rafmacalaba/datause-encoder-data huggingface dataset:
income groups often have no or extremely limited access to electricity, improved water and sanitation, or basic telephone services. (Figures 2-3).

<!-- Start of picture text --> 1 0 0 %<br>9 0 %<br>8 0 %<br>7 0 %<br>6 0 %<br>U r b a n<br>5 0 %<br>R u r a l<br>4 0 %<br>3 0 %<br>2 0 %<br>1 0 %<br>0 %<br>Im p r o ve d Im p r o ve d G r i d T e l e p h o n e<br>w a te r s a n i ta ti o n e l e c tr i c i ty<br>Households with access<br><!-- End of picture text --> Figure 2: Different Access of Urban and Rural Households in Sub-Saharan Africa. Source: Estache (2005).

<!-- Start of picture text --> 1 0 0 %<br>9 0 %<br>8 0 %<br>7 0 %<br>6 0 %<br>5 0 %<br>4 0 %<br>3 0 %<br>2 0 %<br>1 0 %<br>0 %<br>1 2 3 4 5<br>In c o m e q u i n t i l e<br>P i p e d w a t e r G r i d e l e c t r i c i t y<br>Households with ac c<br><!-- End of picture text --> Figure 3: Access Across Income Quintiles in Sub-Saharan Africa. Source: Estache (2005).

# **_Infrastructure, growth, and decline of infrastructure investments in South Africa_**

In South Africa, infrastructure has also been found to affect growth (see, for example, Fedderke, Perkins and Luiz 2005) with electricity having the largest and most robust impact on aggregate growth. Investment in infrastructure does appear to lead economic growth in South Africa, both directly and indirectly by raising the marginal productivity of capital; there is also weak evidence of feedback from output to infrastructure.

Since the 1970s, however, there has been a clear, long-term decline in infrastructure investment and capital stock, becoming an area of potential policy concern. Investment per capita fell from 1976 to 2002 (1995 prices) by 72%. As a percent of GDP, investment fell from 8.1% of GDP to only 2.4% of GDP, below the international benchmark of 4
besides R&D. In this paper, we also consider investment in design and industrial engineering. The reason for this is twofold. First, in several types of manufacturing (such as making wearing apparel), knowledge is incorporated mainly in the production process design. Because potential complementary factors are measured at the sectoral level, we need to be sure we incorporate the sectoral specificities of knowledge stock formation. Moreover, industrial engineering is particularly relevant for reverse engineering and technology adaptation, which is the typical first stage in innovation learning (Katz 1982). Second, design and engineering implies in-house efforts that often cannot be distinguished from R&D (especially development) efforts (Cox 1990). In many firms, these types of knowledge activities are performed mainly by the same human resources and are very difficult to disentangle one from another. Indeed, all guidelines for innovation surveys globally explicitly discuss the difficulties of differentiating both types of knowledge. This is also the case for ENDEI.<sup>24</sup> Thus, for conceptual and methodological reasons, it is better to add design and engineering to R&D (RD&D, research design and development) in our measurements of firms’ knowledge stock formation. Figure 7 presents average levels of RD&D per worker per productivity percentile, clearly showing a positive relationship, as expected with a good measure of knowledge investments.

**Figure 7. Average Investment in RD&D per Worker per Each Percentile of Labor Productivity**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Percentiles of Labour Productivity<br>20000<br>15000<br>10000<br>Average RD&D per worker<br>5000<br>0<br><!-- End of picture text --> _Note:_ RD&D = research design and development.

> 24 We cite the ENDEI questionnaire: “Industrial Design and Engineering Activities: they <mark>are those activities carried out within the firm: technical functions for production and distribution not included in R&D, drawings and graphics for establishing procedures, technical specifications and operational characteristics; installation of machinery; i</mark> ndustrial engineering; and production start-up. These activities can be difficult to differentiate from R&D activities; for this it can be useful to check if it is a new knowledge or a technical solution. If the activity is framed in the resolution of a technical problem <mark>, it will be considered within the Engineering and Industrial Design activities. It should include the annual salary of the staff devoted to these activities according to the time dedicated.”</mark>

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these migrants are available. However, it is the most comprehensive source available on the characteristics of new Filipino migrants, and importantly, does contain information on the destination country and whether this is the first time an individual is migrating or not for contract work. We use data from the 1992-2003 surveys.

In Table 5 we use this data to test whether the age, sex, marital status, place of origin in the Philippines, and education of new migrants going to a particular destination varies with GDP shocks at destination. To do this, we estimate equation (1) with these characteristics as the dependent variables. We find no statistically significant relationships between GDP changes at destination and the characteristics of the migrants going to that destination. The dependent variables are in levels, and GDP is in logs, so to interpret the magnitude of the coefficients, we divide them by 100 to get the impact of 1 percent change in GDP at destination. Thus not only are the coefficients not statistically significant, but we also see they are very small in magnitude. For example, 1 percent higher GDP at destination is associated with a decrease of 0.049 years in the mean age of migrants going to that destination and an increase of 0.024 years in the mean education of migrants going to that destination.

