rafmacalaba/data-use-mentions-extended huggingface dataset:
.000245||-0.000221|0.000361|||||
||(0.000245)||(0.000247)|(0.000362)|(0.000224)||(0.000230)|(0.000393)|||||
|Share of hhlds with computer||-0.0518|-0.0809|-0.0989||-0.0198|-0.0413|-0.0482|||||
|||(0.0763)|(0.0693)|(0.0749)||(0.0557)|(0.0411)|(0.0396)|||||
|IDN exports per worker *||||-0.00182||||-0.00348|||||
|Share of hhlds with computer||||(0.00126)||||(0.00138)**|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.976|0.976|0.976|0.976|0.870|0.870|0.870|0.870|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban
# 3 DATA AND CONSTRUCTION OF VARIABLES

## 3.1 DEPENDENT VARIABLES

We use the World Bank World Development Indicators (WDI) as a data source for our dependent variables spanning the last four decades and more than a hundred developing countries. Appendix Table A.1 provides a list of all the countries included in this study and their regional classification.

For robustness purposes, we consider five relevant dependent variables (Table 1). We expect renewable energy policy packages to negatively affect the first three energy mix measures in Table 1. In contrast, we expect renewable energy policy packages to positively affect the remaining two energy mix measures in Table 1.

To standardize our estimation results across different specifications, we multiply the estimated coefficients of interest for the first three energy mix measures by minus one to indicate that a positive coefficient leads to impacts aligned with decarbonization. Conversely, a negative coefficient denotes impacts that are not aligned with decarbonization.

Appendix Figures A.1 and A.2 show the times series of each dependent variable over time, aggregated over regions to preserve space. Temporal patterns indicate clear differences across regions in all outcome variables. For example, the share of fossil fuels in the energy mix is consistently higher in relatively oil-dominated regions (e.g., the Middle East and North Africa) than hydro-dominated regions (Latin America). These patterns reinforce our rationale to estimate the regressions separately across regions.

10
monitors its school enrollment records each year through site visits conducted by officials based in each region and in the central office, rather than relying on self-reports from school administrators. Nonetheless, in recognition of concerns about over-reporting we rely on enrollment data from the Ministry sparsely, largely for descriptive statistics. We address the potential bias from enrollment data used in more formal analysis when discussing results.

# _Descriptive Statistics_

Given our focus on grade 12 outcomes in this paper, it is instructive to understand how Gambian students’ progress through secondary school. The enrollment data do not track individual students over time, preventing us from constructing true grade progression rates. However, we can approximate progression through secondary school by comparing enrollment totals in grade 7, the first year of secondary school, with grade 10 enrollment three years later, when students transition from Upper Basic to Senior Secondary School. We can do the same for grade 12 enrollment and test taking five years later. Although these estimates will be biased due to mortality, migration, and grade repetition, they nonetheless give a sense of secondary school continuation and completion. For the seventh grade cohort entering in 2000, the year before the program began, enrollment in tenth grade three years later was only 29% of initial cohort size. Grade 12 enrollment and test-taking were each 25% of the initial seventh grade enrollment. These estimates suggest that secondary school progression is rare, but that students who persist to the upper secondary grades are likely to continue. Table S1 of the supplemental appendix presents these results, with additional breakdowns by gender, urban/rural, and region, as well as how grade progression changed over time for subsequent seventh grade cohorts.

