rafmacalaba/data-use-mentions-extended huggingface dataset:
rural cooperative medical scheme (RCMS) and specifically through subsidies for hospital delivery may have been associated with cost inflation, limiting the reduction in absolute out-ofpocket payments per intervention (figure 10 showing the development in cost and out-of-pocket expenditures per hospital delivery in absolute terms – on left axis – and as a share of annual income per capita – on right axis -- by urban and rural typology).

# _Some inequities are closely related to the existing inefficiencies_

Since the early 1980s, China’s public resources have facilitated rapid improvements in availability of advanced care in specialized urban hospitals. As public resources are biased toward higher-level facilities, they disproportionately benefit higher-income households. (12) A similar bias in the allocation of public resources toward higher-level facilities and higher-income population groups is also seen in the education sector, where “key schools” at each level benefit from higher levels of public funding, and are more easily accessed by the children of the higher income households.

In China’s cities, advanced care is available at levels comparable with advanced economies (for example, the availability of advanced technologies per person in large Chinese cities matches the levels in most OECD countries since the mid- to late-1990s (13) and various life-cycle indicators match or exceed those of the developed world). Beijing and Shanghai, for instance, report prevalence of magnetic resonance imaging machines and other advanced medical equipment – funded partially by public resources – exceeding levels common in European cities. (14) Primary care in urban areas, however, has been unnecessarily expensive, often delivered by hospitals and specialized doctors instead of health centers and general practitioners with qualified nurses. (12) By contrast, the fraction of public resources that benefits rural (township) health centers is disproportionately small (figure 11), while poor quality of care, over-servicing and irrational use of drugs remain serious problems there, as well as in urban areas. (1) Poor rural households still face financial obstacles in accessing cheap effective care (such as management of newborn and respiratory illnesses
# **1 Introduction**

As Herrendorf et al. (2014), among many others, argue, the process of structural transformation, that is, the reallocation of inputs to more productive activities, is recognized as an important feature of successful economic development. Focusing particularly on the three largest sectors of the economy, we can further characterize this process as the reallocation of productive resources from agriculture to manufacturing and services, and later from agriculture and manufacturing to services.<sup>1</sup> Access to public infrastructure services and, particularly, to modern electricity services is another important process that has recently gained a prominent place in the development economics literature. Electricity is as an important public good, and consequently improving electricity access in developing countries is a significant goal of the international community. The Sustainable Development Goals and the United Nations initiative Sustainable Energy for All (SE4All), for example, call for providing electricity to the 1.1 billion hitherto nonelectrified people worldwide by 2030.

The economics literature has demonstrated that improved access to electricity positively affects employment (Dinkelman, 2011), industrial output (Rud, 2012), human development and housing values (Lipscomb et al., 2013, Lewis and Severnini, 2017), household income (Chakravorty et al., 2014), industrial and agricultural productivity (Fisher-Vanden et al., 2015, Allcott et al., 2016, Assunção et al., 2018), infant health (Lewis, 2018), and firms’ entry and exit (Kassem, 2018).

However, our understanding of how improved electricity access interacts with the structural transformation process and affects national income levels is limited. To fill the gap, this paper assesses the significance of electricity access, and the different constraints that surround it, in the allocation of resources among sectors and in the formation and growth of

> 1We adopt the definition of these three sectors based on Herrendorf et al.’s (2014) survey of the literature on structural transformation. In particular
This paper takes on this challenge by focusing on 51 countries (21 industrial and 30 developing) for the period 1970-2014. Given the lack of readily-available data on average marginal individual and/or corporate income tax rates on a global scale, we focus our e¤orts on building a new series for quarterly standard value-added tax rates (henceforth VAT rates). We believe that this signi...cant e¤ort in collecting VAT rates is crucial for any study analyzing tax policy in Europe as well as in the developing world, where indirect/value-added taxation is the main tax revenue instrument.<sup>4</sup> VAT rates were obtained from various primary sources including countries’revenue agencies, national libraries, books, newspapers, tax law experts, and research and policy papers. We identify a total of 96 VAT rate changes in 35 countries (18 industrial and 17 developing). As sources for the narrative analysis, we use contemporaneous International Monetary Fund (IMF) documents, OECD Economic Surveys, and news articles to gather evidence on policymakers’intentions and primary motivation behind each VAT rate change.

