rafmacalaba/data-use-mentions-extended huggingface dataset:
the data sets we use. Section 3 considers the implications of test score standardization and of the PISA sampling frame for the measurement of inequality in educational achievement, and reports the standard deviation in test scores for our sample of countries. Section 4 proposes our measure of inequality of educational opportunity (IOp), discusses some of its properties, and presents results. Section 5 applies the proposed measures by examining how they correlate with two educational policy indicators across countries. Section 6 concludes.

# **2. Data**

Two broad kinds of data are used for the analysis in this paper. The first is the complete set of PISA surveys, for all 57 countries that participated in the 2006 round. The second is a group of four household surveys, for Brazil, Indonesia, Mexico and Turkey, which are used as ancillary surveys in the two-sample non-parametric sample selection correction procedures described in Section 3. We briefly describe each of these in turn.

> 3 See Micklewright and Schnepf (2007) for a careful comparison of rankings from each of the PISA tests, as well as from TIMSS and PIRLS.

> 4 But the low variance for Indonesia is a good example of the sensitivity of these measures to assumptions made about the nature of selection into the test-taking sample. Under our scenario of “extreme” selection on unobservables, the variance of math scores for Indonesia triples. See below.

4
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The second change was that PVR contracts began to be routinely used in the late 2000s, before the change of the law by the Ministry of Public Works and since then have become the main means of procuring transport PPPs. This provided an efficient allocation of demand risk to the public.

The third relevant change required that any significant additional works must be procured in a competitive auction under the supervision of the Public Authority.<sup>6</sup> This means that the PPP no longer has an interest in pressuring for additional works through renegotiations.

We find evidence suggesting that renegotiations are significantly lower with the new law. Projects tendered under the new law are renegotiated less frequently and for smaller amounts at similar stages in their lifecycle. In addition, we find evidence suggesting that PVR contracts are renegotiated less, even under the pre-reform law. The lack of renegotiations suggests that risks are shifted to a larger extent to concessionaires, and off the public budget.

We also provide estimates of ex post internal rates of returns (IRRs) for highway and airport PPPs in Chile. Ex post IRRs can inform about the cost to society and profitability of PPP projects. We find that the average IRR of PPPs in Chile is 6.8 percent (median 7.5 percent), These values lie in the lower range of the 7 -10% returns on assets obtained in Chile by regulated industries such as electricity transmission and distribution, and water utilities. Interestingly, ex-post IRRs of PVR concessions are significantly lower than IRRs of fixed-term concessions. Also, a robustness check we performed suggests IRRs may be a couple of percentage points higher, in line with the average estimate from a survey of 24 experts we conducted.

We also find that there is a large dispersion in the IRRs of individual projects, ranging from ‒23 percent to 25 percent, with seven PPPs with negative IRRs. This suggests that public infrastructure projects are intrinsically risky, and that private participation entails significant risk shifting from the budget to concessionaires and their financiers.
**MONGOLIA'S INFORMAL SECTOR** -

**Size and Trends**

**8**

# **2. Observations on the** Size **of** the Informal Sector

I1. An understanding of the size of the informal sector, and the changes over time of the relative importance of informal activity in the economy, are essential for Mongolia's policy makers. The size of the informal sector has direct implications for policies aimed at alleviating poverty and forecasting insurance fund contributions, to name a few. The trends of informal sector growth also present policy makers with a view of how their policies are driving entrepreneurial energy. This section of the report will present analyses of several independent bodies of data providing information on the size of the informal sector and how it is changing during the transition period. First, employment in Ulaanbaatar's informal sector will be examined. It will be argued that official employment statistics unwittingly demonstrate rapid growth of the informal sector, and that roughly one third of the official labor force in Ulaanbaatar are engaged in informal activities. Second, the SSO's household survey data will be used to estimate the contribution of informal earnings to household income in Ulaanbaatar. Informal income has increased in importance during the transition and has come to dominate wage income for many households - nearly half have some informal income, and informal earnings make up about one third of aggregate household income in Ulaanbaatar. Third, a well-known technique for inferring the size of the "underground" economy from monetary aggregates will be employed. This technique suggests that for Mongolia as a whole, underground activity is in the neighborhood of 35% of officially recorded GDP. A summary will be provided at the end of this section.'