Thus we find no evidence of large selectivity in which individuals migrate over the business cycle, at least in terms of these observable characteristics. We speculate that this composition effect is much less important for the type of migrant labor examined here than it is for examining the pro-cyclicality to domestic business cycles of native wages because of the much greater distortions in global labor markets.

# **5. Analysis of a Change in the Minimum Wage for Domestic Helpers**

The results presented thus far are consistent with the case of binding minimum wages presented in section 2.4 above. To bolster this interpretation of the results, we provide direct evidence (via a natural experiment) that minimum wages bind for an important subset of overseas jobs, domestic helpers (maids). In addition, this analysis will also rule out the possibility that true wages paid to OFWs are in fact changing in response to GDP shocks, but overseas employers are simply misreporting (failing to report changes in wages).

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# **1 Introduction**

In late 2013, the World Bank set out two goals for the institution: ending extreme global poverty and promoting shared prosperity. The two goals are often referred to as the ‘twin goals’. The poverty goal is defined as “reducing to no more than 3 percent the fraction of the world's population living on less than $1.25 per day” by 2030 (World Bank, 2014). Meanwhile, the shared prosperity goal is defined as “fostering income growth of the bottom 40 percent of the population in every country”, without a particular target value. The choice of a distributionally sensitive growth measure, in addition to poverty, constitutes a shift away from a past focus on economic growth, and reflects a broader change in development policy and research towards increasing attention to inequality.<sup>2</sup> The latest estimates from World Bank (2015) suggest that 14.5% of the world’s population lived below the $1.25 threshold in 2011, showing a rapid decline over the past two decades. However, despite impressive progress, reaching the 3% goal by 2030 remains ambitious. The 2015 Policy Research Report (World Bank, 2015) shows that even under a relatively optimistic distribution-neutral growth scenario where countries grow at their 2001-11 historic growth rate, the global poverty target will not be reached. The global poverty headcount would decline from 14.5% in 2011 to 4.8% in 2030.<sup>3</sup> In this paper, we ask how different scenarios for shared prosperity affect the feasibility of reaching the 3% global poverty by 2030, thus ‘twinning the goals’. We simulate the income distribution for all developing countries for which there is at least one survey in PovcalNet, up to 2030 based on assumptions about growth in the mean and growth incidence, i.e. the distribution of growth. Specifically, we simulate a set of growth incidences where the bottom 40% of the population grows at a different rate than the mean.

While sustained growth in the mean remains a necessary condition for the eradication of poverty, our paper highlights the potential additional effects of making this growth more pro-poor. Our main findings are that: First, as shown in World Bank (2015) we confirm that the poverty goal will not be reached with distribution-neutral growth, anchored to the observed growth rates over the last decade. Second, relaxing the assumption of distribution-neutral growth and boosting the growth of the bottom 40% (while maintaining growth in the mean) makes the poverty goal much more viable. If the mean of the

> 2 Arguably, World Bank (2006), International Monetary Fund (2014), and Kanbur and Lustig (1999), inter alia, can be seen as reflecting this shift. Also see Berg et al. (2012) and Ravallion (2001).

> 3 This projection assumes that each country’s mean per capita household income or consumption expenditure grows at the country-specific national accounts growth rates achieved in the past 10 years, keeping country-specific distributions constant. For further details, see Table 1.4 in World Bank (2015).

2
lending interest rates up. Compared to other regions of the world, financial systems in Africa also have higher levels of overhead costs (Figure 5). High spreads, margins, and overhead costs can be explained by the same factors as the low levels of financial depth, shown above in Figures 1 and 2. The absence of scale economies and the very high risks due to weak and underdeveloped contractual frameworks and economic and political volatility drive up banking costs and reduce time horizons for both investors and borrowers. These costs make outreach to savers and borrowers who need small transactions commercially unviable. Decomposition of interest spreads and cross-country comparisons of interest margins typically point to the small size of African banking systems, deficient contractual frameworks, and limited protection of property rights as an explanation of the excess spreads and margins in Africa compared to other regions.<sup>8</sup> Most striking is the difference from South Asian countries, such as Bangladesh, India, or Pakistan, which—in spite of similar challenges in governance and volatility—are all in better positions to exploit scale economies. In spite of high costs and high risks, banks are, however, very profitable (Figure 6). Indeed, subsidiaries of foreign banks in Sub-Saharan Africa have higher returns on assets and equity than subsidiaries of the same banks in other regions of the world.<sup>9</sup> This trend will most likely be even stronger in the months and years to come, as global investors’ risk appetite is fading. This might reflect partly the very high risks of banking in Africa and partly the lack of competition. This lack of competition in turn, is again related to the lack of scale in most African financial systems, which limits the number of financial institutions that an economy can sustain. However, it is also related to the widespread incidence of informality, which reduces the base of potential clients of the formal financial system.