While closely following RR’s identi...cation strategy, we also incorporate some new elements that arise due both the global nature of our sample of countries and the speci...c tax measure used. In particular, we allow endogenous tax changes to include countercyclical tax changes (as in RR) as well as procyclical tax changes. While the latter type of policy behavior is not found by RR in the U.S., it is of critical importance in the developing world as well as in many other industrial countries (particularly industrial European countries after the 2007-2008 Global Financial Crisis). When focusing on exogenous tax changes motivated by inherited ...scal factors, we consider inherited de...cit-driven tax changes (as in RR) as well as inherited debt-driven tax changes. Since, over the last 60 years, the U.S. has not faced
# **Economic Crises and Returns to University Education in MiddleIncome Countries: Stylized Facts and COVID-19 Projections**<sup>**1**</sup>

**Tazeen Fasih** The World Bank

**Harry A. Patrinos** The World Bank

**M. Najeeb Shafiq** University of Pittsburgh

JEL Classification: J11, J24, J31, I26 Keywords: COVID-19, education, wages, returns

> 1 We thank Koen Geven for access to relevant micro data for Pakistan under the Data Depot Education for Pakistan initiative. All remaining errors are our own. The views expressed here are our own and should not be attributed to the World Bank Group. Corresponding author: <u>hpatrinos@worldbank.org.</u>
occupations. These occupations require few digital skills, and the digital skills that they do require are mostly basic digital skills. Cooks, who might use computers or smartphone for purchasing or inventory management, and Early Child Development teachers, who may use digital technologies to access or provide lessons, are examples of occupations in the very low digital occupations category.<sup>26</sup>

## **3.3. Matching the occupational skills profiles with country employment data: The Southeast Asia Digital (SEAD) data set**

To analyze the skills profile of the employed population in the four countries we study, we create the Southeast Asia Digital (SEAD) data set that matches the occupation skills profiles to country employment data. We use the occupation variable to do the match with data from four Southeast Asian countries: the 2020 Cambodia Socioeconomic Survey and the 2017 labor force surveys in Malaysia, Thailand, and Vietnam. Each country data set is representative of the population at the national level, allowing us to quantify the number of people working in an occupation. As such, we have a data set of occupation skills profiles for 127 occupations and weights representing the occupations’ employment share in the four countries.

# **4. Methodology**

To explore the complementarity of digital and other skills and the levels of occupation digitalization, we use descriptive statistics, pairwise correlations, a factor analysis, and linear probability model (LPM) regressions.

## _Similarity of skills required in very low-, low-, medium-, and highly digital occupations_

After an inspection of summary statistics, we apply an LPM to estimate the probability that a skill _i_ in occupation _o_ is found in a particular digital occupation group _g_ . We use an LPM to estimate marginal effects of binary outcomes following Friedman (2012) and Bellemare (2015, 2018). The LPM is as follows: where _yog_ is a binary variable representing occupation _o_ dependent on the (digital occupation level) groups _g_ of interest (e.
Macroeconomic effects of fiscal policy volatility**

So far, we have documented the pattern of fiscal policy volatility with a focus on EMDEs and commodity exporters. We have shown that countries with certain institutional characteristics and policies have a more volatile fiscal policy. But what are the macroeconomic consequences of this additional volatility?

We measure these macroeconomic effects on both the volatility of GDP growth and the growth of GDP per capita by using the methodology of Fatás and Mihov (2013). We start with a regression of the type ∆y ε Where σi is the volatility of GDP growth and σi is the measure of fiscal policy volatility. Xi represents a set of controls.

Table 6 presents the results, with fiscal policy volatility measured using primary expenditures. Whether by itself or when including some of our standard controls, fiscal policy volatility is related to higher output volatility. Interestingly, neither EMDEs nor commodity exporters as a group

> 21 We use the average of Brent, Dubai and WTI oil prices (in nominal US dollars) from the World Bank’s Commodities Price Data (the “ _Pink Sheet_ ”).

15
census and survey data are not from similar periods, though the same issues noted above for the ELL method would likely persist in a model using only arealevel covariates. Masaki et al. (2020) use a similar modeling approach to Nguyen’s (2012), but take measures to address some of the shortcomings of a standard ELL approach and obtain EB estimators of Molina and Rao (2010) which appear to be more precise. The authors conduct a design-based validation study using a wealth index constructed with principal component analysis using census data for Sri Lanka and Tanzania. Their results show the approach may hold promise. In the sections that follow, the different procedures discussed here are tested under model-based and design-based simulation experiments.

# **3 Model-based simulation experiments**

The simulation experiment described here is based on those conducted by Marhuenda et al. (2017) in which the true data generating process is a twofold nested error model. Such a model will better accommodate the usual applications of poverty mapping, where household surveys use two-stage sampling. In fact, the traditional ELL method includes random effects only at the cluster level but estimates are given for a higher level.