## **Employment in the Informal Sector**

12. Mongolia's official employment statistics reveal much about the changing size of the informal sector, despite the fact that they were not designed to capture informal sector employment. Ironically, it is for precisely this
charged in the _Informal Sector Income Tax Law_ caused widespread dissent among informals. Although the new, higher, rates seemed modest relative to the _average_ incomes of informals, the new rates were clearly excessive for the poorer informals. Kiosks, for example, saw their monthly taxes go from 4,000 togrogs per month to 14,000 togrogs per month. Even under the old tax rate, 4% of kiosks were generating incomes below the official poverty line. **Under the new, higher, rates an additional 5% of kiosks were driven into poverty.** But even the more fortunate proprietors sensed the inequity of the law. Under the old rates, half to three quarters of kiosks were paying more tax than they would have under the _Economic Entities Income Tax Law_ or the _Citizen 's Income Tax Law,_ respectively. **After the rates were increased, 92% of kiosk proprietors were paying more tax than they would have under the** _Citizen's Income Tax Law._ Furthermore, the broadness of the categories of activities in the _Informal Sector Income Tax Law_ allowed - (required) discretion on the part of the tax collectors many petty street vendors with no permanent location were forced to pay the same tax as the largest, most profitable kiosks.

The popular discontent with the higher taxes on informal proprietors led to a policy reversal. The proposed vehicles taxes were vetoed, and once the dust settled, the _Informal Sector Income Tax Law_ rate applied to kiosks (for example) had been lowered from 14,000 to 6,000 togrogs per month.

# **Box 6**

_Source:_ Informal sector surveys conducted by the author.

they can. On the other hand, once someone has taken the informal path, the taxes they pay have no effect on the incentive to work hard, save, reinvest and grow. The simplicity of taxation of the informal sector may, on balance, be stimulating growth of informal activities. But it comes at the cost of
, as Table 8 shows. In 71% of all village funds, the maximum loan is 20,000 baht; in 10% of cases the

> 2 The average rural wage is about 300 baht per person per day.  We define volunteer time as any time spent by VF committees that is not compensated, in the form of honoraria, at this rate, and then multiply the quantity of volunteer time by the average rural wage to get the value of volunteer time.  The data come from the 2010 Village Fund survey.

10
regulation. We used the standard curriculum, which is homogeneous across contexts and demographics. Thus, we can think of this curriculum as a blueprint that is cheaper and easier to replicate and scale up than other psychology-based programs, including _Virtue_ .

To measure the impacts of the ASPs on students’ outcomes and explore potential mechanisms, we collected data before the start of the intervention (baseline) and right after its completion (follow-up). To measure the main outcomes—namely, behavior at school, school dropout, and academic performance (math scores)—we rely on administrative records submitted as printed reports by teachers from the three countries. Data on behavior at school and math grades were collected at baseline and endline, whereas reports on school dropout are available only at endline.

Moreover, we undertook an innovative and exploratory investigation to provide evidence on three potential mechanisms underlying the effects of psychology-based interventions on students’ main outcomes—namely, socio-emotional skills, fluid intelligence,<sup>5</sup> and emotion regulation.<sup>6</sup> To gather measures of socio-emotional skills, we used taskbased games available in an application developed and used by Danon et al. (2023). We collected measures to proxy for perseverance, self-control, and risk-averse behaviors. To assess emotion regulation, we examined the impact of our intervention on indicators of emotional reactions, which we refer to as _emotion regulation_ markers. These markers were estimated using a software platform based on computer vision and AI called Reactiva (Amangeldiyev and Egana-delSol, 2018) and a platform designed to conduct research on behavioral economics in the field.<sup>7</sup> Last, we use a progressive matrices test (similar to a

> 5Fluid intelligence is consistent with Mullainathan and Shafir’s (2013) idea of mental bandwidth. The ASPs’ psychology-based curriculum may increase concentration capacity and, thus, improve fluid intelligence, which proxies for logical thinking.

> 6The literature defines _emotion regulation_ as a mixture of cognitive and emotional processes
).<sup>10</sup> In 2020, the IMF Fiscal Monitor considered state-owned firms as those with state participation as low as 20 percent and also relied on other criteria to define SOEs, such as national legal forms (IMF, 2020).

To resolve this lack of harmonization, we propose an economic definition to define a firm as an SOE based on the government’s control as well as their role in the market. That is, an entity is considered a stateowned enterprise for the purpose of the Global BOS database if it satisfies the following conditions:

- I. It is controlled by government units or by other public corporations, proxied by a level of direct or indirect (i.e., through subsidiaries) participation of above 10%;<sup>11</sup>

- II. It is recognized by law as a legal entity separate from its owners;<sup>12</sup>

- III. It can generate profit or other financial gain for its owners;<sup>13</sup>

> 8 This classification is described in further detail in Dall’Olio et. al. (2022).

> 9 For instance, in Indonesia the state-owned enterprises are denoted as two different legal forms controlled by the governments: Badan Usaha Milik Negara (national) and locally owned (Badan Usaha Milik Daerah). For instance, SOEs are defined in Azerbaijani as public interest entities (PIEs), and in Mozambique as public enterprises and shareholding companies (World Bank, 2016).