The Gambia made considerable strides in reducing the gender enrollment gap since implementing the scholarship program. Figure 3 shows Ministry of Education data on enrollment in grades 7–12, aggregated across all public schools in regions 2–6. Panels (a)–(b) rescale time so that _t_ = 0 corresponds to the first year of program receipt
<!-- Start of picture text --> FIGURE 3: PROGRESSIVITY IN ABSOLUTE AND RELATIVE TERMS OF THE COLOMBIAN HEALTHCARE SYSTEM<br>Panel A. Progressivity in Relative Terms Panel B. Progressivity in Absolute Terms<br>80 20<br>70<br>60 15<br>50<br>40 10<br>30<br>20 5<br>10<br>0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Income Decile<br>Income Decile<br>Scenario 1 Scenario 3 Scenario 2 Scenario 1 Scenario 2 Scenario 3<br>Source: World Bank Group and staff estimates based on the 2017 ENPH<br>Note: The deciles were put together using market income + per-capita pensions<br>Given this particularity, in the last scenario, only employees whose contributions are less than the sum of the<br>costs of the average health cost of the contributor and their beneficiaries **** is considered eligible to receive<br>transfers in kind for health services. However, it should be mentioned that the contributory health system<br>considers cross-subsidies, that is, that the surplus of the contributions of some people is used to pay part of the<br>shortfall of the others. This situation means that on average, 82.4 ***** percent of the difference between the<br>cost of the average premium and the contributions made by the contributor is paid with resources from the<br>National General Budget. Figure 3 presents the results of the three scenarios.<br>When calculating the Kakwani coefficients and the marginal contribution to the reduction of inequality for each<br>of the scenarios, it is found that: under the first scenario, the Kakwani coefficient associated with health care<br>transfers is 0.7210, while the marginal contribution is 0.0239.
from the DCRTA database), and it is equal to zero otherwise. Thus, by construction, the observations that take a value of one in the _DTA_ variable are a subset of the observations _ij,t_ that are equal to one in the _PTAij,t_ dummy from column (1). The main nding from column (2) of Table 1 is encouraging and expected. Specically, we obtain a positive and statistically signicant estimate on _DTAij,t_ , which suggests that, on average, the deep trade agreements in our sample have led to a 16.1% (std.err. 3.184) increase in bilateral trade among member countries. This result is consistent with and reinforces the general message from Fernandes et al., eds (2021) that DTAs have been eective in stimulating international trade.

Finally, in column (3) of Table 1, we use the DTA database to construct a continuous variable ( _DEPTHij,t_ ), which counts the number of provisions within each of the DTAs in our sample. The number of provisions across the DTAs in our sample varies between 12 and 432. The main result from column (3) is that, on average, the deeper the agreement, the more it would promote trade among its members. Specically, we obtain a positive and statistically signicant estimate on _DEPTHij,t_ ( 0 _._ 00047 , std.err. 0 _._ 00023 ), which is consistent with the ndings from Osnago et al. (n.d.). Our estimate suggests that, depending on the number of provisions that they include, the DTAs in our sample have led to trade increases between 0.576% (std.err. 0.289) and 23.012% (std.err. 12.744). We capitalize on this variation in Section 4.2, where we obtain corresponding GE eects on FDI.

Our estimates of the eects of
we do not know their ultimate destination which makes these export transactions difficult to compare to the rest of the transactions.<sup>11</sup> Sales to free zones/customs warehouses represent about 2-5 percent of aggregate exports in Turkey. Also regarding the destination variable, we account for the changes in name that some statistical territories have undergone over time due to spatial divisions.<sup>12</sup> After applying the cleaning procedures the dataset includes a total of 8.8 million firm-HS 12-digit product-destination-year observations and 7.3 million firm-HS 6-digit productdestination-year observations for the 2002-2011 period.

To assess the quality of our dataset we compare the corresponding aggregate export flows versus the aggregate export flows available in the WITS/COMTRADE. The ratios indicate that the exporter-level customs data represents 100% of the export flows reported in WITS/COMTRADE, which is not surprising since both are sourced from the same institution _Turkstat_ .

Table 1 provides summary statistics on the cleaned exporter-level dataset. The number of exporters in Turkey increases substantially from 30,219 in 2002 to 45,818 in 2007 before the global trade collapse that accompanied the global financial crisis, and 51,371 in 2011. Turkey has a very large number of exporters for its income level, its size in terms of GDP, and its degree of openness (Cebeci, Fernandes, Freund, and Pierola, 2012). Over the sample period, Turkish exporters send as a whole more than 4,500 HS 6-digit products to more than 200 destinations.<sup>13</sup> The range of HS 6-digit products exported and the range of destinations served increases over the sample period. The range of products and destinations is substantially larger than that of exporters in Costa Rica shown in Lederman, Rodriguez-Clare, and Xu (2011), which is not surprising given Turkey’s economic size, level of development, and strategic geographical location.

At the firm-level, the average exporter in Turkey exports 1.1 Million USD in 2002, 2.2
(or better), a tile or cement floor, electric lighting, piped water, a flush toilet and whether it owns a radio, owns a phone and owns an iron.

Our asset index combines housing and other assets with amenities. Yet, amenities (such as contentment with local public services and area security) often explain more of people’s intention to migrate than satisfaction with their personal assets and living standards (Dustmann and Okatenko, 2014). To help distinguish between the effect of these two types of wealth (private/public) on attracting migrants, we also create a separate asset index based on variables that relate purely to housing and other asset wealth (number of bedrooms per person, type of roof, wall, or floor and ownership of radio, iron and phone), as well as an amenities index based purely on the subset of variables relating to amenities (availability of electric lighting, piped water and flush toilet).