7
they find that terrain ruggedness and population density increase unit costs, whereas long roads ( _>_ 50km) decrease it. Furthermore, conflict, corruption, the business environment, and the origin of construction firms affects unit costs. ROCKS version 2.3 used by Collier et al. (2016) runs up to 2008. In 2018, the World Bank’s transport unit released an update including only projects with complete information (Bosio et al., 2018). Unfortunately, most projects in this database are road upgrades, but a few new road projects are included as well and summarized in Table 5.

Table 5: Road Building Costs: Road Costs Knowledge System (ROCKS) - 2018 Update

|Work Type|N|Length|(km)|Cost (M$’15)|Cost/km|
|---|---|---|---|---|---|
|_Continental Africa_||||||
|New 1L Road|1||99.0|21.15|0.214|
|New 2L Highway|4||122.0|59.53|0.611|
|_All Low and Lower-M_|_iddle _|_Income _|_Countr_|_ies_||
|New 1L Road|6||59.5|7.65|0.135|
|New 2L Highway|6||84.5|44.09|0.611|
|New 4L Expressway|1||84.8|328.08|3.869|
|_All Countries_||||||
|New 2L Highway|7||50.0|43.03|0.682|
|New 4L Expressway|24||74.9|213.01|5.152|

_Notes:_ Statistics are aggregated across projects using the median. Costs are in millions of 2015 USD. The raw costs in current
whether process innovations could also be categorized as automation.

including manufacturing and services. In total our estimation sample is based on over 15,000

> 8Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata on the basis of employment, value-added, and total number of establishments’ figures. Geographic regions within a country are selected based on which cities/regions collectively contain the majority of economic activity. Enterprise Surveys implemented in Eastern Europe and Central Asian countries are also known as Business Environment and Enterprise Performance Surveys (BEEPS) and are jointly conducted by the World Bank and the European Bank for Reconstruction and Development. For more details see Enterprise Surveys (http://www.enterprisesurveys.org).

> 9Overall both product and process innovation rates fall from 37% to 31%.

13
explained by municipality and year fixed effects, the remainder may be too weak to act as a statistically strong source of variation. This problem can be visualized in figure 5 for the 298 municipalities that are included in any of the ENCVs over 2010–13 and that are used in our analysis. With the exception of a few sudden spikes in some municipalities, which are likely to be caused by errors in the data on beneficiaries made available to us by the Consorcio, the trends over time are almost parallel in all municipalities.

> 4 The National Administrative Department of Statistics of Colombia provides population projections for five-year groups. (See “Demografía y Población: Proyecciones de Población” (database), National Administrative Department of Statistics, Bogotá, Colombia) The total number of age-eligible residents per municipality has been constructed by assuming a constant age distribution within each group.

17
municipalities and over time to control for general income shocks. The reduce form specification is thus defined as follows: shocks. The reduce form specification is thus defined as follows: L m 0 1L F m 2L m 3 m 3 m m (1) mm mm where m is the outcome of interest used to proxy for the welfare of municipality LL WW = ββ + ββ LL F + ββ LL F FF + ββ XX + ββ DD + δδ + γγ _m_ , and this is only observed + εε in each population census indexed by year _t_ . Welfare is measured using household per capita income and other WW measures of well-being, such as the poverty headcount and inequality and nonmonetary deprivation measures. The coefficient of interest is 1, which captures the effect of the FAIS on welfare once standard orthogonality conditions are satisfied. F F m is measured as the monthly average per capita FAIS received by municipality ββ over the three years before the census year t. Similarly, mm F m controls for all the fiscal resources other than the FAIS. It is measured as the monthly average of per capita revenues over the three years preceding the census year t. m is a vector of variables that account FF for the level and rate of utilization of production factors: (a) number of blue-collar and white-collar workers, XX (b) total value of the assets owned by the economic units, and (c) productivity (output per worker). m are dummies that control for population size to consider scale effects that may affect local welfare.<sup>14</sup> Finally, is DD a set of time fixed effects to control for macroeconomic shocks and national policies, and is a set of mm δδ municipality fixed effects that absorbs any unobserved heterogeneity affecting both the FAIS allocation and mm γγ local economic welfare over the long run.

