first term mayors misappropriate 27% fewer resources than second term mayors, which they also link to electoral incentives.

18
available for estimation since the literature on the real exchange rate confirms that it responds to the structural development of an economy.

# (2) Control variables

To isolate the effect of the macroeconomic policy on the variation of real exchange rates, other than income inequality, we use control variables which are identified in the literature as having long-run stable relationship with the real exchange rate.

Acronyms and definitions are as follows. TOT: terms of trade, NFA: Net foreign assets which are measured by accumulating current account balances with the bench mark figure of 1978, LL: Liquid liabilities of the economy as a measure of liquidity or a measure of inflationary pressure in the economy, PCGDP: Real per capita GDP, TROPEN: Trade openness as measured by export plus import divided by GDP, FIXK: Public investment on fixed capital, OECD: OECD member country dummy, EASIA: East Asia regional dummy, LATIN: Latin America regional dummy.

# 2. Estimation of Inequality and Real Exchange Rate

# (1) Data

The analysis is based on 73 countries and all variables are averaged over 1980-89. Time averaged data are used since the focus of the study is explaining the sustained level of real exchange appreciation or depreciation rather than temporary movements in the real exchange rate. The most important data series is the Gini coefficient which is averaged over ten years and reported in Table 1 .

12