> 8 Honohan and Beck 2007; Beck and Hesse 2009.

> 9 Honohan and Beck 2007.

5
one year to the next. They also can be used to compare different countries' scores on each of the individual indicators, recognizing however that these types of comparisons too are subject to margins of error. We caution users however not to compare directly the scores from different sources for a single country, as they are not directly comparable. To take a specific example, it does not make sense to compare a question rated on a 1-10 scale from a data source covering only developing countries with a similar question rated on a similar scale, but covering developed countries, as the distribution of true governance is likely different in the two groups. As discussed in detail in Kaufmann, Kraay, and Mastruzzi (2004), our aggregation procedure provides a way of placing such different sources in common units that allows for meaningful aggregation across sources.

The only data sources we have not been able to obtain permission to publicize fully are the World Bank's Country Policy and Institutional Assessment, and the corresponding internal assessments produced by the African Development Bank and the Asian Development Bank. We do note however that starting in 2002 the World Bank began publishing limited information on its CPIA assessments on its external website. For the years 2002-2004 the overall CPIA ratings are reported by quintile for countries eligible to borrow from the International Development Association (IDA), the soft-loan window of the World Bank. Starting in 2005, the individual country scores for the IDA allocation factor, a rating that reflects the CPIA as well as other considerations, is now publicly available. The African Development Bank's CPIA ratings are also publicly available by quintile only since 2004, and are fully public since 2005, and the Asian Development Bank's scores have been fully public for its concessional borrowers since 2005.

# **2.2  Revisions to Underlying Data Sources**

In this round of the governance indicators we have added five new data sources. The Gallup World Poll is a new multicountry household survey, first conducted by the Gallup Organization in 131 countries in 2006.<sup>4</sup> This source is particularly noteworthy for

> 4 However not all of the questions we use from this survey were asked in all countries, so that country coverage in our indicators is slightly smaller than in theirs.  Annual updates for this survey are planned, with the first for 2007 well underway

7
of the items produced may be consumed. Many FDPs store wealth in jewelry or gold to carry these values with them, and it is unknown how much of this wealth is used for regular or occasional expenditures. These facts make the estimation of income for FDPs rather hard so that most often consumption-based measures are used.

However, measuring consumption among FDPs has its own challenges, including misreporting and questionnaire fatigue. It relies on a list of all potential products consumed by a household. For each consumed item, the household needs to provide specific information on the consumed quantity. Usually, additional questions are asked about the amount that was purchased, including the outlet and price, to be able to estimate the monetary value of consumption. However, FDPs have a strong incentive for misreporting if they are aid-dependent and believe that their responses affect future aid. Kaplan, Walsh and Pape (2018) find in numerous rounds of data collection in Somalia and South Sudan that IDPs report significantly lower levels of consumption than non-IDP households. For example, 45 percent of Somalia IDP households report food consumption below subsistence levels and approximately 80 percent below recommended levels. While the data may be accurate, there are two reasons to suspect that it is not. Such high levels of below-subsistence consumption would be associated with high rates of mortality due to starvation, which is not borne out in mortality data. Kaplan, Walsh and Pape (2018) use a randomized experiment to test the effectiveness of a bundled nudge including a truth primer and more investigative reporting controls. They find that the bundled nudge induces higher reporting in lower quintiles of reported consumption. This treatment pattern is driven by aid reliant IDPs and vanishes when considering the comparison group of non-IDPs. This suggests that IDPs – and possibly more generally aid-dependent populations – are indeed misreporting. However, the study does not yield a ‘true’ estimate of consumption, which is needed to assess the overall level of misreporting and the different approaches that could be used to mitigate misreporting.

Questionnaire fatigue is another important issue. Administering consumption modules often takes more than 90 minutes and can require multiple household visits. Particularly in a deprived setting like an IDP or refugee camp, it can be challenging for enumerators to justify spending so much time on asking about items that the respondent cannot afford. However, reducing the number of items either by aggregating or by removing items will bias estimated consumption (Beegle et al., 2012). This explains the frequent use of imputed consumption methodologies.

Scholars have experimented with alternative methods to do that while striving to maintain accuracy. For example, Pape (2021) refines a methodology (Pape and Mistiaen, 2018) that combines an innovative questionnaire design with standard survey imputation techniques without relying on a previous baseline survey.