Housing, utilities and assets outcomes improve as one moves from villages over towns to cities, in line with what Gollin et al. (2021) document for much of the developing world (Table 3). This is reflected in the constructed wealth index which is much higher in towns than in rural areas and higher still in cities compared to towns (see also Figure 2). We also see that, with respect to the constructed wealth index, the 10th percentile in cities already outperforms the 90th percentile in rural areas. More broadly, the town distribution of wealth mostly dominates this of rural areas, while the city wealth distribution dominates both (Figure 3).

Furthermore, urban households are smaller, younger and their members have more years of formal education. City households are also slightly more educated than town households (0.6 more years of formal education on average – maximum years of education in the household is 8.64 years on average in the city versus 8.01 in towns). The census asks whether the household experienced any death among
The answers to these questions are likely to provide important building blocks towards a more complete assessment of the state of infrastructure in South Africa, its performance, and any need for reform. It would also help frame the ongoing discussion in South Africa in a comparative perspective based on well-defined and widely accepted performance indicators.

The main results suggest the following. Compared to the benchmarks and other country groupings, South Africa’s utilities provide solid service at reasonable quality and, in some cases, very competitive prices. Despite recent gains, however, access remains a major issue in electricity and, especially, in water and sanitation (particularly in rural areas where most of the poor reside), and so does performance in local telecom services. Even transport performance appears comparatively less strong than what would be expected in an upper middle-income, though more in-depth analysis of comparative performance of transport may be warranted to develop a more nuanced picture, especially across different modes of transport. The main policy implication is that there remain significant needs to scale up infrastructure investments––especially in the yet unserved areas––and improve efficiency in all four major infrastructure sectors if South Africa’s infrastructure performance is to catch up with its group of upper middle-income countries.

The organization of the paper is the following. In the next section, we provide a brief overview of the data, the chosen comparators, and caveats. Then, in the following four sections, we present comparative assessments of the infrastructure performance in electricity, water and sanitation, information and communications technology, and transportation. This is followed by a brief section summarizing some of the larger deviations from the comparator groups of countries along with some reflections on policy implications. The final section provides concluding remarks.

# **The benchmarking database**

The data used for comparing South Africa’s performance comes from the World Bank research data base that was recently developed with a specific objective to be used for this type of benchmarking exercise (Eustache and Goicoechea, 2005). The database pulls
growth, this may explain the stronger negative results in cross-country regressions relative to the mixed results of panel data studies. In general, with many potential channels affecting the relationship, inconsistent findings may be expected with differences in country coverage, sample period, time horizon, model specification, and econometric method.

# **4.2. Channels from Poverty and Inequality to Growth**

# **4.2.1. Channels by which Inequality Can Boost Growth**

# **_Incentives_**

Inequality provides incentives to work, save, and invest—those who do will receive higher returns than those who do not. Differential returns incentivize good behaviors that promote growth. Milton Friedman (Friedman 1962; Friedman and Friedman 1980) based his opposition to redistributive policies aimed at reducing inequality of outcomes on the grounds of efficiency, arguing that they could distort incentives and induce an inefficient allocation of resources. In a capitalist system, the distribution of income is consistent with the ethical principle, “To each according to what he and the instruments he owns produce.” This implies that in a free market economy, people should be rewarded according to their marginal productivity, resulting in some inequality of outcomes. Friedman emphasized that this inequality of outcomes could be necessary to provide incentives to perform certain types of tasks that could be risky or tedious (Friedman and Friedman 1980). Moreover, compensation schemes that reward relative performance and thus generate inequality can provide incentives for workers to invest in skills and exert strong efforts (Lazear and Rosen 1981).

# **_Savings_**

Different savings rates between rich and poor can affect growth. Kaldor (1957) hypothesized that since the richer save more of their income, higher income inequality can lead to a higher national savings rate, a higher investment rate, and greater accumulation of capital, and consequently higher economic growth. Evidence for the United States (Dynan, Skinner, and Zeldes 2004), for instance, supports the notion that both saving rates and the marginal propensity
Data on household consumption and detailed price data for the COICOP two-digit group for 19 countries in EMDE ECA is our primary sample of analysis.<sup>6</sup> This sample is complemented by data on 25 countries in the European Union and European Economic Area.<sup>7</sup> For these countries, we have detailed price data and aggregate household consumption statistics by quintile of the consumption distribution.