> 10 Even within different OECD workstreams, there is no harmonized definition. For example, an SOE survey conducted in 2015 denoted SOEs as corporate entities recognized by the national law in which only the central government exercised ownership and control (OECD, 2017), which differs from the criteria defined within the Product Market Regulation Indicator.

> 11 The WB SOE policy tracker revealed that during the COVID-19 pandemic firms with as low as 10% could indeed receive significant support
||Enterprise Survey in the country. In cases<br>where the fiscal year spanned more than one<br>calendar year, the figure used is the weighted<br>average over the calendar years covered (and<br>lagged by 2 years), where the weights used are<br>the proportion of months covered in the<br>calendar year.<br>Source: Doing Business, World Bank.|
|---|---|
|Firm-size fixed effects|Two dummy variables indicating the size<br>group of the firm. Firms are either small or<br>medium. Small firms have between 11 and 20<br>full-time permanent workers three fiscal years<br>ago (or 2 fiscal years ago in some countries)<br>and the rest are medium firms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|City fixed effects|A set of dummy variables indicating the city<br>where the firm is located.<br>Source: Enterprise Surveys.<br>ti|
||www.enerprsesurveys.org|
|Growth rate of GDP per capita (lagged)|Annual percentage growth rate of GDP per<br>capita lagged by 2 years from the fiscal year<br>covered by the Enterprise. In cases where the<br>Enterprise Survey spanned more than one<br>calendar year, the GDP per capita figure is the<br>weighted average over the calendar years<br>covered (lagged by 2 years), where the weights<br>used are the proportion of months covered in<br>the calendar year.<br>Source: World Development Indicators, World<br>Bank.|
|Employment Growth|Total number of permanent full-time workers<br>in the final year (last fiscal year) minus the<br>same in the initial year (beginning of the<br>
accurate modeling. Instead, the model relies on a simple, non-parametric assignment rule consistent with the existing evidence.

Equations (1) to (14) complete the model. Total household income is a nonlinear function of the observed characteristics of the household and its members and of unobserved characteristics of household members. This function depends on two main sets of parameters: those of the occupational choice model for each skill level; and those in the earning functions for each employment sector and skill level. It is assumed that no variation exists in the composition of the household. In other words, the number, age, and gender of the members of a household remain constant over time. The demographic change is incorporated via calibration of the survey weights. For further details on the estimation strategy of these parameters, see Olivieri, S. et al. (2014).

# **Simulation**

The second step consists of _replicating_ the projected macroeconomic changes (i.e., sector of employment, total output, or public and private transfers) between the baseline and each projected year. These projections derive from various possible changes in different components of the household income generation model (i.e., labor, and non-labor income). This process is divided into three sub-steps ordered in the following sequence: population growth, labor market status and income, and non-labor income.

The population growth adjustment is particularly important in countries with high fertility rates or significant immigration flows, or in cases where the last available national household survey is relatively distant from the projection year. In the first of these instances, the number of labor market entrants rises faster than the overall population. In practical terms, this allows us to explicitly consider changes in the size of the working-age population and hence to distinguish between employment growth driven (or rather absorbed) by demographic trends and net (or additional) employment growth. A simple approach is adopted in this paper to account for population growth with low computational requirement. The estimation weights for all
Our findings can be summarized as follows. First, for industries with high levels of import penetration, trade policy changes have a significant short-run effect on income risk. Second, the effect of the tariff _level_ on income risk is insignificant. Third, while the tariff _level_ has an insignificant mean effect, it nevertheless changes the degree to which macroeconomic shocks affect income risk. Finally, the welfare costs associated with the estimated increases in income risk are substantial.

As we have pointed out before, the welfare results reported in this paper have to be interpreted with caution keeping in mind several limitations of our analysis. More specifically, we focus _exclusively_ on the link between trade policy and individual income risk, and do not study how trade reform affects the mean of income growth. Second, our welfare calculations do not allow for the possibility that an increase in income risk might lead to a simultaneous rise in insurance opportunities (endogenous market incompleteness). Third, we follow a longstanding tradition in economics and measure risk by the variance (second moment) of the relevant distribution, which is justified if (as assumed in this paper) the economic variables of interest are (log)-normally distributed. Finally, the Mexican household survey we use to implement our general approach is a rotating panel that follows individual workers for five quarters over time, which means that the panel dimension of our income data is somewhat limited. In short, the welfare results presented here do not show that trade liberalization is necessarily costly, but they do provide strong evidence that any comprehensive welfare analysis of trade liberalization ought to take into account the cost of increased labor market risk.

34
We find that 4.5 billion people, more than half the global population, are exposed to one of these four weather events. Of those exposed, 2.3 billion are poor at the $6.85 line, and 390 million are extremely poor (they live on less than $2.15). This means that about a quarter of a million people that are extremely poor are not exposed to the direct impact of extreme weather events. However, these households may be susceptible to experiencing less severe events or the indirect impact of extreme weather events.