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|**Total (US$Bn.)**|||||
|---|---|---|---|---|
|Lower|41.70|244.49|220.50|173.43|
|Central|60.99|350.73|316.48|249.18|
|Upper|92.78|545.90|492.29|387.11|
|**Saving (US$Bn.) from R**<br>**Health Damages**|**educed**||||
|Lower|||23.99|47.07|
|Central|||34.25|67.30|
|Upper|||53.60|105.18|

**Table 4:  Different Taxes (%) for a 10% Reduction in PM Emissions by 2030 -- derived from the CGE simulations**

|**Tax Regime**|**Applied to**|**2014**|**2030**|
|---|---|---|---|
|Coal Tax|Coal|14.0%|38.5%|
|PM Tax|Coal, Oil|3.4%|16.2%|

# **Conclusions**

33. The study shows that policy interventions such as environmental taxes are likely to yield positive net environmental benefits for India. The CGE analysis also shows that addressing "public bads" via selected policy instruments need not translate into large losses on GDP growth. The environmental cost model developed in this study can thus be used to evaluate the benefits of similar pollution-control policies and assist in designing and selecting appropriate targeted intervention policies (such as a SO2 tax, a CO2 tax, or emission trading schemes). Once the impact on ambient air quality of a policy to reduce particulate emissions is estimated, the tools used to calculate the health damages associated with particulate emissions can also be used to compute the welfare impacts of reducing them. The monetized value of the health benefits associated with each measure can be calculated, using the techniques developed in this study, and compared with the costs.

36. The comparisons made between the BAU scenario and the Green Growth scenarios reveal that a low carbon, resource-efficient, greening of the economy should be possible at a very low cost in terms of GDP growth. This would make the Green Growth scenarios attractive compared to the Conventional GDP Growth scenario. A more aggressive low carbon strategy (Green Growth Plus) comes at a slightly higher price tag for the economy while delivering higher benefits. The extent to which GDP growth would be impacted under more severe cuts on polluting emissions has to be determined by further study using the CGE model. On the other hand, the modest GDP impacts indicated in this study depend on the availability of minimalcost mitigation options (energy efficiency improvements, embodied technological improvements, improved daily operating practices of boilers). With fewer such options, the GDP cost of hitting the 10 percent and 30 percent targets would be 18
# **D. Sensitivity Analysis**

We conducted a sensitivity analysis around the key parameters of the numerical model. Results are summarized in Tables D1 – D3. We focus on three parameters: the substitution elasticity between energy and factor inputs; the capital transformation elasticity within a sector; and the substitution between electricity and non-electricity inputs.

The substitution elasticity between energy and factor inputs determines how easily energy intensity can be reduced within a given subsector or sector. Higher elasticity indicates that production facilities can more easily substitute factor inputs for energy inputs. In the central case of our numerical model, the elasticity values for different electricity generation technologies are calibrated based on data on the cost of reducing the heat rate in the electricity sector, ranging from 0.16 to 0.37, depending on the specific technology. For industrial sectors, the elasticity value is sourced from the literature and is set at 0.4.<sup>27</sup> To account for uncertainties associated with these parameter choices, we considered two alternative settings: doubling and halving the elasticity. The results are summarized in Table D1. Higher energy-factor input substitution elasticity facilitates the use of factor inputs to replace energy inputs, thereby reducing abatement costs and achieving greater emissions reductions, as shown in Table D1. Additionally, increased elasticity correlates with a lower relative cost of DEPS-NO compared to CAT-NO. Greater elasticity signifies less difficulty in reducing emissions from fossil-based power plants, enabling more substantial emissions reductions by these plants. As a result, the need for augmenting renewable energy sources and reducing electricity demand diminishes, mitigating the distortive effects associated with DEPS’s exclusion of renewables and its implicit subsidy on output.

Consequently, with higher elasticity, the need for overlapping renewable subsidies or indirect emissions pricing to support DEPS decreases. This is evident from the diminishing difference in the relative cost of DEPS-R2 and DEPS-NO compared to CAT-NO in Table D1.

> 27 The estimated substitution elasticities between the energy composite and the factor composite for non-electricity sectors in China range from 0.4 to 1.2 (Cao et al., 2020; Feng & Zhang, 2018; Su et al., 2012; Zha & Zhou, 2014). This wide range is due to the level of sector aggregation and the time scope of the empirical studies. The model adopts the lower bound of this range because of our model’s detailed sectoral disaggregation, recognizing the increased challenge of input substitution at more disaggregated levels as highlighted by recent empirical studies (Oberfield & Raval, 2021). More information on the parameter settings can be found in Goulder _el al._ (2023).

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