# **5 Inflation dispersion and consumption patterns in ECA countries**

Differences along two dimensions explain differences in COL indexes across households: variation in prices across goods and services and variation in consumption patterns across households. In periods of low inflation, adjustment of prices of individual goods and services occurs infrequently; the dispersion of relative prices over time is, therefore, low. When inflation increases, however, price changes become more frequent, and the relative price dispersion expands over time <u>(Alvarez et al., 2019). Median annual inflation in Europe jumped from 2 percent in</u> January 2021 to around 12 percent in October 2022 (Figure 1) – values the region had not seen in more than two decades. This high inflation could lead to larger price dispersion in the region.

# _5.1 Heterogeneity of inflation across countries_

A simple way to measure price dispersion across the 12 broad COICOP groups is to calculate the root mean square deviation (RMSD) in price changes with respect to the mean.<sup>8</sup> <u>Figure 2 presents</u> the RMSD for the 47 countries from which the data is available and where price changes correspond to the 12-month variation by December 2022.

The country with the widest dispersion in price changes across consumption groups is the Netherlands, where the RMSD in price changes was almost 22 percentage points in 2022, but this was driven by a substantial increase in the education category, which differed from a more

> 6 The countries are Albania, Armenia, Azerbaijan, Belarus, Bosnia
22 hospitals in the country. The quality of the templates for facilities to fill in and the data cleaning process have also improved over time and a standard coding system for hospitals has recently been introduced to facilitate verification of data and comparison over time. For this study, substantial data-cleaning was required. This involved adjustment of units to VND for facilities reporting in thousand VND, identification of trend outliers of magnitude 10 times or more in the time series and adjustments by a factor of 10, identification of misrecorded aggregates, and replacement by the sum of component parts. The revenue data have been converted to real values using the Vietnam consumer price index for medical goods and services.

# Autonomy

We define autonomy in terms of when a hospital was formally autonomized under decree 10 or 43. Some hospitals were autonomized earlier than others: the process started in 2003, and by the end of 2008 all hospitals had been autonomized. Figure 1 shows the pace of hospital autonomization in six districts in three provinces. The figure illustrates the point that the pace varies, even within a province.

To identify the timing of autonomization according to Decree 10 or Decree 43 we relied in part on questions in Part B of the hospital inventory, and in part on data gathered from health bureaus. The hospital inventory in 2004 and 2005 asked each facility to report whether they had implemented Decree 10 yet. In 2006 and 2007 a similar question was asked about whether the hospital had implemented Decree 43. This information was incomplete: we cannot tell from these data if a hospital was autonomized in 2002 or 2003. To fill this gap, we used data gathered from the provincial health bureaus in 2007 about the timing of autonomization according to Decree 10 and 43 in each of the government hospitals in their jurisdiction. The 2008 hospital inventory no longer asked whether facilities had implemented
a 15km radius with data on credit constraints and green management; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius are allowed to vary by region. Table 4 provides the first stage of the IV regressions. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

To some extent, lack of more significant results could be because we do not know the actual capital intensity of each green investment the company reported. Investment in green energy generation on site, 26
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Moving along corridors within South Sudan and between South Sudan and Sudan often requires the use of multimodal transport, further increasing transit times. Multimodal transport is the only option during half the year, when roads are unavailable during the rainy season. River transport services are underdeveloped, ports are underdeveloped, and commercial vessels are old. The services are limited between Juba and Kosti, mainly transporting goods delivered by train from Port Sudan to Kosti, about 1,400 km away from Juba. Freight that has to move via river takes six days longer than if it were to travel only by road (table 3).

Since South Sudan is landlocked, it depends on its neighbors to access coastal gateways. One option is accessing Port Sudan via Khartoum in Sudan. But the harsh intervening terrain makes this a difficult option. Further, compared to regional benchmarks, container dwell time at Port Sudan is over four times that of global best practices, truck cycle time for the receipt and delivery of cargo is 24 times higher than global benchmarks, and crane productivity is less than a third of

**Table 3. Time and costs associated with transport within all parts of Sudan**

|Route|Mode of<br>transport|Time (days)|Cost per tonne-<br>km (US$)|
|---|---|---|---|
|Khartoum–Malakal|Road and river|7|75|
||Road|5|107|
|Khartoum–Juba|Road and river|13.5|127|
||Road|7.5||
|Malakal–Juba|River|2|270|
|_Source:_AICD calculatio<br>FAO (2005); and Keer-|ns based on data from<br>MISC ( http://www.keer|Yoshino and others<br> -misc.com)|(2009); UNJLC