The FAIS is defined using the three-year average before the census year utilized for poverty maps for two reasons: First, this allows for a control by the local political cycle
literacy are**correlated with**:<br>-Retirement planning (found across many<br>national studies)<br>-Investments (participation in stock markets,<br>portfolio diversification)<br>-Debt and credit performance, including<br>mortgages| **National surveys**are still lacking for<br>most middle-income and low-income<br>countries<br>Likewise, there is not enough**cross-**<br>**country**analysis of financial literacy<br>levels and financial knowledge<br>In Africa, the**FinScope surveys**do<br>not test financial literacy as such, and<br>may be missing other measures of<br>financial capability as well<br>What is the**relative importance**of<br>financial literacy? To what extent is it a<br>proxy for other indicators?<br>We lack more specific knowledge of<br>gaps in**business literacy**|

44
where ε _iT_ ≡η _iT_ −η _i_ 0 . The difference-in-difference estimator<sup>βˆ</sup> 1<sup>measures the average differential</sup> in performance between plants in the treatment group and plants in the control group, after accounting for general trends influencing the performance of all plants equally, trends in performance related to observable plant characteristics, and unobserved time-invariant plant effects.

Several variants of Equation (1) have been estimated in the literature, using data from both industrial and developing countries, considering diverse treatment and control groups and time horizons ( _T_ ). For example, Bernard and Jensen (1999) define their treatment group as U.S. manufacturing plants that export in the initial year of the sample, and estimate Equation (1) using different time periods - 1984-1988, 1989-1992, and 1984-1992 - and different time horizons - short run ( _T=_ 1), medium run ( _T=_ 3, 4), and long run ( _T=_ 8).

One extension of Equation (1) developed in the literature has been the inclusion of more than one dummy identifying different, mutually exclusive treatment groups. Bernard and Jensen (1999), for example, consider three groups: (1) plants that do not export in _t=_ 0 but export in _t=T_ (entrants), (2) plants that export in both years (continuous exporters), and (3) plants that export in _t=_ 0 but do not export in _t=T_ (quitters). The control group consists of plants that do not export in either year. Another extension developed in the literature has been to select the treatment and control groups from a subset of the plants in the sample. For example, Aw et al. (2000) use three nonconsecutive years of plant-level data for Korea and Taiwan and consider (i) regressions where only entrants to export markets are the treatment group and nonexporters are the control group, 6
investigate the effect of leaders on growth. They found that leaders have a significant effect on growth in both directions: specifically,

> 3 Aruoba et al. (2019) estimate the probability that US state governors are good, based on job approval ratings.

> 4 For example, Deng’s _New York Times_ obituary states: “In the 18 years since he became China's undisputed leader, Mr. Deng nourished an economic boom that radically improved the lives of China's 1.2 billion citizens.” [link]

2
Figure 11: Shocks in Sudan: Risk-Coping Strategies, 2009 and 2014/15 <!-- Start of picture text --> (a) 2009<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Sell Assets/Use Savings/Rent Farm Increase Labor Supply<br>Borrowed Money Received Help<br>Reduced Expenses or Consumption Kids Migrate or removed from School<br>Other<br>(b) 2014/15<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Sell Assets/Use Savings/Rent Farm Increase Labor Supply<br>Borrowed Money Received Help<br>Reduced Expenses or Consumption Kids Migrate or removed from School<br>Other<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS data.

41
where Yj is the either the proportion of informal workers in fi rm j’s total employment or a measure of fi rm performance (log value added per worker, log average wage, log sales per worker, log pro fi t per worker, log capital per worker, log total employment and a dummy variable indicating use of advanced technologies), Ej is enforcement of regulation faced by fi rm j,<sup>24</sup> Xj is a vector of exogenous variables and εj is a i.i.d. error term.<sup>25</sup> As described in the previous section, enforcement of labor regulation in Brazil is decentralized at the level of the subdelegacia (subregion). This is the relevant level of variation for the enforcement data. We have administrative data on the number of labor inspections and on the number of fi nes issued in each subregion. We prefer to use labor fi nes rather than labor inspections as a measure of enforcement because we can discriminate fi nes by type of violation whereas we cannot discriminate inspections by type of offense (because inspections are general). In particular, we know the number of fi nes related speci fi cally to irregularities that concern worker registration, registration fi nes. This is ultimately the enforcement measure that is most relevant for fi rms when deciding to evade (or not) the law and hire informally. Because some subregions are larger and have more fi rms than others, we normalize number of fi nes by number of fi rms in the subregion.<sup>26</sup> A priori we expect β to be negative when Yj is informal employment, but as we suggest in section (2.2) β can be either negative or positive in the case of all other outcomes. We hope that our measure of enforcement is reasonably exogenous, and varies due to arbitrary differences in inspections across regions. However, for several reasons, it may happen that

> 24 We use contemporaneous measures of enforcement instead