**Table 6: Number of people exposed and poor to extreme weather events (millions)**

||Total<br>population<br>|Share of total<br>population (%)|Population exposed in<br>countries with<br>|Exposed<br>|and poor<br>|
|---|---|---|---|---|---|
||exposed||poverty data|$2.15|$6.85|
|Flood|988|13|962|57|424|
|Drought|1,412|18|1,383|179|723|
|Heatwave|2,793|36|2,737|242|1,653|
|Cyclone|634|8|601|24|219|
|Anyshock|4,460|57|4,335|389|2,276|

Notes: Poverty estimates are available for 1,733 subnational regions in 168 economies, accounting for 97% of global population. The total number of poor people is 692 million at $2.15 and 3,560 million at $6.85 when overlaying 2019 subnational poverty rates with 2020 gridded population data.

Exposure by region and income classification is presented in Table 7. South Asia (SAR) is the region whose population is most exposed to
the coefficient of SCI is also smaller than that of the same-year forecast error, indicating a lesser importance of SCI in the accuracy of two-year ahead forecasts.

Logically, the more forecasts are projected in the future, the more they carry an element of uncertainty. Therefore, information in the time of forecasting may be rendered obsolete the longer the horizon of forecasts. In addition, the risk of more unforeseen events increases, as well as their unpredictable impacts. An example is forecasts for the year 2022. Looking at forecasts made in 2021 for 2022, the focus was on post-pandemic recovery, with no knowledge of a potential conflict that would impact all international markets.

# VI. Robustness Checks

## VI.1 Alternative Measure of Data Transparency: Statistical Performance Indicator

The estimations are replicated using an alternative index—the Statistical Performance Indicator (SPI) — which, as detailed above, is more comprehensive. For instance, the SPI includes availability and frequency of labor force surveys, establishment surveys, and establishment censuses that are not included in the SCI. The SPI also covers the periodicity and timeliness of the Sustainable Development Goals (SDGs) indicators as well as indicators of data openness that were not covered in the SCI. More importantly, the SPI is more global in nature, including both developing and developed economies, while the SCI largely includes developing economies. Yet the SPI is available for a shorter period (2016–2019) than the SCI (2004–2020). Appendix Table A7 presents the findings for the lagged log of SPI indicator with both a global sample and a largely developing economies sample. These samples are determined by the availability of IMF-WEO Global sample and World Bank GEP sample respectively. The findings largely mirror those with the SCI—improvements in data transparency are positively correlated with forecast accuracy (absolute forecast errors) and reduce forecast optimism (forecast errors). However, the findings for forecast optimism are statistically significant, while the findings for forecast accuracy are not. Global sample coefficients are greater than those for
becomes a member of the EU it can no longer be targeted by EU antidumping, safeguard, or countervailing duty policies – as such, 12 countries that were significant EU trading partners (and hence potential antidumping targets) in 1999 cannot be included in the sample because they were part of the EU by 2007. Furthermore, the expanding membership means the definition of the economies comprising domestic employment and being subject to a common EU exchange rate are changing over time, which creates potential additional issues of measurement error.

14 The Appendix provides a list of the fifteen trading partners included for each of the five policy-imposing economies’ bilateral relationships. These trading partners cumulatively account for the source of 75 percent (EU) to 94 percent (Canada) of each policy-imposing economy's non-oil imports during the sample periods. We condition on trading partners targeted by TTBs because non-targeted trading partners would be dropped from the estimation given the nature of the included country fixed effects.

15 Even in the case of the application of global safeguards – which are supposed to be applied on a nondiscriminatory basis against all import sources of a product – economies typically apply them in a discriminatory fashion to exclude PTA partners or some developing countries. For a discussion, see Bown and McCulloch (2003). The construction of the policy variables in our bilateral panel data set only counts global safeguards against trading partners that were included in the policy.

10
7|
|Cost recovery|% total cost||90.0|**80.0**|**92.0**|125.7|
|Total hidden costs as % of revenue|%||67.5|**37.3**|**118.3**|3.5|
|U.S. cents|**Mid 2000s**|**Ghana**|**Late 200**|Predom<br>ge<br>**0s**|inantly hydro<br>neration<br>|Other developing<br>regions|
|Power tariff (residential at 75 kWh)||**8.4**||**13.1**|10.27|5.0 – 10.0|
|Power tariff (commercial at 900 kWh)||**13.9**|||11.73||
|Power tariff (industrial at 50,000 kWh)||**9.1**|||11.39||

Source: Eberhard and others 2009. Derived from AICD electricity database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data 22